How do you tell if AI is paying off at a small company?

The one measure that matters: time per process, before and after

Pick one recurring task. Time how long it takes today, the old way. Put the AI tool into daily use on that same task. Time it again. Compare the two numbers. That is the whole method — no survey, no feeling, one clock.

How to measure it in your own company

1. Pick one recurring task, not the whole job — a report, a monthly run, a write-up that happens on a schedule.
2. Time it before, honestly, including the parts that feel like "just checking email while it runs."
3. Put the AI tool into daily use on that task, not a one-time demo.
4. Time it again after it is in daily use, on the same task, the same way.
5. Keep a person checking the result before anyone relies on it — a faster wrong answer is not a win.

What good numbers look like

These are from Summit's own measured client work, checked by a person before anyone relied on them, and never tied to a named company:

• A monthly commission run: 4 days became 2 hours, with a person checking the result before anyone was paid.
• A recurring report: 2 days became 5 minutes, built from the same source files, ready for a person to read.
• One leader measured up to 50% more productive per person, on the same recurring work, before and after, over one week.

Figures from client work.

A small company will not match these exactly — they came from specific processes — but they show the shape of a real result: one task, timed twice, checked by a person.

What to watch for

• No "before" number. If nobody timed the old way, there is nothing to compare the new way to.
• Counting activity, not time. How many messages went into the AI tool is not a result; how much time came off the clock is.
• A demo instead of daily use. One fast run in a meeting proves the tool can work, not that it is paying off — that needs real, repeated use.
• "It feels faster" standing in for a number. Feelings are a reason to go measure it, not a substitute for measuring it.
• Stopping the human check once it looks good. A result that is not checked is a risk, not a saving.