Signal
The real AI rollout metric might be exceptions, not time saved
Every AI case study leads with hours saved. The number that tells you whether it's working is how often you step in to fix something.
The move For one week, note every time you correct something the AI produced before you use it. Write down what you changed and why. That list tells you where the AI falls short and where to improve your setup.
The short version: Time saved tells you what got faster. Exception rate tells you what's actually working. Track both, and you'll know whether your AI use is earning trust or just moving work past the moment you used to catch the problems.
You've been using AI on your own work for a month. If someone asked, you'd say it saves you maybe 40 minutes a day. You'd skip the part where you fixed the client name, rewrote two lines that sounded off, and caught a number the AI pulled from the wrong quarter.
Those corrections tell you more than the time you saved. OpenAI's tax team moved into "checking mode," reviewing every prefilled tax form the AI prepared. HSP GRUPPE, a German tax advisory network, kept professional review on every output. Both report time savings. Both also maintained something the headlines skip: a clear structure for catching where the AI falls short.
Time saved tells you what got faster. Exception rate tells you what's actually working.
The move
For one week, note every time you correct, rewrite, or override something the AI produced before you use it. Write down what you changed and why. After five days, look at the list. If your corrections cluster around one kind of input, improve your prompt or setup there first. If they're scattered, the AI may not be ready for that task yet.
What to skip
The "X hours saved" talking point. Time saved measures speed, not reliability. A faster draft you rewrite still costs the rewrite.