For modern retailers, staying competitive means more than just having an online store or a mobile app. It means creating a seamless, intelligent, and highly personalized customer experience that blends physical and digital. But achieving this vision requires a technology strategy beyond the cloud. It requires moving away from rigid in-store systems towards a flexible, modular architecture.
The question for forward-thinking retail leaders is no longer if they should modernize their store tech stack, but how. Adopting an edge computing platform that’s both flexible and built on open standards future-proofs your retail store technology architecture.
The Problem with Monolithic Store Tech
For decades, many retailers operated on a “purpose-built” model, investing in proprietary, expensive, single-use edge computers for point-of-sale (POS), inventory, and display signage. Marketing has a new idea that needs to be deployed in stores? That requires building a whole new system and months to roll it out across thousands of stores. New computer vision use case? New build, same problem.
An architecture of monolithic “little black boxes” is difficult to update, lacks interoperability, and adds operational overhead. They were designed for a single purpose, not for the rapidly evolving, omnichannel environment of today.
This outdated approach creates a host of problems:
- Slow Innovation: Deploying new features like a smart display or self-checkout system requires a massive, costly overhaul.
- Data Bottlenecks: New ways to use valuable in-store data, such as video feeds or RFID tags, end up being backhauled to the cloud, where innovation is easier for developers, but this causes delays and incurs huge bandwidth costs.
- High Latency: Real-time AI applications, such as loss prevention or dynamic pricing, are impossible when every decision must wait for a round-trip to the cloud.
- Vendor Lock-in: You’re tied to a single provider, unable to integrate best-in-breed solutions that might better serve your specific needs.
Given these problems, what is the best retail store technology strategy for adopting a modular, flexible technology architecture that can adapt to future changes?
The answer: a modular, edge-centric strategy comes into play; this will truly future-proof your business, with a more agile approach.
The Power of a Modular, Edge-First Architecture
A modular architecture breaks down the monolithic stack into reusable and independent components. This “composable” approach allows retailers to add new applications without requiring an entirely new system.
An edge computing platform can empower this approach by providing a crucial foundation for a modular strategy, enabling multiple use cases in retail stores. The platform simplifies the operating model, allowing new applications to be deployed and orchestrated without requiring new infrastructure or local IT resources.
One global fashion retailer is using the ZEDEDA edge computing platform to replace dedicated in-store appliances with a single, commodity server that can run IT services (firewall, SD-WAN) alongside a music and content streaming application. The streamlined approach reduces both the physical and carbon footprints, while also accelerating the deployment of new software to manage inventory in real-time using in-store RFID data.
Increasingly, retailers are now trying to run new AI-based applications in stores, but are facing challenges. Taking the existing monolithic approach is risky and slow. Retailers need to be able to test and experiment, which is cost-prohibitive if each new idea (for example, an AI agent) requires a new system build. Yet, local processing of data is what unlocks the true potential of AI.
Rapid software innovation has transformed e-commerce for retailers, but physical stores have been left behind. The AI-powered, omnichannel experiences consumers increasingly expect require a platform approach in stores.
Edge AI in Action: Real-World Retail Use Cases
AI is poised to transform brick-and-mortar retail. According to a ZEDEDA edge AI survey, retail organizations are leading all other industries in adopting edge AI, a trend driven by powerful use cases that directly impact the bottom line and customer satisfaction.
Edge AI improves the customer experience in several ways:
- Seamless, Tailored Experiences: Edge AI allows retailers to tailor displays and promotions based on real-time customer behavior, past purchases, and saved preferences. By processing data on-site, you can synchronize in-store experiences with a customer’s online and mobile journey, and immediately switch the display as a different customer comes to view it.
- Frictionless Shopping: Edge computing powers technologies like “smart checkout” and self-service kiosks, reducing wait times and removing points of friction. This innovation is especially critical as customer expectations for speed and convenience continue to rise.
- In-Store Intelligence and Inventory Management: AI-powered computer vision, RFID tags, and smart shelves enable real-time inventory tracking, providing store associates and merchandising teams with instant updates on stock availability. This approach not only improves operational efficiency but also ensures customers can always find what they’re looking for, increasing satisfaction.
Beyond customer experience, edge AI is a powerful tool for risk management. The survey found that while improving customer experience is a top motivator for 93% of retail CIOs, risk management is a close second. Edge AI can be used for things like real-time video analytics to detect theft risk and avoid shrinkage at self-service checkout. For grocery, convenience stores, and quick food service, predictive maintenance models can identify potential equipment failures before they happen. By anticipating issues in advance, operators can avoid costly downtime, reduce waste from spoiled goods, and ensure a consistent customer experience.
Each of these use cases presents technical challenges on its own. Trying to test and do all of them, alongside existing store services, magnifies the complexity. Edge AI can either amplify the operational challenges of traditional, monolithic architectures – or it can be the catalyst for taking a new, platform-oriented approach.
The Challenge of Orchestration and How ZEDEDA Helps
While the benefits are clear, deploying and managing a distributed network of edge devices and applications across hundreds or thousands of retail stores is a significant challenge. The ZEDEDA survey found that the top obstacles to edge AI adoption are security risks (42%) and high operational and maintenance costs (40%). Retailers need a way to manage this new complexity at scale without requiring IT staff in every location, which would dramatically reduce net profits in an industry where margins are already slim, ranging from 1.5% to 5%.
To solve these challenges, an edge computing platform with orchestration capabilities is indispensable. ZEDEDA provides a comprehensive solution designed to simplify the deployment and management of edge AI. The ZEDEDA platform offers:
- Zero Touch Provisioning: Devices can be securely configured and deployed without requiring a trained technician on-site, . This dramatically reducing operational costs and streamlining expansion.
- Centralized Management: A single pane of glass provides full visibility and control over all edge devices, applications, and data, no matter how geographically dispersed.
- Intrinsic Security: A zero-trust security model is built into the platform, ensuring data integrity and protecting against cyber threats from the ground up, . This addressing the top security concerns identified in the survey, providing peace of mind.
- Open and Flexible Architecture: ZEDEDA’s platform is hardware- and software-agnostic, allowing retailers to use their preferred devices, applications, and AI models, and eliminating vendor lock-in while enabling a truly modular, composable strategy.
By leveraging an edge computing platform with orchestration capabilities, such as ZEDEDA, retailers can transform their physical stores into intelligent, data-driven hubs. They can reap the benefits of AI and real-time insights without the operational headaches of traditional systems, allowing. This allows them to move faster, innovate more freely, and adapt to whatever the future holds.
A New Era of Retail is Here
The best retail store technology strategy is one that is not only flexible and modular but also built on the foundation of edge computing. This approach enables retailers to address the dual challenges of enhancing the customer experience and optimizing in-store operations. With the right edge computing platform, you can unlock the full potential of AI at the edge, transforming your physical stores into a powerful, competitive advantage.
Ready to learn more about how to modernize your retail technology stack and get the most out of your edge computing strategy? Find ZEDEDA this month at NRF Paris at booth #N111, ShopTalk Chicago at booth #C22, or book time to talk to a ZEDEDA expert today.
FAQ:
Q: How can we use real-time inventory tracking (e.g., RFID and IoT sensors) to prevent out-of-stocks and improve supply chain efficiency?
A: By processing RFID and IoT sensor data locally in stores on an edge computing platform, you eliminate the latency and delays of sending that data back to a data center or cloud, allowing you to track inventory levels in real-time and trigger alerts to prevent out-of-stocks. Summary data can be sent more efficiently back to the cloud for broader trend analysis, which doesn’t need to be as real-time.
Q: What is the most effective way to modernize our legacy point-of-sale (POS) systems and other hardware?
A: Moving to a hardware-agnostic approach, paired with an edge computing platform, as you modernize in-store technology, gives you the most flexibility to solve for multiple use cases and avoid vendor lock-in with interoperability challenges. An edge computing platform like ZEDEDA also allows retailers to consolidate their hardware footprint inside thousands of stores by virtualizing and containerizing multiple applications on a single server. Future-proofing is also a factor, as AI is rapidly evolving and much remains unknown about how retail stores of the future will use AI alongside traditional store services.
Q: How do we address security concerns and protect customer data, especially with the use of AI and personalized services?
A: Starting with a zero-trust foundation is essential to addressing security concerns as retailers look to use customer data in stores for personalized services. In-store technology doesn’t inherit the physical and perimeter security benefits that cloud and data center systems and applications have. Hardware-level root of trust is the first line of defence using Trusted Platform Modules (TPM). Not storing credentials on-site by using a remote orchestration controller, which uses strong, crypto-based authentication, prevents an attacker with physical access from logging in and compromising the device. Keeping data locally in the store means that only anonymized, non-sensitive insights (e.g., “customer count,” “product picked up”) are sent back to the cloud.
