Agentic AI and Supply Chain Resilience, with e2open's Pawan Joshi, Northeastern's Nada Sanders and Andre Simha (ex-MSC)

Earlier this year on the Freightbuys Club, we used a single moment, the release of an AI model called Claude Mythos deemed too dangerous for open access to ask a bigger question. Who controls the most powerful tools in this industry and what happens once they end up inside a business? Today we're picking up where that conversation left off but turning the lens around because the control of the technology, if you think about it, that only really half of the story. The other half is what you actually do with it when the ground underneath you, your operating environment, won't stop moving. I'm talking about trade wars, I'm talking about COVID, the Red Sea, the Black Sea, the Strait of Hormuz, sanctions lists that expand every quarter, tariffs that rewrite the economics of trade lines overnight, six years in, disruption, it's not really an event anymore, it's essentially the day-to-day environment that we all operate and work in. So the question today isn't whether AI is exciting, it's whether earlier risk identification, deeper data and better decision infrastructure actually hold up against the world that simply refuses to behave predictably. And to that, I've got three people who've each stood in a different corner of this problem and operator who ran digital transformation at the world's largest shipping line and spent seven years trying to get rival carriers to agree on sharing data. We've also got the strategy chief who's building the platform that's supposed to tend all that data into a decision and I'm delighted to say we have an academic who's literally being called into court to test whether a company's forecasting claims actually hold up. Along with the way, we're going to pressure test the industry's newest buzzword head on agentic AI, what it promises and who's accountable when it gets it all wrong. So let's crack on. Welcome to the Freight Buyers Club, I'm Mike King and well, let's put some names to those three vantage points. Andre Sima spent nearly 39 years at MSC, the largest shipping company in the world. The last five and a half of them as global chief digital and information officer, before stepping down this spring. He also chaired the digital container shipping association supervisory board for seven years, essentially the job of getting fiercely competitive container lines to agree on shared data standards. He's now an independent board member and strategic advisor, Andre. Welcome to the Freight Buyers Club. Yeah, Mike, thanks for having me and looking forward to this conversation with my old friend Bowen and my new friend Nutter. Well, that will come into those two people right now, thanks Andre. Bowen, Joshi is chief strategy officer at E2 Open, where he's worked since 2003 across product strategy and sales leadership. He holds a PhD in industrial engineering from Wisconsin, Madison, E2 Open, of course, was acquired by Wise Tech Global last year in a deal worth around $2 billion. So Bowen now sits inside the same group as cargo wise. Bowen, welcome to the show. Thank you, Mike, appreciate the opportunity and great to see you again and Nutter, great to meet you. But forward to the discussion. Thank you, Bowen. And the aforementioned Nutter Sanders is distinguished professor of supply chain management at Northeastern universities, the Maureen McKim School of Business and passed president of the production and operations management society who even created an award in her name. She's published more than 100 papers on supply chain strategy and business forecasting is a fellow of the Decision Scientist Institute and she's ranked in the top 2% of scientists worldwide by Stanford University, no less. Oh, and she's also the award winning author of the Hugh machine, human kind machines and the future of enterprise. Nutter, welcome to the show. I hope I got the book title right there in my rush intro. You did. And Mike, thank you for having me on and I'm really looking forward to the discussion and just even the little bit of what we had before beginning recording has been wonderful. So I'm looking forward to this. No, we're on a top 2% of scientists in the world where I know I can't wait to hear what you've got to tell us. Let me set up this section because I think the framing for this matters. For years, this industry has tweeted disruption as a series of one-off shocks that we react to. We used to call them blacks ones, trade war, COVID, the Red Sea, Hormuz, sanctions, tariffs. And each time really the instinct has been to respond faster once it's already happened. The argument now is that the real shift isn't reacting faster. It's identifying risk earlier and using data and network depth, not just software features to move from prediction to decision to action before that disruption hits everybody's supply chains. AI sits inside that as an enabler and more support actor than a headline act. So Andre, you have spent so much time at MSC, basically through each one of those shocks. So from that position, has the industry, do you think, gotten better at spotting risk or have we just gotten faster at reacting to it once it's already happened? And is technology being used to help with either managing risk or reacting to it if I can put it like that? Yeah, that's a hefty question. Yeah, there's a lot in there. There's a lot in there. I'll give you my perspective, not necessarily from obviously from my experience at MSC, but I think we've mostly gotten faster at firefighting. And a lot of the reasons for that I'm sure Power New will agree is that you can't really anticipate risk if the data you're using is still dirty or incomplete or sitting in silos. And so if that data isn't clean and standardized, all tech does is give you a faster high definition view of a mess that's already happening. So I think it's not about fancy dashboards and continuous reporting. It's about being able to automate execution that actually does something to solve the problem before it hits. And that's not an easy task. So a lot of what I'm going to say is going to be based on data. And if we have the right data today, I'm sure everyone will come away from this thinking, yeah, clean data might be foundational to however you manage risk. But we'll come back to that in a bit more depth in a moment. Now that we've moved from surprise disruption to sustained disruption, if I can put it like that, for shippers and forwarders, what does good planning look like now? And how is it different from say the playbook five years ago? And a secondary question, can technology help balance the trade off between resilience and optimization? Thank you. And thank you for asking me. And I'm also going to add that we will talk about the data because boy, that is something I'm passionate about as well, because we have so much dirty data. But to answer the question, so five years ago, really, if we look at prior to COVID, right, it would have been 2020, a good supply chain, good supply chain planning was largely about creating the best plan you could, right? You forecast a demand, you optimized inventory, picked the most efficient routes. And then you had exceptions if something went wrong. But that model assumed, and you mentioned this, is that, you know, this is something that occurs occasionally, but we operate for perfect conditions. Good planning for now really means designing for continuous change. So I think it's a very different approach. You still optimize, but you optimize now across multiple possible futures rather than around one future. This is where my own background in forecasting comes in, and I've really pulled out a scenario planning, which has been part of our toolkit for years, but it is something we are leaning into heavily now, scenario planning, alternative suppliers, routes, visibility into the supply network, all of this. And this is where technology really helps us manage this tradeoff between resilience and optimization. And I think in the past resilience meant, and we all know this, adding buffers, so met more inventory, more suppliers, more capacity, I think now we're really optimizing for resilience. So we used to ask questions like, we know what's the lowest cost supply chain. If everything goes according to plan now, as you already mentioned, it's not going to go according to plan. We could almost guarantee that. So now we're looking at what are the best performing supply chains across a range of conditions and we optimize across that being very flexible to be able to switch over. Thank you, Nade. Yes. Yes, scenario planning. Plan B, if the straight of hall moves was closed, for example, something that we're all grasping with at the moment, power and your ocean shipping index tracks 70 million containers annually across your network. That's just a visibility under another, just sort of reference slightly there. This ability to see patterns before they cascade. before we see these domino effects, what are you actually seeing in that data are companies using it to plan ahead and build resilience? Are they still mostly reacting after things have gone wrong? I think it's a combination of both. I'll take a step back and really talk about the index, right? What it really does is it actually takes a very large panel of activity that's going on in this supply chain, especially on ocean shipping, and puts it into context. And the context is when was when did somebody say I need a container to when that container was actually delivered? And we actually look at every single step in the middle across, you know, 70,000 containers on an annual basis and increasing, right? So that panel is pretty broad, but it's also very timely because right when you actually book, it's not when you receive the shipment that we are creating the panel. It's as it as a container is moving, you know, we continue to add to it. Now there are some of our clients that are actually using that information to better understand when the container is going to arrive, which is great, right? But going back to another and under this point is you're still reacting based on when the containers coming in, which you're not realizing what you what oftentimes the customers don't do is take a step back and say what if that container did not arrive on time? What would I do? Where are my buffers? What's my resiliency plan? What am I alternate supplies? What am I alternate modes of transportation? If that container is running late, right? That's where the scenario planning comes in. But some of our customers actually use it in a much more fundamental way. Like we saw during Covid, the booking to receive time was extremely large, but none of our customers went into their TMS systems and changed the lead time around ocean shipping, right? It almost doubled in many cases because the port blockages, container shortages and all that. But the TMS that was actually creating the bookings continue to operate on a lead time, which was half of what it really was during that period. So guess what happened? Every container was running late. Why was it running late? Because it was not booked ahead of time because the journey took twice the amount of time as an example. So there are these pockets of visibility and use cases of information that get used, but they don't get used in a holistic manner, right? If my supply plan actually assumed my lead time to move from Shanghai to Long Beach is going to get doubled, I would actually create my lead times differently. I would create my forecast horizon differently. I'll plan my transportation differently and it should automatically readjust based on my current way of working. But if we don't, if we continue to operate in silos, the resiliency and the scenario planning come more and more difficult. And that is really, I think, at the crux of it. There is a lot of information that is flowing through our networks. It's just that it's not being used in the right time in the right place. Powan, just a quick one on that one. What was the resistance to that? We've done multiple podcasts on the Freight Buyers Club on container shipping reliability, right? So people know that there's going to be delays. Maybe Andre, I love it a few on this later. Why aren't people taking those, you can plan around this, right? This information is available. It's available, but it sits in silos. It goes back to the point Andre was making earlier, right? It is, it is sitting inside systems, it is sitting inside systems that don't belong to the buyer. You want to think about the brand owner who's who's freight is moving. It is sitting inside the ocean shipping liners, systems, it is sitting inside the forwarders systems. Even if it moves into the brand owner's system, it's probably sitting inside the transportation department, which is not often, not talking to the supply department often enough, not talking to the replenishment department often enough, not even talking to the demand planning in department often enough. And I'm talking about really talking, right? I'm not even talking about information sharing happening automatically. And this is something that we've been working on. I mean, the whole inception of Edo Open back 25 years ago was exactly this, was to solve this problem. And in my mind, the root cause of this problem is not that data is not available. It's just that the way our businesses are organized, and our departments within business are organized. They're broken up into silos. Each silo buys their own system and software and they manage their own data. There's very little visibility across the across the silos. And I'm still talking within a company, right? And supply chains 99% of the activity happens outside the supply chain. If you look at any big brand chances are they're not making their stuff. They're definitely not moving all their stuff. They're for the most point of time, not selling their own stuff, right? So if I'm sitting in the middle, I don't have visible indeed the matter of optimizing my manufacturing, in my manufacturing process. I can't move stuff because I'm relying on carriers and forwarders. And then I'm relying on retailers and distributors tell my stuff. If I'm still broken up inside my silos inside organization, imagine the scale and size of the problem outside, right? And that is really the orchestration problem. It is, it is an orchestration problem that has to that is anchored in the balance between optimization and resilience. And we're seeing that more and more important in this day and age where changes are happening across, you know, very fast speed. Fascinating. Nada. So I mean, following up on this really, you've got a back catalog of books and research papers about forecasting and resilience. What concrete practices within businesses or within teams within these silos that power and was referenced in there? What gets people to move from reacting to events towards identifying risks, planning those scenarios that you talked about before? So before their operations are affected, what's the best ways or a benchmark people can aspire to? Yes. So I'll give you a few examples of a few practices. But what I want to do if I may just for a second underscore what we've already said because I think it is so critical. It's the issue of the data and dirty data and the silos. And as someone that has been doing this for a while as this team has as well, it is somewhat frustrating to see that we're still talking about this, right? That these internal silos that don't have visibility with with one another and that aren't talking to one another. These are some really fundamental problems. So now as we see this advancement in AI, agentec AI and all of it in terms of what's happening, we haven't even gone back in terms of restructuring, making sure we have flows inside the organization let alone within our, you know, with our partners. But back to what you're asking, I think it's really important to distinguish between visibility and then early warning capability. So right now more companies have more data than they've ever had before, but they still react late. And I think in large part it has to do with the fact that they haven't designed the process to turn that signal into action. And it goes back to what has already been said. I'd say there's just a lot of things I can add to it, but I think I can think of at least a few really concrete examples. One, companies have to really move and look beyond transactional data, traditional data that we use relies heavily on orders, on shipments, historical demand. But those are really lagging indicators, right? And I think we need to as organizations really begin to look at external signals that are going to give early risk notices. This would be supplier behavior, poor congestion, weather, geopolitical events that are happening on a moment-by-moment basis. So we have a lot of other kinds of things. So it's a different kind of data. Two, I think we need to move, and I've already mentioned this, from a single forecast to a range of possible futures. So rather than saying, here is what we think will happen. I think teams should be saying, okay, what are the three or four plausible ways this could unfold? And then how would we act to each one of these? And then a third and I think you've alluded to this is I think it's really important for organizations to pre-defined trigger points, right? It's not enough to identify risk, but you need to have trigger points that are set in advance. For example, you could say something like if lead time increases by 20%, if a supplier's reliability falls between a certain threshold, if geopolitical risk crosses a certain level. So you have these thresholds you're very similar to what we've done in quality control for decades, but you have them pre-set and what they do is they trigger automatic reaction. I think it's really important to do that. So I think what we need is to shorten the distance, if you will, between that signal, understanding and be able to take action. The difference between that trigger, that risk trigger that you just talked about. Can we look at that through the reference point of your book, you machine, which argues that AI isn't necessarily replacing people. There's a future where we have a genuine integration where each side gets better together, you're stronger, that type of integration. I'm interested how that works in terms of forecasting risk. Is there a case from that book maybe, or a real life example where you've worked with a shipper or a manufacturer where you've watched that integration work, or maybe fail in terms of those trigger points? Yes, let me start with a general example, and then I'll give you one that has to do more with shipping, but I think the central argument in the book, the human machine, is that the winning model was never going to be human versus machines. And I think that is something that is more true than ever. So all along, we've been saying, what's better is AI going to replace humans, and so forth in the book, and there have been two editions now. We're going to work on a third one, and then I'm actually working on another book, the "Agentic Enterprise." All of that, the foundation, and really the crux of my work for a really long time, decades now. I'm getting older here, is that they have to work in tandem. Humans and machines have to work in tandem. I saw this decades ago when I was a young doctoral student that was, you know, we had the birth of neural nets and all of it, and we cannot replace decisions with the information, the data, because at the end there are humans and trust and other kinds of things. That's the essence of what the human machine is about. We have many, many cases in the book. Just a good example that I want to start with, it was unilever, because I think it's just a really good example of something where you could take things that are very rote. You give it to machines, and in this case, I think the example that we started with was the hiring process. We're basically, they're using technology to handle this high-volume, early-stage screening, while preserving human involvement for the consequential decision. This is, I think, an overall meta example that I think is really important, because human decision-making, expert judgment, the kind of things that listeners have, they have experience. It's a scarce resource. As humans, we do get hired, right? We get decision fatigue, so we have to preserve decision-making for the things that really matter. So what Unilever did is they basically redesigned the process. So technology AI was going to process the, you know, screen through the candidates in a very automated way, identify patterns, narrow the field, but then when it comes time to hiring, it remained up to the human. That is a really important thing, and then when we look at other examples, principles in supply chains, I've personally seen this with Merck with Amazon. The amount of information that is confronting an operator is simply beyond the human scale. It's beyond what an operator can process. You could have thousands of shipments moving. I've seen this with Merck, changing demand, port congestion, inventory position. You've got supplier issues, whether all of this is coming at them, they can only process so much. This is where AI can be extraordinary. It can monitor all of that. Then what it does, it detects anomalies. It can predict where a bottleneck might emerge, and then it gives alternatives and you pass it over to the human. The human that has the experience, the judgment. So we really then ask the questions, should we reroute the shipment? Should we pay more to protect this particular customer? This is where human judgment comes in. It's the humans that understand the customer, supplier. But you can't waste that judgment, Mike, on tire people out on the road things. That was the lesson in the hum machine. What I'm seeing now is it is more true than ever. Where does the hum machine end on an agentic AI begin? How does that factor through into the future of risk forecasting? A secondary question to that is, there's an awful lot of AI companies out there promising all sorts of different things. I mean, you've been an expert witness in cases along these lines. What should people be wary of when vendors tell them that AI can make these decisions for them? Oh, boy. Once you get me started, so I haven't done a lot of expert witnessing. I haven't recently only because I commit so much, Mike, and I really try to do a good job. And we get paid well for that. But let me tell you, you earn every cent. For me, personal, the expert witnessing was especially valuable, because you get to see under the hood, right? Everybody talks about how everything is great. But I can't tell you how many times I've seen companies that have bought software packages. I'm not picking on anyone or any consulting company to be surprised as to how many hidden costs there are. I could tell everybody out there, I can almost guarantee you you will encounter hidden costs. So you need to really plan for that add-ons, even silly things like I've seen companies say, but what if we want to query the algorithm in this way versus that way? And then you get the usual, well, we can do that, but it will cost you extra. So that is definitely something to be cautious of. I've already mentioned it has been said, the data issue, the process flow. Those are things that are really fundamental. I think so many promises are being made right now, especially as AI moves from gen AI to agentic AI, which is the next stage of the integration model. It's not the end. What I am working on, I can tell you, I'm working on this right now. I was working on it yesterday. I'm talking to companies is where the human enters the process. So the hum machine and everything that I've set up to this point is completely valid. It's true more than it's ever been. But the issue now becomes where does the human come in? So with traditional AI, we often imagine a person sitting next to the algorithm, the AI makes some kind of a forecast, the human decides whether or not to act, you know, can press, you know, accept or not, agentic AI changes all that because now the system can do everything, can take action, it can observe the outcomes, it can continue acting. So the agentic AI doesn't invalidate the hum machine, but it makes the design of this entire process more consequential. It also brings in the question of where does the human come into play early on checking in the data or somewhere throughout the process. And that is something, again, like I said, like I am literally working on at the moment. And it's going to vary in terms of the size of the company, the decisions that are being made, the human may no longer approve every shipment, rerouting or every inventory adjustment. So it's also going to be about now setting up those boundaries. What level of uncertainty is accept acceptable? When do we escalate the models to the human level and when do we automate? These are really hard things to put into processes. And this is where I think the key is redoing, readjusting processes. One of the things that, you know, as an operations, I'm operations supply chain person, we're pulling out the old playbook not just in scenario planning, but also in terms of workflow analysis and workflows and bottlenecks because we have to actually rethink what that process is and where do we put the humans and where do we put AI, not to create bottlenecks, but to actually optimize the entire decision slow. And it's really hard. One other thing, if I may, that I want to add that I think is really important is also to include when does the AI stop acting. So I can tell you AI is going to continue generating endlessly and creating, as we all know, a lot of slop. And we've all seen this, right? You get one iteration, two iterations, and then it just continues and then it's sort of, you know, just really is a downhill process. One of the things companies need to do is also putting when is good enough and when does the system stop acting? So all the things that I had mentioned, the trigger points, when do we escalate? I need a handbrake. And when do we stop? Yeah, I want to get your view on what this means for the workforce a bit later, but let's bring power in here. From where, from E2 opens perspective, from your perspective, power and running one of the largest supply chain networks in the world, connected over 500,000 enterprises and roughly 18 billion transactions processed annually, I read. How far has AI actually taken us so far in terms of real prediction, real forecasting, or real resilience, or is all of this promises at the moment? What does your AI strategy look like if it's simplified in that way? Yeah, so I would talk about it more in terms of the practice rather than what we're building right now. I think there's been a lot of use of AI, you know, in systems. The challenge is that's not the right way to unlock a lot of things in the AI. And I'll build upon what NARA said, right? Our systems, our processes, our organizations are designed on the technology constraints from 50, 60, 70 years ago, right? When we did not have the connectivity that we have right now, right? There was no internet bad then. We could barely talk over phones bad then and those phones are landlines, you know, hardwired. We did not have compute the way we have it right now, right? Forget, forget, you know, cell phones where we can actually check stuff. Barely desktops were barely there. And even if they were there, they were green. screens, no graphical interfaces, so you actually had to tap through and do that, right? And the largest memory, the largest processors that we had on those desktops were very, very limited. And if you had to do something, you had to actually go borrow time on a mainframe to run systems and processes. Now in that technology constrained environment, guess what happens? Processes get broken down into bite-sized chunks that can be solved with the compute and the power that we have, right? And when you do that, all of a sudden now you're designing your people around that. So all of a sudden your org structure looks like not a planning department. It looks like a demand planning department, a supply planning department, an inventory planning department, a transportation planning department, right? Fast forward 70 years from the end, we still have organized our systems, our processes and our people are still organized the same way for the most part, right? Very little change. Now, technology has moved so far ahead, right? At the click of a button now, we're having this conversation pretty much live, right? There's very little lag in our communication process. We have systems now that are hyper-connected where you can on the cloud run such a large compute payload in milliseconds and bring that data back into desktops. We have more power in our cell phones than we had when we launched Apollo to the moon, right? That's the technology. Now, if you take a step back and think about what are we doing now, in many ways we are doing what we were doing 50, 60, 70 years ago. I run my forecasting process. I shoot it over, my supply plan runs, I shoot it over, you know, procurement process happens. I send up here out that that process is not changed for the most part. Now, what has really changed? And when I say AI, there's a lot of AI being used. I would say I would use AI as a technology, right, evolution of the technology. It's there when we've been doing concepts around demand sensing which takes not just last years forecast and last month's forecast. We're looking at open orders. We're looking weather patterns. We're looking at inventory availability on the retail shelves and coming up with a forecast that can be executed in light mode. But guess what? That's just one piece, one use of AI in one place just around forecasting. The true power of AI is to take a step back and really rethink and reimagine what the processes should be now that you have hyper connectivity, real time visibility and compute at the tip of our fingers. And not just at the tip of our fingers at the edges, like the edges of capturing it data to be able to make decisions around what happens. Like a good example. I'll use an ocean container example. I mean, we move a lot of freight on reefers or freezerated containers, right? If you can monitor the temperature on a real time basis on that and make that available to the kitchen center, anytime you break that coal chain, you're not only recognizing that container is gone bad or the coal chain is broken. You can actually say, regardless of whether the container arrives on time or not, I will have a shortage at my distribution center because all that material that I'm bringing in is probably going to get quarantined or thrown away, right? And it may be a six week long journey or a four week long journey. And I get that signal way ahead of time that I can now process, not in terms of agent, but just as a raw input into my supply planning process and say, you know, container worth of stuff is now offline. I've got to figure out three weeks to figure out what I do and that is really the unlocking the power of technology. I would say not even AI of technology is to really be able to connect the dots and close the loop, right? One of the first classes that I did when I did when I came to the US and in grad school was control systems, right? And you realize an open loop control system where you tell somebody do this and don't find out whether that person did that or not is an open loop system is designed for failure. And never a closed loop system is what you want, but if closing that loop takes you four weeks, it's too long in this container example. If it takes you four milliseconds that over the last seven days have constantly seen temperature going out of band, that is what I want to bring in. And that gives me an opportunity to actually open up the aperture and come up with multiple serve decisions. That philosophy and that concept is really what we built our technology on at ready to open in that heritage as well as cargo wise on the forwarding side, right? So that is really what drives us is to be able to unlock the power of technology, bring in all sorts of technologies as available, including AI to solve the problem based on the technology based on the right technology at hand. Yeah, not attached upon this is not always the right way to do things. In fact, in many ways, it's a cost prohibitive way of doing things because there are better ways to actually connect and provide visibility. I still remember, Andre, you presented to us when when intro was formed, you know, the communication hub that sits on a ship. It's still sitting in our office. It still inspires us to say, look, it worked. It works on a ship. It doesn't work when you're connecting multiple enterprises across the globe, right? So if we take that tool set and apply it to the current complexity of our supply chains, it'll not work. And then we'll create all these banded and throw people at it. And that is really the power that we think is to start thinking and reimagining. It's not an easy process, you know, it's like changing engine of an aircraft while it is still flying. You can't bring a supply chain to a stop and say, come back in five years when we redesigned it and then we'll sell you the product. You've got to continuously do it. And that's really where the challenge comes in is, how do you change? In my mind, the order of operation should be, how do you reimagine your business process? How do you reimagine the people that are going to support the business process? And then technology comes in to say, here's the best way to solve that problem. And it is, it's going to be an incremental evolution. That recognizing that need is, I think, the first and foremost thing. Thank you, power. I just, I think we had an unexpected guest there and I wouldn't want any of our listeners to feel a bit left out. So have we got a name for the dog, please? Just in case they can hear the barking. I wouldn't want to welcome them properly. I apologize for that, but that is a Lambo. We're named Lambo. Lambo. Yeah. What's the breed? He's an Aussie doodle. Oh. A group global citizen. He's what I'd say, a global citizen. Well, welcome Lambo to the Freight Barrier Club. You're here for the highlight. Power and e2oprum just became part of Wise Tech Global last year. It was a deal I mentioned daily with $2 billion. Brought e2oprum's network planning trade and supply chain capabilities in to the same group as cargo wisers, global logistics, exact execution platform. Sorry, stumbling over that. That most freight forward is used. Does that kind of scale and that amount of data? Does that change what's possible in future? Or is more data with sort of been referenced in its data pointless? Does it matter how much you've got until something else changes? How does that play out? Do you see it? No, for us, there are two pieces of inspiration. One is going back to reimagining the business process. If you think about the bringing products and services to market to how you and I consume it, it's kind of, I would largely break it up into three broad categories, right? People who are making stuff, right? All the compute that we're using today to have this webcast is there's one group of people. Second group of people is who actually help sell that stuff. Right? We all bought our stuff from different kinds of, you know, Amazon or whatever the other stores we bought them from. And then the third group is people who helped move the products from where they made to where they sold. And oftentimes it's components raw material, but in oftentimes it's actually finished goods that get into it. So if you look at those three broad audiences, why is tech in need to open recognized a similar design pattern of the problem 30 years, 20, 30 years ago, that came from different directions. Cargo wise looked at the world in terms of the movers of product and said a lot of that stuff is disconnected, especially around international shipments, right? And that's why Cargo wise was born and it incrementally built over a period of 30 years or so to get to a point where now we have everything unified where if you do an international shipment, whether you do air, ocean, audio, the regional shipments, or you actually going through just pure customs clearance, bought a crossing, all those capabilities are unified as a process flow on that platform. And the underlying participants are wired into that system through an underlying network. What that really means is if I'm connected to a ocean carrier or port or to a particular government agency for customs clearance, I can use that connection for all my customers, all my forwarders that are operating on that platform, right? That was Cargo wise's vision. Edo open came from us for solving a similar design problem from the perspective of people who are making products and selling products, right? So very large brand owners that had actually outsource their manufacturing process, outsource their distribution process, outsource their selling process and transportation process, needed a platform for orchestrating that entire into end process and that's where you open was born, right? So if you think about the aspirational way of designing a system that connects these processes together and connects these entities together, that is really what we're trying to do. We're bringing one of the largest quote-unquote BCO manufacturer brand owner platform with the largest transportation platform into one quote-unquote group structure that allows us to reimagine the business process and not stop at the boundaries of what a brand owner does, but continue that boundary beyond the brand owner to somebody who's moving their products within. And taking the boundary where the forwarders processes start and stop and blending that into the brand owners boundary. So if I need stuff to be moved internationally, well, I do all my planning, forecasting, planning, transportation, all that stuff and collaborate with my suppliers and then hand it over to the forwarder and say, "Okay, move this 10,000 widgets from point A to point B." Guess what happens? That is a process drop. We continue that process into through cargo wise and when cargo wise drops and says, "Oh, it's been delivered," then you start the Edo Open President and say, "Okay, it's available at the distribution center." Now, how do I get into the hands of the end consumer? How do I orchestrate the distribution process? How do I actually better plan? my supply and now that it's available within region, how do I transport it or how do I actually get it to that? That is really the ultimate vision. So the underlying process definition is in the platform. The underlying network that comes with the platform is what really brings the data in. So if we want to think about data quality, oftentimes people will give you the data that they have. It's not it's bad data. It's just that they don't understand the context in which you want to use the data, whether it's meaningful to you or not. They don't provide the context along with it. And this is what Andre's DCSA is trying to do. He's not just talking about data standards, but also talking about the process standards that go along with it. In the high tech space, we did that with Rosette. And at 20, 30 years ago when Edo Open started, we were on the board of that where we're basically saying, look, everybody has data. But how do you actually transport the context of that data so that somebody actually understands it and can use it? It is bad data when you don't even know what it really means. It's bad data when I give you, here's my inventory position. And you look at it and say, hey, is this actually a netted inventory based on your forecast? Or is it actually total on hand inventory where I have to do the netting, right? If you don't have that context, you don't know. If the shipment is arriving late, well, it is late as of what date, right? If you give me that information, those are all very important. So coming back to what we really want to do is to be able to think about the end-to-end process and let our customers drive us towards what level of end-to-end nest do you need? Because it's their readiness that allows us to put that in as a technology provider. We have signed up for the technology challenge in the people process technology part. But we still need the other two dimensions of the stool, which is how do you reorganize your processes and how do you reorganize your people to leverage your technology? Coming back to what I earlier said, technology has moved so far ahead in the last 70 years or so. We need to move our processes and our technology in it. It's a question or imagination. So what we are really doing is we are saying, how can we as technology providers be ready for that challenge as soon as our customers sign up for the challenge on the other side? - Is there any part of that chain power and that is adapting to what you're offering or to the technology that's become an available faster than another part of that chain? So your footprint runs from manufacturing, planning all the way through to the last mile delivery. Are they moving at different speeds if I can put it like that and across that value chain? - 100%. I think it, like I said, at the end of the day, everybody that buy software has a business to run. They're not going to pause that business and say, let me reimagine my process, let me rewire my systems and then I'll come back to it. So it always is a function of where the burning problem is and it changes industry by industry and in some cases, customer by customer. Like during COVID, the biggest problem was, where's my container? Am I going to get that container or not and how is it going to get from where it is to where it needs to be? Am I going to be able to fill it up because I don't have the people to run my physical operations at the right facilities? That was problem to sure and a lot of people actually solve that problem either through brute force or through deployment or technology. Right now, it's all about what happens when the tires change because it's not if they will change. We've been conditioned to talk about it when it will change. So how do I actually make my transportation such that I'm bringing as much as I can within a region so I don't have to get exposed to the tires that may I may not change? That's, you know, topic to sure right now with, you know, the whole USMCA and US Canada and just between in the US region. But even globally, it continues to have a play there, right? So really what I'm going with this is our clients oftentimes gravitate towards the problem at hand. But the clients that are actually further ahead in terms of the maturity of thinking realize that the business environment has changed. 70 years of very stable global structured way of dealing has been upended in the last 15 years, 10, 15 years, right? Which means that all that wiring that we built and all the interconnected that can it miss that we built is actually exposing us to a lot more risk that needs a lot more resilience and the clock speed of change is very, very dramatic. So some of our more mature customers are actually taking a step back and saying, look, the way of doing this with our systems and people and technology has to change. And that's where a lot of the transformation activity is happening and a lot of activity that's happening around being able to run the business but being able to make these incremental decisions with the long term vision and site that these things have to change right now but things that also have to evolve. - I want to bring Andre in a second on container shipping and data and standards but just before we finish power on your network, where's the biggest pushback across that network? Obviously, one of your orders might go through the entire value chain manufacturer supply logistics provider, retailer, where's the pushback or where's the bottleneck is it standards, is it trust? - So it definitely is trust, I would say trust is number one. I think standards is super important but I think going back to the point Andre was making earlier, we have over the last 25, 30 years used technology to quote unquote work around the standards because one of the things that oftentimes is true is unless the standards are well thought out and adopted, people will say I adopt the standard but they'll always be work around. It's standard as long as you can do these five things around it. And that is a true definition of a standard that is either based on just information exchange that has not thought through the business context of that and that's why I think DCSA is extremely unique in that mode. So our resistance really is number one, I don't trust you with my information, right? I don't know what you're gonna use it for and this is getting even worse in the day of AI because I don't trust you, I don't trust the AI that you're gonna use because any information that I give you might show up tomorrow someplace else because I don't know what governance you have, right? So trust is an absolute important issue. The second is the standards but we can work around some of the standards. I think the third is helping people understand what is it, what is in it for me if I give you my data? How do I benefit my business? And that's where the collaborative nature of a network becomes really important. If I'm a supplier and I actually tell you what's happening in my factory, you get better information on whether the certainty on you as a supplier being able to deliver to the PO commit or the promise that I had, that's great. But what do I get in return? Well, if the customer says what you get in return is anytime things change on my side, right? Forecast is a forecast. If my plans change, I'm gonna immediately tell you what it is so you can adjust your manufacturing process. Well, if there's a give and take in terms of business outcomes, then data sharing becomes a part of, okay, I understand now what we're trying to solve for mutually. So I'll give you my data in the context of that mutual solution of the problem and you provide me that data. And now I'm not talking about data and moving. I'm actually talking about solving a problem with information that is being changed to help me solve that problem. And all of a sudden you move away from noisy, bad data to a contextualized way of sharing information to solve a specific problem. And that is really, I think, an evolution that needs to happen. We play a role from a technology standpoint, but at the end of the day, it comes back to process evolution. How am I gonna work with my suppliers, not sending, throwing a PO over the file fall and telling them what, tell me every set, single step. But when I throw up your over the wall, this is what I expect you. And in time, I make a change. I'm gonna be cognizant about the fact that this change will be too late in the manufacturing process. So I will buy that inventory from you, but I'll give you advance visibility. - Andre, I'm not sure I need to ask you a question. I think I can just go trusting container shipping. Please discuss. I think Power and Setup are perfect pivot. You chaired the DCSA, the Digital Container Shipping Association for seven years and spent 15 years on interest board. From that perspective, where's the blocks on progress in terms of shipping in terms of trust, the visibility standards? - Well, actually, it's funny, Mike, the way you mentioned that I was listening to Power and with a lot of interest because, of course, we've been dealing with Intra and E2O and YSTEC for years. But one of the reasons that I really pushed with Mersek at the time for the DCSA was because of the 15 years at Intra. I knew that when at Intra we created standards in 2001, 2002. - Just explain what Intra did for anyone who's not familiar with it. - Sure, Intra was, let's say, the first, we called it an E-business platform, but there wasn't actually any business. It was an E-commerce. I don't know. E-sharing platform for essentially for bookings and shipping instructions, developing a standard format that everybody could use and more or less understand each other. So that was the goal. At the same time, we started to create standards. So I think that was, it was a positive way forward, but in the 20 odd years of Intra, there wasn't that many standards being developed, simply because nobody probably thought it was, it was of any interest. So when we created DCSA, there the goal was, we've been doing this for 20, 25 years, now we need a solution. We need to create these standards to help us create a foundation where people can build solutions and speak the same language. So I think you were asking me if that was progress or what's blocking progress. I think we've progressed somewhat. And again, the tech is not the issue. It's really about adoption and, as Paul is saying, about trust, trust in sharing information, trust in adopting standards to share that information. You know, sometimes it's a vicious circle. So seven years with DCSA taught me one thing is that getting fierce competitors to agree on basic definitions was hard, but it was feasible. But getting the adoption, I think, is another story And that's-- the difficult task that DCSA has today. And again, without common standards, everyone could have visibility, but they're not necessarily looking at the same thing, and we still don't get predictability, which I think is still the talk of the town. This is what customers want. Let's look at predictability from the technology perspective. We were discussing earlier, which taught me back in time to my own formative years. As well, you were telling me about, when you first started at MSC, container positions were up on cards, on a wall. And I remember in my family's trucking business, you know, they'd be like a blackboard. And now we've got all this real-time, tracking AI networks. But is there a different way of working now? Or is it really the same people doing the sort of something similar, but it's sort of dressed up as modern? Where is 40 years of technology taking us? The answer to your question is yes. Yeah, we moved, of course, from these physical cards on the wall to, let's call it semi-real-time digital tracking. I think that works for everything, mostly relying on EDI, by the way, still today. Still EDI. Oh, yeah, well, you have smart containers, of course, that provide their location and sometimes what they're doing. But because smart containers are still a very, still have a very low penetration, it's not yet something you can actually use. And the thing is, as soon as an exception hits, what do people do? They just pick up the phone again, write an email, or open some offline spreadsheet that was updated three weeks ago. You were talking about updating rates and things like that bound. But so digital transformation, and that's something that I've tried to work on for the past couple of years, it's not about just buying new software, and it's really still, and always will be, about getting people to break all these habits. And as both of you mentioned, to redesign the processes, and that's a tough process, with or without AI. Well, OK, on AI, then, usually in similarities with this, to the blockchain evangelist of 10 years ago, or is AI different, do you think? It's funny, because the blockchain evangelists of 10 years ago are the same, are some of the same AI evangelists of today. So there's a vendor now that was talking about, watch out for them. But the funny thing is, you know, you'd get 10 years later, the people would contact me and say, "Oh, I'd like to have a talk with you when I was with the company." And I would say, but don't you remember? You already talked to me eight years ago about blockchain, and what have you been up to, and so blah, blah, blah, blah. And I want you to buy in crypto. Yeah, exactly, exactly. And well, some colleagues do that, but it's relatively simple. I mean, show me where any technology can solve a problem, right? If we have a problem to solve. But don't show me another demo. Don't show me another 300 slide PowerPoint. If you can take a simple routine exception away from an operations person, for example, or read documents without somebody typing everything in again, then I'm interested. Otherwise, we've all seen this movie before, and we're not going to be able to benefit from a lot of this new tech until we fix the underlying processes and look at documents, how many companies have come to us in the past? I keep saying us, two MSc in the past 30 years, will read all your documents for you. Well, nobody's succeeded. So you serve not people, reeking and reeking. I can give you an anecdote. And this has to do with interactually. So you're back, why stack, e2 open, intra. One day, my boss, the big boss of the company calls me up to his office. He says, oh, we're meeting somebody from Merck today. Somebody that I still talk to today. And I said, OK, so we had this meeting, and they explained to us what they were trying to do, creating the e-business platform. And so I listened. I listened very carefully. And I was a bit of a techie, still long hair, coffee, smoking, of course. And so I was writing take notes. And the guy leaves, you know, bye-bye, great seeing you. And the boss says to me, what do you think? And I said, boss, what is EDI? And, you know, we don't have all that. We just have simple databases. And the boss said to me, then let's do it. And it's thanks to that meeting that we actually got into modernizing what we were doing. And a little bit the way we were working. But the truth is, we haven't changed much in the way we work. And when I say we, I think it's everybody, you know, in the ecosystem, maybe a few are a bit more modern. But that's where we are. So let's do AI, blockchain, trying to think of another one. But your general point is that shipping does need more technology. It's implementation. And adoption. I'm sorry, but I interrupt you, Pa. I was saying implementation and adoption. Yeah, I mean, especially on the standard side, if we don't get adoption. And, you know, you take, for example, the E-Bill of Lading, which is something MSC, and myself, we pushed a lot during COVID. If you still only have 10, 15, 20% of original bills that are E-Bills, then you don't have critical mass. So you cannot change your processes. And-- Well, that's been slow. That's been a slow adopter, though, hasn't it, E-Bills of Lading? It's been a slow adopter. And not because it's not useful, but because it's not enough. Because, you know, if you look at-- OK, you can also blame the banks, but it's not really true anymore. If you look at the pile of documents you need for a shipment, you take out the Bill of Lading. You've taken out the title of property, but you still have all the other documents. So now the work is on getting all these other documents digitalized and having them being authorized and accepted by the different regulators that need to accept them. So that's part of the work in progress. Yeah, with DCSA, for example. OK, thanks, Andre. Let's just do a little-- I want to go through an agentic AI with all of you just to see what we're all thinking. And that's sort of summarized where we've all referenced it a little bit. So basically, if a quick definition-- traditional AI tells you what's happening and what you should do, agentic AI decides what to do and does it. Here's where I want to start. And I want all three of you to answer this one. It's essentially a possible because this might be the longest podcast we've ever done. Everything about the maturity of your agentic AI and supply chains, like a football match. So 90 minutes has kickoff actually happened yet. And if it has, what minute are we in? Please feel free to elaborate on the why bit as well as giving me a number. Another. Sure. Thank you. Kickoff has definitely happened, OK? But I would say we're about 10 minutes in with a lot of teams still figuring out formation. So the idea here is obviously we're no longer talking about agentic AI in a lab. We're seeing all the capabilities that we discussed. But I wouldn't personally put it beyond the 10th minute because companies are still really-- and we've heard this a long way from true autonomous supply chains. And also, if I may, we heard a lot about trust. One of the things that in my experience, and I recently talked to people in shipping, one of the things that they've said to me with this, with the agentic AI, with AI, all the capability, they're saying, especially in shipping, relationships matter more than ever, the human element. And one of the things-- and I'll stop here-- but what I want to add that is a real concern for me is that we are going to potentially lose the human expertise to be able to develop these relationships, to share, to talk, negotiate, know what to do in the clutch. Because what's happening is we don't have a pathway of people that are coming in and training them. If we take people now that are early coming into the company early in their careers, they don't have the expertise that people that are well along have. And we're not creating the pathway because we are now really relying so much on the AI, making sure that we keep the talent and develop the talent I think is going to be a really key thing. A very good point. Yeah, I mean, we've seen this with talk about reassuring manufacturing or something. Well, that's great. But if you haven't got the people or the experience to do it, that's a lot. It's not just about the cost. Powan, the floor is yours, agentic AI. So I look at football. It's a team play, right? And I look at agents on a team play. I don't see agents playing as a team right now. I think we still have each of those players in their heads are using AI, right? But is there the choreography that is needed for the business to actually make up order of magnitude change? No. So going back to your question, I think individually, it's probably in a 10, 15 minute period. But as a team, that is reimagining how they're going to play now that they're wired differently or have the ability to communicate differently and not just through voice. We still try to figure out what that would mean. And I would equate that to the process of reimagination. the technology's there, but what does soccer look like? What does football look like when you're actually able to communicate telepathically? That is really the analogy I would bring in, and I think that's where we are. We're still trying to figure out, is it still gonna be a game of football, or is it which should we call it something else? - Your description could have perfectly described exactly how I'm feeling about Liverpool at the start of this new season in the Premier League, in fact. Andre, please, your views. Look, I'm definitely not a football expert. So, I was gonna say we're past kickoff, but maybe five minutes in, but I'll say 10, so that I'm sort of average out with the speakers here. I, you know, honestly, honestly, if you've been in this business for a couple of years, do you really wanna let a machine re-route a ship or paint in voice, or move a container without any human intervention or human approval? Of course not, so I understand you need to start somewhere, but I think before anybody trusts an algorithm to decide, we need to see, actually, you know, we need to see it work on a small scale. So what I would say is we need more pilots with proper data, and it all goes back to the data. If we don't have the data, we're not gonna get any good results. And at the end of the day, if I look at it from the carrier perspective, customers don't choose a carrier because of a nice app or a shiny website or because they use AI. They use a carrier, they choose a carrier, they stay, because the carrier is reliable, easy to work with. So tech, in my opinion, shouldn't make that happen in the background, but it shouldn't be the selling point. And I won't name names, but we've seen it in the past. Technology, sorry, doesn't keep your customers happy. No, we've definitely seen that in the past. Now, I wanna bring you in on that. You talked before about where the line stops, right, with AI. This is where the integration, where it works or not the human machine. Is this the danger, whether we've got the data and the standards, is the danger that, I mean, do we actually want AI to reach full time in the football match, full autonomy? - I think, in my opinion, absolutely not. It's really frustrating that we're still talking about data, silos, process redesign. And I say frustrating because we've seen time and time again, right, new initiatives come on board. And then we simply think it's a plug and play. This is not a plug and play. AI is not a plug and play. So on the one hand, we have so many shiny objects. But I think when we're talking about shipping, we're talking about the criticality of timeliness, of route, of it all comes down to trust being able to communicate and being able to know what to do in the clutch. So to me, the people element is absolutely critical. And I think we have to be careful that we don't have this sort of, what I call symbolic, human oversight or symbolic human in the loop. We actually have to have meaningful human engagement. And this is gonna go back to the redesigning the actual process. My biggest concern, as I already mentioned, is that we are not looking forward and preparing the workforce. One other aspect of this is who is accountable? So you cannot delegate authority and accountability to the machine. So, agent at AI, right, it can act. But who is ultimately accountable when things go wrong and things will go wrong? And this is where the human needs to have oversight. They need to have action, they're accountable. They also need to know what to do. I personally think that the companies that are preparing for this, making sure that their workforce really is seasoned and is growing with the organization and gets this knowledge and works with their partners, those are the companies that are gonna be the winners in the long run, not the ones that are chasing the shiny object. And it's not to say that we're not gonna use AI. Obviously we are, we're gonna implement it, but I think we need to do it cautiously. - Powan, feel free to come in on any of those points for another, but I was gonna ask you about the deployment of this of agent at AI. So over the next two to three years, where do you see these capabilities that maybe create an incredible customer value? Is it about identifying exceptions, recommending actions, is it about coordinating workflows or maybe taking bounded action under predefined rules? Or is it something else that I've not thought of? - I think over the next few years, you'll see AI helping in more of the mundane basic tasks. Right, we've seen some good results around document scanning and digitization. It's way better than what it was during the OCR days, but again, I agree with Andre, the proof is in the pudding, we gotta take it out for a spin, but those would be pockets where humans are doing mundane repetitive work that is basically mind-numbing at some point in time, goals would get automated and they would be better at option there, because nobody wants to continue to build a career on typing printed document into the computer, right? So I think that's a lot of that. It's going to be workflow orchestration when somebody new joins in. How should you go through a workflow, even though you're manually clicking on it because that going back to another point, you need oversight, you need some of that stuff, even though the agents can do it, can the agents actually guide you towards 90% of the time? This is what you do, 10% of the time when there is an exception, you call your boss, right? That kind of thing. There's a lot of that work that will get done. A lot of the work that will get done is around generation of those documents. There are often times you type documents in because yes, it is in some system. That system, the only way can communicate to you is through a PDF document. You've got to retype it, re-key in the PDF. What if you would actually connect to that system and actually get that information because it's already digitized there? So there, I think, again, the emergence of leveraging of standards and adoption of standards is required, but if they're not there, maybe there is an agent that can actually take the two in from it and map it up, right? Automate that process. We've done that historically through traditional EDI maps and all that, but there's ways in which we can do it much more quicker where you're not letting the humans, you don't have the humans document everything and then code it. The agents can kind of self-evaluate the from and to and come up with their maps. Those are all areas that I think are right for handing it over to the AI. And we should do that. Just like when Internet came in, we actually took the phone call on the communications out and replaced them with emails and all that. That absolutely will happen and will happen very quickly. In many ways, it's already happening. The question then is, when you free up people's time and get them out of this typing business and can you actually get them out of the Excel business? 'Cause right now, Excel is where people are pivoting over. Okay, it's in the system. Now, I want to download it because I want to process it in my way. That's the individual brain thinking. Can you get them out of the Excel? When you get them out of the Excel, that's when you're beginning to unleash the potential and the unleash the potential is thinking about resiliency, contingency, thinking about scenarios. Not worrying about data in that because they trust the data because it's incrementally built up to a point where you said, "Machine, I can connect to that machine, "get that data and it's in my context. "I can connect to that particular thing, it's available, "I can instead of processing it myself, "I trust the system, that's processing." Okay, now what do I do with my free time? Well, I start thinking about what if this happens? What if that happens? And I think that's the evolution that'll take. Yeah, it's definitely gonna help in these incremental chunks, but eventually, the process redesign is where scenarios, scenario planning and evaluation and resiliency will get codified and that's where the humans will come in. It should I push the button or not to reroute the shipment? Well, what am I alternatives? What does it really mean? What is the cost profile? What is this? What is that? What does it mean to my customers? What does it mean from a port congestion standpoint do I have capacity of that port? Great, all those decisions come in and yes, the agents can surface that information over to the thing and saying, "Look, here's a 360 view of option one, option two, option three, which one do you wanna pick?" And it could very well be that the person picks up the phone, talks to the person on the other side, going back to another's point in relationships and says, "Look, here's the three things that I have available to you, which one do you recommend because you're closer to the situation I've had?" And that is where I think we need to move up people and our intelligence to and take them away from this mundane business of, "Hey, is my EDI failed because this segment "did not have the right piece of information?" That is from 50 years ago. We got to move past that. We're still stuck in that mode, right? And that's where I think the future is there. It would evolve in those incremental chunks in my mind. - Thank you, Pao, and we're running short of time, so I'm gonna finish up here. And we've covered an awful lot of ground. I wanna give each of you one minute, maybe not a prediction, but a, maybe give me a product recommendation. The one thing you think this industry is currently not taking seriously enough about what's coming down the pipeline. Nada, do you wanna go first? - Yes, thank you. I've already alluded to this, but I think they're definitely not taking seriously enough the erosion of human expertise. I think it is much less obvious than say data quality governance and the kinds of things that we were talking about. But I think as AI takes over so many decisions, what's gonna happen five years from now in terms of the people that are not making decisions how will they know and learn. I don't think that we've figured out how to do that are really thought about it because they're going to have to have the expertise and knowledge. That's actually one of the reasons. If you've seen the numbers now, the latest numbers and hiring are tending more towards more senior people because I think a lot of companies have let go of too many senior people, but we need that knowledge. I think erosion of human expertise, particularly in this industry that needs a lot of knowledge, trust, relationship building, I think it's something we're not taking seriously enough. Andre? Well, I think what Nada said is critical. I think that's a very, very, very important topic for me. You know, I think when I listen to people, I think the industry wants to jump straight into the exciting part. You don't want to build a house. You just want the house to be built from one day to the other. And so everybody wants AI, but nobody wants to spend the time cleaning up data, fixing basic processes or reviewing processes. So if you don't do that first, you're not solving the problem. You're just doing the same back process faster. And on top of that, which we haven't mentioned a lot, you're adding costs. And that reminds me of the third tech that I wanted to mention was the cloud. We didn't mention the cloud. We don't talk about the cloud anymore. But today everybody understands that it's cool, but it costs a lot of money. And I think that AI is going to reveal the same thing eventually, not that you shouldn't do it. But yeah, we got to change the processes. Pawan, do you want to come in on that? I'll just build on that. I think the most important thing in my mind is to really think about, rethink the reimagine the business process, not inside your four walls, but what happens outside your four walls, how do you actually connect all of it together? And to do that, you need people. You need seasoned people who understood the problem, who have lived the problem, who are open-minded enough to reimagine and design the operation for the future. Technology will come in. And you cannot take one piece of technology, which is technology, digior, and say, that's the solution. You have to pick from an array of technologies. We have massive evolution of technologies across the board. Should not be enamored by just cloud or blockchain or AI. You've got to fit for purpose technology. It's perfectly fine to have computer running in your own data centers or your own offices, leveraging some aspect with cloud that may have some component of blockchain embedded for maybe the financial reasons and financial side of the world. Maybe you're using AI for some parts of the thing, maybe thinking agent against some parts, but definitely making sure that there's human in the loop with what Nada talked about, of Procreate Trigger Points. Like we have to be, that's I think the imperative. Technology, there's so much technology that's available. I don't think technology is a problem. It is what is it that you want it to do in the future? Okay, thanks. And this is definitely the final question. Really quick on this as well. And I want people to go hopefully away with some sense of optimism, because it's easy to be very pessimistic about things like job displacement and AI taking decisions that used to be made by humans and the knowledge that can be lost. So power into you first, what's the version of technology in our trading world that is positive? Just one would be good. I think all of this is positive, right? One of the thing is how do you actually take knowledge to the future, right? And if you look at just that one piece, when we could not, right, or we could not print that scale, right? What was the means of communication? Well, we communicated verbally, and we've made people remember things, right? It was books that were people had it in the heads. What printing press came along and all of a sudden you didn't have to remember. You could read it. Then back there, we lost everything. Exactly. Now, fast forward internet, everything is at a fingertips, right? Fast forward AI. It's not just data that's at a fingertips. It's actually process information, right? We got to look at that positiveness. We can find whatever we want to find if you're knowledgeable about what you're looking for. And we are also thoughtful enough about understanding whether it's right or wrong, because there is a lot of hallucination that's still going on. Like if we have that mindset, imagine that being at the at our fingertips, that is what we have to look forward to. The technology is up there. We have a lot more, a lot of things going for us. It's not all doom and gloom. I think what we're really talking about is how do you actually take it to the next level? We're already in many ways at that level where we're actually benefiting from where we were as an evolution of humankind, if you want to think about it that way. Now, tell us why this is in the new dark ages. Oh, I agree with what has been said. I think this is a really fantastic opportunity in terms of the data and information that we have access to and the knowledge. But I think the future for companies that really succeed is not going to be how much intelligence we automate, but really how wisely we combine that machine intelligence with human judgment. And that's something that I've been talking about all along. I think it is absolutely essential in to maintain the human judgment. And the other thing too, if I can add, because AI and this technological capability is becoming more commonplace, right? We're all going to access it. We already are. It's going to become standardized. So what's going to be the competitive advantage, the distinct unique thing that helps our company excel? It's really going to be talent because that is something that is not going to be easily copied. So I think talent cultivating it and then having that talent know how to use machine intelligence and AI and be able to extract knowledge, intelligence signals, and mesh with them is really where the future is going to be. Andre, why is this good for container shipping or supply chains? Or why is this like, let me put another way? Why is this like blockchain, but positive on steroids? You know, shipping has a lot of problems to solve, but I think one of the biggest issues where technology would finally be helpful is keying in data. And if you look at the processes today, I mean, people are keying the same information five times, 10 different systems, blah, blah, blah, blah, a complete inconsistency. I think if you the day we managed to solve that and it's not, it hasn't come yet. That'll free people from playing, you know, where's my box? From the customer side, where's my box 24/7? Instead, they could be handling difficult exceptions. They could be taking care of customers. And I think what Nada was saying about using tech intelligently reminds me of two days ago when I was trying to solve a problem on a professional platform that Microsoft purchased in 2015. And it took me three days to go through five different bots who were asking me the same question and replying the same silly response until I got a human. So somebody must have thought that was a great implementation of an AI bot. Well, that person should be fired. So I think I think what Nada said is critical. You need the right people to do the right things. And but people need to know what they're doing. And therefore, keeping people in shipping, particularly, aware of how shipping works. And, you know, what part works and what part needs to be improved is critical. Otherwise, it's going to be lost. A lot of knowledge is going to be lost here here. Well said, Andre. Thanks. Thanks for that. Thank you, Powan. Thank you, Nada. Honestly, this is the longest podcast we've done, but I could easily go another hour because I've been absolutely fascinated listening to all three of you. Yes. So thank you all for joining me on the Freight Buyers Club today. Thank you for having us. Thank you. Thank you. Thank you for everyone. It's been a lot of fun. Yeah. We should do it again. Yeah, let's do it. Let's do part two. Let's do part two. The revenge. The revenge. The dark age. No, not the dark age. It's Renaissance. Yep. Thank you all. And thanks as ever to Karen Bourne, Tom Muff, for their excellent editing. If you enjoyed this episode, please do subscribe. Follow us wherever you're podcast and share it with someone in your network who needs to hear it. We're on YouTube and Spotify with the full video on every other podcast platform for audio and you can find us at thefreightbuyersclub.com. Thank you for listening and watching. This is the Freight Buyers Club, and I'm Mike Kingd. [BLANK_AUDIO]