Edge AI in Retail: Bringing Intelligence Back to the Storefront

How Edge AI is reshaping in-store intelligence, operations, and shopper experience.

AI in retail is evolving. But the next leap forward won’t happen in the cloud. It’s happening at the edge: on store shelves, POS systems, mobile apps and more. Stores are becoming intelligent.

Before the latest buzz and excitement around "Agentic AI" took centre stage, there was a growing conversation around ‘Edge AI’; a concept that promises to transform industries by bringing intelligence closer to where the action happens, rather than relying solely on distant data centres. Now, with the release of lighter, more efficient AI models, the conversation is back. We now have the ability to run cost-effective AI directly on in-store platforms, unlocking unprecedented levels of efficiency, personalisation and competitive advantage. For retailers and brands, this is a game-changer.

But what exactly is Edge AI, and why does it matter now?

The concept of "edge computing" isn't entirely new. Many of us, particularly those with a background in the tech domain (like my own), will recall conversations about pushing computation closer to the data source to reduce latency. For a long time, this vision was primarily about optimising data transfer and running traditional software locally. It was largely the domain of hardware specialists focused on network efficiency. This is no longer the case, as the launch of highly efficient and specialised AI models has now added true intelligence to these edge devices.

It's crucial, though, to distinguish between general Edge Computing and Edge AI. Let's look at this first.

• Edge Computing (Broader Concept): This involves processing data near the source (at the "edge" of the network) to reduce latency, conserve bandwidth, and enhance reliability. It can involve simple data filtering, aggregation, or even running traditional software applications locally. Think of it as the foundational infrastructure that enables Edge AI. For example, a local server in a store that caches product information so POS systems can access it faster than going to the cloud, or a sensor that sends aggregated temperature data from a freezer, but doesn't interpret that data.

• Edge AI (Specific Application of AI at the Edge): This specifically involves running AI models ( like machine learning, deep learning, or generative AI models) directly on edge devices to perform tasks that require intelligence, pattern recognition, and decision-making locally. It's not just moving data processing; it's moving the smart part of the processing to the edge.

So, while "Edge Computing" provides the proximity and local processing power, "Edge AI" adds the brain, allowing for truly automated, intelligent responses at the source.

What's different today, and what makes Edge AI truly potent, is the advent of lighter, more efficient AI models. The emergence of compact yet powerful models like Google's Gemma, alongside other optimised frameworks ( such as TensorFlow Lite, PyTorch Mobile, and ONNX Runtime ) and smaller language models (SLMs) like Meta's Llama 3 (8B) or Microsoft Phi, has fundamentally shifted the landscape.

These innovations mean that sophisticated AI capabilities are no longer exclusively tied to massive cloud data centres. They can now run effectively on less powerful, more cost-effective edge devices – from in-store cameras and POS systems to robotics and smart interactive devices. This is a game changer as it finally enables retailers to truly "raise the bar for in-store execution and shopper experience" by embedding intelligence directly where it matters most.

The Business Case: Is Edge AI Worth the Investment?

The short answer is yes, and the Return on Investment (ROI) can be substantial. Reports from industry leaders like KPMG and Google Cloud's 'The ROI of Gen AI in Retail and CPG' consistently show strong returns, with Google's research indicating that 74% of enterprises leveraging Gen AI are seeing ROI within the first year, and 86% of those achieving revenue growth estimate gains of 6% or more. While this isn't exclusively Edge AI, it speaks to the broader value AI brings, and Edge AI specifically targets many high-value use cases that drive these returns.

The benefits around cost savings, increased revenue potential and improved operational efficiencies are reasons enough as to why retailers and CPGs should seriously consider investing in this capability. Let me explain this.

• Significant Cost Savings:

  • Costs associated with cloud infrastructure and bandwidth can be significantly reduced

  • Labour costs can be reduced through automation of in-store tasks like inventory checks, merchandising audits, and even checkout. In addition, reduced losses from shrinkage and improved inventory management make a significant impact. For instance, industry reports and case studies show Edge AI surveillance has been instrumental in reducing theft-related losses by up to 25% (or even higher in some instances)

• Increased Revenue Potential:

  • Improved product availability due to real-time stock management can prevent lost sales.

  • Effective personalisation and targeted promotions can drive higher conversion rates and basket sizes; Edge AI for personalised marketing has been shown to increase conversion rates by approximately 10-15%.

• Operational Efficiency:

  • Faster decision-making and response times to in-store events can directly lead to better in-store execution.

  • Streamlined workflows for store associates allow them to focus on higher-value customer interactions.

Early adopters of Edge AI can gain a significant edge by delivering superior customer experiences and operating with greater efficiency than their competitors. A better shopping experience leads to higher customer satisfaction, loyalty, and repeat business.

For a 500-store retail chain, for example, a mere 30-basis-point reduction in shrinkage could translate to millions in annual cost savings. Likewise, preventing persistent stockouts could unlock significant incremental sales. Benefits from such investments are compelling.

While the benefits are clear, let's take a realistic view of Edge AI adoption as it comes with its own set of challenges.

Often, these challenges revolve around hardware limitations, data management complexities, and integration with existing systems. Edge devices may have limited processing power, memory, and battery life, requiring careful model optimisation. Managing data across distributed edge devices and ensuring seamless updates can also be complex. Furthermore, a shortage of AI skills and concerns about data privacy and security (despite Edge AI's inherent benefits) need to be addressed.

While I say this, it's not just doom and gloom as continuous innovation is happening as companies look to develop edge AI platforms to simplify deployment, monitoring and upgrades across a large set of devices, much like cloud software deployments.

So, how do Retailers & Brands adopt Edge AI? Where to start from?

Adopting Edge AI doesn't necessarily mean a complete overhaul of existing infrastructure. It's more about a strategic re-think and incremental steps.

Start by pinpointing specific pain points or opportunities where real-time, localised intelligence can deliver the most significant value. Loss prevention, out-of-stock prevention, or enhancing customer service at peak times are often good starting points.

Once you understand your high-value use cases, begin with small-scale pilot deployments in a few stores or a specific product category. This allows for testing, learning, and refining the solution before a broader rollout. Focus on proving the ROI in these initial pilots.

While Edge AI aims to reduce reliance on the cloud, it still requires capable hardware at the edge (e.g., smart cameras, edge servers, robust Wi-Fi). Evaluate existing infrastructure and make necessary upgrades to support AI processing on-device. And remember: Edge AI isn't about completely abandoning the cloud. A robust strategy involves a hybrid cloud approach:

1. Edge: Real-time data processing and immediate decision-making.

2. Cloud: Long-term data storage, model training and refinement, enterprise-wide analytics, and remote management of edge devices. This allows for the continuous improvement of AI models based on aggregated insights.

While the above-mentioned points around infrastructure are important, don't forget about data governance and privacy. Establish clear policies for data collection, processing, and storage, especially concerning customer data. Transparency in how AI systems use data will be key to building customer trust.

Invest in talent and train staff on how to interact with and leverage Edge AI systems, ensuring they understand the benefits and how these tools augment their roles. Lastly, choose solutions which offer flexibility- by that I mean, opt for solutions that offer modular architectures and can easily integrate with existing retail systems (POS, ERP, CRM) via APIs. This ensures that as your AI strategy evolves, your technology can adapt.

Edge is where the action is.

The conversations around agentic AI are exciting, pointing to a future where AI systems act with greater autonomy. Yet, for industries like retail and CPG, the true immediate impact often lies closer to the ground, at the "edge."

If generative AI is about creating intelligence, Edge AI is about translating that intelligence into immediate, tangible action.

The global Edge AI market is projected to grow significantly, with retail being one of the leading sectors for adoption. The convergence of Edge AI with 5G networks, for instance, promises even more seamless and powerful applications in smart retail spaces.

The next wave of AI-driven innovation in retail won't just reside in massive data centres. It will live on the shelf, in the hands of associates, and within connected experiences where milliseconds matter. By embracing this shift. Retailers & brands gain more than just speed; they gain control, context, and a powerful competitive advantage in an increasingly dynamic market.