Retailers worldwide will spend $388 billion on technology by 2026, with AI investments growing at nearly 25% annually, according to Gartner. The focus has shifted from whether to invest in AI to how strategically it is being implemented. Pragmatic AI is leading the way — retailers are moving past experimentation and deploying it where it delivers the most impact: repeatable processes that affect core business operations. Those with strong data foundations and modern architecture will excel. Those still operating on legacy systems will struggle to keep up. Here is where I see AI already bearing results — and where retailers need to act now.
Where AI Earns its Place
AI in retail is no longer a nice-to-have. What I see across the industry is that leaders have already moved it into production and are deploying it where it delivers real, repeatable results.
Generative AI writes product descriptions and manages customer service interactions. NLP-powered chatbots manage routine inquiries around the clock — which means store associates spend less time monitoring support queues and more time with actual customers. Agentic AI goes further and executes multi-step tasks autonomously, reordering stock when inventory drops, adjusting prices, and updating demand forecasts without anyone having to trigger it manually.
Retailers are also using AI to understand what happens in-store. Recommendation engines analyze purchase history and browsing behavior to suggest products customers are actually likely to buy. Computer vision cameras — the contextually intelligent kind — combine multiple AI techniques to explain shopper behavior, product placement, and store conditions. One European grocery chain reduced food waste by 15% with machine learning for predictive resource analysis. The key was clean data and the right model, not a complex rollout.
Good Data First, AI Second
Retailers collect enormous amounts of data — transactions, loyalty programs, website behavior, and in-store sensors. The potential is real. But in my experience, most of it sits in systems that don’t talk to each other. Inventory data lives separately from ecommerce, loyalty information doesn’t connect to the point-of-sale system and analytics teams pull from sources that are weeks out of date by the time decisions get made. In that environment, AI just makes bad decisions faster. I’ve seen this more than once: the data foundation is always the bottleneck, not the AI model itself.
Retailers who succeed treat data as a strategic asset, not a byproduct. They use unified platforms to eliminate silos between ecommerce, POS and CRM. Their data is clean, current and accessible to every system that needs it.
The Omnichannel Era is Over
Unified commerce is replacing omnichannel — not as a concept, but as an architecture. Where omnichannel connected channels separately, unified commerce brings everything into a single platform with real-time synchronization across every customer interaction. Inventory, pricing and customer data are centralized, so what a shopper does online is visible in-store and vice versa. Store associates can view full purchase histories and process orders across all channels in a single system.
This architecture is called MACH: microservices, API-first, cloud-native and headless. Retailers assemble composable systems from best-in-class components. Success depends on the supply chain keeping pace. Retailers need full visibility, machine learning for delivery optimization and real-time shipment tracking. The real advantage comes from intelligent fulfillment orchestration — AI determining the best source for each order based on inventory, cost and delivery commitments. Retailers who consistently meet delivery windows are winning customers from those who do not. This is no longer only a logistics story. It is a revenue story.
Fast, Personal, Frictionless
Shopping is more digital than ever. Customer-centric technology is changing the experience both online and in-store: virtual clothing try-ons, smart cameras that recognize customers and relay their preferences to associates, hyper-personalized experiences based on shopping data and advanced 5G connectivity. Experimental retail is also gaining ground — stores with interactive displays, customized consultations and immersive experiences that customers simply can’t get online.
Gen Z signals where expectations are heading. They have significant purchasing power and behave differently: buying through social platforms, expecting sustainability data with product specs and seeing same-day delivery as standard. Younger consumers also have distinct expectations for digital experiences. That is why retailers are shifting to mobile-first design and influencer-driven sales rather than traditional advertising.
Payments have changed as well. Customers want fast and frictionless — and the technology is there to deliver it. Computer vision and sensor fusion let customers place a basket on a sensor pad and walk out, transaction complete. Biometric authentication is replacing PINs. Buy now, pay later is integrated at the point of sale, not added on afterward. Every one of these changes removes a step that used to cost a retailer a customer.
Security with AI
Shrinkage from theft, fraud and operational error lingers as one of retail’s most persistent cost problems. AI is changing the economics of addressing it.
Computer vision cameras detect suspicious behavior on the shop floor. Machine learning flags fraud patterns in transaction data in the background, without slowing down checkout. RFID and smart cameras cut losses from both external theft and internal errors. Retailers are careful not to make security intrusive. The most effective systems work quietly — flagging known shoplifters and suspicious transactions without putting cameras in every customer’s face.
Infrastructure, not Experiment
The retailers pulling ahead are not those with the largest technology budgets — they are the ones who turn AI ideas into tangible assets quickly. And from what I see, that gap is widening fast. One practical implementation route is using AI-powered prototyping. Functional prototypes connected to real POS, CRM and ERP data can prove technical feasibility and business value in weeks, not months. Retailers can test everything from personal shopping assistants to inventory analytics tools against real customer behavior before scaling — without overcommitting budget to something unproven.
Treat AI as infrastructure, not as an experiment. Success will come to those who focus on the fundamentals: listening to customers, managing inventory efficiently and delivering on promises. That’s the standard being set in 2026.
Natalie Medved is an experienced retail technology expert serving as VP, Global Head of Retail & CPG at Intellias, where she manages transformative digital commerce and data-driven solutions for leading B2B retailers, CPGs and brands. With a proven record in deploying AI-powered personalization, omnichannel platforms and legacy modernization in retail, she leads projects that connect innovative technology with real-world business results.
