UserTesting deployed 700+ custom GPTs across 800 employees, but Michael Domanic's core insight cuts against conventional wisdom: organizations fixated on hallucination risks are solving the wrong problem. That concern reveals they're building assistants for summarization when transformational value lives in using AI as strategic thought partner. This reframe shifts evaluation criteria entirely. Michael connects today's moment to 2015's Facebook Messenger bot collapse, when Wit.ai integration promised conversational commerce that fell flat. The inversion matters: that cycle failed because NLP couldn't meet expectations shaped by decades of sci-fi. Today foundation models outpace organizational capacity to deploy responsibly, creating an obligation to guide employees through transformation rather than just chase efficiency. His vendor evaluation cuts through conference floor noise. When teams pitch solutions, first question: can we build this with a custom GPT in 20 minutes? Most pitches are wrappers that don't justify $40K spend. For legitimate orchestration needs, security standards and low-code accessibility matter more than demos. Topics discussed: Using AI as thought partner for strategic problem-solving versus summarization and content generation tasks Deploying custom GPTs at scale through OKR-building tools that demonstrated broad organizational application Treating conscientious objectors as essential partners in responsible deployment rather than adoption blockers Filtering vendor pitches by testing whether custom GPT builds deliver equivalent functionality first Prioritizing previously impossible work over operational efficiency when setting transformation strategy Building agent chains for customer churn signal monitoring while maintaining human decision authority Implementing security-first evaluation for enterprise orchestration platforms with low-code requirements Creating automated AI news digests using agent workflows and Notebook LM audio synthesis

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