Most mainframe users who turn to AI for help migrating legacy code to alternative platforms are going to be very disappointed, according to analyst firm Gartner.
“More than 70 percent of mainframe exit projects initiated in 2026 will fail to produce the intended benefits due to an overestimation of generative AI tooling capabilities,” states a paper the firm published last week titled “Too Big to Fail: Why Mainframe Exit Projects Are Likely to Fail in the Age of Generative AI.”
Gartner also thinks that the market for AI-powered mainframe migrations is set to pop.
“By 2030, 75 percent of vendors operating in the ‘mainframe exit’ market will either pivot their business models or cease to exist,” the firm advises.
The main reason for Gartner’s pessimism is the role of the mainframe as home to mission-critical applications and decades’ worth of data.
“For most large-scale enterprises, the sheer volume and interconnected complexity of this data make wholesale migration a physical and financial impossibility,” wrote Gartner’s Dennis Smith, Alessandro Galimberti, and Tobi Bet.
The trio also acknowledge that mainframes are a significant source of technical debt and note that generative AI is very useful when helping organizations to detect and describe that debt.
But the analysts find generative AI has “significant limitations when it comes to the automated conversion and migration of legacy code.”
“It also does not account for the unique capabilities that the mainframe offers (e.g., ensuring that the same performance and throughput is achieved after the migration).”
Gartner’s team thinks one reason vendors are suggesting AI for mainframe exit projects is “Aggressive investor demand for AI capabilities as the sole indicator of a vendor’s long-term health forcing vendors to deploy AI even where unnecessary.” That pressure meets users’ concerns about difficulties finding staff to operate mainframes, and technical debt. AI can sometimes feel like the answer.
Gartner advises wariness due to what it describes as “The gap between the ‘marketing promise’ of generative AI and its actual capabilities in code transformation.”
“The stakes of a miscalculation are immense,” the analysts wrote. “Poor decision making regarding migration is not merely a budgetary overage; it is a threat to business and operational continuity.”
“Falling for “seemingly magical solution’ migration promises while ignoring a platform-smart approach (i.e., diligently evaluating your workloads and choosing the best platform for the relevant work) leads to massive technical debt and critical enterprise risk.”
The paper offers advice on how mainframe users should plan to use their big iron in future and suggests most should continue to look for ways to improve their systems rather than making a move.
“The drive to abandon the mainframe is diminishing,” the analysts wrote. “Customers are increasingly recognizing the near-impossibility of a mainframe exit at an acceptable cost and risk, leading them to give up on the long-held hope for a perfect tool to achieve this migration.”
Gartner’s opinion will go down very well at IBM, which saw its stock price slide sharply after Anthropic touted the COBOL-conversion powers of its Claude Code tool, sparking yet another round of speculation that the mainframe might be on its last legs.
Big Blue’s revenue – which is currently swelling due to unusually high mainframe sales – suggests its big iron has plenty of life in it yet. Indeed, Gartner’s paper ranks the mainframe as “still the leading platform for certain mission-critical applications, even with the ongoing drive toward cloud-native architectures.” ®
