I used to dread meetings with non-technical stakeholders about ML projects.

Now they're my favorite part of the job.

What changed: I stopped presenting accuracy numbers and started presenting stories.

Before: "The model achieves 92% precision and 87% recall on the test set with an F1 of 0.89."

After: "Out of every 100 transactions the model flags as suspicious, 92 actually are fraudulent. It catches 87 out of every 100 actual fraud cases. Last week, that saved approximately $45,000."

Same information. Completely different impact.

The translation framework I use: every metric becomes a scenario. Precision becomes "when it says yes, how often is it right?" Recall becomes "of all the things it should catch, how many does it actually catch?" Latency becomes "from when the user clicks to when they see a result."

I also always prepare the "what could go wrong" section. Stakeholders trust you more when you proactively address risks rather than waiting for them to ask.

Technical communication isn't about dumbing things down. It's about choosing the representation that makes the information useful to the audience. That's an engineering skill, not a soft skill.