The Rise of AI and ML in Supply Chain Optimization

Written by Travis Hinkle

On March 22, 2024


Longbow Advantage’s COO, Ryan Uhlenkamp, and Guy Courtin, VP of Industry and Global Alliances at Tecsys, Inc., discuss The Rise of AI and Machine Learning in Supply Chain Optimization.

Do Artificial Intelligence and Machine Learning live up to the hype?

The pressure is on for these technologies to begin reshaping traditional supply chain processes. 2023 brought us an AI revolution and we expect that pressure to continue in 2024. The abilities of these technologies to analyze vast amounts of data, identify patterns, and make decisions is revolutionizing how companies are thinking about their decision-making capabilities.

The question is, how should you think about these technologies? Dip your toes tentatively in the tech-waters or take a cannonball off the high-dive? Watch now to find out what Ryan and Guy have to say about our current cultural fascination with AI and ML.


Ryan Uhlenkamp
Chief Operating Officer
Longbow Advantage

Guy Courtin
Vice President of Industry and Global Alliances
Tecsys, Inc.

Webinar Replay

About the speakers

With two decades of supply chain experience, Ryan Uhlenkamp oversee sales, consulting and professional services, and strategic partnerships for Longbow Advantage. In his prior position as Senior Vice President of Alliances and Strategic Accounts, Ryan showcased his profound knowledge and expertise, playing a pivotal role in driving the company’s rapid growth in 2023 and establishing a solid foundation for a strong 2024.

Guy Courtin is a seasoned supply chain expert with decades of experience in the technology and supply chain space. Currently serving as Vice President of industry and advanced technology for Tecsys, Inc., he has held leadership roles at 6 River Systems (a Shopify company), Infor Retail, and i2 Technologies (now Blue Yonder). He has likewise served as an industry analyst at Constellation Research, SCM World (Now Gartner), and Forrester Research. Guy is a broadly curious professional not afraid to color outside the lines.

Webinar Transcript

Ryan Uhlenkamp

Hello. Welcome to our webinar on, “The Rise of AI and Machine Learning in Supply Chain Optimization.” Quick administrative note, we will be taking questions at the end. Travis is also monitoring the chat, so if anybody has any questions, put them in the chat. Travis willl get them in front of Guy and I as we go through and we’ll see when we can address them, right? Although Guy and I can be a little long winded as you’ll probably see, I am your host—

Guy Courtin

I like to say “dense and succinct.”


That’s right. I’m your host, Ryan Uhlenkamp, COO of Longbow Advantage. with me is Guy Courtin, VP of Industry and Global Alliances at Tecsys. How’s it going, Guy?


Good, Ryan, how are you?


I’m good, man. I was—I’ve been looking forward to this. It’s a fun topic. And I know you and I have been talking about it for a while. So…


Yeah, I think it’s a really good topic. And you and I’ve had way more conversations about this. And now we need to make it public. So I’m really looking forward to it as well.


That’s right. And I thought—because I know where it’ll go.—so I had this thought that I should start with more of a level set on why this is such a topic. Like I’ve heard people say, “Well it’s 2024, you gotta talk about AI, you gotta talk about ML.” Maybe we should spend a little bit of time about—because, you know, we were both at MODEX last week, saw a lot of AI/ML, right? Not as in-your-face as I probably thought and I know you’ve made that comment. But it’s definitely there. And it’s definitely on the front of a lot of vendor pages. Definitely on the forefront of conversations. Why is this technology so important and so discussed right now?


Yeah, and I think this graph shows that the investment in AI, of course, continues and, you know, no surprise there. But to your point, Ryan, I think it’s one of those aspects—and for me, with technology, it’s always these things, right? It’s the latest thing, it’s the latest shiny object that we chase after. If I go back, you know, for those of you who don’t know, right, I used to be an industry analyst many years ago, covering this space. And I remember back then the whole hot topic was cloud. And I can’t tell you how many times I had briefings from vendors or other service providers, software providers, and the first thing they would tell me was, “we’re cloud based,” and I would always, you know, be like, “Okay, that’s nice, but why, like, why are you cloud based? Why is it so important?” And then you would get that look on their face? Like, well, it’s kind of what everybody should be, isn’t it? 

And I feel like we’re getting the same thing with AI. And we could go back through, you know, through history, if you will, I mean, let’s go back to the Dot Com days, every had to be a dot com something, etc. I mean, even a smaller side, I go back to a couple years ago, chatbots were the hottest thing, everybody had to have a chat bot to do something.

AI is the latest thing and for me, in a way, in a weird way, it is—from a technology perspective, I do think it’s something that we have to pay attention to, right. It’s not a fad, by no means. But let’s put it in perspective too Ryan. You know, I studied computer science in college. It wasn’t this decade, last decade, or this century. It was last millennium, right. When I went to college, my diploma starts with a one nine for the year. I won’t tell you the last two numbers, but regardless, you know, this was in the early 1990s and I was studying AI back then. I remember talking to my father, who’s been in computers, computer science all his life. He was talking about AI when he was doing his PhD. This was even before, obviously a long time before. So the notion of AI has been around for a very long time. We know it all started at, you know, a conference in Dartmouth, talking about this notion of artificial intelligence and what it means.

So I think the fact that today we are now seeing more investments in AI—we’re seeing, as you said, a lot of buzz. You mentioned MODEX, I didn’t see as much as I thought I would. I was at NRF earlier this year, I saw AI everywhere there, it was almost a little too much. So I think part of it is we’re just, we’re caught in a cycle, which happens all the time and technology, whatever the latest and greatest, we feel as if, as a technology solution provider, we have to put it in some of our marketing material, in some of our talk tracks, otherwise, we’ll be left behind. But I think part of why we want to talk about it today is to sort of get away from the notion of why we just need to talk about it as to the realities of AI.

But yes, net net AI is here. It’s here to stay. It’s been around for a while, this is nothing new. I do you think there’s sort of three convergences, which I’m sure we’ll talk about: better computation, more data, more storage. Why AI is truly becoming what we think it could have been back in the 60s, back in the 90s, back in the early 2000s. But it’s here to stay. But let’s let’s cut through, you know, what is sort of marketing BS to what is reality?


Right. No, totally agree. And I think the point we’ve seen this before on other topics, and it seems like it’s repeating, right. It’s the kids soccer game, where everybody’s running to the ball and everybody’s surrounding, because that’s where I have to be. But I think that will be, as we discussed, well, you know, the pragmatic step of making sure, where does it fit? I think the data does show though, to your point, it’s here to stay, it’s not going away. Right?

Investment’s going up. I think the more wide adoption, we see it, like in our homes, everybody has smart devices. Now we’re seeing that start to come into supply chain and cross-industry, and this is a really good graphic for that. I think, now it’s, “how do I leverage it in my business and really use it to differentiate,” right?


Absolutely. I think this graphic is telling, right, look at where supply chain sits on this, right? It’s, what is it, nine or ten? It’s fairly low in the stack and if you look at some of the categories that are on the top, right around banking and things of that nature, what’s the one thing that we all, as an audience, need to think about? Is these industries are these areas where we’re seeing investment? It’s because, why, they’re closed loops, right? It’s where that industry has a lot of control of its data. Doesn’t necessarily mean it’s always going to be clean and right, but it’s a lot better off, as you and I both know, in supply chain, right?

Supply chain data is messy, it’s all over the place. You know, I’ll give you an anecdote: I worked at a company once, we collected data from large, large retailers, who then dissect and give back to CPG providers to understand better how to fulfill and what demand looked like. Well, one incredibly large retail drugstore, who shall remain nameless, but they’re in Rhode Island, they gave us data as basically CSV files. I mean, it’s like, man, you guys are, you know, one of the top 10 retailers in the world, you’re just giving us data as my kid would on a CSV file. And then you don’t know what that data says. And then we have to then spend hours cleaning it up, translating it to be to make it useful.

Now try to say, okay, we’re going to do this across the supply chain, where as you and I both know, there’s still supply chains, where you got supply chain players, if you will, in the supply chain that are still sending you orders via fax. So all of a sudden, you’re asking me to employ this incredibly powerful tool to a system, a supply chain, that’s going to have a lot of different players who are going to have a lot of different data policies, data hygiene, and now you’re saying, well, AI is gonna solve all my world, my issues, for my supply chain, we’ll do better planning, I’m gonna do better forecasts, I’m gonna do better procurement. It’s like, well, wait a minute, let’s think about that first, before we make that leap.


You know, I like it, because we both have experience with WMS implementations. And I think of how hard it is even now with customers when you talk about master data being clean and harmonized. And then like, all across all these systems and like, just to get that for the algorithms in the elements inside of the four wall WMS to properly run its algorithms. Now you explode that to something like AI, I think that is really core to the consideration of, you need to really look and be thoughtful and smart about where and how you leverage it, right. You need good models, good process, good data. So I think it harkens to that, you know, you need to be pragmatic in how you do this in supply chain. It’s not clean. It’s not completely controlled and sanitized. So it means, as a consumer, you have to be even more diligent and saying, “how do I leverage it?” and, “where do I leverage it?”


Absolutely, I think it’s a question of, you know, the analogy is true, right? If AI is the hammer and everybody is a nail, right, it’s going to look around and try to smack us all on the head. But the reality to your point, Ryan, I think this is where I feel like a lot of companies get in trouble, is they just rushed to implement this new shiny object without, as you said, thinking about what are the use cases we’re trying to employ? And I would say this too, not only think about the use cases, but is this tool, the appropriate tool to handle this use case?

You know, you and I have joked about this, but it’s reality. You know, I’ve been in supply chain for over 20 years, I started out in supply chain planning at i2, right, I think a fantastic company, we had a fantastic product. But guess what, we still went in and said to major, major Fortune 50 companies saying, “Well, we have a better planning engine.” “Yeah, well, my planners use spreadsheets.” “Oh, okay.” Spreadsheets have been around—I mean, let’s let’s really date ourselves, right—Lotus 123 first came up with that first notion of a spreadsheet. That was a long time ago. So we’re still using this tool, which is a very powerful tool, very simple tool, but very powerful in its simplicity, to do major things like supply chain planning.

I mean, I can tell you I’ve had conversations, I remember once, when I was an industry analyst, I talked to a very large supply chain company. (I think they were in the CPG space). And they literally—we talked about their planning—And they said, “Well, we have this big spreadsheet. And then you know, the planners on the East Coast, United States wake up on Monday, and they play around with it. And then they send it to the West Coast. And then the West Coast, sends it to APAC, and APAC sends it to Europe, and then it comes back around.” And I’m just thinking to myself, Ryan, like, “whoa, like you guys are planning multi billion dollar’s worth of inventory and a forecasting based on a spreadsheet.” And any of us who played with spreadsheets, know man, you screw up one cell and the whole thing crashes. But to them, it was like, well, this works, like, we know. And guess what, we have other things to worry about. And we’re not going to worry about getting out of this spreadsheet when we can use it.

So the analogy I bring to this is the same thing, as you said, companies and users need to not be afraid when solution providers come up to them say, “Oh, you gotta use AI for this.” Okay, well, why? What’s the use case? What are the feeds I need to make that use case, or to make that AI engine work? Like we always say garbage in, garbage out. Never more true when it comes to AI. And especially when it comes to supply chain.

I think your analogy where you’re talking about WMS, just look how hard that is. And that’s in a closed system, right? That’s the four walls of the warehouse. Now you’re telling me if I’m Procter and Gamble, and I have 6,000 different suppliers, I’m going to put them all on one data model? Ooh, that’s tough.


Yeah, I’m gonna manage to clean all that up, make it synchronize, harmonize, look great and be able to build on top of it. Yeah, it’s a it’s a tall, tall ask. I think the ability—people get wrapped up in what it potentially can do. But the check has to be what is—how can it be leveraged now? Right?

I’ve had customers actually say this to me, where, “hey, is there an AI element to this? Because that accelerates our approval process.” And I get the idea of, hey, AI/ML has to be part of our strategy. I’m 100% on board with that. It does. You have to be thinking about these new technologies, how to leverage them. But when I hear that it’s a massive red flag to me to say, you’re not asking the tough question of how and why would I use it in this space. Right? I think that’s the thing that both of us are pretty passionate about, like, that’s where we want people questioning and making sure that there’s a business problem that’s being solved and I have the right model that I can put it on top of to be successful, because it’s not magic code that just solves things. It’s not fairy dust that’s gonna make everything better.


It’s a tool, right, Ryan. It’s a tool, like everything else. And I love you’re bringing this up, because, you know, I’ll give you sort of two anecdotes from my past. I’ll go back, I mentioned—maybe I didn’t, but you know, I worked at Forrester Research, late 90s right—Dot Com boom—early 2000s. And this true story, everybody thought they had to have a website, obviously. That was the latest and greatest. At that time, that was the cool shiny object, we all need a website and we all need to do e-commerce. 

We had a large water company, a very large water company, right that provides you know, they sell bottles of water but also mostly, you know, provides for offices and other parts, the big, big jugs of water and also water filtration and all those things. They hired—very smart people—they hired a head of e-commerce and his underling, they came to meet us wants to talk about this and we literally asked him like, why do you need an e-commerce site? Like, do you understand how hard it is to break bulk on water and ship like one bottle of water to me because I ordered it? Like, just because this company called Amazon is doing it, you don’t need to do it. And they literally told us, our CEO read an article in Wall Street Journal about Amazon and said, we got to do this and launched 1,000 ships to create this e-commerce for a water company. And we’re like, well, you know, the internet, all this is valuable maybe like for your CRM, maybe for your supply chain communication, maybe if your HR, you should look at the Internet, but to do e-commerce?

So that’s one example. Fast forward—you mentioned automation—and for those don’t know, like, I worked at 6 Rivers. So I was in an automation or before my current job. Same thing, I remember I talked to one prospect, and they said, well, we want AMRs in our warehouse. Okay, well, what what’s the use case you’re looking for? Our AMRs do picking, is that what you need? They literally told us, “well, our CEO wants to be able to bring people in and see a robot zooming around.” And I thought to myself, “well, I’ll take your money for that. But that ROI is pretty flimsy at best.”

But it’s an example again, to your point, Ryan, where I caution everybody to say, if—again, I’m gonna beat a dead horse on this. But push back on your service providers, push back on those who come and tell you, “Oh, you got to put AI and do this with AI, do this, do that with AI.” Understand, to your point, what’s the use case we’re trying to solve? Why is AI applicable to that use case? Are there alternatives to AI to make that use case work or to solve for that problem? Because, like you said, it’s not pixie dust. This is not—this is a tool. When I thought about this, the analogy to me is, we go back to the Stone Age, right? We would hammer in a nail with a stone and then we created a hammer. And now we have nail guns. They all do the same thing. Some do it much more efficiently than others, but they’re all tools. AI is the same thing. And I think you’re right, I think we rush to this shiny object. We—we, meaning the industry—at times, have made grand promises about how this is going to solve all our ills. But the reality is, it’s a tool, and how you use that tool goes a long way to the practicality, or the usefulness, of the tool.


It’s a tool. And I think that the funny thing is, is we’ve learned this lesson before, to your point: webpages cloud, everybody has to be on cloud, gotta be on cloud, AMRs got to have AMRs. And then oh man, I didn’t get any return. Well, yeah, because nobody asked him a simple but tough question: What business problem does this solve? And really vet the return and how it’s going to work. Because foundationally, you have to have a lot of really solid data and information for that to be an unlock. Right? And that’s—you to double click, right. Think we hear the strategy from the C-suite like. “oh, we have to,” and we run before we crawl and walk and make sure we ground ourselves.


I think too, Ryan, one of the things that, I think you and I have seen this before, not just with AI but a lot of technology, is the impact it’s going to have also on your change management, right? You mentioned AMRs and it’s not an AMR webinar, but well, I just want to use that analogy again. One of the things I’ve seen with adoption of robotics in the warehouse, right, the latest hot technology was, “whoa, the change management it creates within my warehouse is something I was not expecting. And all sudden now I have other issues within my warehouse.”

It’s the same with all technology, AI notwithstanding, the same is what is your tolerance of change management? So to your point, if your C-suite says, “run, run, run, do Ai, do AI.” Alright, one of the questions: What’s the use case? Is this worth it? Another question is, what is the impact on our organization? What is it change management that’s going to come from this?

You know, I was just reading the other day McKinsey showed, or had a study that said, only 13% of the C-suite believes the organization is ready for any type of change management. So what is your organization ready for, right? A lot of times, we ignore that aspect, because all of a sudden, you’re rushing to this shiny object—maybe you do have the use case—let’s say that you might have a use case to solve for. Maybe you also have the right data and you can start doing it. The other question ask is: is your organization ready for the change management that’s going to necessarily, or invariably, come out of that adoption of that tool? I think that’s something that we too often either overlook or ignore because it’s hard to understand, it’s hard to quantify, and let’s not let’s not let that get in the way of investing in his new cool, shiny object that I can say, “hey, I’ve got AI in here.”


It’s easy to gravitate towards the, you said, shiny, happy answers, the good the, “Oh, this is what it could do. This is the capabilities it could unlock.” It’s harder to kind of step back and say, “Well wait, what are the downsides? What are the challenges? (Change management being one of them). The other one—and you wrote an article in Forbes about sustainability and ESG and AI and what is—and I thought, for the people listening if you haven’t gone and read it, read it, it’s really, really good.

But I think that was a really insightful piece about out, “hey, if I have a sustainability program, who’s looking and saying when I deploy these AI strategy—and really at scale or volume—who’s looking at that impact to my ESG program, right?” How are those tied together? And do you think we have to talk about the downfalls and be honest about both pieces.


Yeah, and appreciate you give me the shameless plug for that piece. But it was, it was interesting, from my perspective, right, sort of looking into it, because as I’ve been hearing more and more of AI, and we are working on AI at our business, like you are, you know, I started finding more of these articles and started thinking about this notion of the impact it has on ESG. So, you know, for those of us, right, ESGs is environmental, social, governance, right. So it’s really all about sustainability. And I do think that’s one area that we should all be running to, because it’s very important. But what we neglect at times is, you know, if I run a query in chat GPT, about you know, who was the best 10, you know, all-time soccer players in the world, ah, it’s really fun I and do it, and it gives me this and I can run it again.

There’s the reality that you’re asking now, a bunch of servers, bunch of computation to happen. And guess what that takes? It takes energy, takes electricity, takes water, it takes cooling, right, it takes all kinds of call outs, if you will, that are tapping into that ESG world. And I think that’s something that we have neglected.

I’ll give you an example. I read somewhere that says, you know, to train a large language model, basically to learn, is the equivalent in carbon of driving your car from Earth to the Moon and back, right? A lot of carbon. Some basic call-outs to AI is, at times like, you know, running a house full bore electricity for a month. So these are things that I know it’s not fun to think about, but it’s something that especially companies—especially in supply chain, Ryan—I think this is where it’s key. Supply chain, in my opinion, a lot of us feel this way, is at the forefront of ESG. Because supply chain is where we can truly make an impact on ESG. Well, part of that then is you need to think about your AI strategy. And to your point, if we’re going to use this at scale, how much electricity am I burning? How much carbon am I using to run certain queries, which back to your earlier point, if that use case is such that, you know what, I could solve that use case by doing something other than AI… And oh, by the way, if part of the AI repercussion is that I’m going to churn up a bunch of carbon, maybe I take that in consideration, and I don’t just gravitate and throw AI at the problem when it could be, hey, you know what, let’s just sit down in a room be smart about this and figure out or, dare I say, let’s go back and use a good old fashioned spreadsheet to figure this out. Or let’s, you know, put our heads together, whatever that may be.

I think it’s something that we, I believe, will start seeing more in terms of measurement of the impact on ESG. I think we’ll start seeing more savvy companies start thinking about it from that perspective. And I think that’s a good thing. I think it’s just something that all of us have to take—we should—take into consideration when we just spout AI, AI, AI.


You have to—and listen—we internally, right, when we talk about some of the stuff where we were thinking about doing ML and AI insertion into our own product, have this debate, like do we need to? And it’s it takes a certain amount of muscle because it’s like, but then we could say we have it, right. And you know, the marketing piece, but really, it’s being smart about where to leverage it. And I think, you know, that that theme keeps coming up as we talk about, understand the positives, but don’t use that to overlook some of the negatives and the challenges. And just to be smart about when you’re going to market, ask those tough questions. Ask the tough questions of the vendors, challenge and make sure it’s the right fit for you that it has the right return and challenge yourself to make sure you have the foundational capability, right?

I think, you know, if people take anything away—and I’ll let you give some closing thoughts—but I think that’s what they should take away, is we really have to ground ourselves. It’s easy to get caught up in the buzz. And it’s super exciting technology, it’s awesome. But the applicability and what it takes to deploy it, and to get the return, you really do have to get into the weeds on this and make sure that this is the right fit for me and I have the right data to support it. And I’m you know, I don’t want to sacrifice these other potential strategies to get there. Right.


You’re absolutely, Ryan. And I think it’s sort of a cautionary tale with all technologies, but especially this one. And part of it, too, I will say this (and I won’t get too philosophical on this) but the whole notion of artificial intelligence in and of itself, is still up for debate. What I mean by that is, how do I measure intelligence right? Is intelligence being able to remember a whole bunch of data quickly?

I mean, I was thinking about this the other day, like, remember, maybe I’m gonna date myself even more here. But when I was in school, and you do your multiplication tables, and all of a sudden, you know, the kid that could rattle off for four times four equals this, and you’re like, “well, they’re, they’re the smartest people in the world,” and you realize, no they just memorized. They just literally memorized the multiplication table, which takes a certain level of skill, but is that intelligence, right?

Is that, again, I don’t want to open this can of worms because we can debate this… I know, philosophers have a debated this forever and we continue to do, what is the measure of intelligence? And I know people will say, well, the Turing tests and all this, but I think that’s part of it. But to your point, I think it’s one of those where the other cautionary side of this, like all technologies, but this one in particular, and I look at it sort of two buckets, one bucket is the solution providers, right, the software providers, the hardware providers, the chip providers, all the people on the vendor side that are trying to sell these wares, these tools.

I would caution, you know, just because your CEO stands on mainstage, and talks about generative AI and how it’s the greatest thing ever, and how it’s just going to reinvent the world, be careful, right, be careful.

Cautionary tale. Again, cloud came out, all I heard was, “cloud, cloud, cloud, cloud, cloud.” And you got lost in that. You know, I know, part of it is you have to say it because you have to follow the market. You know, I’ve been in talking to customers who, in sales cycles, you know, the first question, “well, what’s your AI strategy? Okay, why do you need AI?” Once you ask them that they’re like, “that’s a good question. I didn’t think about that. But this other solution provider could talk about AI, AI, AI.” Okay, well, to do what? So I think that’s one bucket, right? If I’m a solution provider, yes, I get it. You have talked about AI, you have to have a strategy about AI, which I think makes sense. But be careful. Be careful with standing on stage and talking about it like, again, it’s pixie dust. It’s not. It’s a tool.

I think the other side of the coin, of course, is the users, right? The people that are going to pay good money to get these products or get these tools to solve their problems. And I think what you said, Ryan, is spot on. And it’s—I don’t want to oversimplify because it’s hard—but start with understanding what are the use cases that you’re trying to solve? Understanding—I would say go down to the granular level. For example, you mentioned the warehouse. Okay, can I use AI to do what… to do a better pick routing for my FTE? Maybe that’s not granular enough. Maybe it’s down to the, “Hey, do I optimize based on this shift, this time of the year, and I just optimize on that.”

The reason being for me, Ryan, is that now, as we were even saying part of it is you can control that environment, control the data, ensure you have good data, good data governance, good data hygiene, and quite honestly, use this AI tool to see what it does, right? Understand what it’s going to give you as a solution. And I think from that perspective, I would say from the user side, is when vendors come in and tell you how great AI is and how it’s gonna solve everything, to your point, don’t be shy, you know, ask the hard questions. Don’t be—this is the other part—don’t feel “stupid” (air quotes here) because the vendor is telling you from their C-suite down, “oh, generative AI, it’s the greatest thing ever. You have to do this, you have to jump on board with us to do AI.”

Okay, as a user, that’s great. It sounds interesting. Let’s go down to the use case. Why is this going to be better than what you’re giving me before or someone else’s? Why is this better than the spreadsheet? For example. Why is this better than another type of solution? And I think that’s the discussion that we, as an industry need to get to, which is, you know, this technology is here to stay. No doubt. There’s a lot of promise in this technology, no doubt. But what are those use cases that make the most sense, right.

Like Bill Gates said, we always underestimate, or we always overestimate technology in the near term, and underestimated the long term. This is what we’re seeing today. But part of that discussion, I think, Ryan, is the users and the solution providers need to have an honest discussion about what that means.


Yeah, it’s an enhancer. It has a lot of power and potential. But you got to ask the tough questions. I completely agree. I know we’re close to time. I don’t know if there’s any questions. If there are maybe we can pause for a minute and just see if anybody wants to jump on. If there’s any questions, we got a couple of minutes left.


Well, just quickly, Ryan, one of the questions I get a lot too is, you know, where do you start? Right? What do you do tomorrow, if your boss is telling you to do this, or if you’re excited about doing this? And I’ll just give my quick two cents on that.

I think first is everything we just said is, understand what use case you’re trying to solve. And I would push on people, if they’re asking their boss, or their boss is asking is, get granular because if your boss says, “hey, let’s use AI so we can do better forecasting.” Whoa, that’s like, we’ve always tried to do better forecasting, right? This is this has never ended. So what does that really mean? And I would say to anybody, like, again, don’t be afraid to ask that hard question and get to the granular before you start embarking on this.


Yep, I think it’s a good callout. I think it’s a really good call out. It’s back to, let’s just be smart. Let’s be what? Let’s just ask them questions. Right. It’s good to understand.


Let’s use the most powerful AI engine we have: between our ears.


Exactly. Exactly. Guy, thank you so much. I really appreciate it. It’s always great to talk to you. I’m pretty sure we could’ve talked about this for like two hours, but we crammed into 30 minutes. We did a good job there.


Thanks, Ryan. Really appreciate it. And thanks for a great discussion. 


Always, man. Thank you, everyone.

[End of transcript]

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