AI readiness is a planning-process question before it is a model question
Supply Chain AI fails when companies automate a weak process, fragmented data and unclear decision rights.
AI readiness is not primarily a model-selection question. It is a planning-process question.
Companies can add advanced models to Supply Chain Planning and still produce weak decisions when master data is unreliable, plan versions conflict and decision ownership is unclear.
Observation
Recent industry commentary is shifting attention from AI capability to organizational readiness. The issue is not whether the model can forecast or recommend. It is whether the surrounding process can use, challenge and govern the result.
Operational implication
An AI recommendation may appear precise while relying on stale lead times, unofficial overrides or constraints that nobody owns.
Decision architecture
Each AI-supported decision needs an authoritative data source, an active plan version, a named owner, approval thresholds and a defined failure mode.
Data requirements
Master data, planning parameters, forecast history, overrides and actual outcomes must be traceable and versioned.
What should remain lightweight
Model testing, feature experiments and controlled prototypes can remain outside the core architecture.
What should be integrated
Approved logic, decision rights, audit history and production data should be integrated into APS, ERP or BI.
The real readiness test is whether the organization can explain who owns the decision when the AI is wrong.
Source discussion: Lora Cecere on LinkedIn.