Artificial intelligence (AI) and automation can help Australian retailers match staffing to demand and manage labour costs.
AI-assisted rostering can help reduce admin so managers may spend more time on the floor, not on spreadsheets.
Automation can support award and roster compliance workflows by surfacing potential issues for manager review.
For workforce management, AI-assisted tools can draft and flag information for people to review and decide.
Picture a normal trading day. The roster's short two people, stock is late, and you're doing sums on labour cost between serving customers. AI and automation in retail won't make that chaos vanish, but they can take a lot of the manual work off your plate.
Australian retail is busy, too: shift jobs now sit about 36% above pre-COVID levels, according to Deputy's Big Shift Report 2026. This guide walks through practical ways to put these tools to work across rostering, forecasting, stock, pricing, and compliance.
Match staffing to demand with AI-assisted labour forecasting
Labour is one of the biggest costs you carry, so getting staffing right matters. Many Australian retailers are now doing more with the teams they already have.
In 2024-25, Australian retail wages and salaries rose 2.9% even as employment fell, according to the Australian Bureau of Statistics. Minimum wage increases drove much of that rise.
Monthly retail hiring fell from around 0.15% in 2022 to roughly 0.05% to 0.08% through 2025, according to Deputy's Big Shift Report 2026. Employers are focusing on optimising existing teams rather than hiring.
This is where Deputy's demand forecasting tools can help. They can use business data to help forecast labour demand for manager review. You stay in control of the final call, adjusting for local events or a big delivery day.
How AI-assisted forecasting matches staff to busy and quiet periods
The goal is simple: enough people on for busy trade, fewer during quiet spells. AI-assisted tools surface real-time sales trends, so you can plan cover for peak periods and adjust planned cover during slower periods.
Retailers like Ace Hardware report using this kind of visibility to track labour cost as a share of sales and help keep it within budget. Juice Press matches labour to projected demand across its stores, which the company credits with lifting revenue and reducing front-of-house labour spend.
In one Deputy customer example, sales per labour hour reached $106, an uplift of about 40%. That figure comes from Deputy's guide to using AI to help estimate labour needs. This is one customer example; results vary depending on factors including team size, roster complexity, configuration, and trading conditions.
Build rosters faster with AI-assisted rostering
Once you've got a forecast, someone still has to build the roster. If you build the roster by hand, it can eat hours each week.
Deputy's auto-scheduling tools can help create a draft roster using configured scheduling and workforce information. Treat it as a starting point: you review, adjust, and approve it before publishing.
Dennis Novak, Head of Showrooms at Proper Cloth, has seen the difference. He says Deputy may save his team thousands of dollars a week by cutting the time spent building schedules manually. Results are specific to Proper Cloth and may vary.
How self-service rostering supports staff retention
Rosters aren't only about cost. They shape whether people want to keep working with you.
When staff can swap shifts, request leave, and see updates from their phone, they get more say over their week. That flexibility can contribute to stronger engagement and retention over time.
Retailer UNTUCKit reports cutting rostering workload by 50% across 45 stores, which freed managers to focus on customers and team engagement.
Keep stock levels right with AI-assisted inventory management
Managing stock by hand is slow, and small errors get expensive. AI-assisted inventory tools read large volumes of data so patterns are easier to spot.
They pull together signals like sales history, customer trends, weather, and supplier information. That helps you match ordering to demand and avoid both overstocking and empty shelves.
Supply Chain Dive reports that one global fashion retailer has used AI across its supply chain to cut waste and reduce markdowns.
Using AI to forecast demand and avoid overstocking or stockouts
Good stock decisions rest on good demand signals. AI-assisted forecasting helps you localise ranges by store and adjust orders as trends move.
That can mean fewer dollars tied up in slow stock, less markdown pressure, and a lower risk of empty shelves during busy periods.
Using AI across supply chain, logistics, and fulfilment
Stock decisions don't stop at the shelf. AI can also help retailers plan replenishment, route deliveries, and manage warehouse tasks so goods move more smoothly from supplier to store.
For a multi-site retailer, that can mean fewer stockouts across locations and less manual coordination between your stores, suppliers, and delivery partners. As with forecasting, these tools work best when a person reviews the recommendations before acting on them.
Optimise pricing to stay competitive
Pricing is another area where AI can support decision-making. AI tools review market data, demand shifts, and seasonal or competitor pricing, so you can adjust prices as conditions change. With 77% of Australians changing their shopping behaviour, according to Deloitte, staying price-competitive matters more than ever.
The same insights can inform tailored promotions. Reading customer behaviour across channels helps you shape discounts that may support loyalty rather than eroding margin.
For retailers running both a website and physical stores, AI can help keep pricing consistent across the two. That consistency can help maintain customer trust and reduce confusion for customers who shop both ways.
Personalisation, recommendations, and customer engagement
Pricing sits alongside another common use of AI in retail: personalising the customer experience. AI-assisted tools can read shopping behaviour to suggest relevant products, tailor recommendations, and shape offers for different customer groups.
Used well, this can support more relevant marketing and a smoother path to purchase, both online and in store. The same care applies here: treat AI outputs as suggestions your team reviews, and handle customer data in line with privacy obligations.
