Zenith Performance Solutions

Honest confession from an in-house ID: I used to spend 30 minutes building SME interview questions from scratch before every kickoff.

Not because it was hard. Because I knew what I needed to find out and it just... took time I didn't have.

Now I give AI the brief, the performance context, and the meeting window. It drafts a starting set of behavior-centered questions in minutes. I edit them for the SME, the context, and the gap I'm actually trying to understand.

30 minutes down to 10. And my kickoffs are better because I'm not scrambling to write questions on the drive over.

This is where AI earns its place in an ID workflow. Not the thinking. The setup before the thinking.

What's one task in your workflow that's technically correct but just... takes too long? Might be worth trying this with.

You're not using AI wrong. You might just be skipping the step that matters most.

AI gives you a starting point. A faster first draft. A pressure-test you didn't have time to run on your own.

It does not give you a finished product.

The IDs who are using AI well in their workflow aren't trusting the first output. They're editing it for their learners, their organization, and the performance problem they're actually solving.

That's the difference between AI content that lands and AI content that feels like it was written by no one, for no one.

One thing to try this week: take your next AI output and read it like your most skeptical stakeholder would. What would they push back on? What doesn't sound like your organization? That's where your expertise shows up.

Where are you in your AI workflow right now? Still exploring, or have you landed on what works?



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