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Too Big to Fail: Why Mainframe Exit Projects Are Likely to Fail in the Age of Generative AI

As Generative AI reshapes modernization strategies, many organizations are reassessing long-held assumptions about mainframe migration. Read the latest Gartner® research on the risks, realities, and strategic considerations technology leaders should evaluate before pursuing a mainframe exit.

Read the Gartner report

In this report

  • Examine how Generative AI impacts code understanding, code conversion, and IT operations

  • Understand the difference between mainframe migration and modernization

  • Explore why many organizations overestimate current AI-driven migration capabilities

  • Learn how environment size and complexity influence strategic decisions

  • Evaluate modernization, optimization, migration, and hybrid approaches for the future

Why it matters

Mainframe strategy decisions carry significant operational, financial, and business risk. This research provides practical guidance to help technology leaders make informed decisions about modernization and migration initiatives in the age of Generative AI.

Read the Gartner report

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What Gartner is saying

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For large mainframe environments, the scale of operations makes a total exit nearly impossible, nor justifiable by an increase in value or business outcomes. Therefore, the typical strategy is one of relentless optimization.

Dennis Smith

Gartner, Distinguished VP Analyst

Gartner, Too Big to Fail: Why Mainframe Exit Projects Are Likely to Fail in the Age of Generative AI, By Dennis Smith, Alessandro Galimberti, Tobi Bet, 8 April 2026

GARTNER is a trademark of Gartner, Inc. and/or its affiliates.

Source:
Gartner®, Too Big to Fail: Why Mainframe Exit Projects Are Likely to Fail in the Age of Generative AI, Dennis Smith, Alessandro Galimberti, Tobi Bet, 26 April 2026
GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.