Who This Playbook Is For
This playbook is for retail operations leaders, store execution teams, and analytics owners that need an execution path from AI pilot to repeatable operating gains.
- Multi-store operators with unstable shelf availability or uneven execution quality.
- Teams collecting POS and inventory data but lacking a weekly decision cadence.
- Organizations targeting measurable impact in under one quarter.
90-Day Rollout Sequence
Use phased rollout gates to reduce pilot risk and protect store adoption.
- Days 1-15: scope 2 categories, 10-20 stores, and 3 target outcomes (availability, labor productivity, shrink).
- Days 16-45: run shelf/compliance workflows and capture store manager feedback daily.
- Days 46-75: tune alert thresholds and assign clear ownership for each exception type.
- Days 76-90: scale only if stockout rate, execution score, and labor minutes improve together.
Operating KPI Stack
Track leading and lagging metrics together to avoid vanity gains.
- Leading: planogram compliance, on-shelf availability checks, alert-to-action time.
- Lagging: stockout rate, gross margin return on inventory, pilot-category sales lift.
- Control: labor minutes per corrective action and forecast error by category.
Failure Modes and Corrective Actions
- Alert overload: rank tasks by value-at-risk and suppress low-impact notifications.
- Low store adoption: route tasks through existing shift workflows, not a parallel process.
- No financial lift: prioritize categories with high substitution risk and margin sensitivity.
- Model drift: schedule weekly threshold review and monthly retraining checks.
FAQ
- How many stores should start in a pilot?
Start with 10 to 20 stores across two category types so you can compare outcomes without heavy operational disruption.
- What KPI should decide scale-up?
Scale only when stockout rate, execution quality, and labor efficiency improve at the same time for at least four weeks.
- Can this work without new hardware?
Yes. Most teams can begin with existing POS, inventory, and tasking systems, then add data capture only where blind spots remain.