All AI News

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.

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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.

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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.

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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.

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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.

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Cognizant InsightsApril 17

The skills reset: Unlocking enterprise growth in the AI era

Cognizant's research argues that the primary constraint on enterprise AI value creation is a workforce skills gap, not technology availability. The piece frames AI adoption as requiring a deliberate 'skills reset' in which organizations systematically identify, develop, and redeploy human capabilities alongside automated workflows. Cognizant positions this as a strategic imperative for sustained growth rather than a one-time change management exercise, with skills strategy becoming a board-level concern tied directly to competitive positioning. The article implies that enterprises failing to align talent transformation with AI deployment roadmaps will see diminishing returns on their technology investments.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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Cognizant InsightsAugust 29

How AI Will Revamp the Healthcare Consumer Journey

Cognizant's AI Inclination Index, derived from a survey of 8,451 consumers across the US, UK, Germany, and Australia, quantifies consumer propensity to adopt AI across the healthcare journey's three phases: Learn, Buy, and Use. Consumers show the strongest AI openness in the Learn phase (index score: 86), dropping sharply to 48 in the Buy phase and recovering modestly to 54 in the Use phase, signaling that trust barriers peak at the point of healthcare decision-making. Counterintuitively, consumers aged 55+ are more inclined than younger cohorts to use AI in both the Learn and Use phases, driven by their higher intensity of healthcare engagement rather than tech affinity. Conversational AI—chat and voice—is the preferred tool format, reflecting demand for human-feeling interactions around sensitive health matters. AI-enthusiastic consumers are projected to represent up to 55% of all purchases across industries, equating to $4.4 trillion in US spending alone, making healthcare AI strategy a material revenue and engagement question for health system and payer leadership.

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Cognizant InsightsAugust 29

How AI is reshaping life sciences consumer engagement

Cognizant's AI Inclination Index, derived from a survey of 8,451 consumers across the US, UK, Germany, and Australia, quantifies consumer propensity to use AI across the life sciences purchase journey—covering prescription drugs, health monitoring, condition diagnosis, and consumer health and wellness products. Life sciences consumers index slightly below the global average for AI adoption overall, with the gap most pronounced in the buy phase (11% below average), though wellness products outperform the global benchmark in the learn phase. Counterintuitively, consumers aged 55+ show higher AI inclination than younger cohorts for learning about and using life sciences products, driven by their greater familiarity with product complexity. Prescription drugs lag all other categories due to regulatory data restrictions and consumer preference for human medical guidance, while the fragmented wellness market presents the strongest near-term opportunity for AI-assisted engagement. AI-enthusiastic consumers are projected to represent up to 55% of cross-industry purchases, totaling $4.4 trillion in the US alone, making segmentation of AI-ready customers a material commercial priority for life sciences organizations.

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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.

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Cognizant InsightsJuly 14

To Manage AI Agents, Start By Demystifying Them

Effective governance of AI agents requires enterprises to first develop a clear, operational understanding of what these systems actually do—rather than treating them as opaque or anthropomorphized entities. Cognizant argues that most organizations struggle to manage agentic AI because they lack a concrete mental model of agent architecture, decision logic, and failure modes. The piece prescribes a demystification framework: mapping agent capabilities, data access, and action boundaries before deploying governance controls. Without this foundational clarity, oversight mechanisms—such as human-in-the-loop checkpoints, audit trails, and escalation protocols—cannot be reliably designed or enforced.

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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.

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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.

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Cognizant InsightsFebruary 17

How Japan Can Accelerate Generative AI by Overcoming Key Inhibitors

Cognizant and Oxford Economics surveyed 200 Japanese business leaders (part of a 2,200-person, 23-country study) and found that Japanese firms plan to invest just under $23 million in generative AI this year—less than half the global average of $47 million. Despite this gap, 63% of Japanese respondents believe their companies are not moving fast enough on AI strategy, and 58% expect competitive disadvantage from delays. Key accelerators include strong market demand rooted in automation heritage (631 robots per 10,000 manufacturing workers vs. 274 in the US), favorable compute infrastructure, and government investment including a $740 million NVIDIA partnership and AWS's planned ¥2.26 trillion cloud build-out by 2027. The primary inhibitors are talent cost and scarcity—exacerbated by an aging, shrinking workforce and high barriers to foreign talent integration—alongside data security gaps, with only 16% of respondents rating their data security as adequate.

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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.

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Cognizant InsightsDecember 5

Gen AI in Canada: Embracing the Future with Confidence

A Cognizant/Oxford Economics study of 200 Canadian business leaders finds Canada's generative AI momentum score is 25% above the global average, with Canadian businesses reporting a median annual gen AI spend of $15 million versus a global median of $12.5 million. Seventy-one percent of Canadian leaders express concern about keeping pace with AI advancements, and 52% fear competitors will gain an advantage. Over the next two years, Canadian leaders prioritize productivity gains over disruptive innovation, with the stated goal of redirecting efficiency gains toward growth rather than pure cost-cutting. Key accelerators include strong market demand—anchored by a national AI strategy and C$2.4 billion in government funding—perceived output quality, and data readiness, though data quality challenges persist beneath the surface optimism.

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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.

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Cognizant InsightsNovember 15

United Arab Emirates: Paving the Way to Become a Global Generative AI Hub

Cognizant and Oxford Economics surveyed 50 UAE senior business leaders (part of a 2,200-person, 23-country study) and found UAE firms plan to spend $47.3 million on generative AI in 2024, marginally above the $47 million global average. Despite this above-average investment, 76% of UAE respondents believe their organizations are not moving fast enough on adoption, and 44% fear delays will cede competitive advantage. Key accelerators include operating model flexibility, data readiness, compute infrastructure, and unusually positive shareholder sentiment toward AI investment. The primary inhibitors are talent cost and availability, perceived immaturity of available gen AI solutions, and employee and consumer concerns about the technology—challenges the UAE government is actively addressing through visa reform and the Mohamed bin Zayed University of Artificial Intelligence.

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Cognizant InsightsNovember 14

Breaking Barriers: Maximizing Saudi Arabia's Gen AI Investment

Cognizant and Oxford Economics surveyed 50 Saudi Arabian senior business leaders as part of a broader 2,200-respondent global study, finding that Saudi firms plan to spend $76.5 million on generative AI in 2024—62% above the global average of $47 million. Approximately 70% of Vision 2030's objectives are tied directly or indirectly to AI, and the government has committed $100 billion in AI investment with negotiations underway for an additional $40 billion. Despite strong infrastructure and government backing, 78% of Saudi businesses say they are not moving fast enough on adoption, with talent cost and availability ranked as the top inhibitor. Saudi firms skew more toward using generative AI for business-model innovation than the global average, while data security gaps and regulatory alignment with global standards remain active work-in-progress challenges.

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Cognizant InsightsNovember 5

Gen AI in Spain: Innovating Despite Limited Investment

A Cognizant/Oxford Economics study of 100 Spanish business leaders finds Spain's generative AI momentum score sits 22% below the global average, with projected per-company AI spending of $23.5 million versus a $47 million global benchmark. Primary inhibitors include scarcity and high cost of AI talent, unfavorable public perception, immature AI product markets, weak infrastructure, and data privacy concerns. Despite lower investment, 73% of Spanish businesses want to accelerate gen AI initiatives, and companies show relative confidence in market demand, data readiness, operating-model adaptability, and compute access. Unlike the global trend toward productivity-first deployment, Spanish businesses distribute expected gen AI impact evenly across productivity gains (35%), business innovation (34%), and operating-model redesign (35%), signaling broader transformation ambitions.

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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.

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Cognizant InsightsOctober 3

Gen AI adoption in the Nordics: Balancing ambition with caution

Cognizant's analysis of generative AI adoption across Nordic enterprises finds the region combining high ambition with deliberate caution, prioritizing responsible deployment over speed. Nordic organizations are investing in gen AI but face friction from data governance concerns, regulatory compliance requirements, and workforce readiness gaps. The piece highlights that while productivity use cases—particularly in IT, finance, and customer operations—are advancing, scaling beyond pilots remains a common challenge. Cognizant frames the Nordic market as a bellwether for how mature, regulation-conscious enterprises balance competitive AI pressure against risk management obligations.

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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.

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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.

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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.

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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.

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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.

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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.

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