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Retail & Consumer Goods AI Strategy: opportunities, use cases & the operating model
AI's margin impact in retail is in the merchandising and supply core: sharper demand forecasting, localized assortment and markdown optimization, and inventory positioning that lifts full-price sell-through and cuts shrink.
Where AI creates value
Demand forecasting — SKU-store-level prediction across seasons and promotions
Assortment planning — Localized range and space-to-sales optimization
Markdown and pricing — Elasticity-based markdown and promo optimization
Inventory allocation — Replenishment, safety stock and store fulfillment
Personalized commerce — Recommendations, search and loyalty targeting
Shrink and supply — Loss prevention and supplier lead-time prediction
Top AI use cases
- SKU-store demand forecasting that reduces stockouts and overstock across the replenishment cycle
- Markdown optimization sequencing price cuts to maximize gross margin on end-of-season inventory
- Localized assortment and planogram recommendations tied to store-level demographics and sales
- Personalized product recommendations and semantic search to lift conversion and basket size
- Computer vision at self-checkout and back-of-house to detect shrink and mis-scans
What has to change (operating model)
- Merchandising decisions shift from calendar-driven to continuous AI-assisted planning cycles
- Dynamic and personalized pricing governed for compliance and fairness across jurisdictions
- Supplier data-sharing agreements to feed end-to-end forecasting and lead-time models
- Customer data governance for loyalty and personalization under GDPR and CCPA/CPRA
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