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More than 70% of mainframe exit projects launched in 2026 will fail to deliver their intended benefits because organizations overestimate the capabilities of generative AI migration tools, according to Gartner.
The analyst firm said growing enthusiasm around AI-assisted code transformation is encouraging enterprises to pursue ambitious mainframe modernization projects without fully accounting for the complexity of mission-critical legacy applications, increasing the risk of cost overruns, technical debt and operational disruptions.
"There is a widening gap between the marketing promise of GenAI and its real-world ability to transform and migrate complex legacy code," said Alessandro Galimberti, vice president analyst at Gartner. "When this is combined with the 'too-big-to-fail' nature of mission-critical mainframe applications and the accelerating loss of experienced talent, infrastructure and operations leaders face a perfect storm of risk."
According to Gartner, many organizations are being influenced by vendor claims that AI can dramatically simplify mainframe migration, even though the technology remains better suited to assisting modernization efforts than replacing decades-old enterprise systems.
The firm warned that organizations pursuing "AI-first" migration strategies instead of aligning workloads with the most appropriate platforms risk creating new operational and business continuity challenges rather than eliminating legacy complexity.
Gartner also sees a significant shift coming in the mainframe migration market itself. The research firm predicts that by 2030, 75% of vendors focused on mainframe exit projects will either change their business models or exit the market altogether as demand for one-size-fits-all migration approaches declines.
That outlook comes even as major vendors continue investing in mainframe technologies. IBM, along with software providers including BMC, Broadcom, Rocket Software and 21CS, and managed service providers such as DXC, GTSG and Kyndryl, continues to position the mainframe as a platform for modernization rather than replacement.
Gartner argues that the right strategy depends largely on the size and complexity of an organization's environment.
For medium-sized mainframe estates, which represent the largest segment of the market, the firm recommends balancing modernization with selective migration rather than attempting full platform exits. These organizations often face the most difficult trade-offs, as large-scale migrations carry high transformation risks and may not deliver the expected return on investment.
For smaller environments, Gartner suggests that mainframe-as-a-service (MFaaS) offerings and targeted replacement of third-party software can provide a more cost-effective path while enabling incremental modernization where business benefits are clear.
The research highlights a broader shift in enterprise infrastructure strategy, where generative AI is increasingly viewed as a tool to modernize existing systems rather than a shortcut for replacing them. As organizations look to reduce technical debt while maintaining business continuity, Gartner says pragmatic, workload-specific modernization plans are more likely to succeed than broad AI-driven migration initiatives.
The analyst firm said growing enthusiasm around AI-assisted code transformation is encouraging enterprises to pursue ambitious mainframe modernization projects without fully accounting for the complexity of mission-critical legacy applications, increasing the risk of cost overruns, technical debt and operational disruptions.
"There is a widening gap between the marketing promise of GenAI and its real-world ability to transform and migrate complex legacy code," said Alessandro Galimberti, vice president analyst at Gartner. "When this is combined with the 'too-big-to-fail' nature of mission-critical mainframe applications and the accelerating loss of experienced talent, infrastructure and operations leaders face a perfect storm of risk."
According to Gartner, many organizations are being influenced by vendor claims that AI can dramatically simplify mainframe migration, even though the technology remains better suited to assisting modernization efforts than replacing decades-old enterprise systems.
The firm warned that organizations pursuing "AI-first" migration strategies instead of aligning workloads with the most appropriate platforms risk creating new operational and business continuity challenges rather than eliminating legacy complexity.
Gartner also sees a significant shift coming in the mainframe migration market itself. The research firm predicts that by 2030, 75% of vendors focused on mainframe exit projects will either change their business models or exit the market altogether as demand for one-size-fits-all migration approaches declines.
That outlook comes even as major vendors continue investing in mainframe technologies. IBM, along with software providers including BMC, Broadcom, Rocket Software and 21CS, and managed service providers such as DXC, GTSG and Kyndryl, continues to position the mainframe as a platform for modernization rather than replacement.
Gartner argues that the right strategy depends largely on the size and complexity of an organization's environment.
For medium-sized mainframe estates, which represent the largest segment of the market, the firm recommends balancing modernization with selective migration rather than attempting full platform exits. These organizations often face the most difficult trade-offs, as large-scale migrations carry high transformation risks and may not deliver the expected return on investment.
For smaller environments, Gartner suggests that mainframe-as-a-service (MFaaS) offerings and targeted replacement of third-party software can provide a more cost-effective path while enabling incremental modernization where business benefits are clear.
The research highlights a broader shift in enterprise infrastructure strategy, where generative AI is increasingly viewed as a tool to modernize existing systems rather than a shortcut for replacing them. As organizations look to reduce technical debt while maintaining business continuity, Gartner says pragmatic, workload-specific modernization plans are more likely to succeed than broad AI-driven migration initiatives.
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