By Eleanor Hecks, Editor-in-Chief of Designerly Magazine

Optimized layouts can lead to improved sales. Traditionally, retailer business owners determined store layouts based on past sales, merchants’ intuition and occasional store audits. While still in use today, these methods offer only retrospective data about how shoppers actually behave in the store.

Artificial intelligence’s ability to analyze store images, product placement and consumer interactions can help retailers make more informed decisions about space allocation, merchandising, shelf and fixture layout, and store design. The result is a more optimal store design that enhances shopper experience and sales performance. Below are several ways AI is helping retailers rethink how physical spaces drive revenue, with some examples of use cases in action.

AI Can Ensure Retail Floor Plan Visibility

Ensuring visibility is a challenge of space planning. Historically, retailers relied on static floor plans or sent district managers to visit stores semi-regularly to understand their spaces before changes. This approach created blind spots — layouts looked one way on paper but functioned differently in practice, and problems often went unnoticed until they affected sales.

AI is changing that dynamic because it can identify bottlenecks and other layout issues faster than humans can. For example, one solution addressing this challenge is Driveline Retail’s SmartMap technology, which uses AI to help with retail space optimization by giving retailers a clearer view of what’s happening on the sales floor without requiring constant manual audits. The technology uses image recognition and spatial data to generate digital maps of real-life stores. By capturing over 250,000 images of store interiors each week, the platform enables retailers to analyze product placement, visibility and store traffic patterns using heat maps.

Retail managers can quickly identify blind spots, evaluate consumer movement around fixtures and assess if planograms are being correctly implemented in stores across multiple locations from a central dashboard. What once required physical visits and manual observation now happens continuously through automated image analysis.

AI Lets Retailers Experiment With Layout Changes

Beyond monitoring existing conditions, AI can simulate the impact of changing the store layout before implementing it. This capability addresses one of retail’s most expensive pain points: the cost of getting a store reset wrong.

Testing layouts in a virtual environment helps retailers avoid costly mistakes, as store resets can be labor-intensive and require extensive planning. The virtual space allows testing the placement of fixtures, aisles and displays before a configuration is implemented with real objects in the store.

The value of this approach shows up in measurable results. East of England Co-op, a small retail business, used localized planograms tailored to store-level demand and saw concrete improvements: it increased ambient goods availability from 93% to 98%, while simultaneously reducing stock by 20% in some areas and boosting sales in newly planned areas by 2.2%.

In practice, insights from AI-simulated layouts can lead to better traffic flow, clearer product placement and a simpler shopping experience in the physical store, all without the risk and expense of trial-and-error implementation.

AI Helps Retailers Analyze Shopper Movement 

Where image-based systems capture snapshots of store layouts, autonomous solutions take monitoring a step further by analyzing customer movement and shelf conditions in real time. Rather than conducting periodic audits or dispatching store personnel to manually check merchandising execution, some retailers are deploying in-store AI systems that patrol aisles continuously.

Regional grocery distributor and retailer SpartanNash illustrates this approach. The company deployed Tally robots from Simbe Robotics into dozens of its grocery stores and distribution centers. The machines patrol store aisles, collecting detailed data at the shelf level and transmitting it into Simbe’s Store Intelligence platform. The result is immediate visibility into out-of-stocks, misplaced items and price discrepancies, allowing staff to address issues before they affect customers.

For retailers, this ensures consistent merchandising across stores, with stocked displays, correctly displayed products and visible promotions. The combination of real-time monitoring and corrective action maintains a consistent environment across locations that encourages customers to move through a greater portion of the store.

AI Can Boost Retail Product Sales

Each of these applications — visibility, simulation and monitoring — contributes to a larger objective: increasing sales through smarter product placement and store design. Research by McKinsey & Company found that generative AI could increase the productivity of the retail and consumer packaged goods industry by 1.2% to 2.0% of their annual revenue, equating to $400 billion to $660 billion across the industry. The gains include improvements to marketing, customer relations, inventory management and broad operational efficiencies.

When retailers use analytics and artificial intelligence for data-driven product placement, they gain better insight into shopper behavior, allowing them to optimize store layout and position products for maximum effectiveness while also identifying and removing underperforming stock from shelves.

Individual changes may have little impact, but when multiplied across hundreds of stores or thousands of stock-keeping units, even small efficiencies can yield noticeable revenue increases.

Smarter Store Layouts as a Competitive Advantage

Retail competition can be tough, so more retailers are using AI-driven technology to understand customer behavior and more effectively plan store layouts. AI can help with retail space optimization by improving product visibility, increasing operational efficiency and boosting consumer loyalty. As more companies adopt the technology, AI will likely become a primary approach to retail space optimization.

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