""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.""
""By 2030, 75 percent of vendors operating in the 'mainframe exit' market will either pivot their business models or cease to exist.""
""For most large-scale enterprises, the sheer volume and interconnected complexity of this data make wholesale migration a physical and financial impossibility.""
""Generative AI has significant limitations when it comes to the automated conversion and migration of legacy code.""
Gartner predicts that over 70% of mainframe exit projects initiated in 2026 will fail to deliver expected benefits due to inflated expectations of generative AI. The firm anticipates a significant shift in the AI-powered mainframe migration market by 2030, with 75% of vendors likely to change their business models or cease operations. The complexity and volume of data on mainframes make complete migration challenging. While generative AI can help identify technical debt, it has limitations in automating legacy code migration and does not replicate mainframe performance post-migration.
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