Across boardrooms, enterprise AI has become the biggest line item in the innovation budget — yet it’s also become the biggest source of anxiety. Companies are deploying large models, generative assistants and predictive systems at record pace, but the results for many aren’t keeping up. Behind the PR and pilots, there’s a growing sense across the industry that something’s broken. AI was supposed to unlock new value but for many, it’s only adding layers upon layers of noise.

AI critics like cognitive scientist Gary Marcus and tech columnist Ed Zitron have continued to question where the true moat for AI companies like OpenAI and Anthropic lies, with Zitron popularly describing generative AI as “a financial, ecological and social time bomb” back in February. For Marcus, it’s possible to build truly great AI systems, but just not with current mainstream models or approaches. He argues that right now, large language models are dishonest, unpredictable and potentially dangerous.

Andrew Frawley, CEO of Data Axle, believes the major problem begins before even a single line of code is written. “The performance gap in enterprise AI isn’t a surprise. This is what happens when ambition outpaces readiness,” he said. “Many companies have invested in AI like it’s a product, not a capability, expecting they could flip a switch to unlock immediate value. But AI doesn’t operate in a vacuum. It’s a high-performance engine and too many are trying to run it on dirty fuel.”

Frawley did not mince words when I asked him the big reason for this problem. “The real issue isn’t the technology itself, but the foundation,” he told me. “Companies are obsessing over models while........

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