Authored by Tharun Mathew. Featured in CIOReview Europe.
Global corporate AI investment crossed $252 billion in 2024, with enterprise generative AI spending growing six-fold year-on-year. Boards are mandating AI strategies. Technology leaders are standing up centres of excellence. And yet the performance gap between AI ambition and AI accountability has never been wider.
According to MIT's NANDA initiative's State of AI in Business 2025 report, only 5% of generative AI pilots achieve rapid, measurable revenue acceleration. The remainder stall without registering meaningful impact on the P&L. S&P Global Market Intelligence found that 42% of companies abandoned most of their AI initiatives in 2025, up from just 17% the year before. The average organisation scrapped 46% of its proof-of-concepts before they ever reached production. And Gartner now forecasts worldwide AI spending will hit $1.5 trillion in 2025 - making the scale of this waste almost incomprehensible.
This is not a technology problem. The models work. The failure is architectural - and it is both predictable and preventable.
Read the full article here: enterprise-data-management.ciorevieweurope.com/vp/-merit-data-tech/why_general_ai_fails_the_enterprise:_the_case_for_domain-specific_ai
