A CEO says “add AI,” a team nods, and suddenly everyone is shopping for tools instead of solving a problem. We dig into why that move derails so many AI initiatives and how to convert a fuzzy mandate into a project you can scope, staff, and measure without burning months on ambiguity.

Eric Vogt and I walk through a practical way to anchor AI implementation to business value: cost reduction, new revenue, differentiation, or risk reduction. From there we get concrete about what leaders must define for engineering to build anything real, including inputs and outputs, constraints, and what success metrics actually mean. We also talk about why overly broad goals create failure, and how a small, well-designed MVP can outperform a grand “AI transformation” plan.

We use email automation as a clear example. Instead of “let AI answer everything,” we break down how to choose the right subset of messages, how to protect customer satisfaction with escalation paths and exception handling, and why data quality and fit for use determine whether your model learns anything useful. We also cover who is best positioned to lead this work, and why an emerging AI solutions strategist role needs both executive fluency and technical realism.

If you are under pressure to “do something with AI,” this is the roadmap for turning that pressure into a measurable pilot with KPIs you can defend. Subscribe for more Field Notes sessions, share this with a teammate who owns AI adoption, and leave a review with the hardest scoping question your org keeps dodging.