An AI tool can produce a draft quickly and still leave the team with as much work as before. Someone has to check it, fix the errors and deal with anything the tool missed.
To judge the return, measure the whole task through to an accepted result. That gives you a more useful comparison than the number of prompts sent, words generated or people with a licence.
Define what “finished” means
Choose a repeated task with an observable endpoint. For a release note, finished might mean checked and ready to publish. For an internal support answer, it might mean the user has received a correct answer with a source they can inspect.
Write down the quality requirements before the trial. Otherwise, a faster but less reliable result can look like an improvement because the missing work falls outside your measurement.
Compare similar work
Collect examples of the current process, then test the proposed approach on comparable tasks. Include straightforward cases and difficult ones. Record what makes each case unusual so you can see whether the tool helps only with the easy work.
For each task, capture:
- preparation and information-gathering time;
- drafting or processing time;
- review and correction time;
- whether the result met the agreed standard;
- any rework discovered later.
Record waiting time separately. Reducing a queue can improve turnaround without freeing an equivalent number of staff hours.
A worked example with visible assumptions
Suppose a team completes 80 similar tasks a month. Each currently takes 25 minutes of active staff time. During a trial, preparing the AI input takes 3 minutes, reviewing and correcting the result takes 10, and final handling takes 2.
The proposed process therefore takes 15 minutes per task. The difference is 10 minutes, or 800 minutes across 80 tasks: approximately 13.3 staff hours a month.
If the business uses an illustrative staff cost of £30 an hour, that represents about £400 of monthly capacity. If the additional tool costs are £100 a month, roughly £300 remains before setup, training, support and other costs.
These figures are invented to show the calculation, not F3N client results or a forecast. They assume comparable quality, steady volume and that the trial timing holds up in everyday use.
Freed capacity needs somewhere to go
Time released is not automatically cash saved. A team may use it to clear a backlog, respond sooner or take on more work. Decide how the freed time would be used and whether the minutes are available in useful blocks.
Keep the business case honest about uncertainty. A small sample may miss rare but expensive errors. An early trial may also involve more attention than the team can provide once the tool is routine.
Make the next decision explicit
Set a review date and agree the evidence needed to continue. You might require less total handling time, no drop in accepted quality and a workable way to resolve exceptions. Use thresholds that fit your task rather than borrowing a universal target.
F3N’s AI Healthcheck helps you assess current AI use and compare the opportunities for improvement. Bring the task, the costs you know and the questions you still need to answer.