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For retailers, the AI revolution has opened up an entire universe of new strategic opportunities at the edge.
The latest research shows AI at the edge skyrocketing in the retail sector as retailers rush to take advantage of what analysts describe as game-changing opportunities.
We at Spectro Cloud see confirmation of this trend, with about 70% of our retail customers already running or planning to run AI workloads in their stores.
Trouble is, the legacy systems (hardware AND software) in most store environments just aren’t ready for today’s demands… modernization is essential.
In this blog, we’ll look at some of the ways retail operations are being transformed by AI at the edge and at the infrastructure modernization challenges that must be overcome to make this transformation possible.
Loss prevention: AI-powered vigilance across the store
With retail theft growing at alarming rates, loss prevention is at the top of retailers’ list of problems to solve.
How bad is the problem? Retailers have seen a 93% increase in shoplifting incidents and a 90% increase in total losses since 2019, according to the National Retail Federation.
AI at the edge can help retailers reduce their losses by spotting and flagging suspicious behavior. Using computer vision to track store movements, AI systems can:
- Spot shoppers loitering around risky items, slipping merchandise under clothing, or ducking behind aisles
- Identify repeat offenders through facial recognition models
- Notify staff in real time about suspicious activities
Unlike human security teams, these systems don’t get tired, bored, or distracted. They continuously watch the entire store with a trained eye.
Digital signage that reacts in real time
Digital signage and kiosks certainly aren’t new, but with a modern infrastructure and AI, retailers can take these screens to the next level.
Imagine a beauty or apparel retailer using computer vision to personalize advertising to the shoppers walking by, even using generative AI to create fresh, custom imagery in real time.
Or imagine combining a host of data sources — real time inventory levels, live competitor pricing, live weather, or customer spending habits — to update screens and eink price-tags with custom promotions.
Or what if your signage could become truly interactive, able to chat directly with shoppers passing by to answer product questions and encourage purchases?
Bringing together different AI models on device, with the right data sources and guardrails, makes all of this possible.
For a real coded example of how AI can create greater interactivity, check out this blog.
Smarter wayfinding and store layouts
Shoppers know the frustration of searching for a product in a giant store. AI-based wayfinding can help customers locate items by:
- Allowing customers to use their mobile devices to get directions that take them straight to the product
- Automatically identifying misplaced or out-of-stock products, keeping navigation accurate and reducing shopper frustration
- Personalizing in-store navigation by showing shoppers faster paths based on their shopping lists, preferences, or mobility needs
AI systems also can help retailers discover patterns and make automatic adjustments, such as:
- Adapting to seasonal trends by automatically triggering new store layouts (for example, placing hot dogs + condiments in the most advantageous areas ahead of July 4th)
- Analyzing in-store foot traffic to identify high-traffic zones, dead zones, and optimal placement for promotions or impulse buys
- Reacting to dynamic weather patterns by promoting cold beverages during heat waves or hot drinks during frigid times.
The infrastructure problem holding edge AI back
Although these AI-powered use cases can deliver measurable results, they also expose a hard truth for retailers: most store technology infrastructure was never designed to support this kind of intelligence at the edge.
Traditional store systems are often proprietary and single-purpose, built to run a small number of predictable workloads such as point-of-sale (POS), printers, and basic store services. More recent store apps might be browser-based or based on client-server models and need constant connection to the internet — fine for occasional use, but far too risky to base every store process on.
Perhaps most important in the age of AI, existing PCs and small form-factor servers under desks and in backroom cupboards may lack the accelerated hardware needed for responsive AI performance, and weren’t architected for high-frequency remote software lifecycle updates at scale across hundreds or thousands of locations.
As retailers look for the best ways to modernize their infrastructure, Kubernetes presents a strong case.
Why retailers are embracing Kubernetes at the edge
Kubernetes brings several advantages to the retail edge:
- A consistent application platform across stores, distribution centers, and the cloud
- Faster deployment and iteration of AI-driven services
- Built-in support for containerized AI/ML workloads
- Greater portability, reducing vendor lock-in and simplifying modernization
For next-generation AI-driven edge applications, containers and Kubernetes are a natural fit. In fact, we’re already finding that more than half of edge AI projects are based on Kubernetes today (and spoiler alert, the most successful projects use Kubernetes!)
However, Kubernetes alone does not solve the hardest edge challenges retailers face.
The challenges Kubernetes doesn’t address on its own
Running Kubernetes in a data center or cloud is one thing. Running it across thousands of remote, lightly staffed retail locations is something else entirely.
Retailers quickly encounter a new set of operational and architectural challenges as they attempt to scale edge AI. Deploying Kubernetes across thousands of stores is difficult without heavy manual effort, custom scripts, or site-by-site configuration. What works in a small pilot often breaks down at scale, slowing rollouts and increasing the risk of inconsistencies between locations.
Managing and upgrading large fleets of edge environments introduces additional complexity. Retailers must coordinate updates, patches, and configuration changes across thousands of locations while minimizing downtime and avoiding disruptions to store operations.
Security is also a major concern. Edge infrastructure is often physically exposed — running in wiring closets, backrooms, or other unsecured areas — making it harder to protect systems, data, and AI workloads using traditional data center security models.
Operational costs rise quickly when edge platforms require frequent onsite intervention. Without remote provisioning, monitoring, and recovery, retailers are forced into costly truck rolls just to deploy, update, or troubleshoot systems at individual stores.
Connectivity adds another layer of risk. Store networks are not always reliable, yet AI-driven applications, POS systems, and store services must continue operating safely and correctly during outages, without data loss or security gaps.
There’s also the thorny issue of what to do with legacy VM-based workloads (for example, point of sale appliances) and how to run them alongside modern containerized applications. Maintaining separate infrastructures for virtualization and Kubernetes significantly increases cost, complexity, and operational burden.
How Spectro Cloud helps retailers modernize for AI at the edge
At Spectro Cloud, we’ve spent years helping some of the world’s biggest retail chains succeed with their edge computing projects, whether they have ambitions for scale or need to tackle cost and availability challenges.
Our Palette platform gives retailers a single edge-ready solution to run AI and modern applications across thousands of distributed locations. Retailers can deploy, update, and manage remotely and consistently across their entire store footprint, eliminating manual, site-by-site operations.
The platform is designed for real-world store conditions. Applications continue running locally during connectivity outages, security is built in for physically exposed environments, and remote management reduces the need for costly truck rolls.
Together, these capabilities make AI at the edge scalable, resilient, and economically viable, while enabling retailers to modernize at their own pace.
The sooner you start, the greater your retail edge
If you’re a retailer wondering how to curb VMware costs, deploy AI at the edge, modernize your stores, and eliminate operational complexity, we have an unbeatable solution for you.
To learn more about the state of the AI at edge, read our latest report. To see how Spectro Cloud can give you a retail edge, book a demo right here.