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How CFOs can harness the transformative power of AI in finance
In this episode of the Better Finance podcast, Glenn Hopper joins Myles Corson to discuss digital transformation in finance and benefits of seeing AI as a tool rather than a threat.
Related topics
In this episode, we delve into the transformative role of the CFO in the rapidly evolving landscape of finance, shaped by the advent of digital technology and artificial intelligence (AI). Glenn Hopper, CFO at Eventus Advisory Group, shares his insights on how the traditional responsibilities of CFOs are expanding beyond mere financial reporting to encompass strategic guidance and predictive analytics.
The episode provides an understanding of how CFOs can become pivotal change agents within their organizations. It discusses the importance of embracing technology and AI to not only enhance forecasting and metrics reporting, but also to drive informed decision-making processes. The episode also explores the value of leveraging data from a variety of sources, beyond financial data, to gain a competitive edge.
The episode also explores the reasons for CFOs to conduct thorough reviews of business processes across all departments and explains how identifying inefficiencies and optimizing data flows can help to improve an organization's overall performance. Plus, it offers some insights into the potential opportunities and limitations of AI in financial planning and analysis (FP&A).
Finally, the episode addresses the strategic leadership skills required by CFOs in this age of digital transformation. Glenn shares insights on the need for CFOs to understand data science principles and responsibly leverage AI tools to enhance organizational efficiency.
Key takeaways:
- Understand the evolving role of the CFO beyond traditional financial reporting, and how it can include strategic guidance and predictive analytics.
- Discover insights into how CFOs can embrace technology and AI to enhance forecasts, metrics reporting and overall decision-making processes.
- Understand how CFOs can review and improve business processes across the entire organization, not just within finance and accounting.
- Develop an understanding of the potential and limitations of AI in FP&A.
- Recognize the strategic leadership skills required by CFOs in the age of digital transformation.
For your convenience, full text transcript of this podcast is also available.
Glenn Hopper
“If you're not using it now because you say, well of all the concerns, I'm worried about trust, I'm worried about bias in the models, I'm worried about compliance and all that, keep those cautions in mind, but go interact with it. Understand what it is, study it and learn the basics of how AI works.”Myles Corson
That was Glenn Hopper, the CFO Director of Eventus Advisory Group. I’m Myles Corson from Ernst & Young, host of The EY Better Finance podcast. A series that explores the changing dynamics of the business world and what it means for finance leaders of today and tomorrow by sharing insights from global leaders on key topics affecting the world of corporate finance.Corson
Glenn joins me today to discuss the transformative impact of digital technology and AI in reshaping the traditional CFO role within finance. He shares his insights into the necessity of understanding business processes and the promising potential of Generative AI. So, without further ado, let's get started.Corson
Welcome, Glenn. Thank you so much for joining us. It's great to have you on. As we get started, perhaps you could give us a little bit of background about yourself and how you've got to where you are in your career.Hopper
Myles, thanks for having me. I'm glad to be here. I guess my career, I'm old, so I could just spend the whole, the entirety of this podcast talking about my career. So I'll try to sum up. I came up through the ranks, not through audit and public accounting the way a lot of CFOs [Chief Financial Officer] do. I came up through Fin Ops [Finance Operations] and FP&A [Financial Planning and Analysis] and started out in Telcom in very early stages in the late 1990s, early 2000s, kind of moved my way up in Telcom until I left that company. It got too big for me at US$600 million or so in revenue and I felt like I needed to get back to the startup space where I took my first CFO role, and I served in the CFO role for several SMB [small and medium-sized business] businesses over the course of about 15 years.I moved from telecom to retail, to tech, to legal tech to service businesses and in that time it was either private placement or private equity backed businesses, and I kind of carved out this niche where I was coming in to help businesses sell and in the course of my career, I've probably done 13 or 14 transactions both on the sell and buy side and because I was always working to try to sell a business or integrate a business post M&A [Mergers and Acquisitions] I really started to embrace technology early on and very early understood the power of data and was doing everything I could to collect data from whatever sources outside of financial sources and figuring out ways to use that in my forecasts and my metrics reporting and in doing that, I saw early on that this was a lot different than a lot of my finance and accounting peers were, but I started to see this vision of where the CFO could take reporting and how we could expand beyond the financial statements and start adding value to the company.
So, I've always been a big proponent of the idea that a CFO can be a real catalyst for change in organizations, focusing on predictive analytics, strategic guidance rather than just the historical reporting. So, that's about as short as I can give you the last 25 years or so.
Corson
That’s alright. It's very impressive. There's obviously a lot to unpack there. So, I appreciate you sort of giving us the background and sort of a diverse history you've got and obviously, I think one of the things you're known for is digital transformation, what that means for finances that you've touched on. Perhaps you can go back and then start there. What was it that really captured your imagination around the potential for digital transformation in finance and what it can mean?Hopper
I don't want to throw anyone under the bus here. But it's been a couple of decades since this happened, but it was, I think a lot of my career has been necessity is the mother of invention and early on in my career, I figured out that the way that I was going to move up was I was going to outwork and outhustle. And I was putting in 60-70 hours a week and taking on more and more tasks and keeping up with everything on spreadsheets on my work laptop, and finally at one point as a reward for all my hard work, I got an employee, which was great, but I didn't get to pick the employee and it was a great guy. So, I hope he's doing well, but this guy was an old school and I'm old school, so this guy was ancient compared to me, but he was a procurement clerk who, this is back in the early 2000s. So, picture those big CRT monitors on the desk and the big desktop computers and he had one in his cube and it never turned on. He actually had a paper ledger and he was tracking.Hopper
So in this role, I was tracking this finance guy. But I rolled up to the COO [Chief Operating Officer] and I was tracking all the capital equipment purchases and telecommunications. And we'd have pretty big orders from our vendors and I think our capital budget was something like US$15 million or US$20 million and we've had a lot of pretty big investors in the company and they were beating us up because this was right around the 2000 that the bubble burst and crash and we were getting beat up pretty good. And we we're doing everything we could to stay within our capital budget. And we get to November, the weekend before Thanksgiving and my paper ledger employee comes into my office and says I just found a duplicate invoice and I wasn't tracking this and turned out it was a US$1.5 million invoice.So right there, we're looking at a 10% budget blown when we're getting ready for this board report at the end of the year. Complete nightmare. It ended up working all through the weekend trying to salvage the thing and at one point I thought I guess I could go become a high school teacher because clearly finance and accounting isn't for me. Anyway, we survived that and after that happened, I realized I'm never going to find myself in this situation again. So, I immediately started finding ways to track and to automate and to have this kind of visibility that I didn't before and it is really, I think back to that moment and how terrible that weekend was and just having that big of a miss and it's really, it’s kind of my origin story for my approach to finance and accounting ever since.
Corson
I could see why that would be a catalyst for some change. It's remarkable. I could eat that story. Yeah, I think you said starts in the early 2000s, right? So you think about the journey we've been on in finance in 20 years to digitize and there's been such a rapid growth curve. As you look back on that 20 years from that going back to paper ledgers through to where we are now, what do you think have been some of the major events and what have been the major disruptive technologies that have really helped finance executives to deliver on some of that promise that you talked about in terms of the business partnering and the value creation?Hopper
Prior to that we were on I think Great Plains back then was the accounting system we were using, and I think about how advanced that was for the time and how it opened up things. But I think the move, I mean you look at what I think NetSuite started in the cloud maybe in the late 1990s and you think about the internet itself was in the mid-1990s, the origin of it and I think about these early systems and running the accounting platform on a small server in an office or on somebody's desktop computer and then moving to cloud-hosted applications and how significant that was and one: because the software just kept getting better and better, but the other was people being to access general ledger data and other accounting data from multiple locations and it spread out.It was a democratization of data for the first time maybe and then we went through and the compute got better and the software got better and then really the last decade, ERPs [Enterprise Resource Planning] are great and that's been a big change in the evolution of ERPs, but also all the SAS [Statistical Analysis System] tools that are out there that are automating things and I think the idea that it's not just these behemoth companies that are controlling all this, that there's these companies that come along as a startup and sort of revolutionize and change things in the way that we've been able to integrate all these different SAS tools for everything from close management to APAR [Annual Performance Assessment Report]. I mean it's hard to think of a function in finance and accounting that hasn't been greatly automated thanks to these tools. So that's been huge and I think maybe not widely adopted but certainly among the Fortune 100 companies and large enterprise companies using machine learning in forecast has been around for several years and being able to use it for fraud detection and things like that and I think right now we're on the cusp of the next great wave that I think is going to be as big if not bigger than the internet or cloud computing or any of that will be the generative AI does to the business world.
Corson
Your background is obviously in more of that startup rapid growth type of environment and one of the benefits that those type organizations have is they don't have the legacy and the history, right. So, as you sort of reflect on your experiences and if you were setting up the finance organization in a rapid growth company now, what would be some of the lessons from your collective experience you'd say were the important ones that you'd be thinking about getting right as you were sort of starting with a cleaner sheet because it's a very different environment there from obviously the Fortune 100 type organizations you met, whether it's just a massive legacy to have to address before you can move forward?Hopper
I think about that because I think now a lot of the clients I work with, they've been on a certain piece of software for 20 years in some cases, in some of the older ERPs that are out there, and you think about they've built their entire finance and accounting practice basically around how they use this software. So, they get very ingrained in what they're doing and you kind of stop thinking about the process in and of itself and you think of the process only as existing within the software. So, the idea that you're going to come in and rip out this old ERP or whatever accounting system and drop in a new one, I mean, people do it all the time, but it's not, you can't eat the elephant all at once. It's incremental and so I think about the challenges that you would face if you were with this old massive system that you'd have to change versus in the startup world where I lived and for either of them, I would say never start with a certain piece of technology or the software in mind. I would say you have to look at your processes and audit and this is, I don't think it will sound as strange as it did a few years ago, but it sounds a little bit overreaching for a CFO, but I'm not talking about processes and procedures just limited to what you do in finance and accounting. I think we need a broader understanding of the business and because all these data points are tools that we can use in our KPIs and in our forecasts and in making strategic recommendations.So, I think an audit of the process from sales funnel, from lead and prospect to a deal that's won to how you onboard a client to the software you’re moving through and looking at and understanding what data you collect along the way. ow that data flows from one system to another or if it doesn't. If you're just dealing with siloed information and if you are dealing with siloed information where you could have duplicate information or where you could have issues with not knowing what is the source of truth. So I think you have to understand the entire landscape and understand the human flow of it and just look at it like going back again, showing my age, but going back to like Kaizen or continuous improvement in understanding where your inefficiencies are, where your bottlenecks are, where data is getting lost and looking at all that and then you know what you're solving for, you know what works well and what areas need improvement. So, rather than just throwing a piece of software at something or a new ERP at something, really define those processes and then you can plug in the software that meets its needs and I've been in situations like this where you have the sort of build versus buy situation and you can weigh that and see what's out there, but you're not trying to make yourself the best user of whatever software. You're trying to let the software work for you as part of the digital evolution to make things more efficient and streamlined.
Corson
I think that's a great point. In this role of data steward, data custodian, I think it is one that will be really important for finance leaders going forward. Particularly as we continue to the proliferation of the volumes of data, getting the governance and the strategy right is going to be so key. I think, to your point, really understanding the whole data lifecycle through the organization, and then actually, do you start to rethink a more data-centric view of how processes and organizational models. So again, you think where data gets sourced from, whether it's from ERP, other technology sources, unstructured data sources internal to the organization, the external sources of data, how do you pull that through? How does it get used in finance? But what are then the downstream uses and do you understand all of those points of connectivity and how do you make sure, to your point, some of those structural inefficiencies are addressed and the thought about as you kind of architect the next generation technologies. That I think is also pretty foundational to getting the real benefits of AI [artificial intelligence] and GenAI [generative AI], right, because I think it's so important to have robust data to be able to inform their models. This brings us back to the AI conversation. What are you most excited about in terms of the opportunities from AI in the finance organization, but also more broadly?Hopper
Going back to my career that has been in the SMB space, thinking about businesses that are under US$50 million or so and don't have resources and the groups that I've worked with trying to get budget to develop some software that we need for back office or trying to buy software that I say we need for back office. It's been hard to get those resources. So, going back to that, necessity is the mother of invention, I'm a terrible coder, but I'm good enough that if I am trying to figure out how to make something efficient, I will figure out how to run some weird little app on my desktop to automate steps in a process. So if I think about the data and how it's used and the tools that we've had to use, it's been very clunky for someone who isn't a great programmer, or if you don't have a data science team or these tools inhouse or these resources inhouse, it's been very hard to do real data science and be able to do things with data because it's just the barrier to entry as my Master’s is in Accounting or Finance or whatever.I'm not a computer scientist, I can't get access to this. What I'm really seeing with generative AI in particular right now is, so I mentioned earlier, the democratization of data. But what I see happening with generative AI is it's the democratization of data science. And what I mean by that is instead of having to know Python or Aura or whatever and three of the right Sequel [SQL] queries, if I can in plain language interact with my data. And instead of having to go build a model out in Excel and say, hey, I want to do a linear regression on this three years of revenue and see where the trend line is. And then do a forecast based on that, but now, well, we have seasonality, let's run something like an auto-regress like ARIMA [Autoregressive Integrated Moving Average], or some kind of model, but I don't have the ability to build that. I can just ask generative AI, and you could do this right now on the platforms that are out there, to generate on the fly the forecast that's adjusted for seasonality and trend, and all that. Then, instead of having to build something in a computer language, you could tell it, Now, run a Monte Carlo simulation, run it 10,000 times, and give me the best, mid, and worst-case scenarios. The power that you have, which would have required you to be a programmer, is now going to be opened up to everyone. But the caveat with that is, just like if you weren't a finance and accounting person and somebody showed you financial statements, you might not know the difference between EBITDA [Earnings Before Interest, Taxes, Depreciation, and Amortization] and net income, or what cost of goods sold is versus an expense.
So you have to have this domain expertise and I think for finance and accounting people, we understand GAAP [Generally Accepted Accounting Principles] and IFRS [International Financial Reporting Standards] or whatever the rules and regulations of what finance people are, but we're also going to have to learn a bit about data science and I think that's been happening because we've been using BI [Business Intelligence], which is a cousin of data science. We've been using that for years, but we're going to have to get better at it because where we're going to start adding value is being able to do this next level of analysis, because being really good at Excel isn't going to be a differentiator before or just because, you know the new lease rules by heart, that's not going be the differentiator. It's going to be incumbent on us to find ways to add value layered on top of as more and more of the base gets automated.
Corson
That's really, really helpful. I appreciate you sharing those perspectives and I've heard people say AI isn't going to destroy jobs. It's going destroy jobs for people who don't understand how to use AI as a tool and I think what you're into is very much how do you think about AI as a tool, and the other point I would make is I think we're going to achieve the outcomes around autonomous finance and the opportunities that creates to free up more time for business partnering. AI is one of a series of tools in the toolbox and it's actually how you bring together a number of those tools to do the right roles to solve the problems that we have currently. Again, just interested in how do you think AI fits in with some of the other technologies to really create that vision for the future?Hopper
When we talk about AI, generative AI obviously is what everybody's talking about right now, but we can't forget the ones I referenced earlier, just your basic machine learning that's been around for 15 years now that it used at scale and to great effect over the last 15 years. So, fraud detection, forecasting, anomaly detection, finding correlations between different components of the business and data, whether it's internal or external and being able to supercharge your forecasting or your financial analysis. I mean those have been around for a while, but it was a small group of people that were able to use them. So now with generative AI, more people will be able to use them and it's going to tie in with think about people who are CRM [Customer Relationship Management] administrators, ERP administrators and people that were experts in all these systems, it requires all the department heads and division heads and leaders in the business to understand not just their small section of the business, but across the business functions and be able to integrate and communicate between them.So, I think rather than having an AI tool that is separate and apart from all these existing software systems, it's going to be the developers, the makers of these are going to just integrate it in. So, the way we interact with, whether it's a dashboard or reporting or looking at our GL [general ledger] or doing our first round of budget variance analysis, it's all going to be instead of having to code and dump things in Excel and slice and dice them and using other reporting tools that are out there, it's all going to be just conversational natural language and we're going to interact with AI. You're going to use AI to interact with our software in a different way than we have. It's going to move beyond keyboard and mouse. I mean, it's going to be a picture of Star Trek or maybe I shouldn't reference 2001 HAL because he was great until he wasn't. But you know, more of that kind of interaction than the kind that we've had up until now.
Corson
We've talked about some of the upsides and the opportunities, but what do you see as being the risks and the challenges, both in terms of your experience to date and what you see as being the challenges in this new world of AI-driven finance?Hopper
The biggest challenge right now is trust. I'm using generative AI every day and I know ways to check it, but if you can't trust the output that is coming from a system or if you don't understand how the output was generated, if I'm a CFO and I have to sign financial statements based on something I don't understand that came from AI, that's a problem, especially if the AI has hallucinated. I'd say trust is the biggest issue. These companies are making strides in solving for the trust issue, but beyond the trust issue, you've got explainability. I mean, for SOC [Security Operations Center] compliance, if you're going to be reporting numbers or using a system, you have to be able to explain how the system works and it has to be replicable and a lot of times with generative AI, you can ask it the same question 10 times and get a different response each time. Well, that doesn't really work. That might be great for creative writing, but for accounting, not so much. It's the trust, the transparency, explainability and auditability and AI doesn't think like we do.There is for now, for the foreseeable future, I don't know what that translates to, but for the foreseeable future, I see it's imperative that we have a human in the loop here. So you're not just deferring everything. If you have a system that you don't understand how it works, that's not always producing the same results. I mean, you might as well be asking a question and shaking up one of those old magic eight balls that you ask the question to. So, don't get me wrong, I'm the biggest advocate and proponent for AI out there in the world. I talk about this stuff every day, but at the same time, I'm not just turning over all my tasks to do it. I've found efficiencies, but we still have a long way to go, but the amazing thing is the adoption rate and the technology improvement on this technology is unlike anything I've seen. I mean, think about how long it took to get broadband to be ubiquitous and smartphones and all these other technological advancements, but AI is just coming so fast and it's just we talked today, this podcast airs. It could be outdated three weeks later because some big new breakthrough is going to come. I mean it's coming so fast and you just look back to even a year ago and it was amazing then, but how much better that all these generative AI tools have gotten.
Corson
Yeah, as you say, the pace is remarkable. As you think about the tasks you mentioned, where do you currently see the best use cases? I know you've put out some stuff around AI, CFO, but what are the kind of things in the very short-term you're seeing as real opportunities to leverage the capabilities?Hopper
That brought up something that I left off that was a very important thing on this risks and concerns, data privacy and how your data is used when you upload it into these models. Right now I'm really pushing the limits of what I can do with FP&A, but I'm using public company data primarily and I've got ChatGPT as my AI of choice and I use the advanced data analysis feature in there and I've got a team account which means my data is not used for training the model and there's supposed to be enhanced privacy and security, but at the same time I'm doing client work, not my own. If the client is okay with it, I'll tell him, look, this is just like if you upload your data into any cloud based there are the risks there, but the risk of your data being used to train a model isn't there. So all that said, there's, I think, HBS (Harvard Business School) had this 2x2 matrix that was a year or so ago, talking about how you pick which projects you're going to do with generative AI. And right now, I'm still for something that's important. I'm in the quadrant where low risk, meaning I'm not uploading PII (Personally Identifiable Information) or anything like that into it, low risk and repeatable process, that if I could knock that out, it's our reward.So the most immediate, and this isn't super exciting, but the most immediate ways that I'm using generative AI in my daily workflow would be putting together scope of works for clients, doing meeting agendas, summarizing meeting notes, consolidating information, but on the financial side, everybody knows that use of it. But if you’re listeners, if you haven't tried out the advanced data analysis tool, it is really amazing. You can like I’ll dump public company data and start asking questions on it. Get it to build charts and graphs and I'm sometimes I'll be intentionally obtuse and just say pick your favorite five key financial metrics and output them and create a chart form and seeing what it comes up with and then seeing the information it gives and sometimes I will lean on it like the example I gave earlier, well, I did a linear regression and just followed the trend line, but there's seasonality here. What would you recommend and it says, oh, let's use this approach and then I have it create the forecast and then I have it create a chart and explain things. I'm finding that it is a pretty interesting assistant. It's like a brilliant but very green intern to use. I don't know, maybe I'm talking out of both sides of my mouth because I have seen the amazing things it can do, but I'm not ready yet just because of that trust and all that. So I'm doing it more of an experimental and testing phase right now, but for anything that is in my actual daily production of work, I'm not ready to go over to that yet.
Corson
I think that mirrors a lot of the experience we hear about and look at the benefits of productivity that you described, the efficiencies there, the time that frees up to do the higher value activities, I think is a very useful application. So I don't think we should change that and I think for me, the opportunities are really going to come as we start to reimagine what AI-enabled processes look like rather than just driving incremental efficiency, but I think we're very early in the adoption cycle to be really seeing those benefits and there are some interesting things with producing first drafts of MDA (management discussion analysis) and forecasting and that's great and it's good, it's useful, it gets people socialized to the potential, but is it a complete game changer? No, I don't think we're at that stage yet.Hopper
I will say when you said that I just realized, so using it to read really big PDFs like I do a lot of M&A work. I've got 150-page asset purchase agreement and I'm trying to find what it said we were doing with deferred revenue. I mean it is being able to ask those questions and interact with it, that is an amazing timesaver, but again, if you're not on one of these premium systems, you need to be careful with your data, but if you are, and you've got the plus or the premium or the enterprise or you're running your own Microsoft Azure instance or whatever you can trust it and that saves so much time being able to talk to a purchase agreement. It's crazy.Corson
We talked a lot about the technology aspects. I mean just coming back to the human elements here, what do you think are going to be the key attributes of successful CFOs and finance executives in this new world? What are the differentiators?Hopper
We've seen the CFO role changing so much over my career and I think it's gotten a lot more and I think a lot of this came out of post Sarbanes-Oxley [Sarbanes-Oxley Act of 2022] where the CFO was kind of stepped up to be the grownup in the room in a lot of cases and we had to get broader in our understanding of the business and that's only expanded, but now as these tools they take away more and more of the repeatable data entry and lower value items then we have to up our game to stay ahead of it. So I think that for CFOs, we've been strategic for some time now, but even more so and we need to be able to data the amount that's out there that we've had access to has grown significantly over the last 20, 25 years, but now not just the data, but the higher level of value for this data that's already available to us.We have to be able to think obviously first and foremost like a finance person, but beyond that, we also have to understand how to work with this data. So there's a component of data science that we're going to have to understand. Like we need to understand what these past data points are telling us and what that means for the future, how to incorporate them, how to explain them. I think it's everyone, it's weird because the technology is more accessible than it ever has been, but if we're going to make effective use of it, we have to understand it more. We don't need to know how our laptop works. We just need to know we turn it on and it works, but if you're going to be using AI because you're going to be offloading, just like you need to understand the roles of the people in your department, what they're doing, you're going to need to understand. I'm not saying you have to become a data scientist completely or a developer, but to understand the fundamentals of this technology that's underlying. First it was fine to just be in your ivory tower of finance and accounting, but then you had to understand the business more and now you have to understand the technology too. So the ask keeps getting more and more. While on the other hand our job arguably gets easier because of increased automation. The easy parts of our job get easier, the value-add parts get more difficult because you have to find ways to add that value.