Insight Tech

Christina Cardoza: Hello and welcome to “insight.tech Talk,” formerly known as “IoT Chat,” but with the same high-quality conversations around IoT, technology trends, and the latest innovations you’ve come to know. I’m your host, Christina Cardoza, Editorial Director of insight.tech, and today I’m joined by Matt Redwood, Vice President of Retail Technology at Diebold Nixdorf. Hey, Matt, thanks for joining us.

Matt Redwood: Hi, Christina. It is great to be here speaking with you today.

Christina Cardoza: So, for those of our listeners who are not familiar with Diebold, what can you tell us about the company and what you do there?

Matt Redwood: Diebold Nixdorf is a technology company of two halves. We provide banking systems to the world’s largest banks, and we provide retail technology to the world’s largest retailers. I’m responsible for retail technology. We provide hardware, software, and services to most of the 25 top retailers globally, as well as quite a few tier-two, tier-three retailers.

And we generally cover front-end technology, which we’ll go into more detail on, software and enterprise software. And then we provide most of the services—break/fix and help desk services—to retailers to make sure all their technology is up and running for the maximum time possible.

Christina Cardoza: Great. And obviously we’ll be focusing on the retail aspect of Diebold Nixdorf today. We’ll have to get someone else on a later podcast to talk about the financial aspects of the company.

But the last time we spoke with you, Matt, it was for an article on insight.tech, and we spoke about POSs transforming retail checkouts to improve customer experiences in stores. But customer experiences—I think that’s just one pain point that retailers are facing today, one challenge. So, that’s where I wanted to start the conversation off today. What are the different challenges retailers face today, in addition to customer service, in stores?

Matt Redwood: So, it’s a bit of a tough time for retailers. And I think, regardless of what sub-vertical of retailer you are in, I think most retailers are struggling with the same challenges. So, on one side, as you said, customer experience is key—the desire or drive to make sure that the in-store experience is as high as possible with this ever-changing horizon or landscape amongst consumers, that their expectations continue to rise. So, the horizon of expectation continues, and retailers are really chasing after that. And we are really starting, post-COVID, to see retailers really investing again very heavily in that in-store experience, which is great to see.

On the flip side, on the top line and bottom line, they’re being squeezed. I think you can read in the press global and economic trends that are driving the cost of goods up, the cost of freight up, the cost of managing and running their stores up. So, their top line is being squeezed, their bottom line is being squeezed, and they must 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, as well as satisfying the needs of your consumers.

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 it means that consumers are very flippant in terms of where they get their experience from and where they shop. If they get a bad experience in a store, it’s easy for them to flip to another brand and get a better experience of potentially better products, better prices. So, it’s a very dynamically changing environment, very difficult one for retailers today.

Christina Cardoza: I’ve seen a lot of retailers start adding new technology, more intelligent technology and sensors, to be able to do some of these things: collect data at the edge in real time so they can make decisions as they’re happening. A lot of this is being powered by artificial intelligence, and I think we’re in a stage or a point today in the industry where AI is everywhere, and everybody’s trying to use it and get the benefits from it.

So, from your perspective, how is AI being able to address some of those challenges that you talked about, and what’s the reality of it? What are the real benefits that are coming? Because I feel like sometimes there’s hype, but where can we start using and getting actionable insights?

Matt Redwood: I think 2023, for most people, will be known as the year of AI. It’s where generative AI really took off in retail, and we started to see more and more AI applications in the retail market. And certainly, some companies really jumped to what I would consider the end goal of AI—which is completely changing the technology landscape, completely changing the customer journeys, the staff journeys, how you operate and run your stores—with this kind of euphoric view that AI could remove all technology that existed within stores.

That’s what I call the hype curve. We’re coming through the trough and we’re going back up again, in that a lot of people realize that that technology, although fantastically advanced, was probably quite a way off being realistically deployable en masse. The cost of the technology was high, there were limitations in terms of the size of the store and the amount of products and the amount of consumers. So, trying to take that technology and apply it to retailers today wasn’t applicable.

So, what we are seeing, and what a lot of retailers have done, is kind of take stock of the situation, re-address what’s really important, focus in on the pain points, and then really go, again, with what we call point-solution AI technology: so, specific AI deployed for a specific use case to solve a specific problem, but is very much for that particular use case. And we’re starting to see more and more of these solutions being trialed across retail stores, not only in grocery.

And the possibilities are really—they are bountiful, and they’re kind of endless. And some of the examples that we’re seeing are everything from health and safety in store—using AI on top of CCTV networks to make sure fire exits aren’t blocked or there’s not foreign objects or liquid spill on the floor where someone might slip over. We’re using it for heat mapping to understand what is the flow of consumers around stores—how do I make that flow easier, but also how do I potentially commercialize that flow?

We’re seeing AI on top of existing technology—so, something very close to my heart: self-service. We’re starting to see more and more AI being applied on top of existing technologies to make them more efficient, to make them easier to use, to close loopholes, to boost the consumer experience. So, technologies like facial recognition for age verification.

I think we’ve all been in the situation where we’re trying to buy paracetamol or a bottle of wine, and you must wait for a member of staff to come over and approve your ID. That’s been compounded, the effect of that situation, by the fact that retailers are struggling to find staff. So now I’m having to wait a little bit longer to have a member of staff be available to come and approve my ID. Using AI in that environment drives greater efficiency at the frontend. It reduces that requirement on members of staff, and it boosts that consumer experience.

We’re also seeing technologies centered around the product. Item recognition, really, really taking off. Not just for non-barcoded items—where we’ve seen fruits and vegetables selection—but also all item recognition. And in some environments, particularly smaller stores, why should you have to scan the barcode when you can identify the item by its image? So that’s an exciting technology.

And then finally, something that we’ve been working on over the last 18 months, which is anti-shrink technology using AI. Obviously shrink is something that’s really gone through the roof in a lot of retail environments, driven by the cost-of-living crisis. And we are now working with a lot of retailers; we’ve got 54 different retailers we’re working with on anti-shrink technology in one form or another to try and close those loopholes and make it more difficult for those that are maliciously trying to steal, making it difficult for them to be able to steal. But also those that may have just been unfamiliar with a process or genuinely have made a mistake. Also making sure that we are catching that, without making that a bad experience for that particular consumer.

Christina Cardoza: It’s interesting; in the beginning of your response you mentioned how retailers, they were adding this technology to really transform everything, and they were sort of jumping to the end. And especially when you’re implementing artificial intelligence, which has so many connotations with it, so many misconceptions. It’s interesting, because I feel like these things need to be gradually introduced to consumers for them to be able to accept it, to understand it, to use it.

I can’t tell you how many times I’ve been in self-checkout, where we’re using AI or computer vision, and I can’t even put an item on the scale after I’m done scanning it because it needs to be in a bag, or I can’t bag it yet because of the bag weight. It’s just so complicated.

I know every retailer has different challenges and different areas of entry, but would you say there is an easier place of adoption happening right now to adding some of this intelligent technology? And then not only easier to adoption for consumers and for the store, but—like you were talking about the facial recognition—I know consumers have privacy concerns around that. So how can stores easily implement this that makes the most sense for consumers and for themselves and their business?

Matt Redwood: Sure. So, complex question. I’m going to break it down into parts. So, when we talked about retailers and some technologists rushing to that endgame, it really was about trying to boil the ocean with AI to try and completely change the landscape of retail. And I think sometimes what we forget is although the technology may exist, forget whether it’s commercially viable or practical to deploy it, you have to also have consumer adoption. If you don’t have consumer adoption, no one will use the technology in it. It’s worthless.

So we very much, we track the consumer-adoption curve, and we track the technology-development curve, and it’s important to find something broadly in the middle of those two in terms of what’s the right technology; what’s the right innovation and technology; why am I deploying it? Making sure consumers adopt it, but, crucially, making sure that it solves a need and it solves a business or a consumer desire. The “build it and they will come” mentality does not work with innovation, and it doesn’t work broadly with retail technology.

Consumers are savvy, and retailers are much, much more savvy in terms of deploying the technology. It must deliver. So we always recommend starting with data. A lot of people talk about data; there’s a lot of data that exists; it’s very easy to be swamped by data. We call it paralysis by analysis. There’s too much data out there. But if you can really segment your data to understand—if I’m looking at my transactional process or my customer journey—making sure that I’m looking only at the data that relates to that and highlighting the problems.

I’d say 98%, 99% of our customers that we work with now, we actually work well on a consultative basis to actually really deeply understand their stores, how they’re being run, and how their consumers shop in their stores. And the data provides a lot of insights to that. So really understanding and analyzing: How is the store operating today? Where is the friction associated with the staff journey or the consumer journey? Understanding and quantifying the effect that that friction really then builds the picture to say, “Okay, I’ve got a problem statement I want to try and 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.

The more problematic piece is then really finding the right innovation to solve that. And very much we try and put the consumer and the staff journey at the center of everything that we do. If it doesn’t provide value for the consumer, if it doesn’t provide value for the members of staff, and it doesn’t provide value for the retailer—that triangle of value is at the center of everything that we do. And if it’s not ticking all three of those boxes, we don’t put it into the range and we don’t put it into the solutions or the stores.

Starting with that data is a bit like the treasure map. It highlights where your biggest areas of inefficiency are and then provides the compass to kind of point you in the right direction of what’s the right technology that you should be deploying to the store to actually try and solve that particular issue. And when you break it down like that, and we start thinking about this kind of AI-boil-the-ocean vision, we start thinking about individual point solution, it becomes much easier because it’s much more manageable to deploy from a technology perspective, it’s much easier to develop a solution that works for a particular use case or problem that you’re trying to solve.

But it’s also then arguably very easy to measure how successful it’s been once you put it into the store. The difficulty then comes is what you don’t want to do is collect a whole group of point solutions that don’t talk to each other, and it becomes very, very difficult to scale. Finding the right AI platform that allows you to scale all of these point solutions on a singular platform is really, really important.

Christina Cardoza: Yeah. I love one thing that you said, which was basically, if it’s not solving a problem or if it’s not benefiting the customer or the business, then don’t do that. I feel like that’s a major problem that we have with implementing technology and seeing shiny new things. Let’s just add it to add it, but why are we adding it? It’s not going to get you a return on investment, and it’s not going to help your business if it’s not really doing anything for you. So, I think that that was a great point.

I want to come back to also that facial recognition example again—how obviously I think we’ve all dealt with self-service checkouts or checkouts where you’re scanning something, it doesn’t recognize it, you need a human cashier to come and help you, and that just bottlenecks the entire process.

But there seems to be a lot more self-checkouts in the store. How does the role of the employees come into this? I know, talking about the consumer misconceptions that they have, there’s a lot of misconceptions that this is going to replace employee jobs, and especially when you see that it—there’s not a lot of cashiers on the floor anymore. So where does the human element come into play with some of these?

Matt Redwood: So, the human element is really, really important to self-service, and it’s an element that’s quite often overlooked. If you look at the evolution of self-service, self-service was originally designed as a POS, an attendant till replacement, to ultimately remove the cost of the staffing from stores. But self-service has been around for 20, 25 years now, and the drivers for deploying self-service are very different today compared to 20, 25 years ago.

I’d say 100% of the retailers that we deal with are either putting in self-service—and they might be on their second or third iteration of self-service because they’ve been in that business for a while—or they’re putting self-service in for the first time. A lot of retailers outside of grocery are just trying self-service for the first time. The approach is very, very different, and it’s very much less about removing staff from the equation, more about staff redistribution. The inability to attract and retain staff in retail is a real big problem for retailers, so they have to use their staff wisely. And where the consumers value the staff interaction the most is where they need it, and where they need it is where they generally they need help either navigating the store, finding an item, asking a question about a particular item, or just general assistance.

What self-service is really playing a major role in retail today is it unlocks that member of staff. So I would say to anyone that looks at self-service and says, “Oh that’s going to replace people’s jobs,” it’s not; it’s very much about labor redistribution now. It frees up a cashier that could be sat behind a till for a 12-hour shift to be up on their feet, engaging with consumers shoulder to shoulder in the aisle where it really makes sense to deliver that consumer experience.

Particularly through Covid we saw retailers that that had self-service had much greater flexibility of operations within their store. Post-Covid we’re now seeing that that allows them to boost the level of consumer experience where it really counts. Obviously, there’s always been friction associated with self-service and the adage of “unexpected item in the bagging area”—all of those common friction points perceived with self-service, they’re starting to really drain away.

A lot of focus has been put on fine-tuning and making sure that the base technology works to a much, much more acceptable level. And we’re now seeing self-service that’s very efficient, that generally most of the time you can sell-through a transaction with no intervention, no requirement for a member of staff to come over. We are now in the fine-tuning era of self-service, and why I say fine-tuning is we’re really looking for that last 5% or 10% of efficiency gains.

So, Diebold Nixdorf, we’ve really focused on three core solutions initially out the bag, and those three core technologies have been developed because we identified via the data where the biggest friction points were. So, age verification: 22% of interventions broadly are age related. That’s a big number. If we can use facial recognition to identify the age of the consumer and remove that validation process that’s happening—A, much better experience for the consumer; B, it means faster transactions. Faster transaction means less staff requirement at the till, but it also means that consumers are moving through the frontend quicker.

So that means less queues. Less queues—queuing is the biggest bugbear of consumers when they get to checkout. So, we’ve removed two of the biggest friction points, associated with checkout with one piece of technology. Item recognition, particularly in grocery for fresh fruit and vegetables, was another area of frustration from a consumer perspective. But also inefficiency from a retailer perspective: spending 20, 30, 40, 50 seconds, trying to find the type of apples that I’m looking to buy is frustrating, but it’s also time consuming. So, using item recognition to identify those apples so the consumer doesn’t have to run that process again. Good consumer experience, great productivity gains.

And then finally shrink. We touched on it a little bit earlier, but obviously retail shrink has really gone through the roof, and I think a lot of retailers are battling to really understand: where is there shrink happening? So of course, the natural progression in that argument would be to say, well, self-service is a natural place for shrink because it’s unmanned in a lot of environments.

But what we’re actually finding is: There’s two different types of people that steal. There are people that maliciously try and steal and those that have just made a mistake and it’s genuinely unmalicious. And how you treat those two individuals has to be dealt with very, very differently, because you don’t want to alienate or embarrass the consumer that’s genuinely made a mistake.

For those that are maliciously trying to steal: unfortunately, if we close all of the loopholes and make it impossible to steal at self-service, they will find somewhere else in the store to go and steal. So, we’re in this kind of Whack-A-Mole-type environment, where we’re trying to close all the loopholes as quickly as possible. We’ve really focused our efforts on AI with behavioral tracking. And the reason why we use behavioral tracking is once you can start to identify behavior, it doesn’t matter where you deploy the technology within the store, you can identify malicious behavior and that shrink environment.

We very much focus on the frontend first: we’re deploying shrink onto self-service checkouts and onto POS lanes. But the idea is that the next natural evolution is that then run that same solution onto the CCTV network, and then we can identify shrink anywhere in the store. The human element of this is really important because it’s relatively easy to identify if someone has stolen. What you then do in that scenario is a difficult situation.

What you don’t want to do is alienate a consumer that might have non-maliciously stolen. If they’re maliciously stealing you also need to deal with that in a particular type of way, but you also don’t want to put your staff, your cashiers in your store: A, in danger; or B, in an environment that they don’t feel comfortable with. So, we are very much putting the human element back into this, that, depending on the use case of the theft, we will then deal with that situation differently.

But what we will always do is put the information in the members of staff’s hands so that they can deal with that situation in the way that they see as appropriate. So with all of our shrink solutions—whether it’s on self-service checkout or POS—once the shrink instance has been identified, an alert is sent to a member of staff’s wearable technology—whether it’s smartwatch or tablet or phone or even their POS lane—they’re notified that there’s a shrink instance that’s happened, they know where it’s happened, and they can even review the video clip.

So now they’re empowered that they know what’s happened in that situation, they know what to look for. And then staff training really comes into play here, and we have a number of great partners that we work with on staff training who actually work through these scenarios to give the staff members the toolkit so that when they approach that member of the public and they’re approaching them knowing exactly what’s happened, they’re trained to be able to deal with that situation in the most agreeable way possible—to disperse any aggression or any risk that might be associated, but also to make sure it’s a good experience for that end consumer. 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.

Christina Cardoza: And I think the change in roles and responsibilities for cashiers to being able to have more meaningful interactions with customers—that’s not only benefiting the customer experience, but that’s also benefiting the employee experience as well, maybe keeping employee retention. I was a cashier in college, and I can tell you that is a tedious and redundant process. I would have dreams of just scanning food and shouting out numbers. And it’s not only retail shrink and loss—I think it’s not only with malicious actors or by accident—but sometimes as a cashier I would hit the wrong number just because I was on autopilot going repeat. It was an error-prone process. So, I can see that helping it as well.

You mentioned that to really be able to be successful you need an AI solution that connects all of these together so that this is not happening in silos and the data is actually actionable. Obviously, we’re talking to Diebold because you guys are a leader in this space. So, I’m curious to hear how you are helping customers—if you have any real-world examples or case studies that you can share with us.

Matt Redwood: Yeah. And I’ll be completely honest: we fell into the most obvious trap, looking back at our journey on AI. We’ve been working on this now for two and a half years, nearly three years. And we originally, we went out to market to try and find the best solutions to solve these three use cases, but what we quickly found were there was lots of different competing technologies. There were a lot of potential third parties that we could have worked with, but the underlying technology was the same.

And we quickly realized that actually as a solution provider who retailers work with to actually build out their technology—not just across their checkout but all the way across the store—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 actually kind of paused our program and redesigned our go-to market strategy, which was very much focused on providing an AI platform, and we work with a third party in this space who have a very, very mature AI platform. We’re entering the retail market and didn’t necessarily have the applications to run on top of it. So, we’ve worked with them to actually develop out these three applications as a starting point in the AI space.

But the nice thing about the AI platform is it effectively becomes the AI backbone for anything the retailer wants to do within their store from an AI perspective. This means that we can really satisfy our openness. It’s an ethos that we drive in our product strategy, which is openness of software. And what we mean by that is we provide the building blocks for retailers. We are the trusted partner, we’re the integration partner, but if there’s a particular third party out there who has got the market-leading solution in a particular area, it doesn’t make sense for us to go and reinvent the wheel.

So, when we talk about openness, the ethos that we take to our retail customers—but also that permeates through our R&D and product-management ethos—is to very much work with the best of breed within the market. And our strategy is to basically provide this AI platform for retailers. We will provide applications that can sit on top of it—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 health and safety, we can plug them on top of the platform.

And what that means is the retailer can build this kind of ecosystem of AI partners, all providing best-of-breed solutions, but, critically, they’re all plugged into a single platform. So, they utilize the same business logic; they utilize common databases, like item database or loyalty schemes and things like that. That makes the solutions very, very scalable. It makes them much easier to manage, but it also means that they’re all talking to each other.

And the beauty of AI is it is self-learning to a certain extent. So, the more applications that we plug into this, the more physical touch points that we have in the store, the more information is flowing through the platform and then the quicker it can develop and the quicker it can learn. So, it’s very much a self-perpetuating solution that we’re very much at the beginning of this journey.

As I say, we’ve got about 54 different customers using AI in one form or another. But we very much see this as a much, much longer journey, where we’re starting to build an ecosystem of solutions that will ultimately move us towards what we call “intelligence store.” And intelligence store for us isn’t necessarily removing the physical touchpoint or removing the technology; it’s about providing intelligence to retailers.

And what I mean by that is every device that sits in the store is effectively a data-capture device. And that’s a two-way street: you can push data down to them, you can pull data back. So, whether it’s a shelf edge camera, or whether it’s a staff device or a self-service checkout or a scanner or a screen—these are all data inputs. There might be AI point solutions associated with them, but the AI platform allows you to connect all of these together and create an intelligent store, where intelligence really permeates every single area of the store.

It does mean 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 this data. Because it can and it should inform every single decision or direction that you take as a retailer—whether it’s how I price my products, where my products are positioned within the stores, how I afford loyalty systems to the consumers, how I staff my stores, how I operationalize them—but also what technology exists within the stores.

So, data—it’s a cliché—but data will form the basis of every single decision that we make—from either a technology perspective, solution-provider perspective, but also from a retail-operations and a store-design perspective as well. So, it’s a really, really exciting journey that we’re on.

Christina Cardoza: Absolutely. And I think it’s really important to find a solution provider that is willing to work with others in the industry and leverage their expertise. I think that helps prevent vendor lock-in; it allows you to take advantage of the latest technologies and enables you to innovate faster, working with some of the best partners in the market. So, speaking of best in the breed, insight.tech and the “insight.tech Talk,” we’re obviously sponsored by Intel, so I’m curious if there’s anything you can tell us about that partnership and the technology that you use to make some of your AI retail solutions happen.

Matt Redwood: Absolutely. So, we work very, very closely with Intel—not just on the AI topic but from our core platform itself. Intel very much underpins a large part of our portfolio, so we have a very, very close working relationship with them—not just on the solutions that we deploy into stores today but also our roadmap on our development. We work very closely with Intel on their developments: where they’re going with their solutions and how we can better integrate them into our solutions to give retailers better solutions but also much, much greater flexibility for the future.

And I think a good example of that is probably the speed of development of technology. If you think about traditional point of sale or self-service checkout, if you go back five or 10 years, a retailer will make a choice for that particular type of technology and that would sit in that store for five, seven, 10 years sometimes, as long as the technology is running. The speed of development of technology is in increased immeasurably. The expectations of consumers have also increased immeasurably. And so balancing those two is really, really key.

Where we work very, very closely with Intel is on some of their scalable platforms. So, knowing that retailers have a requirement 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 to the requirements today. So, providing retailers the ability to scale this technology so that whatever they deploy today is not throw-away in two years’ time. That they can evolve it and scale that technology to meet their technology requirements at that particular time is an absolute game changer. And that’s something we’re working with Intel very, very closely on.

Christina Cardoza: Yeah, absolutely agree. Things are changing every day: not even five, six, seven years from now, but five weeks from now things can be completely different. So being able to scale and to adapt is especially important in today’s landscape.

Well, it’s been great hearing about all of these solutions, especially how Diebold is helping retailers from end to end with the item recognition, facial recognition, and retail shrink. We are running out of time, but before we go, Matt, I’m curious if there are any final thoughts or final takeaways that you want to leave our listeners with today.

Matt Redwood:  I think there’s a lot of misconception, particularly around AI. What I would say is: start with the data. Identify the business requirements or the problem 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 also gives you that longevity of scalability. Because AI is a journey; it’s very much a solution that learns over a period of time. So, choosing your solution provider is extremely important, because it is a marriage, and it is a long marriage, and you have to make sure that you’ve made the right choice. So, use the data to help inform those decisions, and, yeah, it’ll be very, very exciting to see where AI and retail technology goes, over the next two, three, five years.

Christina Cardoza: Yeah, absolutely. And I would say also: choose a partner that you can trust and is transparent about how they are using the data. Like with the age verification for instance, you want to make sure that that data isn’t being saved or that anything going into that—that system is going to protect your privacy and your information.

Matt Redwood: Absolutely. Data privacy is absolutely key and is a very, very careful consideration when you are designing or choosing the solution that you want to deploy to stores.

Christina Cardoza: Excellent. Well, thank you again for joining us. I invite all our listeners to visit the Diebold Nixdorf website: see how else they can help you in the retail space. As well as insight.tech, where we’ll continue to cover partners like Diebold and the latest trends in this space. Until next time, this has been “insight.tech Talk.”

The preceding transcript is provided to ensure accessibility and is intended to accurately capture an informal conversation. The transcript may contain improper uses of trademarked terms and as such should not be used for any other purposes. For more information, please see the Intel^® trademark information.

This transcript was edited by Erin Noble, copy editor.

Christina Cardoza: Hello, and welcome to insight.tech Talk, formerly known as “IoT Chat”, but with the same high-quality conversations around Internet of Things, technology trends, and the latest innovations you’ve come to know and love. I’m your host, Christina Cardoza, Editorial Director of insight.tech. And today we’re going to be talking about how technology can uplevel event experiences with Sarah Vickers from Intel.

But as always before we get started, let’s get to know our guest. Hi, Sarah. Thanks for joining us.

Sarah Vickers: Hi, it’s great to be here.

Christina Cardoza: What can you tell us about yourself and what you do at Intel?

Sarah Vickers: So, I’ve been with Intel about nine years, but I’ve been working on our Olympic and Paralympic program for about seven. Currently I’m responsible for all aspects of our Olympic program, which includes games operations, our guest experience, and anything to support the ecosystem.

Christina Cardoza: Great. And of course, the Olympic and Paralympic Games are happening in Paris soon. So, very exciting. I wanted to start the conversation around there. We’re going to be talking about event experiences, but since the Olympics is such a timely event, I wanted to see if you could give us an overview of Intel’s involvement at the event. What motivated you guys to become a technology partner? You said you’ve been doing it for the last couple of years, so how has it evolved?

Sarah Vickers: Sure. One of the things that Intel loves about the Games, is that it is really the largest sporting event, and most complex sporting event, on Earth and has billions of watchers around the world. So it’s a really exciting opportunity for us to demonstrate Intel’s technology leadership in a really scalable way.

We’re not doing this to have proofs of concept; we’re actually integrating our technology to help with the success of the Games. And we think about that in a variety of ways, because there’s so many different aspects to call a Game successful. You’ve got the really complex operations to deliver the Games—moving athletes and fans and volunteers around, getting people from A to B. That’s complex in itself, but do that across 17 days across so many sports. It’s super complex.

You’ve got the broadcast experience—so, billions of people watching at home. That’s just evolved and become more complex when you think about all the different devices and how people consume media. So we do a lot of applications working with Olympic Broadcasting Services to deliver outstanding experiences based on Intel technology.

You’ve got the fan experience, whether that be, again, operationally, ease of getting around, versus actually how do you entertain during the Games. The sports itself provide great sense of entertainment, but there’s all that in-between time. What can we do to help them make that experience even better?

Christina Cardoza: And you talked about how this is such a large event over the course of a couple of days. I can imagine how complex it is during those days, but how is Intel technology being used behind the scenes—not only during the event itself, but how are you preparing before the event, making sure everything is up and running and it’s a smooth experience? And then what happens after the event? Because I’m sure it’s not just for the actual live sessions.

Sarah Vickers: We start working on the Games years before, with the International Olympic Committee (IOC), with the International Paralympic Committee, the organizing committee—in this case, Paris 2024—to really try to understand what are we trying to solve? How can we take what we’ve done in the past and make it better? Or, what are the new challenges that have evolved since the last Games?

So we really work it as a partnership and really think about what are we trying to do. We have taken solutions that we’ve done in Tokyo and made them better. So, in a good example of that is we do what we’re calling digital twinning. Digital twinning is the opportunity to look and have a digital twin of all the venues and really understand what the venues are going to be like in a 3D way.

How this helps is, if you think about broadcasters, they really need to understand where camera placement’s going to be and how that’s impacted by different things. If you think about the transition from the Olympic Games to the Paralympic Games, you’ve got a lot of changes that you need to do for accessibility and things like that for the athletes. This makes it possible to do those things in advance, rather than doing it as it happens and figuring out, oh, this solution actually doesn’t work.

So there’s a lot of benefit to that, as well as just the opportunity to reduce travel. You can do it from anywhere, you can do it from your PC. So it makes it really easy. Another use case that we’re helping out with, from an operational perspective, is really just understanding the data. So there’s a lot of people behind the scenes, right? If you think about all the media that’s on the ground, all the workforce, we’re helping the International Olympic Committee and Paris 2024 understand that people movement to optimize facilities for them.

So that could be either making sure that we’ve got the right occupancy levels, making sure that people have the right exits and entries—really using that data to make real-time decisions based on that data. But what that also does is it helps inform the next Games because they’ve got a base set of data that they can use to help model and plan for those complicated situations.

A final example that I’ll give, just from an operational perspective, is on the athletes’ side. This is the athletes’ moment. For some of them it’s the highest moment in their career. And really what you want to do is make it as uncomplicated as possible. You want them to be able to focus on their performance and not think about the things that they have to think about to get to that performance. So whether that be food, whether that be transportation, whether that be accommodations—there’s so many different things while they’re there that they need to think about.

We’ve worked with the IOC, and we’re implementing a chatbot for them for these Games. So, really a chatbot based on our AI technology platforms. What that’s going to do is it’s going to enable athletes to ask questions, get conversational answers about day-to-day things. And that will continue to get smarter as we get more answers and understand what’s working. So that’s going to be used throughout the Games, which I think is going to be a game changer for athletes.

Christina Cardoza: Yeah, absolutely. And talking about things getting smarter, I think it’s probably so exciting to see how technology has evolved over the last couple of years, and now you’re able to leverage all of these new tools to help make the Games better.

You mentioned digital twins. I’m sure you are using some Intel^® SceneScape behind the scenes. And then there’s all this AI and all the processors that Intel has to really make everything, like you mentioned, real time: make sense of the data, make sure that you can make informed decisions in real time. And all of this, I’m sure, is happening at the edge so it is low latency and we’re getting all this information as quick as possible.

You said you guys start preparing for the Games years in advance. I’m sure there’s a lot of planning and preparation that goes into this. Just looking at not only how are we going to integrate all this technology and make it make sense and eliminate some of the silos, but what it looks like during the event, what it looks like after the event. So, how do you start off years in advance? Walk me through the process of getting from: the Games are coming up, this is how we prepare, and then this is how we launch it.

Sarah Vickers: Really what we do is we sit down and say, “What are the things that need to be delivered?” Right? There’s a set of expectations for every Games, and then there’s that set of expectations of what do we want to do that’s different? And it’s really a process where we sit down and we ask those questions: both, what are you trying to solve, what are you worried about; and what are the things that need to happen?

And then we do an assessment and say, “How can Intel’s technology help?” And we work very closely with a number of partners to try to figure that out. Then we develop a roadmap of solutions. And then we have—for each roadmap of solutions,—it’s typical technology integration, where then we have a plan and a PM that works closely with those stakeholders to deliver that.

Some of those solutions are delivered in advance. So, digital twinning for example, that’s not really—the benefit of that is not during the Games. The benefit is really before the Games and months before the Games. So that solution’s been being used over a big period of time. And then you’ve got other solutions that are obviously for during the Games. So it really depends on the technology integration of what that process looks like. And then hopefully during the Games everything goes smoothly, and we just can enjoy it and watch our technology shine. But we have staff on site to make sure that everything runs smoothly and goes off without a hitch.

Christina Cardoza: Is there anything that happens after the Games? Any more work that’s being done on Intel’s side to make sure that if there were recorded sessions or recorded games, or anything that we point to post-event?

Sarah Vickers: I think if you think about what happens for the Games, there’s so much data, right? So there’s so much data, and data means so many different things. So you’ve got content, right? When you’ve got broadcast data, you’ve got all the highlights and all those things that are being done. You’ve got all the data that we’re helping the IOC collect to understand people movement and things like that. So that data is definitely being used to help plan the next set of Games. When you think about broadcast, that broadcast information is being used to create models and understand for future Games as well, or future entertainment.

One of the really interesting use cases that we’re working on with Olympic Broadcasting Services is AI highlights. We are actually creating highlights using artificial intelligence platforms, and that’s going to help create highlights that just weren’t possible before because they were all generated by people, and there were only a certain amount of people that could do that over time.

But if you think about what we talked about earlier, where how people consume broadcast is changing, people are much more demanding on their expectations of broadcast and want things that are a little more personalized. And you’ve got 206 different countries participating in the Games, multiple languages, multiple sports. And there’s countries where certain sports are really important and that aren’t important—some of the bigger countries that you would see that usually dominate this space.

So what the AI highlights can do is generate highlights that are really customized based on certain things. This is really exciting, and we’re going to see this evolve over time, because what will happen is the models will learn over time and they’ll get smarter, and then you’re going to have even better and more awesome highlights for the fans.

Christina Cardoza: Yeah, I was going to ask if there were any lessons learned that you have experienced over the last couple of years that you’re bringing into this event, or if there’s any new technologies and innovations out there that you’re excited to use. It sounds like it’s AI and digital twinning this year. Is there anything you wanted to add to that?

Sarah Vickers: I mean, I think when you think about AI and Intel and the whole idea behind “AI Everywhere,” really this is really excellent grounds to demonstrate how Intel’s AI platforms will really change a lot of aspects of the Games. So we’re really excited about a lot of our activations that are demonstrating what we can do with AI, and I think what’s happened over time is just technology and AI have gotten smarter; it’s becoming more mainstream. So you’re just going to see more of that, because that’s what the expectations are. And we can use that data—the compute is possible now—to build those models. So we’re going to have a lot of different AI applications throughout the Games.

Christina Cardoza: It’s interesting looking at an event at such a global scale, because at insight.tech we write a lot about Intel^® Partner Alliance members, how they partner together with Intel to make various different things happen: digital signage in stores, the data analytics, the cameras, the people occupancy. It sounds like all of this is happening at the event. So, all of these technologies that we’ve been talking about, that our partners are working with Intel to make happen, it’s such a scale that it’s this end-to-end solution. Everything is happening at the Olympics: the networking, the real-time analytics, everything at the edge.

So I’m curious, what is the value of Intel’s ecosystem and the partnership to make something like the Olympics and the Paralympics happen?

Sarah Vickers: Intel doesn’t do things alone, right? Like you said, we rely on strong partnerships to help deliver that. We really work and try to understand what solution is best and then work with that ecosystem to help deliver that. And that can be a variety of types of partners. So, we have the lucky opportunity to work with some other top Olympic partners, and then we work through some of our other partners at the local level, and we work across our ecosystem to help make this happen. We definitely cannot do it alone.

Christina Cardoza: And of course we’ve been talking about all of this technology in context of the Olympics and Paralympic Games, but there are other events and other use cases I think some of this could be applied to. So I’m curious, how can Intel technology be used beyond the Olympics? What are some other industries or sectors that you see some of the things that you’ve been doing to prepare during, before, and after the event in other areas?

Sarah Vickers: Sure. I think there’s—almost every application that we have—there’s an application for that both at other events, but also beyond sport. So I think the way we think about it is, how does this demonstrate what we can do, and then how does that scale?

I’ll give another example of a use case that we’re doing that’s a really fun application of AI platforms, which is really what we’re calling AI Talent Identification. We are using AI to do biomechanical analysis to help fans that are going to be at athletics and rugby understand which Olympic sport they’re most aligned to. So they’re going to do a bunch of fun exercises, we’re going to mash up that data, and then tell them, “Okay, you are most likely to do this.” And that’s just a fun application of AI.

But if you think about what that biomechanical analysis can do, that can be used in a variety of ways. If you think about physiotherapy, if you think about occupational health, there’s a lot of different ways that this can help improve people’s lifestyles that you can use this same application. You think about digital twinning—that application has gone beyond, and you’re seeing a lot of that in manufacturing, in cities, in all of these different aspects that this type of technology will have that opportunity to help benefit the outcome of whatever their goals may be.

Christina Cardoza: Yeah.  That reminds me of the demo Pat Gelsinger did last year at Intel Innovation, where he was trying to—I believe it was being a soccer player and learn how he could improve his skills using AI and some of these biometrics. So it’s great to see that from last year how it’s advancing, and how it can actually be used in the real world, and how it is actually being implemented in some of these areas. So, exciting to see this technology.

I’m curious—I know we’ve covered a lot about the Olympic Games, are there any key takeaways that you think our listeners should know about doing an event at such scale using Intel technology? Any final thoughts you want to leave us with today?

Sarah Vickers: The Games are going to be a massive event, and in this post-pandemic era I think we’re all excited to see the Games back to their glory, where there’ll be fans in the stands. It’s really exciting, but it’s obviously very complex. Paris is a giant, complicated city without an Olympic Games or Paralympic Games, and so bringing that on is going to be really hard. But by working with Intel and trusting with your partners we can help develop the solutions to deliver an amazing Games. And we’re really excited to be a partner of the International Olympic Committee and the International Paralympic Committee to help make these Games the best yet.

Christina Cardoza: Absolutely. Well, I can’t wait to see the Games in action and some of this Intel technology we’ve been talking about. I invite all of our listeners, if you have any questions, or are looking to partner with Intel and leverage some of this technology in your own event experiences, to visit the Intel website and to keep up to date on insight.tech where we’ll be continuing to cover some of Intel’s partners and what Intel is doing in this space.

So I want to thank you again, Sarah, for joining the podcast today, as well as our listeners. Until next time, this has been “insight.tech Talk.”

The preceding transcript is provided to ensure accessibility and is intended to accurately capture an informal conversation. The transcript may contain improper uses of trademarked terms and as such should not be used for any other purposes. For more information, please see the Intel^® trademark information.

This transcript was edited by Erin Noble, copy editor.