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COMPEL Body of Knowledge
AITP M2.5-Art16 v1.0 Reviewed 2026-04-06 Open Access
M2.5 Measurement, Evaluation, and Value Realization

Measuring AI Adoption: Active Use, Time-to-Value, and NPS

Measuring AI Adoption: Active Use, Time-to-Value, and NPS — Value Realization & ROI — Applied depth — COMPEL Body of Knowledge.

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MEASURING AI ADOPTION
1 Awareness Target users who know the tool exists 2 Trial Users who attempt first meaningful task 3 Active use Monthly active-user rate against target population 4 Time-to-value Days from first use to first useful outcome 5 Advocacy (NPS) Promoters minus detractors on peer recommendation
Figure 282. The adoption funnel: from awareness through active use, time-to-value, and advocacy.

Why this dimension matters

The license-seats fallacy. A common failure mode: the procurement team buys 5,000 seats of an AI assistant, the deployment team installs it everywhere, and six months later the vendor renews on the same 5,000 seats. Active-user rate is 14%. Nobody noticed because nobody was measuring. The spend was real; the value was theater. Adoption measurement is how that failure mode becomes visible in time to do something about it.

The first-week cliff. Most AI tool users decide in the first week whether the tool is worth their time. If the first session does not produce value, the second session often does not happen. Time-to-value measures whether the onboarding path actually works.

The peer signal. NPS captures whether the users who do use the system would recommend it. A high active-user rate with a low NPS is a captive audience, not a successful product.

Core metrics

Metric 1: Active-user rate

Definition. The percentage of provisioned users who performed a value-generating action within the measurement window.

Formula. active_user_rate = (active_users / provisioned_users) × 100, where “active” is defined by a use-case-specific action (not just a login).

Cadence. Weekly and monthly — report both (WAU and MAU).

Owner. Product owner with change lead.

Activity definition is load-bearing. “Logged in” is not activity. “Sent one message” is thin activity. “Completed a task that produced output the user kept or acted on” is real activity. Document the definition in the metric definition sheet.

Metric 2: Time-to-value

Definition. The elapsed time from user provisioning to the first recorded value-generating interaction.

Formula. ttv = first_value_action_timestamp − provisioned_timestamp, aggregated as median or p75 across the cohort.

Cadence. Per cohort; trended monthly.

Owner. Enablement lead.

Cohort segmentation. Segment by role, by geography, and by enablement track. An enterprise average time-to-value hides the teams where onboarding is broken.

Metric 3: Net Promoter Score (NPS)

Definition. Standard NPS — the percentage of users answering “how likely are you to recommend this tool to a colleague” with 9 or 10, minus the percentage answering 0 through 6, on an ongoing in-product survey.

Formula. nps = promoters_% − detractors_%.

Cadence. Rolling 30 days; reported monthly.

Owner. Product owner with UX research.

Companion qualitative signal. Every detractor response must include a free-text reason, and every month the top five detractor themes are reported on the trust scorecard. A number without themes is noise.

How to measure

  1. Define “active” for the use case in the metric definition sheet, signed off by the product owner.
  2. Instrument the value action at the application layer — do not rely on inference-count proxies that conflate exploration and real use.
  3. Stand up a provisioning-to-activity join so time-to-value can be computed per user.
  4. Embed the NPS micro-survey after the user has had time to form an opinion (at least one successful task, ideally several).
  5. Segment every metric by role, geography, and cohort. Aggregate numbers hide the teams that need help.
  6. Report all three on the trust scorecard with trend arrows and the top three qualitative themes.

Targets and thresholds

  • Active-user rate. WAU above 60% of MAU is healthy for a daily-use tool; above 40% for a weekly-use tool. Below those thresholds, the value case is likely overstated.
  • Time-to-value. Median under 7 days for most enterprise AI tools. A month-long time-to-value is a broken onboarding path.
  • NPS. Above 30 is healthy; above 50 is outstanding; negative means the program has a product-quality problem masked by a compliance mandate.

Common pitfalls

Counting logins as adoption. A login is a provisioning event, not an adoption event. Measure the action, not the door.

Averaging away the failure cohorts. An overall metric that looks acceptable can hide a geography or role where adoption is zero. Segment.

Surveying at the wrong time. An NPS survey popped at install time measures expectations, not experience. Trigger after real use.

Treating adoption as a marketing KPI. Adoption is an operational metric tied to the value case. If the value case assumed 70% active use and the actual is 30%, the value case is wrong — not the number.

No feedback loop. If detractor themes do not drive product improvements, the NPS number rots into vanity metrics.

M2.5People and Change Metrics M1.2Training and Adoption Plan M1.2Adoption Review Report M1.1Defining AI Transformation vs AI Adoption M2.5Measuring AI Value