In this guide 6 sections
Measure it like any process change. Baseline the task’s current time, cost and error rate, then agree what accuracy is acceptable once review effort is counted. Compare that against the full cost of running the AI version, not only its subscription fee. A short controlled pilot gives you real numbers instead of a guess.
Baselining the task before changing it
Measure today’s version first. Record how long the task takes, how many people touch it and how often an error needs fixing, because without that baseline “faster” is only a feeling.
Take an auto-parts distributor piloting AI to draft first replies to availability and pricing enquiries, where the baseline is enquiries handled per person per hour. Add the share of replies later corrected. The pilot must beat that.
Defining accurate enough, including review time
Agree an accuracy threshold before comparing costs, and agree how much human review the system will need once it clears that threshold. A highly accurate system that needs every output read can be worth less than a slightly weaker one that needs only spot checks.
Teams often forget review effort. If a staff member still reads and edits every AI-drafted reply, the saving is only the gap between drafting from scratch and reviewing a draft. Real, but smaller than it first looks.
Counting the full cost
The fee or API charge is the visible part of the bill, but the rest includes connecting the tool to your systems and indexing the documents it reads. Add the hours someone spends monitoring and correcting output, plus the work of updating the tool when your products, pricing or processes change.
Prices for AI models move quickly. Get a current quote for your expected usage, then price the drivers: usage volume, integration effort and review headcount, since any fixed figure ages quickly.
Running a controlled pilot with written assumptions
Run the AI version beside the existing process for a period on real enquiries, and write down the baseline, the accuracy threshold and the cost assumptions before it starts. Never set them afterwards. Once the results are visible, the definition of success shifts toward them without anyone noticing, which is why the assumptions must exist in writing first.
For the distributor, run two weeks of AI-drafted replies beside normal practice and have a person compare a sample against what they would have written. Log minutes saved and every correction.
Deciding to scale, revise or stop
Compare the actual results with the baseline and the written assumptions, not with a vague sense of whether the tool felt useful to the people who tried it. Three outcomes are honest. Scale it if it clearly beat the baseline on cost and quality, and revise it if the shortfall traces to a fixable cause such as a document gap. Stop when the numbers say stop.
Stopping is not failure. A pilot that shows AI is not worth it for one task has done its job, because it prevented a larger rollout that would not have paid off.
AI pilot evaluation scorecard
A three-column sheet puts baseline figures for time, cost and error rate beside the pilot-period figures and the difference between them in every row. Rows cover task time, correction rate, review effort and total cost, and a final field records the scale, revise or stop decision once the pilot ends.
Open a blank worksheet to printQuestions owners ask
Should token cost be the main metric?
Token cost rarely decides it. Integration effort, review time and upkeep usually decide whether a project is worth scaling, and watching token cost alone can make an unviable project look cheap.
How do we include review time?
Give it its own line. Measure the minutes a person spends reading or correcting AI output per task, because review time often separates a real saving from a paper one.
How long should a pilot run?
Long enough to include your usual mix of easy and awkward cases plus at least one product or price change if one is due. Fix the end date in writing beforehand.