Measure the outcome, not the activity
Use an outcome, baseline and guardrail to assess AI workflows. A fictional handover scorecard and reusable measurement worksheet.
Measure an AI workflow against a defined business outcome, a baseline and a guardrail. Count activity to understand workload, but do not treat more outputs as proof of improvement. Decide what evidence would justify continuing before the trial begins.
The practical question is not “How many things did the agent do?” It is “What became better for the person or process we were trying to help, and what did that improvement cost?” A useful scorecard makes both sides visible.
Choose a measure close to the intended result
If the aim is to make internal handovers usable, the number of generated summaries is an activity measure. A closer outcome measure is the share of handovers that the receiving team can act on without asking for missing ownership or next steps.
Define what counts before collecting results. Who judges whether a handover is usable? Which fields must be present? What happens when the underlying conversation lacks the information? A clearly marked unknown can be an accurate result; inventing an owner to fill the field is not.
Keep your definition stable during a comparison. If it must change, record the change and avoid presenting the old and new scores as equivalent.
A worked scorecard
All figures below are fictional and chosen to illustrate the method. They are not Ben's operating results, a customer case study or Walter performance claims.
Imagine a team comparing two sets of ten synthetic handover cases. A reviewer applies the same checklist to the current manual process and the AI-assisted draft process. The sample is small, so the team is looking for errors and practical lessons, not statistical proof.
| Measure | Definition | Illustrative baseline | Illustrative trial |
|---|---|---|---|
| Usable handovers | Issue, verified owner or explicit unknown, and next action are clear. | 6 of 10 | 8 of 10 |
| Review time | Reviewer minutes for the entire ten-case set, including corrections. | 40 minutes | 35 minutes |
| Invented ownership | Any owner assignment unsupported by the supplied source. | 0 | 1 |
| Outputs prepared | Number of draft handovers produced. | 10 | 10 |
The apparent improvement in usability does not cancel the invented owner. If the team set unsupported ownership as a stop condition, the decision is to fix that failure and test again. The scorecard prevents one attractive number from hiding a consequential problem.
Likewise, five minutes less review time is not automatically five minutes saved overall. Include setup, source preparation, retries and exception handling before making a claim about total effort.
Copy this measurement worksheet
- Outcome: What should be better for whom?
- Unit: What is one case, and which cases are included?
- Baseline: How does the current process perform on a comparable set?
- Success measure: What observable evidence indicates improvement?
- Guardrail: What must not get worse, or must never occur in the trial?
- Effort: What human and system work is needed, including corrections?
- Review: Who checks the results and by what definition?
- Decision rule: What result means continue, revise or stop?
The NIST AI Risk Management Framework includes measurement, monitoring and ongoing risk management. The worksheet here is a small operational tool; it does not replace the broader assessment a consequential deployment may require.
Keep the comparison honest
Compare like with like. If the baseline includes difficult cases but the trial only includes clean ones, an improved score may reflect the selection rather than the workflow. Keep a record of excluded cases and explain why they were excluded.
Show counts as well as percentages. Eight out of ten is easier to interpret than “80% success” without a denominator. Separate synthetic tests from live observations, and label estimates rather than giving them the appearance of measured data.
Do not infer causation from a simple before-and-after change. Workload, staffing, input quality and customer mix can all differ. A small trial can support a decision about the next test without establishing that AI caused the entire change.
Use the scorecard to make one decision
Pick a review date and decide what happens next. Continue within the same boundary if the evidence supports it. Revise a specific failure and retest if it does not. Stop when the result fails a necessary guardrail or no longer serves the goal.
A useful dashboard should make that decision easier. If all it shows is a rising number of completed actions, add the outcome, the cost and the exception that would change your mind.
Explore Always Running: visit AlwaysRunning.ai to learn more.
