Build a Scorecard for AI-Assisted Service
Build a four-perspective scorecard that distinguishes outcomes from initiatives, states causal hypotheses, governs measures and creates a decision-producing review.
28 minutes
Scenario
A regional service company adopted an AI reply assistant. Leadership reports adoption and licence savings as strategic success, while retention for complex-account customers has fallen and repeat complaints have risen.
- Your role
- Strategy lead redesigning the quarterly performance review
- Method
- Balanced Scorecard
Evidence pack
Financial
Service cost per case fell 11%, but gross retention for complex accounts fell from 91% to 86%.
Customer
Overall satisfaction rose two points; complaint recurrence for complex cases rose from 8% to 14%.
Process
Median first response fell from 90 to 18 minutes; correct escalation fell from 88% to 76%.
Capability
Eighty-two percent of agents use AI, but only 54% pass the sampled complex-case escalation check.
Data quality
Satisfaction excludes customers who abandoned the digital channel.
Initiatives
Leadership proposes broader automation and a diagnostic-skills programme.
Constraints
- Do not treat adoption as customer value.
- Preserve segment and denominator definitions.
- Do not infer causal effects from correlation alone.
Case steps
Work through each prompt using the evidence pack. These guided cases support self-directed practice; server-scored attempts are not available yet.
State the strategic choice, scope, desired results and deliberate exclusions.
Create one linked objective for each perspective and state two causal assumptions with time lags.
Define measures, baselines, targets, owners and guardrails using the supplied evidence.
Design the next review decision and evidence needed before scaling automation.