Thursday, August 6, 2026

    CFO Insights

    Future ready or falling behind?

    This week in CFO-relevant AI signals
    Updated 19 hours ago · past 7 days · 36 sources

    The clearest signal this week: CFOs are still stuck between AI ambition and execution, while the economics underneath finance and IT budgets are shifting fast enough to force new capital-allocation decisions now.

    • EY's DNA of the CFO survey: 60% of CFOs want to lead value creation, but only 25% actually own uncertain-return investment decisions; just 21% call finance AI-ready.
    • BCG: only 5% of companies capture AI value at scale; underinvestment (not overspend) is now the bigger risk, with AI leaders posting 3x cost reduction and 2.7x ROIC.
    • Deloitte Tech Trends: only 11% of organizations have agentic AI in production versus 38% piloting — the gap FP&A must model for.
    • OpenAI cut GPT-5.6 API prices up to 80%, compressing the cost basis finance teams use to justify AI business cases.
    • California and Colorado will tax SaaS starting 2027, adding a new compliance and cost-forecasting variable for tech spend.

    For finance leaders, the message is consistent across sources: piloting is not the constraint anymore — operating-model redesign, data quality, and governance are. Budget cycles should assume falling AI unit costs but rising execution and tax-compliance overhead.

    Top signals this week
    • Dna Of The Cfo SurveySource

      A 35-point gap between CFOs who want to own value-creation decisions and those who actually do exposes the real bottleneck: it's authority and data quality, not appetite, holding back AI-driven FP&A.

    • Why Most Ai Cost Reduction Programs Fall ShortSource

      Only 5% of companies realize AI value at scale because most bolt AI onto existing workflows instead of redesigning them — one client hit 30% opex savings on a $15B cost base by combining offshoring, vendor renegotiation, and elimination, not just automation.

    • Tech TrendsSource

      With only 11% of organizations running agentic AI in production against 38% still piloting, finance leaders should treat agentic workflows as a 2027 budget line, not a current-quarter deliverable.

    • Advancing The Price Performance Frontier With Gpt 5 6Source

      An 80% price cut on GPT-5.6 Luna resets the cost baseline FP&A teams use to build AI business cases — vendor pricing volatility, not just capability, is now a planning variable.

    • California And Colorado Will Start Taxing Saas In 2027Source

      New sales-tax exposure on SaaS and digital products starting 2027 adds a compliance and cash-flow variable that finance and tax teams need to model into vendor contracts now.

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    CFO insights, read aloud

    Monday, August 3 · about 6 min

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    From published research

    Industry benchmarks

    Published industry research — not Koko Knows reader data. Reader benchmarks appear here once enough survey responses accumulate.

    78% of organizations expect to increase overall AI spending in the next fiscal year

    Deloitte · The State of Generative AI in the Enterprise: Generating a new future (Q4) · 2025

    39% of organizations attribute any level of EBIT impact to AI — and most put it under 5% of EBIT

    McKinsey & Company · The State of AI: Global Survey 2025 · 2025

    Only 26% of companies have moved beyond proofs of concept to generate tangible value from AI

    Boston Consulting Group · Where's the Value in AI? · 2024
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    KPMG

    38 articles
    KPMG Thought Leadership

    Business transformation services with KPMG

    KPMG's business transformation practice positions the firm as an end-to-end partner for organizations seeking to redesign operations, technology, and strategy simultaneously rather than in isolation. The offering spans strategy through execution, integrating capabilities across finance transformation, supply chain, digital operations, and workforce to drive measurable enterprise value. KPMG emphasizes that transformation initiatives must be anchored in data and powered by emerging technologies, including AI and cloud platforms, to achieve sustainable competitive advantage. The practice combines industry-specific expertise with alliances across major technology ecosystems to accelerate implementation timelines and reduce execution risk. KPMG positions its differentiation on the ability to connect advisory insight with hands-on delivery, reducing the gap between strategy design and realized outcomes.

    3 minRead
    KPMG Thought Leadership

    capital advisory

    KPMG's Infrastructure, Capital Projects and Climate Advisory practice positions the firm as an end-to-end advisor for asset-intensive organizations navigating capital strategy, project delivery, financing, and operations across four sectors: energy, transportation, digital infrastructure, and real estate. The practice covers the full asset lifecycle, from site selection and procurement through major project delivery and asset management. Six core service lines address energy transition, capital program strategy, major projects, project procurement and financing, site selection, and infrastructure funding. KPMG ranked first for quality AI advice and implementation in the US according to the 2024 Source Global Research study, and the firm integrates AI-enabled tools directly into infrastructure program delivery. The practice draws on a global professional network with deep subsector expertise spanning power generation, renewables, highways, ports, fiber, data centers, and affordable housing, among others.

    3 minRead
    KPMG Thought Leadership

    Customer and Operations

    KPMG's Customer and Operations practice positions itself as a full-spectrum consulting capability linking customer experience strategy to operational execution, arguing that sustainable growth requires both to be transformed in tandem rather than in isolation. The practice spans six core domains: customer experience, marketing, sales, service, supply chain, and operations, applying AI and data-driven tools across each. KPMG emphasizes that companies achieving the highest returns treat customer-facing functions and back-office operations as interdependent systems, not separate workstreams. The firm leverages proprietary frameworks alongside alliances with major technology platforms to accelerate implementation and reduce time-to-value for clients. Sector coverage spans financial services, retail, healthcare, industrial manufacturing, and technology, enabling industry-specific solution design rather than generic consulting approaches.

    3 minRead
    KPMG Thought Leadership

    Finance and Accounting

    KPMG positions its Finance and Accounting advisory practice as a catalyst for transforming the finance function from a traditional back-office reporting unit into a strategic business partner that drives enterprise value. The practice targets inefficiencies across the finance operating model, including process automation, data and analytics capabilities, and organizational design. KPMG integrates technology enablement — spanning ERP modernization, AI, and intelligent automation — with talent and operating model redesign to reduce cost-to-serve while improving decision-support quality. The service portfolio spans CFO advisory, finance transformation, accounting operations, and managed services, addressing both near-term performance gaps and longer-term structural change. The underlying argument is that finance leaders face simultaneous pressure to cut costs, accelerate close cycles, and deliver forward-looking insights, requiring a fundamentally different operating model rather than incremental improvement. KPMG presents itself as a full-spectrum partner capable of spanning strategy through execution across these priorities.

    3 minRead
    KPMG Thought Leadership

    Global Business Services and Outsourcing Advisory

    KPMG's Global Business Services (GBS) and Outsourcing Advisory practice positions GBS transformation as a strategic lever for enterprise value creation, moving beyond traditional cost reduction toward integrated operational models that combine people, process, and technology. The practice advises organizations on designing, building, and optimizing GBS structures—including shared services, outsourcing, and hybrid delivery models—to drive efficiency, agility, and scalability. KPMG applies AI, automation, and data analytics to modernize service delivery and help clients extract measurable performance gains from their operations. The advisory scope spans the full lifecycle: strategy and business case development, sourcing decisions, implementation, and ongoing governance. With a global delivery network and cross-functional capabilities spanning finance, HR, IT, and supply chain, KPMG aims to help clients achieve both near-term cost targets and longer-term competitive differentiation through their GBS operating models.

    3 minRead
    KPMG Thought Leadership

    Harness the power of data to modernize operations

    Organizations that treat data, analytics, and AI as strategic operational assets — not standalone technology initiatives — gain measurable competitive advantage. KPMG's position centers on integrating data modernization across the full enterprise, connecting cloud infrastructure, governance, and AI-driven automation into a unified capability. The firm offers end-to-end services spanning data strategy, engineering, advanced analytics, and AI implementation to help clients move from fragmented data environments to scalable, insight-driven operations. This approach targets outcomes across cost reduction, revenue growth, risk management, and customer experience simultaneously rather than sequentially. KPMG combines sector-specific domain knowledge with technology alliances to accelerate deployment and reduce implementation risk. The underlying argument is that data maturity is now a prerequisite for operational modernization, not a byproduct of it.

    3 minRead
    KPMG Thought Leadership

    Human Capital Advisory

    KPMG's Human Capital Advisory practice positions workforce strategy as a core driver of enterprise value, arguing that organizations must align talent, culture, and operating models to achieve sustainable business performance. The practice addresses the full spectrum of human capital challenges, including workforce transformation, HR function effectiveness, organizational design, and change management. KPMG integrates AI-enabled tools and data analytics to help clients make faster, more precise decisions about workforce planning and talent deployment. The advisory offering spans mergers and acquisitions, large-scale technology implementations, and ongoing operational improvements where people-related risks most frequently determine outcomes. Clients are guided through connecting HR strategy directly to financial performance metrics, moving human capital from a cost center framing to a value-creation mandate.

    3 minRead
    KPMG Thought Leadership

    KPMG Managed Services

    KPMG Managed Services positions the firm as an ongoing operational partner to clients, not merely a project-based advisor, by embedding KPMG talent, technology, and processes directly into business functions to run them on a sustained basis. The model targets functions where organizations face persistent complexity, regulatory pressure, or capability gaps—including finance, risk, compliance, and technology operations. Rather than transferring work to a low-cost offshore provider, KPMG differentiates on delivering measurable outcomes tied to quality, speed, and risk reduction while retaining accountability for results. The offering integrates proprietary and third-party technology platforms with KPMG's domain expertise to drive efficiency at scale. This approach allows clients to redeploy internal resources toward strategic priorities while maintaining the governance and control standards required by regulators and boards.

    3 minRead
    KPMG Thought Leadership

    Risk services

    KPMG's Risk Services practice positions integrated risk management as a competitive differentiator, not merely a compliance obligation. The practice spans five core domains: financial risk, operational risk, cyber and privacy risk, regulatory compliance, and ESG risk, delivering end-to-end advisory from strategy through implementation. KPMG combines deep sector specialization with technology-enabled solutions to help organizations build resilience while pursuing growth objectives. The practice emphasizes embedding risk intelligence into business decisions rather than treating risk as a siloed back-office function. Clients range across financial services, healthcare, energy, and other regulated industries facing escalating regulatory scrutiny and digital transformation risk.

    3 minRead
    KPMG Thought Leadership

    Supply Chain Operations Consulting: Build a Resilient, AI-Enabled Supply Chain

    KPMG positions AI-enabled supply chain transformation as the primary lever for building resilience against ongoing disruptions from geopolitical volatility, climate risk, and shifting consumer demand. The firm's consulting approach integrates five capability areas: supply chain strategy and design, procurement and sourcing, logistics and fulfillment, inventory and planning, and digital and AI enablement. KPMG argues that organizations must move beyond reactive risk management toward predictive, data-driven supply chain models that embed AI across planning, procurement, and operations. The practice targets measurable outcomes including working capital reduction, service level improvement, and cost-to-serve optimization. KPMG combines industry-specific expertise with technology alliances to accelerate implementation from strategy through execution.

    3 minRead
    KPMG Thought Leadership

    business transformation

    88% of companies are simultaneously managing multiple transformation programs, yet most struggle to convert data and change initiatives into measurable business value. KPMG's central argument is that AI and GenAI close this gap by detecting patterns, predicting outcomes, and generating actionable insights at speed and scale — making disruption a competitive advantage rather than a threat. The firm positions transformation not as incremental change but as a fundamental shift in how organizations operate, structured around five elements: Evolve, Grow, Operate, Innovate, and Trust. KPMG deploys a modular entry-point model under its Velocity framework, allowing organizations to scale AI-enabled solutions across functions and geographies with minimal disruption. The firm was ranked number one for overall consulting quality in the US by Source in 2025 and recognized as a worldwide leader in enterprise governance, risk, and compliance by IDC MarketScape, underscoring its claim to execution credibility across the full transformation lifecycle.

    3 minRead
    KPMG Thought Leadership

    Infrastructure, Capital Projects and Climate Advisory

    KPMG's Infrastructure, Capital Projects and Climate Advisory practice positions the firm as an end-to-end advisor for owners, investors, and developers navigating large-scale capital deployment across infrastructure and energy transition assets. The practice integrates project development, financing structuring, delivery oversight, and climate risk advisory into a unified service model, reflecting the convergence of traditional infrastructure investment with decarbonization mandates. KPMG emphasizes that capital projects face mounting complexity from supply chain volatility, regulatory shifts, and ESG requirements, demanding integrated advisory support across the full asset lifecycle. The firm targets sectors including transportation, energy, water, digital infrastructure, and social assets, leveraging federal funding mechanisms such as the Infrastructure Investment and Jobs Act and Inflation Reduction Act to accelerate client investment. KPMG's value proposition centers on reducing cost overruns, schedule slippage, and stranded asset risk by embedding financial, technical, and sustainability expertise at each project stage.

    3 minRead
    KPMG Thought Leadership

    maximize value through transactions

    KPMG positions its M&A advisory practice around a deal architect model designed to reduce value leakage and accelerate value creation from pre-deal through post-deal phases. Dealmakers face mounting complexity from tariff uncertainties, political volatility, and increasingly strategic deal objectives such as AI capability acquisition and operating model transformation. KPMG's response is a tech-enabled, AI-driven approach that delivers faster insights across the full deal cycle, including strategy, due diligence, integration, and separation. A single point of accountability synthesizes functional and industry expertise to keep teams aligned on the investment thesis throughout. The firm was recognized by Forbes as one of America's Best Management Consulting Firms in 2025, reinforcing its standing as a credible M&A partner across sectors including financial services, healthcare, technology, and industrial manufacturing.

    3 minRead
    KPMG Thought LeadershipJuly 1

    KPMG reframes enterprise AI value as an architecture problem, not a model problem: a 10-layer full-stack (nine layers plus a Trust, Ops & Control wrapper) with a central Work Layer where agents turn intent into governed action — skip a layer and you gain a liability, not leverage.

    **The thesis:** swapping one LLM for another or bolting an assistant onto an app raises usage and token spend while value stays elusive — models and interfaces are fragments of a larger whole. When work becomes computational, *architecture* becomes the operating system of the business. **The stack (9 layers + a wrapper):** Applications · Agents · Assistants · Context · Models · Refinery · Data · Compute · Energy — all inside a **Trust, Ops & Control** boundary. **Context + Agents** form the central **Work Layer**, where intent is translated into governed action by agents operating with shared context. **Why it matters:** value compounds only when every layer works as a system; skip one and you gain a liability. Token economics is a design constraint (not an operational afterthought), governance is the boundary (not a bolt-on), and energy is an architectural input (not a facilities issue). Start from a measurable outcome and trace it through the stack to find weak layers before committing budget. Cites the KPMG Q1 2026 AI Pulse Survey — 73% automating cross-function workflows, 53% routing critical information between teams.

    4 minRead
    KPMG Thought Leadership

    Achieve transformation and make the difference

    KPMG's business transformation practice positions the firm as an end-to-end partner for organizations seeking to redesign operations, technology, and strategy simultaneously rather than sequentially. The core argument is that sustainable transformation requires integrating people, process, and technology changes together, as piecemeal approaches consistently underdeliver on value. KPMG draws on cross-industry experience and proprietary frameworks to help clients move from strategy through execution, reducing the gap between transformation intent and realized outcomes. The practice spans functional areas including finance, supply chain, customer experience, and enterprise technology, with AI and digital enablement embedded across all workstreams. KPMG emphasizes that transformation must be tied to measurable business outcomes, not activity milestones, to generate competitive advantage that endures beyond the initial program.

    3 minRead
    KPMG Thought Leadership

    Global Business Services and Outsourcing Advisory | KPMG

    KPMG's Global Business Services (GBS) and Outsourcing Advisory practice helps organizations design, transform, and optimize shared services and outsourcing models to reduce costs, improve performance, and scale operations. The practice covers the full lifecycle, from strategy and business case development through vendor selection, contracting, transition management, and ongoing governance. KPMG advises across key functional towers including finance, HR, IT, procurement, and supply chain, applying both captive and third-party delivery models. The approach integrates automation, AI, and digital enablers to modernize service delivery and extract greater value beyond traditional labor arbitrage. Clients benefit from KPMG's combination of operational, commercial, and technology expertise to navigate increasingly complex sourcing decisions and multi-vendor environments.

    3 minRead
    KPMG Thought Leadership

    Procurement Advisory Services | AI-Enabled Procurement & CLM | KPMG

    KPMG's procurement advisory practice positions AI-enabled transformation as the primary lever for converting procurement from a cost center into a strategic value driver. The firm offers end-to-end services spanning strategy, sourcing, contract lifecycle management, and technology implementation, with AI embedded across each phase to automate manual processes and surface actionable insights. KPMG combines its advisory capability with alliances across major procurement platforms to accelerate deployment and reduce implementation risk. The practice targets measurable outcomes including cost reduction, supplier risk mitigation, and improved contract compliance. Organizations that treat procurement as a strategic function rather than a transactional one are presented as better positioned to manage supply chain disruption and capture sustainable savings.

    3 minRead
    KPMG Thought Leadership

    Supply Chain Operations Consulting & AI-Enabled Supply Chain | KPMG

    KPMG positions AI-enabled supply chain transformation as the critical lever for building resilience against persistent disruptions including geopolitical shifts, tariff volatility, and demand uncertainty. The firm's supply chain consulting practice spans strategy through execution, covering network design, procurement, inventory optimization, logistics, and S&OP/IBP. KPMG integrates proprietary and third-party AI tools to drive measurable outcomes, targeting improvements in forecast accuracy, inventory reduction, and cost-to-serve. The practice serves clients across industries with sector-specific solutions, combining functional expertise with technology implementation capabilities across platforms such as SAP, Oracle, and Blue Yonder. KPMG frames supply chain not merely as an operational function but as a source of competitive advantage requiring continuous investment in data, analytics, and organizational capability.

    3 minRead
    KPMG Thought LeadershipMay 13

    KPMG 2026 M&A Deal Market Study

    Global M&A activity is positioned for a measured recovery in 2026, with deal volume and value expected to grow modestly after years of rate-driven suppression, according to KPMG's 2026 M&A Deal Market Study. Technology, energy transition, and AI-related assets are the primary sectors attracting acquirer interest, as strategic buyers prioritize capability acquisition over pure scale. Private equity faces continued pressure to deploy an estimated $2.6 trillion in dry powder, accelerating sponsor-to-sponsor transactions and carve-outs as GPs seek liquidity and portfolio exits. Valuation gaps between buyers and sellers remain the single largest deal impediment, though narrowing interest rate differentials are gradually closing that spread. Regulatory scrutiny, particularly in cross-border transactions involving data, defense, and critical infrastructure, adds material execution risk and timeline uncertainty to deals in those categories. Respondents broadly expect deal activity to accelerate in the second half of 2026, contingent on macroeconomic stability and continued easing by major central banks.

    3 minRead
    KPMG Thought LeadershipMay 13

    The modern CFO

    The modern CFO role has fundamentally expanded beyond financial stewardship into strategic enterprise leadership, with finance chiefs now expected to drive growth, manage enterprise-wide risk, and guide technology transformation simultaneously. CFOs face mounting pressure across four core dimensions: delivering on traditional financial oversight, enabling data-driven decision-making, leading digital and AI adoption, and serving as a key partner to the CEO and board on long-term strategy. Talent retention and workforce transformation within the finance function represent a critical operational challenge, as automation reshapes which capabilities the function needs. CFOs who successfully navigate this expanded mandate invest in modernizing finance infrastructure while developing their teams' analytical and strategic competencies. The role increasingly demands fluency in technology, sustainability reporting, and stakeholder communication well beyond conventional accounting and treasury expertise. Organizations whose CFOs operate at this broader strategic level demonstrate stronger alignment between capital allocation decisions and enterprise value creation.

    3 minRead
    KPMG Thought LeadershipMarch 10

    KPMG CFO life sciences roundtable first edition

    Life sciences CFOs face a dual mandate: driving growth through innovation and M&A while simultaneously cutting costs to fund R&D pipelines under intensifying margin pressure. The Inflation Reduction Act's drug pricing provisions represent the most disruptive near-term regulatory challenge, forcing CFOs to reassess portfolio strategy and capital allocation earlier in the product lifecycle than previously required. Talent costs and retention remain a top operational concern, with finance functions competing for specialized skills in data analytics and digital transformation. CFOs are accelerating investment in finance automation and AI-enabled forecasting tools to reduce the cost-to-serve of the finance function while improving decision support speed. Supply chain resilience has been elevated to a board-level financial risk, requiring CFOs to weigh the cost of redundancy against vulnerability to disruption. The overarching tension for life sciences CFOs is balancing short-term investor pressure for profitability with the long-cycle capital commitments that define the industry's value creation model.

    3 minRead
    KPMG Thought Leadership2025

    6 Key Questions Finance Leaders Are Asking About Data and Analytics | KPMG

    Finance leaders are converging on 6 critical questions as they work to extract business value from data and analytics investments, with the central challenge being how to move from reporting what happened to driving decisions about what to do next. The questions span data governance and trust, the right operating model for analytics, talent and capability gaps, technology architecture choices, AI integration into finance processes, and how to measure the return on data investments. Data quality and trust remain the foundational barrier, as analytics initiatives stall when business stakeholders don't believe the numbers, making governance a prerequisite rather than an afterthought. Finance functions are being pressed to shift from centralized data ownership to federated or hybrid models that balance control with speed, requiring deliberate choices about where analytics capability sits organizationally. AI and generative AI are accelerating the timeline for finance transformation but also raising new questions about model risk, auditability, and workforce redesign that CFOs must address before scaling. Leaders who treat data and analytics as a strategic asset tied to specific business outcomes, rather than a technology program, are closing the gap between investment and measurable impact faster than peers who lack that business-case discipline.

    3 minRead
    KPMG Thought Leadership2025

    AI-Enabled Financial Close as a Service | KPMG

    KPMG's AI-Enabled Financial Close as a Service offering reframes the traditional month-end close as a managed, AI-augmented process rather than an internal finance function, targeting the persistent inefficiencies that plague most organizations' close cycles. Finance teams typically spend the majority of close time on manual, low-value tasks such as reconciliations, journal entries, and intercompany eliminations — areas where AI and automation can eliminate significant labor and error rates. KPMG's model combines its accounting and process expertise with AI-powered technology platforms to deliver close activities as an ongoing managed service, shifting fixed finance costs toward a variable, outcome-based structure. The approach targets measurable outcomes including faster close timelines, reduced cost per close, and improved data accuracy, while freeing internal finance talent to focus on analysis and decision support. Human oversight and controls are embedded throughout to satisfy audit and regulatory requirements, positioning AI as an accelerant rather than a replacement for professional judgment.

    3 minRead
    KPMG Thought Leadership2025

    AI-Powered Finance as a Service KPMG

    KPMG's Finance as a Service (FaaS) model positions AI as the core driver for transforming finance functions from transactional processors into strategic business partners. The model combines AI-enabled automation, cloud platforms, and outcome-based managed services to allow organizations to consume finance capabilities on demand rather than building and maintaining them internally. KPMG projects that AI-powered FaaS can reduce finance operating costs by 40–60% while simultaneously improving speed, accuracy, and analytical depth across core processes including close, reporting, FP&A, and compliance. The approach shifts finance talent away from manual execution toward higher-value interpretation and decision support, with AI agents handling routine tasks end-to-end. Adoption is structured around three maturity stages—standardize, automate, and augment—enabling organizations to scale at a pace aligned with their technology readiness and risk tolerance. KPMG frames this not as incremental improvement but as a fundamental redesign of how finance operates, with competitive advantage accruing to organizations that move earliest and most decisively.

    3 minRead
    KPMG Thought Leadership2025

    Finance AI Solutions: Transform Your Finance Operations | KPMG

    KPMG positions AI-enabled Finance as a Service as the path for finance functions to move beyond transactional processing toward strategic decision support. The offering combines AI, automation, and cloud-based platforms to reduce manual effort across core finance processes including record-to-report, order-to-cash, and procure-to-pay. By embedding AI agents and intelligent automation into these workflows, organizations can compress close cycles, improve forecast accuracy, and redeploy finance talent toward higher-value analysis. KPMG frames this not as incremental technology adoption but as a structural redesign of the finance operating model, integrating tools with process and talent transformation. The firm presents itself as providing both the technology implementation capability and the functional finance expertise required to scale AI solutions with controls and governance intact.

    3 minRead
    KPMG Thought Leadership2025

    Future of Financial Planning: Smarter EPM with AI | KPMG

    AI-embedded Enterprise Performance Management (EPM) is moving financial planning from periodic, backward-looking reporting to continuous, forward-looking decision intelligence. KPMG argues that integrating generative and predictive AI into EPM platforms enables finance functions to automate routine planning tasks, compress forecasting cycles, and shift FP&A talent toward higher-value strategic analysis. The piece identifies three core capability shifts: AI-driven scenario modeling that processes more variables at greater speed, natural language interfaces that democratize access to financial data across business units, and autonomous anomaly detection that improves forecast accuracy and control. KPMG cautions that realizing these gains requires organizations to first address data quality, governance, and model explainability — without which AI outputs lack the reliability needed for executive decision-making. The firm positions itself as an implementation partner capable of bridging EPM platform capabilities with enterprise AI strategy, change management, and finance operating model redesign.

    3 minRead
    KPMG Thought Leadership2025

    Future of the Financial Close | KPMG

    The article's content beyond navigation elements was not provided, so I cannot summarize the substance of KPMG's findings on the future of the financial close. The title "The Future of the Financial Close: Smarter, Faster, Trusted" signals a focus on modernizing period-end reporting through automation and AI, but no data, arguments, or recommendations from the body of the piece were included in the text supplied. To deliver an accurate 4-6 sentence executive summary, please provide the full article body. I will not fabricate findings or statistics that were not present in the source material.

    3 minRead
    KPMG Thought Leadership2025

    How Finance Leaders Unlock the Power of Data | KPMG

    Finance leaders who treat data as a strategic asset rather than a reporting byproduct are gaining measurable competitive advantage, and those who fail to make this shift risk being left behind as AI-driven decision-making becomes the standard. The article identifies a persistent gap between data availability and data utility, with many CFOs still spending the majority of their time on data gathering rather than analysis and strategic counsel. High-performing finance organizations share three common traits: a unified data architecture that eliminates silos, clearly defined data governance ownership, and finance talent that combines analytical fluency with business acumen. Investment in cloud-based platforms and automation is accelerating close cycles and freeing capacity, with leading organizations reducing manual data processing by 30 to 50 percent. KPMG argues that the CFO must take an active role in enterprise data strategy, not just consume outputs from IT, positioning finance as the connective tissue between operational data and enterprise value creation. Organizations that align data infrastructure investment with finance transformation initiatives are realizing faster ROI and stronger forecasting accuracy than those treating the two as separate workstreams.

    3 minRead
    KPMG Thought Leadership2025

    Integrating Finance as a Service into GBS for Higher Value | KPMG

    Global Business Services organizations that remain focused on cost reduction and transactional efficiency are leaving significant value on the table. KPMG's central argument is that integrating Finance as a Service (FaaS) into GBS transforms finance from a back-office function into a strategic value driver by combining advanced technology, data analytics, and flexible service delivery. This model shifts GBS finance capabilities beyond standardization toward predictive insights, real-time reporting, and decision support that directly enables business growth. FaaS within GBS leverages AI, automation, and cloud platforms to reduce manual processing costs while simultaneously elevating output quality and speed. Organizations that make this transition can expect measurable improvements across cycle times, cost-per-transaction metrics, and the ratio of finance staff focused on analysis versus transaction processing. The path forward requires deliberate redesign of operating models, talent strategies, and governance structures rather than incremental layering of technology onto existing GBS frameworks.

    3 minRead
    KPMG Thought Leadership2025

    The Future of Finance with AI

    AI is poised to fundamentally reshape the finance function, shifting it from a backward-looking reporting role to a forward-looking, intelligence-driven operation. KPMG argues that finance leaders who treat AI as a strategic capability rather than a back-office efficiency tool will gain measurable competitive advantage. Automation of routine tasks such as reconciliations, forecasting, and close processes can free finance professionals to focus on higher-value analysis and decision support. The transformation requires deliberate investment in data quality, governance frameworks, and workforce reskilling, as technology alone will not deliver results without organizational readiness. CFOs are urged to act now by piloting AI use cases, building scalable data infrastructure, and embedding AI literacy across finance teams. Organizations that delay risk ceding ground to peers who are already converting AI investment into faster, more accurate financial insight.

    3 minRead
    KPMG Thought Leadership2025

    The Future of the CFO

    The CFO role is undergoing a fundamental structural shift, expanding well beyond financial stewardship into enterprise-wide strategic leadership across technology, talent, and transformation. Modern CFOs are expected to operate across five distinct dimensions simultaneously: financial guardian, strategic advisor, technology champion, talent leader, and external stakeholder communicator. AI and automation are compressing the time finance teams spend on transactional work, freeing CFOs to allocate more bandwidth to forward-looking analysis and decision support for the CEO and board. The expanding mandate creates a capability gap, as fewer than half of finance organizations report having the digital skills required to execute on this broader agenda. KPMG positions the path forward as deliberate investment in finance talent development, operating model redesign, and technology adoption — with CFOs who delay on any of these three fronts risking strategic irrelevance. The CFOs most likely to succeed are those who treat the finance function itself as a business to be continuously transformed, not merely a control function to be managed.

    3 minRead
    KPMG Thought Leadership2025

    The future of the close with AI

    AI is poised to fundamentally transform the financial close process by automating routine tasks, reducing cycle times, and shifting finance professionals toward higher-value analytical work. KPMG argues that organizations applying AI to the close can move from a reactive, backward-looking reporting function to a continuous, forward-looking insights engine. Key use cases include automated journal entry preparation and review, anomaly detection in account reconciliations, and AI-assisted variance analysis and commentary generation. The technology targets a close process where manual effort still dominates, with many finance teams spending 60–70% of close time on low-judgment data gathering and formatting tasks. Realizing these gains requires foundational investments in data quality, standardized processes, and clear human oversight protocols before AI can operate reliably at scale. KPMG positions the transformation as phased, beginning with task-level automation and progressing toward an autonomous close supported by agentic AI systems.

    3 minRead
    KPMG Thought Leadership

    4 ways AI can empower Finance

    AI is poised to fundamentally reshape the Finance function by shifting it from transaction processing toward strategic value creation. KPMG identifies 4 core application areas: automating routine financial operations, enhancing forecasting and scenario planning through predictive analytics, strengthening risk management and compliance monitoring, and enabling Finance teams to deliver deeper business insights faster. These capabilities collectively reduce manual effort, compress reporting cycles, and improve decision quality across the enterprise. Realizing this value requires Finance leaders to address data quality, talent upskilling, and governance frameworks before scaling AI deployment. Organizations that move deliberately on these foundations stand to convert Finance from a cost center into a forward-looking strategic partner to the business.

    3 minRead
    KPMG Thought Leadership

    From digital to intelligent close

    Finance organizations must evolve from digitally enabled close processes to AI-driven intelligent close operations to remain competitive and meet rising demands for faster, more accurate financial reporting. The intelligent close moves beyond automation of discrete tasks to embed AI and machine learning across the entire record-to-report cycle, enabling continuous accounting, real-time anomaly detection, and predictive analytics rather than periodic batch processing. This shift reduces close cycle times, lowers error rates, and frees finance talent from transactional work to focus on analysis and decision support. Achieving this requires organizations to address three interdependent layers: data quality and integration, process standardization, and AI-enabled technology platforms. KPMG positions the transition as a strategic imperative, not an incremental upgrade, requiring deliberate investment in governance, talent upskilling, and technology architecture to unlock the full value of an intelligent close.

    3 minRead
    KPMG Thought Leadership

    Global Business Services: The future is AI—and it’s here.

    AI is fundamentally reshaping Global Business Services (GBS), moving the function beyond traditional cost arbitrage toward a strategic driver of enterprise value. Organizations that integrate AI into GBS operating models are achieving measurable gains in process automation, decision intelligence, and workforce productivity that exceed what prior generations of shared services delivered. The window for competitive differentiation is narrowing, as early adopters are pulling ahead while laggards risk structural cost and capability disadvantages that will be difficult to close. Successful transformation requires more than technology deployment — it demands redesigned processes, new talent models, and governance frameworks that embed AI accountability from the outset. GBS leaders are being called to move from pilots to scaled adoption, treating AI not as an enhancement to existing workflows but as a foundational reset of how services are delivered. Organizations that act now with intentional strategy and executive commitment will define the next performance benchmark for GBS.

    3 minRead
    KPMG Thought Leadership

    Fifth Annual CAO Survey: Fast-tracking the future

    KPMG's Fifth Annual CAO Survey finds that chief accounting officers are accelerating the pace of finance function transformation, driven primarily by automation, AI adoption, and mounting regulatory complexity. The majority of CAOs report increasing pressure to deliver real-time data and insights to the C-suite while simultaneously managing a growing volume of compliance and reporting obligations. Talent remains a critical constraint, with CAOs citing difficulty attracting and retaining professionals who combine technical accounting expertise with data and technology fluency. Investment in cloud-based ERP systems and advanced analytics tools is rising as organizations seek to reduce manual close processes and improve forecast accuracy. CAOs are also expanding their strategic remit beyond traditional controllership, taking on broader roles in enterprise risk, ESG reporting, and digital finance initiatives. The survey signals that organizations lagging on technology investment and workforce upskilling face measurable risk of falling behind peers on both efficiency and decision-support capability.

    3 minRead
    KPMG Thought Leadership

    Supercharge your Finance workforce with GenAI

    GenAI presents a significant productivity opportunity for finance functions, with the potential to automate or augment a substantial share of routine analytical, reporting, and forecasting tasks. Finance organizations that deploy GenAI strategically can redirect workforce capacity from transactional processing toward higher-value decision support and business partnering. KPMG identifies use cases across the finance value chain, including variance analysis, management reporting, audit preparation, and scenario modeling, where GenAI can compress cycle times and reduce manual effort. Successful implementation requires deliberate investment in data quality, governance frameworks, and workforce upskilling, as technology alone does not deliver value without the right operating model. CFOs are advised to prioritize a phased adoption roadmap that balances speed to value against risk controls, particularly around data integrity and model reliability. Organizations that move early and build GenAI fluency into their finance talent base are positioned to gain a durable competitive advantage.

    3 minRead
    KPMG Thought Leadership

    Will generative AI mean the death of service delivery centers?

    Generative AI will not eliminate service delivery centers but will fundamentally transform their purpose and composition. The labor arbitrage model that justified offshoring — moving high-volume, repetitive transactional work to lower-cost locations — is directly threatened as AI automates the same routine tasks those centers were built to perform. Rather than shutting down, organizations that adapt will reposition their centers around higher-value work: AI model governance, exception handling, process design, and judgment-intensive activities that require human oversight. The workforce profile will shift materially, demanding fewer transactional processors and more technologists, analysts, and specialists capable of managing AI-augmented operations. Centers that fail to evolve their talent mix and operating model risk becoming obsolete, while those that reinvent themselves as hubs for AI-enabled service delivery can capture new productivity and capability advantages. The strategic imperative is not whether to maintain these centers, but how quickly leaders redesign them before the cost-and-efficiency rationale that built them disappears.

    3 minRead

    Accenture

    12 articles
    Accenture Insights

    AI Maturity and Transformation

    Accenture's research of 1,600+ C-suite executives and data-science leaders finds only 12% of large organizations qualify as 'AI Achievers'—firms advanced enough to attribute nearly 30% of total revenue to AI on average and realize 50% greater revenue growth versus peers in the pre-pandemic period. Companies are segmented into four maturity tiers (Achievers, Builders, Innovators, Experimenters) based on performance across foundational capabilities—cloud, data platforms, governance—and differentiation capabilities including C-suite sponsorship and innovation culture. AI Achievers are projected to more than double from 12% to 27% of large firms by 2024, and AI-influenced revenue is expected to roughly triple between 2018 and 2024. Critically, Accenture estimates AI transformation will proceed approximately 16 months faster than digital transformation did, with 42% of executives reporting AI initiative returns exceeded expectations and only 1% reporting underperformance.

    3 minRead
    Accenture Insights

    How to Scale AI for Value

    Accenture identifies three foundational blockers to enterprise AI scaling: misaligned talent priorities between workforce and C-suite, fragmented data infrastructure, and legacy systems incompatible with AI ambitions. Only 20% of C-suite leaders are redesigning end-to-end processes with AI, and just 11% of organizations are equipped for effective human-agent co-learning—despite 59% of executives citing AI's role in continuous development. Accenture's response centers on three pillars: workforce upskilling at scale (779,000 employees in agentic AI training, $1B+ annual learning investment), data modernization with governed cloud infrastructure, and a continuously evolving digital core. 85% of C-suite leaders plan to increase AI spending this year, with 49% indicating they would accelerate investment even in a recessionary environment.

    3 minRead
    Accenture Insights

    Advertising Playbook: A Game Changer for Advertisers

    Accenture research spanning 500+ advertising decision-makers finds the industry at a critical inflection point driven by the deprecation of third-party cookies, tightening privacy regulation, and economic pressure on budgets. Forty-five percent of US and UK advertisers have not changed their approach in five years, and 60% are underprepared for the loss of third-party identifiers, risking a 10–30% decline in marketing efficiency. Despite 77% of CMOs feeling pressure to prove ROI, only 17% of day-to-day advertising decision-makers list return on investment as a top objective—a misalignment Accenture identifies as a core barrier to adaptation. The report prescribes seven actions organized around centralizing first-party data, testing cookieless targeting methods such as contextual and interest-based strategies, shifting to revenue-based ROI metrics, building incrementality-focused measurement models, and embedding generative AI into content and optimization workflows. Accenture's own performance data shows advertisers using interest-based targeting on Pinterest achieved 45% higher ROAS than retargeting-only approaches over a 30-day window, framing privacy-resilient strategies as performance opportunities rather than compliance costs.

    3 minRead
    Accenture Insights

    The Velocity of Work

    Accenture surveyed 250 senior U.S. federal leaders and found that nearly one-third expect generative AI to increase productivity by at least 30% within three years, with more than 90% believing AI will help their agencies absorb cost-reduction and efficiency demands. Despite high ambitions, actual GenAI implementation in federal agencies is lagging private-sector benchmarks, a gap Accenture attributes to insufficient coordination across data, IT systems, workflows, and workforce transformation. The firm argues that productivity gains must be engineered at the business-process and functional-worker level rather than pursued through isolated pilots. Accenture identifies five high-value starting points—personal productivity, customer service, fraud prevention, system modernization, and workforce enablement—and recommends an analytics-driven approach to prioritizing use cases by measurability, repeatability, and mission impact.

    3 minRead
    Accenture InsightsMay 14

    Talent Supply Chain: Future Workforce

    Accenture projects U.S. supply chain role demand will grow by 1.34 million positions (19%) between 2026 and 2035, while labor force growth of roughly 3.2% will add only 221,000 workers, leaving a structural gap of nearly 1.1 million roles. The report argues this is not a cyclical shortage but a design flaw: supply chains have historically scaled by adding headcount, a model that cannot close a gap of this magnitude. Accenture's scenario-planning model shows that pairing technology deployment with deliberate workforce redesign can compress workforce growth requirements from +18.7% to approximately −3.0% over the same period. Three strategic moves are prescribed for Chief Supply Chain Officers: building talent foresight, redesigning work as automation autonomy scales, and developing skills continuously. Leaders who delay will face a decade of managed scarcity—persistent vacancies, service failures, and reduced transformation capacity—while early movers can shift the scaling mechanism from headcount to intelligent systems.

    3 minRead
    Accenture InsightsMay 14

    Turning the supply chain talent shortage into strength

    Accenture's scenario-planning model projects US supply chain workforce demand will rise by 1.34 million roles (19%) between 2026 and 2035, while the labor force adds only ~221,000 workers, leaving a gap of approximately 1.1 million roles. The structural mismatch cannot be resolved through accelerated hiring alone; supply chain complexity is expanding faster than any realistic labor supply growth can offset. Accenture's model shows that pairing technology deployment with deliberate role redesign can compress workforce growth requirements from +18.7% to roughly -3.0% over the same period, with up to 48% of routine, high-frequency tasks automatable when multiple technologies work in concert. Leaders are advised to execute three moves in parallel: build predictive talent foresight tied explicitly to AI adoption roadmaps, redesign roles and decision rights as automation absorbs transaction-heavy work, and shift reskilling from one-time events to continuous, role-specific capability development linked to real career mobility.

    3 minRead
    Accenture InsightsMarch 18

    Agentic AI in M&A | Transaction Advisory

    Accenture argues that agentic AI is fundamentally reshaping the M&A lifecycle—from target screening and due diligence through integration—by deploying autonomous, multi-agent systems that can process data volumes and decision cycles beyond human capacity. The piece positions agentic AI not as a productivity tool but as a structural change to how deals are sourced, evaluated, and executed, compressing timelines and surfacing risks that traditional advisory workflows miss. Key applications span financial due diligence, contract analysis, synergy modeling, and post-merger integration tracking, with agents operating across structured and unstructured data simultaneously. Accenture frames adoption as a competitive differentiator, implying that acquirers and advisors who deploy agentic systems will have material advantages in deal speed, pricing accuracy, and integration outcomes over those relying on conventional processes.

    3 minRead
    Accenture InsightsMarch 18

    AI-Ready Cloud Foundation

    Accenture argues that enterprises cannot fully capitalize on AI innovation without first modernizing their cloud infrastructure into an AI-ready foundation. The piece contends that legacy cloud architectures—designed for application hosting rather than AI workloads—create bottlenecks in compute, data access, and latency that limit model performance and agent scalability. Accenture frames the cloud foundation as a strategic prerequisite, not a background IT concern, requiring deliberate choices around networking, storage, security, and data fabric design. The report positions CIOs and technology leaders as the primary owners of this transformation, with direct implications for AI investment returns and total cost of ownership.

    3 minRead
    Accenture InsightsFebruary 10

    Reinventing Biopharma From Lab to Line

    Biologics now represent 55% of the clinical pipeline, and 64% of FDA Complete Response Letters issued between 2019 and 2024 were tied to chemistry, manufacturing, and controls failures — making biopharma manufacturing a critical bottleneck. Accenture research finds only 35% of surveyed executives describe their organizations as 'connected' in manufacturing and technical operations, with most stuck mid-journey due to siloed pilots, weak data infrastructure, and fragmented digital initiatives. Companies that successfully scale intelligent technologies across the product lifecycle — including robotic high-throughput process design and AI-augmented real-time analytics — can reduce time-to-market by up to 40%, cut batch lead times by 50%, and achieve yield improvements exceeding 400% in upstream cell culture processes. The report argues that closing the gap between digital ambition and execution requires an interconnected foundation of people, data, and technology, with a focus on three priority areas to build resilient operations capable of absorbing geopolitical and supply-chain disruptions.

    3 minRead
    Accenture InsightsJuly 22

    Intelligent Service Center

    Accenture proposes an AI-powered Intelligent Service Center model for U.S. federal agencies, projecting a 40% reduction in operational costs through a three-phase GenAI and agentic AI transformation. The framework targets three levers: contact elimination via self-service tools (cutting per-contact costs by 86%), contact containment via AI virtual agents handling 85% of calls, and agent efficiency gains that reduce after-call work by 50%. A phased rollout begins with quick wins inside existing technology stacks—yielding an initial 8% cost reduction—then expands to custom language models and full-scale modernization, cumulatively reducing agent headcount by over 40%. The Federal Retirement Thrift Investment Board case study illustrates real-world results: a 32% drop in average wait times and a 93% participant satisfaction score in 2024. The report also recommends consolidating vendors to platforms such as Google Cloud and Salesforce and shifting procurement from cost-plus to fixed-price or outcomes-based contracts.

    3 minRead
    Accenture InsightsJune 7

    Learning from AI Leaders

    Accenture's research distinguishes companies that are scaling AI enterprise-wide from the majority still experimenting at the margins. The piece, originally published in Ivey Business Journal (May/June 2024), identifies behavioral and structural traits of AI leaders versus laggards. AI leaders move beyond isolated pilots to embed AI across core business processes, capturing measurably greater returns than peers. The article provides a framework for executives seeking to replicate the operating model, governance, and investment discipline that separates high-performing AI adopters from the rest.

    3 minRead
    Accenture InsightsAugust 8

    total enterprise reinvention banking

    Accenture research across 1,516 C-suite executives and 131 banks finds that only ~10% of banks have adopted a Total Enterprise Reinvention strategy, yet these 'Reinventors' outperform peers by 120 basis points in pre-tax return on equity, 130 basis points in cost-to-income ratio, and 37 basis points in operating expenses over assets. Reinventors across all industries generate 10% higher incremental revenue growth and 30% more financial value within the first six months of transformation investment. The strategy requires a digital core built on cloud, AI, data, and security infrastructure that replaces legacy systems and enables enterprise-wide data flow. Beyond financials, Reinventors achieve 32% better sustainability outcomes, 31% better customer and employee experience scores, and 11% higher talent retention metrics. Siloed or incremental digitalization is characterized as insufficient; continuous, organization-wide reinvention across operations, business models, customer experience, and talent is positioned as the differentiating path forward.

    3 minRead

    Alvarez & Marsal

    13 articles
    A&M Insights

    A&M Crypto Advisory

    Alvarez & Marsal's Crypto Advisory practice offers end-to-end institutional digital-asset services spanning stablecoin adoption, tokenization of real-world assets, custody infrastructure, compliance, tax, accounting, and on-chain investigations. The practice targets traditional corporates, financial institutions, and crypto-native businesses seeking to operate digital-asset programs with institutional-grade controls and reporting. Key offerings include corporate treasury vehicle (DATCO) setup for Bitcoin, Ethereum, and Solana reserves; IPO readiness for crypto-native firms; M&A due diligence; and custom on-chain dashboards that translate blockchain data into board- and regulator-ready analytics. A&M also advises governments, central banks, and regulators on stablecoin frameworks, custody licensing, and DeFi oversight.

    3 minRead
    A&M Insights

    Harnessing the Three C's: Cash, Cost and COGS - A Strategic Blueprint for CFOs

    Alvarez & Marsal's CFO Services team outlines a framework built around three financial levers—Cash, Cost, and COGS—that CFOs should actively manage to improve performance and competitive positioning amid economic uncertainty. On cash, the firm emphasizes direct and indirect forecasting accountability, ERP-driven working capital analytics, and unlocking 'trapped' cash through operational improvements in receivables, payables, and inventory. Cost optimization is framed as a continuous discipline rather than a one-time initiative, requiring leadership alignment, outside-in benchmarking, and digital transformation of processes. COGS management demands granular cross-functional visibility into production inputs, procurement, and inventory systems, with improvements typically yielding longer-horizon margin gains that enable more competitive pricing without quality trade-offs.

    3 minRead
    A&M Insights

    How To "Share" Responsibility in Setting Up Your Own Shared Services Center

    Organizations scaling back- and middle-office operations face a structural choice between outsourcing and building a captive shared services center (SSC). A captive SSC concentrates accountability questions around leadership alignment and internal client relationships, making governance design a critical early decision. Success depends on clearly defining ownership, rules of engagement, and how the SSC interacts with business units — particularly in large organizations where headcount impact is significant. The article outlines frameworks for establishing accountability structures that prevent the common failure modes of captive SSC buildouts.

    3 minRead
    A&M InsightsJuly 6

    Global Restructuring & Turnaround

    This page is a capabilities overview for Alvarez & Marsal's Global Restructuring & Turnaround practice, which the firm positions as its flagship service line after 40 years of operation. A&M describes itself as one of the largest privately held global professional services firms, with restructuring practices across North America, Europe, Latin America, the Middle East, India, and Asia Pacific. The practice focuses on stabilizing financial and operational performance, preserving stakeholder value, and driving rapid change in distressed situations. The page is primarily a leadership directory and firm credentials showcase rather than a substantive thought leadership article with original findings or data.

    3 minRead
    A&M InsightsJuly 2

    Disputes and Investigations

    This page aggregates recent thought leadership from Alvarez & Marsal's Disputes and Investigations practice, covering four distinct topics published in late June and early July 2026. Key outputs include analysis of English Premier League financial regulation overhauls affecting clubs, lenders, and advisers; an examination of how procurement reform, AI convergence, and national security priorities are drawing commercial companies into Defense Industrial Base compliance requirements; recognition of A&M experts in Lexology's 2026 Energy guide; and Chambers and Partners recognition of A&M's Disputes, Cyber Risk, and Reputation Advisory practices. The defense industrial base piece is the most substantive, arguing that commercial organizations face material new obligations as national security procurement expands. No single quantitative finding anchors the collection; the page functions primarily as a capability and recognition showcase.

    3 minRead
    A&M Insights

    Private Equity Services

    Alvarez & Marsal's Private Equity Services practice covers the full investment lifecycle—pre-acquisition due diligence, post-acquisition value creation, and exit preparation—integrating operational, financial, tax, IT, and commercial capabilities. The firm combines Big Four-quality accounting and tax expertise with hands-on operational consulting, serving PE firms across sectors including healthcare, financial services, energy, and software/technology. Post-acquisition offerings include CFO services, cost optimization, merger integration, carve-out support, IPO readiness, and interim management. A&M has also launched a dedicated Generative AI group within its PE practice, developing AI-enabled tools and solutions specifically for private equity firms.

    3 minRead
    A&M InsightsJune 19

    Corporate Finance

    This page is a service overview and team directory for Alvarez & Marsal's Global Corporate Finance practice, covering M&A advisory, capital raising, strategic advisory, and restructuring investment banking. The practice serves founders, entrepreneurs, financial investors, and large corporations across all industries globally. It operates across five regional hubs: Asia, Europe, Latin America, Middle East, and North America. The page contains no proprietary research, data, or analytical content — it is a capability and personnel listing.

    3 minRead
    A&M InsightsJune 16

    Corporate Performance Improvement

    Alvarez & Marsal's Corporate Performance Improvement practice positions the firm as an execution-focused partner for CFOs and senior operators, spanning finance transformation, ESG integration, supply chain, technology enablement, and AI/ML analytics. Recent thought leadership highlights a persistent enterprise AI value gap: MIT's Project NANDA finds 95% of AI pilots deliver zero measurable P&L impact, with A&M attributing failure to organizational design rather than technology. Additional analysis covers telco AI investment outpacing enterprise value creation due to fragmented data and system complexity, and consumer goods valuation research showing revenue growth accounts for roughly two-thirds of multiple differentials, with growth-led earnings valued at twice cost-driven gains. The practice also addresses NetCo/ServCo carve-out structuring, where operating model decisions are characterized as the primary determinant of transaction value.

    3 minRead
    A&M InsightsJune 10

    Digital

    This is the Alvarez & Marsal Digital practice landing page, not a substantive thought leadership article. It describes four internal service teams — Digital & Technology Services, Business Technology, A&MPLIFY, and Software Technology — and lists 10 managing directors without providing analysis, data, or findings. The page surfaces four recent thought leadership titles covering AI deployment gaps, agentic AI operating models, edge AI, and product/platform IT models, but contains no article-level content. As a navigational hub page, it offers no original thesis, evidence, or actionable insight for enterprise decision-makers.

    3 minRead
    A&M InsightsJune 8

    Global Valuation Services

    Alvarez & Marsal's Global Valuation Services practice offers independent valuation, financial modeling, and advisory across private credit, structured securities, M&A transactions, disputes, and regulatory compliance. The practice covers asset-backed finance, equity compensation valuation, divorce financial analysis, and government funding strategy, positioning A&M as a broad-spectrum valuation partner. Recent thought leadership spans German and European bank equity valuations—where rate cuts and compressed cost of capital are pushing price-to-book multiples to record levels—and a Spanish energy M&A market that saw a 30% decline in deal volume in 2025 due to grid constraints and price cannibalization in solar. A CFO-focused checklist highlights that over 70% of ERP initiatives miss original goals, urging private equity finance leaders to scrutinize vendors on integration, security, and total cost beyond the demo.

    3 minRead
    A&M InsightsMay 28

    Corporate Transactions

    Alvarez & Marsal's Corporate Transactions Group (CTG) offers end-to-end M&A advisory services spanning strategy, due diligence, transaction execution, and value capture, positioning itself as conflict-free relative to audit-affiliated competitors. The firm differentiates on interim executive leadership, performance-based fee structures, and a global network of industry specialists. Recent CTG activity spans banking sector consolidation (2026 deal volume on pace for a seven-year high), joint venture structuring, biopharmaceutical M&A, and automotive sector trends. The practice also promotes AI-powered M&A tooling under its A&M Assist platform, targeting faster due diligence and deal insights.

    3 minRead
    A&M InsightsAugust 19

    Environmental, Technical and Sustainability Services

    Alvarez & Marsal's Environmental, Technical and Sustainability (ETS) Services practice positions environmental, health, safety, and sustainability advisory as a value-creation lever rather than a pure compliance or risk-management function. The practice covers four core mandates: lowering risk profiles through strategic assessments, eliminating regulatory barriers via compliance and reporting support, optimizing costs and capital ROI, and unlocking sustainability-driven business value. Recent thought leadership highlights EHS due diligence as an EBITDA improvement tool, embedding sustainability executives in M&A deal teams, ESG value creation in private capital across Southeast Asia and India, and a maturity model for sustainable supplier relationship management. The practice serves clients across the full corporate and transaction lifecycle, with specific readiness offerings such as California SB 261 climate risk compliance.

    3 minRead
    A&M InsightsAugust 1

    Regulatory & Risk Advisory

    Alvarez & Marsal's Regulatory & Risk Advisory practice provides services across risk identification, compliance, cybersecurity, and insurance industry regulation. The practice addresses heightened regulatory scrutiny on non-bank financial institutions, including stress testing, capital measures, and AML requirements. Recent thought leadership focuses on insurance sector themes: M&A transactional risk insurance, London market digitalization via Blueprint II, and five megatrends reshaping complex claims management. The 2024 outlook highlights profitability pressures as higher-rate tailwinds fade and geopolitical, cyber, and technological risks intensify insurers' operational and risk models.

    3 minRead

    BCG

    40 articles
    BCG Publications

    The Cost of Caution with AI Investments

    BCG research finds that AI leaders invest 1.7% of revenue in the technology versus 0.8% among laggards, and the gap is producing measurable financial separation: 3x greater cost reduction, 60% higher profit margins, and 2.7x return on invested capital. The central argument is that underinvestment, not overspending, is now the dominant AI risk for most enterprises. BCG prescribes three actions: treat AI as core infrastructure (not a side initiative), self-fund AI transformation by first harvesting 5–25% savings from traditional cost levers, and abandon the requirement for exhaustive pre-scale business cases in favor of fast iteration. The firm emphasizes that value creation follows a 10/20/70 split—10% algorithms, 20% technology and data, 70% people, process, and behavior change—meaning organizations that fund tools without funding operating model redesign will capture only a fraction of the potential return.

    3 minRead
    BCG Publications

    AI-First Cost Reduction: How to Drive Sustained, Structural Advantage

    BCG research finds that only 5% of companies generate AI value at scale, with most cost-reduction programs failing because they layer AI onto existing workflows rather than redesigning operations around it. The firm identifies five structural traps—fragmented initiatives, additive rather than replacement deployment, absent proof points, delayed investment, and productivity targets disconnected from P&L—that prevent savings from materializing. AI leaders achieve three times greater cost reduction than laggards, and BCG cites a major tech client that realized 30% opex savings against a $15 billion cost base by combining traditional levers like offshoring and vendor renegotiation with an Eliminate-Simplify-Automate framework and direct CEO accountability. BCG prescribes concentrating investment in core workflows, redesigning processes around decision automation rather than task automation, funding early AI investment with traditional cost-cutting wins, committing before the business case is perfect, and setting hard headcount and P&L targets rather than efficiency percentages.

    3 minRead
    BCG Publications

    Building Investor Confidence in AI Strategy

    BCG's 14th annual Global Investor Survey, covering 500+ institutional investors managing ~$35 trillion in AUM, finds 87% expect AI to materially improve corporate fundamentals within two years, yet 56% view markets as too optimistic on AI valuations and 73% believe elevated AI expectations will create future TSR headwinds. Investors broadly expect AI to lift labor productivity (74%), corporate margins (69%), and revenues (62%), but only 22% see AI as a source of sustainable competitive differentiation—meaning most view AI gains as broadly distributed or temporary. Execution skepticism is high: 70%+ of investors are concerned about whether companies have the technical and organizational capabilities to deliver, only 57% say companies report appropriately on AI agendas, and just 41% will tolerate margin dilution above 1–2 percentage points to fund AI investment. The survey's core message to corporate leaders is to pair bold AI investment with financial discipline—78% of investors now avoid companies with net debt/EBITDA above 3x (up 12 points from 2024)—while building explicit, milestone-driven AI narratives that close the current credibility gap with the capital markets.

    3 minRead
    BCG Publications

    Improving Governance in Joint Ventures

    BCG research finds that one in three failed joint ventures can trace problems partly to ineffective boards, making poor JV governance the third most common cause of JV failure. JV boards face structural disadvantages absent from public, PE-backed, or family-owned company boards: directors often carry dual loyalties to parent companies, lack financial incentives tied to JV performance, and have little board experience, while operating with minimal support structures. BCG identifies three governance failure modes—unclear decision rights, management paralysis, and partner distrust—and recommends JV boards borrow specific practices from public company boards (strategic narrative discipline, operating cadence, CEO succession planning), PE boards (capability-based director selection, high-conviction strategy, rapid cadence adjustment), and family-owned boards. Practical remedies focus on governing parent-company service agreements, clarifying seconded executives' reporting lines, and maintaining ongoing alignment on strategic objectives across owners.

    3 minRead
    BCG Publications

    Beyond the Machine: How Industrials Are Creating Customer Value with Digital and AI

    BCG's analysis of industrial AI commercialization identifies a persistent gap between value creation and value capture: customers actively use AI-enabled solutions but resist paying for them, citing legacy expectations that digital tools should be bundled with equipment purchases, skepticism toward intangibles, and confusion over data ownership versus analytical value. The report finds that the primary barrier is not technical performance but commercial readiness—specifically, legacy sales organizations unequipped to sell SaaS-style offerings and lacking capabilities in pricing, packaging, and customer-specific ROI quantification. BCG prescribes a parallel-track go-to-market framework spanning value proposition design, co-development with anchor customers, value-based pricing, and sales enablement, emphasizing that monetization requires embedding AI into redesigned customer workflows with clear 'moments of truth' rather than leading with the product itself. Case examples from food processing machinery and fleet leasing illustrate that customers who experience AI value through process integration—rather than standalone analytics—convert to paid programs and advocate for additional features.

    3 minRead
    BCG Publications

    AI-First Field Service Operations

    BCG argues that AI adoption in field service operations demands full operating-model reinvention, not incremental tech upgrades, with 70% of value unlocked through change management rather than code. The talent crisis is acute—50% of U.S. HVAC technicians are over 55, and healthcare equipment training programs produce just 400 graduates annually against 7,300 needed—but the broader problem is fragmented decision-making and underused asset data across sales, operations, and customer functions. BCG projects productivity gains of 20%–30% and profit-per-technician increases of up to 80% for organizations that deploy AI agents end-to-end, spanning smart dispatch, predictive parts restocking, augmented-reality field support, and outcomes-as-a-service revenue models. A BCG transport-sector engagement demonstrated a 40% reduction in rework and 25% faster maintenance execution after deploying extended-reality devices paired with deep change management. The recommended path is a three-phase approach—rapid diagnostic, proof-of-concept, and iterative full workflow redesign—anchored by deliberate activation of the management layer between C-suite and frontline technicians.

    3 minRead
    BCG Publications

    The AI-Powered Transformation Office

    BCG argues that AI—specifically agentic AI—is approaching an inflection point that will fundamentally restructure the corporate transformation office (TO) by 2030. Rather than replacing human roles, agentic AI will automate routine coordination, progress tracking, and risk detection across three core TO domains: program management, financial and impact tracking, and change management. In financial and impact tracking, AI-driven predictive modeling will shift the function from periodic validation to continuous value-delivery transparency, integrating with ERP and core finance systems and enabling executives to substantiate value-creation narratives with investors. BCG cautions that organizations must establish minimum viable governance frameworks—defining what AI can draft, what requires human review, and what must remain a human decision—to preserve accountability and avoid eroding institutional trust. The path to an agentic TO requires a clearly defined mandate, a human-led operating model with explicit decision rights, and early investment in capability building to scale AI augmentation across complex transformation portfolios.

    3 minRead
    BCG Publications

    Sustainability Reporting in Europe: From Compliance to Strategy

    BCG analysis of FY2024–FY2025 CSRD disclosures finds most European companies have adopted a compliance-driven reporting posture, producing lengthy disclosures disconnected from strategic priorities, with recurring annual costs for large organizations exceeding €1 million. The forthcoming ESRS Set 2 standards, applicable from FY2026, reduce mandatory data points by approximately 60–70% and shift the regulatory emphasis from completeness to decision usefulness, creating a structural reset opportunity. BCG argues companies should use FY2026 to transition from compliance-driven to strategy-led sustainability reporting by realigning materiality assessments with business strategy, redesigning end-to-end reporting processes, and deploying AI and governance capabilities at scale. Despite widespread vendor claims, actual AI deployment in sustainability reporting remains limited due to misaligned use cases, fragmented data landscapes, and inadequate organizational readiness—gaps that must be addressed to realize efficiency gains.

    3 minRead
    BCG Publications

    Taking a Page from Commodity Traders: A New Playbook for Commercial Real Estate Investors

    BCG estimates commercial real estate investors forfeit roughly $200 billion annually in 'dark value'—short- and medium-term optimization opportunities that go uncaptured due to the sector's entrenched buy-and-hold mentality. Analysis across ~500 C-suite executives ranked commercial real estate as the highest-potential sector for value creation via systematic optimization, yet 67% of sector executives deploy such strategies only occasionally or not at all. Applying all six optimization levers—geography, quality, time, industry reconfigurability, immediate buffers, and capital flows—can lift return on assets by 3–5 percentage points per annum, representing roughly a one-third increase above the sector's historical ~9% average return. AI-enabled mark-to-market tools, supply/demand modeling, and yield arbitrage analytics are identified as the core enablers, while culture change and a revamped operating model are prerequisites for systematic capture. BCG positions commodity-trader playbooks as the template, with only a handful of leading players currently approaching systematic dark value capture (step three on a five-step value ladder).

    3 minRead
    BCG PublicationsJune 22

    Cloud Cover: There's More to Cloud AI Cost Than Token Price

    BCG argues that token price is a misleading proxy for total cloud AI cost, and that enterprises systematically underestimate the true economics of AI workloads in the cloud. The full cost picture includes infrastructure overhead, data egress, orchestration layers, latency-driven compute scaling, and vendor lock-in effects that can dwarf per-token fees. Organizations that benchmark AI investments solely on published model pricing risk material budget overruns and flawed build-vs-buy decisions. BCG recommends a TCO framework that captures all cost layers—compute, storage, networking, and integration—before committing to cloud AI architecture at scale.

    3 minRead
    BCG PublicationsJune 22

    Why We Still Need a CIO in the AI-First Era

    BCG argues that the CIO role remains essential in the AI-first era, not despite AI's rise but because of it. As AI agents proliferate and business units increasingly deploy technology autonomously, enterprises face mounting risks around fragmented architectures, ungoverned data flows, redundant vendor contracts, and security exposure—all domains requiring centralized technical leadership. The CIO's mandate is evolving from infrastructure custodian to strategic integrator: orchestrating AI platforms, enforcing governance standards, managing total cost of AI ownership, and ensuring interoperability across business functions. Without a CIO anchoring these decisions, organizations risk shadow AI sprawl, inconsistent risk controls, and eroded ROI on technology investments.

    3 minRead
    BCG PublicationsJune 19

    Agentic AI Turns Every Team into Its Own Transformation Engine

    BCG argues that agentic AI fundamentally shifts transformation from a centralized IT-driven initiative to a distributed capability owned by individual business teams. Rather than waiting for enterprise-wide programs, teams can now deploy AI agents that autonomously execute multi-step workflows, analyze data, and iterate on processes without constant human intervention. This decentralization compresses transformation timelines and lowers the cost of experimentation, but it also creates new governance and oversight demands at the CIO and CDO levels. BCG frames the organizational challenge as designing guardrails and operating models that let teams move fast without accumulating uncontrolled AI sprawl or data risk.

    3 minRead
    BCG PublicationsJune 18

    A Real-World Game Plan for AI in Renewable Energy

    BCG outlines a practical framework for deploying AI across renewable energy operations, arguing that the sector's data-rich but organizationally fragmented environment requires a sequenced, use-case-driven approach rather than broad platform bets. The piece identifies high-value AI applications across asset performance management, grid integration, energy trading, and project development, where early adopters report measurable gains in yield optimization and O&M cost reduction. BCG emphasizes that scaling AI in renewables demands investment in data infrastructure, cross-functional operating models, and governance before advanced automation can deliver ROI. Companies that treat AI as a strategic capability—not a point-solution overlay—are positioned to compress costs and improve capital allocation across the asset lifecycle.

    3 minRead
    BCG PublicationsJune 18

    Beyond AI: How Tech Is Transforming Commodity Trading

    BCG argues that technology transformation in commodity trading now extends well beyond AI, encompassing integrated digital platforms, advanced analytics, and automation across the full trading value chain. Leading commodity traders are investing in capabilities that span trade execution, risk management, logistics, and back-office operations to capture margin and reduce operational exposure. The piece positions technology adoption as a strategic differentiator, with laggards facing compressing margins as digitally mature competitors gain pricing and speed advantages. Firms that treat tech as a holistic operating model shift—rather than a point-solution deployment—are projected to outperform on risk-adjusted returns and cost efficiency.

    3 minRead
    BCG PublicationsJune 18

    How AI-First Banks Are Rewriting the Rules of Retail Banking

    BCG's June 2026 analysis argues that AI-first banks are structurally repositioning retail banking by embedding AI across the full customer and operational value chain, not merely automating discrete tasks. Leading institutions are deploying AI agents for hyper-personalized advisory, real-time credit decisioning, and autonomous back-office operations, achieving cost-to-income ratios and customer acquisition economics that traditional banks cannot match at scale. The piece contends that the competitive gap between AI-first challengers and legacy incumbents is widening faster than most boards anticipate, driven by compounding advantages in data flywheel effects, talent concentration, and technology unit economics. Incumbent banks that treat AI as a point-solution overlay rather than an operating-model transformation risk permanent margin compression and customer attrition. BCG frames the strategic choice as a fundamental redesign of the banking business model, with capital allocation, workforce structure, and technology architecture all requiring simultaneous reconfiguration.

    3 minRead
    BCG PublicationsJune 16

    From Hindsight to Foresight: The CEO Mandate for an AI-First Chief Financial Officer

    BCG argues that CEOs must redefine the CFO role around AI-first operating principles, shifting the function from backward-looking reporting to forward-looking, predictive decision support. The thesis is that finance organizations still structured around historical data and manual processes are leaving material value on the table, while AI-enabled CFOs can compress planning cycles, automate routine accounting tasks, and redirect talent toward strategic capital allocation and scenario modeling. The piece outlines a mandate for CFOs to lead AI adoption across FP&A, treasury, and financial close—not merely adopt tools incrementally—and positions this transformation as a CEO-level governance priority, not a finance-IT initiative. BCG contends that companies whose CFOs operate with AI-native workflows will achieve faster, more accurate forecasting and stronger return on AI investment than peers who treat finance automation as a back-office efficiency play.

    3 minRead
    BCG PublicationsJune 15

    Reinventing the Operating System of Work with AI

    BCG argues that AI is now capable of reinventing the enterprise operating model itself — not merely automating discrete tasks but restructuring how work is orchestrated, decisions are made, and value is created across the organization. The piece frames this shift as moving from AI as a productivity tool to AI as the core operating system of the firm, with autonomous agents coordinating workflows end-to-end. Companies that redesign processes around AI-first principles — rather than layering AI onto legacy structures — are positioned to achieve structural cost and speed advantages over peers. Leaders are urged to make deliberate choices about which work humans own, which AI owns, and how governance frameworks ensure accountability in hybrid human-AI operating models.

    3 minRead
    BCG Publications

    Making the Agentic Marketing Transformation a Reality

    BCG's 2026 survey of 300 CMOs finds marketing has seized ownership of enterprise AI investment, with roughly half of CMOs now leading AI decisions within their functions versus 14% driven by CEOs or boards. CMOs are split into three maturity tiers: 32% are Leaders deploying orchestrated multi-agent workflows, 26% are Followers scaling beyond pilots, and 42% remain At-Risk, still using GenAI only as a task-level assistant. AI investment is accelerating—43% of CMOs report marketing AI spend exceeding $15 million this year, up from 28% last year, with the third wave of investment shifting from discrete tools toward integrated agentic operating infrastructure. Early leaders report 20–30% cost efficiency gains, 3x improvement in marketing ROI, and 10x faster campaign cycle times, with 31% of B2C CMOs already citing measurable revenue impact from agentic transformation.

    3 minRead
    BCG PublicationsJune 11

    AI: The Answer to Process Industries' Talent Cliff

    BCG argues that AI represents the primary strategic response to a looming talent cliff in process industries—sectors such as chemicals, metals, paper, and industrial goods where an aging workforce is creating acute knowledge and skills shortages. The piece contends that AI tools can capture and operationalize retiring workers' institutional knowledge, augment the productivity of remaining staff, and reduce dependence on deep specialist headcount for routine and complex operational decisions. BCG frames this not as a long-term aspiration but as an near-term operational imperative, given demographic timelines already in motion. The recommended path involves deploying AI across process optimization, maintenance, and operational decision-support—with leadership expected to make capital allocation and change-management commitments to realize the productivity offset.

    3 minRead
    BCG PublicationsJune 11

    Agentic AI Will Industrialize Financial Scams. Are Banks Ready?

    Agentic AI is poised to industrialize financial fraud by enabling bad actors to automate, scale, and personalize scams at a speed and volume that outpaces traditional bank defenses. BCG argues that autonomous AI agents can now orchestrate end-to-end fraud workflows—from target identification and social engineering to transaction execution—reducing the cost and skill barrier for financial crime dramatically. Banks face a structural asymmetry: legacy fraud detection systems were built for human-paced, pattern-based attacks, not AI-generated, adaptive, high-frequency campaigns. BCG calls on financial institutions to redesign fraud controls around agentic threat models, invest in real-time behavioral detection, and establish cross-institution data-sharing frameworks to close the defensive gap before industrialized fraud becomes systemic.

    3 minRead
    BCG PublicationsJune 11

    The Process of Change: Navigating the Future of the Workforce with AI

    BCG's June 2026 report argues that AI is fundamentally restructuring workforce composition and the nature of work, requiring organizations to actively manage the transition rather than passively adapt. The piece frames AI adoption as a process-level change problem, not merely a technology deployment challenge, with implications for role redesign, headcount planning, and capability investment. Companies that treat AI-driven workforce change as a structured change-management exercise—with clear process mapping, reskilling pathways, and governance—will outperform those that treat it as an IT initiative. Leaders are urged to assess which tasks, roles, and workflows are most exposed to automation and to build new operating models around human-AI collaboration rather than simple substitution.

    3 minRead
    BCG PublicationsJune 10

    AI Is Turning M&A into a High-Impact Learning Machine

    BCG argues that AI is fundamentally reshaping M&A by enabling acquirers to systematically capture, analyze, and apply lessons across deals in real time, converting what was historically an episodic, judgment-driven process into a continuous learning system. AI tools are being deployed across the full deal lifecycle—target screening, due diligence, integration planning, and post-merger performance tracking—compressing timelines and surfacing patterns that human teams would miss across large deal portfolios. Companies that institutionalize AI-driven deal intelligence are expected to achieve measurably better integration outcomes and synergy realization compared to those relying on traditional playbooks. The piece positions M&A capability as a compounding strategic asset when AI is embedded in the process, with implications for how boards and executive teams should resource and govern their deal functions.

    3 minRead
    BCG PublicationsJune 10

    From AI Upskilling to AI Performance: Five Questions Every CEO Should Ask

    BCG argues that AI upskilling programs are failing to translate into measurable business performance, and frames five diagnostic questions CEOs must ask to close that gap. The core thesis is that skill acquisition and performance improvement are distinct problems requiring distinct interventions — most organizations have conflated the two. BCG identifies the breakdown points as insufficient workflow integration, lack of accountability structures, and misaligned incentives between learning teams and operating units. The framework pushes leaders to move from tracking training completion rates to tracking AI-attributable productivity and output metrics. Closing the loop between capability building and performance realization is positioned as the defining enterprise AI execution challenge of 2026.

    3 minRead
    BCG PublicationsJune 10

    Meet the New Generation of AI Disruptors

    BCG identifies a new generation of AI-native disruptors that are deploying artificial intelligence as a core architectural element rather than a bolt-on capability, enabling them to compress cost structures, accelerate product cycles, and challenge incumbents across multiple industries simultaneously. These companies are building with agent-based and autonomous AI systems from the ground up, giving them structural cost and speed advantages that traditional enterprises cannot easily replicate through incremental AI adoption. Established firms face a dual threat: erosion of existing revenue pools and the inability to match the operating economics of AI-native entrants without fundamental operating model redesign. BCG's analysis implies that incumbents must make deliberate, near-term choices about where to defend, partner, or transform—decisions that carry direct implications for capital allocation, technology investment priorities, and competitive positioning. The findings are materially relevant to enterprise leaders assessing AI investment strategy, platform architecture, and board-level strategic risk.

    3 minRead
    BCG PublicationsJune 8

    Future of Finance 2026: Time to Shift Gears?

    BCG's 2026 Future of Finance report finds that finance functions have reached an inflection point where incremental efficiency gains are no longer sufficient—CFOs must now reorient the function toward value creation and strategic decision support. Despite years of digital investment, most finance organizations remain anchored to transactional work, with AI adoption delivering isolated productivity wins rather than structural transformation. The report argues that leading finance functions are shifting from cost-center to insight engine, deploying AI agents across planning, forecasting, and reporting to compress cycle times and redeploy headcount toward higher-value analysis. BCG identifies three priority shifts: accelerating agentic AI deployment in core finance processes, redesigning the operating model around outcomes rather than tasks, and elevating the CFO's role as a strategic partner to the business. Organizations that delay this transition risk a widening capability gap as early movers compound productivity and analytical advantages.

    3 minRead
    BCG Publications

    How CIOs Can Prove the Value of Technology in the Age of AI

    BCG proposes replacing single-metric ROI measurement for IT investments with a three-part framework—Operate, Expand, Innovate—arguing that forcing technology spend into a single ROI yardstick causes chronic underinvestment and misaligned governance. A BCG survey finds companies plan to more than double AI investment to 1.7% of revenues in 2026, yet 15 years of data show IT spending has remained essentially flat as a percentage of both revenue and operating expense across all major industries, suggesting value capture—not capability deployment—is the core problem. The CIO-CFO tension is structural: CFOs require attributable, timely, and repeatable financial outcomes, while CIOs are accountable for competitive positioning on timelines that don't map to quarterly financials. BCG's 10-20-70 rule underlies the diagnosis—10% of AI value comes from technology, 20% from data and algorithms, and 70% from people, process, and operating model change—meaning organizations that invest in AI without complementary structural redesign risk repeating the 1980s productivity paradox.

    3 minRead
    BCG PublicationsJune 4

    Vibe Coding Is Coming to Finance. CFOs Need Guardrails

    BCG argues that 'vibe coding'—AI-assisted, natural-language-driven software development—is reaching finance functions, enabling non-technical finance staff to build scripts, models, and automations without formal engineering oversight. While this democratizes development and can accelerate FP&A, reporting, and analysis workflows, it introduces material risks: ungoverned code touching financial data, controls gaps, and potential SOX compliance exposure. BCG contends CFOs must act now to establish guardrails covering code review, data access permissions, audit trails, and acceptable-use policies before ad-hoc AI-generated tools proliferate across the finance organization. The piece frames this as a governance and operating-model challenge requiring coordination across finance, IT, and audit leadership.

    3 minRead
    BCG PublicationsJune 2

    How AI Agents Are Transforming Supply Chains

    BCG argues that AI agents are reshaping supply chains from reactive, human-coordinated systems into autonomous, continuously optimizing networks — what the firm calls the "AI-first supply chain." Agentic AI can monitor demand signals, inventory positions, supplier risk, and logistics variables simultaneously, triggering decisions and executing actions across procurement, planning, and fulfillment without human intervention at each step. Early adopters are targeting measurable outcomes including inventory reduction, service-level improvement, and working capital release, with BCG positioning multi-agent architectures as the next competitive differentiator beyond single-use-case AI deployments. The piece outlines a maturity progression from assisted decision-support to fully autonomous supply chain operations, and stresses that data infrastructure, governance frameworks, and change management are as critical as the AI models themselves.

    3 minRead
    BCG PublicationsJune 1

    From Recovery to Resurgence in Global Fintech

    Global fintech is entering a resurgence phase after a multi-year post-2021 correction, with BCG's 2026 report mapping the sector's recovery trajectory and identifying where durable growth is concentrating. Funding and valuation metrics have stabilized, and a cohort of scaled fintechs are demonstrating profitable, sustainable business models rather than growth-at-any-cost economics. AI adoption is accelerating differentiation across payments, lending, and wealth management, compressing incumbents' product and cost advantages. The report argues that financial institutions and investors must reassess competitive positioning now, as the window to partner with or acquire structurally advantaged fintechs is narrowing. Strategic priorities highlighted include embedded finance, cross-border payments infrastructure, and AI-native underwriting and compliance stacks.

    3 minRead
    BCG PublicationsMay 29

    Want Consumer Insights Faster? AI Can Help.

    BCG argues that AI can materially accelerate the speed and scale at which companies generate consumer insights, compressing research cycles that traditionally take weeks into hours or days. The piece positions AI-powered insight generation as a competitive differentiator for consumer-facing businesses, enabling faster decision-making on product, pricing, and marketing. AI tools can synthesize large volumes of qualitative and quantitative consumer data—surveys, social signals, transaction data—at a fraction of legacy research costs. BCG frames this as an operating model shift for insights functions, requiring investment in data infrastructure, prompt engineering, and human oversight to ensure output quality and reduce hallucination risk.

    3 minRead
    BCG Publications

    How the Factory of the Future Is Reshaping the Economics of Manufacturing Competitiveness

    BCG analysis of 1,000 manufacturers finds that AI-enabled Factory of the Future (FoF) configurations can reduce labor requirements by up to 60% and total conversion costs by more than 40 percentage points, fundamentally reordering the economics of global manufacturing location decisions. Roughly $1.03 trillion of manufacturing value is at risk of relocation out of Western Europe and the Nordics, with another $440 billion at risk in the United States, making footprint strategy a board-level economic decision. BCG's 42-factor Manufacturing Competitiveness Index shows highly differentiated outcomes by sector: a European food manufacturer adopting FoF gains a 14-percentage-point cost advantage over relocating to China, while an electronics manufacturer still faces a 15-point gap even after full FoF deployment. The decisive variable has shifted from relative labor cost to a facility's capacity for end-to-end production redesign integrating agentic AI, physical automation, and high-fidelity simulation. CEOs must evaluate six dimensions—localization strategy, FoF cost impact, ability to realize productivity gains, tariff exposure, local digital and workforce readiness, and brownfield vs. greenfield context—before committing capital. The implication is that AI investment economics and capital allocation for manufacturing assets now require a fundamentally new analytical framework.

    3 minRead
    BCG PublicationsMay 27

    Physical AI Will Reshape the Economics of Automation

    Physical AI — AI systems that perceive, reason, and act in the physical world through robotics and autonomous machinery — is poised to fundamentally restructure the economics of industrial automation. BCG's analysis argues that physical AI lowers the cost and complexity barriers that have historically confined automation to high-volume, highly standardized production environments, opening automation economics to a far broader range of industrial use cases. The shift compresses payback periods on capital deployment, alters make-vs-buy decisions for manufacturers, and creates new competitive dynamics as software-driven flexibility displaces purpose-built hardware. Enterprises across manufacturing, logistics, and industrial goods must reassess capital allocation frameworks, workforce cost assumptions, and vendor strategies as physical AI matures from pilot to at-scale deployment.

    3 minRead
    BCG PublicationsMay 26

    Unilever CFO Srinivas Phatak on How to Perform and Transform

    Unilever CFO Srinivas Phatak argues that the modern CFO must simultaneously drive near-term performance and long-term transformation rather than treating the two as sequential priorities. Phatak emphasizes that finance leadership requires active stewardship of capital allocation decisions that balance current business delivery with investment in structural change. He outlines how the CFO role has expanded beyond financial reporting to encompass strategic partnership with the CEO and operating units, requiring finance leaders to engage directly with business model evolution. The piece positions disciplined resource reallocation and a clear-eyed view of value creation as the connective tissue between strategy and execution.

    3 minRead
    BCG PublicationsMay 21

    Always-On Retention: How AI Is Rewiring Insurance Growth

    BCG argues that AI is transforming insurance customer retention from a periodic, renewal-driven activity into a continuous, data-driven engagement model. Insurers deploying always-on AI retention systems can identify churn signals earlier and intervene with personalized offers, reducing policy lapses and improving lifetime customer value. The shift requires integrating AI agents into existing distribution and servicing workflows, supported by robust data infrastructure and governance. BCG positions this capability as a primary growth lever for insurers competing on retention economics rather than acquisition cost alone.

    3 minRead
    BCG PublicationsMay 21

    M&A Is Not a Coin Flip—If You Manage the Right Risks

    BCG challenges the widely cited statistic that most M&A deals fail, arguing that poor outcomes are not random but stem from manageable, identifiable risks. The research distinguishes between deal types—scale, scope, and transformational acquisitions—each carrying a distinct risk profile that requires tailored mitigation strategies. Execution risks such as integration planning, talent retention, and cultural alignment are shown to be more predictive of failure than deal thesis quality, and acquirers who address these systematically outperform peers. BCG's data indicates that companies with repeatable M&A capabilities and disciplined risk management generate materially higher total shareholder returns than infrequent or reactive dealmakers.

    3 minRead
    BCG PublicationsMay 20

    Four Ways to Accelerate Growth with AI and Analytics

    BCG identifies four growth acceleration levers that combine AI and analytics: smarter commercial decision-making, faster product and service innovation, optimized pricing and margin management, and more effective customer acquisition and retention. Companies that deploy these capabilities in an integrated way outperform peers on revenue growth and profitability, according to BCG's analysis. The framework positions AI not as a back-office efficiency tool but as a front-line growth driver requiring cross-functional coordination across commercial, technology, and data functions. Realizing the full value depends on aligning data infrastructure, talent, and operating model changes alongside the AI deployments themselves.

    3 minRead
    BCG PublicationsMay 20

    The Logic of Partnerships with Chinese Firms Has Flipped

    BCG argues that the strategic rationale for Western companies partnering with Chinese firms has fundamentally inverted. Historically, multinationals sought Chinese partners primarily for market access, accepting technology transfer as the price of entry; today, Chinese firms in sectors from EVs to AI to industrial equipment have become genuine technology and capability leaders, meaning Western companies now risk being the ones transferring competitive advantage rather than gaining it. The geopolitical environment—export controls, decoupling pressure, and dual-use technology scrutiny—has simultaneously raised the regulatory and reputational cost of such partnerships. BCG frames this as a strategic reassessment imperative: executives must audit existing and prospective Chinese partnerships against a new calculus that weights capability parity, IP exposure, and geopolitical risk far more heavily than market access potential. Boards and senior leadership teams in globally exposed industries should treat this as a governance and capital-allocation question, not merely a business-development one.

    3 minRead
    BCG PublicationsMay 19

    AI Is Rewriting M&A’s Tech and Digital Playbook | BCG

    AI is fundamentally changing how acquirers assess and integrate technology and digital assets in M&A transactions, shifting diligence from a technical checklist exercise to a strategic evaluation of AI readiness and capability. Deals are increasingly being won or lost based on a target's AI infrastructure, data assets, and talent pipeline, with acquirers paying premiums for companies that have scalable AI foundations. BCG argues that traditional post-merger integration timelines and playbooks are inadequate for AI-native businesses, requiring faster decisions on tech stack rationalization and model governance. Acquirers must now evaluate targets across three dimensions: AI-enabled revenue potential, data quality and proprietary training assets, and organizational capacity to sustain AI development post-close. Failure to embed AI diligence into deal structuring and integration planning materially increases execution risk and value leakage.

    3 minRead
    BCG PublicationsMay 18

    The Capital Opportunity in AI-Enabled Sustainability | BCG

    BCG identifies AI-enabled climate and sustainability sectors as a significant private capital opportunity, arguing that AI is accelerating the commercial viability and scalability of cleantech and sustainability solutions. The convergence of AI capabilities with decarbonization imperatives is creating investable platforms across energy transition, grid optimization, carbon markets, and sustainable agriculture. BCG frames this as a multi-decade capital deployment theme, with early-mover private investors positioned to capture outsized returns as these sectors move from pilot to scale. The piece is directed at private equity, venture, and institutional capital allocators evaluating how to position portfolios at the intersection of AI and sustainability.

    3 minRead
    BCG PublicationsMay 12

    The CFO’s AI Agenda: From Automation to Advantage | BCG

    BCG's May 2026 report positions the CFO as the primary owner of enterprise AI strategy, arguing that finance leaders must move beyond cost-reduction automation toward using AI to generate competitive advantage in planning, forecasting, and capital allocation. The piece outlines a staged AI adoption agenda for finance functions, emphasizing that CFOs who limit AI to back-office efficiency will cede strategic ground to those deploying it in FP&A, scenario modeling, and decision support. BCG identifies three capability gaps most finance organizations must close: data quality and integration, AI governance frameworks, and talent with hybrid finance-technology skills. The report frames capital allocation toward AI infrastructure as a CFO-level fiduciary decision, not a CIO budget item.

    3 minRead

    Deloitte

    26 articles
    Deloitte Insights

    FSI Predictions 2026

    Deloitte's FSI Predictions 2026 report identifies converging technological and structural forces—agentic AI, stablecoins, blockchain, and regulatory shifts—as drivers of the most significant operational transformation in U.S. financial services in decades. On the consumer side, agentic AI in wealth management is projected to deliver 30%–100% productivity gains by 2032, potentially freeing 25%–50% of adviser time and expanding industry capacity by the equivalent of $10–$35 trillion in additional client assets. AI-enabled life insurance distribution could add approximately $2 billion in annual incremental premiums by 2030, growing the individual life market to $21.2 billion. Private capital exposure is forecast to reach one in six U.S. retail investor funds by 2030, while stablecoins and blockchain are reconstructing payment rails and fund operating infrastructure. The report's eight chapters span both consumer-access expansion and the underlying operational rebuild, framing these shifts as mutually reinforcing: infrastructure breakthroughs make broader access economically viable, and rising demand justifies transformation investment.

    3 minRead
    Deloitte Insights

    The $9 trillion knowledge exodus: How organizations can turn baby boomer retirements into a competitive advantage

    Deloitte estimates a $9 trillion knowledge exodus as baby boomers retire, and argues that organizations must implement structured knowledge management programs before that expertise disappears permanently. The framework centers on five sequential steps: establishing a single authoritative knowledge foundation, using analytics to identify the critical 20% of content that resolves 80% of issues, systematically capturing departing expertise through structured transfer models and AI-assisted documentation, aligning culture and incentives to drive knowledge-sharing behaviors, and embedding knowledge capture into daily workflows. AI deployment on fragmented knowledge infrastructure amplifies existing gaps rather than closing them, making foundational consolidation a prerequisite for reliable AI output. Case examples include a European telecom that improved first-contact resolution by 37% and cut new-hire ramp time by 50% after consolidating four knowledge silos, and a European energy utility whose documented gas-leak diagnostic protocols—originally held only by retiring field engineers—enabled a two-week-tenured agent to direct a customer to evacuate a home that subsequently exploded.

    3 minRead
    Deloitte Insights

    How stablecoins could power the next era of retail payments

    Deloitte projects stablecoins will enable more than $200 billion in US retail payments by 2030, with the instruments supporting an estimated 2.5% of US noncash transactions through backend settlement, processing, or funding mechanisms. Near-term value accrues primarily to multinationals reducing cross-border transaction costs, but adoption is expected to broaden to domestic retail commerce as card networks including Visa and Mastercard launch stablecoin-backed debit and credit products that convert holdings to fiat at point of sale, lowering merchant processing fees that currently exceed 2% per transaction. Three catalysts are identified: stablecoin-linked payment cards riding existing network rails, agentic AI executing autonomous consumer purchases that favor programmable settlement, and branded merchant loyalty programs creating stablecoin network effects. Broader adoption is forecast to reach a tipping point around 2028, with financial institutions simultaneously building tokenized deposit infrastructure and exploring on-chain collateralized credit products.

    3 minRead
    Deloitte Insights

    Tech Trends 2026

    Deloitte's Tech Trends 2026 report identifies five interconnected forces reshaping enterprise technology over the next 18–24 months: AI-robotics convergence in physical operations, the gap between agentic AI pilots and production deployments (only 11% of organizations have agents in production vs. 38% piloting), AI infrastructure economics (token costs down 280-fold in two years yet enterprise bills reaching tens of millions monthly), wholesale IT operating model restructuring (only 1% of IT leaders report no major changes underway), and AI-driven cybersecurity threats. The central thesis is that existing infrastructure, processes, and operating models built for cloud-era and human workforces cannot scale AI to production—organizations must redesign rather than automate. Gartner projects 40% of agentic AI projects will fail by 2027, primarily due to automating broken processes rather than transforming them. Leaders who succeed prioritize business-problem framing, velocity over perfection, and continuous organizational evolution.

    3 minRead
    Deloitte Insights

    TMT Predictions 2026: The gap narrows, but persists

    Deloitte's TMT Predictions 2026 report covers 13 technology, media, and telecom trends shaping enterprise and consumer markets. Key findings include: inference will account for two-thirds of all AI compute by 2026, concentrated in data centers and enterprise servers worth over $650 billion combined rather than edge devices; the autonomous AI agent market could reach $8.5–$45 billion by 2030 depending on enterprise orchestration maturity; and agentic AI will likely shift SaaS pricing away from seat-based models toward consumption- and outcome-based structures, increasing complexity in financial planning and vendor management. Semiconductor supply chains face new chokepoints as trade restrictions expand beyond EUV lithography to additional advanced chip technologies and software tools. Technology sovereignty investments will accelerate globally across cloud, semiconductors, data centers, and AI infrastructure, with direct implications for multinational operating models and procurement strategy.

    3 minRead
    Deloitte InsightsJuly 29

    Weekly Global Economic Update

    Disruptions at the Strait of Hormuz and Strait of Bab el Mandeb have effectively cut two major global oil-transit corridors for over four months, pushing crude briefly to $100/barrel and European natural gas to a four-year high. The more consequential price signal is the crack spread—the gap between crude and refined products—which has more than tripled since the conflict began, placing the true end-consumer cost of oil near $140/barrel equivalent and raising freight costs as diesel refining capacity shrinks globally. US Strategic Petroleum Reserve holdings are at their lowest since the early 1980s, and China's return to crude import markets could trigger a sharp additional price spike. Bond markets are repricing inflation risk: US 10-year Treasury yields rose to 4.66% and futures markets now assign a 54.9% probability of two or more Fed rate hikes in 2026, while ECB rate-hike probability for September stands at roughly 85%.

    3 minRead
    Deloitte InsightsJuly 24

    Oil prices and AI investment play major role in the US economic forecast for 2026–2031

    Deloitte's Q2 2026 US Economic Forecast presents three scenarios for growth through 2031, with oil prices and AI capital expenditure as the dominant variables. The baseline projects real GDP growth of 2.0% in 2026 and 1.8% in 2027, supported by AI-related fixed business investment revised up to 6.1% in 2026, though offset by inflation running at 4.2% year-over-year in May and an expected Fed rate hike before year-end. The downside scenario assumes Brent crude averaging $106/barrel in 2026 and an AI investment bust mirroring the dot-com collapse, producing a 1% real GDP decline in 2028 and unemployment reaching 6.5%. The upside scenario presumably reflects lower oil prices and sustained AI productivity gains; longer-term, Deloitte has revised its 2030 real GDP forecast upward to 2.1% from 1.7% on stronger AI-led productivity expectations. Fiscal policy is expected to turn modestly contractionary as the stimulus effect of the One Big Beautiful Bill Act fades, consumer savings sit at an extreme low of 2.6%, and working-age population growth approaches zero due to reduced immigration.

    3 minRead
    Deloitte Insights

    CFO-ready, or not? 5 gaps that can quietly hold finance leaders back from advancement

    Deloitte identifies five readiness gaps that prevent high-performing finance leaders from advancing to the CFO role: insufficient executive presence and communication skills, limited cross-functional influence, narrow operational exposure beyond finance, failure to delegate and develop teams, and inability to operate as an enterprise strategist rather than a functional leader. A 2024 Deloitte survey of 200 North American CFOs ranked communication skills as the most valued quality in a CFO successor, cited by 39% of respondents. The article notes that AI leadership is emerging as a concrete test of strategic readiness, with aspiring CFOs expected to translate AI initiatives into enterprise value through funding decisions, process redesign, and governance — not merely piloting tools. Deloitte's Finance Trends 2026 survey found 57% of finance leaders already position themselves as top strategy influencers, reflecting the role's continued shift toward enterprise leadership.

    3 minRead
    Deloitte Insights

    FSI Predictions 2026

    Deloitte's FSI Predictions 2026 report outlines forward-looking forecasts across banking, capital markets, insurance, investment management, and commercial real estate. The piece is structured as Deloitte's annual financial services industry outlook, covering strategic, technological, and regulatory shifts expected to shape the sector. However, the article body as submitted contains only navigation chrome and no substantive prediction content, making it impossible to extract specific findings, numbers, or actionable theses. Financial services executives should access the full report directly for sector-specific forecasts relevant to their planning cycles.

    3 minRead
    Deloitte Insights

    2026 Global Human Capital Trends: From Tensions to Tipping Points — Choosing the Human Advantage

    Deloitte's 2026 Global Human Capital Trends report frames AI adoption as a strategic inflection point where organizations must actively choose to preserve and amplify human capabilities rather than default to automation-first approaches. The report identifies mounting tensions between workforce efficiency gains from AI and the organizational risks of eroding human judgment, creativity, and accountability. Leaders are urged to redesign work around human-AI collaboration models that sustain competitive differentiation through distinctly human skills. The research draws on global survey data across industries and geographies to benchmark how organizations are navigating talent strategy, workforce architecture, and the governance of AI in human capital decisions.

    3 minRead
    Deloitte InsightsJune 9

    Tech Investment Boom

    Real tech investment in the US has grown 2.6% per quarter on average since 2023—more than double the 1.2% pace of total business investment—and now accounts for roughly one quarter of real GDP growth, a share that doubled over the five quarters through Q1 2026. Productivity in the nonfarm business sector has grown at 2.6% per quarter since 2023, more than double the prior decade's pace, but gains are concentrated in tech sectors (2.2% per quarter output-per-employee growth) versus just 0.5% for the broader private sector. Despite rising productivity and declining tech-sector payrolls (down ~0.4% per quarter since 2023), there is no evidence of broad AI-driven job displacement; AI-exposed occupations such as computer and mathematical roles have grown 5.7% since 2023. Deloitte economists flag a material downside risk: an unwinding of the AI investment bubble could weigh on capital spending and equity markets, with the Nasdaq up 34.3% since early 2025, amplifying wealth-effect exposure for consumer spending.

    3 minRead
    Deloitte Insights

    The dual mandate redefining the future of tech leadership

    Deloitte's 2026 Global Technology Leadership Study, drawing on 660+ tech leaders globally, identifies a widening gap between the enterprise mandate for tech C-suites and how those leaders actually define success. While delivering measurable business outcomes ranks as the top enterprise priority, CIOs and CTOs omit it from their top three personal success metrics, instead centering self-evaluation almost exclusively on AI-linked KPIs. Structural fragmentation is intensifying the challenge: 71% of surveyed organizations now have five or more C-suite technology roles, and 89% allocate no more than 25% of tech budgets to AI despite its stated priority status. Technology spend remains near 6% of revenue—unchanged from 2023—even as leaders are asked to fund operational stability, growth, and transformation simultaneously. The study argues that the emerging mandate is dual: deep technical fluency in AI, architecture, cybersecurity, and emerging technology combined with enterprise leadership capable of translating technology vision into measurable business value. Leaders who treat AI as a lens of self-evaluation while underdelivering on broader outcomes risk reinforcing the fragmentation and inefficiency AI is meant to eliminate.

    3 minRead
    Deloitte Insights

    Agentic AI is scaling faster than guardrails

    Deloitte's survey of business and IT leaders finds that AI agent deployments are outpacing the governance frameworks designed to oversee them, creating material risk exposure as organizations scale autonomous systems without commensurate controls. A significant share of respondents report that agentic AI is already operating in production environments, yet fewer than half have established formal guardrails covering accountability, error handling, or audit trails. The governance gap is particularly acute for multi-agent architectures, where decision chains span systems and ownership of outcomes becomes ambiguous. Deloitte recommends that enterprises treat agent governance as a board-level and cross-functional priority, requiring joint ownership across technology, data, finance, and risk leadership to close the gap between deployment velocity and risk management maturity.

    3 minRead
    Deloitte Insights

    Rethinking Skills-Based Talent Models: 4 Paths to Business Value

    Deloitte's analysis of skills-based talent models identifies four distinct outcomes organizations pursue: becoming an employer of choice (54% of analyzed organizations), improving productivity and efficiency (46%), building organizational agility (36%), and driving innovation and growth (28%). Regardless of which outcome they target, leading organizations share four foundational practices: a simplified job architecture, a skills library mapping supply and demand, identification of a prioritized set of critical skills, and robust change management. The research found that skills initiatives fail not on design or technology but on adoption and behavior change, making trust-building and embedding skills into decision-making essential. Organizations should tailor their approach beyond these foundations to their specific goal—for example, employer-of-choice pursuits favor self-reported skills and internal mobility platforms, while productivity paths require shifting workforce planning from headcount to skills-based capacity matching.

    3 minRead
    Deloitte InsightsMay 12

    3 actions top executives and board leaders can take to help safeguard data credibility

    Deloitte argues that data credibility has become a board-level strategic risk, not merely a technical concern, as AI systems amplify the downstream consequences of poor data quality. The piece outlines 3 concrete actions top executives and board directors can take to strengthen data governance frameworks: establishing clear data ownership and accountability structures, embedding data quality controls into core business processes, and building oversight mechanisms that make data provenance and lineage visible to decision-makers. As AI and automation rely increasingly on enterprise data as a primary input, organizations with weak governance face compounding risk across financial reporting, regulatory compliance, and strategic planning. The authors position data credibility as a prerequisite for trustworthy AI outputs, making governance investment directly tied to ROI on broader digital transformation programs.

    3 minRead
    Deloitte InsightsMay 7

    North American CFOs express concerns about AI governance and risk management

    Deloitte's Q2 2026 CFO Signals survey of 200 North American CFOs at organizations with $1B+ in revenue finds that only 43% feel confident in their organization's current AI governance, while 53.5% feel only somewhat confident. AI adoption has accelerated sharply: 93% of respondents now use AI extensively or modestly across multiple functions, up from a majority still in the experimentation phase less than three years ago. The top governance challenge for 59% of CFOs is balancing pressure to deploy AI quickly against managing associated risks. Internally, 46% cite cost uncertainty and lack of pricing transparency as their biggest AI concern, driven largely by consumption-based vendor billing models, while 43% flag litigation risk related to protected or private content as their primary external concern.

    3 minRead
    Deloitte InsightsMarch 23

    Rewiring the enterprise operating model for AI scale

    Deloitte's 2026 Global Technology Leadership Study of 660+ technology executives finds that scaling AI is no longer a technology challenge but an enterprise operating model challenge. While 81% of executives say they can deploy and govern AI at scale today, nearly 75% acknowledge their operating model must change within 12–18 months to sustain progress. The report identifies five structural shifts required: integrated tech leadership (71% of firms already have five or more C-suite tech leaders, creating coordination risk), AI-human work redesign, dynamic funding models moving away from project-based capital allocation, deeper ecosystem partnerships, and more frequent operating model redesign cycles. Leaders are warned that hierarchical decision-making, siloed governance, and static funding structures built for an era when technology was a support function will constrain AI value realization at enterprise scale.

    3 minRead
    Deloitte InsightsMarch 12

    How AI-native banking products could reshape institutional banking

    Deloitte's 2026 FSI Predictions piece argues that AI-native products—banking offerings designed around AI capabilities from inception rather than retrofitted—are poised to fundamentally restructure institutional banking's product and service model. Unlike incremental AI deployments, AI-native products embed real-time data synthesis, autonomous decision-making, and continuous learning directly into core banking functions such as lending, treasury, trade finance, and risk management. The shift moves competitive differentiation away from balance sheet scale and relationship coverage toward data architecture, model quality, and speed of product iteration. Banks and their large corporate clients face parallel strategic decisions: incumbents must determine whether to build, partner, or acquire AI-native capabilities, while corporate treasurers and CFOs must assess how AI-native banking products will alter pricing, covenant structures, and access to liquidity. Governance and regulatory frameworks lag product development, creating execution risk that boards and CIOs must factor into adoption timelines.

    3 minRead
    Deloitte InsightsMarch 12

    How AI-native banking products could reshape institutional banking

    Deloitte's Center for Financial Services predicts AI-native banking products could represent up to 25% of institutional banking revenues among the top 50 US banks by 2030, equating to approximately $66 billion in the base case and exceeding $75 billion in an upside scenario. The report draws a key distinction between AI-enabled products—which improve existing features—and AI-native products, where AI is built into the core architecture from the ground up. Target product categories include treasury-orchestration platforms, intelligent payment-routing engines, intraday liquidity optimizers, trade-documentation agents, receivables-reconciliation systems, and continuous credit-monitoring tools. In this model, AI functions as the operating engine rather than an enhancement layer, with autonomous agents making decisions within governance guardrails and executing actions while escalating exceptions. The shift signals a structural move from AI as an internal productivity tool to AI as a direct driver of topline revenue growth in institutional banking.

    3 minRead
    Deloitte InsightsOctober 16

    SaaS meets AI agents: Transforming budgets, customer experience, and workforce dynamics

    Deloitte's 2026 TMT Predictions forecast that AI agents will structurally disrupt the SaaS market by shifting enterprise software procurement from seat-based licensing toward outcome- or consumption-based models, compressing traditional SaaS budgets. As AI agents automate tasks previously requiring human-operated SaaS workflows, enterprises face simultaneous pressure to rationalize existing software spend while funding new AI agent infrastructure. The convergence is expected to reshape customer experience delivery—with agents handling more end-to-end service interactions—and accelerate workforce restructuring as roles tied to manual SaaS operation become redundant. CIOs and CFOs in particular must reassess software portfolio economics and operating model assumptions, as the cost and value calculus for enterprise platforms shifts materially.

    3 minRead
    Deloitte InsightsJuly 3

    New technologies and familiar challenges could make semiconductor supply chains more fragile

    Deloitte's 2026 TMT Predictions warn that emerging supply chain technologies—including AI-driven demand forecasting, digital twins, and blockchain-based traceability—are unlikely to resolve the structural fragility of semiconductor supply chains and may introduce new failure modes. The semiconductor industry remains concentrated, with TSMC accounting for roughly 90% of leading-edge chip production, creating single-point-of-failure risk that no software layer fully mitigates. Geopolitical tensions, export controls, and the capital intensity of fab construction (often exceeding $20 billion per facility) constrain how quickly geographic diversification can reduce exposure. Deloitte projects that despite significant technology investment, most enterprises will remain vulnerable to supply disruptions through at least the mid-2020s, and boards and operations leaders should treat supply chain resilience as a strategic priority rather than a procurement problem.

    3 minRead
    Deloitte InsightsJune 23

    4 shifts are shaping technology infrastructure. How can leaders avoid creating systems they can't change?

    Deloitte identifies 4 structural shifts reshaping enterprise technology infrastructure — AI workload demands, edge computing proliferation, sustainability pressure, and accelerating cloud complexity — and argues that leaders who make rigid infrastructure commitments today risk building systems they cannot adapt as these forces compound. The central risk is infrastructure lock-in: organizations optimizing for current AI and compute requirements may find their architectures obsolete as model sizes, inference patterns, and energy constraints evolve rapidly. Deloitte urges a composable, modular infrastructure philosophy that prioritizes reversibility and optionality over short-term cost efficiency. Capital allocation decisions made now — particularly around data center capacity, cloud vendor concentration, and on-premise AI hardware — will define organizational agility for the next decade.

    3 minRead
    Deloitte InsightsJune 18

    Have organizational functions outlived their function?

    Deloitte's 2026 Global Human Capital Trends report finds that 66% of C-suite leaders consider it very or extremely important to push beyond traditional organizational function boundaries, yet only 7% report making great progress. Functions such as HR, finance, IT, legal, and procurement—originally built for efficiency and specialization—are increasingly misaligned with the cross-functional collaboration required to capture AI value, execute transformation, and respond to market volatility at speed. The core problem: functional silos limit end-to-end data and process flows, headcount and leadership layers have grown without driving business growth, and capability is mismatched to dynamic, multidisciplinary needs. Deloitte argues organizations should deconstruct traditional corporate functions and reassemble their capabilities around human and business outcomes rather than structural inheritance.

    3 minRead
    Deloitte InsightsMay 20

    Reinventing workforce planning for an AI-powered, uncertain world

    Traditional annual workforce planning cycles are structurally misaligned with the pace of AI-driven disruption and macroeconomic volatility, making static headcount models obsolete. Deloitte argues organizations must replace point-in-time forecasts with continuous, scenario-based workforce planning that integrates skills data, business strategy, and AI-augmentation assumptions in real time. The reinvented model shifts the unit of planning from roles and headcount to skills and work, enabling dynamic reallocation of human and AI capacity as conditions change. Finance and technology leadership are directly implicated, as the new planning architecture requires renegotiating workforce cost structures, replatforming talent data systems, and redefining the ROI calculus for AI investment versus human labor. Organizations that fail to modernize their planning operating model risk both talent misalignment and capital misallocation as AI reshapes which work requires human execution.

    3 minRead
    Deloitte InsightsAugust 13

    A time to pivot: Four ways US M&A leaders are adapting to 2025 conditions

    Deloitte's May 2025 analysis identifies four strategic pivots US M&A leaders are making in response to elevated interest rates, tariff uncertainty, and volatile valuations that have suppressed deal volumes from post-pandemic highs. Dealmakers are narrowing target screens toward assets with supply-chain resilience and domestic revenue profiles to hedge tariff exposure, while also accelerating AI-capability acquisitions as organic build timelines stretch. Due diligence processes are being compressed through AI-assisted analysis, but simultaneously deepened on regulatory and geopolitical risk dimensions that were previously treated as secondary. Portfolio rationalization and carve-outs are gaining traction as companies shed non-core assets to fund strategic acquisitions and improve balance-sheet flexibility in a higher-cost-of-capital environment.

    3 minRead
    Deloitte InsightsApril 11

    More compute for AI, not less

    Deloitte's 2026 TMT Predictions argue that AI's next phase will require substantially more computational power, not less, directly countering narratives that efficiency gains from models like DeepSeek signal a coming reduction in compute demand. The piece contends that lower inference costs historically expand usage volumes enough to increase total compute consumption — a dynamic consistent with Jevons' paradox. Agentic AI workflows, longer reasoning chains, and multimodal capabilities are identified as primary drivers of accelerating compute demand at both training and inference stages. Enterprises planning AI infrastructure investments should expect capital requirements to grow, not plateau, making compute capacity a strategic constraint rather than a cost to optimize away.

    3 minRead

    EY

    18 articles
    EY Insights

    DNA of the CFO Survey

    The EY Global DNA of the CFO Survey finds a significant gap between CFO ambition and action on enterprise value creation: while 60% of CFOs say they should define and shape how their organization creates value, only 25% actually lead investment decisions where returns are uncertain or long-term. AI adoption in finance remains limited, with just 21% of CFOs rating their function's AI preparedness as leading or advanced, and 61% citing data quality and bias as their top barrier to securing AI investment. Where AI has been deployed effectively—such as an agentic purchase-to-pay solution that reduced required capacity by 85%—the impact is material, but most finance functions have not moved beyond defensive applications like fraud detection. The survey, covering responses from a global CFO cohort, concludes that finance transformation is fundamentally human-centric: closing the ambition-action gap depends as much on CFO mindset, skill development, and operating model redesign as on technology investment.

    3 minRead
    EY Insights

    Four AI misconceptions that deserve greater scrutiny

    EY identifies four persistent AI misconceptions that enterprise leaders must reassess to extract measurable value from their investments. The piece argues that AI's impact will be real but more gradual than hype suggests, requiring leaders to calibrate expectations against actual deployment timelines and productivity curves. Organizations that treat AI adoption as a binary milestone rather than an ongoing management discipline risk misallocating capital and misreading ROI signals. The framework urges executives to move from adoption metrics toward value-realization governance, scrutinizing token costs, use-case prioritization, and operating model fit as the primary performance variables.

    3 minRead
    EY Insights

    Unlocking agentic value: a new investment discipline for the agentic era

    EY's analysis establishes that agentic AI requires a fundamentally new investment discipline, distinct from traditional software or generative AI budgeting, because token consumption in multi-step autonomous workflows scales non-linearly and unpredictably with task complexity. Enterprises deploying agentic systems face a new cost unit—the token—that accumulates across reasoning loops, tool calls, and context windows, making per-task economics difficult to forecast under legacy IT spend models. EY argues that CFOs and CIOs must build token cost visibility into business cases from inception, treating inference spend as a variable operational cost rather than a fixed capital outlay. Without active token governance—including model selection, context pruning, and workflow design discipline—agentic deployments risk eroding ROI even as they deliver automation value. The piece frames token cost management as a board-relevant strategic capability, not merely a technical optimization, as AI inference spend scales enterprise-wide.

    3 minRead
    EY Insights

    EU Taxonomy Barometer 2025: Key Insights and Next Steps

    EY's EU Taxonomy Barometer 2025 examines how evolving EU Taxonomy regulations are reshaping sustainability reporting obligations for large companies operating in or exposed to European markets. The framework classifies economic activities by environmental sustainability criteria, requiring companies to disclose the proportion of revenue, capital expenditure, and operating expenditure aligned with EU-defined thresholds. Compliance demands significant data infrastructure investment and cross-functional coordination across finance, accounting, and reporting teams. As the regulatory perimeter expands and assurance requirements tighten, companies face mounting pressure to operationalize taxonomy alignment at scale and demonstrate audit-ready disclosures.

    3 minRead
    EY Insights

    How focusing on cash can support a value-added finance transformation

    EY argues that embedding a 'cash culture' into finance transformation programs generates measurable value beyond cost reduction by making working capital and liquidity optimization central to the finance operating model. The piece positions cash visibility—spanning receivables, payables, and inventory—as a strategic lever that finance leaders can use to fund transformation investments and demonstrate near-term ROI. Finance transformation efforts that lack a cash focus risk delivering process improvements without tangible balance sheet impact, limiting the business case for continued investment. EY recommends aligning KPIs, incentives, and data infrastructure around cash metrics to sustain behavioral and structural change across the enterprise.

    3 minRead
    EY Insights

    How sustainability and technology will transform CFOs into Value Architects

    EY argues that CFOs are evolving from financial stewards into 'Value Architects' — strategic leaders responsible for integrating sustainability metrics and AI-driven technology into long-term value creation. The piece positions the CFO as the central orchestrator of non-financial reporting, ESG data governance, and technology transformation investments alongside traditional financial oversight. CFOs are expected to lead scenario planning that incorporates climate risk, regulatory sustainability mandates, and emerging AI capabilities into capital allocation decisions. The article frames this role expansion as both a competitive imperative and a governance responsibility, requiring CFOs to build cross-functional influence across technology, operations, and sustainability functions.

    3 minRead
    EY Insights

    How can the financial controller transform to shape the future with confidence?

    The 2024 Global EY DNA of the Financial Controller Report, based on a survey of more than 1,200 financial controllers and senior finance leaders, finds that 86% of controllers expect their role to change significantly over the next five years, with 39% anticipating a shift from value protection and optimization toward value creation. AI adoption is already high among surveyed controllers, with 89% having adopted AI tools and 65% using generative AI on a frequent basis. The report identifies three transformation levers: converting compliance-grade financial data into strategic insights, building enterprise AI confidence by evaluating reliability and transparency of AI outputs, and integrating sustainability reporting with business performance. EY frames the controller's evolving mandate as the 'Age of And'—simultaneously maintaining traditional reporting and compliance responsibilities while acquiring forward-looking skills in data, AI, and sustainability to serve as a strategic copilot to the CFO.

    3 minRead
    EY Insights

    EY Global DNA of the Treasurer Survey

    The EY Global DNA of the Treasurer Survey examines how corporate treasurers can create new value amid geopolitical volatility, economic uncertainty, and accelerating technology change. The survey highlights the evolving strategic role of the treasurer — moving beyond traditional liquidity and risk management toward broader finance leadership contributions including capital allocation, scenario planning, and AI-enabled decision support. Key themes include treasurer adoption of advanced analytics and automation tools, the integration of treasury with enterprise-wide financial strategy, and the talent and operating model shifts required to execute that transformation. The article positions the CFO-treasurer relationship as increasingly central to enterprise resilience and value creation in uncertain macro conditions.

    3 minRead
    EY Insights

    How corporate disclosure committees are adapting in a time of change

    Corporate disclosure committees are under pressure to evolve as companies face an expanding and rapidly shifting disclosure landscape—spanning new SEC rules, ESG reporting requirements, cybersecurity incident disclosures, and AI-related risks. EY argues that many existing committee structures, charters, and processes were designed for a narrower set of obligations and now require deliberate reassessment to remain effective. Key recommended adaptations include broadening committee membership beyond finance and legal to include functional experts (e.g., cybersecurity, sustainability, HR), refreshing charters to reflect current regulatory scope, and improving information-gathering processes to surface material topics earlier in the reporting cycle. The piece frames disclosure committee modernization as a governance imperative, not an administrative update, given the legal and reputational consequences of disclosure failures in a heightened enforcement environment.

    3 minRead
    EY Insights

    How IPO candidates can navigate uncertain and selective markets

    The global IPO market entered 2026 with momentum but has become increasingly selective as tariff uncertainty, geopolitical tensions, private credit concerns, and software sector weakness dampened activity. Investors are concentrating capital on larger, scaled issuers with strong fundamentals, particularly in aerospace and defense, AI infrastructure, and energy sectors. Regional dynamics vary: Europe saw the largest global Q1 IPO driven by defense spending; Greater China faces a dual regulatory and market window constraint with an estimated 180 of 400+ applicants completing listings in 2026; and the Americas anticipates several historically large IPOs in H2 2026. Dual-track processes are proliferating, especially among sponsor-backed companies with extended holding periods, as M&A alternatives grow more attractive. Companies that invest early in IPO readiness and preserve transaction optionality will be best positioned to act when windows open.

    3 minRead
    EY Insights

    Five questions banks must ask to unlock tech value | EY - US

    Banks are failing to convert rising technology investment into measurable business value, and EY identifies five diagnostic questions leadership must answer to close that gap. The framework targets whether tech spending is aligned to strategic priorities, whether operating models have been redesigned to capture AI and automation benefits, and whether governance structures are adequate to manage technology risk at scale. Banks that treat technology as a cost center rather than a value driver consistently underperform peers on ROE and efficiency ratios. The piece argues that without clear ownership of outcomes—spanning finance, technology, and business lines—banks will continue to see diminishing returns on modernization programs.

    3 minRead
    EY Insights

    How risk can operate at the speed of trust | EY - US

    EY argues that organizations must redesign their risk functions to operate at what it terms 'the speed of trust' — enabling rapid, confident decision-making amid geopolitical volatility, regulatory flux, and technology disruption. The piece frames traditional, backward-looking risk models as structurally inadequate for environments where conditions can shift overnight, and calls for risk to become a real-time strategic enabler rather than a compliance checkpoint. EY advocates integrating AI-driven risk sensing, scenario modeling, and continuous monitoring into enterprise operating models so that executives can act decisively without sacrificing governance or accountability. The consulting pitch positions risk transformation as a board-level imperative tied directly to competitive advantage and stakeholder trust.

    3 minRead
    EY Insights

    2025 EY Global Climate Action Barometer | EY - US

    The 2025 EY Global Climate Action Barometer examines the relationship between corporate climate leadership and business performance, positioning proactive climate action as a competitive differentiator rather than a compliance obligation. The report draws on global survey data to assess how leading organizations are embedding climate strategy into core business operations and capital allocation decisions. It identifies a performance gap between climate leaders and laggards, suggesting that firms with mature climate programs are better positioned for long-term value creation. The findings are framed around strategic, financial, and operational dimensions of climate action, with implications for enterprise risk management and sustainability reporting.

    3 minRead
    EY Insights

    Cash leadership office for Bristol Myers Squibb | EY - US

    EY-Parthenon partnered with Bristol Myers Squibb to establish a Cash Leadership Office designed to optimize working capital and strengthen liquidity management across the enterprise. The engagement focused on building sustainable, internal cash management capabilities rather than delivering a one-time improvement, embedding governance structures and processes that BMS could operate independently. EY worked across procurement, accounts receivable, accounts payable, and inventory functions to identify and capture cash flow improvements. The program aimed to instill a cash-focused culture with clear ownership, accountability, and performance metrics at the leadership level. The result was a scalable operating model giving BMS ongoing visibility and control over its cash position to support strategic priorities including debt reduction following major acquisitions.

    3 minRead
    EY Insights

    How tax and finance can drive talent transformation | EY - US

    Tax and finance functions face a critical talent gap as automation and AI eliminate routine compliance work, requiring leaders to fundamentally rethink workforce composition and skill requirements. Organizations must build a structured transformation roadmap that maps current roles against future-state operating models, identifying which positions will be eliminated, redefined, or newly created. The shift demands investment in three core capability areas: data and technology proficiency, strategic business partnering, and higher-order judgment skills that machines cannot replicate. Upskilling existing staff through targeted learning programs is more cost-effective than wholesale hiring, but requires honest skills gap assessments tied to specific business outcomes. Leaders who treat talent transformation as a one-time project rather than a continuous operating discipline will fall behind, as the pace of technology change outstrips traditional workforce planning cycles. Success depends on CFOs and tax leaders taking direct ownership of the agenda rather than delegating it entirely to HR.

    3 minRead
    EY Insights

    tfo survey

    The 2025 EY Tax and Finance Operations Survey of global tax and finance leaders finds that continuous, embedded transformation — not episodic change — is the defining capability separating high-performing functions from the rest. 81% of organizations plan moderate to significant business changes in the next two years, more than double the prior year's rate, driven primarily by geopolitical pressure, tariffs, and supply chain restructuring. 86% rank data, AI, and technology as a top priority, with leaders projecting AI will improve effectiveness by 30% and free up 23% of budget for reallocation to strategic work — yet most acknowledge their data foundations remain inadequate to realize that potential. Pillar Two global minimum tax compliance is the single most acute regulatory burden, cited by 81% as the top legislative change affecting their business, with 85% expecting their overall tax liability to increase as a result and only 21% describing themselves as very prepared to comply. Tax transparency obligations are also accelerating, with the share of companies voluntarily disclosing total taxes paid more than doubling to 80% from 37% two years ago. The survey's central prescription is that tax and finance functions must reframe transformation as a permanent operating discipline, building agile structures and AI-ready talent that allow them to act as real-time strategic advisors rather than reactive compliance processors.

    3 minRead
    EY Insights

    Three strategic priorities for banking CROs in 2026 | EY - US

    The EY-IIF Global Bank Risk Management Survey identifies three strategic priorities for banking Chief Risk Officers heading into 2026: navigating an increasingly complex and volatile macroeconomic and geopolitical risk environment, accelerating the integration of AI and advanced analytics into risk frameworks while managing associated model and operational risks, and adapting to evolving regulatory expectations amid shifting capital and liquidity requirements. CROs report that data quality and infrastructure remain persistent constraints on risk management effectiveness. The survey highlights a growing mandate for risk functions to operate as strategic partners to the business rather than purely as control functions. Banks that lag in modernizing risk technology and governance are viewed as competitively disadvantaged in both regulatory standing and capital efficiency.

    3 minRead
    EY InsightsMay 31

    Three critical areas of change faced by CAOs and Controllers

    CAOs and Controllers face three critical areas of change reshaping the accounting and controllership function: the accelerating adoption of AI and automation in financial reporting and close processes, evolving regulatory and accounting standard requirements demanding greater agility in policy and disclosure, and a structural shift in the talent model as routine tasks are automated and the function is expected to deliver higher-value strategic insight. EY positions these pressures as simultaneous, not sequential, requiring CAOs to modernize operating models while maintaining control integrity and audit readiness. The article emphasizes that technology transformation within the controllership must be paired with governance frameworks to manage data quality, AI-generated outputs, and internal control design. Organizations that treat these changes as isolated initiatives rather than an integrated transformation risk falling behind on both compliance and strategic finance capability.

    3 minRead

    IBM Consulting

    19 articles
    IBM Think

    AI Academy

    IBM AI Academy is an AI-for-business educational program led by IBM thought leaders, designed to help business leaders build the knowledge required to prioritize AI investments that drive growth. The curriculum targets enterprise decision-makers across functions, focusing on practical AI strategy rather than technical implementation. The program is structured as a video/podcast series hosted on IBM's Think platform, with content spanning generative AI and broader artificial intelligence topics. No specific performance data, ROI figures, or curriculum modules are surfaced in the available page content, as the article resolves to a landing/index page rather than a substantive thought leadership piece.

    3 minRead
    IBM Think

    AI Security Costs Rise: Cost of a Data Breach Report & Claude Opus 5 | Mixture of Experts

    This IBM 'Mixture of Experts' podcast episode (episode 118) covers IBM's Cost of a Data Breach report findings alongside coverage of Anthropic's Claude Opus 5 release and related AI model developments. The episode centers on AI's growing role in cybersecurity economics, examining how AI both increases breach costs and serves as a mitigation tool. Additional topics include LLM benchmarks, neuromorphic computing, and new model releases from multiple vendors. The content is primarily aimed at technology and AI practitioners tracking security cost trends and frontier model developments.

    3 minRead
    IBM Think

    Banking AI has an action problem: Event-driven systems can close the gap

    Banking AI systems generate alerts and insights but consistently fail to trigger timely, coordinated action — a gap IBM terms the "intelligence-to-action" problem. The article argues that most bank AI deployments are still passive: models flag risk or opportunity but depend on human intermediaries to route, escalate, and execute responses, introducing latency that erodes value in fraud, credit, and customer scenarios. IBM's proposed remedy is event-driven AI architecture — systems that detect a triggering condition and autonomously initiate downstream workflows across channels and systems of record without waiting for human handoffs. IBM watsonx Orchestrate is positioned as the orchestration layer that connects AI-generated signals to operational actions, enabling banks to move from AI as an analytical tool to AI as an operational participant.

    3 minRead
    IBM Think

    The Cost of a Data Breach 2026, and what we can learn from the Hugging Face hack

    IBM's Security Intelligence podcast (episode 44, July 2026) covers two converging cybersecurity developments: the release of the Cost of a Data Breach 2026 report and a breach of Hugging Face, the widely used AI model-hosting platform. The Hugging Face incident is material to enterprises because the platform hosts open-source models and datasets that many organizations integrate into AI pipelines, making supply-chain compromise a direct enterprise risk. The Cost of a Data Breach 2026 report provides updated benchmark figures on breach frequency, financial impact, and detection economics that inform security investment decisions and risk quantification. Topics flagged in the episode metadata include prompt hacking and security, red teams, and skills gaps—all relevant to organizations scaling AI adoption without proportionate security controls.

    3 minRead
    IBM Think

    Agentic AI Is Rewriting KYC and AML in Banking

    Agentic AI is fundamentally restructuring Know Your Customer (KYC) and Anti-Money Laundering (AML) operations in banking by replacing rule-based, largely manual compliance workflows with autonomous, adaptive AI systems capable of continuous decision-making. Traditional KYC and AML processes are plagued by high false-positive rates, slow onboarding cycles, and escalating regulatory scrutiny — problems that static rule engines cannot solve at scale. Agentic architectures enable AI agents to orchestrate end-to-end compliance tasks — document verification, risk scoring, transaction monitoring, and suspicious activity report generation — with minimal human intervention, compressing cycle times and reducing operational cost. The IBM Consulting perspective holds that institutions embedding agentic AI into compliance infrastructure will gain measurable advantages in audit readiness, regulatory responsiveness, and analyst productivity over those maintaining legacy approaches. Banks that delay adoption risk falling behind on both cost efficiency and the quality of financial-crime detection as regulatory expectations continue to rise.

    3 minRead
    IBM Think

    The great AI chip rush

    AI companies are racing to develop custom silicon as a strategic differentiator beyond model development, with OpenAI, Google, Meta, Microsoft, Amazon, and IBM all investing heavily in proprietary chip designs. OpenAI's Jalapeño chip and IBM's sub-1nm processor represent competing approaches to reducing inference costs and latency while decreasing dependence on Nvidia's dominant GPU supply chain. The economic logic is straightforward: custom chips optimized for specific AI workloads can deliver substantially lower cost-per-token at scale, making chip ownership a long-term margin and competitive-moat play. For enterprises, this hardware fragmentation signals that AI infrastructure strategy—including which cloud providers and model vendors to partner with—carries increasing lock-in and TCO implications. The shift from software-defined AI competition to silicon-defined AI competition is reshaping capital allocation decisions across hyperscalers and AI labs simultaneously.

    3 minRead
    IBM Think

    Tokenmaxxing is dead, long live valuemaxxing

    IBM argues that 'tokenmaxxing'—driving maximum AI usage volume across the workforce—is producing diminishing returns and must give way to 'valuemaxxing,' a discipline focused on measuring and optimizing business outcomes rather than AI consumption metrics. The central thesis is that enterprises miscalibrated their AI adoption KPIs by treating token throughput and tool utilization as proxies for value, when the actual signal should be business impact per AI interaction. The authors contend that software development workflows (SDLC) are a primary proving ground, where indiscriminate AI use can inflate code volume and technical debt without improving delivery quality or speed. Valuemaxxing requires organizations to instrument AI deployments with outcome-linked metrics, tighten governance over when and how AI agents are invoked, and realign executive incentives away from adoption dashboards toward measurable productivity and quality gains.

    3 minRead
    IBM Think

    Nearly half of AI projects are stalling due to data problems, new study finds

    A 2026 study by Confluent surveying 4,625 IT leaders finds that nearly half of enterprise AI projects are stalling due to data problems, with data integration and quality issues identified as primary blockers to AI adoption. The research highlights that real-time data streaming infrastructure is increasingly viewed as a prerequisite for moving AI initiatives from pilot to production. Organizations that have invested in robust data streaming architectures report higher rates of successful AI deployment and measurable business outcomes. The findings underscore that AI execution gaps are fundamentally data architecture gaps, making data governance and integration strategy central to enterprise AI ROI.

    3 minRead
    IBM Think

    Making it up carefully

    Banks, governments, and researchers are deploying synthetic data to circumvent privacy constraints that limit access to sensitive real-world datasets in finance, healthcare, and public administration. Synthetic data generation—powered by large language models, GANs, and diffusion models—creates statistically representative datasets that carry no direct link to actual individuals, enabling model training and analysis where raw data sharing is legally or ethically prohibited. The central challenge is the fidelity-privacy tradeoff: synthetic datasets engineered for strong anonymity tend to lose the statistical edge cases and rare-event distributions that make models accurate in production, while datasets tuned for high fidelity risk re-identification. Practitioners are pursuing hybrid approaches—combining real anchor records with synthetic augmentation—alongside formal privacy guarantees such as differential privacy to navigate this tradeoff. The article treats this as an active engineering and governance problem rather than a solved one, with regulatory uncertainty adding pressure on organizations to document and validate the provenance and accuracy of synthetic training corpora.

    3 minRead
    IBM Think

    One giant leap for AI

    Engineers and researchers are actively developing orbital data centers as a potential solution to AI's escalating infrastructure and energy demands, despite widespread skepticism — including characterizations of the concept as 'peak insanity.' Space-based data centers could theoretically leverage near-unlimited solar energy and natural cooling in orbit, bypassing the terrestrial land, water, and power constraints that increasingly bottleneck AI compute expansion. Several startups and research programs are working to overcome the formidable engineering hurdles: launch costs, radiation hardening, latency, and on-orbit maintenance. The piece frames this not as science fiction but as an emerging frontier that serious infrastructure planners should begin tracking, given the pace of AI energy demand growth.

    3 minRead
    IBM ThinkJune 12

    LLMs corrupt the documents they work on. Does agentic AI make it worse?

    Microsoft research found that LLMs progressively degrade document content the more they interact with it—a phenomenon the study calls 'document corruption.' The research quantified how repeated LLM passes introduce factual drift, omissions, and hallucinated additions, with degradation compounding across iterations. Agentic AI architectures, which route documents through multiple sequential LLM calls across orchestrated workflows, amplify this risk by multiplying the number of model-document interactions. The findings carry direct implications for enterprise deployments using AI agents for document-intensive processes such as financial reporting, contract management, and compliance workflows, where content fidelity is non-negotiable.

    3 minRead
    IBM Think

    Can APAC power the AI boom without overloading its grid?

    APAC faces a compounding constraint: AI infrastructure demand is accelerating faster than regional power grids can expand, with data center energy consumption in markets like Japan, Australia, Singapore, and India projected to multiply within this decade. The article argues that APAC's grid fragmentation—varying by country in renewable mix, regulatory maturity, and transmission capacity—means there is no single regional solution, requiring country-specific energy strategies tied to AI deployment roadmaps. IBM Consulting positions the path forward around three levers: accelerating renewable energy procurement and power purchase agreements, deploying energy-efficient AI hardware and workload optimization techniques, and engaging governments early on grid modernization policy. The piece frames this as a strategic inflection point where enterprises that plan AI infrastructure with energy realism now will avoid stranded-asset risk and regulatory exposure as carbon disclosure requirements tighten across the region.

    3 minRead
    IBM Think

    The world still runs on mainframes

    Mainframes remain the backbone of global economic infrastructure, processing the majority of the world's financial transactions, airline reservations, and government data. Discussed at New York Tech Week, IBM experts argue that mainframe relevance is not declining but evolving, with modern IBM Z systems increasingly integrating AI workloads alongside traditional batch and transaction processing. The case against wholesale cloud migration centers on mainframe advantages in throughput, security, and regulatory compliance that cloud-native alternatives have not fully replicated. Enterprises face a strategic choice: modernize mainframe estates in place—augmenting with AI and hybrid cloud connectivity—rather than pursue costly and risky rip-and-replace migrations.

    3 minRead
    IBM ThinkJune 5

    Why the human brain may hold the key to cheaper, smarter AI

    New research suggests that emulating the human brain's architecture could dramatically reduce the cost and improve the efficiency of AI systems. Current transformer-based large language models are computationally expensive, requiring massive data centers and significant energy consumption. Brain-inspired approaches—such as neuromorphic computing and sparse, event-driven processing—could cut inference and training costs by orders of magnitude compared to today's GPU-intensive workloads. For enterprise leaders, this trajectory signals a potential structural shift in AI economics, with implications for infrastructure investment decisions, AI TCO projections, and the competitive landscape for AI hardware and platform vendors.

    3 minRead
    IBM Think

    Open data architectures still need a performance engine

    Open data architectures built on lakehouse paradigms deliver flexibility and openness, but flexibility alone does not guarantee query performance at enterprise scale. The article argues that organizations adopting open formats such as Apache Iceberg or Delta Lake must layer a dedicated performance engine on top of their data platforms to avoid latency and throughput bottlenecks that erode business value. Modernization is framed as simultaneously a performance, governance, and cost-efficiency strategy—not merely a technology migration. Without an optimized execution layer, enterprises risk accumulating infrastructure spend while failing to meet the SLA demands of analytics, AI workloads, and operational reporting.

    3 minRead
    IBM Think

    The long game: Businesses investing in AI infrastructure need to push to the finish line

    A new IBM Institute for Business Value study finds that AI infrastructure investment is accelerating but businesses are struggling to convert spending into operational AI capacity. While AI infrastructure budgets are rising, many organizations have not yet been able to meet their internal AI compute and data demands. The research positions this as a critical execution gap — companies that stop short of full infrastructure buildout risk losing competitive ground. IBM's core argument is that sustained, finish-line-oriented investment, rather than incremental or paused spending, is the differentiator between AI leaders and laggards.

    3 minRead
    IBM Think

    the billion dollar misfire?lnk=thinkhpaic2us

    IBM Consulting's research finds that enterprises are spending billions on AI initiatives while capturing little measurable return, identifying a widespread execution gap between AI investment and business value. The article argues that most organizations are misallocating AI spend by deploying technology without aligning it to high-value workflows or clear ownership of outcomes. Leaders generating real returns are distinguished by disciplined use-case prioritization, governance structures that tie AI deployment to P&L impact, and operating models that embed AI into core business processes rather than treating it as a standalone capability. The piece positions AI ROI failure as a strategic and organizational problem, not a technology problem, requiring C-suite-level intervention to course-correct.

    3 minRead
    IBM Think

    ai agent scientist cfos?lnk=thinkhpagents1us

    This IBM 'Mixture of Experts' podcast episode (Episode 100) examines the broadening adoption of AI agents across domains including scientific research, enterprise finance, and retail commerce. The episode covers developments such as ChatGPT-assisted home sales, Anthropic's Claude Code gaining enterprise traction, and Adobe's internal AI research lab. The discussion frames AI agent deployment as moving beyond early technical adopters—data scientists and engineers—to executive-level business functions including CFO organizations. The episode references activity from major AI players including OpenAI, Anthropic, Shopify, and NVIDIA GTC, signaling accelerating cross-industry agentic AI momentum.

    3 minRead
    IBM Think

    c suite gap?lnk=thinkhpaic1us

    IBM's research identifies a significant alignment gap between C-suite executives and their organizations on AI strategy and execution readiness. Senior leaders systematically overestimate their companies' AI maturity relative to assessments from managers and frontline employees closer to implementation. This perception disconnect translates into misallocated investment and failed AI initiatives, with IBM framing the risk as a 'billion-dollar misfire' when enterprise AI spend outpaces actual organizational capability. The piece argues that closing the gap requires structured governance mechanisms that surface ground-level signals to executive decision-makers before capital commitments are made.

    3 minRead

    McKinsey

    9 articles
    McKinsey Insights

    Why Accelerated Resource Allocation Matters in the Age of AI

    The article content was inaccessible due to a server permission error, preventing extraction of the underlying thesis, data, or recommendations. Based on the title, the piece addresses why faster capital and resource reallocation is a strategic imperative in an AI-driven competitive environment. McKinsey research consistently links dynamic resource reallocation to superior total shareholder returns, and AI likely amplifies both the speed advantage and the cost of inaction. No specific findings, statistics, or frameworks can be attributed to this article given the access failure.

    3 minRead
    McKinsey Insights

    The Operating Model Advantage: Why AI Winners Are Rewiring Their Organizations

    McKinsey's analysis finds that despite near-universal AI deployment, only 21% of companies have fundamentally redesigned their operating models around AI, and fewer than that track ROIC on AI investments. Top performers—those attributing 5% or more of EBIT to AI—are three times more likely to pursue broad operating model redesign and twice as likely to redesign workflows before selecting tools. The core argument is that AI's competitive advantage is shifting from the technology itself to the organizational structures that deploy it: specifically, how companies rewire governance, decision-making layers, talent, and data workflows. AI is distinctive from prior technology waves because it targets the coordination layer directly, enabling companies to route decisions and workflows through centralized orchestration rather than expanding person-to-person management overhead. Companies that merely bolt AI onto existing structures risk propagating errors at machine speed while leaving coordination costs—and the 'complexity scissors' gap between revenue growth and overhead—largely intact. The durable winners will be those that use organizational rewiring to build operating model advantages that cannot be quickly purchased or replicated.

    3 minRead
    McKinsey Insights

    The Real Future of Work in Healthcare

    US healthcare labor productivity has declined roughly 1% over the past two decades while the broader services economy gained more than 55%, despite $150 billion in annual IT investment by clinical-care organizations. McKinsey argues the sector is automating inefficiency rather than eliminating it, and that meaningful improvement requires end-to-end operating model redesign—not incremental point solutions—targeting 40–50% process improvements. On the care delivery side, redesigned staffing models incorporating virtual RNs and ambient documentation tools can yield labor cost reductions exceeding 20% and first-year RN turnover reductions of more than 60%. On the shared services side, up to 50% of administrative work is automatable, and agentic workflows in revenue cycle management alone can deliver up to 40% productivity gains, but only when broken workflows are eliminated before automation is layered on. Partial automation creates capacity without immediately changing cost structure, requiring coordinated redesign of workflows, technology, and staffing to translate productivity gains into economic impact.

    3 minRead
    McKinsey Insights

    How Top Economic Performers Lean Into Their Competitive Advantage to Guide Their Strategy

    A McKinsey Global Survey of more than 1,250 executives finds that most organizations fail to actively validate or manage their competitive advantage, while top economic performers—roughly the top quintile by revenue growth and EBIT over three years—are 2.5 times more likely to maintain a fully aligned, organization-wide understanding of their advantage. 79% of all respondents expect their business model will need moderate or significant change within three years to remain economically viable, and one-third anticipate their competitive advantage will significantly or completely shift within five years. Top performers differentiate through granular performance monitoring below the business-unit level, external market data validation, and AI-enabled scanning of investment flows, patents, and new entrants. They are also substantially more likely to reallocate budgets year-over-year and to use competitive advantage insights to drive R&D focus, geographic expansion, and new business development—translating strategic clarity into measurable growth.

    3 minRead
    McKinsey Insights

    The Rise of the Agentic Shopper: ASOS's AI Investment

    ASOS CTO Przemek Czarnecki outlines a phased AI deployment strategy that has already routed 50% of inbound customer care requests through AI agents and achieved 90% workforce adoption of Copilot tools. The company structures its roadmap in sequential phases: starting with software development productivity, moving to call-center automation and enterprise-wide Copilot rollout, then deploying back-office agents across HR, legal, and finance, and finally applying agentic AI to core fashion functions—buying, design, and merchandising. Czarnecki identifies three non-negotiable enablers for scale: data quality and accessibility, a robust API layer that allows agents to take action across systems, and deliberate talent development including an internal 'AI strategist' capability to identify high-value use cases. The central organizational warning is that the hardest scaling challenge is not technical but strategic—companies that fail to enforce commercial discipline fragment investment into low-impact projects that never deliver material returns.

    3 minRead
    McKinsey Insights

    AI in Life Sciences Explained: The Technology That Could Reinvent Medicine

    McKinsey's life sciences AI explainer argues that the current AI wave is categorically different from prior technology cycles in pharma and biotech—broader in scope, faster in adoption, and capable of transforming the entire enterprise rather than isolated functions. The firm estimates that roughly 80% of life sciences workflows are 'agentifiable,' with scaled AI deployment producing approximately 5–10% improvement in growth and 3–5% improvement in margin. Agentic AI—systems that can plan, execute, and iterate autonomously—represents the sharpest inflection point, enabling continuous lab workflows, autonomous clinical site management, and parallel modeling of drug development decisions that currently proceed sequentially over a decade. McKinsey's practitioners emphasize that implementation failures are predominantly human and organizational rather than technical, requiring leadership role modeling, retraining, and incentive realignment to capture value at scale.

    3 minRead
    McKinsey Insights

    State of the Consumer 2026: When Tech Acceleration and Cost Pressures Collide

    McKinsey's State of the Consumer 2026 report identifies two dominant forces reshaping consumer behavior globally: rapid technology advancement and sustained cost consciousness. These forces underpin four trends—a new tech-driven path to purchase, a health revolution, the experience economy, and the rise of the resourceful consumer—each accelerating across five surveyed markets. AI is restructuring the purchase journey: 28% of Gen Z already use generative AI for shopping, open web traffic is down 8% since 2023, and agentic commerce is creating a 'dual front door' where AI agents complete purchases autonomously, widening the performance gap between digitally mature and laggard retailers. GLP-1 medications are a material demand variable, with roughly one in six U.S. households having tried them and users reducing grocery spend by ~6%, while Brazilian patent expirations in March 2026 are expected to expand a $3B market. Brands face structural erosion of traditional search and organic discovery advantages, requiring upstream investment in LLM-visible content and recalibration of retail platform strategy as large digital marketplaces embed AI to consolidate their role as discovery and purchase destinations.

    3 minRead
    McKinsey InsightsJune 23

    The AI Advantage in B2B Pricing

    AI adoption in B2B pricing is accelerating, with a McKinsey survey of 419 pricing leaders indicating that gen AI is already in use by roughly 10–30% of organizations across eight core pricing activities, with adoption expected to reach 40–50% within one to three years. Agentic AI adoption is currently below 10% but is projected to climb to 20–45% across the same activities, with the steepest gains anticipated in market intelligence, cost tracking, list price setting, and promotion pricing. Adoption is most mature in market and competitive intelligence and cost tracking, while higher-stakes activities such as contract compliance lag due to the need for additional safeguards. The findings signal that AI-driven pricing is moving from experimentation to operational deployment, with agentic AI positioned as the next major inflection point for B2B commercial strategy.

    3 minRead
    McKinsey InsightsJune 21

    McKinsey at Cannes Lions 2026: How AI Is Rewiring Growth

    McKinsey's presence at the 2026 Cannes Lions International Festival of Creativity centers on how AI and agentic systems are restructuring growth across advertising, marketing, and commerce. Key themes include autonomous growth engines, agentic marketing workflow automation, and the emergence of AI-driven commerce media and creator ecosystems. McKinsey research highlighted at the event covers the agentic commerce opportunity for consumers and merchants, an 'automation curve' in agentic commerce, and a full-stack approach to commerce media. The agenda signals that strategic choices around AI adoption in marketing and sales functions will be decisive competitive differentiators in the near term.

    3 minRead

    Other advisory

    73 articles
    OpenAI News (firm Scan)

    Building Abundant Intelligence

    OpenAI CFO Sarah Friar outlines the company's economic framework for scaling AI infrastructure around a self-reinforcing cycle: falling inference costs expand addressable work, broader adoption funds R&D, and R&D improvements drive further efficiency gains. Recent pricing moves include an 80% price cut on GPT-5.6 Luna (now $0.20/M input tokens) and a 20% cut on GPT-5.6 Terra, with GPT-5.6 Sol delivering 2.5x speed in Fast mode at 2x cost. On the efficiency side, GPT-5.6 Sol helped reduce model-serving costs by 20% and improve token-generation efficiency by over 15%; context management improvements tripled ARC-AGI-3 benchmark scores while using 6x fewer output tokens. OpenAI now serves over 1 billion active users and 2 million businesses, with agentic workloads via Codex representing 99.8% of weekly output tokens, signaling a structural shift from query-based to task-completion AI use across enterprise functions.

    3 minRead
    OpenAI News (firm Scan)

    Advancing the Price-Performance Frontier with GPT-5.6

    OpenAI is cutting API prices for GPT-5.6 Luna by 80% and GPT-5.6 Terra by 20%, effective July 30, with Luna priced at $0.20 per million input tokens and $1.20 per million output tokens, and Terra at $2 per million input tokens and $12 per million output tokens. The price reductions stem from model-level and infrastructure efficiency gains, including a 20% reduction in end-to-end serving costs and a 15% improvement in token-generation efficiency, partially achieved through GPT-5.6 Sol autonomously optimizing production kernels and running experiments. OpenAI is also replacing its Priority Processing tier with Fast mode for GPT-5.6 Sol, delivering up to 2.5× faster speeds at twice the standard price with no change in intelligence. The updates are designed to make high-volume AI workloads economically viable at scale while preserving access to frontier-speed processing for time-sensitive enterprise tasks.

    3 minRead
    OpenAI News (firm Scan)

    How GPT-5.6 Fuses Frontier Intelligence with Frontier Efficiency

    OpenAI's GPT-5.6 model family is engineered to optimize across the full cost-intelligence curve, with three tiers: Sol (max reasoning), Terra (GPT-5.5 parity at half the price), and Luna (80% cheaper than Sol). The efficiency gains are distributed across three layers: model training optimized for task success per token, inference stack improvements—including load balancing, speculative decoding, and kernel optimization via GPT-5.6 Sol itself—reducing end-to-end serving costs by 20% and increasing token-generation efficiency by over 15%, and an agentic harness built in Rust that reduces context bloat and maximizes prompt-cache hit rates. Notably, GPT-5.6 Sol autonomously rewrote production kernels, ran hundreds of speculator architecture experiments, and intervened in training instability, demonstrating a self-reinforcing efficiency loop. OpenAI frames these compounding optimizations as central to its mission of distributing AI benefits across 1 billion users and 2 million businesses while preserving frontier intelligence.

    3 minRead
    OpenAI News (firm Scan)

    How AI Is Expanding What People Do at Work

    OpenAI analysis of 800,000+ U.S. ChatGPT work-related messages finds that 43.5% of occupation-specific AI use involves tasks traditionally belonging to a different job category — a pattern the researchers term 'task crossover.' Customer experience workers (77%), designers (75%), and HR workers (69%) show the highest rates of borrowing tasks from other occupations. Marketing and engineering tasks travel farthest across organizational boundaries, with marketing work appearing in 8.9% of messages from workers outside marketing. Task crossover is more pronounced in smaller organizations (18.9% outside-occupation share in 2–5 seat workspaces vs. 16.3% in 100+ seat workspaces), suggesting AI functions as a generalist resource where specialist functions are scarce. OpenAI frames these usage patterns as a leading indicator of occupational restructuring that traditional labor-market statistics will capture only later.

    3 minRead
    Anthropic News (firm Scan)

    Introducing Claude Opus 5

    Anthropic has released Claude Opus 5, a frontier-class model priced at half the cost of Claude Fable 5 while achieving state-of-the-art results on coding and knowledge work benchmarks including Frontier-Bench and GDPval-AA. On ARC-AGI 3, Opus 5 scores three times higher than the next-best model; on Zapier AutomationBench, its pass rate is approximately 1.5× competitors at equivalent cost. Early enterprise adopters report material gains in financial modeling (9 percentage points higher accuracy, 60% less time), due diligence (17% improvement), legal agent work (26% fewer tokens at comparable quality), and scientific research workflows. The model introduces configurable effort settings allowing organizations to trade off intelligence against speed and token cost, and becomes the default model on Claude Max.

    3 minRead
    Anthropic News (firm Scan)

    The Anthropic Economic Index Connector

    Anthropic has launched the Economic Index connector for Claude, enabling users to query its AI labor-market dataset directly through natural language in claude.ai. The connector requires no installation and works across all Claude models, allowing queries such as which occupations use AI most, how task automation has shifted over the past year, and regional usage patterns. The underlying dataset tracks how Claude is actually being used across the economy and has previously served researchers, journalists, and policymakers; the connector makes the same data accessible to any user. Anthropic notes the Index reflects Claude usage patterns rather than the broader labor market, and the full datasets remain publicly available on its website.

    3 minRead
    Anthropic News (firm Scan)

    Supporting Ambitious External Research Through the Anthropic Economic Futures Research Fund

    Anthropic is committing $200 million to the Economic Futures Research Fund to support external research on preparing society for AI-driven economic disruption. The fund will prioritize grants in the $5–30 million range across five areas: AI's impact on workers at the firm level, retraining and workforce transitions, modernizing income support for displaced workers, building worker ownership stakes in AI-driven growth, and generating evidence on public investments. Eligible applicants include accredited universities, independent research institutes, and nonprofits with field-experiment experience; individual applicants are excluded. The initiative represents a strategic shift from the prior Economic Futures program toward fewer, larger, higher-impact bets, with an emphasis on pilots scalable enough to inform policy before disruption arrives.

    3 minRead
    OpenAI News (firm Scan)

    David Vélez and Robin Vince Join OpenAI Boards

    OpenAI has appointed David Vélez, founder and CEO of Nubank (135 million customers), and Robin Vince, Chairman and CEO of BNY, to the boards of both the OpenAI Foundation and OpenAI Group PBC. Both appointments signal OpenAI's intent to deepen its relationships with global financial services institutions as it scales enterprise and consumer AI deployment. Vélez brings experience building a regulated digital bank across Latin America and into the U.S. market; Vince brings governance and risk management credentials from BNY and 26 years at Goldman Sachs, including roles as Chief Risk Officer and Treasurer. The additions strengthen OpenAI's board with financial-sector expertise in regulated technology deployment, AI infrastructure investment, and institutional governance at a moment when OpenAI is restructuring its corporate form and expanding enterprise partnerships.

    3 minRead
    OpenAI News (firm Scan)

    Introducing OpenAI Presence

    OpenAI has launched OpenAI Presence, an enterprise-grade AI agent deployment product that pairs model reasoning with configurable policies, guardrails, escalation rules, and a Codex-powered continuous improvement loop. The product targets high-value production workflows—customer support, outbound sales, insurance claims, IT service requests—where reliability and policy compliance are non-negotiable. OpenAI's own English-language phone support channel runs on Presence, resolving 75% of inbound issues without human assistance and reducing human handoffs by 15 percentage points in 10 days after launch. Presence is currently available to eligible enterprise customers through a limited general availability program led by OpenAI Forward Deployed Engineers and select global systems integrators, and is not yet self-serve.

    3 minRead
    OpenAI News (firm Scan)

    Introducing the ChatGPT for Small Business Program

    OpenAI has launched the ChatGPT for Small Businesses program, pairing its ChatGPT Work agent—now powered by GPT-5.6—with structured enablement resources including virtual training webinars, in-person AI academies across the US, and partner integrations with Dropbox, Shopify, Intuit, Slack, Atlassian, and Wix. ChatGPT Work is designed to execute multi-step, end-to-end tasks by connecting to a business's files, applications, and memory, positioning it as a substitute for outsourced functions such as marketing, accounting, and operations. At prior Small Business AI Jam events, 78% of participants built a functional AI workflow in a single day and 42% reported saving more than five hours per week. The program is available now across all ChatGPT subscription tiers, making GPT-5.6 and agent capabilities accessible at SMB price points rather than enterprise-only contracts.

    3 minRead
    OpenAI News (firm Scan)

    A Scorecard for the AI Age

    OpenAI proposes 'Useful Intelligence per Dollar' as the primary metric for evaluating AI ROI, replacing legacy software measures like seat counts or cost-per-token. The framework answers four questions: how much useful work AI completes, what each successful task actually costs (including retries, latency, and human review—not just token price), how dependably AI produces usable results, and whether value per dollar improves at scale. The piece argues that a higher-priced frontier model can deliver lower total cost per outcome than a cheaper model if it reduces failed attempts and human correction. OpenAI uses the framework to position its new GPT-5.6 model family, citing a 54% reduction in output tokens versus a leading competitor on a coding benchmark while achieving higher task success rates.

    3 minRead
    OpenAI News (firm Scan)

    Managing AI Investments in the Agentic Era

    OpenAI argues that token price is a misleading proxy for AI value, and that enterprise leaders should instead optimize for 'useful work per dollar'—measuring tasks completed, time saved, and decisions improved. The piece outlines five investment disciplines for the agentic era: building visibility into usage and spend at the workspace, team, and model level; evaluating models on cost per accepted outcome rather than per-token cost; establishing governance frameworks before agentic workflows scale across enterprise systems; managing AI investments as a tiered portfolio that funds exploration, validation, and production at different levels; and matching capacity and support models to proven workflow demand. GPT-4 to GPT-5.4 saw a 97% drop in price per million tokens, with GPT-5.6 delivering 54% fewer output tokens and 57% less time per task, but OpenAI cautions that cheaper models can generate retry loops and correction costs that erode savings. Governance—covering data access controls, zero data retention options, spend limits, and approval paths—is positioned as the operating layer that determines which AI workflows can safely reach production scale.

    3 minRead
    OpenAI News (firm Scan)

    GPT-5.6 is now the preferred model in Microsoft 365 Copilot

    OpenAI has designated GPT-5.6 as the new preferred model powering Microsoft 365 Copilot across Word, Excel, PowerPoint, Chat, and Cowork. The model is positioned as delivering stronger performance per dollar and improved capability on complex tasks, with Microsoft accessing it via the OpenAI API. For enterprise users, the upgrade targets measurable productivity gains: fewer prompting iterations in Word, faster data-to-insight cycles in Excel, and reduced manual coordination in cross-functional workflows via Cowork. The integration reaches millions of daily Microsoft 365 users and extends the existing OpenAI-Microsoft commercial partnership into the latest model generation.

    3 minRead
    Anthropic News (firm Scan)

    Ben Bernanke appointed to Anthropic's Long-Term Benefit Trust

    Anthropic has appointed former Federal Reserve Chair Ben Bernanke to its Long-Term Benefit Trust (LTBT), the independent oversight body charged with holding Anthropic accountable to its mission of responsible AI development. Bernanke led the Fed from 2006 to 2014, guiding the U.S. economy through the 2008 financial crisis, and won the 2022 Nobel Prize in Economic Sciences for his research on banking and the Great Depression. The LTBT holds authority to appoint Anthropic board members, advises leadership on AI risk and societal impact, and its trustees hold no equity in the company. Bernanke's appointment is explicitly tied to Anthropic's focus on AI's macroeconomic effects, including impacts on workforces and economies globally.

    3 minRead
    OpenAI News (firm Scan)

    ChatGPT for Your Most Ambitious Work

    OpenAI has launched ChatGPT Work, an agentic capability powered by GPT-5.6 that executes multi-step enterprise workflows across connected apps including Slack, Microsoft Teams, Google Drive, SharePoint, CRMs, and Microsoft 365. The product moves beyond Q&A to producing finished outputs—spreadsheets, slides, documents, and interactive web apps—and can run autonomously for hours via Scheduled Tasks even when users are offline. Internal OpenAI data shows finance teams reduced month-end close and forecasting from days to hours, while sales teams compressed proof-of-concept development from weeks to 24 hours. ChatGPT Work is available today on Pro, Enterprise, and Edu plans, with Plus and Business rollout within days; enterprise admins retain governance controls over data access, tool connections, agent permissions, and audit visibility via the Compliance API.

    3 minRead
    OpenAI News (firm Scan)

    GPT-5.6: Frontier Intelligence That Scales with Your Ambition

    OpenAI has launched GPT-5.6 for general availability, a three-tier model family (Sol, Terra, Luna) targeting enterprise workloads across coding, knowledge work, cybersecurity, and scientific research. GPT-5.6 Sol achieves a 53.6 score on Agents' Last Exam—13.1 points above Claude Fable 5—while delivering comparable or superior results at roughly one-quarter the estimated cost at medium reasoning settings; Terra and Luna outperform Fable 5 at approximately one-sixteenth the cost. A new 'ultra' mode coordinates up to 16 parallel agents to accelerate complex, long-horizon tasks, trading higher token consumption for faster time-to-result and stronger benchmark scores across browsing, terminal, and SEC-related evaluations. Enterprise integrations cover Slack, Notion, Microsoft 365, and Google Drive, with materially improved output quality for documents, spreadsheets, presentations, and financial models. Cybersecurity performance nearly doubles GPT-5.5's exploit-generation pass rate, with a tiered trusted-access program for verified defensive security work. The performance-per-dollar improvements are central to the release's value proposition, making cost and ROI analysis directly relevant to enterprise procurement and technology decisions.

    3 minRead
    Anthropic News (firm Scan)July 1

    Introducing Claude Sonnet 5

    Anthropic has released Claude Sonnet 5, its most capable mid-tier model to date, designed specifically for agentic workflows including autonomous multi-step task execution, tool use, browser control, and code generation. The model closes the performance gap with Opus 4.8 while launching at introductory pricing of $2 per million input tokens and $10 per million output tokens through August 31, 2026, rising to $3/$15 thereafter. Safety evaluations show Sonnet 5 has a lower rate of undesirable behaviors than its predecessor Sonnet 4.6, improved resistance to prompt injection, and reduced hallucination and sycophancy, though it trails the more capable Opus 4.8 on behavioral alignment metrics. Cybersecurity risk is assessed as low—Sonnet 5 cannot develop working exploits—and real-time cyber safeguards are enabled by default. The model is immediately available across all Claude plans, in Claude Code, and via API, with increased rate limits to support higher token usage at elevated effort levels.

    3 minRead
    OpenAI News (firm Scan)

    HP Inc. Launches Frontier Strategic Partnership with OpenAI

    HP Inc. has announced a scaled strategic partnership with OpenAI under the OpenAI Frontier program, moving from successful pilots initiated in February 2026 to enterprise-wide deployment. Early results include one engineer completing 122 pull requests across 43 projects in weeks, a security team compressing month-long bug remediation to a single day, and an estimated 82 hours per week of security-team capacity unlocked via ChatGPT. The partnership targets deployment across customer and partner-facing workflows, device fleet management, cybersecurity, employee productivity, and software development — with more than 100,000 partners interfacing through HP's Partner Portal. OpenAI Frontier serves as a unified governance and orchestration layer connecting access controls, context management, deployment patterns, and outcome evaluation as HP scales from proof-of-concept to production.

    3 minRead
    OpenAI News (firm Scan)

    How Agents Are Transforming Work

    OpenAI's internal data on Codex adoption shows agentic AI is displacing chatbot-style interactions as the primary mode of knowledge work, with Codex now accounting for 99.8% of weekly output tokens generated inside OpenAI. By May 2026, 70.2% of sampled individual users submitted at least one request estimated to require more than one hour of human work, and 25.6% submitted requests exceeding eight hours. Adoption has spread well beyond engineering: Legal, Finance, and Recruiting shifted to Codex as their primary AI tool around April 2026, and non-developer organizational users grew 189-fold since August 2025. Over one-quarter of Codex output from business-function workers involved engineering or coding tasks, indicating agents are enabling cross-functional work that previously required specialized technical support. The pattern suggests enterprises should expect agentic tools to reshape workflow design, skill requirements, and labor cost structures across all departments, not just technical ones.

    3 minRead
    Anthropic News (firm Scan)

    Introducing Claude Tag

    Anthropic has launched Claude Tag, a Slack-native AI collaboration product available in beta for Enterprise and Team customers, allowing teams to tag @Claude in channels to delegate tasks asynchronously. Claude Tag is multiplayer—one Claude instance per channel, visible to all members—and builds persistent context over time across connected channels and data sources. Internally, 65% of Anthropic's product team code is now generated by the tool, with adoption spreading to functions including data analysis, support, and debugging. Administrators control tool and data access at the channel level, set token spend limits for the organization and individual channels, and retain a full audit log of all activity. The product runs on Claude Opus 4.8 and replaces the existing Claude in Slack app, with a 30-day migration window and introductory launch credits for eligible organizations.

    3 minRead
    OpenAI News (firm Scan)

    OpenAI and Broadcom Unveil LLM-Optimized Inference Chip

    OpenAI and Broadcom have unveiled Jalapeño, OpenAI's first custom AI inference chip, designed from the ground up for LLM workloads and developed from design to tape-out in nine months. Early testing indicates performance per watt substantially better than current state-of-the-art accelerators, with engineering samples already running GPT-5.3-Codex-Spark at production target frequency. The chip is the first in a multi-generation compute platform built with Broadcom and Celestica, targeting gigawatt-scale deployment with data center partners including Microsoft beginning in 2026. OpenAI frames the move as a full-stack vertical integration strategy—owning chip architecture, kernels, networking, and deployment systems—intended to reduce inference costs, improve reliability, and expand access to advanced AI across enterprise and consumer applications.

    3 minRead
    OpenAI News (firm Scan)

    New usage analytics and updated spend controls for enterprises

    OpenAI has released credit usage analytics and updated spend controls for ChatGPT Enterprise, giving organizations granular visibility into AI consumption across users, products, and models. Admins can now track credit trends over time, identify top users, and break down spend by model and product through a unified Global Admin Console. Spend controls have been extended to support workspace-level defaults, group-specific limits, and individual overrides, so power users can request additional credits without triggering blanket limit increases. Usage data is also accessible via a Cost API for integration into external financial systems, enabling deeper ROI and cost analysis.

    3 minRead
    Cognizant InsightsJune 15

    No skills, no payoff: Why AI value lives or dies with the workforce

    Cognizant research across 1,100 senior business leaders and 4,400 employees at G2000 companies finds that AI skilling is the critical missing link between AI investment and business returns. Trained workers outperform untrained peers by 28 percentage points in reporting productivity gains of 20% or more, yet companies allocate just 0.2% of annual revenue to AI training—a fraction of their AI technology spend. 72% of senior executives report that fewer than half their employees received any AI skilling in the past year, and only 50% of business leaders believe their current programs effectively equip employees. Organizations spending more than $10 million on AI skilling report productivity gains at a 60% rate versus 40% for lower spenders, and measurable productivity impact begins when just 25% of workers are trained. Cognizant presents a five-stage AI capability maturity model—awareness, skilling, adoption, productivity, ROI—arguing that companies that deploy AI before their workforce is prepared consistently see tools underused and investments unreturned.

    3 minRead
    OpenAI News (firm Scan)

    Introducing the OpenAI Partner Network

    OpenAI is launching the OpenAI Partner Network, a formal ecosystem program backed by $150 million in investment to accelerate enterprise AI adoption through systems integrators, management consultants, and technology partners. The program addresses what OpenAI identifies as the primary barrier to enterprise AI value: not model capability, but repeatable use-case identification, workflow redesign, systems integration, and change management at scale. Partners will operate across three tiers—Select, Advanced, and Elite—based on sales performance, technical capability, and deployment experience, with additional specializations in areas such as Codex, cybersecurity, and agents. OpenAI also plans to certify 300,000 consultants by end of 2026 and is piloting a Forward Deployed Experts program to embed partner practitioners alongside OpenAI engineering teams on complex deployments. The network is designed to extend OpenAI's enterprise reach into markets and verticals it cannot serve directly, making partner strategy a core component of its commercial model.

    3 minRead
    OpenAI News (firm Scan)June 12

    Introducing the OpenAI Economic Research Exchange

    OpenAI has launched the Economic Research Exchange, a structured platform to fund and collaborate with external researchers studying AI's economic effects on workers, firms, and institutions. Selected researchers will gain access to OpenAI tools and datasets under defined data governance and privacy safeguards, enabling empirical work beyond traditional datasets. The program targets applied researchers in labor economics, productivity, inequality, public finance, and related fields, with proposals evaluated on methodological rigor, feasibility, and potential for credible independent evidence. Applications are open through July 5, 2026, with selections announced by July 31, 2026. The initiative extends OpenAI's existing measurement efforts, including its OpenAI Signals program, and aims to build an evidence base for policymakers, businesses, and the public navigating rapid AI-driven change.

    3 minRead
    Anthropic News (firm Scan)

    DXC will integrate Claude into the systems banks, airlines, and other regulated industries rely on

    Anthropic and DXC Technology have announced a multi-year global alliance in which DXC will deploy Claude across the mission-critical IT systems it operates for major banks, airlines, insurers, manufacturers, and government agencies. DXC will certify tens of thousands of forward-deployed engineers through Anthropic Academy to embed Claude directly inside client environments under regulated, compliance-heavy conditions. DXC validated the model in its own 115,000-person operations first, using Claude to generate more than 95% of the code for OASIS, its new AI-native managed-services platform, while achieving a claimed 10x acceleration in software development. Initial deployment focus areas include insurance modernization, legacy codebase refactoring, cybersecurity SOC operations, and application maintenance. DXC has also joined the Claude Partner Network, extending Anthropic's reach into large-enterprise regulated verticals at scale.

    3 minRead
    Anthropic News (firm Scan)

    TCS and Anthropic Partner to Bring Claude to Regulated Industries

    Anthropic and Tata Consultancy Services (TCS) have announced a partnership to deploy Claude across regulated industries globally. TCS will roll out Claude to 50,000 of its own employees across 56 countries and build Claude-powered, industry-specific products for clients in financial services, healthcare, public sector, life sciences, aviation, telecom, and medical technology. Concrete deployments are already underway: Diligenta will use Claude to serve 22 million UK life and pensions policyholders, TCS banking teams will use Claude Code for software engineering productivity, and TCS iON will deliver Claude training across 75 million annual assessments in India. TCS joins the Claude Partner Network as a systems integrator and implementation partner, packaging Claude into vertical offerings such as claims adjudication and lending advisory, with Anthropic citing India as its second-largest market.

    3 minRead
    OpenAI News (firm Scan)

    New OpenAI Academy Courses for the Next Era of Work

    OpenAI has launched three structured courses through OpenAI Academy—AI Foundations, Applied AI Foundations, and Agents and Workflows—designed to move enterprise employees from basic AI literacy to operating agent-assisted, repeatable workflows. The curriculum is built in partnership with BCG, Accenture, and BBVA, and is positioned as an evolving learning standard updated alongside OpenAI's models and safety practices. Learners receive shareable completion certificates, and organizations can embed the courses in onboarding, L&D programs, or broader AI adoption initiatives. OpenAI frames workforce learning as integral to deployment, arguing that the gap between AI access and realized business value closes only when employees develop consistent, practiced skills.

    3 minRead
    Anthropic News (firm Scan)

    Introducing Claude Corps

    Anthropic is launching Claude Corps, a national fellowship program backed by an initial $150 million commitment that will place 1,000 paid fellows at nonprofits across the United States. Fellows receive a $85,000 annual salary, benefits, and ongoing AI training, and are employed through CodePath while spending 12 months full-time embedded at host organizations. The program targets at least 400 nonprofits in its first year, spanning food banks, veteran services, workforce development, and conservation. Anthropic frames the initiative as both a direct investment in workers absorbing AI-driven economic disruption and a scalable model for broadening AI's benefits, with Social Finance leading measurement and a longer-term funding vehicle to enable expansion.

    3 minRead
    OpenAI News (firm Scan)

    Built to Benefit Everyone: Our Plan

    OpenAI has published a strategic manifesto outlining its third organizational phase, shifting from research and product deployment toward making advanced AI broadly abundant, affordable, and accessible to every person and organization on Earth. The company states its internal expectation that by March 2028, a significant fraction of its research will be conducted by AI systems working alongside human researchers, accelerating alignment work and the path to AGI. OpenAI frames its core commitment as preventing power concentration—explicitly warning that transformative AI could consolidate control among a small number of companies, governments, or individuals—and calls for an international coordinating body with authority to slow frontier development when safety and societal resilience require it. Three stated organizational goals are: building an automated AI researcher, accelerating broad-based economic and scientific growth, and delivering a personal AGI to every person on Earth. The piece positions widespread access, open ecosystems, public oversight, and safety standards as prerequisites for the technology's benefits to be broadly shared rather than captured by incumbents.

    3 minRead
    OpenAI News (firm Scan)

    OpenAI on Oracle Cloud

    OpenAI and Oracle are partnering to make OpenAI frontier models and Codex accessible through Oracle Cloud Infrastructure (OCI), allowing enterprises to apply existing Oracle Universal Credits toward OpenAI usage. The integration routes AI procurement through Oracle's established purchasing workflows and governance frameworks, reducing the need for separate vendor relationships or new contract structures. Organizations with existing Oracle cloud commitments can align AI adoption with planned cloud spend rather than treating it as an incremental budget item. Availability begins in the coming weeks; enterprises should contact Oracle sales for specifics on timing and eligibility.

    3 minRead
    Anthropic News (firm Scan)June 9

    Claude Fable 5 and Claude Mythos 5

    Anthropic has launched Claude Fable 5 for general use and Claude Mythos 5 for a restricted set of cyberdefense and infrastructure partners, both priced at $10 per million input tokens and $50 per million output tokens—less than half the cost of Claude Mythos Preview. Fable 5 leads on nearly all tested benchmarks, with documented enterprise results including Stripe compressing two months of Ruby codebase migration work into a single day and IMC reporting near-perfect scores on trading-analysis evaluations. Mythos 5, deployed initially through Project Glasswing in collaboration with the US government, carries lifted cybersecurity safeguards and demonstrated a roughly 10x acceleration in internal drug design workflows, with 9 of 14 protein targets yielding viable drug candidates. Fable 5 ships with conservative safety filters that redirect flagged queries to Opus 4.8, triggering in fewer than 5% of sessions on average, while alignment assessments show misaligned behavior at levels comparable to Opus 4.8. Anthropic intends to expand Mythos 5 access through a broader trusted access program and plans to publish novel genomics research conducted autonomously by the model.

    3 minRead
    Cognizant InsightsJune 9

    Closing the Enterprise AI Gap

    Cognizant research of 1,100 G2000 senior executives finds only 32% can demonstrate tangible business productivity gains from AI, while 25% have already paused or abandoned deployments at an average sunk cost of $4 million per company. The study identifies two variables that separate high performers from low performers: mature technology infrastructure across 10 scored dimensions, and a 'focused' AI investment strategy that prioritizes compute, data readiness, and customized AI platforms over broader initiatives like talent acquisition or product innovation. Organizations in the highest-performing segment outperform the weakest by 31% on a composite outcome score spanning worker productivity, business productivity, revenue gains, and cost reduction — a gap worth an estimated $1–2 billion in annual returns for a typical G2000 company, and $2.5 trillion in unrealized value across the G2000 combined. A key risk finding: organizations with weak infrastructure that invest in non-tech AI initiatives first are 60% more likely to discontinue deployments than peers with similarly weak infrastructure who prioritize tech fundamentals, and even a single 'adequate'-rated infrastructure dimension materially degrades AI outcomes.

    3 minRead
    OpenAI News (firm Scan)June 8

    Confidential submission of draft S-1 to the SEC

    OpenAI announced on June 8, 2026 that it has submitted a confidential draft S-1 registration statement to the SEC, a required step toward a potential IPO. The company disclosed the filing preemptively, expecting the submission to leak. OpenAI explicitly stated it has not set a timeline for going public and acknowledged that some strategic initiatives may be easier to execute while remaining private. Management characterized the filing as preserving optionality — enabling a faster path to public markets if conditions or strategic priorities shift — rather than signaling imminent execution.

    3 minRead
    OpenAI News (firm Scan)June 3

    Introducing GPT-5.4

    OpenAI has released GPT-5.4, positioning it as its most capable and token-efficient frontier model for professional and enterprise work. On the GDPval benchmark spanning 44 occupations, GPT-5.4 matches or exceeds industry professionals in 83% of comparisons, up from 70.9% for GPT-5.2. The model achieves 87.3% on internal investment banking spreadsheet tasks (vs. 68.4% for GPT-5.2), scores 91% on BigLaw legal document benchmarks, and reduces hallucinations by 33% at the individual claim level relative to GPT-5.2. GPT-5.4 is the first general-purpose OpenAI model with native computer-use capabilities, achieving a 75% success rate on OSWorld-Verified, surpassing human performance at 72.4%. It supports up to 1 million tokens of context, improves agentic tool selection, and delivers significantly lower token usage and faster speeds than prior models—directly affecting enterprise AI deployment economics.

    3 minRead
    OpenAI News (firm Scan)June 2

    Codex for every role, tool, and workflow

    OpenAI's Codex platform has reached 5 million weekly users, with non-developers now comprising approximately 20% of the user base and growing more than 3x faster than developers. OpenAI is launching six role-specific plugins covering data analytics, creative production, sales, product design, public equity investing, and investment banking, collectively integrating 62 apps and 110 skills. The investing and banking plugins connect to data providers including Moody's, FactSet, S&P, PitchBook, LSEG, and Datasite, enabling tasks such as earnings review, comparable company analysis, and client-ready pitch material preparation. Two additional features are being introduced: Sites, which allows users to generate and share interactive web apps via URL, and annotations, which enable inline refinement of Codex outputs. Planned future plugins include Corporate Finance, Private Equity Investing, Marketing Strategy, and Legal, with OpenAI positioning toward an open plugin ecosystem spanning ChatGPT and Codex.

    3 minRead
    OpenAI News (firm Scan)

    OpenAI Frontier Models and Codex Are Now Available on AWS

    OpenAI frontier models and its Codex software engineering agent are now generally available on AWS, including in GovCloud regions, giving enterprises a direct path to deploy OpenAI capabilities within existing AWS security, compliance, procurement, and governance workflows. Codex, used by over 5 million people weekly, is accessible via Amazon Bedrock and targets code writing, review, debugging, and modernization use cases. The integration is designed to compress the evaluation-to-production cycle by eliminating the need for separate security reviews and procurement processes outside of AWS. OpenAI also previewed Daybreak, a forthcoming cyber-focused offering combining secure code review, threat modeling, patch validation, and remediation guidance, which will also be available through AWS.

    3 minRead
    Anthropic News (firm Scan)

    Anthropic Confidentially Submits Draft S-1 to the SEC

    Anthropic, PBC has confidentially submitted a draft Form S-1 registration statement to the U.S. Securities and Exchange Commission, formally initiating the process for a potential initial public offering of its common stock. The filing preserves optionality; a public offering will proceed only after SEC review and subject to market conditions. Share count and offering price have not been determined. The announcement follows Anthropic's Series H raise of $65 billion at a $965 billion post-money valuation, positioning it as one of the most highly valued private technology companies ahead of a potential listing.

    3 minRead
    Anthropic News (firm Scan)

    Introducing Claude Opus 4.8

    Anthropic has released Claude Opus 4.8, an upgrade to its flagship model available at the same price as its predecessor. Key capability improvements include benchmark gains across coding, agentic tasks, and reasoning, with the model being approximately 4 times less likely to allow code flaws to pass unremarked compared to Opus 4.7. Fast mode now runs at 2.5× speed at 3× lower cost than prior models, and Databricks reports a 61% reduction in token cost versus Opus 4.7 for multimodal workloads. Accompanying features include dynamic workflows in Claude Code enabling hundreds of parallel subagents for large-scale engineering tasks, user-controlled effort levels, and a mid-task system instruction API that preserves prompt cache integrity. Alignment evaluations show Opus 4.8 has substantially lower rates of deceptive or misuse-enabling behavior than Opus 4.7, comparable to Anthropic's best-aligned model.

    3 minRead
    Anthropic News (firm Scan)

    Anthropic raises $65B in Series H funding at $965B post-money valuation

    Anthropic has closed a $65 billion Series H round at a $965 billion post-money valuation, led by Altimeter Capital, Dragoneer, Greenoaks, and Sequoia Capital. Run-rate revenue crossed $47 billion earlier this month, up from the Series G close in February 2025. The raise includes $15 billion in previously committed hyperscaler investments and adds Micron, Samsung, and SK hynix as strategic infrastructure partners. Proceeds will fund safety and interpretability research, compute expansion, and product scaling; Anthropic has already secured agreements for up to 10 gigawatts of new compute capacity across Amazon, Google/Broadcom, and SpaceX. Claude is now available on all three major cloud platforms—AWS, Google Cloud, and Microsoft Azure—with AWS remaining the primary cloud and training partner.

    3 minRead
    Anthropic News (firm Scan)

    KPMG integrates Claude across its core business and workforce of more than 276,000 in strategic alliance

    KPMG has formed a global strategic alliance with Anthropic to deploy Claude across its entire workforce of 276,000+ employees in 138 countries, with immediate integration into Digital Gateway, KPMG's core client-facing platform built on Microsoft Azure. Claude is being embedded directly into tax and legal workflows via Claude Cowork and Managed Agents, reducing AI agent build times from weeks to minutes. Anthropic is naming KPMG a preferred partner for private equity, enabling KPMG to deploy Claude into PE portfolio companies through offerings including KPMG Blaze, which integrates Claude Code to accelerate IT modernization. The alliance extends to cybersecurity vulnerability detection and remediation, guided by KPMG's Trusted AI framework, with joint research from UT Austin's McCombs School of Business informing responsible human-in-the-loop deployment practices.

    3 minRead
    Anthropic News (firm Scan)

    Introducing Claude for Small Business

    Anthropic launched Claude for Small Business, a product targeting the 44% of U.S. GDP and nearly half the private-sector workforce represented by small businesses. The offering integrates with QuickBooks, PayPal, HubSpot, Canva, DocuSign, Google Workspace, and Microsoft 365 via toggle installation inside Claude Cowork, delivering 15 pre-built agentic workflows and 15 task-level skills across finance, operations, sales, marketing, HR, and customer service. Key workflows include payroll planning, month-end close, cash-flow dashboards, invoice chasing, and campaign execution—all requiring user approval before any action is taken. The launch includes a free AI Fluency for Small Business course in partnership with PayPal and a 10-city hands-on workshop tour beginning May 14 in Chicago.

    3 minRead
    Anthropic News (firm Scan)

    Agents for Financial Services

    Anthropic has released ten ready-to-run agent templates targeting high-volume financial services workflows, including pitchbook construction, KYC screening, general ledger reconciliation, month-end close, and statement auditing. The templates deploy as plugins in Claude Cowork or Claude Code, or as autonomous Claude Managed Agents with audit logs, credentialed data access, and long-running session support. Claude now integrates directly with Microsoft Excel, PowerPoint, Word, and Outlook via add-ins, with context persisting across applications so work initiated in a financial model can flow into a presentation without manual re-entry. Eight new data connectors—including Dun & Bradstreet, SS&C Intralinks, Verisk, and a Moody's MCP app covering 600 million entities—expand the governed data ecosystem, and Claude Opus 4.7 leads Vals AI's Finance Agent benchmark at 64.37%.

    3 minRead
    Anthropic News (firm Scan)

    Higher usage limits for Claude and a compute deal with SpaceX

    Anthropic has signed a compute agreement with SpaceX granting access to 300+ megawatts of capacity (220,000+ NVIDIA GPUs) at the Colossus 1 data center, effective immediately. This deal joins a portfolio of infrastructure commitments totaling over 10 GW across Amazon (up to 5 GW, with ~1 GW online by end of 2026), Google and Broadcom (5 GW, online 2027), a $30 billion Microsoft/NVIDIA Azure deal, and a $50 billion Fluidstack infrastructure investment. As a direct result of expanded capacity, Anthropic is doubling Claude Code's five-hour rate limits for Pro, Max, Team, and Enterprise plans, removing peak-hour throttling for Pro and Max, and significantly raising API rate limits for Claude Opus models. International capacity expansion is underway with a focus on data residency compliance for regulated industries including financial services, healthcare, and government.

    3 minRead
    Anthropic News (firm Scan)

    PwC Expanded Partnership

    Anthropic and PwC have expanded their strategic alliance, with PwC deploying Claude across its global workforce of hundreds of thousands of professionals to build agentic technology, execute deals, and reinvent enterprise functions for clients. PwC is launching a dedicated Office of the CFO business group—its first standalone unit anchored in Anthropic technology—targeting regulated industries where accuracy and auditability are critical, including banking, insurance, and healthcare. The partnership includes a joint Center of Excellence and certification of 30,000 U.S. professionals on Claude, with Claude Code and Claude Cowork rolling out firm-wide. Production deployments are already delivering measurable results: insurance underwriting cycles cut from 10 weeks to 10 days, cybersecurity incident response reduced from hours to minutes, and overall delivery time improvements of up to 70% across live client engagements. Anthropic's $100 million Claude Partner Network investment underpins this collaboration, which it describes as the deepest commitment within that program.

    3 minRead
    OpenAI News (firm Scan)

    OpenAI and Dell Technologies partner to bring Codex to hybrid and on-premises enterprise environments

    OpenAI and Dell Technologies have announced a partnership to deploy Codex, OpenAI's fastest-growing enterprise product, within hybrid and on-premises environments via the Dell AI Data Platform and Dell AI Factory. Codex now reaches more than 4 million developers weekly and is expanding beyond software development into business workflows including reporting, lead qualification, and cross-system coordination. The collaboration allows enterprises to run Codex agents closer to internal data—codebases, documentation, and operational systems—while maintaining the governance controls required in regulated or security-sensitive environments. Dell's on-premises infrastructure addresses a key adoption barrier for large enterprises that cannot or will not move sensitive workloads to public cloud. The partnership also scopes integration of ChatGPT Enterprise and API-based solutions with Dell AI Factory for data preparation, system-of-record management, and AI application deployment.

    3 minRead
    OpenAI News (firm Scan)

    A New Personal Finance Experience in ChatGPT

    OpenAI is launching a personal finance feature within ChatGPT, initially available to Pro subscribers in the U.S., that allows users to securely connect accounts from more than 12,000 financial institutions via Plaid. The tool provides a spending dashboard, transaction categorization, and conversational budgeting guidance powered by GPT-5.5 Thinking, grounded in each user's actual account data and stated financial goals. OpenAI is partnering with Intuit to extend capabilities toward action-oriented outcomes—such as credit card applications and tax estimates with live advisor access—directly within ChatGPT. Financial data is read-only; OpenAI cannot view full account numbers or execute transactions, and users can disconnect accounts or delete synced data at any time.

    3 minRead
    Cognizant InsightsMay 21

    The Talent Architecture Imperative for an AI Workforce

    Cognizant argues that AI adoption requires enterprises to redesign their talent architecture from the ground up rather than retrofitting existing workforce models. The piece contends that traditional role definitions, competency frameworks, and hiring pipelines are structurally misaligned with AI-augmented operating models, where human work increasingly centers on orchestrating, supervising, and exception-handling rather than executing routine tasks. Organizations that treat AI workforce transformation as a training initiative rather than an architectural redesign risk compounding capability gaps as AI systems take on broader task ownership. The imperative is to rebuild job taxonomies, skills frameworks, and organizational structures around human-AI collaboration as a foundational design principle.

    3 minRead
    OpenAI News (firm Scan)May 14

    Company Announcements

    This page is an index of recent OpenAI company announcements and product releases, not a substantive article. Headlines span June 2026 back to April 2026 and cover product launches (Codex, GPT-5.5, ChatGPT Images 2.0), enterprise partnerships (Dell Technologies for hybrid/on-premises Codex deployment), a new OpenAI Deployment Company aimed at helping businesses build on AI, advertising tests in ChatGPT, and a research piece on how leading enterprises are differentiating through AI adoption. No single thesis or finding is developed; the page functions as a navigational feed. Enterprise-relevant items include the Dell partnership for on-premises AI deployment, the Deployment Company launch, and the frontier-firms adoption report.

    3 minRead
    Anthropic News (firm Scan)May 4

    Introducing Claude Opus 4.7

    Anthropic has released Claude Opus 4.7, now generally available across Claude products, API, Amazon Bedrock, Google Cloud Vertex AI, and Microsoft Foundry at $5 per million input tokens and $25 per million output tokens—same pricing as Opus 4.6. The model delivers measurable gains in advanced software engineering, with early-access partners reporting 13% higher coding task resolution, 3x more production task completions versus Opus 4.6, and a 70% pass rate on CursorBench versus 58% for its predecessor. Opus 4.7 introduces higher-resolution vision, stronger instruction-following, and self-verification of outputs—capabilities cited as enabling hands-off execution of complex, long-running agentic workflows. The release also marks Anthropic's first deployment of real-time cybersecurity safeguards under its Project Glasswing framework, with a Cyber Verification Program for legitimate security professionals, as the company tests guardrails ahead of a broader release of its more capable Mythos-class models.

    3 minRead
    Cognizant InsightsApril 28

    Agent Experience for Businesses: How to Get Ready for the Agentic Internet and the Rise of Agent Experience

    Cognizant argues that the internet is entering an 'agentic' era in which autonomous AI agents—not humans—will initiate, evaluate, negotiate, and execute the majority of digital transactions. The firm forecasts that AI-powered consumers will account for 55% of all US consumer spending by 2030, exceeding $4 trillion. Only 17% of senior executives believe their existing infrastructure can support this shift, yet 91% are pursuing large-scale modernization programs. To compete, organizations must build a distinct 'agent experience' (AX) discipline—designing digital services for machine consumption through structured data, stable and fully documented APIs, and programmatic clarity—distinct from traditional human-centered UX. Businesses that fail to make their services legible and trustworthy to AI agents risk losing discoverability and relevance as agent-mediated transactions become the dominant interaction model.

    3 minRead
    Cognizant InsightsApril 28

    How to Get Ready for the Agentic Internet and the Rise of Agent Experience

    Cognizant argues that the internet is entering a structural transition toward an 'agentic internet,' where AI agents replace humans as the primary initiators and executors of digital interactions. Cognizant's consumer AI research forecasts that AI-powered consumers will drive 55% of all consumer spending by 2030, representing over $4 trillion in the US market. Only 17% of senior executives believe their existing infrastructure can support agentification, yet 91% are pursuing large-scale modernization programs to close that gap. The strategic response centers on building 'agent experience' (AX)—a design and engineering discipline analogous to UX but optimized for machine consumption, requiring structured data schemas, stable and fully documented APIs, and programmatic clarity over visual or narrative design. Organizations that fail to make their services legible and trustworthy to AI agents risk losing discoverability and relevance as agent-mediated transactions become the dominant commercial channel.

    3 minRead
    OpenAI News (firm Scan)April 24

    Introducing GPT-5.5

    OpenAI has released GPT-5.5, positioning it as its most capable model to date with particular strength in agentic coding, computer use, and knowledge work automation. On key benchmarks, GPT-5.5 scores 82.7% on Terminal-Bench 2.0, 73.1% on Expert-SWE, and 84.9% on GDPval across 44 occupations, outperforming GPT-5.4 and leading competitors including Claude Opus 4.7 and Gemini 3.1 Pro. Notably, the model achieves these gains while matching GPT-5.4 per-token latency and using fewer tokens on coding tasks, delivering what OpenAI describes as state-of-the-art coding intelligence at half the cost of competitive frontier models. Internal enterprise adoption data from OpenAI itself is concrete: Finance teams used the model to process 24,771 K-1 tax forms across 71,637 pages two weeks faster than the prior year, and over 85% of OpenAI employees use Codex weekly across functions including finance, marketing, and data science. GPT-5.5 is now available to Plus, Pro, Business, and Enterprise ChatGPT users and via API, with GPT-5.5 Pro available to higher-tier subscribers.

    3 minRead
    Cognizant InsightsMarch 5

    The bridge to AI value will be built, not bought

    Cognizant's central argument is that AI value cannot be purchased off the shelf—it must be deliberately constructed through organization-specific integration of people, processes, and technology. Off-the-shelf AI tools deliver commodity capabilities, while durable competitive advantage requires custom implementation aligned to each enterprise's workflows, data assets, and workforce. The piece emphasizes that workforce empowerment—reskilling and change management—is as critical to AI ROI as the underlying technology investment. Organizations that treat AI adoption as a build-and-integrate challenge rather than a procurement exercise are positioned to capture measurable productivity and margin gains.

    3 minRead
    Cognizant InsightsMarch 5

    The Great AI Misconception and Why AI Builders Are Essential

    Cognizant's central argument is that large enterprises harbor a critical misconception about AI adoption: that deploying off-the-shelf AI tools is sufficient for enterprise-scale transformation. The piece contends that generic AI solutions cannot address the complexity, legacy infrastructure, and domain-specific requirements of large organizations, making dedicated "AI Builders" — professionals who design, integrate, and govern custom AI systems — an operational necessity rather than a luxury. Without this builder capability, enterprises risk surface-level AI adoption that fails to generate measurable business value or integrate with existing workflows and data environments. Cognizant frames AI Builders as the connective tissue between foundation model capabilities and enterprise-grade deployment, encompassing skills in data architecture, model customization, integration engineering, and governance — roles that span both technology and business strategy.

    3 minRead
    OpenAI News (firm Scan)February 1

    Samsung Electronics Brings ChatGPT and Codex to Employees

    Samsung Electronics is deploying ChatGPT Enterprise and Codex to all employees in Korea and all Device eXperience (DX) division employees worldwide, making it one of OpenAI's largest enterprise rollouts to date. The deployment spans R&D, manufacturing, software development, marketing, and corporate functions, targeting both technical and non-technical productivity gains. Codex, originally a developer tool, is being extended to non-technical staff to convert ideas into working software, internal tools, and automated workflows; globally, Codex now reaches more than 5 million weekly active users. Codex weekly active users in Korea have grown nearly 800% since February 1, 2026, signaling rapid workforce adoption at scale. The agreement also deepens an existing Samsung–OpenAI relationship that includes supply of advanced memory semiconductors for AI infrastructure, expanding the partnership from hardware to enterprise workforce transformation.

    3 minRead
    Cognizant InsightsJanuary 22

    Bubble? Hardly. AI Can Already Perform Tasks Worth $4.5T

    Cognizant argues there is no AI bubble by quantifying AI's current economic potential: today's AI can already perform tasks representing $4.5 trillion in annual value, grounding the technology's investment case in measurable task-level capability rather than speculative future promise. The analysis maps AI performance against the full spectrum of economically significant work activities, finding that a substantial share of high-value tasks across industries are already within AI's demonstrated competency. This $4.5T figure is presented as a floor, not a ceiling, as agentic AI and continued model improvements expand the addressable task set. The piece directly challenges bubble narratives by arguing that current enterprise AI ROI is real and calculable, not dependent on unproven future breakthroughs. For executives weighing AI investment decisions, the implication is that deferring deployment carries quantifiable opportunity cost rather than prudent risk management.

    3 minRead
    Cognizant InsightsOctober 20

    AI's two-year timeline: The path to meeting the legacy modernization mandate

    Cognizant surveyed 1,000 Global 2000 senior executives and found that AI integration has become a top-three driver of legacy modernization, with 85% expressing serious concern that their current technology estate will impair their ability to deploy AI. Three-quarters of respondents expect to complete major modernization milestones within two years, but the funding math does not support that timeline: 93% have retired 25% or less of their tech debt, and only 18% will have retired half or more by 2030. Budget allocations tell a similar story — organizations plan to cut legacy maintenance spend from 61% to 27% of budget by 2030, but tech debt savings alone will cover less than half the modernization cost burden for most firms. Cognizant proposes a self-propagating flywheel model that sequences modernization investments to generate operational savings and incremental revenue first, then applies those proceeds to tech debt retirement, and finally funds growth-oriented new platform initiatives — prioritization discipline is identified as the critical success factor for meeting the two-year window.

    3 minRead
    Cognizant InsightsSeptember 22

    Agentic AI and the Future of Sustainable Business Models

    Cognizant argues that agentic AI—autonomous systems capable of multi-step reasoning and action—represents a structural inflection point for enterprise business models, not merely an incremental productivity tool. The piece contends that organizations deploying agentic AI can achieve continuous operational adaptation, reducing reliance on static processes and enabling real-time responses to market disruption. Resilience and sustainability are framed as the primary business outcomes, with agentic architectures positioned as the mechanism for compressing decision latency across functions including finance, supply chain, and customer operations. The article implicitly sets up an enterprise transformation agenda in which the CIO and CDO own architecture and data governance decisions, while CFOs and boards must weigh investment economics and strategic risk posture.

    3 minRead
    Cognizant InsightsSeptember 18

    How AI will change the relationship between consumers and consumer goods manufacturers

    Cognizant's AI Inclination Index, drawn from a survey of 8,451 consumers across the US, UK, Germany, and Australia, quantifies consumer propensity to use AI throughout the consumer goods purchase journey across five product categories and three journey phases (Learn, Buy, Use). Consumer goods AI inclination meets or exceeds the cross-industry average, with AI-enthusiastic consumers projected to account for up to 55% of all purchases—representing $4.4 trillion in US spending alone. High-income consumers show disproportionately higher AI adoption, with Buy-phase scores twice those of low- and medium-income groups, while conversational AI is the preferred tool across all segments. Manufacturers are best positioned to capture AI-driven consumer engagement in the Learn phase (index score: 88/100) and in post-purchase Use-phase embedding, though AI inclination drops sharply for large-ticket and luxury goods. With 47% of manufacturers already using generative AI in operations and 70% planning customer-facing AI deployment by year-end, the report argues that a nuanced, segment-specific consumer AI strategy is now a competitive necessity.

    3 minRead
    Cognizant InsightsAugust 29

    Big Changes Are Ahead for Health Insurers as Consumers Adopt AI

    Cognizant's AI Inclination Index, drawn from a survey of 8,451 consumers across the US, UK, Germany, and Australia, finds that health insurance consumers are roughly 7% less inclined to use AI than the cross-industry global average, with the gap most pronounced in the Learn and Buy phases. Despite this relative reluctance, consumers aged 55+ show the highest AI inclination in health insurance—driven by familiarity with the complexity and financial stakes of coverage decisions—while younger cohorts, who face fewer near-term insurance decisions, score lower. AI interest peaks in the Learn phase across all four product categories (health plans, prescription drugs, health monitoring devices, and health services), where conversational AI is the preferred tool; interest drops sharply in the Buy phase before partially recovering in the Use phase. Consumers who are enthusiastic about AI are projected to account for up to 55% of all consumer spending across industries, representing $4.4 trillion in the US alone, making precise consumer-facing AI strategy a material revenue and engagement priority for health insurers.

    3 minRead
    Cognizant InsightsJuly 21

    Modern Businesses Require an AI-Driven Data Strategy

    Cognizant argues that conventional data strategies — built around dashboards, reporting, and batch analytics — are no longer sufficient for AI-era business demands, and that enterprises must rebuild their data foundations specifically to support AI workloads. The piece contends that AI systems require real-time, contextualized, and semantically consistent data at scale, meaning data quality, governance, and architecture decisions now directly determine AI ROI. Cognizant outlines a roadmap anchored in four priorities: unifying fragmented data estates, implementing AI-ready semantic layers, embedding data governance as an operational control rather than a compliance exercise, and shifting from reactive to predictive data pipelines. The practical implication is that CIOs, CDOs, and technology leaders must treat data infrastructure investment as a prerequisite for AI value capture, not a parallel workstream.

    3 minRead
    Cognizant InsightsJune 30

    How 4 Types of AI Are Transforming Business Strategy

    Cognizant's framework argues that effective enterprise AI strategy requires matching the right AI type—predictive, generative, agentic, or physical—to specific business problems rather than defaulting to the most-hyped option. Predictive AI applies statistical models to historical data for forecasting and anomaly detection, while generative AI produces novel content and enables natural-language interfaces. Agentic AI executes multi-step autonomous workflows with minimal human intervention, and physical AI governs robotics and real-world sensing systems. The piece positions AI type selection as a foundational architectural and investment decision, with misalignment between problem type and AI category cited as a primary cause of failed deployments. Enterprises are advised to audit use cases against this taxonomy before committing budget or platform resources.

    3 minRead
    Cognizant InsightsApril 29

    Legacy Modernization as the Catalyst for AI Transformation

    Cognizant argues that legacy system modernization is a prerequisite—not a parallel track—for enterprise AI transformation, positioning technical debt as the primary barrier to AI-fueled innovation. Organizations running on outdated infrastructure cannot effectively deploy AI agents, integrate real-time data pipelines, or achieve the operational scalability that modern AI workloads demand. The piece frames modernization as a strategic investment with direct ROI implications: reducing maintenance costs on legacy stacks while unlocking the platform architecture needed for AI-driven productivity and competitive differentiation. Cognizant outlines a phased approach to modernization that balances business continuity with accelerated migration to cloud-native, AI-ready environments, emphasizing that the cost of inaction compounds as AI adoption widens across industries.

    3 minRead
    Cognizant InsightsJanuary 22

    Why France Is Positioned to Lead in Generative AI Adoption

    A Cognizant/Oxford Economics study of 2,200 business leaders across 23 countries finds France's generative AI momentum score is 60% higher than the global average, driven by favorable perceptions of data privacy, regulatory environment, business model flexibility, and output quality of local models such as Mistral. Despite this structural advantage, French businesses plan to spend approximately $23.7 million on generative AI in 2025—less than half the global average of $47 million—and 69% of French leaders believe they are not moving fast enough. Productivity enhancement, rather than business-model disruption, is the dominant near-term strategic priority, mirroring the global trend. Key headwinds include talent cost and availability, technology maturity concerns, and legacy infrastructure that could constrain data accessibility gains.

    3 minRead
    Cognizant InsightsDecember 2

    Capitalizing on the Benelux Gen AI Advantage

    A Cognizant and Oxford Economics study of 90 Benelux senior business leaders finds the region plans to spend a median of $18.5 million on generative AI in 2024—48% above the global median of $12.5 million—yet posts a momentum score 47% below the global average, reflecting low confidence in execution. Seventy-three percent of Benelux respondents believe they are not moving fast enough on gen AI strategy, and 59% fear competitors will gain ground as a result. Key inhibitors are talent cost and availability and concerns about gen AI technology maturity, while data readiness and operating-model flexibility are the region's relative strengths. Near-term investment is skewed toward productivity gains rather than business-model disruption, and the study argues that overcoming talent and accessibility gaps is the critical path to converting high spend into realized AI momentum.

    3 minRead
    Cognizant InsightsOctober 30

    Gen AI Is Taking Hold in DACH Businesses

    Cognizant and Oxford Economics surveyed 2,200 business leaders across 23 countries, including 200 in the DACH region (Germany, Austria, Switzerland), to assess generative AI adoption momentum. DACH businesses plan average gen AI spending of $37 million in 2024, below the global average of $47.5 million, and the region's momentum score sits 8% below the global baseline—dragged down by talent scarcity, cost concerns, and cautious consumer and employee perceptions of AI. Despite this, 71% of DACH respondents believe they are not moving fast enough with their gen AI strategies, and 56% fear competitive disadvantage from delays. Near-term investment priorities skew toward productivity gains over disruptive innovation, though DACH leaders are notably above the global average in plans to redesign operating models—signaling intent to channel efficiency gains into growth rather than pure cost-cutting.

    3 minRead
    Cognizant InsightsSeptember 26

    How Singapore's Thriving Digital Economy Could Drive Generative AI Adoption

    Cognizant and Oxford Economics surveyed 100 Singapore business leaders as part of a 2,200-respondent global study to assess generative AI adoption momentum. Singapore companies plan a median generative AI spend of USD $16 million, above the global median of $12.5 million, yet Singapore's momentum score sits 27% below the global average, driven by pessimism around compute availability, data readiness, and cost of capital. Sixty-six percent of Singapore respondents feel they are not moving fast enough on generative AI strategy, and 58% expect competitive disadvantage from delays. The primary near-term use case is productivity augmentation rather than business model innovation, with talent shortages, consumer data-trust concerns, and technology maturity cited as the leading adoption inhibitors.

    3 minRead
    Cognizant InsightsSeptember 25

    The AI advantage: why ANZ is positioned for gen AI success

    A Cognizant/Oxford Economics study of 2,200 business leaders across 23 countries found ANZ businesses plan a median gen AI spend of $15 million, above the global average of $12.5 million, yet 69% feel they are not moving fast enough and 52% fear competitive disadvantage from delays. ANZ's 'momentum score'—a composite of 18 regional and internal factors affecting adoption readiness—ranks 15% above the global baseline, driven by stronger data readiness and operating model flexibility relative to peers. Despite this relative optimism, technology infrastructure remains a significant inhibitor: only 9% of ANZ respondents cite it as an accelerator, and just 16% rate data accessibility as good or excellent, creating a gap between data quality and usability. Talent cost and availability rank as the top adoption inhibitor, while near-term investment is weighted toward productivity gains over business-model disruption. Sustainability impact and employee perception concerns pull the momentum score back below its potential.

    3 minRead
    Cognizant InsightsSeptember 19

    UK and Ireland: A Beacon of Progress for Generative AI

    Cognizant and Oxford Economics surveyed 200 senior business leaders in the UK and Ireland (UKI) as part of a 2,200-respondent global study across 23 countries, finding that UKI firms plan to spend $57.6 million on generative AI in the current financial year, well above the $47.5 million global average. The region's 'momentum score'—measuring business confidence in executing a gen AI strategy—is 5.6% above the global baseline, driven by strong market demand, flexible operating models, and favorable output quality assessments. Over the next two years, UKI business leaders prioritize productivity gains over business-model disruption, with the stated goal of redirecting efficiency gains toward growth investment rather than cost-cutting. Key inhibitors include consumer skepticism toward AI, talent shortages, data privacy and security concerns, and gaps in technology infrastructure, all of which threaten to slow adoption despite the region's strong investment fundamentals.

    3 minRead
    Cognizant InsightsSeptember 11

    Generative AI: The New Frontier for US Business Ingenuity

    A Cognizant/Oxford Economics study of 2,200 business leaders across 23 countries finds US companies plan to spend an average of $67 million per company on generative AI in 2024, versus a global average of $47 million. The US momentum score runs 11% above the global baseline, driven by stronger confidence in market demand, data readiness, and compute availability. Despite this optimism, 74% of US respondents say they are not moving fast enough on gen AI strategy, and 66% fear competitive disadvantage from delays. Key inhibitors include a shortage of skilled AI professionals (cited by 58% of US respondents), weak technology infrastructure, and inflexible business models, while near-term investment skews toward productivity gains over transformative innovation.

    3 minRead
    Cognizant InsightsJuly 30

    Global generative AI strategies: accelerators, inhibitors and a new focus on productivity

    A Cognizant survey of global enterprises identifies the primary accelerators and inhibitors shaping generative AI adoption strategies, with productivity now emerging as the dominant business case replacing earlier revenue-growth narratives. Organizations accelerating deployment cite leadership alignment, clear use-case prioritization, and access to quality data as the top enablers, while data governance gaps, talent shortages, and unclear ROI measurement frameworks are the most cited inhibitors. The research finds that companies with formal gen AI governance structures are advancing to production deployments at measurably higher rates than those still in pilot phases. The report positions productivity-driven AI investment as the near-term value lever, with implications for workforce planning, technology spend, and operating model redesign.

    3 minRead
    Cognizant InsightsJune 18

    Turning potential to profit: building consumer trust in AI

    Consumer trust is the primary barrier converting AI's potential into measurable business value, according to Cognizant's analysis. The piece argues that organizations deploying AI-powered customer experiences must treat transparency, explainability, and data stewardship as commercial imperatives rather than compliance checkboxes. Companies that close the trust gap stand to capture higher adoption rates, greater willingness to share data, and stronger revenue conversion from AI-driven interactions. The recommended framework centers on clear AI disclosure, human-override options, robust data governance, and continuous feedback loops to demonstrate accountability to end users.

    3 minRead

    PwC

    28 articles
    PwC Insights

    Spending too much on AI? How CIOs and CFOs can scale AI with discipline

    PwC argues that AI cost control tools alone are insufficient for competitive advantage — organizations need a new operating model built around four disciplines: upfront cost underwriting, architecture redesign, outcome-linked governance, and savings reinvestment. Token costs are falling yet AI bills are surging because volume growth and agentic workflows compound costs invisibly across planning, retrieval, reasoning, and orchestration layers that most budgets and dashboards cannot see. PwC details a framework requiring coded budget gates, mandatory routing controls, and human-oversight triggers embedded directly in agentic pipelines — not bolted on afterward. In a documented case, one global technology company applied this model to a key pipeline and cut cost per run 65–80%, tripled processing speed, and maintained output quality, effectively achieving 3–5x more AI capacity for the same budget.

    3 minRead
    PwC Insights

    PwC Case Studies

    This page is a PwC case study library index listing 100 client engagements across industries and technology platforms, with no single thesis or finding. Recent entries highlight cloud modernization (Guidewire, Oracle Cloud, SAP S/4HANA), AI-enabled data platforms (Microsoft Azure, Microsoft Fabric), compliance remediation (SOC 1/SOC 2, risk controls), and ERP implementations (Workday). Specific quantified outcomes cited include 50–60% reduction in modernization timelines, 70% backlog reduction, 10x research output increase, and 66% reduction in ledger accounts. The page functions as a navigational index rather than substantive thought leadership content.

    3 minRead
    PwC Insights

    AI-Native Engineering for Faster Software Delivery

    PwC argues that bolting AI tools onto existing software delivery models produces only marginal gains because team structures, prioritization processes, and governance layers remain unchanged. Its AI-native engineering model instead replaces the operating model entirely across three simultaneous dimensions: small 5-7 person pods trained in prompt engineering and agent orchestration, rolling backlogs with AI-compressed elaboration and code generation, and a pre-built AI-enabled platform stack wired to existing repositories and design tools before sprint one. A live insurance engagement produced quantified results: delivery time cut in half, team size reduced from 12 to 5.5 engineers, discovery compressed from weeks to hours, mean time to resolution down 77%, and product owner time freed by 45-50%. Human approval gates remain at every merge, architecture decision, and release, making the model viable for regulated industries.

    3 minRead
    PwC Insights

    From AI Noise to AI Advantage

    PwC's framework argues that most enterprises are failing to scale AI value because they remain overfocused on technology and pursue high-profile use cases that do not move financial or operational needles. A small cohort of breakaway companies is generating outsized returns by aligning four interlocking pillars: strategy, people, technology, and execution discipline. The central prescription is that moving from isolated experimentation to enterprise-wide AI advantage requires deliberate integration across all four dimensions simultaneously, not sequential pilots. PwC positions this framework as the bridge between inflated AI expectations and measurable, scalable outcomes.

    3 minRead
    PwC Insights

    AI Readiness Assessment for Enterprise Transformation

    PwC has launched an AI Readiness Assessment designed to give senior enterprise leaders an evidence-based diagnostic of their organization's actual AI maturity rather than a self-reported estimate. The tool scores organizations on a 0–100 scale across 12 enterprise domains — including Strategic Vision, Data Governance, Talent, Risk, and Business Model Resilience — and benchmarks results against proprietary data from 500+ organizations across eight industries. A notable output is leadership alignment visibility: inter-executive scoring gaps of 14 points on the same domain are common, surfacing misalignment before strategic decisions are made. Recommendations are classified as Critical, High, or Quick Win, tied to specific domain gaps, and mapped to a 90-day, 6-month, and 12-month action roadmap with auto-updating scores as initiatives progress.

    3 minRead
    PwC Insights

    Responsible AI in Finance

    PwC identifies three priority actions for finance leaders deploying AI: establishing data integrity, validating AI outputs through human-led review, and embedding governance into financial reporting controls. The piece targets CFOs, CAOs, and controllers at public companies, where AI use in forecasting, impairment assessments, and revenue recognition directly implicates ICFR frameworks and auditor engagement. One-third of CEOs report GenAI has already increased revenue and profitability, creating pressure to scale adoption while managing compliance risk. Practical guidance centers on data lineage systems, tailored review protocols calibrated to use-case risk, and iterative refinement of AI models—illustrated through an ASC 606 revenue recognition case where AI initially misclassified performance obligations before human oversight corrected the error.

    3 minRead
    PwC Insights

    The Uncomfortable Truth About AI: Your Technology Is Ready. Your Organization Isn't

    PwC's workforce solutions leader argues that most enterprise AI transformations are failing because companies address only one dimension of change—the work itself—while neglecting workforce structure and individual worker roles. Using a 3W framework (Work, Workforce, Worker), the piece contends that the real AI dividend lies not in headcount reduction but in redesigning roles, team configurations, management layers, and operating models to unlock new growth. PwC's 2026 AI Jobs Barometer data shows the strongest gains are in professionalized roles where AI amplifies human expertise and judgment, not where it simply automates tasks. Organizations advancing beyond basic tool adoption into process automation and operating model reinvention require CHROs and people strategy to evolve in lockstep with technology deployment, or the productivity dividend is lost to margin rather than captured for expansion.

    3 minRead
    PwC InsightsJuly 8

    AI creates a workforce dividend. Invest it for growth.

    PwC's central thesis is that AI is generating a 'workforce dividend'—freed human capacity—that leaders should reinvest into growth rather than extract as cost savings. The 2026 PwC AI Jobs Barometer, analyzing one billion job postings globally, found headcount growth at AI-exposed organizations is double that of least-exposed peers, suggesting AI augments rather than contracts labor demand. Unlike prior technology transitions that took decades to produce economic impact, AI is delivering measurable effects now, including a rising share of GDP and rapidly accelerating enterprise spending. PwC argues the critical strategic question is not how much labor cost AI eliminates, but what new value—products, capabilities, market positions—leaders build with the capacity AI frees. Reskilling alone is insufficient; the recommended 'AI dividend test' requires growth investment to take priority over headcount reduction to avoid cutting off future competitive optionality.

    3 minRead
    PwC Insights

    AI Reality Check: Find the Signal in All the Noise

    PwC's latest global CEO survey finds 43% of companies are realizing AI-driven revenue or cost benefits, while 42% remain stuck and unable to unlock either. The firm identifies three compounding misconceptions holding back enterprise AI: overestimating model readiness, conflating agent volume with scaled deployment, and mistaking additive experimentation for systemic transformation. PwC argues that true AI transformation requires deliberate integration of data, workflows, controls, and operating model decisions — not scattered, bottom-up experiments — and introduces a 'bi-modal' framework that separates high-value, leadership-directed initiatives from worker-led experimentation. Both early adopters risking AI fatigue and laggards paralyzed by perceived catch-up costs still have time to act, but only if C-suite leadership moves decisively to concentrate resources on the highest-value use cases and drive measurable P&L impact.

    3 minRead
    PwC Insights

    How Cloud and AI Modernization Accelerates Data Strategy

    PwC positions data modernization as the foundational prerequisite for scaling cloud and AI capabilities across the enterprise, arguing that fragmented or legacy data architectures directly limit AI and cloud ROI. The piece frames cloud migration and AI adoption as interdependent: organizations cannot realize AI at scale without first establishing governed, accessible, and high-quality data infrastructure. PwC's approach integrates data strategy, cloud architecture, and AI enablement into a unified modernization program rather than treating them as sequential or siloed initiatives. The content is oriented toward enterprise leaders evaluating how to sequence and fund technology transformation investments to maximize business outcomes.

    3 minRead
    PwC Insights

    PwC and Palantir's approach to tariffs and supply chain

    PwC and Palantir have partnered to deliver real-time scenario modeling capabilities aimed at helping enterprises navigate tariff volatility and supply chain disruption. The offering combines Palantir's data integration and AI platform with PwC's advisory and industry expertise to enable dynamic, data-driven decision-making rather than static planning cycles. The joint solution targets CFOs and supply chain leaders who need to model cost impacts, sourcing alternatives, and margin exposure across multiple tariff scenarios simultaneously. The approach positions rapid scenario analysis as a strategic competitive advantage in an environment of ongoing trade policy uncertainty.

    3 minRead
    PwC Insights

    America in Motion: How Companies Can Power Growth and Innovation

    PwC's 'America in Motion' program positions geopolitical shifts, tariff uncertainty, and US-first industrial policy as a growth opportunity requiring integrated strategic response across five domains: manufacturing and reshoring, AI and data centers, energy supply and demand, capital sourcing, and operational modernization. Companies considering reshoring face critical gaps in infrastructure, workforce skills, and energy systems, requiring rigorous scenario analysis before committing to new facility investments. PwC offers end-to-end advisory spanning site selection, financial modeling, supply chain restructuring, capital strategy, and AI/compute infrastructure planning. The firm emphasizes cross-functional integration—arguing that manufacturing, energy, data, and financing decisions cannot be addressed in silos—as the defining differentiator of its approach.

    3 minRead
    PwC Insights

    Why the biggest strategic decisions are being made in the next 12 months

    PwC argues that the next 12 months represent a defining window for strategic investment decisions—spanning data centers, automation, supply chain reconfiguration, and AI deployment—that will set competitive trajectories for the next decade. The firm segments companies into three groups: a wait-and-see majority deferring commitment, stalled strategists who have done the analysis but cannot execute, and a first-mover cohort actively stress-testing growth strategies and reallocating capital. The central finding is that decision-making lag—not information scarcity—is the primary vulnerability, and that quantifying the cost of inaction (e.g., the balance-sheet impact of tariff-driven growth attrition or the competitive cost of a 12-month AI deployment delay) is what converts risk awareness into executive action. PwC recommends building structured, scenario-based decision infrastructure that enables confident, sequenced moves rather than waiting for environmental clarity that may never arrive.

    3 minRead
    PwC Insights

    Scaling the Agentic Enterprise

    PwC argues that enterprises must replace fragmented AI pilots with a unified agentic architecture built on three structural shifts: centralized state and context management, purpose-built specialized agents, and governed orchestration that limits human intervention to exceptions only. Clients who have implemented this architecture have seen costs of core operational workflows drop roughly 30%, achieved through reuse of shared orchestration, runtime controls, and governance infrastructure across workflows rather than rebuilding them per point solution. The architecture comprises five layers—tech stack, governance, orchestration, workflow design, and agents—and is designed to layer on top of existing ERP and IAM systems rather than replace them. PwC recommends five actions to begin: build an agentic blueprint, select a foundation with a buy-versus-build framework, operationalize governance from the start, upskill IT staff as agentic system architects, and identify a small number of high-value workflows to scale first. The central economic argument is that centralized platforms contain complexity and cost earlier, while fragmented deployments create compounding remediation and integration expenses.

    3 minRead
    PwC Insights

    Quarterly Outlook: Insights on the US and Global Economy, Sector Implications, and Investing Landscape

    PwC's Q2 2026 quarterly outlook identifies a collision between two opposing macro forces: AI-driven investment growth and a severe energy supply shock triggered by the Iran military conflict, which has disrupted nearly 20% of global daily oil and LNG flows through the Strait of Hormuz. Under the baseline scenario, WTI averages around $80 for the remainder of 2026, trimming U.S. GDP growth from 2.1% to 1.9% and slowing euro area growth to approximately 0.5%; an adverse scenario could push WTI to $96–$100, while a severe scenario involving a full Hormuz closure could send oil to $120–$150 and decelerate global GDP to roughly 2.2%, likely triggering recession. AI capex—which drove a 24% year-over-year surge in U.S. software and computing investment in Q1—faces new constraints, as energy-intensive data centers and compute infrastructure become more costly to operate under a sustained supply shock. Fed rate cuts are likely pushed to early 2027 under the baseline, with a rate hike no longer implausible if labor markets tighten and core PCE rises materially above 3%. The report advises companies and investors to reassess capital allocation strategies across energy, industrials, technology, financial services, and healthcare, as traditional diversification strategies show meaningful limits in this environment.

    3 minRead
    PwC InsightsJune 4

    Dynamic controls testing with AI

    PwC's dynamic controls testing framework applies AI to transform internal controls from periodic, sample-based reviews into continuous, comprehensive testing processes. Rather than testing a fraction of transactions on a scheduled cycle, AI-enabled dynamic controls testing analyzes full populations of transactions in near real-time, surfacing exceptions and control failures as they occur. This shift materially reduces the lag between a control breakdown and its detection, lowering financial misstatement risk and strengthening SOX compliance posture. The approach also enables internal audit and accounting teams to redirect manual testing effort toward higher-judgment activities, improving operating leverage across the controls function.

    3 minRead
    PwC InsightsJune 4

    The intelligent enterprise in the age of AI

    PwC's intelligent enterprise framework argues that AI's transformative value is realized only when it operates across the entire organization as a unified system rather than in isolated functional deployments. The model envisions AI agents, data, and workflows integrated end-to-end—connecting finance, operations, technology, and customer functions—so that decisions made in one domain automatically inform and accelerate action in others. This cross-enterprise coherence requires deliberate architectural choices around data governance, platform standardization, and AI agent orchestration, making it a C-suite and board-level commitment rather than an IT project. PwC positions this shift as the defining operating model transformation of the current AI cycle, with competitive differentiation accruing to firms that unify their enterprise intelligence layer fastest.

    3 minRead
    PwC InsightsJune 3

    Market Volatility 2026: How Consumers and FS Execs Are Responding

    PwC's 2026 Market Volatility Survey of 1,004 consumers and 204 financial services executives finds widespread behavioral retrenchment driven by inflation and macroeconomic uncertainty. 83% of consumers now prioritize financial resilience over long-term returns, with 84% managing finances more cautiously and 76% focused on liquidity and emergency preparedness. On the corporate side, 73% of FS executives have delayed or canceled M&A activity, 84% increased stress testing and scenario planning, and 96% are monitoring liquidity thresholds more closely. A significant AI trust gap has emerged: 91% of FS executives view AI tools as increasingly critical for client navigation, yet only 53% of consumers trust AI-powered financial tools during volatility, with 87% still preferring human guidance. Generationally, younger cohorts are most affected—64% of Gen Z and 68% of millennials are delaying major financial decisions, compared with 49% of baby boomers—and 75% of consumers characterize their behavioral changes as lasting shifts.

    3 minRead
    PwC InsightsApril 9

    Agentic AI in procurement: PwC

    PwC argues that agentic AI is poised to fundamentally reshape procurement by enabling autonomous, multi-step decision-making across sourcing, supplier management, contract execution, and spend analysis—moving well beyond prior generations of procurement automation. AI agents can operate end-to-end across the source-to-pay cycle with minimal human intervention, surfacing savings opportunities, flagging supplier risks, and accelerating cycle times at scale. The piece is directed at Chief Procurement Officers and frames the shift as a strategic imperative requiring governance structures, data readiness, and human-in-the-loop controls to manage agent autonomy. Implementation priorities include identifying high-value use cases, ensuring clean underlying data, and establishing clear accountability frameworks before deploying agents at scale.

    3 minRead
    PwC InsightsMarch 30

    AI predictions for tax leaders: PwC

    PwC's piece for tax leaders identifies 4 action-ready AI predictions aimed at guiding tax function transformation. The article positions AI adoption in tax as moving from experimentation to operational deployment, with tax leaders expected to lead—not just react to—enterprise AI agendas. Key themes include agentic AI handling compliance and data-intensive workflows, the need for tax-specific data governance, and upskilling tax teams to work alongside AI tools. PwC frames tax leaders as strategic owners of AI decisions within the tax function, requiring deliberate investment in technology, talent, and process redesign.

    3 minRead
    PwC InsightsMarch 20

    C-Suite Outlook: Executive Views on Policy, Risk, and Growth

    PwC's April 2026 C-Suite Outlook survey of U.S. executives finds 90% believe their company is stronger than two years ago, a sharp reversal from May 2025 when 57% said they were missing opportunities due to slow decision-making. Executives report broad action across technology/AI investment (38%), proactive risk management (36%), and trade strategy adjustment (35%), averaging 3.7 strategic actions since January 2025. However, PwC warns that convergence on identical playbooks is eroding differentiation—73% of respondents took at least one of the three most-cited actions, meaning execution quality, not strategy selection, now determines competitive separation. AI adoption is accelerating (74% plan to begin or increase AI investment in the next 12 months), but 81% say meaningful returns beyond efficiency are still at least a year away; simultaneously, 65% lack the data needed to assess geopolitical risks, and 87% are already planning for higher business taxes driven by U.S. fiscal pressures.

    3 minRead
    PwC InsightsMarch 11

    Invoice automation for finance operations: PwC

    PwC outlines an AI-enabled invoice automation framework targeting accounts payable operations within finance functions. The approach leverages AI agents to extract, validate, and route invoice data, reducing manual processing time and exception handling overhead. Key benefits cited include improved straight-through processing rates, faster cycle times, and stronger internal controls over disbursements. The piece positions invoice automation as an entry point for broader AI-driven finance transformation, with implications for working capital management and AP team redeployment.

    3 minRead
    PwC InsightsMarch 6

    AI-powered IT separation planning for deal execution: PwC

    PwC's article argues that AI can transform IT separation planning during M&A divestitures, a process traditionally plagued by manual effort, compressed timelines, and high execution risk. AI-powered tools can rapidly analyze application inventories, map interdependencies, and generate separation blueprints that would otherwise take weeks of consultant and IT staff time. The approach is positioned to reduce time-to-close risk by accelerating the IT workstream, which is frequently a critical-path bottleneck in carve-outs and spin-offs. For enterprises executing divestitures or acquisitions, this translates to faster Day 1 readiness, lower transition service agreement (TSA) costs, and reduced integration risk. The piece is fundamentally a capabilities showcase for PwC's AI-enabled deal execution practice rather than an empirical study with disclosed metrics.

    3 minRead
    PwC InsightsOctober 1

    Fueling US growth: innovation and agility in AI, energy and manufacturing

    PwC's 'America in Motion' report identifies a structural realignment in US business driven by geopolitical uncertainty, tariff regimes, AI-fueled data demand, and a reshoring push in manufacturing. Data center capital spending could reach $2.35 trillion by 2030, while US energy demand is projected to rise 15–20% by 2030, with data centers potentially consuming up to 9% of total supply. Manufacturers reconsidering reshoring face complex decisions spanning site selection, cost modeling, tax incentives, and supply chain restructuring—many having not built a US facility in decades. PwC frames five strategic pillars—AI and data centers, manufacturing reshoring, and energy supply among them—as the basis for a connected, scenario-driven strategy that builds agility into long-term operating models.

    3 minRead
    PwC InsightsApril 28

    PwC's AI Agent Survey

    PwC's May 2025 survey of 300 senior U.S. executives finds 88% plan to increase AI-related budgets in the next 12 months due to agentic AI, and 79% report AI agents are already being adopted at their companies. Of those adopting, 66% cite measurable productivity gains, 57% report cost savings, and 55% report faster decision-making. Despite broad adoption, fewer than half are fundamentally rethinking operating models (45%) or redesigning processes around AI agents (42%), meaning most companies are capturing efficiency gains without achieving structural transformation. The primary barriers are not technical: organizational change readiness, cross-functional workflow integration, and employee adoption rank as the most underappreciated obstacles, while trust gaps persist for high-stakes use cases such as financial transactions (trusted by only 20%) and autonomous employee interactions (22%).

    3 minRead
    PwC InsightsJune 11

    PwC Pulse Survey: Executive Takes on Election 2024

    PwC's October 2024 Pulse Survey of 709 executives finds broad economic and political pessimism heading into the US presidential election, regardless of which candidate wins. Recession expectations jumped to 61% from 49% in June 2024, driven by geopolitical tensions, labor market uncertainty, and election-related volatility. On policy risk, 75% say a 10% universal tariff would significantly hinder growth, and 75% say they would reduce domestic investment if the corporate tax rate rose to 28%—meaning both candidates' flagship proposals draw near-equal concern. Cyber attacks remain the top business risk (75%), followed by margin pressure (70%), geopolitical tensions (68%), and AI legal and reputational risks (63%), while 71% of executives believe post-election trade and tax policies will hurt US competitiveness no matter who prevails.

    3 minRead
    PwC InsightsMay 29

    Generative AI

    This PwC hub aggregates 30+ generative and agentic AI thought leadership pieces published between mid-2024 and mid-2025, spanning enterprise AI strategy, responsible AI governance, workforce redesign, and function-specific agent deployment. Key themes include agentic AI applications across finance, procurement, IT, HR, and marketing; a dedicated series on responsible AI covering data governance, privacy, regulatory readiness, internal audit, and model testing; and workforce restructuring away from traditional hierarchical models toward AI-augmented operating models. PwC's 2026 AI Business Predictions and a midyear 2025 update frame focused, outcome-oriented AI strategies over broad model proliferation. Finance-specific content explicitly addresses how AI agents reshape the CFO operating model and whether top-performing finance functions have reached terminal value.

    3 minRead
    PwC InsightsFebruary 28

    Risk & Responsible AI webcast

    This PwC webcast replay from February 2024 addresses the intersection of AI risk management and responsible AI principles for enterprise leaders. The session covers governance frameworks organizations should adopt to deploy AI in a controlled, auditable manner. Key themes include regulatory risk, ethical AI design, and the organizational structures needed to oversee AI systems at scale. The content is oriented toward cross-functional leadership responsible for risk posture, compliance, and strategic AI governance.

    3 minRead
    FAQ

    Frequently asked questions

    Common questions from finance leaders on deploying AI across the close, FP&A, and controllership.

    All firms

    17 firms tracked

    Accenture

    Accenture · Ireland/global
    Accenture

    Largest publicly-traded consulting/IT services; multi-billion AI bookings.

    ai-bookingsindustry-aialliancestraining-programs
    9 events · 2 in last 30dVisit

    Alvarez & Marsal

    Boutique · US
    Boutique

    Performance improvement, turnaround, corporate advisory.

    cfo-transformationoperational-aidigital-technology
    No events tracked yetVisit

    Anthropic

    Other · us
    Other

    AI safety + research company; maker of Claude.

    generative-aireasoningsafetyenterprise-ai
    74 events · 14 in last 30dVisit

    Bain & Company

    MBB · US
    MBB

    MBB; OpenAI services partnership.

    openai-partnershipstrategy-ai
    4 eventsVisit

    Boston Consulting Group

    MBB · US
    MBB

    MBB; BCG X is the AI/digital build arm.

    bcg-xstrategy-aibuild
    9 eventsVisit

    Capco

    Boutique · US/UK
    Boutique

    Financial services management consultancy; Wipro-owned.

    financial-servicesriskdigital-transformation
    No events tracked yetVisit

    Cognizant

    Other · us
    Other

    IT services + consulting; growing AI services focus.

    ai-servicesgenerative-aidata-analyticsmodernization
    No events tracked yetVisit

    Deloitte

    Big 4 · UK/global
    Big 4

    Largest of the Big 4 by revenue; deep AI consulting + AI Institute.

    ai-strategyagentic-systemsindustry-aialliances
    11 eventsVisit

    EY

    Big 4 · UK/global
    Big 4

    Big 4; EY.ai platform; abandoned Project Everest split.

    ey-aiassurancetax-techalliances
    17 events · 2 in last 30dVisit

    IBM Consulting

    IBM Consulting · US
    IBM Consulting

    IBM Consulting + IBM Garage; tightly coupled to watsonx.

    watsonxhybrid-cloud-aiindustry-ai
    No events tracked yetVisit

    KPMG

    Big 4 · US
    Big 4

    Audit, tax, advisory; Big 4. The audience.

    ai-in-audittax-techagentic-financetraining-programsalliances
    37 events · 8 in last 30dVisit

    McKinsey & Company

    MBB · US
    MBB

    MBB; QuantumBlack is the AI/analytics arm.

    quantumblackstrategy-aianalytics
    5 eventsVisit

    OpenAI

    Other · us
    Other

    AI research lab + enterprise platform; maker of GPT and ChatGPT.

    generative-aireasoningenterprise-aisafety
    79 events · 37 in last 30dVisit

    Publicis Sapient

    Boutique · US/global
    Boutique

    Digital business transformation; part of Publicis Groupe.

    digital-transformationmarketing-airetail-ai
    No events tracked yetVisit

    PwC

    Big 4 · UK/global
    Big 4

    Big 4; declared $1B AI investment; ChatPwc internal tools.

    ai-investmentassurancetax-techalliances
    11 events · 3 in last 30dVisit

    Slalom

    Boutique · US
    Boutique

    US-headquartered tech-and-business consultancy; Microsoft + Salesforce strong.

    microsoftsalesforceawstransformation
    No events tracked yetVisit

    West Monroe

    Boutique · US
    Boutique

    US business and tech consultancy; PE-services emphasis.

    pe-servicesfinancial-servicesdata-analytics
    1 eventVisit
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