AI Powered Retail Execution For Modern Success

Modern retail execution means keeping up with a retail landscape riddled with complexities due to quickly changing market demands. So getting products in front of consumers at the right time and place (and in the right amount) is often a complex dance that involves consumer goods companies, retail partners and supporting technology that optimizes but doesn’t get in the way.

Cait Will, chief revenue officer for Repsly, shares strategies for using tools such as AI to level up work in retail execution and leveraging data-driven initiatives to provide reps with holistic and granular views of inventory.

How AI is Modernizing Retail Execution

CGT: How are technologies such as AI being used in retail execution to modernize practices and create more effective strategies?

Cait Will: For starters, AI’s intended goal in retail is to speed up and level up work in the retail environment for both brand reps and for retail workers, so that these critical, valuable workers can focus on higher value work. Make them smarter, faster.

We see it being used in very smart, accessible ways by our customers.

There’s the widely discussed image or visual recognition technology. This one replaces the day-to-day audits and shelf checks that brands or service providers need to stay on top of their displays and entire categories. They can now replace that work with a picture to gather all critical data on how things are displayed in stores.

AI can scan those photos and instantly spot if things are out of place, if the layout isn’t following approved product lists or planograms or if competitors have snuck into prime spots. And alerts can go off in real-time, or more often, in reports back to the home office, to get the right folks to take action.

Will: Imagine having a super-fast, never-tired, never-wrong merchandiser in every store. For example, real-time, in-store alerts that tell reps what to do. There’s no second-guessing how to take action. It's, “Hey, Sarah, this is what products should be here, this isn’t right, go fix it, and if you can’t, tell us why, right now.” Take the guesswork out of it. Then let your reps move quickly onto higher value work, or more coverage, to drive meaningful sales and relationships.

AI can also support real-time inventory management. One customer, Novamex, uses AI image recognition inside their owned coolers. Instead of waiting for someone to notice low supply, AI cameras snap a photo every time the cooler door closes. They track compliance and use those images to alert Novamex teams and distributors when something is running low. That means products stay on shelves, Novamex’s retailers and loyal customers of Topo Chico and Jarritos stay happy, and sales don’t take a hit just because someone forgot to restock or the rep missed a store visit that day.

There are more approaches and examples, but these are my favorite use cases driving real shifts in go-to-market and sales impact.

Overcoming Challenges in Adopting AI Tools

CGT: What are the challenges brands are encountering in adopting these tools, and how can they overcome obstacles?

Will: When it comes down to it, the No. 1 issue is price. It's been priced in such a way that most brands under $1B in revenue can’t afford to dabble in AI and image recognition. We’ve found a way to democratize image recognition by productizing it — that is to say, making it not only turnkey to get it going, but also accessible from a cost perspective — while still working with a best-in-class AI model partner, who also wants the same.

Another challenge, and barrier at times, is data quality. These AI models need good data to learn from — clear, well-labeled images from real store environments, and strong product databases to ground them. But in reality? Shelves are messy, lighting varies, products look different in different stores and approved product lists are hard to create, so the AI can get confused. Teams need to dedicate time and effort to collect good, solid data to train. And that’s where experts come in, implementing these models and putting smart tools in reps' hands to get good data from the field. It's not hard, it just requires a solid roadmap.

Lastly, don’t boil the ocean in your launch plan. I’d recommend establishing a pilot plan, with quick wins in your sights. Start with a program in a specific territory and one to two categories, max. Prove the value before going big. And it helps to focus on those quick wins, such as reducing stockouts or increasing coverage by 10%. Then begin scaling and seeking additional value and KPIs.

Measuring the Benefits and Value of AI in Retail

CGT: What are the benefits, and how is the value being measured?

Will: Well, we’ve talked about a few examples above, such as brands immediately and accurately spotting out-of-stocks or scaling their reps’ territories and even tracking competitor products weekly or daily. That level of visibility helps reduce lost sales and ensures marketing efforts actually show up on the shelf.

So how does that translate to value? The KPIs we work to realize with our customers include reduced out-of-stock rates, where brands are tracking how much they improve product availability thanks to faster shelf replenishment. One of our customers ran an AI trial in the candy category with shelf scan technology. Of stores visited with IR tech in trial vs stores not, they saw an increase of 6% in on-shelf availability. That is massive, tangible value creation. Extrapolate that at scale and you have a game-changing plan that makes you a hero.

There is also a sales lift because when products are always on shelves and displayed correctly, the result is obvious. Our brands often A/B test stores with and without image recognition tools in their reps' hands to compare results. A big one is labor efficiency because when brand or service provider teams don’t have to manually audit shelves anymore, that frees them up for higher-value tasks, increased coverage or larger scaled responsibility — and it’s easy to calculate time saved and the impact of that time savings.

Gaining a Competitive Edge with AI in Todays Market

CGT: With increased competition due to inflation and private label growth, what tactics can consumer goods companies implement to maintain a competitive advantage?

Will: Yes, perfect example. Consumer goods brands are getting squeezed — rising costs, price-sensitive shoppers, private labels looking more attractive… How can they use image recognition to stay ahead? It's the perfect tool for it, actually. You can spot and respond to this competitive movement much faster with these tools. Image recognition lets brands track shelf space and pricing of competitors, including private labels, in near real-time when reps scan shelves.

This data would reveal if share of shelf is falling, if a retailer starts pushing a private label harder or if there are undercuts on price. Then the brand can see it and respond, maybe with promotions, better placement negotiations or targeted marketing. These shifts are happening fast, and brands need real-time visibility to keep pace. The good news is that tools like this are more accessible than ever. Now’s the time to lean in and act.