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.<\/p>\n
\u201cMore 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,\u201d states a paper the firm published last week titled \u201cToo Big to Fail: Why Mainframe Exit Projects Are Likely to Fail in the Age of Generative AI.\u201d<\/p>\n
Gartner also thinks that the market for AI-powered mainframe migrations is set to pop.<\/p>\n
\u201cBy 2030, 75 percent of vendors operating in the \u2018mainframe exit\u2019 market will either pivot their business models or cease to exist,\u201d the firm advises.<\/p>\n
The main reason for Gartner\u2019s pessimism is the role of the mainframe as home to mission-critical applications and decades\u2019 worth of data.<\/p>\n
\u201cFor most large-scale enterprises, the sheer volume and interconnected complexity of this data make wholesale migration a physical and financial impossibility,\u201d wrote Gartner\u2019s Dennis Smith, Alessandro Galimberti, and Tobi Bet.<\/p>\n
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.<\/p>\n
But the analysts find generative AI has \u201csignificant limitations when it comes to the automated conversion and migration of legacy code.\u201d<\/p>\n
\u201cIt 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).\u201d<\/p>\n
Gartner\u2019s team thinks one reason vendors are suggesting AI for mainframe exit projects is \u201cAggressive investor demand for AI capabilities as the sole indicator of a vendor\u2019s long-term health forcing vendors to deploy AI even where unnecessary.\u201d That pressure meets users\u2019 concerns about difficulties finding staff to operate mainframes, and technical debt. AI can sometimes feel like the answer.<\/p>\n
Gartner advises wariness due to what it describes as \u201cThe gap between the \u2018marketing promise\u2019 of generative AI and its actual capabilities in code transformation.\u201d<\/p>\n
\u201cThe stakes of a miscalculation are immense,\u201d the analysts wrote. \u201cPoor decision making regarding migration is not merely a budgetary overage; it is a threat to business and operational continuity.\u201d<\/p>\n
\u201cFalling for \u201cseemingly magical solution\u2019 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.\u201d<\/p>\n
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.<\/p>\n
\u201cThe drive to abandon the mainframe is diminishing,\u201d the analysts wrote. \u201cCustomers 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.\u201d<\/p>\n