A few weeks ago, a startup founder told me that they had purchased an expensive AI tool for sales automation.
Six months later, the results? Zero.
The tool looked great in demos but collapsed in real-world use.
Here’s the mistake: they trusted the pitch before testing it in their own environment.
That’s where shadow evaluations come in.
It’s like a test drive, but for AI.
Here’s a simple framework anyone can use:
→ Step 1: Run in parallel. Don’t replace your existing process. Test the AI tool side by side for a few weeks.
→ Step 2: Track clear metrics. Define what “success” looks like. Faster? Cheaper? More accurate? Measure against it.
→ Step 3: Stress-test edge cases. AI looks smart on easy examples. Throw it messy, real-world data and see if it breaks.
→ Step 4: Compare cost vs. gain. Calculate if the tool saves enough time or money to justify itself.
→ Step 5: Involve end-users. Don’t just test technically. Get feedback from the people who’ll actually use it.
Do this, and you won’t just fall for the AI hype.
You’ll know whether a tool deserves a budget or belongs in the recycle bin.
Have you ever run a shadow evaluation before rolling out AI in your team?
