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The Agile Brand with Greg Kihlström®: Expert Mode Marketing Technology, AI, & CX
#768: Infios Chief Innovation Officer Eugene Amigud on use-case driven success with AI
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The Agile Brand.
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Welcome to season seven of the Agile Brand where we discuss the trends and topics marketing leaders need to know. Stay curious, stay agile and join the top enterprise brands and martech platforms as we explore marketing technology, AI, e commerce, and whatever's next for the omnichannel customer experience. Together we'll discover what it takes to create an agile brand built for today and tomorrow and built for customers, employees and continued business growth. I'm your host Greg Kilstrom, advising Fortune 1000 brands on martech, AI and marketing operations. The Agile Brand podcast is brought to you by Tech Systems, an industry leader in full stack technology services, talent services and real world application. For more information, go to teksystems.com to make sure you always get the latest episodes, please hit subscribe on the app you listen to podcasts on and leave us a rating so others can find us as well. And now onto the show. How can large organizations embrace the speed and adaptability of a startup while navigating the complexities of established processes and legacy systems? Especially in the rapidly changing world of AI driven consumer experiences? Agility requires adopting new technologies while fundamentally shifting how teams operate, experiment and learn from data. It demands a cultural evolution that embraces change and empowers individuals to respond quickly to market demands. Today we're going to talk about the transformative potential of AI powered shopping and the significant implications it has for brands, retailers and the entire supply chain. To help me discuss this topic, I'd like to welcome you, Eugene Amigood, Chief Innovation Officer at Infios. Eugene, welcome to the show.
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Hi Greg, thank you for having me.
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B
Yeah, looking forward to talking about this with you. Definitely top of mind for many, myself included. But before we dive in, why don't you give a little background on yourself and your role at infios.
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Sure. So I've been in Infios for nine months. My role is Chief Innovation Officer, which a big chunk of that is bringing AI gen AI. And we'll talk a little bit about how we define it into the organization. Right. We approach it in a very use case driven purpose. But before we dive into AI, just a quick intro about infuse. Right. So in Infuse we concentrate specifically on supply chain execution software. Right. And we bring in capabilities that, you know, typically tier one customers implement to the masses. Right. So we make supply chains run smoother, better, faster.
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Great. So yeah, definitely you're the right person to talk about this topic today. So let's dive in and I want to start with how shopping is evolving and really that process and certainly there's a lot of hype. There's a lot of companies and platforms that talk about AI and sort of hype around it. But what are some tangible ways that AI is already practically changing the shopping experience? And how should brands be preparing for the next wave of disruption?
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Yeah, so if you kind of look at how we shop, and I've been doing for almost 30 years now, right, Brick and mortar. Everyone started there. And then retailers very quickly pivoted, saying, well, we can now sell online. And then Covid comes in, probably the first major disruption. Well, there was Internet, then online shopping, that was first. But then Covid was kind of recent major disruption and everyone started talking about flexibility of buying online and all different fulfillment options, right. I can order online and pick it up in the store, right? During COVID I couldn't walk into the store, but I could pick up from the store. And so that added a little bit of complexity, right? Then some stores would be closed, some stores because of the different areas. So there was quite a bit of disruption. And digital kind of became even stronger, right? Larger, more important channel. And now with AI, right, we all going to ChatGPT and we ask, right, what's the best item the, you know, reviews, everything. And so now, right, in the very quickly in the future, you may not never come to the website at all. You never come to the store or to the website. You'll buy from some channel. So one big difference is this kind of multitude of channels. Again, in digital commerce, we always talk about omnichannel, right? So before it was to be used to be kind of one channel, then one more channel, then maybe a few more, you would sell from Instacart or somewhere else. But to me, like, what's very interesting and not too many kind of, I don't see too many people talking about is this AI will introduce almost infinite number of channels. Because really I don't need to go to the website to buy it anymore, right? I'll just ask saying, hey, what's the, you know, I am looking for the short, what's the best recommended shirt out there for the, you know, cool latest retailers, where do I buy it? And then I can see in the very quickly in the future say, well, this bike. And guess what? They already know my address. They have all the information and you can get there like really fast. So again, going from, for typical retailer shopping experience from one channel to this infinite number of channels becomes pretty massive, right? Everyone talks about personalization and kind of more data around it, but like again, this Explosion of channels is key. And the enterprise, the customers, right. If they're not architected, if they're not built correctly, that will become overwhelming really fast. Right? Because if I'm spending all this time and energy on my website and now I'm starting getting all these other channels, well, how do I react to it? How do I prepare inventory, how do I educate chatgpt or whatever the channel is on my products? Right. If I may not even have that kind of approach before, so that becomes really interesting and quick. Real quick.
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Yeah, yeah. And I think, you know, from one aspect, from the consumer aspect, I think that omnichannel, you know, kind of everywhere approach is good because it's, you know, easier access, quicker access. I think there's another way of looking at it as well, though, that I think a lot of brands fear that AI may depersonalize shopping. You know, so again, you have infinite options, but the connection with the brand is still key. Right. So how can a brand leverage AI to enhance instead of kind of erode that human connection with our customers in this kind of new landscape of infinite options?
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Yeah, spot on. Right again. Now, customer is not coming to the website anymore. I'm just buying maybe, Right. Typically, the way I think about shopping is what are you buying, how much does it cost, and when can I get it? Those are like really three fundamental needs, right. For a shopper, if you think about it that way, now what are you buying? And it's all about the brand. So that's where the brand needs to kind of build out the capabilities and use these additional channels to send the content. Right. You know, whether it's through their website or through the wiki or through the Reddit or whatever else. Right. There's so many ways to publish it. You know, pricing, kind of. Yes. There's the dynamic pricing, et cetera. And where we spend a lot of time, right. Is how can I get it? So in this kind of less personalized world, if you will, what you're asking for, there's actually opportunity to get closer to the customer. Right. If I know. Just give you some examples. Right. If I know that in this region, bad weather is coming, and I know your address. Right. When I'm talking to you as a customer, throughout, regardless of what channel you're purchasing it from, maybe you're on the website, et cetera, I can be very kind of saying, oh, because I know where you're getting it and I know you're getting it for your birthday, you should probably order in a different way because you will not get it on time because now through AI, it gives me access to all this information. So it actually can be very powerful. And if you kind of use it right, I know where my inventory is, I know where I need to ship it from, I know the transit times, I know that during peak or just one of our customers is a Halloween. They do a lot of business on Halloween and they know that, hey, I need to get it by end of October, right, so the kids can go trick or treating. Well, there's a lot of intelligence that could go in to say like, hey, how likely you will get it by end of October? Because guess what, after, you know, if I get it, maybe November 2nd, it's kind of useless to me, right? So you can get all this AI to really personalize it. So on one hand, like we were talking about, the customers are coming last to the website, they're infinite number of channels. But on the other hand, if I know this data, if I get everything correctly and if the customer obviously wants to share that information, right, which typically shopping sites will have addresses, geolocations, items preference, et cetera, I can be very accurate. And again, if you put the right tag in, very prescriptive of how do I fulfill the items to you and again that becomes super powerful. Before I would kind of run typical algorithms, but now with new power I can make it very conversational. I can take additional data points that I could never consider before I can learn from other customers. So if you are also in this address somewhere and your shipment was delayed, very likely my shipment was also delayed. So I can start this communication proctor with the customer saying, well, we need to figure this out earlier in the process. So again, on one hand it becomes less personalized. On the other hand, if you use the tech right, it can get really accurate in a very powerful way.
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Yeah, and I think basically we talk to a lot of customers, et cetera. One of the number one topics, right? Boards, executive kind of the conversations, et cetera is supply chain. Right? Because what's happened now again as you talked about Before COVID was the first disruption, one of the first disruptions. And then everyone said, well, Covid is over, things will get back to normal. And then now we are faced with tariffs and labor shortages and various disruptions around even recent around ports, ports closures or weather conditions, et cetera, et cetera. And they're just so much. And at the same time, consumer and customer expectations keep increasing, right? Amazon probably is a big part of it, right? Because I go on Amazon, I can get almost anything within two days. And so everybody else, even if they're not Amazon, and more Interestingly, even in B2B, not just talking about consumer shopping, but at the same time, right? Businesses buy from businesses as well. The expectations keep rising. I want to know, when will I get it? I want to be proactive in communicating with customers. I want to have multiple options. I want to be able to offer saying, well, you can get it tomorrow, right, for maybe $10 or you can get it, you know, as economy shipping for free. So increasing consumer customer business expectations and the same time increasing complexity. So if you look at this, right, And I'm an optimist, so if I look at it one way, like, this is probably the toughest environment we've ever been at and supply chain is absolutely key to that. But if I look at it from the AI side of it, saying, there's so much opportunity and for those who will kind of jump on that bandwagon, right, In a very purposeful way. One of the pillars within ethers, we always talk about purposeful innovation. And so purposeful innovation is all about saying, hey, how do we align the tack that we have with the business needs, with the use cases that solve specific customer needs, right? And so if you kind of bring it all together, that becomes actually very promising. Where before you would say, well, I don't know how to kind of put it all together, but now with the kind of power of AI, you can actually start reacting, not being just reactive, but start being actually proactive around saying, hey, I know there's labor shortages in this port. Let me figure out how to orchestrate the transport to a different port. Well, okay, so orchestrated the transport to a different port. Now within the other system, I need to say, like, okay, I have a different visibility now. I know inventory is coming ten days later, okay, how do I start promising on my website based on this disruption, right? Oh, and by the way, there's bad weather coming in, okay? And, and there is a tariff. So I may want to move a product proactively to US versus you Know, some customers to keep it in Canada. Well now to move products on Canada to us they might have some tariff situation. So let me just source it out of US and you know, upfront. So all of those decisions, again, they become kind of infinite number of them. And AI really helps power through that. And by the way, when I talk about AI, right, it's not just gen AI, obviously, it's kind of latest and greatest, but there's right. Machine learning and forecasting. There are some very powerful optimizers to kind of look at all different options. How do you make promise to the customer and how do you keep it? So that all becomes, so it all gets powered by AI and kind of helps you accelerate supply chain. But supply chain at the core is probably one of the most stressed kind of variables in this whole equation of shopping. Again from shopping perspective, you know, maybe I'm on a website, but all of this orchestration has to happen before that. Right. And so that's, that's the power of it.
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Yeah, yeah. And you know, to that point I think consumers took it for granted all the, all the behind the scenes things until you know, there were some challenges in the supply chain and stuff. And so, you know, I think it's, it's certainly, I think it's elevated just the importance of that to the, to the customer experience, you know, to do some of the things that you're talking about. I mean certainly, you know, AI, a variety of AI methods would be employed in this. Are there other adaptations in the supply chain beyond technology that retailers should be embracing so that they can take the best advantage of AI driven supply chain and shopping environments?
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Yeah. So probably the most power and innovation can come from this actual change. Right. We all know that it's never about pure tech, Right. It's all about organizational side of things. And so the way we kind of evolving is before retailers or our customers, right. They would look at and kind of say, okay, I'm opening a new warehouse and I need to like, I need to buy the software. Right. That's kind of how software really started originated. Right. I need to buy this software that drops like warehouse and management system or I need, you know, I'm adding additional channels so I need to buy another software. Right. Or maybe some of them like, well, we're using ERP and we'll just keep adding within the erp kind of the more and more tack to solve my needs. I think what we're seeing, the biggest change is around saying, hey, forget about the software altogether, figure out the business needs and objectives and start realizing it through aligning with the tech side. So what I mean by that is forget which software you're getting, whatever, but almost like it's a delivery of a function, right? So I was talking to one customer and they said, you know what, my revenue is coming 80, 20. So 80% is from brick and mortar and 20% from DJ. And my board told me that in three years it has to be 50, 50. It has to be 50 from digital and 50 from stores. And to me that was kind of say, you know, my kind of light went on. I'm like, oh, that's exactly what kind of how you should be thinking about it, right? It's not about opening a warehouse or buying another 20% inventory, but it's really kind of okay, what's your business need? And then you break this business need to individual functions, right? So if I need to do more around digital, right, I want to be able to offer more personalized options. I want to be able to go to product detail page and start communicating to the customer early on. I need to make my website more attractive, et cetera. So instead of bringing this big monolith systems in, take a lot of time to implement it. It's a lot more now kind of a modular approach saying, hey, this is my business need. These are all my organizational kind of departments or business functions. How do I enable each one of them to drive this business need? And how can the technology be the unlock for these business functions? So very different kind of, right? And very often every business function may have slightly different sequencing, different priorities, et cetera. So if you get, if you drop this kind of one major big application, it becomes quite difficult to consume. And that's how projects fail, that's how misalignment happen, et cetera. So instead now you go with this modular approach and function driven approach where to say, okay, you need to get more on digital. That means these are the steps. This is how every business function has to change. And in order to change, yes, you may, in some cases you need the IT function there, right? Some specific solve. In some cases you don't, you use what you have. But it's kind of becoming more agile. Moving to this kind of more modular approach where you can drop something in and realize the benefit in maybe 30 to 60 days instead of before having this kind of two year plans, right? Or like a one year kind of full transformational, you can have this kind of vision of two five year plan. But the closer you get to these functions, probably the faster you'll be able to realize the benefit.
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Yeah. And an initiative like that, that you have to wait five years to realize any kind of return. I mean from what I've seen, those are rarely successful versus to your point, those that, you know, there's maybe it doesn't take a matter of weeks, maybe it's a few months or something, but there is some kind of return on that investment, even if it's just efficiency gains or something. Right.
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And tying it to the disruption that we are talking about. Right. Tariffs. Right.
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Yeah.
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And again there's interesting in our industry there's a planning and execution. Right. So you can plan. Right. And usually it's a long term planning. You plan for a year. Right. When do inventory, where is my peak? When do my, you know, most customers come in and shop? So you have all these great plans and then tariffs come in and you have to react like immediately. And so your plans are good, but now you have to kind of react immediately and right. Say okay, now I need to realign my business and be able to do it within couple months becomes absolutely instrumental. And again, just one example, Covid was another example. But that kind of is very interrelated in my mind.
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And of course in any of these conversations about AI, a key component of this is, is data. And just having, you know, having the right data, having accessible data, all of that and certainly first party data, there's you know, consumer privacy concerns along with that kind of stuff as well. So you know, from, from your, from your perspective, what role does data play in enabling AI powered shopping experiences and what's the recommendation for companies to. AI needs a lot of data, but also ethically sourcing of data and all that stuff to enable what's the best way to guide this approach?
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Yeah, and we deal with this question all the time. Supply chain cannot function without the data. Right. Basically it's the most basic foundational block around the data. Right. Obviously without customers, you need to make sure there is the right data governance EU versus US. There are different requirements around how do you accumulate data. And even for us, very frequently we cannot use data from one customer for another customer. Every customer data is segregated. So there's a lot of thought going in there. But to your point, again, to me, AI just unlocks a power of data even more. Right. We've been always saying for the last 20 years that data is everything. And to be frank, we couldn't always use the data. We would kind of collect the data. And it's supply chain, industry everywhere, you collect the data and then you can freely effectively use it. AI allows you to unlock the data, right? It allows me to change the paradigm, how I interact with the customer. Right? So when a customer before is in the call center and say, hey, where is my order? When will I get this order? Oh, it's order been delayed and you're talking to this kind of completely oblivious call center agent who say, well, what's your order number? Well, let me kind of look it up, et cetera. Now, with all the data, this whole experience, again, tying it back to the personalization can be so much more personalized. But the consumers obviously need to agree to share the data, right? And again, you need to absolutely have the right governance, right security, the right kind of controls there. But again, if you get the data, it's amazing what you can do with it. Like in terms of personalization, right? Like I know exactly what you purchased. I seen you interactions before. I know that it could be as simple as I know that you call every time the order is delayed and you call a day later, right? Because you're not placing an order for the first time. Now I can use the data to proactively initiate a call. And by the way, not necessarily using a human right, but doing kind of an agentic call who will say, hey, I know you're wondering where your order is and unfortunately it's being delayed due to the weather situation, but it's coming eight hours later than you were expecting and Right. That becomes super powerful, right? Because again, you accumulate the right data. There are some of the data, again, I'm seeing some specific delays in some region for transportation and again, I can apply that kind of disruptions across. But to me, again, I'm quite optimistic of if you kind of handle the data in the right way, if you put the right security controls. For instance, in our company we started AI Console and AI Console is nothing but saying, hey, let's put AI brains together. But one of the founding members of that is our ciso, right? To make sure that the data is we put the right controls, we put the right kind of thinking ground up from architecting the whole AI kind of approach, right? With the gen AI or machine learning or forecasting to make sure that it's used in ethical way and secure way, et cetera.
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Yeah, well, and in several of the examples that you gave as well, I feel like when a lot of people say AI, you've said this already as well. They think of ChatGPT and Claude and the gen AI stuff. Obviously AI is much, much broader and there's there's certainly other ways, you know, other types of predictive analytics and predictive AI that are hugely helpful. Again, you gave a few examples of that. How do you recommend that brands leverage predictive analytics to, you know, anticipate some of these, not only some of these supply chain issues, but also some of these customer needs and proactively address, you know, potential pain points in the process?
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Yeah, to me I'll probably sound like a broken record, but it's all use case driven, right? If you will. Because if you have a hammer, everything becomes a nail. That's probably not the right approach. And that's what I see sometimes in industry is happening, right? Like okay, gen AI, let's go solve. I always give this example, right? If I think about it, one of our kind of solutions, warehouse solution. And so in the warehouse I have a scanner and every shipment I scan and so I scan hundred thousand, sometimes 100,000 shipments per day. And I need to imagine, right, the response is required is like low milliseconds. It's very difficult, right? And now tied back to ChatGPT where you ask something and the first thing it will say, well, thinking more to give you a better answer, right? In the warehouse, I cannot think more to give you a better answer when the person is scanning. And I have, you know, 100,000 employees across various locations just for one customer doing that, right? Just fulfilling everyone's Christmas presents, right? Just one extreme example. So to me, retailers or pretty much anyone kind of who isn't playing supply chain, it's always about very specific use case driven, right? So to give you an example is within the warehouse how to optimize speaking path, right? Maybe the best solution there is an optimizer and guess what? I think of optimizers, very intelligent optimizers as fully part of AI, right? Because these are sophisticated optimizers can say, hey, based on historical data, based on everything I saw, this is your best peak path, which is very fast, quick, but helps to reduce the cost. Here's another example where we have carriers and carrier picks up the load and they go on delivery and the delivery, I don't know, maybe they're getting from Albany, New York and going to Chitado and let's say they left Albany two hours later, no updates, nothing is going on. Well, typically the old ways it would be somebody kind of monitoring manually or you get an alert from the system and then you have a call center person calling the driver or the driver would have to notify on the app and they're busy driving. So it Becomes quite messy. One use case we're introducing is to say like, hey, you will have an agentic bot monitoring these orders, initiating the call, talking to the driver and updating the system all proactively with not a single human on the shipper side being involved. That's Genai use case. And then you have a third type where you just have, look, I need to know how much inventory I need to have for each individual store because right during my Halloween traffic will increase because I sell Halloween related items. But for some other type of items, traffic monitoring. So the reason I'm giving this example is I think the power there is. You need to have a comprehensive platform around AI and comprehensive thinking. But then you need to kind of be laser focused on enabling specific use cases and applying the right tools to solve these use cases instead of kind of blowing it up saying hey, now everything I do is through Gen AI, right? Again, there is awesome space and place for gen AI with very powerful use cases, changing the different kinds of interactions, exposing new capabilities, et cetera. There are great ones for machine learning, analytics, forecasting. You have a bunch of historical data. How do you predict the future based on the historical data? And there are lots of very solid algorithms that guess what, those algorithms barely hallucinate. They almost don't hallucinate at all. Versus on Genai, there is still hallucination. For some use cases, hallucination is great. I mean not great, but it's tolerable, right? And in some cases it's not. So again, it's so important to match the right tool for the right use case and kind of, that's the starting point.
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Yeah, yeah, absolutely. Well Eugene, thanks so much for joining today. I really appreciate hearing your insights. One last question for you before we wrap up. What do you do to stay agile in your role and how do you find a way to do it consistently?
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Yeah, especially on innovation. You know, I do spend a lot of time on tech, right. Whether it's conferences, whether it's, you know, just staying up with the innovation, right. Kind of being hands on. Right. Literally, you know, a couple of days ago I said, well, let me go and create a new agentic bot with the new tool that I saw. Right. Like just being hands on as well. But I think the most important part I'm always kind of thinking about from a domain perspective, like hey, where is the industry going? What are the use cases? Because again, to me, AI and innovation is just a tool to get what our customers, what business requires. And so that kind of almost helps me to a little bit stay in somewhat constrained but also focused on what I do. So that's kind of couple steps that I do to stay up to date. But Greg, thanks for having me and inviting.
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B
Yeah, thanks so much. Again, I'd like to thank Eugene Amigood, Chief Innovation Officer at Infios, for joining the show. You can learn more about Eugene and Infios by following the links in the show. Notes thanks again for listening to the Agile Brand brought to you by Tech Systems. If you enjoyed the show, please take a minute to subscribe and leave us a rating so that others can find the show as well. You can access more episodes of the show@theagilebrown.com that's the agile brand.com and contact me if you're interested in consulting or advisory services or are looking for a speaker for your next event, go to www.gregkilstrom.com that's G R E G K I H L S t r o m.com the Agile brand is produced by Missing Link, a Latina owned, strategy driven, creatively fueled brand production co op. From ideation to creation, they craft human connections through intelligent, engaging and informative content. Until next time, stay curious and stay agile.
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The Agile Brand.
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Before we continue, I wanted to share a key strategic resource that a majority of the Fortune 500 are already aware of. Finding the best technology, business and talent solutions is not easy. With business demands and competitive pressures mounting, you need to be able to design, deploy and optimize your technology to provide leading customer experiences while driving business growth. Those of you that have been listening to this show for a while know that this podcast is brought to you by Tech Systems, a global provider of technology, business and talent solutions. For more than 80% of the Fortune 500, tech systems accelerates business transformation for their customers. Whether you're looking to maximize your technology ROI, drive business growth, or elevate customer experiences, TechSystems enables enterprises to capitalize on change. Learn more at techsystems.com that's T E K systems.com now let's get back to the show.