How FP&A Teams Can Improve Decision-Making with Scenario Planning & Financial Models - Michael

Michael Gould: I think people are still wallowing in spreadsheets and that's a problem. People are, uh, even with the bigger companies, with the planning systems in place, don't necessarily have the kind of flexibility to move things on in the way that they want to and speed of change because of the teams involved in that process. And I think where that comes out in terms of limitations of what people are able to achieve as an FPA function, supporting the business, things like evaluating scenarios.

Paul Barnhurst: Welcome to welcome to another episode of FP&A unlocked where finance meets strategy. I'm your host, Paul Barnhurst. The FP and a guy. Each week we bring you conversations and practical advice from thought leaders, industry experts and practitioners who are reshaping the role of FP&A in today's business world. This week I am thrilled to be joined by our guest, Michael Gould. Michael, welcome to the show.

Michael Gould: Great to be with you, Paul. Really good to be here.

Paul Barnhurst: No, excited to have you. And so let me start just a little bit of Michael's background. But first I'll say why I'm so excited is Michael's one of the, uh, biggest tools, I think kind of a huge one, this industry that everybody knows about. So when you said yes to come on, I have to admit I was really excited because I enjoy, uh, talking to people that have been in this space for a long time. And I think you're over 40 years now.

Michael Gould: That's right, yes. Yep. More than 40 years in this space.

Paul Barnhurst: Now you know where all the bodies are buried, so to speak.

Michael Gould: Certainly do.

Paul Barnhurst: All right, so a little bit about Michael's background and then we'll jump into the interview. So Michael Gould is a technology entrepreneur and software Engineer with over 40 years experience building financial planning and modeling systems. He began his career in the early 1980s on IBM mainframes. After studying mathematics and computation at the University of Oxford, he went on to co create Adatum, a leading early business planning platform and later founded Anaplan, a globally successful enterprise planning platform and British tech unicorn, which listed on the New York Stock Exchange. Today, Michael is the founder of Kaleidoscope, his latest venture focused on rethinking, modeling and planning for modern teams. He continues to pioneer in the space, exploring how better systems can improve the structure of financial models and drive clearer, more effective decision making. So how did you get into the EPM space over 40 years? Obviously you've enjoyed the journey, but kind of take us back to how it all started.

Michael Gould: Yeah, it seems to have kind of got me hooked, hasn't it? Yeah. So I started out, got a job as an intern at IBM. It was a gap year between school and university. Didn't know what it was about. It was a programming job, learned on the job in terms of programming, uh, programming in APL back in the day, goes back a while, not used so much nowadays, but it was used quite extensively at the time. And the system I was working on was, had been developed in house within IBM by a great guy who's kind of my mentor, George Kunzel, who had built a, essentially a multidimensional modeling system for internal use in IBM uk. And so it was a kind of very similar to the sort of technologies that then emerged over many years to come in terms of being a kind of alternative to the spreadsheet as a vehicle for describing what's going on in the business and modeling it and planning it. So that's where I kind of got into this journey, found out about it, learned on the job and took it from there.

Paul Barnhurst: What's kept you interested in it all these years? Obviously you must have found it kind of quite interesting trying to solve that multidimensional modeling problem versus just using a spreadsheet, so to speak.

Michael Gould: Yeah. So I think why it's kept me fascinated is essentially the problem we're trying to solve is how do you represent what's going on in a business? And that is a uh, complex thing. There's a lot of different parts in a business, a lot of different business functions, how they interrelate with each other. There's a lot of data and essentially trying to come up with a representation of that business that's a natural representation of the activities of business in all their complexity and not dumbing it down, not trying to make it over simplistic, but not so complicated that you can't work with it and model it. And obviously when you're forward looking for financial planning, financial modeling, you don't know. You can't model transactional level detail. You've got to model at a much higher level. So just trying to understand that problem, how do you translate what's going on in the business into a effectively a computer model of it that allows people to make good decisions.

Paul Barnhurst: Yeah, and I think you made a great point. Right. You're not going to model or nobody should model at the transactional level. Right. Just the amount of work that would be makes no sense. So it's how do you consolidate it and build something that's still realistic.

Michael Gould: Yeah, absolutely. And represents the real complexity of what your business does.

Paul Barnhurst: So would love to go back I know you helped, uh, found Adatum and then you helped found Anaplan. What led you kind of to that entrepreneur route to starting a business? And what's that journey been like? I mean, Anaplan, well known in the marketplace, one of the, you know, leading enterprise tools out there now.

Michael Gould: Great journey with Anaplan. So I've been, as you said, at Ed Atom, and then we got acquired by Cognos and essentially recognized it was an unsolved problem. The problem which I'd, by that stage been working on for 20 years, this challenge of how do you represent business, how do you model it? Seeing that spreadsheets, which were so Excel was launched in 1985. So by the time I started on Anaplan, Excel had been out 40 years now and counting. The motivation really was seeing that companies big and small still on spreadsheets, still struggling to get this kind of the right framework for modeling business decisions. And even though we'd made some progress with Adatum, which was essentially taking this IBM mainframe system that had worked on previously and taking it onto, uh, initially to DOS and then to Windows and then onto the Web, it still wasn't the right representation of how a business functioned. Anaplan was effectively a chance to just start again, clean slate, try and rethink it. And we moved along, we moved a significant step along the journey with Anaplan. Um, I don't think we got the whole way, but it made a substantial, you know, substantial progress, um, which is what it, you know, how it became established as a, as you know, a leading player in that, uh, space, um, in terms of the journey. Uh, so I left my job at Cognos and I was in a position where I was able to work by myself, unpaid for a couple of years. Um, one of the things I've learned because I've been around this loop quite a few times now, is it takes a long time from writing the first line of code to having a product. These are complex problems. So at Anaplan, it was actually four and a half years from when I wrote the first line of code to when we signed our first customer, which, ah, is a long lead time. Uh, so I worked by myself in a very, um, cold stone barn a couple of miles up the road from where I am now, um, rural North Yorkshire. Writing the core calculation engine showed it to Guy and Sue Haddleton, who invested in the business. Guy came in as CEO and sue started out as in head marketing, and then took another two years with a small engineering team, um, to build the Product out and launch commercially. And so that was a long, long lead time. Got it out and then started to see the success with it and started signing up some really amazing, uh, big name customers, uh, and took it from there.

Paul Barnhurst: Great journey. I've, I've heard the uh, barn part. I've seen that a few places you kind of started in a barn and that's always fun. It's kind of like you know, uh, HP and starting in a garage.

Michael Gould: That's the one. Yes.

Paul Barnhurst: Yeah, yeah, those type of stories. The other thing I heard you saying it, it doesn't surprise me now when I started it, did you know I really started talking to FP&A software vendors about five years ago now. And now I've talked to well over a hundred, I've talked to a lot in the market. And when you said four years, I'm like, yeah, I started thinking of all the tools, they show me a demo, then all of a sudden I wouldn't hear anything for a long time. Like, yeah, we went back and rebuilt the product. It'd be a year later, two years later, whatever, because they'd come out to market, just realize, oh, this isn't competitive or we haven't really solved the problem or one guy was talking to, you know, he got a year and a half in and he started over because he just, he didn't feel comfortable with the product. And so hearing you say that kind of ties with what I hear a lot of these founders say. Everybody thinks, oh, uh, I just need a spreadsheet and some reporting. And this is a simple problem. I think especially after Covid and when I saw so many tools prop up and I'm like, yeah, okay, maybe for a startup, but then they're just going to use Excel, enterprise, anything of real scale. Love your thoughts. It feels like this becomes a very complex challenge to manage the dimensionality, the calculations and build out what they really need to support a business.

Michael Gould: Well, I think one of the things I've seen that's really interesting is the complexity. I mean obviously at Anaplan we were dealing with some of the biggest companies in the world, uh, the likes of hp, Diago, people like that, huge companies. But in many ways the small businesses have the same problems, they have the same complexities, the detail. It's obviously much smaller scale, but you've got a lot of um, the same challenges about supply chains and choices, decisions you're going to make about do you outsource manufacturing or you do in house, do you manufacture sub components or buy them in all These kind of decisions that you have to make and they really are the same challenges. Whereas for small businesses you don't have the resources to bring in to deal with relatively complex enterprise software systems.

Paul Barnhurst: Sure, yeah, it can get very expensive in a hurry. If you're doing a lot of customization. You got supply chain planning, you got sales off, you got finance for using them all in the same tool, whether it's Anaplan or somebody else. I mean, you know this right? There can be a big consulting bill to get that all set up. The engine may be great, but there's a lot of bespoke work, so to speak, that goes on to meet each business's needs. So I'm curious if you could do it over again, anything you do differently at Anaplan, maybe what are some of those kind of key learnings from that journey?

Michael Gould: Really good, really good question. So one of the things about Anaplan was obviously we were VC funded. We got as it got the, after getting the initial product out, started to get a few customers, we got investment from venture capital firms. Uh, I don't think I appreciated because first time I'd been on that journey, I don't think I quite appreciated at the time what that implied. It was in terms of jumping onto a, like a, you know, moving walkway that was just going to accelerate, it had to accelerate. So you're jumping onto a trajectory that requires fast growth, further investment, further growth and you're always striving for the next milestone. Um, which was, you know, in hindsight that, you know, that was the only way we could have done it. It was extraordinary. It was a uh, you know, building the business. We, we grew from um, we went from like 150 to 300 to 600 employees in successive years. So you, and you, you lose a few along the way. So by the time we were at 600, 500 of those employees had only been with the business for like a year or so. You, it's, it's extraordinary kind of, yeah, churn and, and, and uh, and change that you get in that high growth, growth stage. So if I was doing it over again, what would I do differently? Uh, in one way, nothing at all. I think that was the right path for what we were doing then. We couldn't have done it in any other way. But what it does do is it forces you into a more short term perspective on what you're building because you're trying to hit the revenue targets for the next quarter. And the next quarter you've got to get the Big deals in therefore you prioritize the features that the next big enterprise deal deal will make the difference to get that deal across the line in time for the quarter close in time for the year end. And that drives growth, then allows for future investment. But what it doesn't do is um, it's not a path that helps you on a long term strategic rearchitecting uh, of the system. And in many ways what you find is whatever you built in to start with, that's what you've got to play with. And then you're building a lot of stuff around the edges. It's very hard to kind of really re architect things. And so I think the one thing which I would I do differently, probably not in a sense because I don't think we could have done this any differently. But the thing which I would love to do and now trying to do differently is just taking that longer term view to say okay, well where we want to get to, we're prepared to take a bit longer um, to architect the system. You mentioned you've seen people already re architecting and they're only a year or two out and in a way I think that's the right thing to be doing. Um, we didn't have that chance down a plan. We were too busy being successful in the market to do that.

Paul Barnhurst: It can be tough. I know of a tool that's been around nearly 30 years and they did a big architecting project and well, continuing to have customers and be good size, that's hard to do. It's really tough because of, you know, all that goes with that. So it sounds like kind of the biggest thing that the learning and probably what you're taking away with Kaleidoscope is really taking that time to build it, get comfortable where it's at, really doing that architecting, making sure you have the tool you want and going to market. If I understand correctly. I don't think you've done really any VC or any of that type of funding with this one. It's a little bit more uh, kind of allowing you to not have the high growth targets as you try to build.

Michael Gould: Yep. Started kaleidoscope back in 2020 on the back of having had the Anaplan IPO so able to, to to fund it. Take our time and uh. Yeah, try and try and do it. Do it right next time round. This is the Kaleidoscope is my sixth attempt at this. So this IBM mainframe version that I worked on back in the early 80s, I count that as V1 and counting through the different, different attempts. This is, this one is V6, there's

Paul Barnhurst: IBM, there's a datum, there's Anaplan, Kaleidoscope. What's the other two in there?

Michael Gould: It count as two because there was the kind of DOS Windows desktop product and then we rebuilt it for the Internet. So the.

Paul Barnhurst: Yeah, yeah, that's a totally different.

Michael Gould: It's quite a different world and it's a dating contributor. And then there was uh, while still at Cognos, we attempted a new platform which never made it out and learned some, some good lessons along the way trying to, to do it. But it never, that one never saw the light of day. So those. There's one, one more in there.

Paul Barnhurst: Got it. Okay, that, that makes sense. I figured there might be a couple within either IBM or the cog the whole Cognos journey there. So that.

Michael Gould: Yeah, you mentioned, uh, know where the skeletons are. Yeah.

Paul Barnhurst: So tell us about Kaleidoscope. You've been working on that I think since 2020. I mean I know you founded the company to build the tool you wanted now, but kind of just walk through why Kaleidoscope, how you're thinking about it, why it's needed in the marketplace. Just give us the story.

Michael Gould: Yeah, so one of the things that I've seen and going back over the years, uh, at Ed Atom, we were working with much smaller companies than we did at Anaplan and then gradually worked towards bigger companies towards the end of Edatum and with Codeglass. Obviously large companies seeing that the, the problems that small companies are trying to solve are really just as, just as real as big companies in terms of decision making. If you think of the, the impact on cash flow of a decision that you make, if you're a bigger company, you may have a bit more leeway in terms of being able to raise capital or restructure or whatever. If you're a small business and you make a bad decision, you just run out of cash and you're out of business. So in many ways the decision making process is more critical. So what we're trying to do with Kaleidoscope is to take the kind of uh, modeling that allows you to come to good decisions that really has been exclusively the domain of big companies with big implementation teams and bring that to smaller companies initially, we may well go to bigger companies in future. But our initial target is to take that same kind of M modeling expertise domain into small businesses, medium sized businesses, and uh, provide them with a platform that allows them to make good decisions and Specifically to explore possible scenarios, model different assumptions about where the business may go and then be able to evaluate options before you commit to a decision. And yet not all decisions are going to be good, but at least you can understand what the, what the risks and benefits are.

Paul Barnhurst: Got it. And when you say smaller company, are we talking, you know, few hundred employees, you're kind of mid marketed or how do, how do you think about that? Like what kind of. Because at some point, you know, if you're too small, probably doesn't make sense. How do you kind of think about.

Michael Gould: Yeah, so uh, that's been part of our journey over the last couple of years. So we started out looking at very small businesses and there was a reason for that really was to almost um, to set ourselves a challenge to say, okay, well you, we've got experience in the mid market with Adatum, experience in the big companies with Alaplan. Um, if you start with really small businesses you've got two, two things going to happen. One is you've got the kind of threshold for usability is just so high if you can't get them up and running really quickly, really easily, you kind of, you've just got to make it, make it easy. Um, uh, and you've got to make it easy to get data in. And we can come back and talk about that because that sees the real, one of the real big challenges and a big opportunity there. Um, the other reason for starting with really small companies is there's this kind of gravitational pull upwards. Um, so once you've got people selling a product, um, every salesperson on the planet, they never come to you and say, hey, can you take all these features out so I can sell smaller deals into smaller businesses? Um, that's not what a salesperson says. They say, can you put this feature in? Because if you put this feature in, I can get this deal at this big company. And so there's this kind of gravitational pull upwards towards the bigger companies. So we thought, well, let's start right at the smaller businesses, see how we get on, uh, and then take it from there. So we initially started looking at uh, sales analysis, demand planning and stock planning for micro businesses. So tiny companies. Um, anything we didn't, we said, okay, we'll exclude sole traders. We assume a sole trader can just, they're doing it on the back of an envelope. Um, but anyone who's got, you know, a couple of employees upwards and are uh, selling goods on E commerce platforms, uh, what we found was that it's actually um, that that size of business. You people, you, you, you don't have anyone who's technical in the company typically, uh, they're the founder who has a passion, um, product and they've, they've got some ideas and they've started producing it and selling it. You don't have a finance person quite often, uh, and found that that sector just too, too challenging to get into. So we've, we've now shifted up but we, we are still looking at small um, to mid market companies. So essentially companies, once you've got, you're big enough to have a finance team, you've got people using spreadsheets, you've got, you've got, you know, anything from. We're probably targeting more like 100 to 500 company but we're certainly interested in the 50 to 100 as well. Um, maybe a bit smaller than that in terms of, in terms of number of employees.

Paul Barnhurst: Yeah, no that, that makes sense. And yeah, I'm not surprised to see you moved up a little bit. The smaller you go, it's just, it's so, it's very hard in this space from everything I've seen. So, and I'm glad you tackled that. Anything else you want to share about Kaleidoscope before we move on? I want to ask you some questions about fpa, but I give you anything else you want to share there.

Michael Gould: AI is pervasive through everything and one of the things we're thinking about very hard at the moment is looking at what's going to be differentiated in the AI space. Because my perception is that things have shifted and changed even over the last few months. There's something has moved in terms of the capabilities, uh, and the adoption of AI. So I think it's fair to assume now that any platform going forward will have AI to help you with bringing data in and cleaning it up, helping you build your model, helping you uh, get insights from it, either queries to get information out of it or to alert you to things that are going on. Those kind of capabilities, they're going to be across everything. They'll be in, they are in spreadsheets, they're in big um, enterprise tools. And so what's going to be differentiated is going to come down to some very different things because uh, to me those things are now just table stakes. If you don't have that then you're not in the business. So um, then it comes down to, well, is your model a good representation of the business such that AI tools or people and, or people can build, um, a really good model and understand it, trust the numbers. Because that's one of the big challenges. Okay, it's all very well for AI to generate lots of data, but do you trust it? Do you know what it's got? So that's the kind of stuff that we're looking at now is to say, okay, we think we've got a building on the multiple previous attempts at this building a framework that allows you to describe your business really well that actually is then amenable to AI tools to do the same thing, but still got that human aspect that you can inspect it, you can understand it and uh, uh, make sense of it and therefore trust it. Um, and just to give an example, I mean extraordinary the developments recently in terms of AI tools generating spreadsheets. They're getting so good at it, um, which is amazing. But an AI tool that generates 100,000 cell formula in a spreadsheet, how do you know it's done it right? How are you going to figure that out? Okay, it might be great and it may be that we'll get to a point where people trust the tools so well and the tools so good, they just don't make the mistakes that humans make. But in the meantime I think it's a real, that's a real challenge. There could be some anomalies in there that you just don't spot. And if you didn't construct the model, actually picking through it is going to be really difficult. So in a way one of the questions I sort of feel like people will have to ask, and we are asking ourselves as a business as we build out our platform is okay, what's the, what are the, what are the constructs that you're actually working with? Are ah, they a faithful natural representation of what's going on in your business so that you can examine them and understand them and see the, specifically the calculation logic. But the data structures, do they help you? Um, and do they play well in that world?

Paul Barnhurst: FP and a guy here Agentic AI is one of the biggest shifts in finance ever and most FPA leaders are, are still struggling to figure it out. On May 21st I'm hosting the FPA and AI Software Showcase with two of the leading AI agents in the marketplace, Concourse and Sapien, plus leading planning tools Drivetrain and Una AI one sitting real demos Register at the fpnaguy.com FPA Software software-showcase. That's fpnaguy.com backslash fpa-software-showcase See you on the 21st. I think you hit on kind of a key point with, you know, all these tools, building models, whether you're building it in a spreadsheet or whether you're having a build in a planning tool, you know, more so in a spreadsheet. Because every cell is typically a different formula. They're not building them with arrays, at least that I see in general. So you're, you know, you're, you're dealing with a hundred thousand formulas if you have 100,000 cells. And I really like what somebody said. I had, uh, an expert modeler on. We did a conversation. He's like, thing everybody needs to remember is we trade in trust. When you have someone build a model, you trust that it's right. He goes, they don't care if the tool built it or you built it. If it's wrong, you lose the trust. Not AI.

Michael Gould: It is an interesting point, that trust aspect. How do you, and how do you know whether it's good or not almost in, like you say, independent of whether it was built by a person or an AI tool? Um, yeah.

Paul Barnhurst: So you need to become better at auditing regardless because you know, the best modeling houses, you know, investment banking, all those big deals, they have a very strict audit process and you're going to see that develop for AI. And some of it will be another tool checking it. Some of it will be software, some will be a human. Until we get, like you said, one day maybe we get to the level where we're comfortable enough that the errors and mistakes are going to be less than the human we let it build. And we probably do get there at some point given how far we've advanced in a few years. But I don't think we're there yet for most models. I wouldn't trust it at this point without, you know, a review. So I think it's a, it's a fascinating area in discussion. It'll be fun to kind of watch it all unfold.

Michael Gould: Yep, indeed.

Paul Barnhurst: All right, so I'd love to get your thoughts. You've supported FPA software for 40 plus years now. You know, six different tools. How would you describe or how do you think of the job of FPA in an organization? What's your thoughts on that?

Michael Gould: So I think fundamentally it's supporting the decision making process in an organization. Uh, I like to think of organizations as kind of two types of people in there. There are the people who actually do the stuff. So people who make staff who do services, um, they go out and sell it and market it. Because if you've got to market it for people to know about it, you've got to sell it for people to have it, um, build the products, whatever it is, support them. So they've got the kind of frontline people and then in organization you've got the kind of people who are one step removed from that. So um, senior management, finance, uh, uh, hr, it. If you're not an IT company, they're all kind of one step removed. Um, kind of you could just say don't really do anything useful to the business. Um, at least they only do things by being one step removed in that sense. So it's what their supporting role is within the organization that uh, is critical. Um, so specifically in terms of the finance team, what are they doing? Obviously they're providing the information, the insight as to what is going on in the business. But I see it fundamentally as being uh, enabling the decision making and therefore providing the, the, the uh, if you like the, the analysis of potential future states. What if I do this? What's going to happen? What are the alternatives? What are the different paths that as an organization a particular decision could, could lead to and putting that in the hands of the right people. So you have senior management but the, the all the different departments in a company making their own decisions about how they spend their budget, how they, what hiring they do, what investment do they do in plant and equipment, uh, all those critical decisions enabling all of that and critically I suppose tying it all together so that the functioning of the business is a coherent whole rather than um, you disconnected, uh, dragging off in different directions.

Paul Barnhurst: I ah, like what you said, there is, you know, FP and A, it really is to enable decision making. So how do you think, you know, as you look at it, how do you think FP&A is doing at the job? How do you think they've, they've done? From your perspective, do you think the field's done a pretty good job there, Things you think that we need to improve on or what's kind of your take on these kind of the, the FP and A profession on how they're doing?

Michael Gould: I think I'd characterize it as doing kind of as good a job as they can with the tools available. I think the motivation is there, the people want to do a good job. Um, I think uh, people are still wallowing in spreadsheets and that's a problem. Uh, people are, even with the bigger companies with the planning systems in place, don't necessarily have the kind of um, flexibility to move things on in the way that they want to and speed of Change because of the teams involved in that process. Um, and I think where that comes out in terms of the uh, limitations of what people are able to achieve as an FPA function supporting the business is things like uh, evaluating scenarios. So very often when you hear people um, talking about scenarios, it's just, okay, well there's a best case and there's a worst case. And I mean guys, that's just not so over simplistic. You've got a whole bunch of levers that are interconnected. You make a decision on, let's take an example. Um, you got a new product launch, you don't know how much it's going to sell. Uh, you've got to decide how much stock you're going to make. Um, do you buy more or make more? And that ties up capital. What profile of sales, what profile of uptake is there going to be? Are you going to. If you don't have the funds to fund that initial, um, stock purchase or stock production, you might have to take out some financing arrangements. What are your risks around the, the, the upside or downside, depending on how you're on a range of, of outcomes or range of timings as well. It's not just about, you know, especially for the smaller businesses, it's not just about how much you sell or don't sell, but when, when's that kind of curve of uptake hitting those kind of things are enough to make or break a small company especially. Uh, so how do you tie all those things together to be able to evaluate lots of different scenarios and identify risk? And that to me is where I feel like in terms of the role of fpa, the desire is there, the willingness is there, but the tools are not to enable people to do that kind of detailed exploration and analysis of potential future, future states, particularly with regard to this kind of interconnection. You know, okay, product launch, I've got to, I've got a new products, I've got to be able to market them. Am I going to be able to sell them? Have I got the finances, have I got the production capacity, have I got the warehousing to, to store the things, the logistics to deliver them. All those parts of the business get triggered from one decision we're going to make. Uh, you know, we're doing to launch this new product line. And how does that all pull together back into the finance function where uh, right. I've actually got to pull this together into a P and L, a cash flow and a balance sheet and see where we would, we would end up if we make that decision.

Paul Barnhurst: I often hear finance leaders say I know I need FP&A software, but I don't even know where to start. That's why I created the FP&A software Showcase so you could see top tools in action. This year we're featuring Drivetrain and Una AI on the planning side. And for the first time ever, we're adding two leading AI analyst agents on the market Concourse and Sapien. These tools are already being deployed at public companies. You will get to see actual demos without the pressure on of a formal sales pitch. Ask your questions and compare tools all from the comfort of your chair. Join me on May 21st for the showcase. Register for free at the fpandaguy.com FPA Software Showcase. That's the FP&A guy.com FPA Software Showcase see you there. It sounds like the biggest challenge you see is really around the tech and being able to m, manage scenarios, sensitivity, variables, really looking at something interconnectedly and saying, hey, we're going to do decision C, go into this new market or build a plant or whatever that might be. How do we really make sure we understand all the implications? What's that do for cash? What does that mean for inventory? What does that mean for, you know, the team, our customer support team, whatever it might be? Because we all know, you know, any big decision touches pretty much everything in a business.

Michael Gould: Yeah. An example of where I see this kind of thing working well is in the S and op process, particularly in larger companies where you're on a monthly cycle deciding how much production to run. Um, you're bringing together the sales team, the marketing team, finance statistical analysis, a statistical forecast based on historic data. Uh, in order to come to a consensus plan that can then drive your production. But that's a very uh, kind of tightly managed, um, structured process on a monthly cycle with specific events going on through the month and specific meetings in order to drive what's effectively a short term planning decision. What are we going to make of which products in the next period of production? Um, it feels like that same kind of uh, ethos is needed on the bigger decisions and the longer term ones getting all the different functions, each function. So one thing that I really believe is critical for finance is how do you kind of look almost like the coordination. Each part of the business have their own expertise. They understand what's going on. What makes marketing work. I don't understand what makes marketing work. There's kind of a uh, whole world out there of people who understand that what levers you can pull and where investment will pay off and, and what you need in order to have a successful product launch or success or ramp up at a particular product line. Um, but how does that code. It's all very well, marketing, doing a great job on marketing a product. What if the production capacity isn't there? So there's constraint or you shouldn't have been marketing that because, um, there wasn't the demand and the statistical forecasts may have shown you that. So there's kind of tying all those pieces together. And it seems like, uh, a key role of the finance team should be almost having a finger on the pulse of all those different parts of the business, how they connect, and then modeling that in such a way that you can explore what potential future states look like.

Paul Barnhurst: I'm sure you've seen this in your role and with companies how many times and I've been guilty of it. You make a decision and you realize, oh, we didn't think about this or we didn't include that. And it feels like the more and more we connect, the less that should happen, the better the data can speak to each other. And sometimes it's an FPA mistake. I've, I've done a few. I've made my, you know, my error, so to speak. But a lot of times it's just a challenge in knowing everything. You need that help. And so I think. You think bring up a really good point and then you add in the complexity of more of a long term. If you're thinking out a year, it's one thing when you're doing production for a month or quarter. Right. The shorter the time, the more detailed you can be, the more accurate you can be. And even if you're a little bit off, the less risk. Right.

Michael Gould: One month, um, yeah. Re. Re. Re.

Paul Barnhurst: Course correct is a lot less expensive than if you get a year into something and you realize you did it completely wrong.

Michael Gould: One of the things which I, I sort of feel like we should be aiming for in this, this space is you. You can't say no nasty surprises because nasty surprises can happen. You know, Covid wars don't want to go into which, you know, plenty humans are human. Plenty going on in plenty going on in the world or just decisions that don't work out in the way you. You hoped. No. No outside influence, you know, affecting it, but just um, yeah, products that just don't take off in the way that you, you expected that they would. So not saying that there should be. Yeah, no, no, no nasty surprise or no nasty events happening, happening. That's part of, part of life. But in a way I feel like there shouldn't be surprises. Um, because if you've modeled it, if you're modeling a product launch, you should have modeled it assuming it didn't go well. There wasn't the uptake you expected. You've had to weigh up a decision. You've had to make a decision. That's what senior management execs are paid to make the call on those things. And they may get it wrong. But what you shouldn't be in a situation is where it goes wrong and you don't know the results of it are not, uh, unforeseen and you're scrambling trying to model something that you hadn't thought about beforehand. It feels like you should know what the result of that be and almost get the very early indicators. Okay, well, it's not going quite as well as we thought. Therefore we're on track to this scenario that we have already modeled out. Uh, what are our, if you like, our ah, pre planned actions that we can then put in, into play. And obviously things change and you have to rework things. So. Obviously you can get surprises, but it shouldn't be a complete surprise. That's what I'm trying to say.

Paul Barnhurst: Yeah, you should have a good idea there. Like you said, there's always going to be big things, macro things that come out of the blue that you couldn't have foreseen. But within that range of foreseen things, there shouldn't be big surprises. Right. There's always a little bit of that, uh, unforeseen world. And even then we all know at some point a black swan is going to come. You can at least model. Okay, what could that mean for the business if it's a bad or good scenario in a black swan? And how would we respond? You may not be able to know exactly what, like nobody knew, you know, Covid and exactly what that would be like, but you still could have said, hey, a black swan's coming if one comes, that has a significant impact on our business. Here are some different things it could have. How would we think about that? And so I'm, I'm with you. It's that idea of there, uh, within that range of known probabilities, you shouldn't have big surprises. Small ones are going to happen, but there shouldn't be big things. And that gets a lot into tools. But I think there's also maybe a little bit of skilling and I'd love to get your Thought because. Right. Monte Carlo and a lot of things that help you do scenario and sensitivity tend to be more on the math side than a lot of finance people are in fpa, you don't see a lot of the statistical modeling. So what's your thoughts there? Do you think as a profession we need a little bit more of that data science skill? I know AI is starting to fill some of that gap now, making it easier. But just kind of your thoughts in general? Because I've used some statistics and frankly I would have liked to use more if I did it again and learned Monte Carlo and some of those things, because I feel like it's a little bit of weakness. But I'd love your thoughts.

Michael Gould: Yeah, I think it's a really interesting question in terms of whether, uh, AI will move the bar because bigger companies will have data scientists, they'll have, um. Used to be called operational planning. I don't know if it's called that anymore. But it's uh, the um, uh, teams who do this who are specialists. And I think it's needed. And I think the question is, is it something that will. My gut feel is that it will come, that it will bring. That kind of, uh, analysis will become more available to much smaller companies who don't have the specialists. They, they probably need to take it with a bit of caution. But it could at least alert people to, like you were saying, scenarios that they maybe hadn't thought of or probabilities, risks. I certainly see a role for that.

Paul Barnhurst: I agree with you. I think that's where we're headed. I think, you know, AI makes it easier to be a data science citizen, I guess, or citizen data scientist. It doesn't. You still need to know things. And so that's kind of the challenge. There's still a fundamental knowledge you need to have, but you can get a lot further with the basics than you could before is kind of how I see it. But you got to take it with the grain of salt, like you said, because the less you know, the more you can be surprised.

Michael Gould: Yeah, I mean, in a sense, I like to think it's not so much taking it with a grain of salt as more saying, okay, uh, I'm not a statistician, I'm not a data scientist, so I know I'm not going to understand the ins and outs of what confidence levels that model has produced or whatever. Uh, but what I do know is I do know my business and I do know how the business works. And so I can at least put that sort of sense Check on it to say, okay, well there's this possibility that there's a risk I haven't thought about. How does that look in my business and bring that kind of human instinct kind of instinct combined with experience, um, to play onto the input you get from the tools that you don't understand. In a sense, in a way, AI is a whole complete bucket. In a way, it's a similar thing to a, uh, statistical model or data science model that. It's a bit of a black box. But both are a bit of a black box. AI is almost 100 black box because it's all happening inside some, you know, the weights inside some LLM and the tool calls that are going on in the background. When it says thinking with a little spinner, you know, it's kind of, there's something happening I don't understand, I don't know what's going on exactly. And some results come out and I have to assess the results. And in a way what you're saying with things like statistical methods, it's almost. If you're a non statistician, you almost have to take them in the same way. Well, yeah, I don't necessarily understand how it got to it, but I can at least I can absorb and assess what it's presented me with. But critically is the important thing, you've got to, you can't be non critical about it, um, but bring your own understanding of the business and of finance and to bear.

Paul Barnhurst: Yeah, you don't have to understand, I think what you're saying there. You don't have to understand all the math behind it, all the details, but you have to be able to do a sense check. You have to understand enough to look at it and say, okay, this is realistic or no, something looks totally off here, I need to dig deeper or rerun this or whatever. So it's kind of that judgment level that you have to bring regardless of whether you understand everything. Because. Right. Black box transparency. None of us understand everything that's going on behind AI, but we have to be able to look at it and decide, is this, does this look right enough that I can use it? Is there enough here that I trust it?

Michael Gould: And of the bits that I can see, is it giving me visibility, uh, to things in terms of things like the calculation, logic and things like that?

Paul Barnhurst: Yep. No make makes sense. So I want to move into our kind of FP and A section. There's a couple questions I ask every guest. There's two I pretty much always ask. And so I'm Curious what you'll, what you'll say, coming from a different perspective than the typical, you know, guest I have that usually works day to day in fpa. What do you think is the number one technical skill that FPA professionals should master?

Michael Gould: So right now we've been talking about it AI, um, and I think that why it's critical is because, um, I think there's a huge shift happening, happening and some, some people are kind of, you know, at the forefront, you know, embracing it and, and exploring it. Some people are skeptical, don't really believe it. Um, but I think, and I've seen this recently within our business in different areas, um, just being prepared to understand and try almost replacing your own job with an FPO with an AI tool to say, can I get the AI tool to do what I'm doing today and uh, see what works and understand, because I think there's a lot of opportunity, there's a lot of pitfalls and things are going to change a lot. So I would say understanding how that impacts your day to day work and what the opportunities are and what the risks are, are you can't do that without actually just trying it hands on. And it's not that difficult to do. You can just spin up, uh, any of the AI tools and try stuff if you are doing it. And I would recommend, uh, a paid subscription because then they come with a license agreement where they're not. Whatever data you share isn't being used to train the AI model. Just to flag that one up just on behalf of anyone who's thinking about doing this, if you stick all your company finances into an AI tool and you haven't got a subscription with a zero data retention policy, you have just provided the AI tool with some learning data for the next time round. So just, you need to be cautious and look at the data retention policies. But using tools to build a spreadsheet, using tools to drive the other, whatever other tools you're using, or just to kind of, um, explore what, what it can do for you. And to me, um, that's a very different answer from what I'd have given even six months or a year ago just because of the shift I've seen, really in a space of a few months, the level of reliability of what's coming out, the ability of tools like we've talked about, generating spreadsheets, um, you've at least got to understand what it's capable of and keep up with it, because what it can do now, you might have your best efforts and think, okay, that was a load of rubbish. It didn't do what I want. I could have built that myself with far fewer errors and done it. That might not be true in six months time or even three months time. And so keeping current on that to me is the number one technical skill that FP&A professionals should be investing in right now. Which is in a way it's interesting because it's not an FPA skill. It applies as much. I've had similar conversation with uh, people on my design team at Kaleidoscope, um, where um, even six months or a year ago if we wanted to do a working prototype we'd write some code to knock up a working version of something just so we could put it in front of people to, to try it out, get a bunch of engineers building it. And we don't need to do that now because we can just use tools that will generate a really convincing UI that you can try out with people. So suddenly the ground has shifted and the designers uh, they can try uh, out prototypes that they couldn't do even six months ago on a very tight turnaround. And so I think just, just keeping, keeping abreast with, with that and seeing how that impacts your, your, your day to day work. And I'd say do that almost do it with everything. Do it, do any, any task. You know, if you're in accounts, could you do reconciling your bank statements? How do you um, how can you automate that and just see what happens and often be, be surprised.

Paul Barnhurst: It's a great point on the uh, AI. It's an answer I expect to see more. I think I've only had it maybe the second or third person so far. But you know, I've been doing this for a couple years and I think we've all seen that become more and more important.

Michael Gould: I think there's something, something is, in my mind something has shifted literally in the last few months experiments that we were doing um, last year and they're getting just very, very different results. I mean I'm a software engineer, um, by background, I'm not a finance person trying, you're seeing what the, and that's the space where, because AI tools are built by software engineers, that's the first thing that they've tried to crack is because the people who are building it understand the problem they're trying to automate. Um, so that's almost a kind of leading indicator on where things are heading. And it is quite extraordinary what tools can do in terms of understanding a complex code base, making Changes quite successfully and safely and presenting you with reasons why those changes were made. Uh, it's extraordinary. And that wasn't, I think that was true that long ago it was people trying it and the people who are really enthusiastic would uh, make progress but normal people wouldn't. Um, something shifted and so I say that's the number one technical skill to invest in all.

Paul Barnhurst: Ah, right. What about soft or human skill?

Michael Gould: Coming back to what we talked about earlier in terms of this kind of coordination. So I feel like two things really. One is that just recognition. Understanding that um, the finance team. Quick anecdote. One of the people I worked with back at, back at um, uh IBM, he used to say well finance, we made our numbers. What the rest of you guys doing, it's like. And uh, but you know one level finance don't. They don't contribute anything to the business, they don't sell stuff, they don't make stuff. Um, so um, understanding. Okay, so what is your role? It's that supporting role in terms of the decision making of the business. So in terms of the soft skills, I feel like the, this kind of um, making the connections is between the different people and trying to make sure that the, you know, almost enabling the communication don't get lost inside the technology. Uh, but understand the extent to which what you're doing is enabling different parts of the business to talk to each other, but not by not talking but talking numbers to each other. Because at the end of the day it's going to have to boil down to specific concrete things. How much you spend, how much you produce, how much you store, all those, how many people you employ, what you pay them, all those decisions. Uh, but acting as that kind of coordination in whatever job you're doing because you're working on the financial models but you've got touch points across the business and so there's no reason why you shouldn't be more than just the, you know, the, the number cruncher who kind of pulls this stuff together by burning the midnight oil hunched over your spreadsheet. Um, it's. Yeah, I feel like there's an opportunity to do something that's far more um, contributes a lot more to the, the ongoing success for business than, than just that.

Paul Barnhurst: Thank uh you appreciate that as a uh, I like both of those half asses because I'm curious as somebody who uh, very much focused on multi dimensional modeling and seen Excel survive for 40 years now and continue to be arguably the most used software in the world, what do you see as its greatest strength? If you had to list one and maybe its greatest weakness.

Michael Gould: So I think greatest strength, there are many. I mean, it clearly wouldn't have survived as a tool, uh, if it hadn't got some extraordinary good qualities about it. I think it's a combination of the fact that it's so quick and simple and easy and natural to get started, you can open up a spreadsheet, you can just type in some stuff or copy and paste some stuff in, do a calculation, which you only, uh, need to learn how to point and click, and you can start to build something. So you can get up and running remarkably easily in expert hands. It can do you. You're almost. You're basically, uh, kind of almost unlimited, I'd say almost. Because there are some big caveats to that, because you, any, any experienced Excel user will understand. They, they can push it so far and then there would be another level of complexity that wouldn't just be a bit more work work, but it would completely become completely, uh, infeasible. So there's a, there is a ceiling on the complexity that Excel can handle. It goes quite a long way. And especially if you start writing macros and things, um, then, yeah, you, you can start to, to push.

Paul Barnhurst: It's amazing how far you can push it.

Michael Gould: You can push it in the hands

Paul Barnhurst: of a capable person.

Michael Gould: But, yeah, and you hit, you hit a limit, but you don't, you don't hit a. It's like you don't hit a brick wall. It's more like you hit a curve that's going up steeper and steeper. Do you see what I mean by the analogy? It's like, yeah, you can get a long way and then it gets harder and the really good people can get a bit further, but it's getting harder and harder and harder. But doesn't actually. There isn't like a hard stop where you've got a tool where it says, okay, well, no, you just can't do that. It's not supported in the tool. Um, you never hit that kind of a, kind of a brick wall. Um, it's incredibly flexible. You can apply it to any kind of problem. Um, so I think those are the strengths. Um, very easy to share and you can email spreadsheets around, so it's great for collaboration. Conversely, that's nightmare for the poor people in FP and A who try and pull the whole stuff to get back together again. Um, greatest weakness, I think, is the, fundamentally, it's not a good representation of your business. If you think, what are the constructs In Excel, in a spreadsheet, um, obviously there are online spreadsheets too. Um, but the constructs are. You've got a workbook which has got sheets, it's got rows and columns themselves, a grid. And your business doesn't work in terms of sheets and rows and columns. Um, your business deals with products and product lines and employees or people, contractors and employees, customers, um, and numerical metrics. So it's basically dealing with a different set of constructs and you're using a spreadsheet metaphor to represent those. But in Excel, um, in a spreadsheet there's no meaning attached to a number. So if I type in 5000 in a cell, the only reason it has any meaning is by looking typically to the left and above and seeing what other bits of information have been put in the headers in the same column or in the same row. Um, and maybe the sheet name as well, or maybe a header somewhere up in the top left. The meaning of that number is only there by inference. Um, remarkably, the AI tools are beginning to get quite good at figuring that stuff out, um, and therefore understanding what's going on. But there isn't any kind of coherent data structure that says, okay, that was a sales figure for this product, of sales forecast for this product in this month, uh, within this region. Um, it doesn't have that information. The information is all implicit. And depending on how tight you've been about constructing the thing, it's quite, um, may or may not be good. You can do it without the labels in there, if you know what's what.

Paul Barnhurst: Sure.

Michael Gould: So I think in a way the weakness is the fact that it's not a close representation of your business. It doesn't deal with the lists of things that you care about, that the people, the products, the locations, all, um, the fundamental metrics, drivers, ratios, all those things that you're interested to all your accounts from your accounting system. Um, those constructs aren't actually represented in it. And then there's obviously a whole bunch of other weaknesses in terms of things like, um, collaborations, sending a bunch of spreadsheets out and everybody inserts a row and it goes all out of, you know, changes the 4 milli or whatever and all that fun and games that any FPA person will be only too familiar with. But that's a, that's a more m. Kind of just practical logistical challenge.

Paul Barnhurst: We've all been there on, on those, so appreciate that. I want to just ask you one or two questions, kind of get to know you a little bit better, help our audience get to know you and then we'll wrap up here.

Michael Gould: Yeah, Great. Thank you.

Paul Barnhurst: First, what do you like to do to unwind? What do you do in your spare time when you're not working on Kaleidoscope?

Michael Gould: So firstly, uh, I'm a Christian. My faith is really important to me. That. Yes. It's not just outside. I see that as something that I try to live out both in work and outside work. I play the guitar, love guitar playing classical guitar and acoustic. Um, I have a little folding guitar. So when I was a Downer plan, I was traveling. I was in San Francisco about every, every six weeks or so. And also trips to other parts of the world. On sales visits, I always had a little travel guitar with me to carry around. So I played the guitar and I started running. And this was after kind of winding down Sanaplan and managing to get a bit more time and trying to, uh, undo a bit of the damage of 10 years of flying around the world, spending too much time in airports and hotels. So I run, um. Yeah.

Paul Barnhurst: Okay. Grief. And then I know. Appreciate sharing, you know, the Christian party. I know some of that. I know you, uh, you and your wife have done a lot of fostering of young refugees. Share a little bit of that experience. How that kind of come about. Would love to just share a little bit.

Michael Gould: Yeah. So we have a big family. We've got four birth children and three we've adopted at the time of the Syrian refugee crisis, which was, I guess, I think about 10 years ago now. Um, we saw images of terrible things happening and we basically said, we can't just sit by and do nothing about that. We've got space in our house, we've got the means. We decided to start fostering and specifically to foster unaccompanied asylum seeking children. And we've been doing that for the last. So we had to go through. We signed up through a local authority, got the training and did that. So we've been doing that for the last nine years. Um, and it's been a wonderful journey. I mean, we specifically, we do fostering for teenage girls who've arrived in this country either as refugees or as asylum seekers. Obviously, you know, it doesn't take much imagination to guess what the experience of girls who've been traveling alone across Europe is like. They come, you know, they're very traumatized, have had a really tough time. It's just wonderful to be able to provide a place where, you know, a big house where they can be safe, they can get back on their feet, they can get back, back to school, get back to college, learn skills. And we've had quite a few now who've moved through now. They're safe, they're happy, they're living independently, making a life themselves. That's just a real joy to see. And, um, yeah, in terms of. You asked what I do in my spare time. Well, we've currently got eight people besides my wife, myself in the house, so it's a busy household as well.

Paul Barnhurst: Is there really any free time with that many in the house?

Michael Gould: Right, yeah, there's a lot going on,

Paul Barnhurst: hence why you're out in the barn, so to speak. Speak.

Michael Gould: Yeah. So I have this little shepherd's hut down the bottom of our field where I hide away, so I get on with the business.

Paul Barnhurst: Understand that one. Thank you so much for joining me, Michael. It's pleasure to chat. I loved hearing some of the backstory and just how you think about these things and all the work you've done. I mean, one of the experts out there, obviously, in this space, with 40 plus years of experience and, you know, before we, uh, finish here, you know, if someone wants to learn more about Kaleidoscope or maybe get in touch with you, what's the best way for them to do that?

Michael Gould: Best way would be. Well, obviously, go to our website, kaleidoscope.com, drop us an email. Hello, kaleidoscope.com. the thing we're particularly looking for. So we're not launched commercially yet, the thing we're looking for. We'd love to talk to people who are particularly people in finance teams who are struggling with spreadsheets. If you're in a finance team in a small business, we'd love to have a chat to understand what are you looking for in a system? What are the tools you would love to have to help you do your job better? We're busy building something and we'd love to just hear from people who would like to dive into an early adopter engagement, participate in the process of building this next platform is an opportunity to jump in early and have a bit of influence about what we build. So, yeah, love to hear from people.

Paul Barnhurst: Well, great. Hopefully you do hear from some people. Really excited to see what you end up building and see the product when you're ready. And thank you again so much for carving out some time. It was a pleasure. Pleasure chatting with you.

Michael Gould: Great, thanks, uh, Paul, really great to spend time with you.

Paul Barnhurst: Thank you. That's it for Today's episode of FP&A Unlocked. If you enjoy FP&A Unlocked. Please take a moment to leave a five star rating and review. It's the best way to support the FP and A guy and help more FPA professionals discover the show. Remember, you can earn CPE credit for this episode by visiting Earmark cp, downloading the app, and completing the quiz. If you need continuing education credits for the FPAC certification, complete the quiz and reach out to me directly. Thanks for listening. I'm Paul Barnhurst, the FPA guy, and I'll see you next time.