Yesterday's AI debate was all about questioning whether AI even had a place in retail. That debate is settled now. Decisively. Today's challenge is more pressing: identifying where AI truly creates advantages and how these benefits manifest not just on presentation slides, but in the execution gap of everyday tasks on the store floor.
In retail, advantage is created or lost at execution. Promotions are set. Shelves are arranged. Pricing changes. Associates are expected to perform hundreds of tasks correctly every day across thousands of locations. And yet, most retail organizations still lack a reliable way to confirm that approved processes are carried out as intended. This gap can be costly. A study from Coresight Research suggests that failing to verify execution can erode up to 5.5% of their revenue due to factors such as improper displays or pricing errors. With clear execution checks, this erosion can be significantly reduced, enabling more strategic resource allocation.
That matters because retail is still a physical business. Roughly 80 percent of sales still happen in stores, with about 20 percent online. While Andy Jassy, Amazon’s Chief Executive Officer, has publicly suggested that this ratio will eventually flip, performance today is still determined by what happens inside stores and distribution centers. People. Execution. Repetition.
Meanwhile, most generative and agentic AI investments in retail have focused on desk-level work and consumer-facing experiences. Analytics. Search. Personalization. Useful, yes. But they stop short of the reality of how work actually gets done on the floor.
You can see this shift clearly in how shopping itself is changing. Prat Vemana, Target’s Chief Information and Product Officer, recently noted that shoppers no longer search for “black coffee mug.” Instead, they describe a moment or an occasion. Sometimes that request runs through a retailer’s own search engine. Sometimes it comes from an AI agent. Vemana noted that roughly 25 percent of searches already follow this pattern, and broader research suggests that 7 in 10 shoppers are open to generative AI guidance.
As a result, content and product pages are being rewritten. The irony is obvious. AI is now helping rewrite pages so they can be understood by other AI systems. Still, behind every query sits a human trying to make sense of options, decide, or finish a task without friction.
To be clear, AI is not new to retail. Retailers have relied on prediction and optimization engines for years. Demand forecasting. Price optimization. Assortment planning. Promotions. Allocation. Replenishment. Supply chain execution. Workforce scheduling. Task prioritization. Retail has become very good at deciding what should happen.
What has been missing is consistent, real-time support for human execution. Not after the fact. Not post-workforce improvements or audits. In the moment. Guidance for associates. Confidence for leaders. Assurance that work was performed correctly when it mattered.
Retail runs on playbooks, task lists, operating procedures, and training content that define expectations. The problem is where those tools live. Usually outside the moment of work. Printed. Buried. Remembered. Or forgotten. They explain what should happen, but they rarely show up when someone is actually doing the job.
Without a bridge between the 'brain' of the organization and its 'hands' on the floor, even the most advanced AI falls apart. This is where an operational knowledge layer comes into focus. Think of it as the 'GPS for store execution,' guiding associates and ensuring every decision is translated into action effectively.
An operational knowledge layer connects AI-driven decisions to the people responsible for carrying them out. It builds a working relationship between associates and AI by delivering clear, visual instructions throughout the workflow and supporting people as they perform tasks. It ensures that approved processes are not only documented and trained, but also understood, executed, and validated across stores and distribution centers.
This layer does not replace planning or optimization systems. Those systems still decide what should happen. The operational knowledge layer ensures those decisions translate into consistent action by people in real environments, under real constraints, on real shifts.
For example, a new associate setting an endcap does not have to remember the latest promotion or flip through a binder in the back office. The guidance appears while the work is being done, step by step, aligned with what was approved. If something is missed, it is caught in the moment, not during a walk weeks later, or worse, never at all. A quantifiable benefit of all this? The vendor trade funds aren’t at risk due to improper display setup.
In another case, a store manager running a morning walkthrough can identify safety issues and confirm that critical tasks were completed correctly across departments without chasing people down or relying on verbal confirmation. That visibility creates confidence without adding more checklists or meetings.
During seasonal hiring, this kind of support becomes even more critical. New associates often start during the busiest weeks of the year, with limited time for formal training and little margin for error. Instead of relying on rushed onboarding or shadowing, they receive guidance while they work. What to do. How to do it. What matters most. That support helps seasonal employees perform confidently from their first shifts, not weeks later.
For associates, this kind of support reduces mental effort and shortens ramp-up time. It helps people do the job correctly without counting entirely on memory or experience. For leaders, it creates confidence that standards are being followed across locations without slowing operations down or adding friction.
Retail does not win or lose on insight alone. It wins on whether people can act on that insight consistently, under pressure, and at scale. As AI continues to shape decisions upstream, the real test will be whether those decisions are accurately reflected in stores and distribution centers. That is where operational advantage lives.
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