""You know, the thing about the AI capabilities is it's enormously powerful in putting together a demo. But when you want to do something that needs to be reliable, you need to have made sure that you've made the right decisions about what AI to use in what places.""
""Well, I would say it's almost entirely strategic error in terms of people not actually having thought through what the different limitations of the AI are and how applicable they are to the particular problem they're trying to solve. Also, there is a completely mind-numbing amount of vendor hype in the market, I think, unduly covering and coloring the normal thought processes that people go through - and because it's beautiful and easy to get something to work a little, people assume that if they keep doing it the same way it'll work reliably.""
Many businesses implement AI features, tools, chatbots, and pilots but experience minimal return on investment. Successful AI projects require clear strategic choices about where and how to apply AI, rigorous evaluation of AI limitations, and avoidance of vendor hype. Demonstrations often mislead because prototypes can be powerful while production demands reliability and correct placement of AI components. Boards and leadership frequently lack understanding of AI constraints and governance needs. Effective adoption requires selecting appropriate AI approaches per use case, designing for reliability, establishing guardrails and data practices, and aligning investments with measurable business outcomes.
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