A Quickstart for Measuring the ROI of Ongoing AI Training and Support

Key Takeaways

• Ongoing training earns its return by keeping people using AI. The value is the adoption you would have lost without it, so that is what you measure.
• You need a baseline before you need a formula. A team that got only launch training, an earlier cohort, or your own usage trend from before support started will all work.
• The worksheet has four inputs. Retained users, net hours per retained user, loaded hourly cost, and the full monthly cost of support.
• Count your own people's time as cost. Champions and office-hours attendees are part of what support costs, and leaving them out flatters the number.
• Find the break-even first. The number of net hours each retained user has to produce for support to pay for itself is the easiest figure to defend in a budget meeting.

Training budgets get the same question at every renewal: what did the last round actually buy us? For a one-day launch workshop, the honest answer is often "a good couple of weeks." For ongoing training and support (office hours, champions, refresh sessions, follow-up with teams that stall) most companies can't answer at all, because nobody set up a way to count it.

There is a decent signal that the ongoing part is where the value sits. EY's 2025 Work Reimagined Survey of 15,000 employees found that people who get more than 81 hours of AI training a year report saving an average of 14 hours a week, against a median of eight. Only 12% said they get enough AI training to get the full benefit. (Those are self-reported numbers, and the people who seek out 81 hours of training were probably keen to begin with. I'd use it for direction and nothing more precise.) Nobody gets 81 hours in a kickoff. You only get there with something that keeps running.

So the question is fair. If you pay for training that keeps going after launch, you should be able to say what it returns.

This post is a quickstart. One formula, four inputs, one month of data you probably already have. It won't give you a perfect answer. It will give you one you can defend.

Launch Training Takes Credit for Habits It Didn't Build

Most training ROI math gets done once, right after the kickoff. Usage is up, session ratings are good, and somebody writes a slide. That slide measures the easy part.

Habits take longer. Microsoft surveyed 1,300 Copilot users and found that 11 weeks is the point where most people say the tool has improved their work, with about 11 minutes a day of saved time as the threshold where they start to feel the value. (Microsoft sells Copilot, so read it with that in mind. The shape matches what most rollouts look like.) Eleven weeks is a long time after a one-day workshop. Whatever keeps people going through weeks two to eleven is doing most of the work.

That's the part ongoing support pays for. And if you measure it with launch-day numbers, the kickoff gets credit for it.

Separate the launch from what comes after

Split your training spend into two buckets before you calculate anything. Launch costs are one-time: curriculum build, kickoff sessions, onboarding workshops. Ongoing costs repeat every month: office hours, champion time, refresh sessions when the tools ship new features, and follow-up with teams whose usage drops. This post is only about the second bucket. Mixing them makes both numbers meaningless.

The Return Is the Adoption You'd Otherwise Lose

I'll put the claim plainly: ongoing training earns its keep by stopping usage from leaking away. It adds a little new usage, too, but the leak is where the money is.

That changes what you measure. The value of ongoing support is the gap between how many people are still using AI well three months in, and how many would have been without it. Everyone who would have drifted back to the old way, and didn't, is a retained user. Their output is the return.

This is an old problem in training measurement, by the way. The Phillips ROI Methodology, the standard way learning teams put a dollar figure on training, makes isolating the effects of training from other variables a required step before you count any benefit. AI makes it harder, because the tools improve every month and usage would climb a bit even if you did nothing. That's exactly why you need a baseline.

Pick a baseline before you pick a formula

You have three practical options, from best to roughest:

• A comparison team. One team gets ongoing support and a similar team gets launch training only. Cleanest, but most companies won't hold a team back on purpose, and I wouldn't either for long.
• An earlier cohort. If your first wave of users got a kickoff and nothing else, their week-12 active rate is your baseline for the waves that got support.
• Your own trend. If support started after launch, use the usage decline in the weeks before it started and project it forward.

Pick one, write it down, and don't switch mid-year. A baseline you change every quarter tells you nothing.

The Ongoing Training ROI Formula Has Four Inputs

Here is the whole worksheet.

Then the math:

Monthly return = (Retained users × Net hours × Hourly cost) − Support cost

ROI = Monthly return ÷ Support cost

Break-even hours = Support cost ÷ (Retained users × Hourly cost)

Define "active" the same way every month. Something like "used AI on real work on at least four days in the last 30" is fine. And measure net hours on one workflow with a clear start and end, rather than asking people to guess.

Run the worksheet on one month

Say a company trained 200 people on Claude and has run ongoing support since launch: weekly office hours, ten champions, and a short refresh session whenever a meaningful feature ships. (Illustrative numbers, not a client.)

• Baseline: An earlier pilot cohort that got only a kickoff was at 45% weekly active by week 12. Applied to 200 people, that predicts 90 active users.
• Input 1: The supported group is at 70%, or 140 active users. Retained users: 140 − 90 = 50.
• Input 2: Timed on one workflow per team, retained users save about 6 net hours a month.
• Input 3: Blended loaded cost of $65 an hour.
• Input 4: $9,000 in trainer or partner fees, plus 10 champions × 3 hours × $65 = $1,950, plus 40 hours of office-hours attendance × $65 = $2,600. Total: $13,550.

Value of retained use: 50 × 6 × $65 = $19,500. Monthly return: $19,500 − $13,550 = $5,950. ROI: about 44%.

Break-even: $13,550 ÷ (50 × $65) = about 4.2 net hours per retained user per month. That's the number I'd lead with. Most people who use AI on real work every week clear four hours a month without trying. If yours don't, that's worth knowing too.

Support Costs More Than the Invoice

Input 4 is where most training ROI numbers quietly cheat. They count the vendor invoice and stop.

Your own people's time is a real cost. A champion who spends three hours a month helping teammates isn't doing their day job for those three hours. Forty people sitting in office hours is forty hours of salary. In the example above, internal time is about a third of the support cost. Leave it out and the ROI jumps from 44% to roughly 117%, which looks great until finance redoes the math.

I'd rather show a smaller number that survives a review than a bigger one that doesn't.

Price the hours your own people spend

Keep a simple log: who ran or attended what, for how long. Champions can note their hours once a month. Office-hours attendance comes from the calendar. It takes minutes, and it makes Input 4 honest. It also tells you something useful about the program, like whether office hours are packed or empty.

Recovered Teams Are the Easiest Return to Prove

Of everything ongoing support does, one piece is far easier to attribute than the rest: going back to a team whose usage dropped.

The sequence is simple. You see a team's weekly active users fall. You run a session with that team on their actual work, or a champion sits with them for an afternoon. Four weeks later, you check again. The people who came back are about as close to a controlled result as you'll get in training measurement, because nothing else changed for that team in that window.

I'd track these separately from the rest of the retained users. If the full-rollout baseline is the number finance argues with, recovered teams are the number they won't.

Log every intervention

Each time someone on the support side steps in, write down the team, the date, what you did, and active users before and four weeks after. After a quarter, you'll have a short list of interventions and what each one recovered. That list is the best evidence you'll have about which kinds of support work, and it tells you where to spend next quarter's hours. We drive this kind of follow-up from usage data through PressW's Token & Usage Analytics, so the support team goes where adoption actually dropped.

The Quickstart Measures Hours, and Hours Aren't Everything

I want to be honest about what this worksheet leaves out.

It doesn't measure quality. Someone who writes a better analysis with Claude in the same time saves zero hours on paper. It doesn't capture the person who would have opened a personal AI account in another tab if the approved tool hadn't been easy to use. And it ignores a real risk: the same EY survey found heavily trained employees were 55% more likely to leave their organization. Skilled people get offers. I don't think that's a reason to train less. It is a reason to pair training with work that keeps people interested, and to admit the return isn't only measured in hours.

So treat this as the floor. If the hours alone clear break-even, you're in good shape. If they don't, the program might still be worth it, but you'll need a stronger case than a worksheet.

Add one quality check

Pick one output per team and look at it before and after: error rate on a report, rework on a draft, turnaround on a request. You don't need to put a dollar figure on it. Put it next to the ROI number so the conversation includes it.

Start With Last Month's Numbers

I don't think anyone has training ROI for AI figured out. The tools change monthly, so the curriculum changes monthly, so the baseline keeps moving. How much ongoing support a team needs once the tools get better at teaching people themselves is an open question, and I'd be suspicious of anyone who claims a firm answer.

What I'm confident about is that you can get a usable number this week. Export last month's active users. Pick a baseline. Time one workflow. Add up what support cost, including your own people. Fill in the four inputs and look at the break-even.

Ongoing support is the core of how PressW runs AI Training: role-based sessions in your own environment, weekly office hours, champions, a curriculum that keeps up with the tools, and adoption tracking by team so follow-up goes where usage lags. If you run the worksheet and want a second pair of eyes on the numbers, or help setting up the measurement, Book a Discovery Call.

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