Enterprise AI programs are usually evaluated on whether the technology works. It almost always does. The interesting question is why capability so often fails to become practice. I'll walk through two modules I built for the same client, on the same platform, delivered within months of each other. One is in daily use: it compresses three to five hours of manual review per case down to about five minutes and lets non-specialists do work that previously required an expert. The other was technically sound, liked by the people who asked for it, and never entered the workflow. The difference had nothing to do with the software. We'll cover the specific conditions that predict adoption — who actually loses time, whose judgment is being replaced versus assisted, where the work sits in someone's day, and what has to be true before a person will trust an output they can't verify. And we'll look at the diagnostic questions worth asking before a line of code gets written, because the cheapest place to discover a workflow won't be adopted is in the first conversation, not after the deployment. Attendees will leave with a practical checklist for evaluating whether a proposed AI workflow will survive contact with the people expected to use it.

Target audience: enterprise technology leaders, CIOs/CTOs, transformation and operations leaders. Preferred duration: 30-45 minutes; also works as a panel contribution. Level: intermediate, non-technical audiences welcome. No special technical requirements.