Retailers face growing challenges across merchandising, pricing, planning and inventory. Traditional AI can provide recommendations, but when it’s a black box, teams hesitate, execution slows and value is limited.

Explainable AI transforms decisioning by making the “why” behind each recommendation visible and turning vague forecasts into actionable insights. Multi-agentic AI goes further, letting each functional agent share reasoning across teams for aligned, confident decisions. 

Inside this guide, you'll learn:

• How multi-agentic AI provides transparency for forecasts, allocations, pricing and inventory
• Role-based explanations that make AI outputs meaningful for merchandisers, planners and executives
• Techniques like feature importance, scenario analysis and causal reasoning
• Practical implementation tips, dashboards and audit-ready tracking
• How to operationalize the AI decisioning loop: observe, recommend, explain, decide, act, learn
• Ways to build trust, track KPIs and embed explainable AI across teams