Before you deploy an AI tool, you'll spend weeks evaluating models, comparing vendors, writing prompts, and setting up integrations. What you probably won't do is define what success looks like thirty days later.
This is the question every AI pilot forgets to ask: How will we know if this is working?
The omission isn't carelessness. It's that the question is harder than it sounds.
Why "engagement" isn't an answer
The default metric for AI pilots is some form of usage: how many people tried it, how often they came back, how many questions they asked. Usage tells you the tool wasn't immediately rejected. It doesn't tell you whether it helped.
Teams with high engagement can still fail to get value. The tool becomes a novelty — people visit, experiment, move on. The meetings still happen. The decisions still take the same amount of time. The new hire still takes three months to reach full productivity.
The question underneath the question
Useful pilot metrics start from what the AI is supposed to replace or accelerate. Not "did people use it" but "what specifically should be different because it exists?"
- If the goal is faster onboarding: what was average time-to-productivity before, and what's the target?
- If the goal is reducing meeting time: what's the decision lag you're trying to cut?
- If the goal is knowledge retention: how many support questions are answered without escalation?
These numbers exist before you deploy. Write them down. If you can't name the before, you can't measure the after.
What this reveals about readiness
Here's the useful side effect of this exercise: teams that can't answer what success looks like usually aren't ready to deploy. Not because they lack AI appetite — because they haven't defined the problem clearly enough for any solution to actually solve it.
The question "how will we know if this is working?" is really the question "what problem are we solving?"
Answer that first. The rest is implementation.
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