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Productivity is time to a trusted artefact, not a pile of drafts
Measure AI productivity as time-to-trusted-output, error rate, and recovered judgement time. Word count is a vanity metric. A model can fill a page in seconds. A professional still has to make that page true, in voice, and fit to send. If your dashboard celebrates tokens or documents generated, you will reward the people who produce the most unread text. The number that matters is how long it takes to reach an artefact a named person will sign, compared with the old path, after review. Everything else is a story the vendor already knows how to tell.
Error rate is the second number because speed without accuracy is rework plus risk. Count misses that review actually found: wrong names, invented figures, bad citations, tone that would damage a relationship. A falling clock with a rising miss rate is not productivity. It is a transfer of cost into incidents and into other people's evenings. The third number is recovered judgement time: minutes that used to go to blank-page labour and now go to sources, to a second read, or to stopping. If those minutes vanish into extra volume, you have not gained a thinking organisation. You have gained a printer.
This essay is a measurement method you can run on one team without a consultancy pack. You will baseline a real artefact, define trusted, sample in the flow of work, and refuse vanity counts. You will also cost the review, because review is part of the new labour, not a tax you ignore so the graph looks kind. ROI that forgets review, errors, and licences is a press release. Write the dull table. The dull table is how you know whether to keep the tool, change the brief, or stop.
If the metric cannot fall when the work gets worse, it is not a productivity metric. It is a usage trophy. Choose numbers that can fail.
Three measures, and the costs that belong beside them
Time-to-trusted-output starts when the brief is ready and ends when a named person would send. It includes the check. It does not include waiting for a meeting that has nothing to do with the tool. Error rate is misses per artefact in a sample, classified by type so you can fix the brief. Recovered judgement time is a diary measure: what did you do with the minutes the draft no longer ate? If the honest answer is more drafts of the same quality, the recovery is zero. Put licence cost, switching cost, and incident cost on the same page. Productivity that ignores those is incomplete arithmetic.
Do not average across unlike jobs. A rewrite of an internal note and a first draft of a client letter are different labour. Measure per workflow, on comparable instances, with the same definition of trusted. Self-reported hours are weak; sampling a few real packs is stronger. Qualitative notes still matter: did the reader ask what did you mean more often? That question is a quality signal. A metric suite that cannot hear it will declare victory on a pack nobody trusted. Listen to that question in the same week you log the minutes.
| Measure | How to take it | Refuse this substitute |
|---|---|---|
| Time to trusted output | Clock brief to signed send, including review | Time to first fluent draft |
| Error rate | Sampled misses by type against source | Thumbs-up in the chat |
| Recovered judgement time | What the minutes were used for | Hours the model ran |
| Full cost | Licences, review, errors, switching | Seat price alone |
Baseline, sample, and keep the definition of trusted still
Pick one artefact. Write what trusted means in five checks. Time three recent instances on the old path, including the human polish you already do. Then time three instances on the new path with the same checks. Sample weekly for four weeks so a lucky pack does not become a legend. Log error types in a shared table. Ask one extra question: where did the saved minutes go? If you cannot answer, you are not measuring recovery. You are assuming it. Freeze the definition of trusted while you compare. Changing the pass mark mid-stream is how every tool looks like a win.
Report in a language a sceptic accepts: minutes, misses, and money. Do not report engagement. When the sample is too small, say so and keep sampling instead of inventing a percentage. If review time rose more than draft time fell, the method is wrong or the brief is wrong. Fix those before you buy more seats. A team that generates faster and reviews longer has not found productivity. It has found a new bottleneck with a chatbot in front of it. Move the bottleneck on purpose, or admit the gain is not there.
Role: You are an operations analyst who distrusts vanity metrics.
Task: Design a one-page measurement sheet for AI productivity on one workflow.
Context: I will name the artefact, what trusted means, and costs I already know.
Constraints:
- Use time-to-trusted-output, error types, recovered judgement time, and cost.
- Do not include word count, token count, or number of prompts as success.
- If a figure is missing in my notes, write missing.
- Keep the sheet usable in a weekly sample of three artefacts.
Output: Definitions, a log table, and a decision rule for keep or stop.
Quality checks: Which number could still look good while the work got worse?Keep the log next to the team's artefacts, not in a transformation dashboard that nobody updates. Review it in the same meeting that already discusses the pack. When error types repeat, change the brief that week. When recovered time is always more volume, change the incentive. Measurement that does not change a brief or a target is decoration. The point of the numbers is a decision about the method, not a slide about modernity.
Vanity counts, moving targets, and ROI that forgets review
Vanity counts are easy to automate and easy to game. People will prompt twice to look active. Moving the definition of good so that this month's drafts pass is another way to lie with charts. ROI that counts only licence savings against an imagined headcount cut will collide with reality when the same heads still review, still correct, and still carry the incidents. Include the labour you still pay for. If a senior person now spends evenings checking fluent errors, that cost belongs in the story even when it does not appear in the vendor invoice.
Beware last-mile blindness. The model shortens the ugly middle of a task and lengthens the anxious end, where someone must stake a reputation. If you only measure the middle, every demo wins. Measure to the signature. Also beware comparing a pilot's best operator with the team's average last year. Compare like with like, or you will scale a hero and call it a system. Heroes do not survive leave. A method that works for the median person is the only one worth keeping. Design for the median, then sample the median.
| Mistake | What it looks like | What to do instead |
|---|---|---|
| Word count | Longer drafts as proof | Time to a signed artefact |
| Usage trophy | Seats logged in | Sampled quality and recovery |
| Headcount fantasy | ROI from jobs not cut | Review and error costs included |
| Hero baseline | Best user versus old average | Like with like instances |
A metric that rises whenever someone types more is not measuring productivity. It is measuring the model's willingness to talk. Count time to signed work instead.
Related reading on StudyGrid
Read next: Adoption and Value How to Run an AI Pilot at Work The Real Cost of AI at Work. Those essays sit beside this one. Use them when you need the neighbouring skill, not as a substitute for the check you still have to make.
What to do this week
Choose one artefact. Write five checks that mean trusted. Time three old instances and three new ones, including review. Log misses by type. Ask where the minutes went. Put those four facts on one page. Keep the tool for that job only if trusted time fell or quality rose without eating the surplus. Otherwise change the brief before you change the story, and do not buy more seats on a vanity count.