Re:Tech Team
June 8, 2026
This is a guest post by Maria Kushnir, Senior Director of AI Research, FORM.
Something interesting is happening in retail technology investment. According to the Re:Tech Retail Tech 2025 Report, the Retail Digitalization & Store Operations category has matured significantly and is narrowing toward solutions that demonstrably move the needle rather than those that simply demonstrate what’s possible. The era of the impressive pilot is giving way to the era of the proven result.
It’s a transition that anyone managing physical retail operations will recognize. Whether it’s a brand’s field representative visiting a retail account or a store associate walking their own floor, the challenge is the same: The gap between what the data shows and what actually happens at the shelf remains wide.
Curious to learn more? Download the full report to access the complete ecosystem analysis, funding insights, and the Top 100 Israeli Retail Tech Startups.
The conversation about AI in retail tends to cluster around headquarters functions: how algorithms can sharpen demand signals, how machine learning is reshaping pricing strategy, and how personalization engines are redefining the digital customer journey. These are real advances. But they exist at a remove from the moment that ultimately determines whether a brand wins or loses with a shopper: the moment that person reaches for a product and either finds it or doesn’t.
At the store level, AI-powered image recognition has become the foundational layer of modern execution. A mobile device moving through an aisle generates a continuous stream of visual data; computer vision processes that stream and produces a real-time picture of what’s on the shelf, what’s missing, and what’s out of place. It’s a capability that has matured substantially—it’s accurate enough, fast enough, and deployable enough to be useful across large retail networks rather than just in controlled environments.
But accuracy at the recognition layer doesn’t automatically translate into better outcomes on the floor. A precise diagnosis is only valuable if it leads to treatment. In retail execution, that means a store associate or field representative taking the right action at the right time.
Here’s where execution intelligence is genuinely difficult. A field representative or store associate arriving at a location doesn’t face one problem. They face 15, and they might only have time to address five. An out-of-stock in the cereal aisle, a promotional display that hasn’t been set correctly, a price discrepancy on a high-velocity item, a competitor product occupying prime shelf real estate. Each of these matters, but they don’t matter equally.
For example, a field representative might arrive at a store and discover both an improperly set seasonal display and an out-of-stock on a top-selling, high-margin beverage SKU. While both issues require attention, the AI system may determine that replenishing the out-of-stock item will have a far greater immediate sales impact than correcting the display, allowing the rep to focus limited time where it matters most.
The difference between effective execution and unfocused execution is prioritization. Which of these issues, resolved first, produces the greatest return? The answer depends on factors that vary by brand, by store, and by what the sales data says about that particular location, context that a human standing in an aisle at 9 in the morning can’t reliably synthesize on the spot.
This is where AI can provide real operational value. Rather than presenting a field team with a comprehensive report they need to interpret, the most effective systems rank actions by commercial impact and provide a short, prioritized list of what to do and in what order. And because that instruction is tailored to the specific priorities of the brand or retailer in that location, it drives action in a way that a broad summary of findings can’t.
When field teams—whether brand reps or store associates—operate with AI-assisted direction, they move faster and more consistently, which means they cover more ground in the same amount of time. In many retail environments, this has translated into measurable operational gains, including faster shelf audits, improved on-shelf availability, stronger compliance with promotional plans, and reduced out-of-stock conditions.
For the teams monitoring performance from headquarters, on either the retail or brand side, that translates into a richer picture of what’s actually happening across the network. Patterns that once took weeks to surface become visible in days. The relationship between central planning and field execution becomes more like a continuous feedback loop, with real-time shelf conditions informing decisions as they happen rather than weeks after the fact.
The next evolution of this technology isn’t difficult to imagine, even if the timeline remains uncertain. The current iteration asks field teams to receive and act on prioritized information. The next models will be more conversational: a field rep arriving at a store and simply asking what needs attention and receiving a response that reflects everything the system knows about that location in that moment.
The role of the person in the store doesn’t disappear in that scenario. Physical retail is still a human endeavor, and the judgment, relationship-building, and adaptability that experienced field representatives bring aren’t easily replicated. But the burden of synthesis—of turning data into decisions—will likely shift significantly toward the technology.
The Re:Tech report describes a retail tech ecosystem where capital is being concentrated in fewer, stronger companies. Those with proven traction, global scalability, and solutions designed for integration into real retail environments rather than experimentation. The shelf is precisely that kind of opportunity: high-stakes, high-frequency, and still underleveraged by the tools available to manage it. The technology to treat it as the commercial asset now exists. The organizations that move to use it are the ones that will define what retail execution looks like next.
The 2025 Israeli Retail Tech Report provides deeper analysis of the ecosystem, including:
Maria Kushnir is the Algorithm Team Lead at Trax by FORM, where she specializes in AI and computer vision solutions for the retail industry. Based in Israel, she leads the development of advanced retail analytics technologies that help consumer packaged goods (CPG) brands and retailers improve shelf visibility, execution and in-store decision-making.
