AI in Retail 2026: Uses and Australian Adoption

AI in retail helps stores predict demand and keep shelves stocked. Customer-facing tools guide shoppers towards products they can buy. Australian retailers use it across buying, pricing, store operations and delivery, with different tools for each job. Adoption figures need their survey year beside them: using an AI writing assistant is a different commitment from automating replenishment.

For a board paper, separate the business problem from the technology. A stockout forecast needs sales history and reliable stock records. A shopping agent needs product information and store policies. Each needs a person responsible for decisions when its output is wrong.

How Retailers Use AI Today

Retailers use AI to predict what will sell, choose ranges and help staff or customers make decisions. Merchandising models compare product performance by store and category. Pricing analytics estimate how demand responds to promotions, while loss-prevention systems flag unusual checkout activity for review.

Generative AI creates material such as product descriptions, campaign images and shopper-chat answers. Predictive models estimate a future outcome, such as next week’s demand. Both can support the same workflow, but a fluent written answer doesn’t prove that a demand forecast is accurate.

Coles’ 2025 AI announcement describes fresh-produce stock monitoring, staff chatbots and order replenishment. Woolworths’ 2025 results presentation documents machine learning for picking routes and algorithms for transport planning. These uses connect the shelf to the supply chain.

Part of the value chain	AI use	Intended benefit	What to measure
Buying and merchandising	Demand and range analysis	Buy stock that matches local demand	Sell-through and unsold stock
Pricing	Promotion-response forecasts	Avoid discounts that erase margin	Margin after promotion
Inventory	Replenishment forecasts	Reduce stockouts and excess stock	Availability and waste
Marketing	Content drafts and audience selection	Reduce manual campaign work	Review time and campaign results
Service and selling	Policy answers and product guidance	Help customers choose and order	Completed purchases and escalations
Store operations	Shelf and checkout monitoring	Direct staff to problems	False alerts and resolution time
Fulfilment and delivery	Picking and transport optimisation	Reduce unnecessary movement	Picking time and delivery reliability

These are possible benefits, not guaranteed results. Detailed AI uses for online stores need a separate assessment of the storefront workflow.

AI Among Retail Technology Trends

AI sits alongside connected retail systems, retail media networks and automated fulfilment. Restive’s 2026 Unified Commerce report covers AI personalisation and fulfilment across channels. It is a report from a commerce consultancy, so its advice reflects a commercial provider’s perspective.

Unified commerce connects stock, orders and customer records across stores and online. Those connections can work through ordinary software rules. AI adds forecasts or recommendations once the shared records are reliable.

A retail media network sells advertising using a retailer’s channels and shopping data. IAB Australia’s 2026 retail media report identifies expected AI uses in audience targeting, campaign planning and measurement. Advertising inventory and sales reporting still exist without AI.

Automated fulfilment uses machinery and software to move goods. AI can decide picking sequences or respond to changing demand. A conveyor running a fixed route is automation, even when the wider warehouse uses AI elsewhere.

AI in Retail Stores

In-store AI can spot shelf gaps, review checkout events and help staff find procedures. Computer vision compares camera images with expected products or activity. Staff still need a way to correct false alerts and act on real gaps.

Coles’ 2025 announcement describes computer vision monitoring fresh produce stock levels and task-specific generative AI chatbots for team members. Its 2026 half-year results transcript describes store object recognition and loss technology. It also names a Knowledge Assistant that helps teams find policies and procedures.

Woolworths’ 2024 annual report records its Scan Assist rollout. Its in-store technology explanation describes cameras that flag potentially missed or mis-scanned items at self-checkout. A short video helps customers and staff review the event. Woolworths says Scan Assist doesn’t use facial recognition.

A shelf alert needs the store’s stock records behind it. If the item is in the stockroom, staff can refill the shelf. If the store has none, the problem moves to replenishment.

AI for Inventory and Demand

An AI retail inventory management system forecasts sales by product and location, then uses those forecasts to propose replenishment. Inputs can include sales history, promotion schedules, supplier lead times and stock availability. Demand forecasts help cut stockouts when orders arrive before the shelf runs empty.

Coles’ 2026 results transcript names AI forecasting, demand planning and ranging as operational uses. Its 2025 announcement links AI with order replenishment and store-specific ranges. These forecasts inform how much stock the retailer orders and which products it carries.

Stockouts can hide demand because a missing product records no sale. New products also have little history. Forecasts need handling for those cases, plus limits on automated orders when a promotion or supplier delay changes the assumptions.

For example, a hypothetical grocer sees rising salad sales before a hot weekend. A model proposes more stock, but the buyer checks delivery timing and shelf life. More accurate demand predictions help only if the stock reaches the shelf while customers still want it.

AI Agents and Voice Commerce

AI agents in retail can search products, compare options and buy under a shopper’s instructions. A buyer’s agent can work across retailers, while a store’s agent helps customers within that store. The guide to agentic commerce explains the buyer’s side in more detail.

Voice commerce is a spoken way to search or place an order. Speech recognition turns the request into text that a shopping system can process. A voice request becomes agentic when the system can perform authorised actions, such as buying an agreed product.

Australia Post’s 2026 eCommerce Report cites its 2025 survey: 32% of shoppers use AI for shopping advice. It reports 44% of businesses as advocates of agentic commerce, compared with 16% of shoppers. These are survey responses about use and attitudes, not evidence that those businesses operate autonomous checkout.

Our 2026 outlook is an opinion: retail agents will be more useful when they connect product advice with reliable stock and ordering. Voice will be one way to use them. The retailer still needs clear purchase permissions and a way to resolve a wrong order.

On-Site Shopping Agents in Australian Stores

An on-site shopping agent answers from the retailer’s catalogue and policies, recommends products and helps the customer order. Page context lets it understand which product the customer is viewing. That helps answer questions such as whether a particular appliance has the connection the customer needs.

shoppa is an AI shopping agent that retailers add to their own site with two lines of code. Customers find and compare products, then pay in the chat using the retailer’s catalogue and documents.

shoppa reads the product page in view, answers policy questions and supports order tracking and returns. Staff can take over when the customer needs a person.

A store’s own agent keeps the conversation within that retailer’s range. A buyer’s agent searches across stores on the customer’s behalf. These serve different interests: one helps sell the store’s products, while the other seeks a purchase that meets the buyer’s instructions.

Large catalogues benefit when customers struggle to narrow the range. Technical categories, such as appliances and electronics, need clear product comparisons. Furniture and fashion also involve visual choices, although appearance previews don’t establish physical fit or measurement accuracy.

AI in Fashion Retail

Fashion AI can suggest sizes, match a photo to products and forecast demand for styles. Fit guidance uses sizing data and customer information. Trend forecasting informs buying, while visual search helps a customer describe an item without knowing its name.

Hype DC’s fit guide describes personalised True Fit size recommendations using brands worn and sizing information. That is a different task from generating a picture of someone wearing a garment.

THE ICONIC launched Snap to Shop in 2017, according to Internet Retailing’s launch coverage. It matched uploaded photos with products using machine learning. That historical example shows how visual search works, rather than establishing what every version of the retailer’s app supports today.

An AI fashion stylist builds outfits around a shopper’s preferences and an occasion. Depending on the service, it can use past orders or an uploaded photo to suggest complementary pieces. The recommendation still needs available products and the customer’s preferences behind it.

shoppa’s photo recommendations turn a photo into editable sets of complementary products from the retailer’s range. Customers can replace items and add them to their basket. Its still-photo try-on shows appearance and doesn’t recommend a size. The guide to virtual try-on explains that distinction.

AI Adoption in Australia

Australian retail adoption depends on which businesses a survey covers and what it counts as adopting AI. The National AI Centre’s 2025 ecosystem report charts retail trade adoption at roughly 46% in its 2024 survey data. This is the chart’s approximate value, not a 2026 retail adoption rate.

A government tracker release in 2025 reports 41% adoption among Australian small and medium enterprises across industries. That broader figure must not be presented as a retailer-only result.

The National AI Centre tracker methodology describes 400 completed surveys each month. Adoption includes broad use, limited use and businesses implementing AI. Responses can come from different businesses over time, and industry results are shown quarterly.

For a board paper, label each figure with its survey year, population and definition. Keep consumer use separate from business adoption. A business using an AI content tool is counted differently from a retailer that has automated stock ordering across its stores.

Choosing Retail AI Solutions

Choose retail AI software by the decision it must improve and the data it needs to make that decision. Inventory tools need trustworthy sales and stock records. Customer-facing agents need product attributes, availability and store policies. Camera systems need a store process for reviewing alerts.

Ask vendors how their platform connects to existing systems and how quickly changed information reaches the model. Establish who can approve actions and who handles failures. A replenishment suggestion and a purchase order need different permissions.

Compare the full cost of the service, including data preparation, integration and staff time. Check whether charges follow usage, store numbers or a subscription. Identify who owns uploaded data and conversation records, whether they train shared models and what you can export when leaving.

Start with one business outcome you can measure against its existing result. For inventory, record availability and waste together. For store monitoring, include false alerts. Assign an operational owner before expanding a successful project to more stores.