How do you measure the ROI of AI training for a team?

Measure the return on AI training with two numbers you collect yourself: each person's score on the same graded test before and after the training, and the hours a month that come back on named recurring tasks, timed by hand before and with AI after, including the time spent checking. Multiply the hours by a loaded hourly cost, set that against the price of the training, and report the people whose scores did not move as well as those whose did. Attendance, completion rates and satisfaction surveys measure that the training happened, not that it worked.

Why are completion rates the wrong measure?

Because a person can finish every module and change nothing about how they work. Completion tells you the content was delivered. Leadership is paying for a change in what people can do and in how long the work takes, so measure those directly.

How do you measure the skill change?

Give every person the same assessment at the start and at the end, graded the same way. A useful assessment is short, built on tasks from the job rather than trivia about AI, and graded against a rubric someone can read. Report it per person and for the team: the baseline, the end score and the difference. The people who did not move are the most useful line in the report, because they show where the training or the role track was wrong.

How do you measure the time that comes back?

Pick the recurring tasks the training targets before it starts: the weekly report, the inbound queue, the first draft of a proposal. For each one, time it by hand for a week. After the training, time it again with AI, and count the checking. Keep a one-line log per task: time before, time after, errors caught in review. Drop any task where checking eats the saving; it is not a win.

The arithmetic is plain. A task that took an hour a week and now takes none returns about 4.3 hours a month (a month is about 4.33 weeks). Two such tasks per person return about 9 hours; three return about 13. Write the basis beside the number, because "13 hours a month" without "three one-hour weekly tasks" cannot be checked.

How do you turn hours into a return?

Multiply hours returned by a loaded hourly cost (salary plus on-costs, divided by working hours), across the people who actually changed how they work, and compare that with what the training cost. Two cautions keep the figure honest:

• Hours returned are not money saved unless they go to something else of value. Say what the time went to.
• Count only measured tasks. Projecting one person's saving across the whole company is how AI business cases lose credibility.

What else should the report show?

What got built, if anything: a workflow, an automation or a template the team now uses, with who maintains it. And what is next: the tasks the team now wants to hand to AI. BCG's guidance on AI programmes puts about 70% of the effort in people and processes, against 10% in algorithms and 20% in technology and data (BCG), so a report that shows only tools has measured the smallest part.

How does Square1 Ai measure a team's progress?

A team starts with a free two-minute AI readiness test, and the training ends with the same graded reassessment. Every exercise and project in between is graded by Nova, the platform's AI tutor, and kept per person on the manager dashboard. Leadership gets a Team Impact Report: each person's baseline and end score, and the hours a month returned with their basis printed beside them. The offerings are on the For teams page.