Everyday AI Podcast – An AI and ChatGPT Podcast - EP 467: Transforming Supply Chains with AI - What’s happening now and what’s next

Episode Date: February 21, 2025

You might not think about the supply chain every day. But every product you use or service you rely on is 100% impacted by the global supply chain. And AI is completely reshaping how it works. Join us to find out how.Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Ask Jordan and Julian questions on AI and supply chainsUpcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: [e-mail] with Jordan on LinkedInTopics Covered in This Episode:1. Role of Generative AI in Supply Chains2. Challenges in the Supply Chain Industry3. Automation and Robotics in Logistics4. Accessibility of AI Solutions5. Data Quality and ManagementTimestamps:00:00 Global Supply Chain Analytics Platform03:56 AI-Driven Procurement Insights09:57 Supply Chain Transparency and Challenges12:37 Meta & Apple Enter Robotics Race17:32 Data Classification Challenges in Industry21:22 Accessible AI: From Chatbots to Agents23:39 Navigating AI Disruption in Product Suites27:02 Data Management and Security EssentialsKeywords:Generative AI, global supply chain, artificial intelligence, machine learning, large language models, data extraction, ERP systems, data classification, supply chain analytics, predictive analytics, scenario planning, robotics, automation, ChatGPT, business impact, ESG compliance, supply chain insights, procurement officer, spend management, minority supplier spend, data quality, predictive insights, scenario analysis, enterprise resource planning, SAP, Oracle, Coders, generative AI applications, supply chain transformation, generative AI impact, technology disruption.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

This is the Everyday AI Show, the everyday podcast where we simplify AI and bring its power to your fingertips. Listen daily for practical advice to boost your career, business, and everyday life. Meet Firefly AI Assistant, now live in Adobe Firefly, the All In One Creative AI Studio. Just describe what you want to create and the assistant handles the rest, orchestrating multi-step workflows across Photoshop, Premiere Express, and more in one conversational interface. You direct the outcome. The assistant accelerates execution. On the show a lot, we talk about how fast generative AI is moving, right?

It's like every day, every hour, sometimes almost every minute. It seems like something is changing when it comes to generative AI, large language models, and their impact on business. So, I mean, today's topic is something that moves even faster, quite literally. So today we're going to be talking about how transforming global supply chains with AI, what's happening right now and what's next. This is something I don't know a ton about. And even if this is something that you think doesn't impact you, it definitely does, right? The products we use, the services, right, when we go out to a local business, they're buying things, right? Everything is actually driven by the global supply chain.

Although artificial intelligence and machine learning has had a big place in the global supply chain scene, large language models has actually changed that exponentially. So I'm excited to talk about that today on Everyday AI. So what's going on, y'all? My name is Jordan Wilson. I'm the host. And this thing is for you. Everyday AI.

It is your daily live stream podcast and free daily newsletter, helping us all not just keep up, but how we can actually get ahead by knowing about everything that generative AI is impacting. So that sounds like what you're trying to do. Please go to our website at your everyday AI.com. Sign up for the free daily newsletter. We're going to be recapping today's conversation as we do every day, as well as keeping you up to date with all of the AI news, fresh finds, and everything else that you need to be the smartest person in AI at your company.

All right. So if you're looking for the daily news that we normally go over right before a show, it's technically a pre-recorded show that we're debuting live. So make sure to check out the newsletter for that. All right. Enough chit-chat y'all. I'm excited to talk supply chain and AI and what's happening and maybe even what's next. All right. So live stream audience, please help me welcome to the show. Today's guest, we have Julian Harris, the CEO and founder of Robo Buy. Julian, thank you so much for joining the Everyday AI show. Hi, Jordan. Thanks very much.

Pleasure to be here. All right. All the way from Sydney, Australia. Yeah, there we go. There we go. I was going to let people know if they couldn't pick up on the down under accent. So, you know, yeah, definitely talking, taking a global viewpoint today, right? But let's start at the top.

Like, Julian, like what is Robo Buy? So Robo Buy is a supply chain analytics platform, global supply chain. analytics platform and at the heart of what we do we extract data for large large corporates it's all be-to-be spend we extract data from all and every ERP system that they have so one of our clients has 43 different ERP systems in their business they've acquired this over the years from various m&A deals so they've got 43 ERP systems and they have no single view of their data. So the problem we're solving is they probably don't know their spend to the nearest billion dollars and they probably don't know their number of suppliers to the nearest thousand

because we deal with very large companies like Coca-Cola and Atlanta MasterCard in New York, really big companies with really big global supply chains. So we extract data using APIs from all of these ERP systems and we've built proprietary AI algorithms to clean and classify that data to a standard global taxonomy, and then surface that information in various insights and dashboards to various people within the company. So the typical people we work with are the chief procurement officer can now see where they can make savings.

They can see which of their suppliers are on contract, which are their suppliers have got purchase orders or not, how many suppliers they've got in a certain category. So if they've got a thousand suppliers in their laptop category, it probably makes sense to pair those down to about two or three, and in which case they'll save a lot of money from efficiency. So we do a lot of work with chief procurement officers. We do a lot of treasurers where we help them look at the most efficient way to pay for goods,

so possibly using virtual credit cards for small payments. And we work very closely with MasterCard and the bankers. banks in that area. And then the third area, which has become bigger and bigger, is all around sort of risk compliance ESG. So we can look through your supply chain and say, hey, you've got some people that are looking like they're flagged on sanctions. Or there's people here that look like they've got modern slavery risk, possibly due to where the goods are coming from or a load of risk factors. There's some suppliers here that look like they've got cyber security flags against them. We can tell people how much they're spending with minority groups,

like in the US, a lot of people want to know how much I'm spending with veterinary-led businesses, women-led businesses, things like that, basically. So a whole suite of insights. And as usual, the big issue is, first of all, you have to source and then clean up that data, because quite often the data in these ERPs is quite dirty over the years. And then use AI to sort of, you know, come up with some smart insights on the data. So that's in a nutshell is what we do. Yeah. No one likes dirty data, right?

That's the, that's the worst part of your, of your AI journey. But, you know, so Julian, so you founded the company, right? Was it about eight years ago? Is that right? Yeah, 2017. We founded the business in Sydney. but we are a global operation. A lot of our clients are in the US.

So, you know, I'm curious kind of starting an analytics company, you know, AI in like right before the degenerative AI boom of, you know, 22. How would you say has generative AI changed what you do, but also the bigger picture, how has it changed the global supply chain? I know that's a huge question, right? Because it's probably a thousand different ways that you can answer it. But from your perspective, what has it been like kind of the before and then the kind of after or during? Well, that's very interesting question, Jordan. This is my second AI company. I set up one in 2016 that we sold a cognizant in 2020 out of London.

and that one specifically focused on, it was a data science, high-end AI consultancy working with the Amazon toolset. And in 2016, even though, as you said earlier, AI algorithms have been there since the 50s, a lot of the maths, that was quite new in 2016. So we were doing a lot of really interesting work.

You know, we were predicting client churn for companies. We were predicting the cost of energy for energy traders. We were doing some really, what I would say, cool stuff with very traditional AI and algorithms we were written ourselves. Same with this business. All the IP we've built is really good at doing its job, which is classifying and cleaning, spend and supply chain data. Last year, when chat GPT comes up, it just throws the whole industry up in the air. I mean, the first thing for us is, I think, very much confusion amongst our clients. So, you know, we would say, hey, we can, we've got very proprietary AI that does, cleans up your supply chain.

And obviously, most executives that have been on a plane and read a magazine are now thinking that chat GPT will do it out of the box. Chat GPT won't do it out of the box. And it's a, you know, it's a very different approach. So I think there's been a little bit of education there, but absolutely the pace of change now with the generative stuff is amazing. And it is just sweeping through every type of organization and every sector. And I think the automation we're going to see for the next couple of years are going to be quite stunning, to be honest. When it comes to large language models and, you know, obviously the big step of the day,

is the ability to work with unstructured data, right? You said you talked about the need to not have dirty data, right? But data is data, unstructured information is a little hard to work with, except for large language models help that. So what potential problems or opportunities, you know, kind of are we in the middle of matching up generative AI with opportunities when it comes to supply? chain, right? Because like I said, I don't know the supply chain very well. You do. What are those

areas just ripe for potential disruption with large language models? Well, people that there are a few problems you always get with supply chain. One of the first things is if you're dealing with a large buyer and their 10,000 suppliers, let's say, the problem you you often have is you don't know what your supplier, supplier, supplier, supplier is. How big is that supply chain? How big is that supply and where does it go? So as an example, you'll be buying batteries in Chicago. Batteries have got cobalt in them. Two-thirds of the world's cobalts mined in the Congo,

probably using child slavery. You don't know that because you bought your batteries from the corner shop in Chicago. So seeing through that supply chain is a tricky deal. We work with one company that actually has cracked that, we've got some incredibly good data and some incredibly good math and basically they're using large language models to actually look through any layer of supply chain. So I think you'll see solutions like that coming up. I think you'll see a lot more predictive analytics, a lot more scenario type planning.

You know, a lot of the tools today are telling you, you know, what you've done in the past and perhaps how you can change things in the future because the past is a predict. of the future, but I think there'll be a lot more tools now doing a lot more scenario type planning. In terms of generative AI generally, I mean, once we get into the point and people are talking about it a lot now where AI will be writing its own AI, and I think Zuckerberg recently said that AI will be replacing his base level coders in Mesa, which is really scary. I mean, I've been in the IT industry forever. And if you think about it, over the last 50 years, we've spent our time offshoring blue-collar workers to China.

And we've lost a lot of that industrial base. That's America, UK, and Australia. Generative AI is going to now replace the white-collar workers, not the blue-collar workers. So if you now take out a level of coders, and all coders become, you know, AI prompt engineers or whatever, but there'll be less of them. That's going to be massively disruptive to the world. But to start with, it makes technology so much cheaper because currently it's an expensive item for most businesses.

But you'll end up with a lot of base coders today looking to upscale, I think. Yeah. You know, speaking of Mark Zuckerberg and meta, you know, a pretty recent story show. that, you know, both meta and Apple are trying to enter the, you know, AI, humanoid robotics space, right? A space that, you know, there's pretty promising, you know, prototypes out there from, you know, Tesla Optimus, from Figure, all of these other companies, you know, at least here in the U.S. It's, I mean, this is a space that's hot on fire.

What are your takes on that, right? Like, especially as it comes to the supply chain, to logistics, right? Is this something? Is it our factories and warehouses going to be just humanoid robots in the near future? Adobe just introduced an entirely new way to create, bringing the power and precision of its creative suite into one conversational experience. Meet Firefly AI Assistant, now live in the Adobe Firefly app, the all-in-one creative AI studio. Powered by Adobe's creative agent, Firefly AI assistant lets you start with your vision, just describe what you want, and shape the outcome as it takes form with the assistant.

The assistant orchestrates multi-step workflows, drawing on 60-plus pro-grade tools across Adobe Creative Cloud apps, including Photoshop, Illustrator, Premiere, Lightroom Express, and more to help bring your ideas to life. You can also get started with creative skills, a growing library of pre-built workflows for common creative tasks, like batch editing photos, creating mood boards, portrait retouching, and creating social variations. Every step the assistant takes is visible so you can refine, redirect, or take over at any time. You stay in the driver's seat as the creative director. Adobe Firefly AI assistant now in public beta. See it today at Firefly.adobie.com. Well, I think if you went and saw an Amazon

warehouse, you'd see that it's pretty automated now. I don't know if you've seen videos of those robots. I mean, for sure, the physical supply chain is getting more and more. automated by robots. I mean, that's that that's a given. I think what's going to happen is, as I said, there's a lot of what I call quite traditional software that's out there. You know, and this is in every space. This will be sales force, let's say, dominates CRM. SAP dominates the RP industry. I mean, in the world of supply chain, at the top end corporate, they're either using Oracle or they're using SAP. But those those guys are, got nearly 50-50 wheelchair.

There's some other things like Microsoft Dynamics and a few other tools there, but at the top end, they're either on SAP or Oracle. And those tools have been around for probably 40 or 50 years, I've thought, certainly 30 years. Now, those guys are scrabbling like crazy, and you'll see all the ads and the PR out there to transform their products into AI products. But obviously, you're just, you're effectively adding onto a code base that's very old. So I think over the next few years there are going to be some pure play AI players

powered by generative AI that just say look at the market share that Salesforce has got in CRM or look at the market share, you know, SAP has got an ERP space and quite frankly probably take them out. I think this, they've got a very, very strong, a big stronghold at the moment, but I think generative AI, particularly when you get to the point where AI is writing itself and it's highly automated, the price of producing software becomes like next to nothing. Yeah. You know, and I'm curious, you know,

you work with a lot of big brands that have a huge global supply chain footprint. What are some of the most common, you know, not mistakes, right, because that's, you know, dirty data happens, but what are some of the most common, you know, redundancies or some of the most common shortcomings that usually have a generative AI solution. Like what are these things that you're seeing over and over? Because maybe that can help us understand, you know, where this intersection is ultimately headed. Sure. Well, supply chain, I would say, is quite traditional, as in it's probably one of the last areas to change. There's a lot of,

there's still a lot of manual input into these ERPs. You know, people getting in, this is where the dirty data comes from. Somebody in the accounts payable team and a large corporate will get these invoices coming through. They may be scanned, hopefully, they may not be, they're double entering into SAP. They make mistakes. Or they're really busy. They've got to enter 500 a day, so they don't bother putting in the details, you know. Instead of saying what each line on.

item is that they bought, they'll just put, you know, stuff at the top, you know, bought some stuff 10,000 bucks. Now, when you're then trying to classify that data for some execs to look at what they're paying in each category, it's very hard if somebody has just titled something stuff. Yeah. So, you know, you need to go back and look at the supplier, perhaps go and scrape some data from the internet about the supplier, you know, throw that into a model and start predicting things that way. But yeah, I mean, the industry has always been, you know, quite traditional,

quite slow at adapting to some of the technologies and not as joined up as it should be. For a while, we've had AI that can read contracts and read documents and things like that. Certainly large language models are making that way more accurate and way more efficient. So, you know, I think to automate the end-to-end supply chain without these sort of gaps where humans are double entering things would make a massive difference. But I think somebody, yeah, certainly using large language models, it's right for somebody to take just a totally different approach to the supply chain. Yeah. So, you know, we've kind of touched on, or at least the way I see it, you know, on some of the front end of the supply chain. So, you know, manufacturing, warehousing.

What about on the on the ladder side, right? So when we look at transportation, distribution, customer delivery, all of those things, where do you think we're headed next, right? Like I always thought like, you know, 20 years ago, if I would have thought about this, I'd be like, oh, you know, we're going to have, you know, fully self-driving cars and drone deliveries and all these things. It doesn't look like we're quite there, right? But at least when it comes to large language models,

How are they going to find their place in the near future in those other places, such as transportation, distribution, customer delivery, etc? Well, I think there's a lot of work being done in that area by the real specialists. I mean, Amazon is probably the leader, I'd say, in those sort of logistics. And I think individual companies just can't spend the way that somebody like an Amazon can in that sort of technology. So you see a lot of the more sophisticated companies outsourcing a lot of that logistics, to people like Amazon. And they are trialing, drone deliveries.

You will get automated vehicles delivering this stuff on show. I mean, the drone deliveries, I think, that Amazon are doing, the trials have been quite successful. When it moves to mass rollout, I don't know. But I mean, all of those are now controlled, because obviously there's a navigation piece, there's a safety piece. All of these are now controlled. going to be controlled by large language models for sure.

So, you know, one thing that, you know, I'm always looking at is how generative AI specifically can help even non-technical people make better decisions. How does that ultimately play out in your space? You know, when it comes to predictive insights scenario planning, right? Is this something that maybe it's only for the big global players when it comes to using this in their supply chains? Or are some of the smaller guys able to finally get some of this technology because of large language models that has maybe only been afforded to those companies that had big data teams for decades? No, I think the technology, you know, starting with Jack GPT and now, you know, all the others that are coming, that are coming. fast behind, it's becoming very accessible, very cheaply, for just your average worker.

I mean, everyone now that's, you know, got a Microsoft laptop has got co-pilot available to them, you know, and co-pilot will just, I mean, they drop in new versions every other month as it appears. I mean, that will just get more and more powerful. And it's, you know, it's designed for the business user to use, not a techie. basically. You know, Anthropic did a big deal with Amazon, so Amazon, you know, pushing more and more products out there. I think, I mean, everyone's talking about this year we're moving from chat bots, effectively AI chat bots to AI agents, which essentially are just smarter versions. But essentially, these are going to be in the hands of the user.

I mean, just from an efficiency point of view in my company now, very few people are building PowerPoint decks, you know, they're telling AI to build a PowerPoint day. Very few people build the start of a Excel spreadsheet. They get chat GPT or co-pilot to build it and perhaps just do a little bit of editing. I mean, I think most people like just office workers are getting into the groove and people like Microsoft are going hard at free training for all these people. You know, it'll take a while, a little bit of a culture shift, but certainly, you know, the next generation of workers coming in, if there's one skill they need, it'll be how to how to maximize the use of AI, otherwise they'll get left behind. Speaking of what's next, you know, I have to ask you that. So as someone that's, that has, you know, has two separate, you know, AI companies.

You said your other one was acquired and you have a successful company now. what do you think is next or what is maybe, you know, keeping you up at night with, whether it's excitement or worry, at least when it comes to generative AI in the supply chain. What are you looking at? So, okay, so I'll start with my, or our suite of products. It is excitement and worry, as you say, because we built some very predictive, sorry, some very specific models over the last eight years. And obviously the question in your head, as soon as chat GPT, pops up is, is that all now replaceable by this simple LLM that's popped up and rendered our whole product worthless?

So, you know, I think everyone at the moment are doing a bit of navel gazing, including all the big players, and looking, stripped down all their product and understanding, okay, what have I got this defensible? And what actually could I replace with a large language model? because obviously if there are parts of my suite of products, I could replace the large language model, and that's what we're looking at now, in many ways that makes it more efficient and easier to maintain and has more features around predictive and things like that, basically. So I think most people are looking at that.

I think as we said earlier, I think some of the big players must be very worried. If I was SAP and Oracle, I would be worried that somebody's just going to come in and build a pure AI version of the ERP because that is a massive multi, multi-billion dollar business. There's obviously going to be a ton more automation. So in terms of supply chain, you know, typically if you're in a Coca-Cola or any or a Walmart, they'll, you know, they might have hundreds of cash. category managers analyzing each category under a procurement head, a lot of those jobs will disappear because a lot of that stuff will just be automated using large language models. You know, what to buy, who to buy from, which suppliers to let go, which suppliers to spend

more with. All that is easily automatable with this sort of product. And I think, as we said earlier, I think you'll see a lot more predictive analytics coming out with the large language models and scenario planning and things like that. A lot more intelligent forward facing stuff. All right. So, Julian, we've covered a lot in today's conversation. But as we wrap up, what do you think is the one most important thing?

And this is a big question. So answer it how you may. What is the one most important thing you think people need to keep in mind when thinking about the supply chain, generative AI? in where we're headed next. The single biggest problem they're going to have is, where is my data? What state is it in? How do I get it into a place where it's even usable for AI? Because it absolutely, I agree with you, large language models can deal with

with unstructured data, absolutely. But if that unstructured data is fundamentally rubbish, it's still going to have problems, yeah? I mean, we've been, you know, we've built search engines and all sorts of things in the past. Unstructured isn't necessarily a problem. There's technology that's been doing that for a while, but the quality of the data. So with most people, they've had hundreds of different systems, they're not joined up. So the first job is, where's my data? How do I access it?

How do I clean it up? Once I've cleaned it up, how do I make sure that I've got policies in place that it is cleaned up? So it's all around data. I mean, the other thing to think about is data sovereignty and a bunch of other things around that. There's too many people at the moment will throw their data into something like chat, GPT, unaware that they're now sharing that with the world. Yeah. Now, you know, you can still use the models on your own infrastructure or private clouds and there's ways around that.

But people really need to think through their data from, you know, a, you know, Where is it? How do I access it? How do I clean it up? How do I make sure I've got policies to keep it clean and accurate going forward? But also, how do I secure it and, you know, make sure that all my privacy settings and security settings are right? All right. So a lot to think about there. I think some great takeaways, whether, you know, you're in the supply chain or not. I think today's conversation is an important one to have. So Julian, thank you so much for joining the Everyday AI show. We really appreciate your time.

Thank you, Jordan. My pleasure. All right. And as a reminder, y'all, we covered a lot. If this was helpful, if you're listening on the podcast, please go ahead, click that subscribe button and leave us a rating if you could. If you're listening online, share this with someone that's in the industry. Keep them up to date.

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