The hidden bottleneck in Enterprise AI: Why data readiness trumps model selection
By Selvi Shanmughavel, CIO, Straive
A pattern is quietly repeating itself in boardrooms today. A company spots a promising AI use case, brings together a strong team, picks a reliable model, and launches a pilot one that often delivers results that are genuinely impressive. Leadership provides the support, the budget is approved, and the move to implementation begins. And somewhere between the pilot and the workflow, the initiative loses momentum, timelines stretch, accuracy drops, and adoption stalls.
The conversation that follows almost always focuses on the model. Was it the right architecture? Did more compute need to be provisioned? Should a different vendor have been chosen? Across enterprises in Financial Services, Life Sciences, Education, and Supply Chain, these are rarely the right questions.
The model was not the problem. The data was.
This distinction is more important than it seems at first glance, because it shapes where an organisation directs...
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