Send us Fan Mail

A shiny AI certificate can be comforting, but it doesn’t answer the only question that matters at work: can you use generative AI to produce something correct, usable, and accountable? We dig into why “completed training” is not the same as “proven capability,” and we share a simple way to evaluate AI skills based on real performance instead of paper credentials.

We use a concrete scenario: an employee drafting a new standard operating procedure with generative AI. The surprising twist is that the AI factor starts offline. Before any prompt engineering, you have to understand the current manual process, the real constraints, and what “good” looks like on the ground. From there, we break down iteration, where strong users actively shape outputs through multiple specific prompts rather than accepting whatever shows up first.

Then we hit the danger zone: the first-draft trap. AI can generate a polished, confident document in seconds, and that polish makes it dangerously easy to skip the hard part. We explain the verification step where human expertise checks AI output against reality to catch errors, prevent downstream rework, and support better AI governance. Finally, we close the loop: the verified procedure improves the workflow and makes coworkers more capable, which is the true sign of scalable AI adoption.

If you want more practical frameworks like this, subscribe, share the episode with your team, and leave a review telling us what you’re trying to automate or improve with AI.

Support the show

By Edith Campbell Mbuyi