Over 70% of Mainframe Exit Projects to Fail Due to Overestimated Generative AI Capabilities

Gartner’s June 2026 forecast indicates that more than 70% of mainframe exit projects starting in 2026 will fail to deliver the intended benefits. The primary reason is widespread overestimation of generative AI capabilities in handling complex legacy code conversion. The gap between marketed AI performance and real-world results on intricate mainframe systems is significant.

📑Table of Contents

1. H2: Mainframe Exit Project Failure Rate Forecast (Gartner June 2026)
2. H2: Specific Gaps Caused by Overestimation of Generative AI
3. H2: Risk Environment Accelerated by Investment Pressure and Talent Shortage
4. H2: Market Transformation Forecast Through 2030
5. H2: Comparison of Realistic Approaches for Success
6. H2: Frequently Asked Questions (FAQ)
7. H2: Related Official and Independent Sources
8. H2: Summary

Investor pressure plays a key role. Vendors feel compelled to integrate AI features even when their contribution remains unclear, simply because AI presence influences purchasing decisions. This leads to under-planned projects where migration risks to core business operations are underestimated, particularly in mid-sized environments where system complexity and talent constraints intersect.

The shrinking pool of mainframe-experienced personnel compounds the difficulty. While IBM and other vendors continue investing in the platform, operational expertise is declining. A failed migration can cause prolonged downtime or major delays, with consequences far more severe than in smaller setups.

By 2030, Gartner predicts that 75% of mainframe-related vendors will either pivot their business models or exit the market entirely. Demand for universal migration solutions is also expected to drop. In this landscape, a full platform exit carries high risk; instead, organizations are better served by preserving existing environments and pursuing targeted modernization.

When generative AI is used, it should support incremental modernization within the current setup rather than serve as the primary exit vehicle. Lower-risk options such as Mainframe as a Service (MFaaS) or replacement of outdated ISV solutions are gaining attention.

The ITmedia coverage of the official Gartner press release (https://www.itmedia.co.jp/enterprise/articles/2606/25/news052.html and https://www.gartner.com/en/newsroom/press-releases/2026-06-18-gartner-predicts-more-than-70-percent-of-mainframe-exit-projects-will-fail-due-to-overestimation-of-generative-ais-capabilities) details these figures and recommended paths. Companies must evaluate their own environment size, talent availability, and risk tolerance on a case-by-case basis.

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H2: Mainframe Exit Project Failure Rate Forecast (Gartner June 2026)

According to Gartner’s June 2026 survey, more than 70% of mainframe exit projects initiated in 2026 are expected to end without delivering the intended benefits. The main cause is the overestimation of generative AI’s ability to convert complex legacy code, with failures carrying significant risks of disrupting core business operations.

This figure is not merely speculative but reflects actual market conditions. Many organizations have launched projects based on inflated expectations of AI capabilities, only to encounter unexpected challenges that force them to abandon the effort.

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H2: Specific Gaps Caused by Overestimation of Generative AI

The concrete gap created by overestimating generative AI lies in the difference between market claims and real-world performance. Vendors integrate AI features under investor pressure even when their actual contribution is unclear, as confirmed by detailed data in the Gartner press release. This lack of realistic evaluation during the planning phase increases the risk of business continuity issues if migration fails.

Legacy codebases often contain decades of complex dependencies and proprietary specifications. Expecting generative AI to handle these accurately in a short time is unrealistic. Ignoring this gap leads to ballooning test and remediation efforts, budget overruns, and schedule delays.

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H2: Risk Environment Accelerated by Investment Pressure and Talent Shortage

In an environment where investment pressure and talent reduction are accelerating, the decline of mainframe-experienced personnel is particularly acute. The impact of migration failure on business continuity is especially complex in mid-sized environments, making decisions difficult. While IBM’s continued investment and the presence of independent vendors and MSPs support the platform as a modern solution, the talent shortage raises the bar for successful migration.

Mid-sized companies often struggle to secure dedicated mainframe engineers and must rely on external partners. A failed migration project is not just an IT issue but a critical risk that directly affects overall business continuity.

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H2: Market Transformation Forecast Through 2030

Gartner forecasts that by 2030, 75% of mainframe-related vendors will be forced to pivot their business models or exit the market. Demand for universal migration solutions is expected to decline, prompting organizations to prioritize lower-risk options.

This prediction suggests that the current hype treating generative AI as a cure-all will gradually be corrected, leading to more realistic approaches becoming mainstream. Vendors will need to shift toward niche solutions leveraging their strengths or exit the market.

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H2: Comparison of Realistic Approaches for Success

Several realistic approaches are available for successful modernization:

Approach	Risk	Recommended Scenarios	Source Note
Full Platform Exit	High (high impact if fails)	Small-scale, clear ROI	Gartner
In-Place Modernization + Generative AI Support	Medium	Most mid-sized environments	Gartner recommended
MFaaS (Mainframe as a Service)	Low–Medium	Cost-effectiveness focused	Gartner
ISV Solution Replacement	Low	Outdated third-party software	Gartner

Full platform exit carries high risk and should be limited to small-scale environments with clear ROI. In-place modernization supported by generative AI is recommended for the majority of mid-sized cases. MFaaS and ISV replacement are effective when cost-effectiveness is the priority. Individual assessment based on Gartner’s guidance is essential.

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H2: Frequently Asked Questions (FAQ)

Q: Why is mainframe migration difficult with generative AI?

The gap between marketed capabilities and reality is large when converting complex legacy code. Decades of proprietary specifications and dependencies are difficult for current AI to handle accurately in a short timeframe.

Q: What is the main cause of the over 70% failure rate?

The tendency to overestimate generative AI tool capabilities combined with insufficient planning due to investment pressure. Projects proceed without clear understanding of AI’s actual contribution, leading to unforeseen challenges.

Q: What will happen by 2030?

75% of mainframe-related vendors are predicted to pivot or exit. Demand for universal migration tools will decline, and specialized approaches will become necessary.

Q: What are the advantages of keeping the mainframe?

Continued investment by IBM, together with independent vendors and MSPs, keeps the platform functioning as a modern solution. Existing assets can be leveraged for stable operations.

Q: What requires special attention in mid

sized environments? A: Decisions are more complex and full exit carries high risk. Individual assessment of system scale and talent situation is necessary.

Q: How should generative AI be utilized?

It should be positioned as a support tool for modernization within the existing environment rather than as a means for full exit. Focusing on partial code modernization and operational efficiency is more realistic.

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H2: Related Official and Independent Sources

Detailed information is available in the ITmedia Enterprise article and the official Gartner press release dated June 18, 2026. Organizations should carefully evaluate approaches that match their specific environment.

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H2: Summary

Successful mainframe exit projects require realistic planning and risk assessment without overestimating generative AI capabilities. Based on Gartner’s forecast, phased modernization that leverages existing environments is expected to accelerate, especially among mid-sized enterprises. When making investment decisions, refer to the latest information from official sources.

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