Breaking 19:00 Morgan Stanley sees momentum stocks recovering as investors return to quality companies 18:18 Visa strengthens digital fraud defense with $2.4 billion BioCatch acquisition 17:10 Pentagon signs agreements to expand THAAD and Patriot PAC-3 missile production 15:51 Sam Altman reveals his TikTok experience and concerns over digital addiction 13:51 The rising Wall Street star defeated by risks: The collapse of the Situational Awareness fund 13:24 Trump Highlights Morocco as a Key U.S. Security Partner in Regional Stability 13:13 Survey shows 70% of Americans believe the economy is in poor condition 12:45 Aircraft window shortages push planemakers and airlines to tighten supply management 12:33 Capital One confirms closure of Trump Organization bank accounts after review 12:00 Japan and the United States intervene together to support the yen for the first time in 15 years 11:06 Spider-Man: Brand New Day spins record-breaking $928 million global box office debut 08:45 Wildfires devastate Washington state as hundreds of homes and buildings are destroyed 08:42 U.S. congressional report highlights Ceuta and Melilla issue and calls for diplomatic dialogue 08:00 Oil prices tumble over 5% as US-Iran talks revive hopes for Middle East de-escalation 07:45 US approval of new pesticides sparks PFAS concerns among scientists and environmental groups 07:30 US Justice chief drops controversial compensation fund to advance Senate confirmation 07:00 Brazil’s Lula launches bid for fourth and final presidential term at age 80

Gartner warns most ai driven mainframe migrations will fail

Thursday 16 April 2026 - 10:20
By: Dakir Madiha
Gartner warns most ai driven mainframe migrations will fail

Gartner has warned that a large majority of enterprises relying on generative AI to migrate away from mainframe systems are likely to fail. In a recent report, the firm estimates that more than 70 percent of such projects launched in 2026 will not meet their objectives, citing an overestimation of what AI tools can deliver in complex legacy environments.

The report argues that generative AI can help identify and document technical debt within mainframe systems, but falls short when it comes to fully automating code conversion and migration. These systems often support critical workloads with high performance and throughput requirements that are difficult to replicate outside mainframe environments. As a result, organizations risk losing key capabilities during poorly executed transitions.

Analysts highlight the scale and interconnected nature of enterprise data as a core barrier. Many large organizations operate systems built over decades, with tightly coupled processes and dependencies. According to the report, this level of complexity makes full scale migration both technically challenging and financially prohibitive in most cases.

The study also points to market dynamics driving unrealistic expectations. Investor pressure has pushed software vendors to position AI as a universal solution, encouraging the development of migration tools that may not be suited to real world enterprise needs. At the same time, companies face genuine concerns about aging mainframe expertise and growing technical debt, which increases the appeal of AI driven solutions.

The warning follows earlier developments involving Anthropic, which promoted its Claude Code tool as a way to modernize COBOL systems. That announcement triggered a sharp market reaction, including a significant drop in the stock of IBM, a long time leader in mainframe infrastructure. Gartner’s analysis challenges this narrative, emphasizing that mainframes remain essential for certain mission critical applications.

Instead of pursuing full migration, the firm advises organizations to modernize existing mainframe systems incrementally. The report stresses that failed migration efforts can have severe consequences, including operational disruption and business continuity risks. For many enterprises, maintaining and evolving current systems may offer a more reliable path than attempting a complete transition driven by immature AI capabilities.


  • Fajr
  • Sunrise
  • Dhuhr
  • Asr
  • Maghrib
  • Isha

This website, walaw.press, uses cookies to provide you with a good browsing experience and to continuously improve our services. By continuing to browse this site, you agree to the use of these cookies.