In this episode of AI and the Autonomous Supply Chain, theCUBE Research’s Dave Vellante and George Gilbert sit down with Wayne Usie, CSO of Blue Yonder, to talk about how AI is reshaping the foundations of global supply chains. Usie shares how today’s volatile world demands cognitive systems that are responsive, predictive and adaptive.
Usie walks through the limits of legacy systems and highlights how Blue Yonder’s AI-driven applications are designed for continuous planning and real-time decision-making. They also talk about the concept of “agentic orchestration,” where AI doesn’t just analyze — it acts. The trio explores how these tools reimagine operational agility.
The discussion also tackles the strategic implications of having a single AI-powered data foundation. Usie stresses how this approach not only synchronizes supply and demand but also elevates sustainability efforts and shifts the roles within enterprises. As AI advances, so too must the mindset of organizations managing global logistics.
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Wayne Usie, Blue Yonder
In this episode of AI and the Autonomous Supply Chain, theCUBE Research’s Dave Vellante and George Gilbert sit down with Wayne Usie, CSO of Blue Yonder, to talk about how AI is reshaping the foundations of global supply chains. Usie shares how today’s volatile world demands cognitive systems that are responsive, predictive and adaptive.
Usie walks through the limits of legacy systems and highlights how Blue Yonder’s AI-driven applications are designed for continuous planning and real-time decision-making. They also talk about the concept of “agentic orchestration,” where AI doesn’t just analyze — it acts. The trio explores how these tools reimagine operational agility.
The discussion also tackles the strategic implications of having a single AI-powered data foundation. Usie stresses how this approach not only synchronizes supply and demand but also elevates sustainability efforts and shifts the roles within enterprises. As AI advances, so too must the mindset of organizations managing global logistics.
In this episode of AI and the Autonomous Supply Chain, theCUBE Research’s Dave Vellante and George Gilbert sit down with Wayne Usie, CSO of Blue Yonder, to talk about how AI is reshaping the foundations of global supply chains. Usie shares how today’s volatile world demands cognitive systems that are responsive, predictive and adaptive.
Usie walks through the limits of legacy systems and highlights how Blue Yonder’s AI-driven applications are designed for continuous planning and real-time decision-making. They also talk about the concept of “agentic or...Read more
exploreKeep Exploring
What is the current state of disruption in the global supply chain and what are the challenges in achieving a unified view and optimization of decisions across the supply chain?add
What are some of the challenges in the supply chain sector and how is data solving these problems?add
What is the relationship between autonomy in supply chain management and the progression towards autonomous vehicles?add
What is an example of how predictive machine learning and generative AI technologies are used to improve in-stock positions and reduce waste in the grocery sector?add
What can a single AI data cloud with execution dimensions and measurements on the platform help organizations achieve in terms of sustainability and financial outcomes?add
What is the biggest challenge companies are facing in preparation for working with new technology?add
What is the impact of rapid technological advancement on businesses and supply chains?add
>> Welcome back to AI and the Autonomous Supply Chain, made possible by Blue Yonder. And we're diving deep into the future of business planning and intelligent AI powered apps. George Gilbert and I are pleased to welcome Wayne Usie, who's the Chief Strategy Officer at Blue Yonder. Wayne, good to see you. Thanks for coming in.
Wayne Usie
>> Thank you for having me. Good to see you both.
Dave Vellante
>> Yeah, you bet. Really interesting to talk to the CSO, maybe zoom out a little bit on this whole topic. Let's start with supply chain management and why it needs a do-over or at least a makeover. AI has clearly been an enabler here, but what factors, from your standpoint, are sort of driving your strategy and the shift away from traditional supply chain models? And explain why older models are not going to be able to keep up in this AI era.
Wayne Usie
>> Sure, sure. Well, it's no surprise or no secret to anyone, the amount of disruption we're seeing, both manmade and unnaturally manmade, in terms of what's happening in the global supply chain. And it's about speed and agility and it's also about understanding the disconnecting things that are existing in the supply chain. It is quite fascinating when you look at all of the sectors out there, it's probably the one sector that you would think would be unified in its data and in its processes would be supply chain. Unfortunately, it's the most bespoke sector I think that we have in all of enterprise software. And therefore the challenge of the remake, if you will, is starting to understand how do you get a collective view across the supply chain, from raw material all the way through the consumer, and start to understand how you optimize decisions about those inventory and raw material moves across the supply chain. It's quite simple in those terms. The complexity comes in at the volume and scale and the frequency of disruption that we're starting to see in the marketplace. And that's what many of our customers across manufacturing, third party logistics providers, and retail are trying to deal with is how quickly can they anticipate and then respond to these disruptions that are occurring in the supply chain. So it's ripe for an overhaul, but it's really now finally doable because of the technology that's upon us.
Dave Vellante
>> Wayne, about a little over 10 years ago, Erik Brynjolfsson and Andy McAfee that were at MIT at the time, wrote a book called The Second Machine Age, and the media's take on it was, "Oh, AI is going to replace humans." And their response to that was that machines have always replaced humans, but this is the first time in history where machines replaced humans in cognitive functions. And I bring that up only because I want to flip that narrative a little bit and maybe turn it into a positive and apply to supply chain. And I wonder if you could talk about the cognitive notion, cognitive solutions in supply chains, what does that mean? What are the core attributes that really make it cognitive? And what does it mean for, ultimately, for organizations and customers?
Wayne Usie
>> Sure, 100%. And look, I think all of us as humans have historically worked on computers, and now it's more about working with computers and working with agents and things that can help that are more cognitive in the way they see the world. And so part of our challenge in the supply chain sector, getting the data problem solved first was the number one thing we had to attack. And that was more because of the data bespoke across so many different systems. And so now we actually have developed and are now at a point where we have a single AI data cloud that is representation of the entire supply chain, not only across enterprises but across a network of suppliers and carriers. Again, all the constituents that are on the network within the supply chain. So when you start to think about agentic orchestration and you think about how do we work with AI and agents, you think about what opportunities do we now have to optimize the decisions across the supply chain that we never could do before? I'll describe it as we're sort of sub-optimize. If you think of a warehouse solution typically optimizes the product coming in, how it gets stored, how it gets picked and how it gets shipped out. But it doesn't really think about the consequences of that shipping out as it relates to the transportation optimization. And so because these things were disparate, we were collectively missing opportunity to optimize across the supply chain. And also when you look at how things have been done sequentially, we typically had, in a demand planning perspective in supply chain, that discipline was you would create an unconstrained demand view of what is possible to sell your product and then you would apply the actual supply constraint against that unconstrained demand, and that's physically what you would execute in the supply chain. And what happened to that unconstrained demand that you never really satisfied went by the wayside. It was a missed opportunity if you will. And so today, because we're operating where we've sort of brought all of these capabilities concurrently, we're now able to have supply chain optimizations that are done simultaneously. Therefore, supply and demand is done concurrently, and, therefore, I can optimize the demand that I absolutely want to go out and serve and satisfy in a more cohesive manner. Same thing for inventory moves in retail, where we've had disciplines of allocation and replenishment. We have historically required companies to make a decision of whether you allocate a product or you replenish a product. And now you're able to actually have the technology determined, on every single inventory move decision, whether it should be based on a pull signal or a push signal. And so this is kind of the opportunity now that we are bringing to market with the cognitive solutions. And in addition, it has an ability now, when you talk about cognitive, think about technology and agents, not only they can see, they understand, they can comprehend the disruption, they can decide, and they can even act. So we can definitely provide root cause analysis, we can make prescriptive recommendations and it's really up to us, the humans, to decide, okay, let the technology then go out and execute the optimization itself. And that's what makes it autonomous.
Dave Vellante
>> And you just laid out, distinguished between autonomous supply chain and the traditional approach. As a strategist, you need to look out into the future, but you also need to intersect that with what's possible in some kind of timeline. So my question is, when people hear autonomous, of course they think of autonomous vehicles and they think about the progression, maybe it helps you with staying in the lane or some kind of warning, or maybe even semi-autonomous or semi-full self-driving. Eventually get to full self-driving. Should we think about it similarly in supply chain, or is it more of a step function I think of autonomous database where, all of a sudden, here it is, and did the DBA's job changed? How should we think about supply chain in that context?
Wayne Usie
>> Interesting question. I think it's been a combination of both, and I'll describe it this way. So when you think of autonomy in the supply chain, for example in retail, we had a period of time where we went through establishing what we called centralized replenishment systems. So in the original retail days, the store manager placed the order for all of the inventory that was necessary. And eventually corporations came in and found out technology could provide a centralized replenishment solution that was automated, had forecasting capability, and eventually you no longer needed that store manager to actually place that order. And so that became autonomous. No one looks at an inventory fulfillment run anymore, especially in large scale retail, because it became autonomous. Similar situation is as we think of the step change, we're looking at how do we humans work with agents? And think of it as like a copilot or an autopilot. And I know that term's been used loosely when you think of agents, but it's like I can have this technology provide insight that I otherwise could not find myself as a planner as an example. I can have it give me prescriptive recommendations of how to address disruptions and shortages of inventory or raw material. I can actually have it execute where it's my comfort level that I have confidence in the solution and what the agent is able to do. And then I can have it not even bother me anymore in the future, just like the replenishment example I gave where it's just not necessary for humans to do that. And where the challenge comes in and why we're changing the way humans work is it's because the time in which we have to respond is what I'll describe as machine speed. That's our biggest challenge and also our biggest opportunity today, is that these decisions have to happen so rapidly at scale, where you have millions of line items, for example, in a bill of material in a manufacturing planning, or you have billions of evaluations happening in AI each and every day. Technology has to be the answer in these scenarios. So it's really a combination of how we step into adopting it, but also the capability is truly game-changing.
Dave Vellante
>> Yeah, thank you. And AI certainly changed that. It makes it an imperative from a competitive standpoint. George, you've done a lot of forecasting in your day. Back to your security analyst days, you had to figure out, "Okay, what are the numbers going to say in the future?" So why don't you take it from here and we can dig into the impact on forecasting and planning.
George Gilbert
>> Let's tie it also back to something, Wayne, you talked about where we've looked at suboptimal or local optima before, like in a warehouse or just in the retail in a single store. Now help us understand what a company looks like when it has such a great broader scope and fidelity. And by fidelity, I'm getting down to where you say like every SKU or every SKU in every store. What does it mean when a company can sense the environment and replan very quickly? How does that change how they compete against their peers? And also if this is going, last part of the question, if this is going across a network, like an ecosystem of partners and customers, to whom does all this value accrue?
Wayne Usie
>> Right. Great question. So I'll give you an example. If we think about the grocery sector for one particular piece around what I'll describe as fresh and ambient product. So typically retailers or grocers are looking to optimize the decision around in stock position and waste. And most of these types of categories are highly impacted by things like events and weather as an example. And so when you look at how traditionally, fundamentally, forecasting solutions were applied to predicting the quantities of those items that were sold in a given store, forecasters were inaccurate to some degree, where the weather may have changed the day before and all of a sudden it caused a spike in demand for a given set of categories because it's the first spring warm day and people are going out and they're going to barbecue as an example. Because that forecast was inaccurate, the way they compensated, i.e. the retailer, is they had a large number of planners doing overrides. And now that we have machine learning, predictive machine learning, and even generative AI technologies we can apply to that specific problem, we're able to leverage the accuracy improvements significantly in terms of the in-stock position for those categories and providing the reduction of waste that they're trying to manage within their organizations. And we've proven it out, that one major large grocer actually not only one holiday two years in a row, but actually have outperformed their competitors, where their in-stock position in all of these categories improved by one whole percentage point, which is quite significant when you think of out-of-stocks with these types of categories. So it's a perfect example where the technology's been able to sort of come in and provide that accuracy. And then how does that translate back to, as you said, the other constituents on the network? Well, the suppliers typically need to provide the order four days in advance of what is going to be physically brought into the store. But if we could give them a preview 10 days in advance, then they'll have a better opportunity to have a higher fill rate on that four day in advance. And so, again, getting the accuracy with these types of technologies, from a predictability standpoint, can go back not only to serve the in-stock position for the retailer in this example, but also gives the higher opportunity for the supplier to have a higher fill rate within these categories. And we've seen evidence of where these types of results are giving an opportunity for the company to outperform their competitors in the exact same markets.
George Gilbert
>> Okay. So now maybe help us understand, I'm thinking about maybe beyond retail where there's this chance for a greater scope and fidelity of planning and then operationalizing the plans, where it's not just you're getting the demand signal from the retailer, but there's all sorts of now probabilities that have to be incorporated along the supply chain and where now agility is critical in terms of... I'm struggling to articulate this, but it's where you have to take into a range of scenarios and then be able react as events change. Like in a non-retail scenario where you're trying to orchestrate an entire ecosystem of activities, what might that look like?
Wayne Usie
>> Sure. Again, the prediction of disruption and how would you respond, you've heard traditional disciplines around scenario planning, which was like I'll have hundreds of planners go through and create a number of scenarios that then I can come back and evaluate what is the lowest risk and the best financial opportunity, or outcome I should say, of what I'm seeking. Today, a lot of the technology can go out and evaluate what are those recommended outcomes in terms of those scenarios. And it can be over thousands of scenarios that get evaluated with AI technology. So therefore you can actually get to a much refined, precise answer. And typically these things get applied where you have disruptions. In your example that you were asking, like for manufacturing for example, if I need to make sure that I have a factory at full utilization at all times, and I have five tiers of suppliers that are basically in my supply chain that are dependent upon making sure that factory stays in full production, and my tier one supplier decommits an order that I need that raw material for this factory run as an example. But the reality was it wasn't the tier one supplier's challenge, it was their supplier supplier's challenge. So when we think of the network architecture, it allows us to have a single bill of material across five tiers of suppliers in this example. And therefore, at the moment in time when I have a predicted disruption or an actual disruption, I can pinpoint that it was a third tier supplier that had the problem. I can have the technology determine an alternate source for that third tier supplier to my second tier supplier. And so what you find is a lot of the manufacturers wanting to take control of their destiny, if you will, in terms of their operation and support where all these interdependencies lie in the supply chain. And if you're able to do that seamlessly in one single set of what I'll call the single version of the truth, which is the single data representation of the network, and specifically of the raw material that made up that bill of material, you'll gain an enormous advantage in terms of your ability to serve your customers where that dependency was going on. So that's just one example where we look at how these massive complex multi-party involvement come into play.
Dave Vellante
>> So it's very relevant now with what's going on in the geopolitics. So take the scenario of tariffs right now. So let's say I'm an organizer of a company and I say, "All right, I want to burn some cash and I want to bring in inventory now before these tariffs take place. I want to run a scenario. If the tariffs stay in place, I know there's an upside here because I brought the goods in at a lower cost. Okay, I can figure that out pretty easily." I can also figure out, "Okay, if the tariffs don't stay in place, if we had a reprieve, I just burned some cash. Okay, what did that cost me? I can afford it. Okay, I've decided that's my base case." At the same time, I might want to do a what if and say, "Okay, my second probable case is these tariffs stay in place. I need to refactor my supply chain. Maybe let's go to India or maybe let's get another third party supplier." Maybe it's bring manufacturing on shore. You're saying that my ability to model that out... Explain my ability to model that out, how that is enhanced today and what role AI plays, especially when given the uncertainty. The probabilities are, each of them are 20%.
Wayne Usie
>> Yeah. I actually had this exact same conversation with a customer of ours, and that's exactly what they were trying to do. And part of the challenge, you can appreciate back to the conversation we had earlier of siloed disciplines within organizations and how they run the supply chain. The category managers, for example in grocery, are the ones who are going to make that decision of whether I forward buy inventory, I'll buy 20 days forward because it's coming across the border and I need to ensure, I want to hedge my bet as one of the scenarios. The problem is what are the physical constraints does that company have in actually being able to execute bringing forward 20 days? It's one thing to make the buy decision, but the second is, do I really understand my warehouse constraints? Do I really understand my logistics and transportation? Where am I going to put the inventory? What's the carrying cost? It could outweigh that hedge scenario. So again, going back to a single AI data cloud that has all of the execution dimensions and measurements on the platform, in addition to the planning components, allow me to make a holistic decision of truly understanding, in all of these different scenarios, what is the best outcome in specifically both sustainability and financially in many organizations, that I'm looking to basically hedge the bet on the uncertainty in your example. But clearly understanding what those constraints are, the differentiation. And that's powerful today. And you can do it over any time dimension as well. This is the other scenario. You've all remembered the sales and operational planning, which ultimately became integrated business planning, looking over 24 month time horizons of demand and supply. I need to be able to translate that into my actual demand and supply planning process, which is weekly planning out 13 weeks. And then ultimately, if I'm a manufacturer, I need to be able to synchronize that into my factory planning order, slotting, and sequencing. And you see this in spades in the auto sector, right? Because they went through a secular trend where all of the vehicles are now being personalized. And so you can imagine how complex that supply chain problem is today to be able to match inventory parts that I can customize with vehicles coming from overseas in order to ensure the dealers and the customers eventually have their vehicle within a day or two based on available to promise. It is a new paradigm.
Dave Vellante
>> Interesting. Especially your example, the relevant one around pulling forward some of the inventory, the brand manager might do that. They have the latitude to do that, but they don't necessarily understand the downstream dependencies, so their strategies is going to fail and they don't even know it until they actually try to execute on it.
Wayne Usie
>> That's correct.
Dave Vellante
>> Go ahead, George.
George Gilbert
>> Another question which is I'm trying to understand how difficult it is or where the state-of-the-art is among the competition in taking what are these really advanced planning systems that can do the scenario analysis, but then the ability to operationalize that. Like not just now, you've taken it where, with Blue Yonder, you can do it with your systems, but how do you do that with your suppliers and their suppliers, other partners? Or do you just have a representation of their capabilities, and then how do you sort of stay in sync with what they're capable of?
Wayne Usie
>> Yeah, so part of our strategy where, again, we have an outbound thesis on M&A is we did acquire a company last fall called One Network, and part of that architecture basically removes the duplication, the synchronization. It's quite fascinating, by the way, how much EDI is still out there as we all know. And that technology is what, 30 years old, probably best case. But the fundamental flaw in it is you're still moving data, you're still trying to synchronize between two entities. The power of a network architecture is its one record that represents the inventory move or the raw material move for all constituents. So that record of an inventory move, as an example, could be a purchase order for one party. It could be the sales order for another party, it could be a shipment to the carrier, but it's one record. And the power of that is ensuring you eliminate all of this duplication of data and all of the overhead in synchronizing data that you truly are. Now, in most industries, the density of the suppliers, so let's say we're in high-tech, for example, semiconductor specifically, if we are able to have most of the suppliers on the network, meaning onboarded to the network that they are available to be able to do commerce, then as manufacturers come onto the platform, their supplier base is already on. And so it's just a matter of bringing on PO collaboration, order forecast collaboration. You have business cases and business use cases that are being applied, but it's a single version of the truth. And these network architectures take a long time to build, which is why we made the decision to acquire one.
George Gilbert
>> That must make for... We have trading relationships with like-minded nations, although we used to, where it takes years to negotiate the rules of commerce. And it sounds like a company and its partners can build up a similar network. And it's not just the rules of commerce, but it's what you were talking about, like the ability to talk the same language and then the ability to coordinate is something where they're competing not one company against another, but networks against each other. And those that have built these sort of tight collaborations represented by this network enables them to sense and respond in a way that their competitors cannot.
Wayne Usie
>> It's true. It's true. And what's the power of the network architecture as well, it's sort of this hub of hub concept, right? So it's not hub and spoke as we would think of in the past. So let's say, for example, I'm a CPG company and I'm actually connected as a supplier to a very large retailer as an example. So in that case, my role is a supplier. But what if I, as the supplier, are very large tier one CPG company? I could also have all 12, or 15, or 30 of my largest retailers on the network and now I become a hub. So when you think about inventory optimization, you remember the old bullwhip effect, how everybody builds up inventory more. You truly do eliminate it because now you're able to optimize the views across the constituents. And that's the power of this architecture is that I now have a better opportunity to serve all my customers as the CPG company. But as the retailer, I now have better supply coming in from the CPG company in that case. And so it's the density of hubs that get played through pharma life sciences, to auto industrial, high-tech, semiconductor and CPG and grocery. And those are the four major verticals that we're focused on.
Dave Vellante
>> You mentioned that. It's like the provisioning before the cloud. We saw that, we overbought, and then it was this whipsaw effect during peak seasons. Wayne, a lot of your customers and organizations got prepared during the pandemic when they were forced to march to digital, but some were slower to respond. I'm interested in the customer prerequisites. And imagine a scenario where maybe their digital strategy was still forming and they really didn't do the best job possible during the pandemic, but they made it through. How should they prepare, whether it's data skills, ecosystem buy-in. What are some of the common pitfalls that you see organizations facing on this journey toward an autonomous supply chain?
Wayne Usie
>> Yeah, I think the biggest challenge we're going to see, and we are seeing it today, with companies in preparation for this injection of agents that are working with humans is one dimension. And again, there are methodologies and approaches for us to be willing to adopt, and engage, and work side by side with this type of technology. But it's this concurrency that we have of an optimization across silos that is probably the biggest change management that I would see. So you look at organizational structures today, in many of, like our manufacturers, they have demand planning centers of excellence as an example. You're never going to do that discipline independently anymore. You're going to have a demand and supply discipline that's done concurrently. So therefore it changes the role and the dynamic of how they see their responsibilities to do that. Same as the old retail allocation replenishment example. You used to have replenishment folks and you had allocators, right? You're not going to have that anymore. It's all about inventory move. And oh, by the way, returns is now being forecasted as part of that inventory deployment decision, because returns and apparel make up a large percentage of inventory that comes back in the stores. So this convergence of disciplines, I would say, even warehousing and transportation, when you think of load building and you think of labor standards and work optimization, they're getting merged into a broader optimization and how we work and what decisions we make, and that will fundamentally change the roles and responsibilities of the people that are running these businesses. And then as agents come into play that take over a significant amount of the effort, then you'll have more orchestrators, if you will, of the supply chain that are able to make informed decisions based on all of the insight that's brought forward. So it's a pretty sizable change management, but I think the biggest thing I would finish this part of the conversation with is it's the speed and velocity at which this is coming into play. It's the fastest introduction and use of technology I have seen in my entire career. And I can tell you firsthand as we today sit here and companies are saying, "Hey, it's next year, it's next year, it's next year," they will be left very far behind, because the acceleration of this technology is the fastest I've ever seen.
Dave Vellante
>> Yeah, we built on the internet, we built on mobile, we built on cloud, we built on big data, and it's all there to be leveraged in intelligence. I wonder if you could talk to sustainability how strategic sustainability is, and in particular, how do you approach sustainability? Do you take it as a whole house or do you have to make a bespoke attempt at each portion of the supply chain? How do you manage that?
Wayne Usie
>> So we look at it in two dimensions. One is when you think of sustainability efforts, there is their whole measurement aspect to how we run our supply chain, sort of the scorecard if you will. What's our carbon footprint? What are we measuring in food waste and apparel waste? How are we measuring against those scorecards? And then there's the optimization dimension where when do I factor in as I'm making an optimized decision on inventory, our raw material for that matter, what are my sustainability objectives and goals? And so we have a fairly comprehensive roadmap. Our chief sustainability officer here at Blue Yonder's driving those efforts with our clients and we are looking at it, again, from the three different lenses of the aspect of reportability and measurement, the aspect of optimization. And then the third dimension now is looking at all of the regulations that are now being set in where certain countries are saying, if you follow these types of metrics, you will get preference from a global macro standpoint. And of course there's a fourth dimension now, which is the disruption that's going on with the tariffs and how those sustainability initiatives are maintained. But it is all brought in a comprehensive view across the portfolio. You can probably surmise logically logistics and transportation and warehouse have larger pillars of capabilities surrounding the sustainability objectives, but it very much is brought back forth into our optimization engines for planning.
Dave Vellante
>> Excellent. George, I'll give you the last segment here.
George Gilbert
>> Let me come back to the change management, but let's elevate it up to the notion that if we go back 100 years to the rise of mass production, the pinnacle of that was Ford's River Rouge assembly line and we remade management around that to squeeze the ultimate inefficiency out of that giant fixed asset. And you're building this network, this digital network, but to utilize that properly, we have to break down the silos of management that used to exist. Do you have customers yet, or have you seen work with integrators or academics, who can advise companies about how management structures themselves are no longer going to be like divisions and functions and divisions? What will companies look like when they're organized around the network and they're delivering management as part software, part human, and it's a collaboration?
Wayne Usie
>> It is a great question. And it's interesting not only the management structure, but we have a lot of conversation here at Blue Yonder around the user experience or what I'll describe as the UI. What is it? Imagine a discipline where it's automated through an agent which is executing a traditional aspect? A planogram as an example, we typically used to have planograms create planograms. And so you can appreciate the software itself. The tool has screens that look like shelves and people are moving things on shelves. And if you have an agent that is now capable of having the individual provide the category strategy and aspects of the product capability that they're looking with their consumer, and it automatically creates the planogram, I no longer need that tool anymore. I no longer need a drag and drop spreadsheet or looking tool that is on a shelf. So therefore, that person now has a different role. It's the role of the category manager saying, "I'm going to provide the strategy, this is what I'm trying to accomplish." And I think that's how we are going to see humans work with. And then where the silos get broken down, I think what you'll find is, because intelligence and information will be pouring in at such light speed, that it is going to enable companies to reform the individuals and the skill sets they have in terms of running the business. So think of a prompt engineer that you hear about with large language models. That's a new role that didn't exist maybe 10 years ago. But it's a consolidation of an ability to now say, "I can have the system write code for me." It will be very much the same thing. My sense of it is, across organizations, you still are going to have the consortiums that come together in the pharma life sciences space and say, "Risk is the biggest challenge for our sector, and how do we ensure we at least align on some common understanding of how we measure it?" And those efforts will still go into play. But within enterprises, I think you will see a pretty quick restructuring of how we think about running the business as the technology and agents are coming in as part of their workforce. And you almost have to think of it that way. Agents are now part of the workforce that are going to be supporting these businesses. And we are seeing firms out there helping companies to understand how that looks and how they should be thinking about their structure.
Dave Vellante
>> Yeah, you tell your agents, "If you don't show up on Saturday, don't bother showing up on Sunday."
Wayne Usie
>> Exactly. Exactly. That's exactly right. And then you think about, how do you manage multiple agents? How do you orchestrate the agents? And we're having that exact same conversation today is like, well, if I have a planning agent here and I have a warehouse agent here, how do I orchestrate them across the supply chain in that same mindset? So it's building upon itself.
Dave Vellante
>> No whining in agentic.
Wayne Usie
>> That's right. No whining. No whining.
Dave Vellante
>> All right, Wayne, thank you very much. I really appreciate you bringing out the telescope and the binoculars and helping us see the future. Really a great conversation.
Wayne Usie
>> Thank you for having me. It was great. Thank you.
Dave Vellante
>> You bet. So George and I are so pleased to participate in this series. We're digging into autonomous supply chains, intelligent applications, and how digital and physical worlds are colliding. What we really like about this program is the depth of expertise the guests are bringing to the community and the insights around the future of business and business planning. Check out all the on-demand assets we have on theCUBE.net and on this site, and check out BlueYonder.com. There's a lot of resources there as well. Thanks for watching. This is Dave Vellante, for George Gilbert. See you next time.
Dave Vellante
George Gilbert
