Your dashboard tells you revenue dropped 8% last quarter. It cannot tell you why. The why is sitting in a Slack thread, 3 support tickets, and a deal review nobody wrote up properly!!!! That split is the most underrated problem in enterprise AI, and it is what I got into with Jayanth Mysore, Product Manager at Glean, at Glean:GO on The Ravit Show in San Francisco.
Jayanth co-founded Iris, an AI analyst that sat directly on the cloud data warehouse and answered questions in plain language with SQL behind it. Before that he was an early product leader on Google Analytics and VP of Product at Sigma Computing.
He has spent a career on the structured side of this problem. Iris is now part of Glean, whose strength has been the opposite half, the documents, conversations, and tickets where the reasoning lives.
Most enterprise AI still picks one side. You get a tool that queries your warehouse but has no idea what your company decided last month, or a tool that reads every document but cannot count.
Neither one answers the question an executive actually asks, which is what happened and why.
We talked about what it really takes for AI to understand a company rather than generate plausible answers, where enterprises still underestimate context, how to decide what AI automates versus where a human stays in control, and what customers are asking for now that they were not asking for a year ago.
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