Sign InAI in Retail: 8 Ways Smart Technology Is Changing How Stores Are Run
Key takeaways
AI is changing store operations in 8 ways: computer vision, demand forecasting, replenishment, store planning, visual merchandising, virtual showrooms, agentic workflows, and immersive training.
Store-level AI depends on accurate spatial data, so retailers should get store data ready first and pilot on 3-5 stores before scaling across the fleet.
Matterport digital twins give planning, merchandising, and operations teams an accurate, shareable record of every store, while cutting the travel, printing, and mockups behind store work.
AI is changing how stores are run. For years, most AI in retail lived in back-office pilots and ecommerce teams, but it now shapes how retailers plan layouts, set merchandise, train associates, and execute in-store work.
The biggest AI retail trends now point to everyday store decisions, like where fixtures go and whether a display matches the directive. Store leaders who wait risk running tomorrow's stores with yesterday's technology.
This article covers 8 ways AI is reshaping store operations and how retailers can get their store data ready to adopt new AI workflows.
AI in retail: From online experiences to the sales floor
For retailers, AI is now a fleet-wide trend that affects productivity, margins, execution speed, and customer experience at the same time. In ecommerce, AI is well established, with retailers using it for marketing, product recommendations, personalization, and search for years.
Physical retail is catching up, too. Retailers now use online behavior to shape assortments, layouts, and staffing in stores. The results are already measurable. In NVIDIA's 2026 survey of retail and CPG companies, 95% of respondents said AI decreased annual costs, and 89% said it increased annual revenue.
A lot of the retail AI still operates in the decision layer, producing forecasts, plans, and recommendations.
The execution layer is what happens on the sales floor across every location, and that’s where retail AI has the most ground to cover. The TCS Global Retail Outlook surveyed 800+ senior retail executives in 18 countries and found only 24% use AI for autonomous decision-making.
Closing that gap starts with accurate store data. A digital twin turns each location into a shared spatial record of layouts, fixtures, and dimensions, so that planning, merchandising, and operations teams can all work from. It’s the input the eight trends below depend on.
8 AI retail trends for store operations
The following 8 AI retail technology trends trace AI’s path from back-office decisions to on-the-floor execution.
1. Computer vision for shelf monitoring and store analytics
In-store cameras paired with computer vision track stock levels, shelf price accuracy, and display execution. As the foundation layer, computer vision and the store data behind it give forecasting and replenishment models the store-level signals they need.
The store data in question includes dimensionally accurate layouts, fixture and shelf locations, shelf capacity, and camera coverage. To make computer vision work, retailers need:
Labeled image data to train recognition models
Camera and edge computing infrastructure
Privacy policies for in-store AI
Accuracy monitoring before alerts reach store teams
Planning is often the hardest part. Teams have to decide camera placement, fields of vision, and overlap zones across hundreds or thousands of stores. With a dimensionally accurate 3D digital twin of each location, planners can test AI and IoT placements and check coverage before physical installation.
Trax Retail shows what that looks like in practice. Working with Matterport partner Treedis, Trax planned in-store camera placements in Matterport digital twins across more than 100 stores. Planning time dropped from 45 days to 10 days per store, and a Treedis image cropping tool cut AI model training time per store by 70%.
2. Demand forecasting and inventory optimization
AI demand forecasting combines sales history, local events, weather, and online signals to predict demand by store and SKU. It builds on the shelf monitoring layer, using shelf-level signals from computer vision and space data like shelf capacity and fixture counts to set inventory levels that fit each location.
Done well, demand forecasting reduces stockouts and overstock and helps teams time markdowns. Demand for the same product can vary sharply between regions, and store-level forecasts catch the difference before excess stock forces a markdown.
To make demand forecasting work, retailers need:
Clean, connected POS, inventory, and sales history data
Clear ownership of each forecast input, so someone is accountable for a stale weather feed or missing sales data
Human review before any markdown or major inventory change
A forecast is useful if stores can act on it, adjusting space and fixtures quickly. A retail space planning process built on accurate store records helps teams act on each forecast at the fixture level.
3. Supply chain and replenishment efficiency
AI replenishment models predict each store’s needs and guide how distribution centers allocate stock to those locations. Like forecasting, they build on real-time shelf data. They also need each store’s backroom and fixture capacity to set store-level replenishment.
Backroom space, shelf depth, and fixture count change by location, even within one store format.
Picture a flagship and a mall store getting the same allocation for a denim launch. The mall store has a smaller backroom and a different wall fixture set, so extra units end up in boxes or crowding the floor while the flagship store runs short.
Without accurate store data, a fleet-wide model treats both stores the same.
To make replenishment work, retailers need:
Integration between forecasting, warehouse, and store systems
Fresh capacity data, updated after every reset
Shared allocation rules between supply chain and store operations, like minimum display quantities and backroom limits
4. Store planning, remodels, and fixture design
AI is reshaping the remodel workflow, with planning tools that recommend fixture layouts, validate designs, and generate virtual walkthroughs, so teams rely less on physical mockups.
To make AI store planning work, retailers need:
Accurate as-built data to recommend layouts, fixture counts, and adjacencies
Historical performance data to train layout models
Clear constraints, such as brand standards and code requirements
Planners skilled at evaluating AI-generated options and rejecting the ones that won’t fit
Accurate layouts don't require reprinted planograms or on-site measuring that disrupts trading hours. Teams can generate schematic floor plans from a digital twin and check clearances with Measurement Mode, keeping store design and construction on schedule without extra site visits.
Apex Imaging Services, which runs multi-site remodel and rebranding programs for Fortune 500 retailers, verifies measurements in Matterport digital twins. This improved its bid accuracy and removed the need to send teams across the country to re-verify.
Fewer site trips and physical mockups can mean lower travel emissions and less material waste.
5. AI-assisted visual merchandising and planogram validation
AI can generate assortment and planogram recommendations, then help validate planograms, keep displays consistent, and execute rollouts and seasonal resets with less rework. However, execution is still the hardest part, because every store has to set the display correctly.
To make AI-assisted merchandising work, retailers need:
Alignment between merchandising and store operations on display standards
Clean assortment and sales data for AI planogram models
Defined compliance thresholds for AI checks
Merchandisers who review and adjust AI recommendations before they reach stores
Digital twins make display standards easier to follow. HQ teams can capture a model display and share it as a navigable 3D reference instead of 2D photo directives, then pin folding instructions, SKU details, and placement guidance at exact points in the twin with Tags and Notes.
GUESS used to mail 80-page paper directives to more than 300 US stores. Replacing them with 3D directives in Matterport digital twins drove a 200% productivity increase and cut paper and printing costs by 95%. For sustainability teams, fewer printed directives and merchandising trips add up to a carbon reduction they can report.
6. Immersive virtual environments and virtual showrooms
AI-powered virtual showrooms let shoppers and wholesale buyers browse a real store or showroom from any device. They can include personalized product recommendations, guidance through AI chat or live-assisted tours, and a direct path to ecommerce checkout.
AI can also analyze visitor behavior to improve the virtual experience and inform the physical store layout.
For Reliance Digital, Treedis built a shoppable digital twin of a store area set up like an apartment to showcase Google smart home devices. Visitors could check product information and add items to their cart.
To make virtual showrooms work, retailers need:
Product catalog and inventory data connected to the experience
Ecommerce integration for checkout
Privacy-compliant visitor analytics
Staff ready to support assisted sessions
With Guided Tours, merchandisers can build a guided walkthrough, adding captions and Tags at each stop. Once an admin enables Auto-Tours, it automatically generates a Guided Tour that teams can edit before sharing.
Retail VR built virtual showrooms in digital twins so luxury fashion buyers could view new collections without flying to Milan or Paris. By the company’s calculation, one Kenzo virtual showroom saved 1,200 tons of carbon emissions.
Virtual showrooms can also cut the freight involved in shipping samples to buyers.
7. AI digital workers and agentic workflows for store operations
AI digital workers are software agents that complete multistep tasks with limited human input, and agentic workflows chain those tasks together.
In store operations, examples include:
Generating merchandising task lists for each store
Routing compliance checks to the right manager
Flagging display drift from the approved standard
Monitoring store performance against targets
Scheduling resets around store hours
Agents act on signals from across the stack. Computer vision and spatial data flag empty shelves or display drift, forecasting triggers stock changes, and merchandising pushes new sets.
To make agentic workflows work, retailers need:
AI governance that sets what agents can do on their own
Integration with task and store systems
Audit trails for every agent action
Human oversight of decisions that affect stores or customers
When agents resolve issues remotely, they also cut visits. To estimate the savings, multiply site visits avoided x people per visit x cost and emissions per trip.
Cost per trip should include airfare, hotels, and per diems. Running the same math on emissions gives a carbon figure for ESG reporting.
8. Immersive training for distributed store teams
AI can personalize learning paths for associates and power assistants that answer store standards questions with real-world examples. Spatial data gives these tools and distributed teams a shared, immersive view of how each store should look and run.
New associates can walk the store layout, back-of-house flow, and display standards in a digital twin before their first shift. And with Views, one scan can serve trainers, merchandisers, and marketers, so no team needs separate materials.
Digital training rollouts also reduce printing and travel for distributed store teams.
To make AI training tools work, retailers need:
Current, approved standards for AI assistants to draw from
Guardrails against inaccurate answers
Manager support, since associates are more likely to trust and use tools their managers back
At GUESS, moving training materials onto store iPads gave managers back 1-2 hours each morning that used to go to printing. Digital twin libraries of the stores also cut training travel costs by about 20%.
A data readiness checklist for store-level AI
Store-level AI needs strong data. A model can't plan fixtures for a backroom without measures, and an agent can't flag display drift without a current standard.
Retail leaders can use this checklist to confirm their store data is ready before scaling AI across stores:
Map which data sources each AI use case depends on, such as POS, inventory, sales history, and store layouts
Confirm every store has a current, accurate spatial record of the layout, fixtures, and capacity
Standardize capture, naming, and data formats across the fleet
Check data quality and freshness, and set refresh triggers after every reset or remodel
Assign data ownership and governance for each source
Connect store data with forecasting, merchandising, and task systems so they share one source of truth
Set data readiness KPIs, such as the percentage of stores with current records and data refresh time, then pilot on 3-5 stores
For the pilot, pick stores with a reset or remodel already scheduled, since their records need refreshing anyway.
For large fleets, Matterport Capture Services can help get every location captured to one standard. Get store data ready first, start small, and expand store-level AI with proof.
Turn every store into data your AI can act on
AI retail trends will keep improving the decision layer, with sharper forecasts and agents that take on more routine work. But store-level results still depend on accurate spatial data for every location.
Matterport digital twins give teams that record. Planners validate remodels remotely, merchandisers share 3D directives, trainers onboard associates before day one, and agents act on real store conditions.
Each of these workflows can also cut travel, printing, and physical mockups, lowering both cost and carbon.
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