Where Vision AI fits in modern fulfilment
High-volume fulfilment is sensitive to error. A single mismatch becomes downstream cost: returns, support load, reconciliation time, and customer trust loss. Vision AI provides value when it strengthens verification and traceability without slowing throughput.
High-value use cases
1) Pick-pack verification
Verify item identity and quantity at packing choke points. The goal is not surveillance; the goal is fewer wrong shipments and fewer disputes.
2) Returns disputes and evidence trails
Evidence-linked packing and dispatch events reduce ambiguity in returns claims and missing-item disputes.
3) Throughput and safety monitoring (selective)
Targeted monitoring for congestion and unsafe movement patterns can support throughput stability when tied to actionable workflows.
Implementation requirements
- Low-latency inference at packing stations
- Clear exception workflow (what happens when mismatch is detected)
- Integration with order management and returns systems
- Evidence retention policies and access controls
- Monitoring for drift as packaging and SKUs change
Metrics that matter
- Reduction in wrong-ship incidents
- Reduction in returns disputes and resolution time
- Pack station exception rate and false alarm trend
- Impact on throughput (must remain neutral or positive)
- Audit completeness for high-value orders
Common pitfalls
- Deploying verification without a clear “exception handling” workflow
- Over-alerting that interrupts operations
- Ignoring SKU/catalog churn which drives drift
- Treating evidence storage and access controls as an afterthought