Key Points

1. AI in inventory management isn’t about replacing people—it’s about removing guesswork so that people can do better work

   
   

2. Old Navy’s partnership with RADAR shows that when AI, RFID, and vision systems combine, customer experience gets more personal, not less
   

3. Before AI can work its magic, organizations must confront messy data, tangled systems, and human hesitation—because the tech isn’t the hard part, the people are

I had a client that had trouble selling flip flops–the sandals. They are a major pharmacy with a significant retail component to the business. Flip flops were causing three issues:

1.      flip flops were piling up in storerooms across the continent

2.      the stockpile of dated, old flip flops was growing significantly

3.      it was taking too much time to scan inventory of flip flops that nobody wanted

The answer to these three pain points was found in AI for inventory management. It was actually three disparate AI systems working in tandem, bundled into a new technological solution. This new solution analyzed historical data to determine when flip flops should be put out on the floor and advertised on sale, triggered automatic replenishment of flip flops (so as to avoid over-ordering), and a system that actively monitored when flip flops would be physically removed from a shelf.

The solution is becoming more commonplace. Old Navy, a subsidiary of Gap Inc., recently made retail headlines: they are embarking on a multi-year plan to integrate RADAR’s AI-driven RFID technology into its stores. The idea is to provide associates on the floor with real-time inventory data so that they can locate items quickly within the store. By combining RFID with AI and computer vision (to physically see inventory), Old Navy is not only aiming to improve associate efficiency and accuracy, but they are also aiming to enhance customer service experience. I don’t know about you, but I’m particularly excited; Old Navy never seems to have my size of jeans–ever.

Much like my previous client who struggled with selling flip flops, AI can make a significant impact on inventory management. Let’s dive into this a bit further.

AI for Inventory Management

Enhanced Demand Forecasting. Much like the flip flop example, AI algorithms can analyze historical sales data and marketing data internally. Those data can be combined with external data, such as market and consumption trends, to anticipate future demand. The benefit of doing so shouldn’t be understated. Smart demand forecasting allows retailers to maintain optimal stock levels, thereby saving costs in terms of reducing overstock. For example, rather than just knowing that swimsuits sell better in July, an AI model might flag an early-season heatwave in a particular region, cross-reference those measurements with historical sales surges, and recommend adjust stock levels in that cluster of stores.

Automated Replenishment. Think of this as the reactive component of demand forecasting the other side of the same coin. In this instance, AI systems work off inventory data to automate the reordering of new inventory. In the past, replenishment often relied on static rules: if stock drops below five, reorder ten. But AI can flip this logic on its head, making replenishment smarter and not just faster. Much like the way Old Navy will do so with RADAR, RFID monitors shelf-level data and warehouse status simultaneously. If a product is selling quickly in one store but not others, the system can auto-generate a transfer request. Or it can pause auto-orders if it predicts a drop in demand due to, for example, weather events or shifts in promotional priorities. This is a particularly attractive capability for retailers because it means that inventory management becomes more granular and adaptive.

Operational Efficiency. Better forecasting and replenishment do not just make inventory numbers look nice. They free up actual people to do better work. When store associates stop manually counting items or looking for hidden stock in the storeroom, they focus on customers. When warehouse teams stop scrambling to process last-minute shipments due to stockouts, they can plan strategically. Take RADAR, for example: this system tracks the movement of every tagged item in real time and allows associates to search for an item using a mobile app and be guided directly to it. It’s a small change, but compounding small changes have a ripple effect, specifically faster order fulfillment. It means a customer can actually find what they came in for. It means an employee gets to spend more time helping someone, and less time on scavenger hunts.

Implementation Considerations

For all the power AI brings to inventory management, integrating it successfully is not just a matter of plug-and-play. In each of the two examples I discussed above–of my former client and Old Navy–the real challenge is not the technology: it’s the people involved. The following points below are ones to consider.

Data Quality. AI is only as smart as the data it’s trained on as well as the data it receives in real time. But retail data is messy. Product SKUs vary across systems, sales data are often fragmented between platforms, and real-time inventory counts can be inconsistent at best. So, for AI to work, organizations must undergo a data hygiene campaign that involves cleaning, labeling, and integrating data sources. It’s a critical step, it’s not particularly enjoyable, and it involves time from people across IT, operations, finance, management, and frontline staff. So, remember that if a system doesn’t trust its data, it can’t act on it, nor will your people be able to trust it. Proper data preparation needs to be preceded by proper communications and proper training plans.

Legacy Integration. Many retailers, especially largescale organizations, operate on a patchwork of legacy tools. Sometimes they are systems that are decades old. Many of these systems are bespoke, custom-built designs. Others are bolt-on afterthoughts. When AI is integrated or interacts with these systems, operations can become prohibitively complicated, not to mention expensive. I’d be remiss not to mention the impact of these changes on your people, too. A guide approach is required, one that takes into account what it means to bring together modern technology with antiquated software–particularly when your staff are more accustomed to the former than the latter.

Ethics and Privacy. RFID and computer vision systems can, intentionally or not, start to resemble surveillance. Tracking product movement is one thing—tracking employee and shopper behaviour is quite another. If AI systems are being used to monitor human productivity or shopper movements without transparency, it will lead to mistrust let alone legal risks and morale issues. It is not a new issue that consumers are concerned about what data is collected on them when they enter stores. When you add AI to the mix, concerns increase. Retailers need to be thoughtful about how they use these systems, what data they collect, and who has access. Ethical use is not just a matter of compliance, but  a matter of culture, priorities, and principles.

The Future of AI in Inventory Management

The trajectory of AI in inventory management points towards increasingly sophisticated applications and use cases. Machine learning, computer vision, and robotics are set to further enhance inventory accuracy and operational efficiency. There is much to be saved and salvaged through these advancements, though it is important to recognize that these advancements are investments. They require planning, time, and reflection. Old Navy's partnership with RADAR precisely exemplifies the transformative power of AI in inventory management. It’s also a reminder that people always matter when working with AI.