Dataleo Insight · 2026-07-12· Explainable Planning AI
Planning AI needs explainability at the decision boundary
Explainability matters most where an AI recommendation changes inventory, supply, capacity or customer commitments.
Recent expert discussion on explainable and agentic planning AI points to a practical distinction: explaining a model is not the same as explaining why a decision is safe.
The affected decision is whether a planner accepts, modifies or rejects an AI recommendation. Value requires the explanation to expose the material assumptions and constraints behind the proposed action.
The principal failure mode is an attractive narrative that describes the model without revealing why the operational consequence should be trusted.
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