u/Cool-Trust-1215

We’ve been trying to improve AI adoption internally, and one thing that keeps coming up is how difficult it is to measure actual AI proficiency before rolling out training.

Most people self-report as “good with AI,” but that can mean very different things depending on the person. Some are strong at prompt iteration, workflow design, and evaluating outputs critically, while others mainly use ChatGPT for simple drafting tasks.

Curious how other L&D teams are approaching this.

Are you using:

• self-assessments
• manager evaluations
• practical tasks
• AI certification programs
• AI readiness assessments
• something else entirely?

Recently tried aisa.to, which was interesting because it wasn’t a multiple-choice AI skills test. It was more of a 20-minute conversational assessment with an AI interviewer, and the feedback was surprisingly specific about strengths and blind spots.

It honestly made me question whether traditional quiz-style AI certifications are enough for workplace upskilling anymore.

Would genuinely love to hear how others here are measuring AI skills before building training programs around them.

Been running into this problem lately.

You spend weeks (or months) building something, features, fixes, weird edge cases, all the stuff that actually matters. But when you try to explain it, it turns into a couple of lines that don’t really show anything.

Even linking the product doesn’t always help. People don’t have time to dig through it, and half the important decisions aren’t visible from the outside anyway.

So it ends up being this weird gap between what you actually did and what people think you did.

Wondering how you guys handle this.

Do you just let the product speak for itself, or do you document your work somewhere in a way that actually makes sense to other people?