Overdue Modernization Stokes Security Fears, Wrecks AI Plans

An article from Dive Brief Rising costs of modernization and lack of a clear vision could also be holding enterprises back in their AI adoption, a GFT Technologies report found. Published Sept. 29, 2026 ciodive.com Getty Images This audio is auto-generated. Please let us know if you have feedback. Dive Brief: Legacy technology systems are blocking AI adoption in the enterprise, a Tuesday study released by IT services firm GFT Technologies found. More than 4 in 5 tech leaders canceled at least one AI pilot because their legacy systems created limitations for the project, according to the company’s survey of 945 CIOs and CTOs. A vast majority, 93% of respondents, said they believe not modernizing their legacy systems while using AI could eventually trigger an enterprisewide security event. If a CIO’s goal is to achieve minor productivity and efficiency gains, bottlenecks caused by legacy systems can often be managed, Rishi Chohan, U.S. CEO of GFT Technologies, told CIO Dive in an email. “But as AI grows increasingly complex and connects to more parts of any given business, like with agentic AI, legacy systems pose significant barriers to scale, speed and security,” he added. Dive Insight: AI and agentic systems call for large amounts of compute, wide data access, persistent IT spending and solid governance plans. As the technology spreads across organizations, legacy infrastructure is showing signs of strain. Cybersecurity risks, slow release cycles and the high costs of legacy tech maintenance were the main drivers for modernization over the last few years, but interoperability with AI systems has emerged as a priority as IT leaders devote large parts of their budgets to AI overhauls. Only 17% of IT leaders had confidence in their tech stack’s ability to support mission-critical AI agents, according to a July report from Google. Cybersecurity risks persist as AI models become more sophisticated, and AI agents breach government sites and other AI institutions. CIOs don’t need to modernize their entire legacy tech stack at once, Chohan told CIO Dive. They should define their goals for how AI will work for their business, identify the systems that are holding those goals back and modernize accordingly. “By doing so methodically, they can build the foundation their organization needs to scale AI,” Chohan said. Most enterprises are working toward the goal of end-to-end modernization, but are at varying stages of development. Nearly half said they’ve started modernizing legacy systems, about one-quarter said they’ve started but feel they’re behind and 1% haven’t started any modernization plans, according to the report. Only 15% of tech leaders said they’re nearly or fully complete. As AI investment continues, CIOs will need to be able to wade through the excess of AI information to clarify their vision for how it fits within their organization, Chohan said. Each tool a CIO chooses to deploy will also require an assessment of the technical barriers preventing them from bringing their goals to life. “Few people know more about how an organization runs than its tech leaders – how it’s built, how its employees work, and how its customers experience its services,” Chohan said. “As a result, leaders are better equipped to build solutions that are shaped by their business needs rather than by market noise.”