CBI 1-11 | Ordering drinks in Italian

The biggest challenge facing end users today in CPM is data, and I would argue that every CFO and FPNA leader should have on their whiteboard, it's the data stupid. And we can, our studies get into more granularity, you know, I'll give you a little preview of our upcoming trend report. We actually break out data management with data security, privacy, and transparency, but it all comes back to the biggest challenge, is solving the data riddle, until you get that solved, you're not going to be as successful with AI as you would want to be. Hello and welcome to the data and AI culture podcast. My name is Kasten Banger, founder and CEO of BARK, and my guest today is my esteemed colleague Kelly Kassa, she's part of our North American analyst team, and a true expert on corporate performance management. We will talk about AI usage in CPM, the biggest trend of the time in also in CPM. We'll talk about what companies are actually doing with AI, where they see benefits and opportunities, but also challenges. I think Kelly has a great view from her perspective being in the North American market, but also working with global companies, but also in the global BARK analyst network, meaning that she can pull from real world examples, from her daily work with our clients, but also from the BARK research, which gives her, I think, a very good overview on how companies are adopting this big trend, the AI and CPM, and where they see opportunities, but also real benefits when using AI for CPM, enjoy the episode. Hello, Kelly, welcome to the data and AI culture podcast. Hello, Kasten, how are you today? I'm fine, I hope you too. I am great, I had my morning rowing practice on the Charles River today outside of Boston, and I'm rare and to go. That sounds marvelous. So AI in CPM in the Office of Finance is our topic today, because you are an expert on that, and it's a big topic in the market as well. Everyone's talking about it, companies are trying to figure out how that works. Why do you think there's such a big interest around AI or using AI in CPM in corporate performance management? That is a big question with lots of different answers. I think on the very high level, there's so much promise with AI, not just specifically for the Office of Finance, but all sorts of data applications. But then when you get down to the specifics for the Office of Finance, they're still faced with a lot of manual challenges. You would be surprised at how many companies still use Excel for planning and forecasting and budgeting. Even though they might also have a dedicated CPM solution, and when you're looking at Excel, in particular, it becomes very manual. So one of the big opportunities with AI is to reduce that manual effort. Our data coming out of the planning survey 26 talks about the opportunities to gain efficiencies to flag anomalies to reduce manual errors. That's the hard core promise, if you will, aside from the really aspirational prospects for AI and finance. So you mentioned reducing manual work, basically raising efficiency as one of the main drivers. What about quality? What about effectiveness? Is there also a driver that companies expect different outcomes or better outcomes? Yes, but I'll get to the button in a minute, in particular with North America's moving a little more quickly than Europe, North America's at this point more likely to target advanced planning use cases. So they're looking at improving forecasting accuracy, in particular with, or by capturing complex drivers, they're also looking at faster creation and evaluation of scenario planning, which is a very interesting aspect of it when companies buy CPM solutions, they're looking at how quickly can I get into scenario planning and all the good fun stuff. And the reality is, as our colleague Christian Fuchs mentioned recently on LinkedIn, that even companies with dedicated CPM solutions, they want to do scenario planning, but they're not there yet. So AI brings that promise of some faster creation and evaluation of the scenario planning. And then the other aspect that I think is very interesting that we can dig into, and this gets into the butt, is that companies are looking at AI to enable more data and broader use of data in forecasting, sort of the hesitate to say legacy because it's only about 10 years ago. But a lot of those systems were very siloed and finance was in its own little silo. And now the promise of AI and where companies can go can be more high level enterprise-wide strategic planning, but they need to get all that data in the right place and in the right ways. And that's the big butt there. Okay. And so you mentioned advanced planning of something that companies in North America are moving faster into compared to Europe, do you see other differences, different types of behavior maybe in adopting CPM solutions, for example? Yeah. So, well, I should say yes and no. The data says the North American market is moving more quickly and they see more upsides. But the commonality is that whether you're in North America or Europe or the rest of the world, there is a significant issue with a lack of data availability, reliable data data quality, and ultimately a lack of trust in the AI results because if you don't have all the right data that's governed that you believe in, then how do you believe in the results AI gives you, whether it's scenario planning, forecasting, consolidations. You know, it's a lot of hard work to deal with the data foundation and that's the common place that all the companies are looking at right now. Yeah, absolutely. Isn't that a place where AI can also help? I mean, I had actually now quite a few guests that talked about how AI improves data management in general. Do you see this also for CPM use cases or in CPM installations? Absolutely. It is a great use of AI and when you think about what the CPM vendors are talking about AI's, they're talking about agent work flows and this agent talks to that agent, the reality is we're still in the plumbing phase of AI and one use case that I've heard of recently is Unilever, Global CPG brand, they're using AI for their data quality and data prep activities that then funnel into the plans and the budgets and the forecast. So that is a great use case and it's a great place to start with AI. You know, you don't want to start with the idea and we hear this a lot, you know, why do I even need CPM software, can I just throw insert your AI tool at my spreadsheets and you know, the answer is no, it's absolutely no. But start to use AI in those pilots in cleaning up your data and fixing your data and governing your data. Where do you see the limitations of using, let's say, general AI platforms or especially gen AI platforms for CPM, especially for planning, for example, we just mentioned no, we cannot do that but why can't we? What's the problem? Well, I would turn that around a little bit, you know, we all use the phrase or most of us now are using the phrase human in the loop and as you might recall from our did an analytics retreat in greater Denver back in May, Ben Schein of Domo actually said human in the lead, okay? And so one of the challenges is you need to understand your data and what's going on in order to then trust your results. So an example, which is not a CPM example, but a real life example. I took my favorite Gen A.I. tool. uploaded a rowing video to it and I said, "I'm in this seat. I'm doing this. I want a longer stroke. How do I fix the problem?" And after six or seven prompts, it kept giving me the wrong answer. But I only knew it was wrong because I knew enough about what I was doing and enough about rowing to say, "No, these seats aren't behind me. They're ahead of me." And I can go on and on, but I won't. But the thing is that you need a human in the lead to understand if those outputs are right or wrong. You can't just trust that what it gives you is correct. And do you see the role of CPM software changing with now a gen tick, or it's a, first of all, Gen A I being in the mix and finance departments do use it, and are also now with the move to more agentech AI. How do CPM vendors react to those trends? I think it's going to help users get to that promised goal, if you will, of having more accurate forecasts, of forecasting more easily, of scenario planning, where you can understand if X happens, the result will be Y. I think that eventually we'll get to the really sexy part. But it's right now, it's a lot of promise, but it still is a lot of hard work. What do the vendors do? Do they build new functionality in AI supported? Let's stick to the example scenario planning. I mean, that has been covered, I think, for the last, I would say, 20 years. So what has changed? So what has changed is obviously AI. But the thing is, when I'm thinking about what has changed. If I'm an end user, there are some very different approaches to CPM. So you're going to have some legacy tools that are kind of bolting on AI features because all of a sudden it's the hot, sexy, exciting thing. Then you've got a lot of emerging players that are claiming their AI native, some are AI native, some aren't. And I think that makes the software selection process challenging. And especially in this age of AI, AI demos and CPM can look very, very exciting and lots of sizzle. And the challenge for an end user is to figure out what fits their specific need and use case. It's not a matter of which vendor is the best, or hey, this vendor's got bolt on AI. But maybe the core features are what you need. It's sort of like, I use the example of hamburgers a lot. You know, the best hamburger might be the $32 hamburger. If you look at statistics, you know, the certain golden arches is probably best if you look at sales volume. But you might just want a $12 hamburger. And it doesn't mean all those other hamburgers are bad. It's what's best for you. And so when you're looking at the CPM software vendor landscape, you know, you need to figure out is AI native best for me is, you know, more of a legacy comprehensive platform approach best for me is their, you know, specific things that I want to solve in my planning that leads to one vendor over another. You can't just believe the demos. And frankly, you really want to look at how those tools play against your own data. And that's how AI is changing the landscape in relation to selecting your software. Yeah, that makes a lot of sense. I was just thinking about our, or is the AI adoption in, let's say, the leading CPM vendors, is that rather similar across the board? Or is it a way for vendors to differentiate greatly? So also for the customers or the prospects, the people that are interested in it, is that something where they will see big differences how vendors are using AI. I think absolutely. You know, if you, if you look at what the vendors are saying, as I mentioned, you know, agents are running off all over the place. One of the things I learned having been to a number of user conferences this spring is you then on the other end of the spectrum have a lot of end users that are just trying to figure out where to start with AI. And so I could see a lot of differentiation in, in not just how, how vendors appear, but also how they guide and, and sort of hand held their, their customers and their prospects in the AI adoption journey. You know, right now everything's in the messy middle of okay, we've got to start. I think maybe not a lot of companies know what the end goal is. They, you know, they've got the idea. You know, one of the things that we're seeing is companies in North America are moving more quickly on pilots. And part of that is we're also seeing that there's a lot of peer pressure for North American CFOs and FPNA leaders to adopt AI quickly. And so I think we're, it's a very interesting time to, to be in the CPM space because there are all these different factors that are going to be driving how, how users change, how they're using CPM software. Absolutely. You mentioned the peer pressure. So there, there seems to be a pressure to apply AI, but we have increasing discussions that companies are also increasingly questioning the value, the return of investments in AI. Maybe a bit of a pointed question, are CFOs doing a better job in using AI? Or do they run into the same issues, into the same problems that they, yeah, maybe because of peer pressure, they're investing in AI, but then see, okay, many of the use cases actually do not yield the returns they were hoping for. Do you see the same thing like as in every other department of a company or is it a bit different in the office of the CFO? Well, I think if you asked one of our data management colleagues, that question they would, they would argue that data management has much better use cases. I have, I have written about the CFO and FPNA leaders as the AI entrepreneurs. And I believe there are really strong use cases, partially because the CFOs and the FPNA group, they've got access to more data across the enterprise than say other groups would. So if I'm, you know, we'll take sales planning. If I'm doing, or we'll take supply chain, you know, I might have limited data, I might just have supply chain data, whereas the office of finance can get access to supply chain data, sales planning data, inventory data, the hard finance numbers, and the more complete picture you have with your data, the more likely you are to succeed with AI. And so I think they're seeing a lot of good results in applying it. And a lot of it also comes down to, again, back to what we talked about, is eliminating some of these manual processes. So those are quick wins when it comes to AI. That makes no sense. You mentioned advanced planning, often application area of AI, where companies also do maybe new things or can do things better. I wonder about enterprise planning in the term of that for quite a while, we had to trend that set companies often are pretty sophisticated and financial planning, but when it came to operational planning, there was a lot of room for improvement, and also the integration of both, was a big driver. First question is, do you still see that, or are companies now pretty advanced with enterprise planning? Second question would be, how does AI help here? So they're still not there with the strategic planning, and I believe Christian had a LinkedIn post recently on it, that they're getting better with the core finance aspects of planning, but that business level strategic, most companies are not there yet. They're just still trying to, as we say, walk before they run, where they can go with AI is that AI is going to enable planning to become more continuous, and that's not just in the office finance, but across the enterprise. Scenario planning gets faster and richer. In some cases, there are some technology solutions that enable almost real-time access to data, and so if you're thinking about maybe a coffee company that's looking at, I want to do some scenario planning around these x-number bags of coffee are going to stay out soon, and how do I move them? When you have more real-time, deeper, richer data, AI is then going to enable you to do that scenario planning as quickly as, "Hey, the coffee on the shelves next month is going to stay out and how do we prevent product loss?" Yeah. I think that's a good example, but that also shows that I would say maybe AI helps companies in general, but also, let's say, in these examples, to use data and AI in a more operational way, meaning with a direct influence on operational processes, and not just maybe for controlling purposes or reporting purposes. Do you agree? Absolutely. The challenge is that's been the promise for 20, somewhat years. As they say, the proof is in the pudding. It's actually getting to the execution. You have some companies that are so advanced, for example. There was a gas company in, I'm going to make dates wrong, but in the early 2000s, that was using operational data against one of our favorite planning tools to figure out when to bring their oil rigs, back in the Gulf of the U.S., back online after hurricanes, Katrina, and Rita. That is a real hard core operational example of where companies can go, but then on the other end of the spectrum, you've got people that are saying, "I finally hit a wall, but spreadsheets, I need the CPM solution. Where do I start?" That's a big spectrum in between. I'm bringing oil rigs back online to, "I need something." I would posit that AI is going to help speed up adoption of CPM so that companies can start to narrow the gap between the leaders and the laggards. Kelly, you've now been on board with Bach for quite a while. You've taken part in a lot of our research studies. I would like to hear from you about the challenges that our companies are running into. What does our data say or what do the latest studies say? What challenges are companies reporting? I think that's always interesting to talk about that because that's something that we can tell people, "Hey, this is what you should look out for. These are problems that others have run into." Probably, you want to make sure that you address these and are aware of it. Can you give us a bit of an overview? The biggest challenge and actually the data, whether you're looking at North America or the rest of the world, the biggest challenge is a lack of trust in the data and AI results in data quality. Our surveys are saying 50% of users just don't yet trust the data to go where they want to go. That's the biggest challenge. I'm going to show how old I am. I'm dating myself now. Back in 1992, when Bill Clinton was running for president, one of his chief strategist, James Carville, had written on his whiteboard, "It's the economy stupid." I think the biggest challenge facing end users today in CPM is data. I would argue that every CFO and FPNA leader should have on their whiteboard. It's the data stupid. Our studies get into more granularity. I'll give you a little preview of our upcoming trend report. We actually break out data management with data security, privacy, and transparency. But it all comes back to the biggest challenge is solving the data riddle. Until you get that solved, you're not going to be as successful with AI as you would want to be. So really focus on that. That's your recommendation. Absolutely. That makes a lot of sense. First, fix the foundation. I'll make sure that you can trust your data. Let's look at the other side of the coin. When there are challenges, there are also opportunities. What do you see there? What are the opportunities that companies maybe want to pursue or that they would like to see when they use AI in CPM? There's lots of opportunities, again, for agents running off and doing all your hard work and then having a four-day work week. But I think what the big opportunity is that your data foundation becomes a strategic differentiator. I don't know a good example, but think of somebody who presents really well and looks really nicely dressed versus somebody who's just thrown on some sweatpants. If your data foundation is buttoned up and where it should be, then you look much better and you're in better shape to be successful. I think the other thing is we're going to see governance as a differentiator. As AI takes on more planning tasks, the governance of the data is going to be more important. That's a big opportunity. Lastly, I think that as much as there's an opportunity, I think companies should show themselves a little grace. North America, at the moment, we're leading an experimentation in CPM. Global maturity is going to vary. Frankly, the maturity of how you're using AI is going to vary company by company industry to industry. One of the things we're seeing, which is obvious when you think about it, is SaaS-based businesses are moving more quickly to adopt and deploy AI than other companies. That makes sense. But there's a lot of opportunity. I think it's a great time to be in the space because things are changing so quickly. I completely agree. We touched on the topic of agents several times now during this conversation. I think if we want to take a look into the future, so what will be next? What do you expect as a market analyst to happen in the next years? In that market, for sure, agents are very high on the list. But before we talk about that, I'd like you to give maybe us an overview. Where do you see agents at work today already in CPM software? What tasks or topics are there practically solving? That's an interesting question. When I think about the CPM market, frankly, I tend to focus more on the planning side than the consolidation side. But the truth is we're seeing a lot more agentic successes on the consolidation side. And particularly if you look at monthly, quarterly, annual close, those are repeatable manual, arduous, not fun tasks. Again, with the example of Unilever using AI and agentic AI to clean up their data, this is where we're seeing real wins that they can put agents, create workflows with agents in the closed and consolidation process. Take care of all that manual. Check the same check list that you do every month. Your agent can go off and do it. And then that gives you more time to actually analyze the data and understand it. The second area we're seeing is related to that which is anomaly detection. With agents can much more quickly figure out this doesn't look right. And then you have the time to figure out why does it not look right versus you spending all that time trying to figure out where the needle is in the haystack. You know, your agent can easily say, "Oh, there's that needle. What are you going to do about it?" But if you're busy trying to find the needle, you don't have time to do anything about it. Yeah, makes a lot of sense. That sounds like a bright future. So what's your take? Where are we heading with this? AI is getting more powerful by the day, it seems. So obviously we have even more capabilities in the future. How would that affect CPM or what other future challenges and opportunities, especially do you see? I think it presents a great opportunity for people within the finance function. Even as few as 10 years ago, finance, CFOs, opinion leaders, we're still seeing as just the being counters. There's people in the corner office that just care about the numbers and the money. Now, their role has changed incredibly quickly. You know, it's no longer just about spreadsheet skills, but about the ability to understand AI and how to use AI within the finance function and how to how to get AI to make you the the exact finance executive smarter, more agile, quicker. And I think that is actually a tremendous career opportunity, career evolution to go from the person in the corner office that's just counting the beans to I now have the right data, the trusted data, trusted outcomes. to be a strategic advisor on the enterprise level. - That's a great outlook. Thanks so much, Kelly. I really enjoyed our conversation. It was great to hear what's going on in North America, the trends you are seeing. And with that, I can only say thank you for a supporting bark in North America. And obviously, I hope to see and speak to you soon again. Bye-bye. - Thank you so much, Karsten. And if you get to Boston, I'll get you out on the river. That sounds great. Let's do that. Bye-bye.