Opinion: Convenience retail needs AI that acts, not just analyses

For convenience retailers, having more data is rarely the problem. It's knowing what to do with it. Every day, store owners and operators balance sales, stock levels, product performance, merchandising, staffing, customer expectations, and margins. Digital systems have made it easier than ever to collect information about what is happening across a business. The challenge is finding the few signals that actually matter, and acting on them while the opportunity to optimally capitalise exists. That is where the next phase of artificial intelligence in retail comes into its own. The value of AI should not be measured by how much information it can produce, but rather by whether it helps retailers make better decisions faster. From information overload to useful answers Convenience retail typically involves managing many variables. A product that sells strongly at one location might struggle at another, a promotion can increase demand overnight, and over-ordering ties up cash and valuable shelf space. Traditionally, making sense of these patterns has required retailers to move between reports, dashboards, and spreadsheets, then use their own experience to determine what to address and prioritise. AI streamlines that process. Rather than asking an operator to analyse another dashboard, an AI-enabled system enables them to ask a straightforward question in plain language, such as: Which locations are underperforming? Which products are selling faster than expected? Where might stock need attention? This is a more useful application of AI because it starts with the retailer's specific issues. Nayax's recent addition of an AI layer to its MoMa mobile app for unattended and self-service operators reflects this advancement. Its AI Assistant allows operators to ask questions about their own business data, while other capabilities include the provision of product-mix recommendations and the use of visual recognition to help create planograms. Turning insight into action For a convenience retailer, knowing that a particular product is underperforming is only useful if it leads to a decision. Should it be moved? Replaced? Reduced in quantity? Is another product performing better in that location? Does the answer differ between stores? This is where AI can move beyond analytics. For example, product-level data can help identify differences between locations and highlight opportunities in the product mix. A product that performs strongly in one environment may not necessarily justify the same amount of shelf space somewhere else. AI-driven recommendations can help surface those patterns, giving retailers a starting point for decisions about ranging, replenishment, and merchandising. But there is an important distinction: AI should support the retailer's judgement, not replace it. A store operator knows things that a dataset cannot necessarily see, such as local events, customer behaviour, changes in foot traffic, or a new competitor opening up nearby. The best technology combines that human knowledge with timely business intelligence. Look for AI that reduces work, not adds to it There is a temptation whenever a new technology emerges to ask, 'Where can we use it?' A better question for retailers is: 'What problems are we trying to solve?' If a new system requires staff to spend more time maintaining data, learning complicated interfaces, or checking another stream of notifications, it may create as much additional work as it removes. The most useful AI should fit into existing workflows. That could mean flagging an unusual sales pattern rather than requiring someone to find it, or recommending which products deserve attention rather than presenting a list of hundreds of SKUs. Or it could mean automating a repetitive task. Visual recognition is a prime example of this. MoMa's visual recognition capability can create a draft planogram from a photograph of a machine, reducing the manual work involved in mapping products and helping operators establish the product-level information needed for more thorough and insightful analysis. The principle applies well beyond vending and self-service retail: automate the repetitive work so people can spend more time making decisions. Start with the business problem For convenience retailers considering AI, the first thing to do is establish whether or not it solves a genuine operational problem. Does it help reduce unnecessary site visits? Does it identify stock issues earlier? Does it improve product ranging and make merchandising decisions easier? If the answer is yes, that's the time to proceed to the test and implementation phase. Retailers want technology that respects the reality of running a busy business: limited time, tight margins, and hundreds of actions and decisions competing for attention. The next generation of AI should be less about giving retailers more information and more about helping them know what matters, decide what to do, and act accordingly, knowing you are prioritising the actions that will generate the best outcomes. For time-poor operators, that is where the real value lies: a shorter path from data generation to reaching the most beneficial decisions and implementing them. This article was written by Dylan Winik, CEO of Oceania at Nayax. To stay up to date on the latest industry headlines, sign up to the C&I e-newsletter.