@guilleflorvs: Uber reportedly burned its 2026 AI budget in four months without ROI. Mckinsey published a report that said 95% of ente…

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Summary

Uber burned its 2026 AI budget without ROI, and a McKinsey report shows 95% of enterprises see no ROI from AI, leading to a discussion on enterprise AI adoption challenges and strategies.

Uber reportedly burned its 2026 AI budget in four months without ROI. Mckinsey published a report that said 95% of enterprises get no ROI from AI. So I asked @jainarvind on what is happening on enterprises. “It’s a wide spectrum, but businesses are definitely getting some value from AI today. If they’re getting none, they would have stopped by now.” The clearest value is basic knowledge-seeking — “every employee in the company becoming more productive, more knowledgeable” — “but it’s hard to measure. The fact that people can work faster oftentimes doesn’t translate into your P&L statement.” Larger companies with intentional programs, good metrics, and systematic process-by-process adoption are seeing results — “it is hard, it takes a lot of time, and AI vendors kind of made it seem like you’ll automatically and instantly get all these value savings.” Glean’s model is built for that reality: immediate productivity savings up front, then “many, many months understanding their business and helping them build those agents and get them to production level of quality.”
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Uber reportedly burned its 2026 AI budget in four months without ROI.

Mckinsey published a report that said 95% of enterprises get no ROI from AI.

So I asked @jainarvind on what is happening on enterprises.

“It’s a wide spectrum, but businesses are definitely getting some value from AI today. If they’re getting none, they would have stopped by now.”

The clearest value is basic knowledge-seeking — “every employee in the company becoming more productive, more knowledgeable” — “but it’s hard to measure. The fact that people can work faster oftentimes doesn’t translate into your P&L statement.”

Larger companies with intentional programs, good metrics, and systematic process-by-process adoption are seeing results — “it is hard, it takes a lot of time, and AI vendors kind of made it seem like you’ll automatically and instantly get all these value savings.”

Glean’s model is built for that reality: immediate productivity savings up front, then “many, many months understanding their business and helping them build those agents and get them to production level of quality.”

Guillermo Flor (@guilleflorvs): MARKET SHIFT: Enterprise AI is not won by the best model. It is won by the founder who understands the sale.

Glean CEO Arvind Jain built two unicorns and crossed $300M ARR at a $7.2B valuation.

His core insight: most AI founders are optimizing the wrong thing.

Enterprise

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