Episode Transcript
[00:00:00] Speaker A: Hello and welcome to the Lodestar podcast. I'm your host, Charlotte Goldstone. In this episode, I will be joined by Emma Crumpton, Enterprise Solutions Director from the freight intelligence platform Portcast. We are going to be discussing where freight forwarders are actually losing money, why the underlying issues are quite difficult to fix, and what better data and AI can realistically do to help these margin leaks be plugged. What is interesting is that a lot of these problems that generate risks to revenue aren't really about people getting things wrong or external volatility, but just kind of the sheer complexity and volume of information that Forwards are dealing with every day. Emma is going to run us through what she has seen as the biggest risks for losing money and her advice on how to protect your bottom line. We will also be joined by Ian Powell, tech and Solutions Director for forward or freight forwarder Metro, to hear about the firsthand forwarder experience in practice and what Metro is doing to monitor its supply chains and mitigate against loss. We've got a lot to get into, so without further ado, let's get straight into the episode.
Emma, hello and welcome to the Lodestar podcast. It's great to have you here.
[00:01:19] Speaker B: Hi. Thank you for having me.
[00:01:20] Speaker A: So, I've already given a brief introduction about you and your company, but I'd love to hear, in your own words, what you do at paulcast.
[00:01:26] Speaker B: So. So, yep. I'm Emma Crumpton. I am the Enterprise Solutions Director at portcast. I joined Portcast around five months ago. I have a background in logistics tech and then prior to that in freight forwarding for about 10 years. So I come with a lot of industry knowledge, a lot of understanding of how our customers operate, what their pain points are, and my role is really an enablement role. So supporting all of the different teams and Personas through our organization to understand how we are building solutions for our customers and how we're better served being
[00:01:59] Speaker A: thrown right in at the deep end. Doing a podcast in your first five months, it's very impressive.
[00:02:03] Speaker B: Yeah.
[00:02:03] Speaker A: Well, we are here today to talk about where forwarders are losing money and how they can protect their margins. So I think it's safe to say that this is a topic that probably forwarders care about the most. So, firstly, just to clarify, when we talk about margin protection in freight forwarding, what are we actually talking about? I mean, is this just about getting better buying rates or is there kind of a distinction between winning margin commercially and then protecting that margin operationally?
[00:02:29] Speaker B: Yeah, I mean, I think it's safe to Say that it's the most important thing for a freight forwarder. They're a customer service business, they rarely own assets. So really it's about the margin between where they're procuring and what they're selling out to their customers and everything in between is where they bring money. So, I mean, when we talk about margin erosion and I think about margin for freight forwarders, there's two different areas that I tend to go to and this is sort of the lens that I like to look at through all of the solutions that we're building is which of these it's serving. So firstly, you can take an individual shipment and there are lots of different things that can happen throughout the lifecycle of a shipment that can impact the margin and the P and L on an individual shipment level. So things like detention and demurrage, unexplained charges like, you know, wasted haulage charges, if something's been misquoted, all of that can quietly chip away a margin at a single shipment level. And then you have the more holistic.
So how do we procure better so that, you know, overall for the business we're buying better rates, how do we ensure that we differentiate commercially so we can win more business and offer better service to our customers so that we retain them? And whilst that's not reflected in a single shipment P and L, it is seen in the P and L. Eventually, when you roll it up to sort of regional and, and different levels of
[00:03:49] Speaker A: P and L, we are going to get into the details about what to look out for and how to mitigate against this. And obviously, whilst Portcast does help forwarders mitigate against this, so I'm sure you have lots of insight. The topic, I think it would be really useful to actually hear firsthand from a forwarder where they might have noticed these issues and also what they're doing to protect themselves and their shipments. So we are now joined by Ian Powell, tech and solutions director at Metro. Hello, Ian.
[00:04:11] Speaker C: Hi, good afternoon. Thanks a lot for having me. Charlotte. Hi, Emma, Good to see you again.
[00:04:15] Speaker A: Wonderful to have you here, Ian. So, to make it clear for our listeners, I think it would be useful to illustrate some real world examples of the things that Emma's just been talking about. So if we followed a shipment from quote to delivery, where might you as a forwarder typically expect to find most common margin leaks?
[00:04:32] Speaker C: Yeah, I think there's a couple of things.
So from our perspective, we, you know, we very much look at it on a shipment level, but that does roll into the P And l. At some point, some of the things, or maybe the key things that we maybe focus on is, you know, the correct buy and sell rates. If we're not, if we're not looking at buy and sell correctly, then there is clearly potential for GP erosion there, the unbilled revenue piece. So do we have mischarges? You know, are there things that we're not billing back that we're receiving costs for? And that's clearly an area where we then potentially have revenue leakage from a freight forwarding point of view. And that directly impacts that job and the GP on that job.
Other areas, operational areas. So operational errors and rework. You know, that from a freight forwarding perspective, that has a material impact in the sense that it increases our cost to serve to our customers, right. If we're constantly reworking activity, if we're raising credits against jobs, re invoicing jobs, right, that's not optimal for us from an operating cost point of view and that materially has a bottom line.
I think some of the other things that Emma raised are quite pertinent, but it's that manual processing, right, the lack of automation, because that in itself is driving margin leakage in the sense that our processes aren't scalable, we have constraints within our processes and ultimately that drives operating costs and impacts bottom line. So we would look at various levers or factors when considering this topic.
[00:05:57] Speaker A: So as Emma said, I mean, there's lots of different factors that do stack up to create some quite considerable costs when all put together.
One kind of major thing that I think of instantly when I think of kind of margin leaks for forwarders is detention and demarrage. This is something that we report about quite a lot, detention and demurrage charges. So Emma, what does a detention and demurrage mistake actually look like in the real world? And why do you think this process is so difficult for forwarders to manage?
[00:06:26] Speaker B: Yeah, I mean, I think, look, D and D is one of the sort of age old problems in the industry. It's something that we hear a lot from customers. There's a lot of noise about in the market and you know, rightly so it is a number of different things that compound to make the problem extremely complex.
So when I think about when I was in operations and some of my customers and I'm still hearing the same sort of stories now, if you imagine that you have every single carrier, every port in every country having a different amount of free time, a different amount of charges per day, and then you add on top of that Some of it's published online, some may be negotiated contracts.
Some of the carriers have different terms. So whether it's calendar days, working days, they start charging on different milestones. When you start to look at that and scale that globally, it becomes an extremely complex problem very, very quickly. And it's almost impossible to ask a human to get that correct 100% of the time. So at an operational level, when a shipment's being handled, an operator has to look at every single delivery they're making to think what is the contract that's applied to this? What are the free time days that I have? Have I informed the customer? If they're going to go outside of the free time, am I giving them options to get this delivered within the free time? Not always possible. Maybe they have constraints within their warehouse, they can't take any more orders. Maybe there's an issue with, with haulers and there's a shortage of labor there. So lots of different things that compound into it and ultimately it hurts the pnl. A lot of detention and demurrage is passed on to, you know, shippers and bcos into a freight forwarders customer. But unfortunately there are often times when mistakes get, mistakes happen and they have to absorb the cost. They can't pass that on because they don't want to damage the relationship with their customers. But for me, when I think about detention and demo, it's a, it's a complexity issue and there are just so many different ways that detention and demurrage can be charged. And even just the terminology with the way that some of the carriers talk about it, is it detention, demurrage combined detention, demurrage, key rent, port, storage. There's, there's just, there's no unity across, across the board. And what happens in the UK or in Europe is very different to the US for example. So all of that compounded just makes any sort of scalable global solutions very, very complex and difficult.
[00:08:42] Speaker A: Yeah, I'm stressed out just hearing you talk about all the different factors. So I can't imagine monitoring all of these for like thousands of shipments as well as you said. So how does, how does a company like Portcast then help protect against these detention? In the case of detention and demurrage specifically, how would a tech solution help against that?
[00:08:58] Speaker B: Emma? Sure. So I think there's a couple of different ways that it can be done. And at Portcast we do take a couple of different approaches. So first is around detention and demurrage prevention.
So making sure that operating teams have alerts and features within the solution that allow them to mitigate any detention into morriage.
When is free time ending? When are you going to be at risk of charges? And then rolling that up as well so that you know, sort of branch managers and regional leaders can see where their risk is in terms of detention and demurrage across the board. And then the second part is around P and L visibility. So detention and demurrage has happened. We know that the charges are complex and understanding the contractual terms is difficult. So how do we ensure that the correct charges both on the buy side and the sell side because often they're different.
What we've procured at is different to what's being sold at lands in the accruals and the financial shipment so that it can be billed correctly. As Ian mentioned, one of the things is charges that aren't getting passed on to customers when they should be. So there's two different lenses that we look at. One is prevention and one is when it's actually happened. How do we manage like the, the, the actual financial process there? And I think it's interesting with detention and demerge because as I said, it is a sort of an age old problem. And one of the great things with you know, generative AI is that it is enabling, you know, organizations and freight forwarders alike to bring a bit more clarity to the chaos of unstructured data.
So that's the approach that we take. We do use AI within that solution to tackle an agile problem.
[00:10:38] Speaker A: Well, I mean having that solution and kind of taking the burden of monitoring like these thousands of shipments across D and D must kind of go beyond just the leaked charges. And it kind of saves operator time as well because obviously if operators have to spend hours finding and then inputting data, that's going to take up a lot of time. And human resources is one of the largest operating costs for a forder.
So that must put quite a lot of pressure on margin if you're not using solutions to monitor it. I mean something we hear a lot when we talk about AI and tech in supply chains is that it's a great tool for kind of doing these menial tasks then free up time to do other tasks. Ian, I'm sure this is something that you've dealt with a lot. So how significant is that to a forders bottom line? I mean do you have any kind quantifiable idea of how much time tech solutions have actually saved your team and how productivity has changed like over the last few years?
[00:11:30] Speaker C: I think it's difficult to say based on the number of processes that we execute as a freight forwarder. If we look at Metro specifically, we're very kind of technology led. We use our industry leading freight forwarding platform. We have our own technology. From a customer facing point of view, we leverage other partners like PortCast. Increasingly technology becomes important because it systemizes a lot of the manual processes that you would have historically been doing. I think as Emma points out, you know, freight forwarding as a, as a, as an industry still very kind of labor intensive in terms of, you know, document, document exchange between parties is all unstructured, right? It's PDFs, it's PowerPoints, it's Excels. Right. So there is still a lot of human effort. But I think the key thing is about understanding, you know, where you can leverage technology to tank out some of that effort. That's the key, that's the key point.
You're not going to take the human entirely out of the loop in freight forwarding, but it's about how you translate all of that unstructured data that your business is reliant upon because it's data exchange between different parties. How do you integrate as one method of exchanging data? But then how do you also use technology to essentially structure that unstructured data to reduce your operational burden? So we, we do that in many different ways at Metro. We use technology clearly we also have an offshore back office function and we deploy technology into our operations that support them to execute their processes. So a key focus for us back end of this year in 2027 is how do we leverage particularly AI to reduce that operational overhead and that operational burden. The technology is very powerful.
It's about adopting it in the right way, identifying the right use cases, piloting it, and then deploying kind of scalable whole scale solutions that we can implement across our branches and across our country operations.
We're quite pragmatic about AI, but we're also quite cautious about how we deploy it into the business.
That approach us using third party technology providers is key to our success.
[00:13:35] Speaker A: And the field changes so quickly and there's so many offerings out there, it must be quite difficult to know what is right for your business. So I can imagine that does take quite a while. But the problems that we've spoken about in this episode must be so familiar to a lot of the forwarders listening. You've illustrated a few examples. So Emma, why do you think forwarders haven't just automated all of this away? I mean, surely they're now inundated with options for AI systems or partners or does it go beyond that? Is it more just like the data itself?
[00:14:02] Speaker B: That is the issue. I mean, the dream is to just automate a lot of these manual processes, isn't it? Unfortunately, I think there's, there's a bit of a misconception in, in. Well, it's actually just globally and in general around AI and that it's this magic button. But there's a few different things that I think of when you mention that. First of all, it's only as good as the data, AI is only as good as the data that is fed into it. So if data isn't clean, there's missing data and that underlying data asset isn't adequate, you won't get the result that you're looking for. So we are very keen at portcast in terms of looking at our data quality and really working on that to make sure that anything that we deploy on top of that will enable our customers to get the most out of it. And you know, within freight forwarding, if you think of a single shipment, there are hundreds, thousands of different data points across the operational file, the financial file, the commercial file. All of this data has to be cleaned to be able to really get as much value as you want out of any artificial intelligence that you deploy in the other part of that is freight forwarding is really complex. It's difficult and it's hard and there are processes that have to be followed, there's legal implications for things that happen. So you can't just trust wholeheartedly.
You know, artificial intelligence, it still needs that human insight and it still needs that human input. And part of what I envision and see for this industry over the next few years is how do forwarders start to co work with agents and with this type of technology to bring about the most value?
Because one of the things that I've seen over the last few years is the big part of it is adoption, because there's a lot of buzzwords, a lot of noise around AI, but ultimately the users need to be able to use it in their day to day to understand it, to trust it.
And without that, any sort of implementation operationally will fail.
So, you know, and you know, big, big global freight forwarders can often have thousands of operators that all need to be trained, need to understand a solution, be trained on new processes. That's a huge task in and of itself. So it isn't just a light bulb moment where you can just switch something on because the processes behind it are so complex. It needs to be thought through, it needs to be appropriately planned and managed as well.
[00:16:26] Speaker A: At that point, you mentioned at the beginning about kind of a solution is only as good as the underly data.
How important is that to your operations, Ian? I mean, what would happen, for example, if you kind of put a sophisticated AI agent on top of poor quality
[00:16:40] Speaker C: data, you'd have very dissatisfied customers if it gives you the wrong output. Right. That's the main, I think that's the point on the main data, reliability is key. Right. And I think there's this misconception, as Emma says, oh, let's implement AI. But the challenge is it's yet only as good as the underlying data, but it's also only as good as the instruction that it is given. Right. And what it is, what it is essentially asked to solve. So, you know, for us, that's where the cautionary tale comes in a little bit in the sense that, you know, don't, don't be fooled by just implementing AI will solve all of your problems. Right? It will most probably, if not done in a controlled way and robustly in terms of what data you're giving that AI access to, what outcomes you want that AI to deliver, it will most likely not deliver the output that you expect. Right. And that's not very good for your customers, but it's also not very good for your reputation as a freight forwarder.
And I think increasingly there is access to so much data today, so much more than five years ago or 10 years ago in the freight forwarding industry. The challenge also becomes is not only is that data accurate. Correct. But is that data really the right data that you want to be using to solve these problems? So I think increasing people, people have to be cautious about, you know, what AI they're developing, what AI they're releasing, but also, you know, what data they're relying on to be able to make those, those decisions for them. I think, you know, it is better, as Emma says, you know, to, to have a position where you are, you're in a kind of a.
Yeah, there's a, there's a kind of collaboration between the human and AI to execute tasks because you always need that human oversight not, not for all processes, but where you're making decisions that impact customers or they impact the profitability of a file or they impact parties down the line. You certainly want a human to be in that loop and a human to be thinking about all of these inputs that they've had and making a kind of, I suppose, an informed decision.
[00:18:38] Speaker A: So if I'm a freight forwarder listening to this and I've got my data and I've selected a partner. Emma, could you kind of walk me through what this looks like in practice? If someone were to partner with PortCast, I mean where in the shipment life cycle would a solution like this realistically help? Could you kind of give some examples? I know you spoke about detention and demarrage at the beginning, but perhaps a few more.
[00:19:00] Speaker B: Sure. I think, I mean any approach in terms of partnership that's successful starts with running like very deep discovery. We really need to understand where the problem lies, where the challenges are. And whilst the processes are very similar from forwarder to forwarder, the actual challenges that they face, the restrictions, their, you know, commercial ambitions, their customer base, et cetera, differ quite a lot. So actually the solutions that they're looking for can be quite different. So we always start with that discovery to really understand where is margin leaking, where have you got a problem with not being able to scale without scaling, headcount, et cetera. And that's where it starts. But if I look at, you know, sort of our offering and portcast offering, there are solutions sort of at most stages during the, during the shipment lifecycle. And a big part of my role at portcast is to be able to leverage those solutions differently for different customers to achieve different outcomes, but using the same technology in the way that we implement, where we implement it, how we embed it into workflows and how the data is used. But I think, you know, it really depends on customers, specific use cases and what they're trying to achieve on where. I would recommend that we use AI in terms of if they wanted to start from something scratch and wanted to partner with us to build something quite new and novel off the back of one of our data assets. Again, it would start with that discovery and we would look at a co build relationship and we're very open to that with our customers and do that with our customers, which we're doing at the moment, which is always very interesting to as you, they do get to have a bit more of an input into how the end product looks.
[00:20:44] Speaker A: We do have one of your customers here with us. It gives me a perfect segue. So Ian, could you kind of walk me through what the adoption process looked like for you with portcast and how their systems work in your operations and perhaps some benefits that you've seen?
[00:20:55] Speaker C: Yeah, sure, sure. I think as Emma says, it's very tailored, it's very, it's not a one size fits all. We actually leverage multiple capabilities that polecast delivers. We've also Done a kind of a collaborative development on one capability.
I think for us, you know, it was very much a case of identify, you know, what problem were we trying to solve. And you know, for example, if I take air freight tracking, you know, we were looking at how do we get some, you know, some more insights into real time air freight tracking. We have integrations with airlines, we have, you know, we have integrations to our electronics, to essentially our operational management system. But the reliability of that was, you know, it's very difficult for us to depend on in terms of then surfacing that information to our customers. So we went out to the market, we looked at various options. We identified Portcast as a, as a potential supplier.
We then went into a phase of pilot, pilot phase ultimately. So we went there for a period of three months where we built the API, we integrated our systems, we then through our own tech stack, we surfaced certain information visualization and data from podcasts and through that POC cycle, you know, we essentially worked through niggles and challenges and issues and we had open dialogue until we got to a point where there was, you know, real confidence from, from our point of view that we could scale this solution to our entire airfreight operations. That period was probably somewhere between, you know, from, from our initial discussion all the way through to going live and deploying this at whole scale was probably around four months.
And it is a collaborative process. You know, things don't always go right, but the point is you have to, you have to pilot it right. How you can't just go in two feet and expect these things to work because, you know, ultimately, you know, polkast is delivering a product, you know, their product is also evolving.
We have certain expectations and we have to really get that like close marriage right. In terms of another approach that we've done is with, with some, some tracking solution that we're kind of ready to deploy in probably like the next four weeks. But we've gone through a very kind of collaborative approach with poor cost. We outlined the problem statement, we talked around the challenges we had. There was a clear gap in the industry around that capability.
So we've gone into a relationship where we're more kind of giving poor cast R and D insights into some of the things we expected to create forward to solve this problem. Ultimately they've built technology to support that which will be scalable more kind of longer term across other customers. But that process has been a bit longer, you know, from discovery phase all the way through to where we are today, which is we're currently testing that specific integration and that specific solution that's probably more like, I don't know, throughput time, probably around eight to 10 months from year to date. But that solution didn't exist when we started our conversations. That lead time's been a bit longer, but yeah, I would say it's dependent on how much time you want to invest in it, how much resource you assigned to it, and having that relationship with podcasts and having that constant, should we say touch base and weekly meetings to make sure you're getting from, you're getting out of the process what you want to.
[00:24:00] Speaker A: I mean, this space really is constantly evolving. As you said, there are new kind of AI and tech capabilities launching what seems like every few days.
So looking ahead, how do you see this idea of visibility and AI actionability?
[00:24:14] Speaker B: That's a great question and I wish we all had a crystal ball to know the answer to that. I think for me it's one of the most interesting reasons for being in this industry is that five years ago you couldn't have imagined the capabilities that we have now because that was pre generative AI. And so it's really hard to envision what we can see in the next five years because we don't know how far and how much more developed AI is going to become. We don't know if there's a new technology that's up and coming that we don't know of. And I think that's what makes it so exciting. But in the shorter term, Visibility now is quite a standard offering and it creates like as I said, a really solid data asset for underlying data.
And I think more and more, you know, whether it's freight forwarders building on their own data assets or it's other companies like portcast building on visibility data, they will apply agents on top of that to, to solve new and novel use cases. We're releasing an agent later this year that's is a, it's an analytics agent and it, that's something that is based on our own data that we had, but we're just actually looking at it through a new lens and applying a layer of AI on top of that to actually solve completely new use cases that we couldn't do six months ago, which is really interesting. I really think that's what we're going to continue to see in this industry.
Because, you know, I think it was, I think it was you, Charlotte, who actually mentioned that there are so many problems to solve in this industry. That's also what makes it exciting because a lot of the time different providers are going after different problems and there's lots for everybody to solve. And, you know, we also see, like, once you implement technology and you streamline one process, it likely opens up other processes and other problems that then need to be solved. And so I think we'll see AI becoming, you know, we mentioned it sort of working in hybrid with humans over the next few years. And I hope, I hope that that's what we see because I think, you know, there's a lot of noise around, like automation and different things. But the way I actually see it in the future is it is a hybrid approach. It's not about. It's not about replacing humans. It's about enabling every operator to have all of the relevant data that they need to make the best decision in that moment. Because at the moment the data's a little bit, it can be a little bit all over the place. It can be difficult to get the right data into the right person's hands at the right time. So I, yeah, I hope to see a hybrid approach over the next couple of years.
[00:26:48] Speaker A: And Ian, I mean, you're a tech and solutions director for an international freight forwarder, so you must be monitoring these things quite closely. What are you looking for over the next few years?
[00:26:56] Speaker C: I think we're kind of, I suppose we're moving to that next phase. Right. If you look at, I think Emma pointed out, but, you know, visibility was a real buzzword and it has been a real buzzword in the industry for, you know, five years or so.
[00:27:08] Speaker B: Right.
[00:27:08] Speaker C: Pandemic time. Post pandemic, everyone wanted visibility, right. You were seen to be a differentiator if you provided visibility. That's our standard offering now. But I think as part of that standard offering, visibility's become a little bit convoluted. Right. And I mean that in the sense that there is so much data now available to people, people don't know what to do with it. That's the biggest challenge, right. There's such a, such a repository of data and such a widespread kind of data assets or data pools there that, yeah, how do you start to digest all of that? How do you start to consume it? So I think a key thing for us is, you know, it's moving away from that purely that visibility side of things to actually how do you, how do you kind of safeguard that data quality and integrity? How do you build, you know, or build technology onto that to drive value for our customers? And I mean that in the sense that, you know, that data needs to be taken. It needs to be, you know, it needs to be put into something that's tangible, something that people can act upon, that's giving people insights, it's aiding people to support the decision making process. And I think that's the next stage that we want to move them to. I think in addition to that, with this huge availability of data. Right. And so also how do you use that data in ways to, you know, to improve our operations, improve the way we work, to drive efficiencies in our business? So there's various avenues to it, but I think that the point, the point now is how do we use all of this data that we all have access to? And I think that was the overriding thing the last three to five years was everyone wanted visibility, everyone that wanted data. We have it in droves, we have it in droves now. And it's about how do we use it in the, in the most constructive way. That's definitely our approach the next 12 to 18 months.
[00:28:44] Speaker A: So I mean, if you had to give your main piece of advice to freight forwarders struggling with margin leaks, Ian, what would you, what would you say? I mean, I guess they are your competitors, but if you had to give, if you had to give some advice,
[00:28:57] Speaker C: I think you need to, I think there's this misconception that AI and technology will solve everything. The underlying point is you have to understand what is the problem you're trying to fix then I think part of that is about, you know, reviewing the way you work today, understanding what the root causes of those problems are and then looking at a structured way of how you're going to solve them. You know, it's, it's not always a case of, well, let's not, let's not deal with the structural challenges and let's try and put technology over, on, over the top of it and think technology is going to save, save us. Right? It's not, that's not the case. It's about making sure you understand the problem. You can identify the root causes. Part of that might be process reengineering, it might be strengthening the way you work just through pure process changes. And then once you have that, you know, that foundation that's about then building and adapting and overriding, you know, overlaying technology to help you do those things more efficiently, quicker, you know, reduce turnaround times to better service your customers, all of those kind of nice things. But fundamentally, if you don't fix the underlying problems or identify what the underlying problems are, you know, technology is not just going to, it's not just going to save you and give you a hallelujah moment.
[00:30:02] Speaker A: What are your thoughts on this? What's your main piece of advice?
[00:30:05] Speaker B: So I think, you know, I echo everything that Ian's just said, but I think one of the things that is really useful in terms of thinking about where to start is to really actually try and get to the root cause of what is actually causing the margin erosion. I mean, a lot of the time it is, you know, it could be that the data integrity isn't there. It could be lack of data, it could be lack of process.
It could be actually a completely new data asset that needs to be acquired. It could be a technology solution that you need to be able to offer your customers.
So really trying to get to the root cause of what is actually causing margin erosion and whether that is specifically a P and L level for a single file or whether it's something more holistic like procurement, procuring rates, it's competitive advantage and then actually just assessing the market and if you already have like technology providers that you're working with, seeing what they can offer, and then there are obviously organizations like portcast and others who are solving problems specifically for freight forwarders and for freight forwarders customers that you can absolutely reach out to. And you know, usually there will be a partnership approach, a collaborative approach and they will be able to support you in uncovering what the actual root cause is and then advising you on what the appropriate technology to apply is. Because you know, as Ian said, it's not always about just applying AI. I mean, a lot of our products, if we can take a deterministic route rather than applying AI, we will, we will do so.
You don't need to apply AI for the sake of it. So it may be that that problem actually doesn't need to be solved with AI. And yeah, and the likes of portcast will be able to help you really get to the root cause of that and give some solid advice.
[00:31:50] Speaker A: Well, that seems like a great place to leave the episode. It was great to chat to you both. Thank you so much for joining me.
[00:31:55] Speaker B: Thank you very much for having me
[00:31:57] Speaker A: and thank you all for listening or watching. We'll see you next time.
[00:32:00] Speaker B: It.
