AI Benefit Tracker

Define a few indicators before the rollout, capture the baseline while the old way of working is still visible, and read speed next to quality. Then decide, use case by use case, whether to continue, adjust or stop.

Consulting practice plus mixed research. The gap between felt and measured gains comes from firm surveys, self-reported and published by firms that sell AI advice (McKinsey, 2026; not checked against the original page). Task-level studies measured real gains in specific settings (Noy & Zhang, 2023; Brynjolfsson, Li & Raymond, 2025). The indicator set and the review rhythm are practice, not research.

Use it when:

• You are about to roll out an AI use case, or already have, and cannot yet show whether it helps.
• Someone says AI is working, or not working, and nobody can show numbers.
• You must report AI results upward and want a reading that holds up when questioned.

Skip it when:

• You have not chosen the use cases yet: start with 10.11 AI Adoption Plan.
• You want to know whether your own personal use pays off: a quick test on one task in 10.7 AI Task Map is enough.

Most people who use AI at work say it helps. Whether a team's results improve is a separate question that few teams can answer. The surveys that describe this gap come from consulting firms that sell advice on adoption, and they rest on what respondents report (McKinsey, 2026, not checked against the original page). Independent studies that measured carefully did find real gains, but on specific tasks and unevenly: in writing tasks (Noy & Zhang, 2023), and in customer support, where the least experienced gained most and the most experienced gained little speed and lost a little quality (Brynjolfsson, Li & Raymond, 2025). Economists add that the measured payoff of a new general purpose technology can lag its adoption (Brynjolfsson, Rock & Syverson, 2021).

So the tracker does three things. It fixes a baseline before the change, because without one every later claim is an impression. It pairs speed with quality, because speed alone measures how fast errors ship. It keeps felt and measured gains in separate rows: the first tells you whether people will keep using the tool, the second whether the effort is justified.

The limit: on one team the numbers are noisy and cannot prove cause. Read direction, not decimals.

Two to four indicators per use case, always mixing speed with quality. Use data the work already produces where you can. Measure the team, never the individual. Count the time spent on checking as a cost, not as overhead to hide.

Strong answer: "Use case: first drafts of monthly project reports. Time: hours per report, baseline 12. Quality: review findings per report, baseline 4. Outcome: days from period end to sent report, baseline 6. Felt: team's own rating of effort, 1 to 5, kept apart. All baseline figures illustrative."

The use case:

How verification time is counted:

Illustrative example: the team and all numbers are made up.

The finance unit of a Danish building contractor tracks two use cases from its adoption plan.

Report drafting. Baseline from the last eight monthly project reports: 12 hours each and 4 review findings each. After one quarter: 8 hours and 3 findings, with checking time included in the hours. Measured gain, quality held: continue.

Contract summaries. The team feels faster, but cycle time is flat and the lawyer downstream reports unchanged workload. The workflow check shows the saved time going into extra polishing of the same summaries. The lead redirects it to the overdue contract archive and sets a follow-up read.

Next quarter. The archive backlog indicator finally moves. The lead tells her manager both stories: one clear gain, and one that looked like a gain until the data said otherwise.