Myron Burke, Leader of Solutions Management at Sensormatic Solutions – Interview Series

Myron Burke, head of global product & solutions at Sensormatic Solutions, identifies and implements new ways of accelerating innovation, increasing speed and delivering greater value to customers through a strategic solution roadmap.

Myron is a proven leader with more than 25 years of experience in retail, including his tenures at Walmart and Sam’s Club, delivering innovation at scale. Most recently, Myron founded Divergent Technology Advisors, a retail technology advisory firm guiding major retailers, technology providers, and start-ups with technology strategy, go-to-market planning, international market expansion, and more.

Sensormatic Solutions, the leading global retail solutions portfolio of Johnson Controls, powers safe, secure and seamless retail experiences. For more than 60 years, the brand has been at the forefront of the industry’s fast-moving technology adoption, redefining retail operations on a global scale and turning insights into actions. Sensormatic Solutions delivers an interconnected ecosystem of loss prevention, inventory intelligence and traffic insight solutions, along with its services and partners to enable retailers worldwide to innovate and elevate with precision, connecting data-driven outcomes that shape retail’s future.

You’ve spent more than 25 years at the intersection of retail operations and emerging technology — from leading RFID strategy and store innovation at Walmart and Sam’s Club, to incubating next-generation concepts at Store No. 8, to now heading Global Product & Solutions at Sensormatic Solutions. How have those experiences shaped your philosophy on how AI and sensor technologies should be deployed inside physical retail environments today?

I take a very pragmatic approach to AI implementation, and I encourage my team and Sensormatic Solutions retail customers to do the same. My experience on both sides of the equation has proven time and again that building in this manner is the key to successful transformation.

Sensormatic Solutions has been operating on a very simple belief for the past 60 years: Technology succeeds when operational effectiveness and real-world retailer challenges are at the center. It seems obvious—especially to those who stay close to emerging tools—but this foundational principle has been somewhat forgotten amid all the hype around AI.

The pressure to move fast and keep up with the market was and remains high both in solution development and customer adoption, but building tools that actually fill gaps is more impactful than attempting to fold AI functionalities into any and every product. We’re focused firmly on finding the places where streamlined collection, fusion, analysis and action drive measurable improvements. This focus also extends to the data sets that AI will use – targeted, controlled and cleansed data sets are key to delivering sustained AI value, especially across differentiated customers.

Something we also keep in mind is that this is true across all possible users: corporate decision-makers, shoppers and associates. With each new solution or update, we ask ourselves whether we’re delivering value to all three stakeholders in equal measure, because each group is integral to retail success.

This internal ethos naturally translates into solutions that help retailers adopt a similar stance, offering tools that support meaningful improvement through practical, tailored system designs. AI deployment isn’t one-size-fits-all, and neither are the programs we build with customers.

Sensormatic Solutions is increasingly positioning AI and ML advanced analytics as core enablers of modern retail operational intelligence. How is AI redefining what “loss prevention” means in an omnichannel world?

The simplest answers are visibility with speed. AI is helping truly demystify shrink and deliver a full view of total retail loss. The reality is that you can only note the losses that you can see, right? For most of the industry’s history, visibility into losses has been possible only at the most basic, surface level, with programs focused on items that should be available for sale but aren’t. You might have some idea of whether an item was stolen, broken in transit, or damaged while on the shelf, but tracking these kinds of things at scale was difficult, if not impossible.

Connected analytics and sensor systems expanded what retailers can see, track and quantify. Think about highlighting the 3% of error that is hidden in the vast amounts of data volume generated today. These sensor systems unlock the what, where, when and who of loss, which—on its own—spark a transformation in understanding around shrink and shift the paradigm toward “total retail loss.” This broadened scope allows retailers to see another layer of operations and a whole new set of potential drivers of losses related to process deviation and gaps, along with wasted time, resources and effort.

When all of that is identified and labeled, you can then transform it. That’s where AI comes in. It connects these new “dots,” often in real time, to surface an entirely different layer of data. Predictive, highly accurate intelligence and modeling can help quantify the impacts of upstream waste, weigh the relative value of possible adjustments and illustrate the cost of inaction. Effectively, it’s enabling retailers to shift their stances from reactive to proactive, helping them reframe losses as opportunities to improve.

With technology like Re-ID and AI-powered foot traffic analytics, retailers can now move beyond simple people counting to deeper shopper behavioral and operational insights. What are the most transformative use cases you’re seeing emerge from this shift?

Re-ID, to me, is a powerful example of small, targeted adjustments that have a huge impact for operational understanding.

Re-ID really does one thing: refines traffic measures. Of course, getting the technology to accurately separate unique shoppers from re-entries, staff and other categories of visitors is complex, but the result is a very simple change to datasets that drives significant improvement in understanding.

Traffic data continues to underpin a wide range of metrics across the industry, with conversion perhaps the most notable example. Just trimming records to reflect the more accurate count of individual visitors can dramatically alter interpretations, enabling retailers to refine staffing, floor plans, messaging and countless other practices to help improve customer experiences and financial outcomes.

It’s the embodiment of the ethos we discussed earlier as central to Sensormatic Solutions success over the past 60 years. We are using AI to make targeted, high-value adjustments that benefit everyone in the equation.

Sensormatic Solutions recently introduced Orbit AI and Video AI as part of its Store Guest Behaviors capabilities. What strategic gap does this Solution solve for retailers, and how does it differentiate from other retail intelligence platforms?

We approach every new solution with a specific challenge in mind. For Orbit AI and Video AI, we were focused on separating the “signal from the noise”, to give retailers reliable, specific and contextualized data that takes the guesswork out of decision-making.

Re-ID’s innovative object recognition technology enables Orbit AI and Video AI to help retailers:

• Understand dwell time patterns across the store.
• Differentiate between shoppers and passersby.
• Track shopper journeys to identify trends that inform merchandise layout, promotion and advertising plans.
• Utilize heatmapping to track where visitors spend the most time.

Orbit AI and Video AI take it a step further, though, as their tailored machine learning models adapt alongside operations. The system learns about each enterprise and location over time, continuously adjusting parameters, identifying sources of bias, and working to remove redundant or incomplete data that skews models. This continuous refinement ensures that each insight reflects the reality of the store right now. Not yesterday; not last week. As things stand today—and this is critical because retail trends, pressures and conditions change at a rapid clip.

Orbit AI and Video AI were built for ease of integration and with key barriers to adoption in mind. The sensors’ integrated design, at-the-edge approach and Re-ID capabilities allow retailers to gain these insights with fewer devices, making deployment easier and analytics tools available for businesses of all sizes. It’s a continuation of our decades of work focused on making intelligent insights available to the industry at large.

You’ve emphasized streamlined data usage and sensor fusion as foundational to retail reinvention. How does combining multiple sensor inputs create a competitive advantage compared to siloed analytics tools?

Cloud-based analytics help connect operations and remove silos, but they also include a range of drivers of waste and inefficiency—and many retailers don’t even realize these are present within their systems. Effectively, sensor fusion shifts initial processing and integration tasks to the device itself (on the edge), reducing the volume of data that needs to be transmitted to central servers and enabling real-time responsiveness across the ecosystem.

Take behavioral analytics, for example. In a traditional cloud-based environment, the sensors would perform basic collection tasks, continuously (or periodically) sending raw data to the central compute for processing, analysis and action. Let’s say that analysis reveals signals of suspicious behavior on the sales floor, which triggers a series of response protocols. Well, that information—the need for a response—also needs to be transmitted. And though the whole process is quick by human standards, you’ve already lost time sending and receiving information from A to B to C to B and so on.

With Video AI and Orbit AI’s fusion capabilities, we can cut out those extra steps. The integrated AI and ML tools analyze raw data as it’s collected and prioritize next steps based on their findings, enabling more timely action. Additionally, by eliminating the need for continuous transfers to larger systems, edge-based fusion reduces energy demand and strain on the central system.

At enterprise scale, integrating global hardware, software, and data platforms is notoriously complex. What architectural principles or systems-engineering approaches are critical to making AI-driven retail infrastructure truly scalable?  

It is imperative to begin with SAFe / Lean – Agile Systems Architecture. This foundation enables secure, economically smart, flexible and customizable (if needed) design thinking and development. I also believe in working to leverage an agnostic approach to partner ecosystems – allowing us to meet partners where they are in their digital journey. This enables us to create leverage at the account level and also opens pathways to support companies that need more of a SaaS offering or those more unique enterprise organizations that want all systems / data on site. Our approach enables multiple pathways to enablement and also supports a broad range of hardware options.

Many retailers struggle to translate analytics into measurable ROI. How do you help organizations connect advanced AI insights directly to financial outcomes and operational efficiency?

That question helped drive Shrink Analyzer’s development. After the first sort-of thrust of digitalization investment, retailers had mountains of inventory, loss and other data but lacked a tool to make sense of it all.

Though its primary purpose is ongoing improvement, Shrink Analyzer’s initial task is always benchmarking at the point of implementation. That’s the first step, and it’s what enables any and all improvements thereafter, as well as serving as a point of reference for tracking that progress in terms that matter to the business. It’s this step that many leaders have missed in the AI hype, and it’s the reason that tracking ROI has been such a challenge across industries.

By uncovering the “what, when and where” of waste and loss at the beginning, Shrink Analyzer can translate it all into something retailers haven’t really had before: a clear, quantifiable picture of how losses happen at scale.

It shows where losses are really happening, the gaps that have the biggest impact on performance, and the opportunities for change that can help bring that number down. From there, retailers can start testing use cases, tracking progress, and adjusting as they go to compile compelling evidence that their AI and other technology investments are moving the needle.

Privacy and trust are central concerns as stores become more instrumented. How is Sensormatic Solutions approaching responsible AI deployment while still enabling high-resolution operational intelligence?

I view this issue as part of what we discussed earlier—building for leaders, shoppers and associates in equal measure. Yes, retailers are the people buying our solutions, but we cannot succeed if associates and shoppers aren’t on board with the systems. Their satisfaction is essential to our customers and to us.

This drives our privacy-by-design approach across all of our research and development processes. In other words, we bake consumer guardrails into the solution from the beginning, which keeps us curious and creative.

Re-ID’s design demonstrates this. Its journey-mapping and traffic-counting capabilities use variations in and combinations of individual, non-identifying details—like hair style and color, clothing design and accessories—to assign unique IDs to visitors. You might think that there’s too much overlap in attire or style for this to be effective, but we found that, when considered together, these types of insights are unique enough to confidently say “that person works here” or “that person visited an hour ago.”

We never would have known if we hadn’t been forced to think outside the box from the start. As regulations change and consumer privacy concerns mount, organizations that adopt this point of view early will likely lead the pack in innovation as they are already accustomed to creative problem-solving.

Retailers are navigating constant disruption — supply chain volatility, organized retail crime, labor pressures, and digital competition. How can AI-enabled infrastructure serve as a stabilizing force rather than just another layer of complexity?

Data-driven systems provide stability by aligning the organization around a single truth and shared goal. Adding AI reinforces that certainty.

Data on its own is still up for interpretation, and stakeholders’ conclusions are colored by their own priorities. AI can mitigate that issue, as it analyzes data across the operation without bias toward one point of view. If the system worked as intended, leaders with competing personal priorities can trust that analyses, recommendations and predictive models reflect the reality of the business’ operations. It levels the playing field, so the best next step rises to the top because its value is clear to everyone.

Looking ahead five to ten years, what does a fully AI-optimized physical retail environment look like, and what strategic steps should leaders be taking now to prepare for that future?

There’s no one-size-fits-all roadmap I can point to our AI-readiness, because it really is about building systems that work for each individual retailer. However, the foundation for this is somewhat universal. Every retailer needs:

• A unified database that provides a comprehensive record of all areas of operations. Without this, even the most capable and advanced models will not be able to provide useful insights. They need context to deliver.
• Reliable benchmarks based on relevant business data. This serves as a starting point for investments and provides a reference by which to measure progress.
• Training and upskilling plans. AI isn’t an independent actor. It can do a lot, but the people using it need to understand its functions and limitations. Retailers need to start planning for and communicating about the technology early and often, so associates and employees are ready when the time comes.
• Leaders that care. Transformation is a long-term project, and leaders need to be ready to commit resources to the initiative for the long haul and excited to guide the organization through it.

Thank you for the great interview, readers who wish to learn more should visit Sensormatic Solutions or Divergent Technology Advisors.