Evaluation: Is This Actually Working?
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Evaluation: Is This Actually Working?
Without measuring, you’ll drift from “this is working” to “I think this is working” to “I’m not sure anymore.” Evaluation closes the loop — and opens the next one.
AI adoption isn’t a project with an end date. It’s a capability you keep developing.
Monthly Check-In Questions:
Set a recurring reminder — first of every month, spend 15 minutes answering these:
- Am I still using this tool regularly, or has it drifted into the “subscriptions I’m ignoring” category?
- What’s my time-per-task compared to where I started? (Check your hypothesis.)
- Has the quality of output improved, stayed flat, or declined?
- Am I adding unnecessary complexity — extra steps, integrations, or tools that aren’t pulling their weight?
- Could I get similar results with a simpler approach?
- What’s the next workflow I should apply this to?
The Simplicity Check:
This one matters. Every month, ask yourself: is this getting more complicated than it needs to be? The shadow AI data tells us that simple tools people actually use beat sophisticated tools they abandon. If your AI workflow is turning into a Rube Goldberg machine, strip it back.
For larger teams: Add quarterly strategic reviews covering capability development, competitive positioning, and build-vs-buy re-evaluation. Are your custom solutions still delivering unique value, or could vendor tools now handle what you built? Should you simplify by migrating custom builds to standard platforms?