How do you calculate AI ROI?

Most AI business cases get built the way software business cases were built, which is why they don’t survive the first serious question. A licence cost on one side, an hours-saved figure on the other: that’s a spreadsheet, not an argument.

There are three benefit lines and three cost lines. Skip either half and the number stops meaning anything.

Three ways it pays

Capacity returned. Hours a week handed back to the team, valued at a blended loaded cost rather than a salary. It’s the easiest number to produce and the weakest one on its own, because “we freed up 130 hours a week” invites the obvious follow-up: freed up for what? It only counts if you can say where the capacity went — more accounts covered, a queue cleared, a role you didn’t have to hire for.

Revenue you’re already leaving on the table. The cross-sell nobody spotted. The renewal that lapsed because it sat in a queue. The quote that arrived two days after the buyer had made up their mind. This is usually the biggest line and always the hardest to defend, because it’s counterfactual — you’re claiming credit for something that didn’t happen.

So measure the leak first. How many referrals actually went unnoticed last quarter, on the record? Now the agent’s contribution is a recovery rate against a real baseline instead of a projection.

Cost taken out. Systems consolidated, contractors not renewed, rework that stops happening. The most credible line of the three, because it shows up on an invoice that either exists next year or doesn’t.

Three ways it costs

Platform. Price the platform per organization and seat count stops being the variable, which kills the annual argument about who “deserves” a licence — usually the thing quietly capping adoption. It does not make the platform free, and for a smaller team the per-user plans are often cheaper. Model both against real usage.

AI actions. New in 2026, and the line most business cases miss entirely. Agent interactions are metered and sold in annual packages, so what you need to forecast is agent volume — how many times a month your workflows will actually run. Not headcount. It’s harder to estimate than seats ever were, and getting it wrong is how a project that looked funded quietly stops being funded in month seven. Work it out before you sign anything.

Build and change. Implementation, integration and the part everyone underprices — getting people to work differently. In our experience the technical build is almost never what decides whether the number lands. Whether the new process survives contact with the people who run it: that’s what decides it.

You cannot compute a return without a before

This is the part that decides whether any of the above is worth anything. A workflow constraint audit — where work stalls today, how often, what that costs you — isn’t the warm-up to the ROI calculation. It is the ROI calculation. Everything after it is arithmetic.

It’s also why a slider on a website is an estimate and nothing more. Ours assumes automation absorbs about 65% of manual entry and triage time, at a blended US$55 an hour. Both of those are our planning defaults, not facts about your business. They’re good for deciding whether the order of magnitude is worth a conversation. They are not good enough for a board paper.

Two ways to tell a real number from a wish

Rank the list, don’t total it. A ranked list of workflows, each with a value against it, is something you can act on. One big aggregate number is not — nobody can act on it and nobody quite believes it.

Include the workflows that shouldn’t be automated. A business case that finds AI pays everywhere hasn’t been tested. Ours routinely concludes that two or three of the candidates should stay human. That shrinks the engagement, and it’s the finding that makes the rest of it credible.

If an assessment hands you a number without the baseline it was measured against, what you’re holding is a proposal wearing a spreadsheet.