Set economic baseline
Capture current volume, handling cost, rework, quality loss, delay cost, and risk exposure before rollout.
Deliverables
- Baseline worksheet
- Value hypothesis
- Owner map
Guides
AI economics control plane
A playbook for managing AI investment through adoption, unit economics, operating cost, and scale decisions.
Target outcomes
Conditions that should be true before expanding this workflow in production.
Guides
AI value reviews connect adoption, cost, quality, risk, and expansion decisions.
Guides
Unit economics are measured per completed outcome rather than per request.
Guides
Scale decisions are tied to cohort evidence, not one-time launch assumptions.
Execution stages
Each stage produces operational artifacts that client teams can review and run.
Model adoption ramp
Forecast adoption by cohort, workflow eligibility, enablement, reviewer load, and abandonment signal.
Deliverables
- Adoption ramp model
- Cohort plan
- Enablement triggers
Measure unit economics
Track cost per completed outcome across model, retrieval, tooling, review, support, and exception recovery.
Deliverables
- Unit economics sheet
- Outcome cohorts
- Cost guardrails
Run scale decisions
Use value reviews to decide whether to expand, tune, hold, or retire each workflow class.
Deliverables
- Value review agenda
- Scale decision log
- Remediation backlog
Recommended resources
Learn references that support this playbook in delivery.
Rapports
Linked operations report
Snapshot view used to track progress and health for this playbook.
Executive operations snapshot
Unified view across queue health, resolution flow, priority pressure, and subscriber footprint.
- Open vs in-progress mix by week.
- High-priority queue share and intervention volume.
- Localization demand by region and language.
Related routes