Overhauling Retail Execution With On-Device AI

Retail execution in the consumer goods industry is rapidly evolving. Organizations must shift from overwhelming, post-visit data collection to real-time, AI-driven decision support. When using a traditional approach, field reps become buried in data and dashboards, slowing down store-level decisions. By integrating on-device AI and real-time image recognition, companies can transform store visits — from pre-call planning to post-visit reporting — into immediate coaching and outcome-based store manager discussions. In this episode of CGT’s Expert Exchange, senior editor Liz Dominguez interviews Sam Barclay, chief growth officer at StayInFront. The conversation addresses how consumer goods companies are modernizing their retail execution strategies by shifting from passive data collection to real-time, AI-driven decision-making.In the current landscape, organizations are collecting more information than people can effectively act upon, according to Barclay. He says the flood of data coming to field reps and supervisors can be overwhelming, and they are being expected to act on that information very quickly without the right tools to help them sift through that data and turn it into meaningful insights and actions. But things are changing. Just five years ago, retail execution was built largely around standardization and historical reporting — trying to create an ideal or perfect store model that followed broad coverage models. Now field teams are operating in a dynamic environment. Store conditions are changing quickly and labor resources are constrained, he says."The tipping point occurs when a company realizes that despite having access to more data and pushing more data out to the field, decision-making is actually slowing down," says Barclay.Reps are spending more time looking at reports, looking at dashboards and preparing for visits. And supervisors are spending more time reviewing reports."It's all about converting that information into actionable guidance at the moment that decisions are being made. That is right at the shelf," he says. "And that's where organizations really need to rethink retail execution around insights delivery, prioritization and decision support."The biggest shift is that AI is moving retail execution from a documentation and reporting role to real-time recommendation and decision support in the store. It is moving the industry toward real-time recommendations. So now conversations with the store can be much more about outcomes, according to Barclay."On-device image recognition has completely changed the game there because I capture the data, I process it and I produce the insights immediately," he says.It takes into consideration inventory levels and sales, POS insights and data captured through the image recognition software. That data is synthesized together to immediately coach the rep. Buy-in for this technology is a culture discussion right now. The most successful organizations are not positioning AI as a replacement, he says. In terms of retail execution, he says companies still need somebody in front of the shelf to actually take those actions on the shelf. It's just about providing tools to make people more effective."If they're coming into store and bringing insights and bringing value to the conversation, making more productive conversations, seeing positive results in the store, seeing their recommendations picked up and driving greater sales, there's nothing that drives better employee satisfaction than success," says Barclay. "I think adoption improves significantly when employees see AI helping them to solve everyday challenges."Most retail execution transformation projects stall because organizations underestimate the operational, organizational and team changes required to support the technology, he says.The first step, says Barclay, is getting teams aligned — cross-functional alignment between category management, sales management, account management. Home office sales and retail execution have to be properly aligned. The second step is around data readiness. Data has to be reliable, well-organized and set up in a way that AI can really use it to make an impact.

Overhauling Retail Execution With On-Device AI

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