How Treasury Teams Can Save 37 Hours a Week Without a Tech Overhaul

The one that some work that we did on cash forecasting for the company. Now this isn't the sort of classic we put in the machine learning tool and nobody had to do anything anymore. Think this was just we took the existing process and we redesigned everything so that effectively we replicated what were the business teams doing? They were using spreadsheets, populate the spreadsheets that were coming in and then for the central team, like how could they make sure that the spreadsheets were received on time? Automated chasers. So we took a process that was taking 40 hours a week across six finance staff and we took it down to three hours and the quality of the output was improved, the speed of everything obviously significantly improved. It wasn't a huge lift. We weren't having to go through and create brand new data sets or anything else. It was simply how do we take what we've got and then incrementally build from there. So we've then gone on and we've gone how do we improve the forecasting for the worst performers and all of that kind of stuff. We're now in a situation where most of the AI tools are good enough that you should be using them. If you're turning around and saying I don't have time to find out how to use this or this isn't important to me, you've got what's effectively a free tool sitting there that can drive significantly improved performance, there is a little bit of a call to action there because I think we are now at a point where there's a level of maturity that means that now's the time to get involved.

Welcome to this week's Treasury Career Corner podcast where I interview treasury professionals about their treasury careers. Each and every week I talk to treasurers about how they build their careers, where they are now, where they see both themselves and the treasury profession going to next. Let's get on with the show. Today's Treasury Career Corner podcast is with James Kelly. Now what you're going to hear in a moment is my original introduction. So I introduce James. Back when I talked to him, he was the SVP of Treasury Risk management in Amsterdam and insurance at Pearson. So you hear that then we actually do the catch up towards the end. So listen for that. It's where now he's co founded a company called your treasury and they transform how multinational treasury teams operate through AI and cash flow mastery programs. So listen to today's episode catch up with James. Well, you hear Pearson when we go up to date some great value there. He talks about his passion for AI and then his passion for AI keeps going into his new role. So enjoy today's episode. In this week's show I'm joined by James Kelly, the SVP treasury and Enterprise Risk Management at FTSE 100 learning company Pearson. During his time at Pearson, James's team have established a reputation as leading innovators, winning awards at the ACT's deal of the Year awards for Pearson's debut social bond issuance, as well as establishing comprehensive in house bank structure employing high levels of automation to drive efficiency. Pearson also uses TIS Cashforce, which is a leading AI enabled cash flow deal asking solution which James will explain a little bit more about. Lots of words. I'm trying to say this all at once but we're going to go back to the beginning of James's career, how he first discovered finance, treasury and then bring up today and then we're going to talk about some of the passions that he shares as well for AI and technology a bit later in the show. James, over to you. Enough of my voice. Over to you, sir.

Sure. Thank you, Mike. So I started out as a bit of a finance geek. I did economics in French at uni, went did a couple of stints at investment banks in my summer holidays as internships and then was looking for a job in September 2001, which wasn't a great time to be looking for jobs. I started thinking what else could I do? And applied for a whole range of things, one of which was Auto Scene. So I, I joined a re reasonable firm called Morph Stevens, did three years with them, took on some interesting tasks. So I had clients like Caterham Cars, Formula one team, one or two others. And then I moved to KPMG and did sort of 18 months at KPMG as an auditor there, where I had clients like Action Aid and Children in need, which was quite fun from the charity sector and a variety of others, say a paper manufacturer, Hawlier, various other people. And I then got a call, would I be interested in joining Kingfisher PLC as a group accountant? They were looking for someone who could speak French and at that point I'd had four years where I hadn't used my French, left uni having thought, okay, French is going to be a big part of my life. And so jumped at that opportunity. Thought I just got to the stage where I was like, I'm going into clients for three weeks at a time, getting to know them a little bit and then you move on and you go to the next one. And I quite like to get my teeth into something and really be able to add a bit more long term value. So that came at a really good time. Joined and just at the point that I joined my then boss, got an internal promotion, moved to investor relations and so it was me and the person I was taking oath from had to run the half year from a group perspective. So that was quite fun. Threw me in at the deep end, meant that I got lots of exposure to lots of things. And so the normal kind of rotation cycle of you'll get to do balance sheet and you'll do that for six, eight months and then you'll do a bit of P and L and then you'll do the next thing that got crunched quite significantly and you know, within about six months I tried almost everything and within about a year I was working on the cash flow, which was considered the most technically challenging of the tasks. And through that I was asked to help the treasury team with a few things, particularly transition to ifrs. And I was helping them with some work on derivatives and things, largely because I was seen as this sort of slightly geeky guy who could probably get my head around what was going on and teach people how to do sort of balance sheet reconciliations and that kind of stuff. So I did that and around that point the then treasury accountant decided to move on and I was asked if I'd like to move across. And so having been thinking I'd quite like specialize in something, I'd actually been thinking tax probably, I thought, why not? I don't have any particular ties to anything. And I was incredibly lucky. I had two really top treasurers in the team in Linda Hayward and Nick Favier. And actually that team is amazing. You've got Granja who's at Walgreens, Gary Burton who's gone on and done some really great things. So it was a really excellent team, really thoughtful, competent people really looked out for each other, but were also really clear about what they could and couldn't do was a really great grounding. And I think one of the things that I in fact two things I took from that. The first was just how incredibly generous Linda was with her time and helping upskill me. And they joke that 6 year old asks 75 questions a day. And I was very much in that camp. It's like treasury is completely new to me. I'm trying to get through year end. I had to brand new green people when I started both pretty much out of uni at that point, my first instinct was to try and go it alone. And then get them to do little bits. And I tried that for about half a week and realized that's never going to work. I've got three people in my team. I'm going to have to make sure that everyone's carrying something, otherwise I'm going to fall over. So that was a really good lesson. And it doesn't matter, you know, who you think you are. The reality is you need to work with others to get things done. And I think Nick was brilliant, just never closing anything down. Everything was. Everything's always possible. It's just you've got to find a way of getting there. And I think the fact that he'd made group treasurer really young was really inspirational for me, actually, because at that point, and to be honest, it's a little bit the same now, if I go to, you go to treasury events, that there aren't too many group treasurers in their early 30s. And so it was one of those things that there was me, 27, thinking right here, it's going to be a long old road to get to that kind of level.

I will say I'll jump in there. And we will put a link in the show notes to previous podcasts I did with Linda. Yeah, so we have Linda on the podcast, Wonderful Lady. She was also mentioned recently by Andy Henley at Tesco. He took over. And actually, funny enough, I placed Granja with Linda back in the day. So, yeah, it was a great place to be at Kingfisher. So, yeah, great team and great people. And then what happened next? Or talk us through because you made some moves.

So I moved to. Moved to sky and I moved primarily to get more front office exposure. So the front office team at Kingfisher were pretty well set. It didn't really feel like there was going to be an opportunity to move across and I wanted. I knew that if I wanted to move up, then I needed both front and back office experience. So sky was a kind of midpoint in that at the point that I joined Mike Hazell, who was deputy treasurer, Alison Dolan was treasurer. And the model effectively was that Mike did the dealing with and myself and Mark Palmer, who's my colleague, the two of us did everything else, including the analysis and the prep, et cetera. So that gave me more exposure to doing the risk management side. Thinking about, in that case, we were working with a shared service center who were doing cash management and things, but just making sure that we were on top of things, et cetera. I stayed at sky for about a year. I think I had amazing experience I got really heavily involved in a couple of M and A pieces. It's a really intense place to work that year. Honestly, in. In my kind of mental timeline feels like it's about four, because I met so many people, I had so many experiences and some of them funny, meeting Ross Kemp in the toilets, some of them more serious. But actually, I look back on that time incredibly fondly and Alison is now on our board of directors, so I sometimes see her audit committee meetings and things. So it's funny how things work out, but. Yeah, so I. And around that time, my first child was born, Willow. And it quite quickly became clear two things.

One was.

So Mike moved on, was replaced by Simon Morley, who's amazing and was incredibly thoughtful and generous with his advice and guidance. But that. But Simon's experience was on the front office side. They decided they were going to bring in a designated treasury manager, Mark and I would become back office. Now, given that the number one reason that I moved to that role was to get front office experience, that wasn't.

Yeah.

So it was time for me and it was. It was a long commute and everything else, so it felt like it was the right time to. To try and find another role. And I had a couple of offers, but the most interesting was with Rent A Kill in. In Crawley. And I remember, like, sweating on that decision because I had an offer come in and then I had this Rent A Kill thing, which looked like it was probably going to happen, but I was a bit earlier stage in that process and trying to decide do I go for the. The. I gamble and I'm incredibly glad that I gambled. I joined as deputy treasurer to Nigel Roberts, who, I think we had a really good working relationship. He's incredibly structured, very good at prioritizing and has a really clear idea of strategy and communication.

And can you explain for our international business when to kill you? And I know them, the household names in the uk, but if you're in the us, you might not know them.

So. And actually, amusingly, Rentokil are quite large in the us, but they go under a variety of different brand names. So Rentakil are effectively pest control and hygiene. So effectively you may see the initial brand sometimes then in washrooms. So if you go into a public restroom, often there are Rent A Kill hand dryers or soap dispensers things. Some of that's been sold since I left, but a lot of that's there. But they're very large in pest control. And so in the us, they're. They're Terminex and various other brands. So Western pest control and various other things. Yeah, so I was there five years. It's a pretty decentralized business, very innovative in terms of the way that people work together. There's a real culture of how do we make things better? So it's Sky. At the point that I was there, the Dave Brailsford sky cycling team was just being set up with the marginal gains theory. And you were starting to see some of that same type of philosophy come through at Rentokil, where one team would come up with an idea that would increase efficiency for route planning. For example, ultimately you've got people who are out in vans who go to a site, perform a service and then move on to the next place. So you want as little time traveling as possible. And actually one of the things that we found was that FaceTime is incredibly important. If you go around and do a great job, but no one knows who you are, you're unlikely to be re repositioned next time the, the RFP comes around or whatever. So actually, in order to make sure that you get those renewals, you need to make sure that you've got good relationships with people. But there's a real kind of culture of, okay, they've had this idea, I'm going to take that and how do I make it a little bit better? Which was really interesting. And I was lucky enough that my financial modeling skills are quite good. And being the keeper of the rating agency purse strings, I was often asked, can we afford such an M and A deal? And so we built an M and A model. And I built the sort of equity.

M and A model.

And that was designed to say, if we do 10 deals of 20 million a year, how long does it take before we start seeing meaningful progress on the share price? How long does it take before the cash flows from those M and A deals start to pay for the next year's M and A deals? And how fast does the ramp up happen? So if you want to get to a point where you can do a billion M and A deal, how do you, in say five years time, how do you get there and how fast does the ramp up happen to be? So that was really great experience. I got to work closely with the head of M and a, the CFO, CEO, etc. Got really great FaceTime. And again, I tried to make sure that I was in the right place at the right time. And so anything that came up I was volunteering for. So I know that, for example, one of the last things I was doing was assessing the management LTIPs and monitoring how was the share price going versus the different targets and how likely were different payout levels. Likely to be. So quite good fun. Not massively challenging, but also one of those things that it meant you got FaceTime every day.

I was going to say they want to talk to you, you're a man in demand, which is great.

Exactly, exactly. So it's one of these things. Just trying to make sure that I really got involved in things. And we did some really fun stuff. I think we did three or four financings on the bond side. We did a couple of bank deals, started to put in a new tms. So lots of kind of process changes, lots of financing, et cetera, lots of M and A, so lots of interesting treasury technical pieces. And I stayed there for about five years and I reached the stage where I felt I, I wanted to work in a slightly different environment somewhere that was a little bit more centralized and also given the experience I'd had in dcm, I wanted to major on that a little bit.

Yeah.

And so I moved to Associated British Ports, which at that point was the, the third biggest issuer of US private placements in the UK. So they own, I think it's 21 ports around the UK. It includes Southampton, which is the biggest cruise terminal, Immingham and Hull up in the northeast. There's effectively four across. Yeah. Port Talbot all over the place. And interesting model. It's infrastructure, pretty highly levered. So six and three quarter times EBITDA and the model basically is you add 15 million of EBITDA every year, that generates another 120 million of debt, capacity or whatever go out, raise the USPP, pay that to shareholders. Nice, relatively easy model. Unfortunately, I joined just at the point where that EBITDA came under a bit of pressure and that was because new taxes came in on coal imports. Coal imports had been between 20 and 25% of EBITDA and effectively the first three months that I was there, I hadn't realized. But they were the honeymoon period because what had happened was everyone had got their coal imports in during that period. From an ABP perspective, it was slightly unfortunate because that marked what was really going on. So there'd actually been a colossal drop off in demand, that drop off in demand and then the kind of bringing forward of orders actually, broadly speaking, came to what normal looked like. And so you looked at the trend and it looked like everything was normal. And then suddenly in the April, the new tax came in and the numbers just dropped away. Almost to nothing. And so suddenly you go back and you speak to customers and you go, what's been going on? And they say, look, we've been stockpiling this for the last three months and you'. Really. It hadn't been apparent, but then subsequently it became clear that the underlying use of coal had been declining for a little while. And in preparation for this. And so I, rather than being busy on debt issuance, as I thought I was going to be, I was busy on ratings and working capital, so trying to make sure that the rating was shored up. There was a little bit of buffer in the capital structure to make sure that the company didn't lock up. But it was one of those things that it was going to be a bit tighter than we would have wanted. The dividend was cut, but clearly that meant that the shareholders were somewhat anxious. And so I did a bit of work, particularly around receivables, to make sure that we were able to generate some leverage that could then be paid to shareholders. So it was interesting. And I was there just a year. Again, really interesting formative experience. I did quite a lot on the insurance side there as well, so got some really good experience there. And then this role at Pearson came up and it was a. We're pretty heavily decentralized. We want to build a centralized model. We're looking for someone with multinational experience. And, you know. Yeah.

What was Pearson like when you joined as a company? What was the situation there? That was seven and a half years ago.

Yeah. So there is still a little bit of a legacy from. We'd had a previous chief exec, Marjorie Scardino, who had effectively delivered relentless performance, you know, 20% growth year on year, for goodness knows how long. And we got to a point where, you know, and so it meant that a lot of the businesses were very highly cash generative, very high margin. And when you're in that situation, sometimes there's only one way to go. And so her successor, John Fallon, had a rather harder job to try and follow that. And he was unfortunate enough to come in at a point where Amazon was really reshaping the world of retail, in particular the world of kind of book selling and student life and everything else. And we had some pretty ambitious growth plans. And unfortunately, we had a number of profits warnings within the first few years that I was with Pearson, as we tried to continue to meet expectations in a market that was changing quite significantly. And John had a pretty tough job because he had to try and take what had been a largely print business with a variety of businesses, including Financial Times and the Economist, that were hugely loved within the, within the organization. And so when they were sold, understandably, there were a number of people who really felt strongly and let down that these businesses were leaving. And so I think at the point that I joined, there was a lot of optimism and a lot of hope and a lot of perseverance and ambition to try and make sure that things worked. But I think also a degree of anxiety that it's a difficult balancing act to transform and meet expectations and have a clear idea of where you're going and where you've been and a clear narrative. And I think that was a difficult piece to pull off. The first couple of years from my perspective, was quite a kind of tidy up exercise. I joined and we had over $2 billion of cash on balance sheet and over $3 billion of debt. And we just needed to respect that. Our net debt was probably going to be about a billion or maybe a bit less for the foreseeable future while we went through transformation. We had a couple of large, very ambitious efficiency programs that we run. We ran. I got pretty heavily involved in the first one, which was how I took on insurance, having looked after insurance elsewhere. I had a colleague who was looking after compliance who took this on and said, I don't really know where to start. So we agreed between the two of us that I'd take on that part of his cost saving task. And it was really interesting, quite challenging at times. You had, oh, absolutely unbelievable transformation effort. Just really interesting because you've got this transformation going on in the business. You've also got, from a finance perspective, we were trying to transform all our processes, move to standard ERP processes, et cetera. And I was going into this thinking I need to make sure that I really think about how best to design processes and practices in a world where accountability, for example, for the balance sheet is going to be very diverse. You can't just go to somebody and say, tell me what's going to happen in the US because it's actually, it's five different teams in five different locations and they'll all know a little bit of the puzzle. And so trying to pull all that together and make sure that you've got a kind of coherent story and a manageable process was really interesting. And that's part of the reason why we put Cash Force in initially, so that we could start to build more of the forecast ourselves and then go out to the different teams and get them to interrogate it, rather than have hundreds of stakeholders who we were trying to get feedback from and try and collate it.

I just wanted to take a moment to connect with all of you amazing listeners, especially those of you who have been earning your CTB credits by taking our CTP quizzes. We had a brilliant response. Every week I hear from more and more of you not only enjoying the podcast but getting value from the quizzes too. So a bit of a roll of honor shout out some of our amazing participants, but I'm going to do them really quick. So listen careful. Adriana, Arturo, Ben, Brooke, Bryce, Chan, Daniel, Dylan, Drita, Emily, Evan, Harrison, Ionis, James, Ken, Kevin, Kimberly, Marcus, Meredith, Milo, Motorbang, Oscar, Phil, Ryan, Sachin, Summit and Trey. And an extra shout out to Aubrey. You've been smashing it and wishing a full and speedy recovery to your dog Toby. Now what's interesting is a lot of you folks are taking part in the quiz are first tier connections fantastic but some of you are second or third tier. What I know your people I haven't met yet. But you're getting value so we need to fix that. You need to connect with me. Reach out you follow the link in the show notes or head over to LinkedIn. Look up Mr. Treasury or Mr. Treasury as the lovely Summer puts it. Thank you once again for supporting the podcast, taking the quizzes, sharing your feedback. It means the world to us, keeps us doing what we love and if we're connected, who knows, I might have a Treasury role for you or some career advice. If you need advice and you're trying to recruit, that's what we're here for. More updates coming soon on where we'll be traveling, the live events we're hosting hopefully chance to meet a lot of you face to face. So look forward to connecting but for now let's get back on with the show and you've always and it nicely segues into you've got this passion for from process, from cash forecasting but all things and I noticed and anyone that does connect to you we'll put you linked in details later but we're not quite there yet. But you love AI and automation and processes and some we were talking just before the show. I recently did one a session for the Manufacturers and stuff alliance association in America. Some of them were talking about how they were doing the scraping and I did a presentation and they were loving it and you and I talked about there's some people that other people that run for the hills but I think it's a lack of understanding sometimes treasuries are later document with you how come you're so passionate about it? Where does that come from? First of all, if you like, say.

I think it's one of these things. I think the first thing is I'm massively practical, so I, I don't have a philosophical love of AI. Makes sense. It's a. I love it because from a selfish perspective, it's a massive help. It's one of these things. And I, I think what's transformed is the ability to be able to do things yourself. So with things like rpa, you've had the opportunity to automate bits of processes again. If you automate bits in your tms, you can make strides forward, but generally speaking, you'll need some kind of consultant in whether it's to build the automation in the tms, whether it's to build the rpa. And getting hold of capital to bring people in can be difficult. And one of the key challenges is that treasury is so varied that you've got 50 processes or something, not five. And so saying, okay, I'm going to spend my budget this year in order to try and automate this process. And then you're brilliant. I've automated one of my 50 processes. The other 49 are still crap. One is just absolutely brilliant. It doesn't really make sense and say you need to have the tools to be able to go after a variety of different bits. I think to date, what's happened is that the tmss have been pretty good at automating processes where there's a high degree of standardization. Broadly speaking, we all execute FX deals in much the same way now, interest rates, swaps, there's a kind of. There's a market convention. They've all got to be registered with a repository, but there's a very standard process. Once you get onto things like P and L reporting or to some extent, hedge accounting, I know you can do it in the tms, but again, it's quite heavily customized. Often, you know, you get into territory where it's quite a high degree of customization for each individual firm. That's not very easy to do in the current business model because A, there probably aren't enough consultants out there for everybody to constantly have someone building things, and B, you never have enough budget. So where things are really starting to unlock is by combining different tools. By using a large language model with Python or a large language model with Power Automate, or in a variety of different contexts, you can really unlock a lot of value. And it's happening at pace, which means that you can now start to go at things that mean that you're able to get to a position of ease. And I think the position of the treasurer hasn't necessarily been one of ease over the last few years. Most of us still struggle to get full visibility of cash balances, let alone build a coherent forecast that covers everywhere. The reality is that there's a lot of satisficing decisions that we have to make. Well, focus on the key markets because that's what we've got time for. We can't focus on everything all at once. And that means that inevitably you've got some blind spots that you're not able to keep an eye on. And the nice thing about some of this tech is that you're able to really make a difference. So you use the web scraping example. We've now got all our, all our banks websites linked to a web scraping tool. So we now pick up whenever their credit ratings change. That automatically sends a message through to us to say, by the way, this has happened. So previously we had a model where we check once a month, we check what the movements were and effectively we'd look at what was it before, what is it now? Okay, it's moved. Do we do anything with it that's not necessarily very timely if you have a Silicon Valley bank type problem. And so again, you can set things up to monitor and those other things. And so I find that really interesting.

James, if you're. And I was, I've been scribbling notes here as well. And this is all very well for you because you love these processes and you've got a gift for it as well. But if you are a treasurer, and again, some of them were this discussion of the day, who's nervous about that? They feel they might be adding to their already overworked stretch schedule. How would you ease their thoughts? How would you calm them?

I think I would always start with something small. I think the things that are talked about in the market are, generally speaking, not necessarily very helpful to the average treasurer. Yes, something like cash forecasting is, I describe it as like the Everest of tasks. Because the reality is that you're looking for exceptions. When you're looking at your cash forecast, you're not really thinking about is my balance going to be 3% higher or 3% lower? What you're actually looking for is the, what's the massive payment that's going out that you weren't expecting? Or like, where's the unexpected receipt? So you're looking for outliers. So that's a really hard Use case. And just as in the same way you wouldn't start running by going, I'll tell you what, we'll go and do a marathon. You'd always start by start with something small. And so like relatively easy things to do are just looking at for example documentation or looking at can you build a quick report that sends information to you about three different things or whatever. It's quite easy to pull data from Excel files, other things. So I did an example quite recently, for example, where I've got a Python built Gantt chart. And so I have a. I have an Excel spreadsheet that's got the 10 tasks that need to happen, the start and the end date. And I just update the Excel spreadsheet that gives me an automated dashboard that looks quite sexy. The reality is it took me 25 minutes to build the script. It's relatively easy. And again, to be honest, you can do that with a large language model. Just say, give me the Python script to build a Gantt chart out of this Excel, upload the Excel and it will do it for you. So there's some really simple stuff. I think you have to be a little bit careful about data privacy. So I always work in terms of kind of general themes. If I'm going to share some data then I might share column headings. I won't share what's the actual data underneath. That then means that, you know, system is able to work out what does it look like and you can always be careful about data privacy. So I always work in terms of kind of general themes. If I'm going to share some data then I might share column headings. I won't say it's got these headings and there's 500 lines or whatever share what's in the actual data underneath.

Fair enough, that's good. Connect to the bus.

That then means that, you know, system is able to work out what does it look like. And you can always. And it will then generate a script. You can then use that for whatever you're looking for. What I find is really useful is that this technology is really good for augmenting data. So often one of the things I find is difficult is say it's got these headings and there's 500 lines or whatever and it will then generate a script. You can then use that for whatever. You'll have one data set that has most of the information that you need. So for example, if I've got my bank balance, this and then I've got another data set over here looking for what I find is really useful is that this technology is really good for augmenting data. So often one of the things I find is difficult is that's got my credit ratings per bank and then I've got another data set over here, perhaps that's got my foreign exchange positions. You can use the technology really quickly to crunch all of that together. It doesn't really. You'll have one data set that has most of the information really require any technical skill. Again, it's one of those things, you know, GPT or use Claude, AI or Gemini or any of those tools. Explain that. This is what I'm trying to do. I'm trying to bring this column in related to this particular item that's in the first thing by bank account. I want to sort for what's the credit rating of the relevant bank and it will do that for you. And that's quite a. That can be quite a fiddly thing to do when you've got different data sets.

Yes, yeah, yeah.

Particularly we're getting to a point where you can feed all of those things together and say, one of the things that I've been looking quite hard at is how we build, how we provide tools that allow you to connect all of these different pieces.

You and I talked about this as well, that treasurers have a natural, correct concern, if you like, about data privacy. You touched on it there. But what you're talking about, as you say, is publicly available models, not giving them private information, but it's giving them public information. And as you say, it's putting together the pipe work, it's doing the plumbing for you, it's so much quicker.

The challenge that we have is that generally speaking, we're kept on the outside because of these data privacy concerns. And fundamentally we're in a situation where generally speaking, the tool that we have is a Treasury management system. And if there isn't something built for that, then we're typically looking at Excel or Outlook, neither of which are particularly designed for project management, like large scale data analysis, data reporting, all of those other things. You've now got these amazing tools out there in the wild and my. The reason why I'm passionate about sharing this is that I think that there are great opportunities. I can't explore them all personally, my team can't explore them all personally. So I'm really keen the other treasurers get out there and try stuff and go, hey, hey, I tried this and I've got this great idea. And I think that as a Treasury community, we can really make big strike strides forward. Because actually, we're in a situation where we're not reliant on. Oh, thank you very much, sir. It's on the implementation list. We'll develop something over the next 12 months or whatever. This is something where we can do something now. And in my 15 years in treasury, that's a pretty rare experience. You look. You need. The barriers to entry on most technology are quite high. Everyone's got a phone in their pocket right now. I can go onto a human AI and I can have a conversation with it and it will understand through facial gestures and tone of voice, et cetera, how I'm feeling. And it'll give me advice on things that I could do to relax or whatever it happens to be. I can go on presentation AI and it will produce a PowerPoint presentation for me if I give it a prompt. There's all these amazing tools out there. Particularly. It's really interesting. Even in the treasury space, there are some mega geeks out there who created some weird, wonderful things. There's a cash management GPT which is actually quite good. I tested it out thinking, oh, this. This isn't going to be able to answer the difficult stuff. So I was like, what are the rules and regulations for opening an account in Nepal? And was pleasantly surprised that it actually.

Got most of those.

Wow. So actually there are all these things out there that can act as research help apart from anything else. And so I use things like Claud a lot just for. I've got a paper to write. Here are my 10 ideas. Again, I won't include anything sensitive, but I'll say this is about.

Give us some guidelines.

Yeah.

And we. Sorry. We had Dan Ferguson on the show a while ago and he talked about being insurance for many years and using their own internal chatgpt to get an answer for a CFO for something that would have taken him hours. Now it's seconds. We had Dana scraping her FX exposures.

It.

It's basically enabling you guys and it enables me. I had three original articles today that I needed to update and bring up and it wasn't AI. People have said, oh, AI generated. No, it wasn't. It was generated by me. But I need. It needed a freshen up. It needed reflection. I said, look, can you look at this? What have I missed? And actually there was a 10.1, which I never liked. I said, condense that to 5 and make it more interesting or tell me where I should focus on my efforts. Well, out it came a really good five point summary.

That's What I want and the reality is that AI is a huge area. It's a little bit like describing athletics or something like that or sport. It covers everything from competitive marbles up to ultramarathons. And there's a kind of what you've got is you've got a situation where large language models have made a lot of these different types of technologies more accessible for people haven't necessarily changed the underlying technology. If you listen to data scientists, they're still going underlying. It's still an ARIMA model or the correlation covariance is still being calculated or whatever it happens to be. But it means that you and I can interact in a way that we don't have to be coding experts.

Yes.

And that's the key change is democratize this stuff. Rather than learning a language for three years and being like I now know Python inside that you can have a pretty good go day one. And I think what's really interesting is that there's tremendous potential in these things that we just haven't as treasurers had access to because of the fact that A tools haven't been built for us and B they've been in other disciplines. And so you the barrier to entry in terms of the amount that you have to learn in order to use the technology, et cetera has been too high.

Coming down as you say. I see the evolution latest chatgpt4.0 come out and now it actually finally even though my developer had built it, so it trying to remember my tone of voice, it kept on saying really cute phrases like no, this is not me. Look back at this. But now because it's iterating based on my previous we're going to feed in more of that it sounds and it's giving me more. It's not writing it for me. It's helping me get faster and produce work quicker as well. So we were trying to be respectful of your time but also people's time as well. So we're going to put your LinkedIn details in the show notes. I think there's some great takeaways for people. What are the takeaways you'd like? You've heard the podcast few times so it's guys starting their careers or people a bit further through the request. What are the takeaways you'd give them? James from today's show?

I think three things probably. I think in terms of people starting out try stuff be it for us and be they've been in other disciplines and so you the barrier to entry in terms of the amount that you have to learn in order to use the technology, et cetera has been too.

High coming down, as you say. And I see the evolution latest chatgpt4.0 come out and now it actually, I.

Think one of the great things about people coming into treasury is they've got the opportunity to bring different perspectives and different ideas and that's really fresh. So ask questions, make life difficult. Ask why are we doing this? Because that's how we get better. I think the second piece is if you're trying to progress in your career and trying to take steps forward, don't be afraid to try something different. If what you're. If what you're trying isn't working, it's almost certainly not you, but sometimes it's the situation that you're in. So think about how do I take a sideways step, try something different, try a different tactic in order to get to where you want to get to. And then I think the third thing is in terms of makes somebody different and what makes you exceptional that get.

Finally, even though my developer had built it so it tried to remember my tone of voice, it kept on saying really cute phrases like, no, this is not me. Look back at this. But now, because it's iterating based on my previous, we're going to feed in more of that it sounds and it's giving me more. It's not writing it for me, it's.

Helping gets you to the top. It's an openness to working with others, trying new things and really being thoughtful about. It's really important to make sure that you get the basics. You need to be a technical expert, you need to be a really strong communicator. You need to be thoughtful. You need to be someone who people want to work with and who open to ideas. Give people a chance and see where things get to.

So as you say, be adventurous. There you go in. Don't be afraid to get out of your comfort zone. Brilliant. And yeah openness and keep going. So amazing. Me get faster and produce work with her as well. So we, we're trying to be respectful of your time but also people's time as well. So we're going to put your LinkedIn details in the show notes. I think there's some great takeaways for people. What are the takeaways you'd like? You've heard the podcast few times, so it's guys starting their careers or people a bit further through using sir, thank you very much. We'll put your details in the show notes so it'll expand your LinkedIn network overnight, who knows? Lovely to have a session with you. Thank you very much, sir.

Thank you, Mike.

So I'm welcoming back James Kelly. When James and I, you'll have just heard the podcast, it was amazing. We were talking about AI and Treasury. James was at Pearson before, but things have moved on a little bit. So this is one of our update podcasts. You can go back through our archives. You've got other ones which are great. Have an original podcast, then you catch up with people. This has only been a year since I spoke to James. James, you've sat back in your chair, armchair. Not much has happened really, since we last spoke. Right, over to you, sir.

Yeah, it's been a completely quiet year, completely new normal. I left Pearson, left the life of a corporate treasurer and I seem to have become a teenage girl as I spend my time now waiting for the phone to call, phone to ring, or checking in on proposals and things. No, it's. It's been really interesting. I've set up my own business with. With a guy called Dominic lynch, who's former treasurer, Bitpanda and ghost student. We brought in a data scientist from Canada. The idea was we wanted to be a varied group to begin with and we wanted to be international.

The name of the company now is.

It'S called your Treasury.

What are the takeaways you'd give them, James?

From today's show, I think three things, probably.

And the idea basically is we're called your treasury because the idea is that we want to be a support network for treasurers.

So I think in terms of people starting out, try stuff, be adventurous. I think one of the great things about people coming into treasury is they've got the opportunity to bring different perspectives and different ideas and that's really fresh. So ask questions.

If I think about my team at Pearson, I was really lucky. In the end I was able to bring in a data scientist and I got carte blanche to do a lot of the work that I. I do on sort of automation in Python and things like that, but that was quite a hard slog. I had a lot of fights with it to get access to the tools that we use. It took a lot of engineering in order to be able to bring in the data scientists, et cetera. And really I felt that's not accessible to most people. And I remember a while ago hearing HSBC do a talk on big data and then going, there's amazing information you can get. All you need is go to your IT department, tell them you don't know what you're looking for, you don't know how long it's going to take and you don't know what your budget is. But could they just do some data crunching and there'll be some gold in there?

That's what they do.

Absolutely. We all completely unfunded business cases always land well. So it's one of these things that this kind of offers the opportunity for people to learn about things, co create, use our data scientists, things like that so that they've got access to tools.

That maybe they wouldn't have otherwise make life difficult. Ask why are we doing this? Because that's how we get better.

So the way that we've set the business up we've got in Microsoft.

I think the second piece is if you're trying to progress in your career and trying to take steps forward, don't be afraid to try something different. If what you're trying to, if what you're trying isn't working, it's almost certainly not you.

But sometimes it's the last year we've got all the key tools that we think people need in terms of large language model, Python environment, we've got automation tools and effectively the way that we work is that we help co create ideas with people and then we deploy them on their network so that they're then available for them to use. Everything is designed to be very editable and something that they can maintain themselves so that effectively you're not having to wait for the market to bring all of these ideas to you, you're not having to wait for a TMS vendor to bring something in. And I think that's important because actually a lot of the things that drag treasury teams down are not the things that tmss currently do.

Yeah, you talked about it. I'd re listened to it. I said I'd re listen to our episode and don't even necessarily they've just heard firsthand. You talked about in tmss this standardization of data is great but actually interestingly.

There'S not situation that you're in. So think about how do I take a side sideways step, Try something different, try a different tactic.

A lot of standard data sometimes and it sounded like you said and again you said there was a specific gap with these little challenges and you and I have talked about this. Find a little problem in treasury, get buy in and then you prove it's worth and then oh hang on, we'll invest in that because you're saving money. Oh wow, hang on. Iterate and iterate can you go talk to speak to that for a moment.

Yeah. So one of the, one of the things that large language models like GPT and Gemini and Copilot etc are really good at summarizing information and translate in.

Order to get to where you want to get to. And then I think the third thing is in terms of makes somebody different and what makes you exceptional that gating between different methods.

So you know, things like account opening. Python and a large language model are quite capable of reading a PDF form.

Yeah.

They're quite capable of sourcing information from databases and from their training and that kind of thing to suggest here's how the form should be completed. Using Python you can then get the form completed in the PDF. Some of the models are allowing you to do that at the moment. We're not quite there yet generally certainly with the enterprise ones. So you're in a situation where effectively if someone sends you an account opening form and most of the information's generic, then you can just self serve on that rather than have to go through fill it in. And that moves you from the position of being doer to being reviewer. And I think one of the big challenges for small treasury teams and most are small is that it's really difficult to be both the gets you to the top.

It's an openness to working with others.

In the detailed doer and the strategic.

Thinking trying new things and really being thoughtful about. It's really important to make sure that you get the basics. You need to be a technical expert, you need to be a really strong communicator, you need to be thoughtful, you need to be someone.

Yeah, exactly. And so the more that you can take some of these basic tools which or basic tasks which really are only being done because there isn't a kind of an obvious automation tool that allows you to translate KYCs. Another example the request comes in through email, through forms, all sorts of things like it's the same questions. They just need converting into a standard format and then you can use automation. So there's lots of opportunities to help people with what feel like little wins but actually when you add them up. Yeah add up to a big chunk of time and you talked about.

Sorry and jump in you, you also talked. You know I've talked about this repetition if you like.

Yeah.

A lot of repetitive tasks and treasury.

And you've even just people want to work with and he open to ideas.

Described it yourselves that manual workload for treasury can be say 20% and that's one day a week. And if you could then free that up. You'd have all that extra time.

But just give people a chance and see where things get to.

So as you say, be adventurous. There you go. And don't be afraid to get out of your comfort zone. Brilliant. And yeah, openness and keep going. So what does that look like in practice? Can you give us any examples? I know you have got some examples. You worked.

So the one that springs to mind ice is some work that we did on cash forecasting with a company. Now, this isn't the sort of classic we put in the machine learning tool and nobody had to do anything anymore thing. This was just. We took the existing process and we redesigned everything so that effectively we replicated. What were the business teams doing? What emails were they sending out all those other things to populate the. They were using spreadsheets. Populate the spreadsheets that were coming.

Sir. Thank you very much. We'll put your details in the show notes. So expand your link.

And then for the central team, like how could they make sure that the spreadsheets were received on time? Automated chasers. If something looked odd then we had automated network overnight.

Who knows? Lovely to have a session with you. Thank you very much, sir.

Thank you, Mike.

So I'm welcoming back James Kelly. When James and I made a validation.

So it would send something through immediately to say, Mike, I'm not sure that your submission looks quite right. Would you mind answering this? Yeah, like it. And doing that.

You'll have just heard the podcast. It was amazing. We were talking about AI and Treasury.

James was immediately rather than doing it later makes a huge difference because you're much more likely to get an answer there and then because people are in that mode. Whereas if you wait three hours it sets a precedent and you probably find it's another three or four hours before someone comes back. That's almost the next business day. And suddenly you find that your whole process has been held up by 24 hours because of the fact that there was one glitch. So we took a process that was taking 40 hours a week across six finance staff and we took it down to three hours and the quality of the output was improved. The speed of everything obviously significantly improved. It wasn't a huge lift Pearson before.

But things have moved on a little bit. So this is one of our update podcasts. You can go back.

We weren't having to go through and create brand new data sets or anything else. It was simply through our archives.

You've got other ones which are great. Have an original podcast then you catch up with people. This has only been a year since I spoke to James. James, you've sat back in your chair.

How do we take what we've got and then incrementally build from there? So we've then gone.

Yeah, armchair. Not much has happened really since we last spoke. Right, over to you, sir.

Yeah, it's been a come on. And we've gone, how do we improve the forecasting for the worst performers and all of that kind of stuff. But actually we just started with, okay, what are the things that are causing this to take 35 hours rather than 3 hours? And then how do we cross those out one by one so that we've then got the time and space to go. Okay, how do we make this really good now? And we see that across the board. Like one of my favorite little wins is around tools that just auto import spreadsheets into your TMS or your ERP or Power bi. So that when you've got completely quiet year, completely normal. Yeah, I left Pearson left the life of a corporate. Like most companies that I've worked in have a situation where the big operating entities are on a standard erp.

Yeah.

And then there's always a few that have been acquired or they're in Latin America or something like that. Treasure and I seem to have become a teenage girl as I spend my time now waiting for the flat where they're not on the standard platform. And the reality is that if you sis. Point into court, pay to ring or checking in on proposals and things. No, it's. It's been really interesting. I've set up my own business with. With a guy called Dominic lynch who is 80% automated, but 20% of the data isn't automated. Your process is not automated because that manual process just overrides everything else. Like the reality is that the automated bit, that doesn't take any time, but all the time is on the manual stuff. So the quicker that you can take the manual stuff, put it into the automated process and then just have a streamlined process, the better it is. And having those little tools that will help you achieve the right result every single time makes a huge difference. And it's just, it's being smart enough to make sure that they. If someone changes the format slightly, it doesn't break.

Yeah. Making sure they all work and you work hand in hand. And again I said, I was interviewed recently myself and then I was asked this question and I will repeat it to you actually, because you and I have talked about it, is how.

Well, former treasurer bitpanda and go student. We brought in a data scientist from Canada. The idea was we wanted to be a varied group to begin with, so treasurers generally.

I found a late adopters. A lot of time, they wait till things.

And we want it to be international.

The name of the company now is.

It's called your treasury. And the idea basically is we're called your treasury because the idea is that.

Have happened particularly, as you say, staff constraints sometimes and bandwidth. But also, how are you seeing Footsie 100, Fortune 500 reacting to AI? Are they skeptical? We'll wait till it's proven some are all in or curious or what's the balance across? Because you're out there and I know that you and I have had private conversations about someone that's 100 in and then suddenly they're 5% in and then 5%. What's that like?

So it depends. It varies quite a lot between geographies. So you've got some geographies, for example, in the Middle East.

Yes.

Where the government and the culture is very behind. AI is the way forward.

Yeah.

And therefore people are very enthusiastic. We want to be a support network for treasurers. So if I think about my team at Pearson, I was really lucky in the end, I was able to bring in adopters, Fortune 500, again, pretty data scientist, and I got carte blanche to do the enthusiastic adopters. There are some nuances in that. A lot of the work that I do on sort of automation and Python and things like that. But some companies, for example, don't pay for training, and that's a slightly different dynamic because the tools are relatively inexpensive. But learning how to use them, particularly in a Treasury context, does require a bit of training in order to really get the most out of them.

So when you mean training, do you mean for the individuals to use them or do you mean in general for.

The group or how so groups is better and we'll probably come back to this, but I think we're getting to a stage where organizational alignment, particularly within a Treasury team, is quite important in order to really get the maximum benefit. Otherwise, what you end up with is pockets of disparate systems that don't really talk to each other and everyone ends up a little bit out of whack. The UK is just changing. So I think if you'd asked me this question six weeks ago, I'd have said that people were focusing on other things. They're focusing on refinancings and other pieces. That was quite a hard slogan. I had a lot of fights with it to get access to the tools that we use. It took a lot of engineering in order to be able to bring in the data scientist, et cetera. And really I felt that's not accessible to most says. I think we're now in a situation where it's starting to move up the priority list. It's not yet at the top, but it's one of those things that every conversation now begins with, what are other people doing? People. I remember a while ago hearing HSBC do a talk on big data. And because quite clearly people are going, I want to be able to say, these people over here, they're running ahead. Let's clap on. Yeah. So I think we're probably three or four months away from seeing a bit of a an up.

And is that down to their maturity levels in terms of them as treasury teams? Because. And also all their size, because you got the top FTSE 1/ hundreds and then you drop down the Footsie 250s with two. Maybe one person is the, hey, I'm the Treasury team. You know, how is that?

So where you've got one person, treasury teams, they're super open. It's one of those things that.

Anything to help.

Yeah. You and I are both in an entrepreneurial environment and if you look at small businesses, there's massively high adoption of AI. Because the reality is, if you're one person, like if someone says to you, mike, you never have to worry about invoicing ever again, would you like your accounting automated? Anything that allows you to focus on your key objectives, then that's awesome. Where you get into larger organizations where effectively a lot of these support functions are already done for you and you have a more kind of narrowly defined remit, the benefits become a little bit harder to achieve because you've effectively. The things that you do also have to coordinate with other people.

Yeah.

Now that doesn't have to be super coordinated, but it is one of those things that you're fitting into kind of other people's objectives and that kind of thing. So when you get to the very big organizations, what you tend to find is that firstly, it's much better to deal with teams because again, you just help everybody through on the same journey. And secondly, from my perspective, anything has to be quite tailored for whichever company you're working with because of what is their general environment, what ERP system are they using, what TMS, what email and copilot type system are they using, etc. Because it all has to fit together neatly. So I think there's an interesting piece there, but it is going to. There's amazing information you can get. All you need is go to your IT department coming together and I think the exciting thing about where we are is that tell them you don't know what you're looking for, you don't know how long it's going to take and you don't know what your budget is. But could they just do some data crunching and there'll be some gold in that. So many of the key questions that existed 12 months ago have been answered.

Yeah. Our podcast is sponsored by icd. ICD are an independent.

Absolutely.

We all completely independent platform trusted by thousands of treasury professionals worldwide to manage short term investments. One global treasurer recently told me it was one of the smartest technology decisions they've made. No license fees, rapid setup and tangible returns. If you're looking for a smarter, simpler way to manage your liquidity then head to find out more at our partners page treasuryrecruitment.com forward/partners and find out much more. Let's get back to the episode. And again I did and you commented very kindly on our latest newsletter this week where I said I was just bringing up here so AI and Treasury isn't an evolute, a revolution, it's an evolution. You and I have talked about this, but I think also and it comes down to and I put it in this week's newsletter on the video as well. And when I commented and I said.

Unf funded business cases always land well. So it's one of these things that this kind of offers the opportunity for people to learn about things, co create, use our data scientists, things like that so that they've got access to tools that maybe they wouldn't have otherwise. So the way that we've set the business up we've got in Microsoft, I.

Use AI in different forms. It doesn't write my newsletter but it helps me improve it. It asks me questions and it's it using as a tool, not as a replacement, as a helper, not someone's. Someone said oh well I can replace all this and I get these emails oh we can replace this. I got even one this morning. Oh we can do your LinkedIn. I thought I don't want you to. I want to do these video. I don't want to have an AI avatar do it for me. I want to do it because then it gives me more time to spend with my clients and with you. I was going to ask but some of these treasure professionals listening are still nervous because there's an Emotional thing because we're not robots. We literally not.

How.

What's your approach to helping teams overcome that hesitation or fear of what is unknown to some people and stretched and things? Yeah.

I think we start from a very human place.

Yeah.

And so in a sense, to a surprise to a lot of people, our training courses all begin with nothing treasury related. We work through last year, we've got all the key tools that we think people need in terms of large language model, Python environment, we've got automation tools and effectively the way that we work is that we help co create ideas with people and then we deploy them on their network. So let's just play and explore. Yeah, we give people that they're then available for them to use. Everything is designed to be very. Edit case studies and go. Imagine you're now running the local Tesco and you're going to create an ad campaign or whatever it happens to be, local community campaign. Just things that get people trying different tools for different things so that you open your mind and start to see the opportunities. Because what we don't want to do and we think it drives bad outcomes is if you start with this is my existing process, how do I make sending that email out a bit faster or how do I make consolidating. It's one of these things that the reality is that the technology is there to be able to make revolutionary change within the kind of the framework that already exists, if that makes sense. There isn't like a new risk management idea like fx, marketable and something that they can maintain themselves so that effectively you're not having to wait for the market to bring all of these ideas to you, you're not having to wait for a TMS vendor to bring something in. And I think that's important because actually a lot of the things that drag trade management that hasn't been invented, it's not doing that. But what it can do is if you look at something like a cash management process that still for many years has been built around this idea of one person does a bank wreck, one person checks the cash positions, another person does actual versus forecast analysis, then you do the commentary, then you look at what the positions are.

Effectively you can do all of that.

In one automatically on upload, because the data's there for the forecast, the data is there for the actuals and it can all just, it can spit all of that out, produce a nice report, etc. But if you went at that one bit at a time, it would take you a lot longer to build than if you went let's just do the whole process as one. So it's one of these things that the fun bit is helping people reimagine because otherwise you end up focusing on three teams down and not the things that tmss currently do.

Yeah, you talked about it. I'd re listened to it. I said I'd re listen to our episode and don't anyone listen earlier. They've just heard firsthand. You talked about in tmss there's standardization of data. It's great. But actually interestingly there's not a lot of standard data sometimes and it sounded like you said.

And again you said there was a specific tiny bit. So you go, okay, I used to work for a shipping company, for example. They used to do a lot of bank guarantees. So you'd be there going how can I improve my communication with the bank? Actually you could. Like the whole piece can be re engineered so that. Why not just reimagine it and say let's have a five minute conversation over a teams call or a zoom call or whatever. Ask people some questions, use the transcripts to populate the form to send off to the bank and you can send the video to the bank. So if they want some background like that's the kind of like it's a much quicker process for everybody. You get much more context. But you don't get that if you start with the idea I want to improve the speed of sending something out.

Yeah. Just think of your day to day problem as you say, I've done that sometimes where I just record. Right. I've got this issue and I do it more and more. Give me some ideas about how to stop and then it comes up with something. Hang on, I didn't even think about that. And I know that you and I have talked about the fact that you're with your treasury as a company. You're very open to having those conversations, aren't you? So we will have your details and show notes. You'll just have that five minute chat because then people go oh okay. And this big gap with these little challenges. And you and I have talked about this. Find a little problem in treasury, get buy in and then you prove it's worth and then. Oh, hang on, we'll invest in that because you're saving money. Oh wow, hang on. Iterate and iterate. Can you go talk to speak to that for a moment? Yeah.

So one of the, one of the things that large language models like GPT.

In general, it's not a sales pitch, it's just more because you've been on both sides and actually that was a question. You've been on both sides now you've led internal treasury teams. Now you're an external advisor helper if you coach, if you like. How has your view evolved or changed? Now you've got both. Because it's a great thing for you. You've got both sides.

I think it's a really sharpened my insights. Yeah, it's one of these things that you realize much more clearly what drives being able to do a project or not do a project. And it's quite easy to have a textbook view of. If you've got something good to do and it's got a decent roi, then it will happen. Yeah, that is absolutely not the case. There are tons of those ideas out there. Getting the ROI is just the minimum hurdle to get over. Actually what you need is something that is going to move the needle that people are excited about because it's got to be the project. People can't do seven projects at once. Therefore you're not competing with and Copilot etc are really good at summarizing information and translating between different methods. So you know, things like account opening. Python and a large language model are quite capable of reading a PDF form. They're quite capable of sourcing hurdle rate. You're competing with the ability to for people to do more if that makes sense. Whether that's better support the M and A deal, drive better outcomes in the broader business. It's one of those things that you need to be able to communicate. What is the thing that if we don't do this, we won't be able to do. And that's where if I think back the things that used to worry me were cash management process not being good enough. You say that if somebody made a forecast error, it was often like an hour and a half or two hours before we were able to get money, fix it, transfer it. That's horrible experience because the CFO probably hears about the problem or you tell the CFO and they're like okay, that's fine, like we didn't know about this payment. So like how's it going? Like that period where you're going with information from databases and from their training and that kind of thing to suggest, here's how the form should be completed. Using Python, you can then get the form completed in the PDF. Some of the models are allowing you to do that at the moment. We're not quite there yet generally, certainly with the enterprise ones. So you were just working on it. At that point the CFO is going, surely they just transfer some money and it's over. Is there a problem that I now need to worry about? So actually like the quicker that you're able to resolve the problem, it becomes a micro drama. At that point the CFO just goes okay, they're crap at forecasting, I don't need to worry about will improve that. But actually there weren't really any dire consequences. Similarly, on the FX side you always knew that there was like data out there in some of the businesses that were off the main ERP systems you'd hear things about their intercompany is a bit of a mess and all of that kind of stuff. And you're there going I know there's a position there, I just don't know how big it is and I'm hoping that there's not going to be like a 30% FX movement. And then suddenly I'm going to have to expl explain something like, you know, being able to get into those weeds and go okay, see the things that were keeping me up at night, I've dealt with all of those. So Mr. CFO, Mrs. CFO, we're in a situation where effectively if someone sends you an account opening form and most of the information's generic.

Yes.

Then you can just self serve on that rather than have to go through fill it in. And that moves you from the position of being doer to being reviewer. You don't need to worry about this now. Like treasury is sorted and I think one of the big challenges for small treasury teams and most asked.

So we're gonna again we've already put your LinkedIn diesel but we're approaching the end of the show again as we did the second time round and I said to you earlier that I'd re listened to our show and I said we were really good. We like we were eloquent. It was about you were adequate anyway. So looking back, people will have heard it 15, 20 minutes ago. One of your takeaways if you're a young treasurer was to try stuff and then I was just doing the screenshot. It said important to get to the basics but you also need to be a technical expert and be a strong communicator. Thoughtful someone who wants people want to work with, who's open to ideas. Give people a chance to see where things get to God. Some great advice here and be adventurous now without being too adventurous as treasury you don't want people you're being crazy. Your reflections now and is that it's.

Really difficult to be both the in the detail doer and the strategic thinking.

Big idea.

Yeah, exactly. And so the more that you can.

Take maybe from an AI textbook to wrap up today's show.

Yeah. So I'm going to use an AI angle because that's one of the things that the higher up you go in an organization within treasury, the more important it is that you're really good at getting the most out of some of these basic tools which. Or basic tasks which really your team. It's less about individual performance, it's about the collective that you're able to facilitate. And so you have to be good at delegating, you have to be good at working with others. And if you've got people that you're not getting the most out of you, it's going to be obvious and people will start asking questions. Now, we're now in a situation where most of the AI tools are good enough that you should be using them. If you're turning around and saying, I don't have time to find out how to use this, or this isn't important to me, you've got what's effectively a free tool sitting there.

Yeah.

That can drive significantly improve performance. It is one of those things you're only being done because there isn't a kind of an obvious automation tool that allows you to translate KYCs. Another example, the request comes in through email, through forms, all sorts of things like it's the same questions. They just need converting into a standard format and then you can use automation. It does take a little bit of effort to learn, to get from. I've experimented and I can do meeting notes and that kind of thing to a level where it's able to write reports for you in your style of a really high quality, being able to integrate data and being able to combine data sets and that kind of thing. But if you've got somebody in your team who's got amazing data skills and who can speak multiple languages and who can take notes for you and who can follow up action points and trigger actions and all sorts of other things, would you really leave them sitting in a corner and ignore them gathering dust?

Yeah, use them. Yeah, go on.

So I think it's one of those things. There is a little bit of a call to action there because I think we are now at a point where there's a level of maturity that means that now's the time to get involved.

And if people still need a decent translator and someone that can be their guide.

So there's there's lots of opportunities to help people with what feel like little wins but actually when you add them up.

Yeah.

Add up to a big chunk of.

Time and you talk to. Sorry. And jump in you. You also talked to you and I've talked about this repetition if you like.

Yeah.

Of repetitive tasks and treasury and you've even just reached out to you and you're there to help them.

Absolutely.

Thank you, sir. Thanks very much.

Thanks, Mike.

Today's episode of the Treasury Career Corner was brought to you with the support of our partners, icd. If you're looking for a smarter way to manage your short term investments, ICD's independent portal gives you access to a full range of investment products, integrated analytics and a simple centralized platform built specifically for Treasury. If you head over to TreasuryRecruitment.com partners you can learn more and we'll be able to connect you with the right person. ICD for both you and your business. Many thanks for listening to the show and thanks for your continued support. Described it yourselves. That manual workload for treasury can be say 20% and that's one day a week. And if you could then free that up, you'd have all that extra time. But what does that look like in practice? Can you give us any examples? Because I know you have got some examples you worked on.

So the one that springs to mind ice is some work that we did on cash forecasting with a company. Now, this isn't the sort of classic we put in the machine learning tool and nobody had to do anything anymore thing. This was just we took the existing process and we redesigned everything so that effectively we replicated what were the business teams doing, what emails were they sending out all those other things to populate the. They were using spreadsheets. Populate the spreadsheets that were coming in. And then for the central team, like how could they make sure that the spreadsheets were received on time? Automated chasers. If something looked odd, then we had automated validation. So it would send something through immediately to say Mike, I'm not sure that your submission looks quite right. Would you mind answering this? And doing that immediately rather than doing it later makes a huge difference because you are much more likely to get an answer there and then because people are in that mode. Whereas if you wait three hours, it sets a precedent and you probably find it's another three or four hours before someone comes back. That's almost the next business day. And suddenly you find that your whole process has been held up by 24 hours because of the Fact that there was one glitch. So we took a process that was taking 40 hours a week across six finance staff and we took it down to three hours. And the quality of the output was improved, the speed of everything obviously significantly improved. It wasn't a huge lift. We weren't having to go through and create brand new data sets or anything else. It was simply, how do we take what we've got and then incrementally build from there? So we've then gone on and we've gone, how do we improve the forecasting for the worst performers and all of that kind of stuff. But actually we just started with, okay, what are the things that are causing this to take 35 hours rather than three hours?

Yeah.

And then how do we cross those out one by one? Say that we've then got the time and space to go, okay, how do we make this really good now? And we see that across the board. Like one of my favorite little wins is around tools that just auto import spreadsheets into your TMS or your ERP or Power bi. So that when you've got like most companies that I've worked in have a situation where the big operating entities are on a standard ERP and then there's always a few that have been acquired or they're in Latin America or something like that where they're not on the standard platform. And the reality is that if Your system is 80% automated, but 20% of the data isn't automated, your process is not automated because that manual process just overrides everything else. Like the reality is that the automated bit that doesn't take any time, but all the time is on the manual stuff. So the quicker that you can take the manual stuff, put it into the automated process and then just have a streamlined process, the better it is. And having those little tools that will help you achieve the right result every single time makes a huge difference. And it's just, it's being smart enough to make sure that they, if someone changes the format slightly, it doesn't break.

Yeah, making sure they all work and you work hand in hand. And again I said, I was interviewed recently myself and then I was asked this question and I will repeat it to you actually, because you and I have talked about it is how are so treasurers that generally I found a late adopters, a lot of time they wait till things have happened, particularly as you say, staff constraints sometimes and bandwidth. But also how are you seeing FTSE 100, Fortune 500 reacting to AI? Are they skeptical? We'll wait till it's proven some are all in or curious or what's the balance across? Because you're out there and I know that you and I have had private conversations about someone that's 100% in and then suddenly they're 5% in and then 5%. What's that like?

So it varies quite a lot between geographies. So you've got some geographies, for example, in the Middle East.

Yes.

Where the government and the culture is very behind. AI is the way forward.

Yeah.

And therefore people are very enthusiastic adopters. Fortune 500, again, pretty enthusiastic adopters. There are some nuances in that. Some companies, for example, don't pay for training, and that's a slightly different dynamic because the tools are relatively inexpensive. But learning how to use them, particularly in a Treasury context.

Yeah.

Does require a bit of training in order to really get the most out of them.

So when you mean training, do you mean for the individuals to use them or do you mean in general for.

The group, or how for groups is better? And we'll probably come back to this, but I think we're getting to a stage where organizational alignment, particularly within a Treasury team, is quite important in order to really get the maximum benefit. Otherwise, what you end up with is pockets of disparate systems that don't really talk to each other and everyone ends up a little bit out of whack. The UK is just changing, so I think if you'd asked me this question six weeks ago, I'd have said that people were focusing on other things, they're focusing on refinancings and other pieces. I think we're now in a situation where it starts.