Retailistic Podcast Q&A: How AI and Robotics Are Transforming In-Store Operations

Deborah Weinswig: Brad, for those who are new to Simbe, can you start by telling us a bit about the company?

Brad Bogolea: Absolutely. I co-founded Simbe about 10 years ago with the goal of digitizing physical retail. Today, we build the intelligence and visibility layer into every store—digitizing every shelf, product, and price tag. This data enables retailers to transform operations and address key challenges like revenue leakage and margin erosion. We've partnered with more than 40 leading retailers globally across grocery, mass, club, DIY, and more.

Q: Retailers seem to be accelerating tech adoption, what’s driving that shift?

A (Brad): We’re seeing a wake-up moment for leadership teams and boards. AI and automation are no longer experimental—they’re strategic. Retailers now see shelf intelligence as a lever to solve persistent issues like out-of-stocks and margin erosion. It’s about understanding where they're losing revenue and taking action with precision.

Q: This year’s report shows promotion execution errors rising to the top of retailer concerns. Why now?

A (Brad): For the first time, retailers can empirically measure execution issues like pricing and promotion accuracy. Many operate with a 5% pricing error rate, which impacts sales and regulatory compliance. As store-level data becomes more transparent, these challenges are getting the attention they deserve.

Q: What’s the biggest operational opportunity for retailers right now?

A (Brad): It all starts with visibility. From price and promo execution to reducing out-of-stocks and optimizing labor, store intelligence touches every facet of operations. Our data helps identify shelf-to-inventory mismatches, misplaced products, and fulfillment gaps—all of which are addressable and often resolved quickly.

Q: Only 20% of retailers have scaled store intelligence. What’s holding the rest back?

A (Brad): Two things: understanding the true size of the prize and cultural alignment. The leading 20% see this tech as foundational, not just another initiative. They have executive buy-in, often at the COO level, and are using objective data over legacy, self-reported metrics.

Q: Let’s talk about robotics. Why are adoption rates so varied?

A (Brad): We’ve seen unprecedented demand. More retailers are moving from experimentation to full deployment. The AI boom—especially around tools like ChatGPT—has accelerated awareness. We’re also seeing strong peer-to-peer knowledge sharing, which helps eliminate redundancy and speed up adoption.

Q: Speaking of AI in the physical world, how important is LiDAR and other sensor tech in this transformation?

A (Brad): Hugely important. Thanks to investments in autonomous vehicles and mobile tech, sensors like LiDAR and 3D cameras are more powerful and affordable. They enable us to build high-fidelity digital twins of store environments making real-time visibility a reality.

Q: How quickly can retailers see results?

A (Brad): Often within weeks. We’ve helped reduce out-of-stocks by up to 60% and price errors by 90% in under 50 days. Once stores are instrumented, they can act on insights almost immediately—whether it’s backroom stock that needs shelving or correcting shrink-inducing errors.

Q: What about the impact on the store team?

A (Brad): It’s a game-changer. 90% of store managers tell us they don’t want to operate without this tech once they’ve tried it. It boosts job satisfaction, reduces turnover, and improves the customer experience, what we call an “omni-win” for managers, associates, and shoppers.

Q: How can store intelligence help address shrink and organized retail crime?

A (Brad): By getting to the root cause. Is the product truly missing, or was it never shelved? Did it arrive from the DC? We help retailers distinguish between theft, execution errors, and supply chain breakdowns with clear, empirical data.

Q: What role does store intelligence play in the booming retail media space?

A (Brad): It provides proof of execution. Brands want to know their products were on shelf during a campaign, and retailers want that same validation. Our data becomes a shared source of truth, helping optimize investment and in-store performance across the ecosystem.

Q: Are you seeing retailers use your data for localization?

A (Brad): Absolutely. Every store is a snowflake. Real-time data enables retailers to tailor assortments, correct merchandising lapses, and optimize seasonal setups. We even see COOs of major retailers logging in to monitor performance at the store level.

Q: Simbe brings together robotics, sensors, and AI. It’s more than just automation. How do you define Simbe today?

A (Brad): We’ve evolved into a multimodal platform. Robots remain the most scalable way to collect high-fidelity data, but we also use fixed cameras and hybrid edge-cloud processing. We're no longer just about identifying issues, we’re helping retailers orchestrate entirely new operating models.

Q: Any advice for companies just beginning their journey?

A (Brad): Start with the data. Run a pilot across a representative store set—not just a flagship—and compare it to what you think you know. Make sure your ops, merchandising, and finance teams are aligned. And elevate the conversation to senior leadership. Once they see the data, the value becomes clear.

Q: Does Simbe support peer learning across retailers?

A (Brad): Definitely. From informal client introductions to a formal customer advisory board and participation in forums like NRF and FMI, we’re creating spaces for retailers to share best practices, define success, and accelerate time-to-value.

Q: We’re midway through 2025, what’s next for Simbe?

A (Brad): We’re seeing AI-first retail go mainstream. Demand is coming from both growth-oriented and turnaround retailers. Internationally, we’re expanding in Europe and beyond. Our vision remains: to make intelligent automation ubiquitous in every store on the planet.