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AI may speed up parts of legacy-system modernization, especially dependency mapping, code generation, and documentation. But the available evidence does not show that AI can fix legacy IT on its own—or that AI-led modernization reliably pays for itself. Andy Thurai, founder of The Field CTO and a former IBM chief strategist, warns that infrastructure sprawl can turn recurring costs into a “black box.”
What AI is being used for in legacy modernization
Legacy systems are difficult to change partly because their dependencies and behavior may be poorly documented. A ZDNET article reports that organizations are beginning to use agentic AI to map hidden technical dependencies, generate code, and create documentation. These are reported early use cases, not evidence that AI can safely replace engineers or other expert reviewers. ZDNET’s account does not establish how often these tools succeed or how their output performs in production.
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That distinction matters: helping teams understand or modify a system is not the same as delivering a complete modernization. The article does not provide a controlled test, comparative cost model, or measured return on investment showing that AI-led work outperforms other approaches.
What the reported survey figures do—and don’t—show
As reported by ZDNET in 2026, a Kyndryl survey of 2,000 senior IT decision-makers found that 10% of technology chiefs said they were applying agentic AI as a modernization tool. The article also reports that almost half of respondents said modernization was behind schedule and experiencing cost overruns; 18% saw limited or unclear value from modernization; and fewer than one in ten organizations were fully confident they knew their system dependencies. These figures are reported through the article, not independently validated here.
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The available account does not give the survey field dates, detailed sample composition, or exact question wording. Treat these numbers as attributed survey responses—not universal rates, proof of causation, or an independently verified measure of modernization outcomes.
Why Thurai says the economics may fail
Thurai’s concern is not simply the initial price of an AI tool. He argues that adding AI-driven infrastructure can make recurring costs difficult to trace: “The economics don’t hold up. When AI drives infrastructure sprawl, the monthly bill becomes a black box.” The article attributes this criticism to Thurai; it does not quantify the costs or establish that AI modernization is uneconomic in every organization.
He also describes modernization as a potentially recurring problem: “IT modernization increasingly looks like a chronic condition.” His criticism is that organizations may add software and infrastructure without retiring older systems. In that case, a modernization project can leave teams managing the old estate alongside new layers of complexity rather than reducing the long-term burden.
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Because the reported article does not compare products or migration strategies, organizations need to evaluate proposals against their own systems and costs. These checks are practical decision criteria, not findings from a comparative study:
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- Recurring and transition costs: Account for ongoing compute and infrastructure alongside implementation, migration, and operating costs. Ask how each cost will be tracked over time.
- Schedule and overrun risk: Request a bounded scope, milestones, and a plan for handling unexpected dependencies rather than assuming AI will make delivery predictable.
- Dependency visibility: Check whether AI-generated maps can be verified against the running system and kept current as changes are made.
- Code and documentation quality: Define who reviews generated work, how it is tested, and who owns corrections and maintenance.
- Security and governance: Establish what system data tools can access, where it is processed, and what approval controls apply.
- Retirement of old systems: Identify which applications or infrastructure will actually be decommissioned, and when. Adding new components without retiring old ones may preserve the complexity modernization was meant to address.
So, can AI fix legacy IT?
AI may make specific modernization tasks faster or more manageable, but the reported use cases and survey figures do not show that it can independently fix legacy IT or consistently deliver positive economics. The practical question is whether a proposed use improves discovery or delivery while producing verifiable results, manageable recurring costs, and a credible plan to retire obsolete systems. The article also quotes Kyndryl’s report: “Cost reduction and legacy escape are no longer the leading motivations to modernize.” That shift in motivation does not, by itself, establish that AI makes modernization worthwhile.
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