How to measure the results of AI training

Treat results as a chain with three links: behavior, work and business. Training first changes what people do; that changes the speed and quality of the work; only then does it show up in the company’s results, mixed in with the market, pricing and everything else that happened in the period. You observe the first two links directly. You estimate the third, with your assumptions written down.

How to think about the ROI of AI training for executives

A defensible ROI for AI training adds up two things: leadership time freed up in recurring work and the value of decisions that got better or faster. The monthly report that took up a whole afternoon, the board agenda that required several rounds with the team, the vendor proposal analysis that sat in the inbox.

Convert into money only what you could defend in a budget meeting: board hours on recurring tasks, rework that no longer exists, a pilot shut down before it ate up budget. Everything else goes in as qualitative evidence, with an example. And be wary of any percentage return promised before the program, including the one you yourself might be tempted to put in the presentation.

What to record before the program

The starting point fits on one page per participant, written before the first session. A short conversation with each leader and a sample of recent deliverables will do. Record:

1. Which tasks each leader already does with AI today and which they do the old way: researching, analyzing, writing, preparing meetings, reviewing.
2. How long recurring work takes, such as the monthly report, the budget review or the board presentation.
3. How much rework there is: how many versions a document usually goes through before approval and how many deliverables go back to the team.
4. Which AI initiatives are open, who owns each one and where they stand.
5. How the team uses AI today, including the use no one has declared.

The fifth item often comes as a surprise. If you suspect it’s large, the guide on the team that uses AI in secret helps bring that use to the table without a witch hunt.

Indicators for each signal

Real adoption is measured in tasks, not logins: which recurring work is now done with AI, by whom and with what quality.

Leaders using AI in their own tasks

• Recurring tasks: which of the month’s work is now done with AI, and which went back to the old way.
• A verifiable example: each leader can show a recent analysis or document made with AI and explain what changed in it.
• First draft: how long it takes to get a draft of a document that used to clog the calendar.
• Asking the team: the leader asks for AI-assisted work by saying what they expect, instead of banning it or pretending not to see it.

The quality of what the team delivers with AI

• Rework: how many rounds a document goes through before approval, compared with the starting point.
• Workslop stopped at review: deliverables that look finished but are shallow or inconsistent go back before they reach the client or the board.
• Written criteria: each area knows what a good AI-assisted deliverable is.
• A library in use: shared, up-to-date prompts, criteria and lessons learned, not a forgotten file in a folder.

The term workslop comes from a Harvard Business Review article and describes work that shifts the effort onto whoever has to review it. To turn criteria into routine, see how to set quality standards for AI-assisted work.

Priorities decided with judgment

• A list of initiatives: each AI initiative has an owner, a goal and a recorded decision: continue, adjust or stop.
• Pilots shut down: stopping a pilot that doesn’t hold up is a sign of judgment, not failure.
• Usage rules: they exist, people know them and they answer the team’s real questions.
• Budget tied in: requests for tools and projects arrive linked to a priority that has already been decided.

What not to measure, and why

Some metrics are easy to collect and say little about what changed in the work.

• Active licenses: they show access. Someone who opens the tool once a week to summarize an email counts as a user.
• Satisfaction scores: they measure the experience in the room. A fun session and a useful session can get the same score.
• Training hours: they measure the effort invested, not what came out of it.
• Number of prompts: volume can grow along with workslop. More generated text doesn’t mean more good work.

Applause isn’t results. Enthusiasm isn’t transformation.

These metrics can go into the report as context. The problem starts when they become the answer to the board’s question.

When to look

• Right after the program: check whether each participant applied what they learned to a real task and whether what was produced in the sessions is in use.
• A few weeks later: see which uses became part of the routine and which stayed in the enthusiasm of the first sessions.
• A few months later: compare the quality of the team’s deliverables and priority decisions with the starting point.

Each check answers a different question. The first shows whether learning was applied; the second, whether it became a habit; the third, whether it changed the way the company works.

Applied learning is also what you want to hear from the people who took part:

How to show the board the results of AI training

One page is enough, as long as it compares before and after with concrete examples. Use this structure:

1. Starting point: what was recorded beforehand, in a few lines.
2. Leadership’s routine: which tasks changed, with an example the board will recognize, such as the monthly report or the board agenda.
3. Quality: what happened to rework and which review criteria now exist.
4. Decisions: prioritized initiatives, pilots shut down, published usage rules.
5. What didn’t change: and what, in your reading, explains it.
6. Next steps: each with an owner and a date.

The fifth item is what gives the rest credibility. A report that only shows progress looks like a marketing piece, and the board notices.

Why follow up after the training

The risk in any training lies in the following weeks, when the calendar tightens again and the old way seems faster. Without someone watching, new habits lose to urgency.

If you’re looking for an AI program with follow-up after the training, ask in the proposal what exactly happens afterwards, with whom and for how long. And set up your own follow-up either way:

• An internal owner: someone in leadership is responsible for continuity, not the provider.
• A fixed slot on the calendar: the topic comes back to the board’s monthly meeting, with the indicators above.
• Managers reviewing: the quality of AI-assisted deliverables becomes part of routine conversations with the team.
• A forum to decide: an AI committee with senior leadership receives the signals and decides what continues, what changes and what stops.