ARTIFICIAL INTELLIGENCE

Has your retail store considered switching to self-service kiosks? Are you worried about potential challenges such as “unexpected item in bagging area,” accurately identifying produce, or ensuring a smooth checkout process without constant staff intervention?

These are common obstacles on the path to digital transformation that both retailers and consumers face. But intelligent retail technology has the potential to significantly enhance the customer experience and streamline operations. Improving the employee experience and retaining skilled staff are additional benefits that can greatly impact your bottom line.

Matt Redwood, Vice President of Retail Technology at Diebold Nixdorf, a retail technology company, guides us through the landscape of retail technology. He discusses AI solutions for common retail inefficiencies, the importance of purposeful innovation, and the value of leveraging technology partners throughout the transformation journey (Video 1).

What are some top challenges retailers face today?

Most retailers are struggling with the same challenges. And making sure that the in-store experience is as good as possible for their customers is a key one. Post-Covid, retailers are really investing very heavily in that again. But they’re also being squeezed both on the top line and on the bottom line—the cost of goods is up, the cost of freight, the cost of managing and running stores—and they have to find ways of driving efficiencies in the store while also delivering that great consumer experience. It’s a real balance between getting the economics of retail right and satisfying the needs of the consumer.

And competition is as high as it’s probably ever been in retail, which is good in certain aspects. It helps with pricing and keeping inflation under control, but on the flip side, if consumers have a bad experience in a store, it’s easy for them to flip to another brand.

How is AI being used to address some of those challenges?

Generative AI really took off in retail in 2023, and certainly some companies really rushed to an AI endgame, with this euphoric view that AI could replace all the existing technology within stores. I think sometimes we forget that although the technology may exist—forget whether it’s commercially viable or practical to deploy it—you have to have consumer adoption. If you don’t have consumer adoption, the technology is worthless. That’s what I call the hype curve.

What a lot of retailers are now doing instead is focusing in on their pain points with what we call point-solution AI technology, that is, specific AI deployed for a specific use case to solve a specific problem—technologies like facial recognition for age verification. For example, if you’re trying to buy a bottle of wine, you have to wait for a member of staff to approve your ID. And that wait is compounded by the fact that retailers are struggling to find staff. Using AI in that environment drives greater efficiency, it reduces that requirement on members of staff, and it boosts that consumer experience.

Another big one is anti-shrink technology and using AI to make it more difficult for those who are trying to steal. But it can also help when someone may have just been unfamiliar with a process or have genuinely made a mistake—making sure that that is being caught without making it a bad experience for that particular customer.

We’re also starting to see AI applied on top of existing technologies to make them more efficient, to make them easier to use, to close loopholes, and to boost the consumer experience. One example is in-store safety—using AI on top of CCTV networks to make sure fire exits aren’t blocked, say. Or heat mapping to understand the flow of consumers around stores—making that flow easier but also potentially commercializing that flow.

What is the best way for stores to implement AI?

The “build it and they will come” mentality does not work with retail technology. We track the consumer-adoption curve and we track the technology-development curve, and it’s important to find something broadly in the middle.

We always recommend starting with data. It’s very easy to be swamped by it—we call it paralysis by analysis. But if you can segment your data, it can provide a lot of insights. You can really analyze and understand: How is the store operating? Where is the friction within the staff journey? Within the consumer journey? You can then quantify the effect that that friction has. It builds the picture to say, “Okay, I’ve got a problem statement that I want to solve. It’s having this impact on consumers and staff, and this is the impact to my business.” And that’s relatively easy to calculate.

We are starting to see #AI applied on top of existing technologies to make them more efficient, to make them easier to use, to close loopholes, and to boost the consumer experience. @DieboldNixdorf via @insightdottech

The more problematic piece is then finding the right innovations to deploy in the store to solve for that issue. But starting with that data highlights where the biggest areas of inefficiency are and then provides the compass to point you in the direction of the right technology. It’s also then very easy to actually measure how successful that technology has been once it’s been put into the store.

Tell us more about matching the right technology to a specific problem.

At Diebold Nixdorf, we’ve really focused on three core solutions where the biggest friction points are. One is age verification, which I mentioned before. Facial recognition provides a much better experience for the consumer. It’s faster, and faster transactions mean that consumers are moving through the front-end quicker. That means fewer queues, and queuing is consumers’ biggest checkout bugbear. So we remove two of the biggest friction points associated with checkout with one piece of technology.

There are also technologies centered around the product, such as efficient item recognition at checkout—particularly in grocery for fresh fruit and vegetables. That is the second solution. And it’s not just for non-barcoded items like produce. In some environments, particularly in smaller stores, why should you have to scan the barcode when you could identify the item by its image?

And then, finally, shrink. Of course, the natural argument is that self-service is a natural place for shrink because it’s unmanned in a lot of environments. But with those who are maliciously trying to steal, even if we close all of the loopholes at self-service, they will find somewhere else in the store to steal from. We’ve really focused our AI efforts there on behavioral tracking. Once you can start to identify behavior, it doesn’t matter where within the store you deploy the technology. Of course, we focus on the front-end first: self-service checkouts and POS lanes. But then we run that same solution onto the CCTV network, and then we can identify shrink anywhere in the store.

Where does the human element come into play?

The human element is really, really important to self-service, and it’s quite often overlooked. Self-service is more about staff redistribution. Attracting and retaining staff is a big problem for retailers, so they have to use their staff wisely. And where self-service is playing a major role is in unlocking members of staff to interact with consumers where those consumers need the most help—finding an item, asking a question about it, navigating the store—places where it really makes sense to deliver that consumer experience. During Covid, retailers that had self-service had much greater flexibility of operations within their stores; post-Covid, self-service actually allows them to boost the level of consumer experience where it really counts.

Let’s go back to the challenge of preventing shrinkage. So, it’s relatively easy to identify if someone has stolen. What you then do in that scenario is more difficult. If someone is stealing maliciously, you don’t want to put your staff in danger or in an environment that they don’t feel comfortable with. You also don’t want to alienate or embarrass someone who has genuinely made a mistake. So we are very much putting the human element into the situation here; the situation will be dealt with differently depending on the use case of the theft.

If there’s an instance of shrink, an alert is sent to a member of staff. All the information is put in that person’s hands so that they can deal with the situation in the way that they see as appropriate. And staff training comes into play here. We have a number of great partners that work on staff training to give employees the toolkit they need. Then, when they approach that member of the public—and they’re approaching them knowing exactly what’s happened—they’re trained to deal with that situation in the most agreeable way possible. So the technology is only one-third of the actual solution; the human element is a massive part of it that shouldn’t be overlooked.

How is Diebold Nixdorf solving customers retail challenges?

As a solution provider that retailers work with to build out their technology—not just across checkout but all the way across the store—we quickly realized that it was unrealistic to think that we could have 20 or 30 different solutions—all in the AI space, all providing different use cases, but none of them talking together. So we work with a third party that has a very mature AI platform, and that becomes the backbone for anything the retailer wants to do within their store from an AI perspective.

We are the trusted partner, the integration partner. We will provide applications that can sit on top of that platform—like age verification, shrink reduction, item recognition, process or people tracking. But if there is a particular partner out there that is market leading in, say, health and safety, we can plug them on top of the platform, too. It doesn’t make sense for us to reinvent the wheel.

And what that means is that the retailer can build this ecosystem of AI partners, all plugged into a single platform, and the solution is very, very scalable. It will ultimately move us towards what we call intelligent store. It isn’t necessarily removing the physical touchpoints or removing the existing technology; it’s about providing intelligence to retailers.

Every device in the store is effectively a data-capture device—a shelf edge camera or a self-service checkout or a scanner—these are all data inputs. And that’s a two-way street: You can push data down, you can pull data back. The AI platform allows you to connect all of these together to create that intelligent store.

It does mean that there’s a huge amount of data available, but I think the retailers that are really going to advance quickly are the ones that work out what to do with it. Because it can and it should inform every single decision or direction that a retailer takes—how products are priced, where they are positioned within the stores, how stores are staffed.

What is the value of technology partnerships in making AI retail solutions happen?

We work very, very closely with Intel—not just on the AI topic but for our core platform itself. And not just on the solutions that we deploy into stores today but also on our development roadmap. And we follow the developments at Intel closely, too—where Intel is going with its solutions and how we can better integrate those into our solutions.

We work particularly closely with Intel on some of the scalable platforms. Retailers have technology requirements today, but—particularly with these AI topics—the amount of computing power that will be required in three or five or seven years will be very, very different from the requirements now. So providing retailers the ability to scale their technology to meet their future requirements is an absolute game changer.

Any final thoughts for those looking to incorporate AI in retail?

I would say, start with the data. Identify the business requirements or problems that you are looking to solve and then find the right provider that’s going to enable you to deliver against those requirements today, but that is also going to give you that longevity of scalability. It is a marriage, and you have to make sure that you’ve made the right choice.

Related Content

To learn more about AI in retail, listen to AI in Retail: Stop Shrinkage and Streamline Checkout and read New Retail POS Solutions Transform the Checkout Journey. For the latest innovations from Diebold Nixdorf, follow them on Twitter/X at @DieboldNixdorf and on LinkedIn.

This article was edited by Erin Noble, copy editor.