The promise of artificial intelligence in retail is no longer theoretical — it’s rapidly becoming a competitive necessity. Gartner predicts key enterprise computer vision markets will pass $386 billion by 2031, with retail among the sectors offering the fastest revenue growth opportunities. But there’s a critical challenge that rarely makes headlines: How do you effectively deploy and manage these AI systems across hundreds or thousands of retail locations?
Consider a typical retail chain with 500 stores. Each location has multiple security cameras that could be leveraged for AI-powered insights. Traditional approaches would require sending massive amounts of video data to the cloud for processing, which is wildly expensive and inefficient. The alternative is processing this data at the edge, right there in the store. However, this introduces new challenges around managing and updating these systems at scale.
What happens when AI models across all 500 stores need to be updated? How do you ensure system security? How do you monitor performance and detect when models start to drift from their intended purpose? These aren’t theoretical questions — they’re practical challenges that retailers must solve to stay competitive in an increasingly AI-powered industry.
Building Smarter Systems at Scale: The Edge AI Advantage
The good news is that solutions are emerging. Retailers can build systems that are both powerful and manageable at scale by combining edge computing with modern AI deployment practices. Take a common use case: using computer vision to detect when customers pick up and return items, enabling automated checkout and real-time inventory tracking. This capability, essential for modern grab-and-go stores, requires processing thousands of video frames per second to track multiple shoppers simultaneously — something impossible for human staff to replicate.
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The AI models that power this system need frequent updates to improve accuracy, adapt to new product layouts and account for seasonal merchandise changes. With the right edge computing infrastructure, these updates can be seamlessly deployed across hundreds of locations without on-site IT support.
These solutions are compelling because the same edge computing infrastructure can support multiple AI applications. Beyond inventory and checkout, retailers can analyze customer traffic patterns, detect potential security issues and monitor store cleanliness — all using existing camera systems with an added intelligent edge computing layer.
Gartner’s research validates this multi-use approach, noting that nearly 20% of companies have already adopted AI-enabled vision systems that combine cameras, computer vision software and advanced AI pattern recognition technologies. But the critical role of edge computing in making these deployments practical and sustainable is often overlooked.
Edge Computing: The Foundation for Retail AI
Edge computing creates a bridge between the cloud, where AI models are trained, and the store environment where they operate in order to make this work and realize the benefits. Each store location has an edge computing node that processes video data locally, running AI models optimized for edge deployment. When a model needs updating — whether to improve accuracy or add new capabilities — these changes can be orchestrated automatically across all store locations through a central management system.
The key is treating edge AI deployment like we do modern cloud software development — with automated updates, continuous monitoring and rapid iteration. This approach enables retailers to quickly deploy new AI capabilities across their entire store network while automatically updating models and software as improvements are made. Retailers can monitor system health and model performance in real time, maintain security through automated patching and smoothly scale from pilot programs to full network deployment.
But that’s easier said than done. Just look at the reality of other retail tech trends, like digital signage. The promise of using digital signage to rapidly change promotions and branding has also brought operational challenges. If the digital signage node isn’t centrally managed and orchestrated, then new content has to be manually loaded by a store manager with a laptop — yet another chore for already overloaded store staff. And when something goes wrong, store staff often lack the technical expertise to troubleshoot.
As retail continues to evolve, the ability to deploy and manage AI capabilities at the edge will become increasingly crucial. Today, the technology exists to make this vision a reality. The question for retailers isn’t whether to implement edge AI but how to do it in a manageable, secure and scalable way. Those who solve this challenge will be well-positioned to lead retail’s next transformation through enhanced operational efficiency, customer service and innovation capability.
Dormain Drewitz is VP of marketing at ZEDEDA, a leader in edge computing platforms. Before joining ZEDEDA, Drewitz was VP of Product Marketing and Developer Relations at PagerDuty and led product marketing and content strategy for VMware Tanzu. Before its 2019 acquisition by VMware, Drewitz led product marketing, ecosystem marketing and customer marketing at Pivotal Software through its 2018 IPO. Before shifting into platform and solutions marketing roles at Riverbed Technology, she spent over five years as a technology investment analyst, closely following enterprise infrastructure software companies and industry trends. Drewitz holds a bachelor’s degree in history from the University of California at Los Angeles.
