Reimagining Space and Assortment Planning with Agentic AI

The journey so far—evolution of intelligence in merchandising

Merchandising intelligence has progressed from simple rules and individual knowledge or experience to key performance indicators (KPI) dashboards and predictive models to determine demand, elasticity, and affinity. While each step improved accuracy, few challenges such as siloed optimization and decision latency still persisted: assortment, price, and space were all tuned separately, often on stale data, with limited visibility into trade-offs, impacting decision cycles.

Agentic AI changes the approach from models that only predict to goal-seeking systems that perceive context, reason across constraints, run scenarios, and propose actions with human oversight. It shortens the sense-decision-action cycle, allowing retailers to respond at pace to market changes while maintaining governance and control.

This means, range analysts and buyers can now be more perceptive. They can uncover the true substitutability of products, ensuring every range when cascaded into smaller format stores reflects customer intent, preserves choice, and safeguards sales. They can manage the unpredictability of the supply chain and quickly respond to a supplier factory fire or a delayed harvest, optimising the allocation for customers. Similarly, they can stock shelves with products based on customers’ behaviour patterns and not just static demographic details drawn from historical data. 

Agentic AI can help merchandisers tackle the toughest, high-value use cases that directly shape range relevance, space efficiency, and drive sales and revenue growth. Retailers can unlock measurable gains such as fewer stockouts and write-offs, stronger gross margins, and faster turns. Top use cases for agentic AI in merchandising include:

• Demand substitution modelling using attribute and image embeddings to anticipate cross-elasticities when stock keeping units (SKUs) are added, shorted, or de-listed
• Dynamic store clustering that adapts to local missions and behaviour rather than static segments to respond in real time by optimising assortments, pricing, and inventory according to changing consumer preferences
• Seasonal and event-driven adaptation by pre-emptively shifting facings and secondary placements based on weather, festivals, and local events
• Price and promotion optimisation by simulating competitor moves and supply constraints to balance margins and market share 
• Markdown optimisation to maximise sell-through while protecting brand equity

Agentic AI delivers exponential gains on its own, but when it is fused with digital twin platforms, its power compounds. Retailers can simulate store space layout and optimisation, new product or variant introductions, pricing shifts and planogram optimisation using digital twins before executing decisions in the real world, thus reducing risk and accelerating time to value. The result is a powerful decision-support ecosystem, overlaying the recommended assortment and planograms, promotions, and spatial planning with precision, helping enhance profitability and customer experience.