Measuring CC AI value honestly

AI in CC · ~7 minute read

Headline AI savings hide selection effects, segment masking and dropped side-effects. The full metric set is the test every deployment passes or fails — and the test most vendor scorecards skip.

The full metric set

Five categories. Operational — AHT, FCR, containment, transfer rate. Customer — CSAT, CES, complaints, repeat-contact rate, vulnerable-customer handling. Agent — override rate, engagement, cognitive load, attrition. Risk — bias across groups, drift, hallucination rate, compliance incidents. Cost — full TCO including integration, training, maintenance.

A deployment that improves operational and degrades risk is not a successful deployment — it is borrowed risk.

Disciplines that prevent distortion

Pre-specified success criteria, written down before the data starts to flow. Control comparison or pre/post baseline. Statistical significance, not anecdote. Cohort comparability — pilot users were selected; production users will be different. Segment-level reporting — vulnerable customers tracked separately. Long-window measurement, not the favourable month.

These are not academic disciplines — they’re the ones that survive challenge from a regulator, a finance director, or a frontline that already knows the deployment isn’t landing.

Distortion patterns to watch

Headline-only on the deck. Cherry-picked window that starts at the low. Selection bias in footnote, not in headline. Composite without decomposition. Side-effects dropped (CSAT down by 2pts; not in the slide). False containment counted as good. Self-comparison ("vs no AI" rather than vs best alternative).

These distortions are not deliberate dishonesty — they are habits. The disciplined operator catches them by checklist, not by virtue.

The leader’s stance

Calibrated honesty — report the gain and the give. Investigate degradation rather than dismiss it. Refuse to overstate. Credibility compounds; over-claim once and the next claim isn’t trusted.

The leader who reports a 12% gain that holds up to scrutiny earns more standing than the leader who reports a 30% gain that doesn’t.

The closing principle

If the AI value claim doesn’t survive the full metric set, the value isn’t there yet. Pre-specify the criteria, measure the full set, segment honestly, and refuse to overstate.