JP Wiggins: 0:03

AI is a tool, not a strategy, but look at hard ROI on the use case. What are you actually saving? If you can't put hard dollars with it, then it's fluff.

Tom Raftery: 0:12

Good morning, good afternoon, or good evening, wherever you are in the world. Welcome to episode 112 of the Resilient Supply Chain podcast. I'm your host, Tom Raftery. If your AI strategy can't show hard ROI. It's not a strategy at all. It's fluff. And in supply chain fluff gets exposed very quickly when your data is fragmented and your partners still fall back to emails and phone calls. In this episode, I'm talking to JP Williams, CEO of 1Logtech, and in this conversation, JP makes the point that if one carrier integration can still take three months and cost about 10 grand in dev work, the problem isn't a lack of intelligence, it's the plumbing. If you're a senior leader in supply chain, sustainability operations or enterprise tech, the big takeaway here is this. The companies that win won't be the ones shouting loudest about AI, they'll be the ones that clean up data flows, connect trading partners properly, and turn manual work into usable operational visibility. And just a quick note before we get started, the full Resilient Supply Chain back catalogue of over 450 episodes, which was available only to Resilient Supply Chain+ subscribers is now open to everyone and searchable too which should make finding a show a lot easier. From now on, subscribers get something different. Bonus episodes with timely topical analysis. The first of these exclusive episodes drops this coming Friday, so if you are a subscriber, watch out for that. And if you're not a subscriber yet, there is still time. So let's get into it. JP, welcome to the podcast. Would you like to introduce yourself?

JP Wiggins: 2:06

So it's JP Wiggins. So I'm a CEO of a company called 1Logtech. Could talk about what we do here in a little bit, but my background comes from I was co-founder of another company called 3G TMS, which is now a TMS part of Descartes Systems. And before that, I spent about 10 years at SAP, on the travel and transportation business side. I was also one of the co-founders of a company called GLog, which is now Oracle Transportation Management, and I also was general manager of Descartes Systems. And then we go into ancient history, but that's before punch cards and computers.

Tom Raftery: 2:36

You've been around.

JP Wiggins: 2:37

Yeah, so specifically I'm a logistics. I'm a supply chain person. I focus on logistics and transportation you know, and, and really, really thick into TMSs.

Tom Raftery: 2:46

And what does 1Logtech do in the broadest sense?

JP Wiggins: 2:50

So, in the broadest sense, we're an integration platform. What we've done is we've taken integration to make it a configuration task versus a development task. We're like a report writer for integration, and that allows a normal business user to just sit and configure a report, configure a report to integrate your truckload carriers, your LTL carriers, integrate your customers, you know, EDI API. You can set up API without knowing how to code and set up EDI without even knowing what EDI is. You can configure an integration. A different way of thinking about how to write software as opposed to a dev task.

Tom Raftery: 3:20

All right. And what pulled you into the world of logistics integration? why does this problem matter enough for you to build a company around it?

JP Wiggins: 3:28

My background comes mostly from transportation management systems, TMSs and warehouse management, so TMSs and WMSs. And if you think of that, every TMS has to integrate to everything out there. It has to integrate to your ERP, it has to integrate to your warehouse systems. It has to integrate to a lot of external parties, such as carriers and 3PLs. And that challenge is a difficult one because every, there literally is a million carriers out there. There's more than a million carriers actually, and then every type, of carrier and shipper talk. There needs to be some type of data normalisation, because everyone set up their TM differently, they use reference fields differently. You have different workflows, and that comes from how you've set up your ERP. So there's customisations required in all those. And the, the problem is, is that. Someone has to write that code. And so we've struggled with this in the TMS industry our, my entire 30 plus years is having to have developers write code. And my co-founder and I thought of a better way, which is let's have the business users write the code. The, technology is there to allow the creation of the solution. It wasn't there 10 years ago, it wasn't there five years ago. But now you can have a solution which allows. When you're setting up a trading partner in your TMS, like you set up a carrier for rates and tariffs, we'll set 'em up for integration also, just at the same time, it becomes a configuration task, not a dev task.

Tom Raftery: 4:41

Why? Why are logistics integrations still so complex and slow? You know, we're decades into digitalisation. What? What's holding things back?

JP Wiggins: 4:51

Well, there's so many different players on the tech stack. Like I said, there's a million carriers out there. If you add in third party logistics, I use the term LSP logistics service providers. There's, there's millions of 'em across the globe. There's literally over a million in North America alone and they're all on different levels of tech stack, different technologies. And while there are standards like EDI standards to how you pass data like a status message has standards associated with it. People use those standards differently, and like I alluded to, everyone's TM system is set up slightly differently. They use fields slightly differently. They use different workflow, they want different types of data, and so typically there needs to be this normalisation. So even though you might have a standard that tells you how to move data. No one's data comes in as standard and it does need to be normalised. So that's why like you can set up a business network for finance. We see this in, supply chain all the time. Suppliers and manufacturers can have a business network, which allows'em to push orders back and forth. Or you can push financial data. Those things are rigid and structured. Well, us in supply chain terms don't mean the same thing everywhere you go. You might have a term that means this status message means one thing here, and it means something different elsewhere. I did a project at Dan and Yogurt and they selected us because we supported FOB terms. we had a full feature to support of the FOB. So we get there and Dan and yogurt says, well, I need this feature to support FOB. And I'm like, yeah, it's right here, and I shouldn't goes. She goes, no, that's not FOB. FOB stands for fruit on bottom. And it's the way they process their fruit on bottom shipments versus their other shipments. So it's that normalisation is a challenge for us in supply chain and logistics is to normalise that data and it means that somewhere someone has to write a point to point integration between every integrations. Yeah, you can come out with fancy systems to move code around, but still there has to be some business analyst that has to normalise the data between every shipper and 3PL.

Tom Raftery: 6:39

And how does this complexity then affect resilience especially when disruptions hit?

JP Wiggins: 6:44

In the modern age, you need to be connected to your partners. Connected to your trading partners is not an option. You have to be and people fall back to manual methods. Literally there's still so much phone calling going on. I'm gonna put an AI chat bot to make a phone call to a driver driving down Interstate 75. And ask 'em where they're at. I mean, that doesn't help. It's not really the right answer, which is the, electronics, the telematics in that truck needs to be sending a message to the carrier's fleet system that carrier's fleet systems need to be sending status messages over to the customer. And that customer then has to have that integrated into their control tower. That's what needs to happen. what's not happening is people just make manual. It goes back to manual modes. I'm gonna send an email, I'm gonna make a phone. And we're still stuck there. We we're not connected, or some people are, but for the most, you still, you fall back into, especially brokerage moves, or one-off carriers. You fall back into phone calling and check calling.

Tom Raftery: 7:39

And what does an organisation typically get wrong when they try to modernise this layer?

JP Wiggins: 7:45

So modernising this layer reduces a lot of manual complexity, but it also opens up the whole, I think we sell a lot to customers like running Oracle Transmission Management or SAP TM or 3G TMS, or you know, a modern TM platform. And they then struggle, they then can then turn that into a true control tower. Those modern TMS have a lot of complexity and business logic built into 'em that you could take advantage of. But you have to have those, that digital integration. When we talk more about AI, AI has to have clean, consistent data for it to be able to do what it needs to do. It can't work if it can't see it. You can't ask an AI to do something it has no integration or connectivity to, or visibility to. And that's the same thing in, logistics. When you're modernising, your modernisation needs to have a connected strategy so that you're digitally communicating to your trading partners. And it's that simple. it's more than just signing up to somebody's network and passing simple EDI files. I mean, that's what we did in the nineties and turn of the century is pass EDI files before. Now you need to have direct real-time communications between the trading partners. And that's really what we're helping facilitate, is that direct real-time communications at an affordable road so that you can do it that you don't have to have, multimillion dollar spend it to some integration layer and dozens of developers that are coding because, what we see is backlog, technical backlog. A dev environment will be overloaded with request. You bring on a new trading partner, you bring on a new customer, you bring on a new carrier, and then you also have like carriers might produce new levels of APIs like LTL in North America. Here we get LTL APIs all the time. They produce new versions of their API. Well, okay, who's gonna go rewrite that? And then who's gonna maintain that? And it's just because it gets so costly and timely to make those connections and maintain them. That's the challenge.

Tom Raftery: 9:29

And how do you see the likes of AI and no-code changing the integration landscape?

JP Wiggins: 9:35

It's one of the things that we've been able to figure out to do is to let the business user make the integration. But the thing is, is underlying, you still have to have code running between the integration layer. Let's pick a status message. I'm going to go to a carrier and get a status message for 'em. This carrier may be sending me some type of EDI feed, or maybe they have an API that I connect to get that status message. Well, that's what the AI can do. The AI can actually look at the API and interpret the payload and set the templates for you. So like, the AI can get you like 80, 90% and the business user goes, oh, no, wait, consignee actually has this characters in it. Or, oh no, your ship dates are wrong. Or, oh, no, you're using status fields in a wrong format. The business user could put their business intelligence. And then with the AI. It can actually then create a working integration. So we like to say AI, augmented humans is really what's taking over us in logistics is really helping. It's one of the big things is it's not like an AI replace someone's job here, but they're augmenting the function of a human and combined with a human in the business intelligence allows some really wonderful things to happen. We're seeing a lot of AI there that, helps in logistics from that side. That's, that's our personal use. There's other use cases. We can talk about SAP and Oracle and, I've been spending a lot of time at their conference having come from both companies. sold mine to Oracle and then also, tenured at SAP and I know you from SAP too. And I'm also selling into those communities too for supply chain. You know, really touch, base with what they're doing.

Tom Raftery: 11:00

There's a lot of mixed feelings for various reasons around AI, so are you getting any pushback on the use of AI or are you just not mentioning that there's AI underneath the surface? Or how does that work for you?

JP Wiggins: 11:13

I'm a startup. I've been around for a little more than two years now. And what was interesting is, is when I first went out to the market. It's amazing how uneducated the investors were on what AI actually is. And if you just put AI in a term you're gonna get invested, you're gonna get financed. And that's, that's how it was. It was just stupid. And then actually even going to a lot of the trade shows these past, this past year, every development feature I've seen in supply chain, whether it's using a large language model, an LLM, like, open AI or anthropic, you know, something like that. Versus let's say you have a developer that wrote a new feature that did some great, if then elsing, everything is being called AI now.

Tom Raftery: 11:53

Sure.

JP Wiggins: 11:53

It's, it's a way overused term. But, the direct answer to your question we see this a lot more now, is as we're bidding on projects, the sophisticated companies, like at the enterprise companies we're selling to, we're getting asked an AI questionnaire on how the AI is used because they're scared of it. They're scared of agentic AI that's making automated decisions. AI that's actually processing data. And does it have access to their private data? Does it have access to data restrictions? And we see this a lot in the security questionnaire, so there's a lot of scared companies about buying AI versus before it was like, oh, lemme just buy it because it's really cool sounding. Too many people have been burned and there's this a whole other security layer you're going to be going through when you're selling a product that has some AI in it. And companies need to be prepared for what that is So the business buyers are becoming much more sophisticated, but also I'm seeing the investors. So many of those investors that invested in AI companies. I didn't go out as an AI company even though it's in my software. But I do see, and I still talk to those investors where they're now learning. While they made a lot of bad investments in AI, they're having to reinvest. A lot of companies, venture cap out there invested in AI two years ago, and they're now finding these companies that are coming back asking for more money because of just bad business model. So this leads me to, one of my core beliefs, which is I see AI as a tool, it's not a strategy. It's a perfect tool. It's another type of development methodology. I mean, I've been developing software and it's software development companies my entire career for 30 some years. I have seen it go from, simple DOS, command line programming to the advanced techniques that we have now, and it's hard to separate these AI abilities from like even some of these modern tools that we have. So like, let's say I go use pro logic and I write business rules and it's machine learning and it figures out how to rearrange some truckload tenders based on the time of day. That's an AI. You know, but it's a nice, nice little piece of tool that's, helpful because it'll help reduce manual workload that's a practical use is my point. It's a practical use of AI. It has, real value. And so I, I like to sum it up saying, AI is a tool, not a strategy, but look at hard ROI on the use case. how, What are you actually saving? If you can't put hard dollars with it, then it's fluff. There's a lot of fluff out there right now. We're sorting through a lot of fluff. But it's like the.com era. we saw the.com era come and there was a lot of.com things that just came outta nowhere, and then they just disappeared into nowhere. But yet we're all using the internet now. It's just so ingrained into our whole business world, into the whole lifestyle. And that's the same with AI is there's gonna be a lot of fluff for AI, but in reality, the tools are undeniably valuable in certain instances, and everything we do is going to have, or a lot of things that we do is gonna have AI embedded into it. So that's why I come back to the term like the AI, augmented human jobs that just have AI that help where AI makes sense.

Tom Raftery: 14:48

Is there fear amongst the business users that this AI is coming in ostensibly to help them, but over time, are they concerned? Is there a concern there or are they just embracing it blindly or is it a spectrum?

JP Wiggins: 15:03

I like to look at what are the big companies doing here? And I focused on SAP a lot on what their strategy is. And then also Oracle's strategy, and we can talk about those in detail if you'd like to. But the bigger companies are gonna struggle with what they can and can't do with AI. A lot has to come back to data privacy and like SAP or even Oracle, you gotta worry about data privacy. you can't just have these models knowing everything like an Open AI or an anthropic, that know everything because that's illegal in a B2B piece of software. You gotta watch what you're able to do with it. And, we're business people. In a business world, you look at, well, do I wanna put an extra five grand a month into this tool, is it gonna add value to me? So the business world is quickly learning what makes sense and what doesn't make sense because they're buying what makes sense and they're not buying what doesn't make sense. I think the days of buying AI just to buy AI are almost over, if not over already. Just invest in AI because too many people got burned too quickly. That bubble already burst for the most part. And business users, when they see a business case that makes sense, and that's what I try to do. I put things through a lens of what makes sense, what's a hard ROI, how is this gonna improve my value? I'm not gonna go buy this AI thing because it's some wonky, great sounding statement. and the overly thing is most people don't even understand what the heck AI is. It's hard. Oh gosh. Here's this. You go to some of these AI conferences. Literally, I'm at this AI conference, and this guy comes out there and he says, this is what he said. I had to write this down. Modern AI systems are transitioning from static parameter frozen interface engines to continually see adapting multimodal self supervised architectures where graded free policy optimisations, retrieval, augmented latency, space modulations and emergent adjunct planning creates dynamic context reasoning pathways. They're not explicitly encoded in the underlining model weights. And I'm thinking, great how does that help me Tender load to JV Hunt? The people that are trying to explain what their AI is versus the people that are trying to use it. You know, you get into this whole gap. But, that's what I try to be, is I try to be this lens of, Hey, I'm the supply chain guy. Let's put a lens on what's AI doing for me now, how's it gonna help me? and what, what's fact and what's fiction.

Tom Raftery: 17:13

Okay. Well then in that scenario, when operational teams, not developers, can configure integrations themselves, what's the kind of practical shift that you see there?

JP Wiggins: 17:25

So for us it's a like it most companies that would have to go write an integration, let's pick a a move where I have to go do tendering booking document, retrieval status messages, and invoicing. Your classic moves that you talk to a carrier or a 3PL with. If that carrier has an API it would usually take a dev team about three months. It would cost me about 10 grand in dev cost in three months time to do one carrier. With this modern tool, we have a business user, a single business user, not a developer, can use the AI and use my tool, and it's more than AI, it's the platform. And actually create a working integration ready for testing the same day. Let's say you used to have to have a report. You know, you'd write up your report, you'd, you'd draft up the report, you'd write up the project specs, you'd turn it over to Dev, and then they'd code it and they'd test it. It'd run through QA and all of a sudden, boom, you got a report. Okay? That might take weeks, if not months, versus now you're using your SalesForce or HubSpot and you need a sales forecast report. It's just there, you're the user, you just build it and then you configure it, you test it, you put it in production.

Tom Raftery: 18:26

Okay. And when you've got these citizen integrators, what does that unlock and what still needs guardrails?

JP Wiggins: 18:34

you're giving the citizen integrator a lot of rope to hang themselves. because they can introduce things that are gonna destroy their integrations. You might have a working integration and they destroy it. And I, that's normal conversation we get into with like our clients is in the sales process, I should say, is, well, what happens when a user does something wrong and it destroys the integration? I'm like, yeah that can happen. But it's also. Like if you're running a TMS and you set up a carrier and you put in their rating guides and you put in their service areas, if you configure that wrong, that carrier's not gonna be selected. That operation isn't, that move isn't gonna happen. So it's a configuration task though. But an operation user can still screw up their operations that they don't configure their tool. Right. They need to configure. Right. And test it. And that's the same thing that we think of in integration is that, if you configure it wrong, well, okay, rollback, and we give 'em rollback abilities. So rollback to the previous version, but you should put it in production, you test it. It's a, but it becomes configuration versus development versus a development effort because it's the actual end user that's actually doing the configuration. And they could see if it works right there at, in, in real time and change it and update it. And if it doesn't work, they, can roll back. It's a different way of looking at the world. Configuration versus a dev task. But you're right, you give 'em a lot of rope to hang themselves because you're giving 'em total control over the integration, but you're also to give 'em total code to create a new integration from scratch. versus wait three months. That's what we also see. It's like, let's say a problem does come up, and this happens all the time, is a, you'll have some data come through and then there's a problem with it. The user can just look at it and correct it. Right there on the fly versus in the past you'd have to put the error over to dev, open up a ticket and then maybe contact the trading partner who sent the data in wrong and get them to fix it. And they open up a ticket and you might be looking two to three months and then it gets corrected It happened to me last week. They were sending over ASCI characters and the sharp symbol came over. And old EDI systems can't interpret those ASCI characters, and it wasn't our system, but it was one of the destinations in the downstream that was puking on the sharp symbol that came in in some reference field. My user just literally just said, oh, wait a minute. Let me put in an Excel formula that says if you see an ASCII character, change it to the word ASCII 35, which is the ASCII number for the sharp symbol. He put it in production. So then he told the carrier, stop sending, ASCII characters, you know, because it's affected my old, EDI tool that I'm using down the road. Yeah, no, but it's, exciting times. It really is. I mean, I'm seeing such advances in, technology. I still say this every day. We're living in the future and it's great.

Tom Raftery: 20:58

Yeah. And what kind of real world improvements do organisations see when the integrations go from weeks or months to hours or minutes?

JP Wiggins: 21:07

ROI is big for just displacing a lot of different tools. You don't need to buy real time visibility tools 'cause you're getting the data directly from your trading partners. Like the carrier knows where their freight is. They can tell you directly a lot more faster. So it eliminates a need for third party control towers. It lets you leverage your benefits of your own TMS let your own TMS become your control tower. Eliminate old school EDI it doesn't need to exist anymore. You don't need those vans anymore. Just eliminate 'em together. use a modern platform that's just more efficient so you can eliminate that. And then there's three or four other types of things. Like if you've got fleets and you wanna bring in telematics data bring your telematics data in directly into the tool so you could just set up these types of integrations. So in supply chain we're all about B2B and trading partners. And that also means digitally connecting to your trading partners, not just in buying materials that's covered under the trading networks, but in operations. And, that's where the, the, we've really struggled is getting B2B operations connected. and that's what we're really fostering is that logistics. That's the name of the company. 1Logtech

Tom Raftery: 22:14

Okay. And how does the shift, the work of ops teams, you know, what can they stop doing and what can they finally start doing that they weren't able to do before?

JP Wiggins: 22:22

Yeah. You know, the, the, the big one is manually, like we talked about, like doing check calls or check emails. We still see in most parts of the world, people still send emails for getting all their loads tendered. It's so they send a carrier, say, Hey, pick up this load, and they send it over in an email. Even if you're running OTM or 3G or SAP, you're still sending emails for a lot of these loads that are one-offs or they're sending it over to a broker, and the broker is then doing it all manually. They're manually connected. They're manually calling a driver, establishing rates. Digitally connected eliminates those manual modes. Every TMS has the ability to to directly tender digitally. To directly receive status messages digitally. They all have it. I mean, it's a core function, so it'll let you use those core functions and eliminate that manual mode. We become much more of an enabler of the AI tools. So there's AI tools that I see SAP and Oracle building into their TMSs and these AI tools sense data and it needs to sense data, sense what's going on. So that it can then start making proactive decisions and so just having good clean data among the supply chain is what's important. And that's what I think, like if you wanna talk SAP for a second, SAP's core strategy, if you look at their AI strategy is, you know, it's run this Joule system, but Joule is actually it's not their own AI, it's, a throttle for others, AI, you can make Open AI or Google or Anthropic run through Joule, so they're not building the LLM, but then they put a work or wrap around it to keep it in balance and keep it throttled in so you have access to those tools versus that then you don't have to rewrite a whole LLM. SAP is not spending billions of dollars creating an LLM. It's, it orchestrates, external LLMs is what I'm trying to say. The AI gets embedded into workflows and supply chain exceptions. I think supply chain exceptions and anomalies is really where these AI's really take off. And I see SAP doing a lot of, making that is their aim is to make it invisible to the tool. It just makes the software run better. So it's like an AI business platform. It's a different way to think of how AI actually works. If you wanna compare and contrast to like what Oracle's doing, Oracle's strategy is they want to be the infrastructure for AI. They want to be the if you look exactly what they're doing with the Oracle DB 23 AI I think in simplest terms, they wanna be the AI supercomputer that the LLMs are created and trained on. Open AI, Anthropic, Google, pick the names. they wanna be the tool that are used to create those AI is and the tools that the supercomputer that they're run on. Doesn't mean they still can't come up with AI agents within their platform. And I see a lot like in Oracle Fusion in the SCM tools and stuff where they're generating, AI agents within those tools themselves. And this is why Oracle stock price is just going through the roof, is that, AI is actually running Oracle. they're the infrastructure, the db, the data, the computering cloud, the, Oracle Cloud Infrastructure is what's running a lot of those major customers. Open AI Cohi or XAI, things like that, that's all. And that's really what Oracle strategy and if I had to summarise the difference between, the two, I kind of gotta put it through the lens of supply chain and, if I put it through the lens of supply chain, I see SAP building a lot more apps for business, and Oracle is selling to the LLMs, which doesn't do a lot for me in, in the world, but that's just right now. But if you look at Oracle, they're putting billions of dollars into that structure and SAP's putting a lot of money into their AI strategy. I kinda like what SAP's doing right now but we'll see what Oracle does down the future. The bottom line though is, is I think if you're running like SAP and Oracle, and we can look at Blue Yonder and Manhattan and some of those other majors, is they're where you should be looking to get most of your AI technology. If you're running one of these ERPs, look to your ERP provider for your AI technology. Because it's just gonna be embedded into your overall supply system.'cause you wanna control your entire supply chain. and I saw some great things at some of these shows where these AI tools are orchestrating across multiple tiers, the supply chain, and trying to identify problems as they happen or before they happen and then recommend changes if you have a tier problem here, you know, change it here, you need some more suppliers here and oh, you're gonna need more transportation here. So find some more carriers here. Those kinds of things across your supply chain. And that can really only be done by your internal ERP. You can't look for that external by some third party. Don't go look for someone that's gonna be selling you some third party app that says, I got AI that's gonna manage your supply chain. No, it's not. It doesn't see enough. It has to see your supply chain to manage it. And it has to see, your inventory level counts. It has to see your production schedule. It has to see so much more than just your logistics. And it needs to see your logistics. You need to see what's going on in your transportation. Not just, you can't do one. You have to have a full picture, and that can only come from your ERP, in my opinion.

Tom Raftery: 27:09

Sure, and obviously this is the Resilient Supply Chain Podcast. So in your experience, have you seen any instances where faster integrations create resilience or sustainability gains?

JP Wiggins: 27:24

The big problem with 3PLs have is connecting to their customer's ERP, connecting to their customer systems. Yeah, it's easy to maybe send EDI messages, but a true 3PL needs to connect to like my customer's SAP or IDS or Oracle or NetSuite. And it needs to be able to insert order data and like insert shipment data and receive order data and then turn 'em into shipments and give you statuses back and insert invoices. And that becomes a digital 3PL, and that's where resiliency comes in because your customer doesn't want to pick up the phone and call a 3PL doesn't wanna send them email. They want it to be digitally connected to their internal supply chain processes. They want to treat a carrier like another vendor where invoices come in and they pay those invoices. They wanna send orders over and have 'em executed, and they wanna know statuses back internally. They might need pricing quotes. So digitally connected provides resiliency for a lot of 3PLs in B2B communications because once you're digitally connected as trading partners, it provides a, platform to grow on. It also reduces costs on both sides. It makes it, if you don't have manual in there, it becomes cheaper for the 3PL to process and manage to do a managed transportation function, like to the shipper customers. So they can lower their costs. The first thing that comes to mind when you said resiliency would be that, is that, digitally connected between trading Partners provides a better resiliency because it gives you a platform to build on. And then how to improve process, and this is in general, shipper, carrier, whatever. Carriers don't want to have to deal with manual methods. You even to, I'm booking a load with a trucking company if that came into their fleet system automatically and then it got processed, that reduces them having to, put a manual person to, to manage that operations and that reduces their internal costs. Resiliency then also tunes into cost benefit. This can now scale. And that's, what I like seeing right now, is that we're able to scale across not just the biggest company with those billion dollar IT budgets now mid-size and small companies can now be digitally integrated. In fact, you almost have a, economy of scale going on. It's an inverse economy of scale because big companies they might have been putting, millions if not tens of million dollars into integration systems over the past 10 years or more. And they're stuck with them. They're stuck with old technologies versus a smaller company that with manual can now go automated and digitally surpass maybe even some of their larger counterparts. Technology is the, the equalizer, right? The global equaliser, which, if I can provide a digital integration service that's better than the, top ten 3PL, then, as a shipper, I'm gonna go for the person that's gonna charge me best and be digitally integrated.

Tom Raftery: 30:01

Sure. And how do you see all this logistics, connectivity, digital integration evolving over the next three to five years?

JP Wiggins: 30:10

I see rates being able to become much more dynamic. I see dynamic pricing becoming much more popular. I wonder about the evolution of brokerage process. You know, brokerage is still very, very manual right now. there's so much technology being putting into brokerage and the brokerage tools themselves on the way they price, the way they're able to find loads the way, able to find carriers and the way able to integrate. But they're still lacking, digitally integrated to their customers. It's still. know, maybe an email comes over and say, Hey, how much to quote for this load? So I think that next step is that where we are eliminating more of that manual, that we're able to just let supply chains be managed across the multiple tiers. And I think that's what's exciting to me is when you see someone that's actually planning across multiple tiers of their supply chain, then they're able to truthfully, really dynamically optimise and truly reduce costs across your supply chain, not just looking at things because we look at things too much isolated right now still, how does it affect me? How does it affect this specific piece? But when you look at a company's supply chain, it's much more of an ecosystem. I, I like that and that's, that's where I've always seen as evolving to, is evolving to ecosystem optimization. But that's kind of why I see us evolving much more that way. And to do that, you gotta be integrated across the ecosystem. And we're not now.

Tom Raftery: 31:26

So if you were advising a COO or a CIO today, where should they start in reducing integration drag?

JP Wiggins: 31:34

well, I would say it seems like every COO, CIO that we talk to, they've gone down that whole hype cycle on AI, and I think that's because their board, and their investors have said, what are you doing with AI? You know, you pick any company and they're gonna have, what's your AI strategy? And I think we're starting to come through that hype cycle, though. I think that hype cycle has now kind of gone away. When it comes to their supply chain, there's, there's only finite things that they can do. What's fact from fiction and they're not gonna get budget approval for an AI tool that doesn't have hard ROI right now. You know, the advice I give a lot of COOs is, bust through that hype cycle even more. Look at AI as a strategy. What is your strategy related to AI? And, it's a tool though. That's what I keep coming back to is, sometimes you're gonna have to push away your investors and board members because they don't understand. They're reading the hypes and they're pushing you to do things, and you need to come back with, here's my AI strategy, which is using AI as a tool, and we're going to be using it in certain use cases where ROI makes sense, and then here's my long-term strategy. But if AI is going to be part of your strategy and it needs to be clean, consistent data across your trading partners is what's super important. So then, yeah, integration becomes so much more important, we're now becoming so much more of an integrated world. and that's why you need a good integration platform. And that's comes back to like, if you have an AI strategy, your B2B strategy needs to be more digitally integrated with your trading partners. that core fits for one of the things we solve. You know, we solve the logistics side of that. So it's, it's core integration.

Tom Raftery: 33:06

Okay. What's one thing leaders underestimate about modernising their data flows?

JP Wiggins: 33:13

A lot of times it's getting the partners to respond. Even if they're a vendor, we see this a lot. It is like, while you can control your own integration in your company, you can make it easy to talk back and forth still on the underside of the equation, you don't know what kind of tech stack your trading partner has and if they're capable of providing it and integrating it, and that's what the challenge gets underestimated a lot of times. It's like, oh, great, I'm gonna integrate these data flows with you, but my partners can't integrate with me because they're literally still just using Excel files. Understanding the, true abilities of your trading partners is one thing that I think gets underestimated constantly. I deal with carriers a lot and every carrier you go to 10 different carriers, you're gonna get 10 different tech stacks. Some are gonna be great, some are gonna be old, some are gonna be non-existent. And, you know, some are gonna be modern, but they've implemented 'em the wrong way. It becomes a herding cats exercise at that. It's everyone is completely different with their own mindset and their own tech stack. So that's one thing that gets underestimated a lot.

Tom Raftery: 34:15

A left field question for you, JP, if you could have any person or character, alive or dead, real or fictional, as a champion for automating integration, who would it be and why?

JP Wiggins: 34:32

You know, I come back to science. I still like to look things from a scientific method more than anything. I, use abbreviated methods for my scientific thoughts. But I also like people that just think outside of the box and just come to answers, but, but also have personality. So I'm a big fan of Stephen Hawkins as a character that was able to apply scientific properties but then to know when to ignore 'em, to come up with some great new, wonderful ideas that, just think outside of the box. So I'd go, Stephen Hawkins.

Tom Raftery: 35:01

Fantastic. Okay, and we're coming towards the end of the podcast now, JP, is there any question I didn't ask that you wish I did, or aspect of this we haven't touched on that you think it's important for people to think about?

JP Wiggins: 35:15

I think one is what AI is. I mean, that's kind of, if you don't know what AI is, it's easy to actually go ask the AI's what they are. The thing to watch is that they're gonna be overly helpful and too positive, and you're gonna run into confirmation bias. Understand that AI at its heart is pattern matching. It's trying to find patterns and to bring those patterns together, but it can go down wrong patterns and take you down completely different rat holes. So it's important to understand what you're getting with these LLMs and how to use 'em, but they are invaluable in everyday use. Like when we first came out with the Google search engines, I think of them now as a very popular Google search engines, but understand. Tune your AI, tell it don't be so positive towards you. It easily wants to do confirmation bias. Tell it to be much more critical. Ask it to be significantly critical of your ideas and approaches, and then you're gonna get better responses because these things develop a personality and they just wanna be super nice and helpful and friendly. You want the right answer versus a confirmation error. Now if you wanna go believe the world is flat, your AI will teach you in under mounting detail that the world is flat. And you could try to train it to do that. That's my point. Versus if you wanna learn the right answer, and that's why I come back to like the Stephen Hawkins and that approach, which is think like a scientist, you know? And if you understand the scientific method, you develop theories, you test those theories, but you could still come back and don't believe it as fact. Everything is still a theory.'cause even your most underlying core beliefs can someday be shown as wrong, but if you're not open to believe it, then you'll go through the rest of your life believing that your core theory was wrong. And that's the same thing with the AI. Once you go down a core belief, it'll keep believing that, even if it's wrong. You can go on believeing, the world's flat, but, you know, trust me, it's, it's not, but, you know, but that's what I mean. So that would be the core thing I would say to more people is tell, tell your AI to be critical and you'll get better results.

Tom Raftery: 37:02

Yeah, kind of a hack I use when I'm using LLMs is I use two at the same time. So I'll test, test one against the other and see this LLM tells me this, is that correct? That kind of thing. So sanity checking, I guess is, is the best way to put it.

JP Wiggins: 37:20

I mean, we've seen, I've seen the dang LLM, same LLM, we do a paid service with Open AI here. And like in the same project, one of my guys searched this term and it came back and says, well, this is an Oracle TMS specific term used for such and such. I used Open AI on my side and it says, no, this term is a SIA specific carrier integration thing for sure. And I'm like, well, what about Oracle? No, it's not in Oracle. And he asked, what about SIA? He goes, no, it's not in SIA. And and and we're in a shared project where we're seeing each other's work, and I'm like, how can it be so wrong? So anyway, don't believe what these things say, but they can be super helpful and it does help. I love like, writing emails where I tell it what I want it to write, but then it formats a nice clean language. I write all my marketing materials, so I give it my points and then I ask it. Like, if I'm doing an email to a customer to bring in some of my marketing points to a customer, it'll bring in some of those points. But I still, as much as possible, I'm telling it what I want it to do versus it going, saying, Hey, gimme a marketing document. If you do that, you know, you're, it's much better when you tell it what you want it to do in more detail

Tom Raftery: 38:21

Cool.

JP Wiggins: 38:22

great.

Tom Raftery: 38:23

JP, if people would like to know more about yourself or any of the things we discussed on the podcast today, where would you have me direct them?

JP Wiggins: 38:30

Yeah just 1logtech.com. You know, obviously everyone's got a website, so 1Logtech .com. I'm on LinkedIn follow me on LinkedIn. JP Wiggins pretty easy to find. ping me on LinkedIn or, you know, hit us up on the website and our contact info is on 1Logtech.com. So easy to find.

Tom Raftery: 38:45

Perfect. Great, JP, that's been really interesting. Thanks a million for coming on the podcast today.

JP Wiggins: 38:49

Great, thanks Tom. Glad to be here.

Tom Raftery: 38:51

Okay. Thanks everyone for listening to this episode of the Resilient Supply Chain Podcast with me, Tom Raftery. Every week, thousands of senior supply chain and sustainability leaders tune in to learn what's next in resilience, innovation, and transformation. If your organisation wants to reach this influential global audience, the people shaping the future of supply chains, consider partnering with the show. Sponsorship isn't just brand visibility, it's thought leadership, credibility, and direct engagement with the decision makers driving change. To explore how we can spotlight your story or your solutions, connect with me on LinkedIn or drop me an email at Tom at tom Raftery dot com. Let's collaborate to build smarter, more resilient, more sustainable supply chains together. Thanks for tuning in, and I'll catch you all in the next episode.