The AI transformation gap isn’t a model problem: It’s an integration problem
By Rama Krishna, Co-founder and Chief Architect at [x]cube LABS
Every few months, a new model is released and the conversation in enterprise technology resets. Benchmarks are shared. Comparisons are made. And then, quietly, the pilots that were already running continue to stall and the gap between what AI can do in a demo and what it actually does inside a company stays exactly where it was.
The Wrong Place to Look
Pilot-to-production failure rates in enterprise AI have remained stubbornly high even as model capability has improved quarter over quarter. If the intelligence of the underlying model were the bottleneck, those rates should have been falling. They have not.
Waiting for the next frontier model release has become a surprisingly comfortable way to avoid the unglamorous work that actually needs doing. It gives teams a reason to pause, a plausible explanation for why the current deployment is not working,...
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