Retail VR & generative AI: save 80% of the time spent on your planograms

AI and large language models are transforming the merchandising chain, from planograms to POS materials. Here is how to automate your store layouts, personalize at scale, and free your teams from repetitive tasks.

Retail is going through an unprecedented transformation in which responsiveness, local precision, and the in-store customer experience make all the difference. In this context, the combination of AI and merchandising marks a genuine breakthrough: thanks to large language models (LLMs), merchandising platforms no longer just arrange products — they orchestrate data, design, and in-store execution. For trade marketing, category management, and market research teams, this has become a strategic priority.

AI and merchandising: automating the chain, from planograms to POS materials

The primary goal of artificial intelligence applied to merchandising is simple: automate the most time-consuming tasks to unlock your teams' strategic value. In practice, this comes down to several complementary building blocks.

• Automated planogram creation: AI analyzes product dimensions, packaging constraints, and volumes to instantly generate optimized 2D and 3D layouts.
• Automated data processing: direct ingestion of sell-out data, profitability per linear meter, inventory, and product visuals.
• Smart recommendation engine: layouts generated from historical performance and shopper traffic forecasts.
• POS and in-store signage integration: promotional materials and signage are automatically factored into the spatial model, in line with brand guidelines and in-store staging.

This automation is a natural extension of our 3D merchandising platform, designed to connect data with the store floor. As an op-ed published by LSA points out, French retail can no longer afford to wait before embracing AI.

80% time saved: scale in the service of local precision

The main strategic challenge remains moving from generic merchandising to ultra-local execution, or clustering. This is exactly where AI changes the game.

What changes in practice

• Creation time: from several days per concept to a reduction of around 80% in production time.
• Granularity: from a single global strategy to precise variations by cluster or by store.
• Updates: from a seasonal rhythm to real-time adjustments based on inventory and demand.

Thanks to this productivity gain, you can generate hundreds of variations in just a few clicks, each tailored to the profile of every point of sale: shelf size, local socio-demographic characteristics, shopping habits. Personalization at scale is no longer a theoretical ideal but an operational reality. The industry's major players have taken note, as shown by Oracle's work on AI for retail.

AI and merchandising: tangible impacts on teams and performance

Beyond the technology itself, it is roles and business results that are being transformed.

For merchandising teams

• A more rewarding role: your teams focus on brand strategy, experience design, and performance analysis rather than manual data entry and graphic assembly.
• Simpler collaboration: immediate alignment between headquarters, category managers, and field sales teams.

For sales performance

• Optimized sales per linear meter: better allocation of space to high-potential products.
• Fewer out-of-stocks: planograms adjusted to actual backroom capacity and product turnover.
• Flawless in-store execution: field teams understand the layout immediately, which reduces implementation errors.

The roadmap: from Claude to a global view of the store

AI integration follows a gradual, pragmatic rollout.

• Step 1, connecting to cutting-edge models: we connect tools such as Anthropic's Claude to analyze text data, structure briefs, and simplify complex queries.
• Step 2, a built-in conversational assistant: a chat interface directly within the merchandising platform. You drive layout creation with a simple text prompt, for example: "Generate a planogram for the Drugstore cluster with 15% more POS materials."

This trajectory is part of a broader industry shift, widely documented by specialized resources such as retail-ia.fr.

Conclusion

AI and merchandising are now an essential pairing for any team looking to combine responsiveness, local precision, and operational efficiency. Automated planograms, cluster-level personalization, massive time savings: the benefits are concrete and measurable. To see how these innovations apply to your store network, explore our Retail VR customer cases and come meet our experts at the Tech for Retail trade show, November 30 and December 1, 2026, in Paris.