Breaking 12:15 Bny reports higher profit driven by strong fees and interest income growth 11:20 Solana teaser on XRP fuels speculation over potential blockchain integration 11:00 Let cofounder Amir Hamza critically wounded in Lahore shooting 10:45 Chanel expands in California with the acquisition of a new vineyard estate 10:20 Gartner warns most ai driven mainframe migrations will fail 09:40 Bitcoin proposal seeks to freeze satoshi era coins over quantum risk 09:20 Researchers hijack ai agents via github prompt injection attacks 09:00 Mars bathtub ring discovery points to long lasting ancient ocean 08:40 Largest gravity test confirms Newton and Einstein across cosmic scales 08:20 Ai models can pass hidden traits through unrelated data study finds 07:50 Nikkei hits record high as US Iran talks lift markets 17:20 Apple expands ads in maps as unified business platform rolls out 17:00 Robinhood and Webull jump after US SEC approves removal of day-trading limits for small investors 16:30 Big advertising agencies settle US FTC probe over alleged boycott of political content 16:20 VW warns China car market may shrink for first time since 2018 16:00 Steve Aoki exits crypto holdings as Bored Ape NFTs lose 88% value 15:40 Anthropic shifts to usage pricing for enterprise AI customers 15:20 European farmers cut crops as Iran war disrupts fertilizer supply 15:00 Tesla completes AI5 chip design with mass production targeted for 2027 14:40 Renewables offset Hormuz crisis as fossil power output falls 14:20 Unitree launches $8,200 humanoid robot globally via AliExpress 14:00 Donald Trump threatens to reconsider trade deal with the United Kingdom

Gartner warns most ai driven mainframe migrations will fail

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.