AI Summary: Treat AI readiness as a measurement program, not a revenue promise. Define observable events, record baselines and implementation changes, use attribution as a declared reporting model, and label causal claims only when the study design supports them.
Start with an observable business action
An audit can identify conditions, but cannot show AI discovery or caused revenue. Start with observable events: audit completion, remediation, qualified CTA click, consented lead, trial, or sale.
Google Analytics calls a business-critical action a key event. Any collected event can be marked and counted in reports. [1]
| Measurement layer | Example | What it can support |
|---|---|---|
| Readiness signal | Passed check | Evidence a condition changed |
| Product action | Audit completion or CTA click | Observed engagement |
| Commercial outcome | Consented lead or sale | Business result with valid records |
| Financial return | Incremental value and cost | ROI with comparison evidence |
Define the baseline before changing the site
Record the window, traffic, consent, release dates, URLs, events, and value rules. Keep a change log.
NIST calls for reviewed metrics, documented limits, test details, deployment-like conditions, monitoring, and generalizability limits. [3]
A pre/post comparison is descriptive, not causal. For higher stakes, use a holdout or rollout. Declare seasonality, campaigns, traffic mix, pricing, changes, and missing data; seek review.
Treat attribution as a reporting view
Google Analytics assigns credit to touchpoints on a key-event path. Its data-driven model uses converting and non-converting paths plus a counterfactual estimate. [2] This is not proof that an AI mention or page change caused it.
| Claim | Evidence needed | Safer wording |
|---|---|---|
| Condition changed | Audit or test record | "The check passed" |
| Channel received credit | Named model and window | "This channel received reported credit" |
| Revenue increased | Causal design and value records | "Incremental impact is unestablished" |
Publish the decision with its limits
Report the baseline, intervention, window, event, model, traffic, consent, missing data, and competing changes. Label descriptive evidence and re-measure after release.
Ask which defined outcome changed after which intervention, under what design, and with what uncertainty. Until then, a score prioritizes work; it is not a financial return.
References
- Google Analytics Help: About key events
- Google Analytics Help: Get started with attribution
- NIST AI RMF Playbook: Measure