Physical retail loses an enormous amount of money in the gap between what headquarters plans and what actually happens on the shop floor. Research from Coresight, produced with technology providers Simbe and RELEX, estimated that store execution failures consume 6.4% of gross sales, and that hardware, mass merchandise and grocery retailers would lose $196.4 billion to them in 2026, up 21% on the previous year. Nine in ten retailers reported difficulty managing their shop floors.
This article covers AI for stores and omnichannel operations: forecasting, shelves, staffing, shrink and merchandising. For selling online, see our ecommerce guides linked at the end.
Start Where Results Come Fastest
Replenishment, demand forecasting and loss prevention cameras are among the retail AI use cases that produce measurable results without a large data transformation project. The prerequisite is honest data. If inventory records are inconsistent across locations or point-of-sale data is not clean, fix that before layering AI on top, or the forecasts will be precise and wrong.
Computer vision deserves a specific caution. Cameras that watch shelves and checkouts raise privacy questions for shoppers and staff. Use clear signage, avoid identifying individuals unless you have a lawful basis and a clear policy, and check biometric and surveillance laws in every market where you operate.
7 Best AI Software for Retail
1. RELEX for Forecasting and Replenishment
RELEX specialises in AI demand forecasting and automated replenishment for retail and grocery, connecting forecasts to space planning so that what is ordered actually fits the shelf. It is particularly strong in fresh and short-shelf-life categories, where forecast error becomes waste or lost sales directly. Its mobile replenishment pushes order proposals to store associates’ devices so local teams can adjust for events and correct inventory errors on the spot.
Watch out for: phantom inventory. Forecasts are only as good as stock records. Pair replenishment AI with regular inventory accuracy checks.
2. Focal Systems for Shelf Intelligence
Focal Systems places AI cameras on the shelf edge to monitor availability continuously, detecting gaps and out-of-stocks as they happen and turning them into specific tasks for store teams instead of relying on manual checks. It won In-Store Technology of the Year at the 2026 Retail Systems Awards.
Watch out for: alerts without action. A camera can tell you a shelf is empty; value comes when that signal flows into replenishment and task workflows automatically.
3. Simbe for Autonomous Shelf Scanning
Where fixed cameras suit high-value shelves, Simbe’s autonomous robots scan entire stores, capturing stock levels, pricing accuracy and planogram compliance across every aisle. For large-format stores, it replaces periodic manual audits with a continuous, store-wide picture of shelf conditions.
Watch out for: store layout and staffing. Robots need navigable aisles and a clear process for acting on what they find. Plan the workflow before the pilot.
4. Legion for Workforce Scheduling
Labour is usually a store’s largest controllable cost. Legion uses AI to forecast demand and build schedules that match staffing to expected traffic and tasks, while taking employee preferences and availability into account. Workforce scheduling is frequently cited as one of the fastest-returning retail AI investments because schedules change every week.
Watch out for: the people it schedules. Optimised schedules that ignore predictability hurt retention. Check local fair-workweek and scheduling laws, and give staff a real voice in shift preferences.
5. Everseen for Loss Prevention
Shrink increasingly happens at self-checkout, through missed scans, ticket switching and deliberate non-scanning. Everseen has one of the deepest libraries for detecting these behaviours and strong references among large grocers. Veesion focuses on gesture and concealment detection on the shop floor. Payback on a first store is reported at several months.
Watch out for: friction and false accusations. Most missed scans are honest mistakes. Design interventions that correct rather than confront, and keep humans in every decision about a customer.
6. YOOBIC for Store Execution
Many head-office plans fail because store teams never receive or complete them properly. YOOBIC is a frontline platform for communication, task management and training, using AI to help distribute and track store tasks and to give associates quick access to procedures, so what is planned centrally actually happens locally.
Watch out for: task overload. Every system that can assign store tasks will. Coordinate what reaches the floor so associates are not buried in notifications.
7. Nextail for Fashion Allocation
Fashion retail has a distinct problem: short product lifecycles, sizes and colours, and little history for new styles. Nextail specialises in AI merchandising for fashion, allocating and replenishing stock across stores based on how each location actually sells, which reduces markdowns and missed sales on popular items.
Watch out for: category fit. Fashion-specific models solve fashion problems. Grocery and general merchandise retailers are better served by broader forecasting platforms.
A Sensible Sequence
Start with inventory: forecasting and replenishment, backed by shelf visibility, because stockouts and waste are the most direct margin levers. Once teams are comfortable acting on AI recommendations, add workforce scheduling and store execution. Loss prevention can run in parallel wherever self-checkout shrink is material. Measure each against a clear baseline, such as on-shelf availability, waste, labour hours or shrink rate.
Related Reading
For selling online, see AI software for ecommerce businesses. For network-level planning, see best AI software for supply chain.
Final Thoughts
The biggest retail AI wins in 2026 are not in flashy customer-facing experiences. They are in making sure the right product is on the right shelf, with the right number of people in the store, and less of it walking out unpaid. Fix execution on the shop floor, and every other investment works harder.
Figures were reported as of September 2026, and several come from vendor-sponsored research. Retail AI is typically priced by store or lane, so confirm terms directly with each vendor.