Overestimating AI threatens legacy mainframe migrations

2026-06-22 · Source: Information and Enterprise Technology News | CIO Dive - Www.ciodive.com · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Software Development & Engineering · Depth: Intermediate, short

Summary

Gartner predicts that over 70% of enterprise projects initiated in 2026 to migrate legacy mainframes using AI will fail, leading to significant disruptions, technical debt, and cost overruns. This high failure rate stems from an overestimation of generative AI's capabilities, particularly its ability to effectively migrate complex legacy code, according to Alessandro Galimberti, VP analyst at Gartner. The report notes a widening gap between AI's marketed promise and its actual performance in this domain, compounded by vendors integrating AI into offerings without clear outcome improvements and a dwindling pool of experienced mainframe talent. While some firms like Chase and Morgan Stanley have successfully used generative AI for specific mainframe modernization tasks, Gartner suggests AI is more effective for optimizing existing mainframe investments rather than facilitating complete platform exits. By 2030, Gartner expects 75% of mainframe exit vendors to alter their business models as demand for universal solutions declines.

Key takeaway

For CTOs and VPs of Engineering planning mainframe modernization, you must critically reassess strategies heavily reliant on generative AI for full platform exits. Gartner warns that over 70% of such projects will fail, incurring technical debt and cost overruns. Instead, focus your AI efforts on optimizing existing mainframe investments and limit full migrations to highly specific, case-by-case scenarios to mitigate significant risks.

Key insights

Overestimating generative AI's capabilities for mainframe migrations will cause over 70% of projects to fail, incurring significant risks.

Principles

• Overestimating generative AI for migrations creates high risk.
• AI optimizes existing mainframes better than full exits.
• Vendor AI integration often lacks proven outcome improvement.

In practice

• Implement governance for AI in mainframe modernization.
• Develop in-house AI tools for legacy code updates.
• Prioritize mainframe optimization over full platform exits.

Topics

• Mainframe Modernization
• Generative AI
• Legacy Systems
• IT Strategy
• Technical Debt
• Risk Management

Best for: Executive, Director of AI/ML, VP of Engineering/Data, CTO

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Editorial summary, takeaway, and curation by AIssential. Original article published by Information and Enterprise Technology News | CIO Dive - Www.ciodive.com.