Most AI projects fail. Here's how to pick the ones that don't with Thomas Schaefer, Uniphore

Enterprise AI projects fail more often than they succeed. The MIT figure Thomas Schaefer cites puts that at 95%. The reasons are rarely the model. They are the architecture beneath it, the data that feeds it, and the use case selection that determines whether any of it was worth starting.

Thomas Schaefer is Director of Sales Engineering at Uniphore, based in Germany. He has over 20 years in enterprise technology, including 12 years at Jacada across solution architecture, professional services and presales leadership across EMEA and APAC. He joined Uniphore in 2021 and works with enterprise clients on AI transformation, agentic workflow design and the operationalisation of business AI at scale.

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