Is AI dipping into the trough of disillusionment? What about all that money flowing into AI tools and technologies?
There seems to be a common denominator emerging among surveys of AI success rates – that is, only one in 20 enterprises are really reaping its benefits. A new FICO finds only five percent of organizations – in this case financial enterprises – have their AI investments truly aligned with their business goals.
By extension, of course, that means nearly 95% of organizations are having a difficult time developing AI systems that serve the very needs they were designed to address. The FICO study is based on insights from 254 C-suite leaders in financial enterprises around the world, conducted in Q2 2025., released last month, that show only five percent of generative AI projects show tangible success. What’s going on? Is AI dipping into the trough of disillusionment? What about all that money flowing into AI tools and technologies? Corporate AI investment for the year 2024 was estimated at $252.3 billion,to Stanford University’s Human AI project. At least $33.9 billion of it went to generative AI alone. It doesn’t sound like a safe bet, hundreds of billions of dollars for technologies that only deliver five percent of the time. Still, the majority of respondents in the FICO study see more benefits than risks in their AI investments. Customer experience, executive pressure, and competitive differentiation are ranked as primary drivers of AI investment, surpassing more traditional concerns such as compliance and cost savings.What excites you most about AI innovation over the next 5 years?Improved real-time AI solutions 39%Agentic AI approaches 31%Why are AI projects taking so long to deliver to the business? “The problem isn’t ambition,” the FICO authors state. "Most organizations now have dedicated teams focused on data science and AI development. But too often, these teams operate in silos, cut off from business leaders, end-users, and risk stakeholders." A majority of survey respondents, 72%, cite insufficient collaboration between business and IT as a major challenge, the survey shows. “It’s not that AI teams lack vision, but rather that the vision isn’t shared, and when it is shared, it isn’t shared effectively.” Interestingly, there appears to be a great deal of organizational support for AI in some form or another. The issue is in implementation. Here is what’s slowing down AI success, the survey shows:Lack of a shared understanding of AI 66%Internal politics 40%The report’s findings illustrate how “realizing the full value of AI requires more than better models. It requires better standards, systems, and collaboration.” The real differentiators “aren’t breakthroughs in generative text or image synthesis,” the FICO authors note. "Rather, they’re structural – robust business processes, strict AI governance standards, better interdepartmental communication channels, and aThey note that AI standards adoption is slow and uneven, monitoring and governance structures are underdeveloped, and AI teams often remain structurally disconnected from the business functions. Ultimately, "the winners won’t be those with the most models. They’ll be those with the most accountability, the clearest strategy, and the fewest silos."
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