I wonder people ever read what they link.

> The core issue? Not the quality of the AI models, but the “learning gap” for both tools and organizations. While executives often blame regulation or model performance, MIT’s research points to flawed enterprise integration. Generic tools like ChatGPT excel for individuals because of their flexibility, but they stall in enterprise use since they don’t learn from or adapt to workflows, Challapally explained.

The 95% isn't a knock on the AI tools, but that enterprises are bad at integration. Large enterprises being bad at integration is a story as old as time. IMO, reading beyond the headline, the report highlights the value of today's AI tools because they are leading to enterprises trying to integrate faster than they normally would.

"AI tools found to be useful, but integration is hard like always" is a headline that would have gotten zero press.

> The 95% isn't a knock on the AI tools, but that enterprises are bad at integration.

You could read this quote this way. But the report knocked the most common tools.

The primary factor keeping organizations on the wrong side of the GenAI Divide is the learning gap, tools that don't learn, integrate poorly, or match workflows. Users prefer ChatGPT for simple tasks, but abandon it for mission-critical work due to its lack of memory. What's missing is systems that adapt, remember, and evolve, capabilities that define the difference between the two sides of the divide.[1]

[1] https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Bus...