The most ambitious CFOs are saying we don't want to simply be someone that works within the finance and accounting function. What we want to do is help shape the way the company thinks about the business, how it makes decisions, and really we think especially in the era of AI, leveraging technology to get better insights faster can help position companies to pivot more quickly as their circumstances and competitive environments change. For McKinsey and Company, I'm Sean Brown and welcome to Inside the Strategy Room. You just heard senior partner Kevin Carmody, touching on the opportunity that today's CFOs C to transform their functions and their businesses with AI. Our recent survey of CFOs around the globe reveals a shift in the function towards strategic thought partnership in the C-suite, with finance chiefs holding a unique position to leverage AI to drive performance and inform key decisions. As you'll hear from strategic planning and cost management to risk mitigation and investor relations, AI is indeed reshaping the function and creating opportunities for value creation that stretch across the organization. In today's episode, we'll explore what the finance function of the future looks like and discuss practical examples of AI in action today. We'll also unpack what makes for a successful AI implementation for CFOs and finance teams, including common barriers and how to overcome them. For those wondering where to start, I can tell you right now that it is not going to be trying to implement the perfect technology or the perfect data. And now I'd like to introduce our guests. Kevin Carmody is a senior partner in our Phoenix office. He leads our CFO practice globally and was a founder and leader of our transformation practice as well. Kevin advises senior executive teams, boards and other key stakeholders through periods of significant change, including CEO and CFO transitions. Kevin has also served an interim chief transformation officer and chief restructuring officer roles at a number of global companies. Kevin, it's great to have you here today. Thanks, Sean. It's pleasure to be here. David A. Grande is a partner in our Milan office. He leads our data and artificial intelligence work in Europe, the Middle East and Africa, as well as our strategy and corporate finance work for banks across the region. David A. Welcome to the podcast. Thank you, Sean. Great to be here today. And Andrea Tricoling is an associate partner in our London office and a leader in our strategy and corporate finance practice. Andrea, welcome. Thank you, Sean. Great to join you today. Excited to have all of you here with us today. And Kevin, I thought we could start with you to set the context. You speak with many CFOs and their finance teams about how they help drive value creation in their organizations. What are you hearing from them on their priorities, on how they use technology and AI to help drive performance, especially in a more disruptive environment? Sure. Well, we're, as we talk to CFOs as they think about how they drive performance across their entire organization, speed is always done at the top of their list. But with the disruption that you're seeing in the marketplace industry, geography, et cetera, and the rapid pace of technological change, what we're seeing is that CFOs are embedding how they leverage technology, how they leverage AI to get better insights and drive performance in a way that hasn't been seen before. So it's an exciting time for CFOs to harness this change. And Andrea, anything you would add, given your focus on AI and finance? I would say it's not just CFOs. I, we are talking also to a lot of finance teams and the opportunity from AI is also coming very live in the daily lives of people that are working in finance organizations through processes for sure, but also in terms of personal productivity. And one of the things that you all mentioned in your article is the real shift in the role of the finance team. And in particular, the CFOs role from that of traditional financial reporting or even operational and risk processes toward more of a value creation focus. Kevin, how much are you seeing this now? And how has this surfaced in your survey and in the conversations you're having with finance chiefs and their teams? Sure. And there are really our six points that routinely come up in conversations with CFOs and folks throughout the finance organization. And the headline really is the most ambitious CFOs that we talk to have really taken the opportunity to redefine the role of the CFO and the organization, but also what their finance team does. CFOs that are the most ambitious will say, I'm a member of the executive team. I want to be a partner to the business, even more so than maybe we've done before or even more so relative to the way the organization or the markets perceive the role of the CFO. And what in my mind, what that really says is that CFOs are looking to elevate that role. And many cases become in the strategic thought partner of the CEO. I like to say the first among equals in the C suite because they have a unique view of the entire business with the technical capabilities that can help drive performance. So their insights, their their natural skills, how they see the business and also how they can interact with business units and different functional leaders is unique, which we think gives them an opportunity to really step up how they think about driving performance across the organization. And the second point, just to contrast would be that the most ambitious CFOs are saying, we don't want to simply be someone that works within the finance and accounting function. That's important. But we don't want to be a classic accountant or a classic treasury management person. What we want to do is help shape the way the company thinks about the business, how it makes decisions. And really we think, especially in the area of AI, leveraging technology to get better insights faster can help position companies to pivot more quickly as their circumstances and competitive environments change. So a real opportunity to move from kind of a bookkeeping role into more of a strategic partner that helps the company make better decisions. The third point that we talk a lot about is how to manage risk. Without compromising how you manage risk in the classic sense across the organization, we see CFOs that take a step back and say, how do we actually enhance that? So we look at enterprise risk management issues in a different way, more forward looking to tackle some of the challenges that companies face. Again, just another opportunity to step up their role as CFO to help advise and counsel to CEO, but also the broader executive team all the way through line management that execute on a daily basis across the company. And what we see is that CFOs have historically been the leadership of the finance function, treasury function, the accounting function. But we see CFOs that want to lean into their role to help make better strategic investment decisions that drive long-term value creation. And we've heard them say we have excellent functional leaders. They want to spend more of their time thinking about how it impacts the business. And in many cases, changing the perception across the organization as to how these CFOs are perceived in non-finance roles. So helping operators that are on the assembly line, for example, think about driving change. Giving them better insights, better tools, better information, different types of reporting. And wrapping that in technical actual change in AI to ensure that they're in the cutting edge to deliver the best possible information insights to the companies. And the final two points quickly, there's something about how to think differently about allocating capital. It goes into the strategic investment decisions that we talked about, but really taking the investor lens to think about how you're spending the company's capital to drive ROI across the enterprise. And the last point is continuing to upskill your team. So we've seen CFOs that have taken finance folks in the finance function, embedding them across different areas of the business, could be functional areas, could be supply chain, procurement, or business units. So they understand the business differently, but they bring the expertise that is natural to them in the finance organization to make the collective team stronger. And NetNet across each of these six dimensions, we see real opportunities and real progress for CFOs and finance organizations to really change the way they partner with their management teams to drive performance. And Kevin, if I were a CFO or a member of the finance team, the notion of contributing to value creation sounds a lot more exciting than the traditional transactional expertise that CFOs are known for focusing on. So how do you make that transition a reality as a CFO? And what are some of the barriers that you're seeing in CFOs and their finance organizations in making this shift? Probably the biggest challenge is culturally, just how does the company historically perceive the role of the CFO and the finance organization? I think the best CFOs that I'm thinking of one in particular that was in retail, you align with his CEO also with the board, but he leaned into it. So simple things like, for example, when the company was putting together its strategic operating plan, he would lean into the business. It wouldn't just come from a finance perspective, but he would ask some difficult and challenging questions about key assumptions. Does this actually make sense? Are we looking at the market right? Can we run our stores harder? Those types of questions typically would not come from the CFO and as he did that because he was very well researched and very smart, he could distance himself from the finance organization and become a better partner. So I think changing the cultural dynamic at a company is really the first thing.
building block than used to be addressed. David, it looks like you have something to add here too. I would say that there's also AI would play a very critical role because ultimately AI will enable, you know, reduce the effort required to manual work, automate most of the transaction activities and really free up time, free up capacity within the finance function and deploy this capacity to really play, you know, strategic corroding, driving business in value creation. Super, thank you both. And in Kevin, it's clear that CFOs and finance teams are increasingly expected to and even seek to create value and reinvent the function, yet they still need to deal with the day-to-day challenges of helping run the business from a financial perspective. Can you speak to how they balance those two different expectations effectively? Sure, and the reality is as bold as CFOs want to be to change the way the finance organization operates and therefore how it's perceived across the organization. The reality is that there are a lot of challenges, a lot of complexity in this day and age that simply don't go away as a CFOs reinventing his or her finance organization and it really in the first instance starts with the complexity that they deal with on a daily basis. Although this is changing with technological advancements, we do see decisions that are often made on inconsistent information or historical data. So one of the disconnects as we compare what happens in the finance organization with other parts of the business is that decisions are based on historic data as opposed to forward-looking data. Well, they still have to run the business as a CFO, but the question is, can CFOs leverage AI to get better information so it is forward-looking so that they can react to changes in their business model much faster without compromising the day-to-day expectations around performance? And that's difficult. The second one is really identifying those opportunities, both cost and I'd even say growth opportunities in a way that's consistent that doesn't put the enterprise at risk and how do you embed technology to get better information faster? I think the final point I'll say in this first carry on complexity is also taking a step back and saying, what is the discussion that the CFO wants to have with his CEO, his executive team, the broader organization, and really changing the performance dialogue that is happening on a day-to-day basis, monthly basis, or quarterly basis, focusing on not just the what, but also why are we doing certain things and should we rethink that? And then bringing the discipline that you would expect from a finance organization that is fact-based, hypothesis-driven, to help the broader team get to better answers. The final two points that we see around common CFO pay points is a time and effort spent on regular finance operations. So we've heard countless stories about CFOs that have literally put all the reports that they generate over time on a table to try to figure out who is using this information. In the most extreme case, they've caught off most of those reports to see whether they're missed across a broader organization and what that is done is really help the CFOs take a step back and say, "What is the information our company "and our managed-between needs to run the business?" And it may very well not be what has been produced in the ordinary course, but it's tough to do that again because of the volume of the reports and the pace at which technology is impacting the finance organization and the broader business over time. And the final point is the financial risk that CFOs are facing on a daily basis. And how do you manage that? Liquidity could be covenant issues, could be capital allocation issues, could be disruptions, seeing a lot of that today and how does that impact the business and how quickly can CFOs adapt to those changes and how quickly can they adapt and utilize technological advancements to get better insights over time. And Kevin, if you consider that the CFO usually directs the CAPEX investments across an organization, how are the CFOs you work with perhaps justifying their decisions in investing in AI in the finance function itself? Because you can't make all of the CAPEX investments that you might want to make. How do they make the investments in the finance function not come across as self-serving? So maybe I'll just have a quick answer and then it would be great to hear from the team as well. But I think one of the best stories I've heard from a high-performing CFO is that there's something around educating the broader organization, not assuming that folks that are not finance, that don't have finance backgrounds, understand how value is created. So there's a bit about this is actually how we create value across the organization. And then leading to the next step, which is really the transparency. If you understand how value is created, then CAPEX is scarce. How do we think about looking at different CAPEX projects and then stacking and racking them? So we make the best decisions for the business. As opposed to assuming that there's an endless supply of CAPEX, it's that discipline and rigor that's required to get to the best answer, which is a combination of what happens in finance, but also what happens with the other business partners that are looking to allocate CAPEX across their business. David A. Andrea, anything you'd add? No, maybe I would just add that the CFOs have typically the privilege to play a double role when it comes to AI. Because they can either deploy AI in their own finance function, but also they can sponsor and deploy AI to generate value for the full enterprise. The CFOs are taking the lead to use AI to decrease no personal cost, right? Because what they had of procurement or cost control, and they generated a lot of value, not for their own function, but for the full enterprise. Or think about also a cash optimization. Again, it's not for the CFO themselves, but it's for their organization. You know, I'm also intrigued about what this means for the arc of the finance function over time. Many finance organizations may have already outsourced, for example, aspects of their back office, or even their front office work, to lower cost locations. And are you seeing situations now where the implementation of AI may be changed the calculation, and the model is now changing in terms of how you staff and where you staff and lead the function as AI and agents are brought in, along with other technologies. Andrea? I'll maybe start offering that in a number of conversations, this is an active topic. The question from being able to delegate to a certain extent, to agents or teams of agents, the execution of specific processes, it's opening the door to a question of what is the right for ensuring a decision for the enterprise, for every specific process. Now, I cannot say that I've seen organizations that have yet to go the full way, but at the same time, it's definitely a very active conversation. David, can you talk a little more extensively about what the finance function of the future looks like, and how AI is informing that transformation? Yeah, thank you. So we looked at this through six domains. So the first one is strategic planning and control, which basically a bit transformed for backward looking. It's a lot of effort in trying to understand the past, describe the past, lots of reports, data crunching, according to our experience, more than 50% of the time of the strategic planning and control as typical as pan, in quickly reports that nobody reads, crunching data, et cetera. So two forward looking exactly what Kevin said earlier, so I've got a bunch of clients that are really building very sophisticated AI models, a chatbot to get real time answers and automated reports. The second domain would be cash optimization and working capital, so moving your way from fragmented processes with a lot of manual efforts in reconciling, for example, in-bosses with contracts and unreliable cash flow forecasting, two, heavy agents that actually automated the full process of order to cash or procurement to pay, on cost, cost management and optimization, moving your way from cost control, so really trying to understand and doing cost allocation, et cetera, to cost the steering. So I've got a lot of clients, a lot of CFOs that are actually leveraging AI to redistir productivity for the full enterprise. Investor relations from having experienced based equity story with a lot of efforts to produce and to prepare the meetings for real invested, for example, now you can have agents and AI to redraft equity story for you, synthesizing investor calls, et cetera. In M&A, we move from a contingent approach with Manuel Tankersumel Research to a programmatic approach where you can basically instantly find the best targets for M&A, automate, synergy calculation, et cetera. And a risk management, a movie, a risk program, a reactive compliance checks to proactive real-time risk detection, think about geopolitics for English, for example, now, which is a big source of risk. So these are the major trends. Now, the reality is that this is not happening already now. Right? Every year, we actually do a bunch of surveys with hundreds of CFOs. We ask many questions. I'm all in this question. One of the questions would be, have you already invested in AI in your finance function? In 2023, 24% of our participants said yes. In 2024, it was 50%, in 2025, 64% of our participants said yes.
5%, so it's really growing exponentially. And the second question would be, are you actually using AI, are you an active user? Are you just piloting or researching? In 2024, 6% were active user, 2025, 51%. So seven times increased in finance in the park. So we're really moving away from piloting thinking to really using AI. And very quickly on this, what is actually AI in the finance dungeon? It's a combination of analytical AI, so basically, crunching data and building models for forecasting. Combined with generative AI, right? So creating your text, the synthesizing, a lot of material, all your code, think about, you're forced to contract compliance or conversational Q&A, and now, authentically AI. So really having agents do the work for you in a report generation or automation of, in both to pay or in order to cash. The most important part of this, I think, is whenever you do AI, be obsessed with value creation. Every year investing in AI, I must return 10 times the investment. And Andrea, I'd like to dive a little bit deeper into how these finance domains are being transformed by AI. Perhaps we could start with the strategic financial planning and control domain. Now, of course, there's very many ways that companies deal with reporting and have focus on reporting in their FPN major. And of course, one of the key elements and aspect of that is ensuring performance management towards supporting value creation, as we discussed earlier. And organizations we are seeing are implementing AI differently in FPN A. In certain cases, this is taking the form of conversational interfaces. You can imagine the popular chat pots out there, where the focus is in enabling a number of questions of different degrees of complexity from the simple water, which is simple, but still very valuable because it basically puts in the hands of the finance organization, as well as the stakeholders within the business, a tool and a level of access to understanding the company financials and KPIs that is now mediated only through natural language, which creates meaningful opportunity for teams to save time, to be on top of what is going on, whether they are in finance and are more skilled in accessing information or they are not in finance. This is the first level. And this type of tooling is announced with analysis that are accessible on giving teams and the stakeholders an understanding of what is the variance, whatever that variance is coming from. I'm thinking variance, of course, of actions versus budget or forecast results as they progress through the year versus what is the expected landing, as well as more in depth root cause analysis of why certain phenomena are happening, following the appropriate, you know, value trees that every company has. And then getting into the world of what could I do? What are the potential actions? What are the scenarios that we could look at, given the certain conditions in the data? And this type of analysis, this type of assistance to decisions have a number of effects. I mentioned that the opportunity to democratize access, but it's also about keeping the kind of back and forth of can you run that additional scenario? What does that scenario mean for me? And really being focused when there are the high power interactions that are review meetings or a source allocation meetings, as we were mentioning earlier, being focused on what are the decisions to be made, what are the scenarios to look at, and can we look at more scenarios? So this type of tooling and this type of implementation of AI is connecting to a change in the way decisions are made within the companies that are doing that. And the other aspect of that is the other side, but similar, is that these synchip abilities, where AI is able to both combine data, but also commentary, as well as tools like root cause analysis or forecasting, is at the core also of the rethinking reporting where agents can effectively walk through workflows. You could imagine them as Q&A, Q questions and answers, but they are chained together to be the first draft of a report that then the appropriate person, maybe either in the business or in the FPN 18 can review a build on, and most likely also connect with those agents and ask for a rerun if there's either something not correct or something that needs deeper or better analysis. So just to summarize, this only leads to saving time but also more effectiveness. And it's really two sides of the same coin, different organizations implementing, and implementing differently, Q&A on one side or reporting on the other side, but in a way they are related on the way they actually perform. - And it would be really good to understand how finance organizations are actually implementing this and are most large finance organizations using internally developed AI, or are they using off-the-shelf solutions because finance organizations typically deal in very sensitive data and wouldn't necessarily want to be putting this into an open LLM for example. - Yeah, let me start. I mean, there's a number of elements that you mentioned there. Let me take the easy one first. And security, I mean, these tools are not different from other tools that have been available in the past in terms of security. Every company has their own security frameworks. We have as well. The way we think about it is very similar to the past, the appropriate security needs to be followed. So as you say, if the company doesn't allow for data to go public, it should probably stay right. And that's one aspect. The more important and interesting it aspect is how to implement and think through these tools. I mean, more famous people than me have said that the future often the future is easier but it's not evenly distributed, right? And indeed, as you say, many companies are, and we have seen in the data, many companies are implemented this, but not everyone is at the same either speed or the same place. It depends on their priorities, but also now they are thinking about is being at the forefront versus waiting for the tool. What we're seeing for leaders is that they are combining their system and architectural landscape with the capability to build their applications on top. And so combining the security as well as reliability they get from backbone systems with the ability to build, for example, an agentech reporting layer on top of that, in combination of that, so that the data comes from the right place and but at the same time is handled and managed by AI through the workflows that suit that specific organization. Maybe let me also add and read that these tools typically need to be trained, right? So one of my clients, for example, they built such a conversational chat button sitting on their data and they tested it with thousands of questions. And this question were asked to business guys, what did you ask, right? In this what, why, in what fashion? Initially, the percentage of questions that were correctly answered was around 50, 60%, and then gradually in the matter of weeks, they reached above 95%. So you need to train the algorithm. The beginning, you will receive sometimes hallucinations, but after training, it will get better. And typically clients do a parallel running so they keep their old approach with a parallel running using AI and gradually shifting towards AI when they feel confident. And then a lot of change management, we're gonna talk about that change management absolute crucial, how do we think about the way you do strategic planning, budgeting, performance reviews, forecasting, using AI? - Andrea, can you share any specific examples that demonstrate how organizations are embedding agent AI and the finance function? - The example I wanted to talk about is the budgeting one, where you can have the first draft of a budget put together by a team of agents that work orchestrated and in sequence. But you can have the analyst agent look at the past budget, what were the variances put together the starting point, of course getting the data from the right systems and with the right analysis in accordance to how the company thinks about it. Then a market research agent puts together the latest trends, the right understanding of the different products, depending, of course, on the industry of the company, what is the most pertinent. Then internal operation agent, that connects to these external, the past view with the external view and the way
with the current setup of the way the company fulfills its mission in terms of either manufacturing or supply chain and so on, and builds an integrated view. An agent that is in charge of planning scenarios, creating both scenarios that are sensitivities, they high and low scenario if you want, as well as more specific scenarios depending on the instructions that it receives. A agent that pressure tests that first draft against a competitive landscape, looking to gather information on competitors or peers or other markets and thinking through what could be the reactions. And then finally an agent that puts together this as an in terms of data visualization and creates the opportunity for the analyst as well as the CFO, as well as the others they call us to interact with this flow. And so Andrea is the process that you've just taken us through one that can be standardized for other companies to follow or does the application need to be bespoke. Every company is organizing these slightly different so the agent, the architecture and workflow needs to be adapted, but the opportunity of getting agents to work through a workflow and a sequence of tasks to support the process is definitely there. And it's coming to different processes as we speak. - And what about other finance domains? Can you apply this logic, for example, to cash optimization or working capital as well? - What I just described on processes is very true here as well. And that is where earlier we were discussing about how these processes which often are distributed across the central and share service centers are also being affected. An example of a specific agent, maybe a narrower agent, but it is particularly valuable. And that is focused on thinking through beyond the typical three way matching in the procure to pay process and really looking at a voice to contract compliance. Our benchmarks show that often organizations live on the table between one and four percent of value in leakage because complex terms, discounts, rebates, or specific hourly rates are not always always reflected in voices. And so either they spend a lot of time monitoring this or they leave value on the table. And one of the real opportunities from AI in this area, in this domain, is really the opportunity to do this at scale, to do this monitoring at scale. - Thank you, Andrea, and Davide, let's now turn to how this could work in a domain like cost control. - Okay, so how as a CFO, you can really leverage AI to decrease, for example, no personal cost for the full enterprise. So this is a case of a company that was already super efficient, right, was another best efficient in their sector. And the question was how can I actually getting even more efficient, right, improve my cost to revenues ratio? And the CFO basically realized that look, we're actually sitting on oil, but we're not deploying this oil. The oil is the data, right? And so they basically said, why don't we leverage the data we have from more than 50 data sources, both financial data, like invoice, this contracts payments, it's not just numbers, but also PDFs, and operational data, like energy consumption, daily football, trips, combine them in one place. By the way, the McKissie, we have one capability, which is called Spanscape, that really brings this together. And once the data is in one place, really leveraging AI to very surgically find opportunities to reduce costs. And they have found more than 100 levers, and they reduced external cost by 9%, 19%, and one example out of this 100 lever, energy costs, and we were in a period where energy costs were to the sky because of the war in Ukraine. And basically, they said are there ways to use AI to really reduce the wastage of energy? And they basically built an AI model where in each branch, they would calculate every day of the year what is the statistically expected energy consumption? And this is calculated based on climate areas, type of system it is gas, heat pump, system age, square meters, et cetera. And whenever in a branch, the actual energy consumption would go above the black line, the CFO team would get an alert and say, "Hey, this branch in this city today "consume the more energy than what it should be." I think it was a very good reason, or maybe there was a bad behavior, like stupid things like keeping the windows open or with their condition is on or whatever. Only with this lever, out of the 100, they said 11% of energy cost, that were in the middle of hundreds of millions. So that means only with this AI model, they said a tens of millions of years. So this is one example out of more than 100. Now in other case, you can still use AI to support re-negotiations with suppliers. So our McKinsey Global Institute has basically built a very sophisticated model that assessed the potential automation of more than 2,000 and 100 activities from vendors. So basically the model works that we have 850 occupations, network and computer system, lawyers, HR extender, especially, et cetera. Each occupation is plitted into activities, we have 2,000 and 10 activities, and then skills. And so we can say that for example, you know a riding code, the computer made by a genticae AI up to whatever, 53%. So what is the result? Is it actually, there are services that you're currently buying from your suppliers that will be automated to a large extent by a genticae AI? For example, digital technology is already automatically 11% and by 2030 it will be at 53%. This means that your vendors will have an extra margin in the coming years. And you can go there and say, hey, why don't we share this extra margin and renegotiate our tariffs because they're going to do the same thing at a lower cost for you. And this is what most of my clients are doing. For example, clients that had to renegotiated the tariffs with their IT service providers in Halvedask. And basically they figured out that leveraging automation, assistant generative AI and a genticae AI, that suppliers would increase productivity by 60%. So they went and said, hey, we're going to share this profits. And so they renegotiated 10% savings right away and 15% to 20% savings in the future contracts. So this is another example on how you can really leverage AI to bring a lot of value. Thank you, Davide. These examples are really bringing these opportunities to life. And Andrea, I was wondering if you had anything else you'd like to add. I imagine there are a lot of other applications for this technology. And I know you've done a lot of work applying AI in investor relations, for example, to help remove decision-making bias. Maybe you could tell us a little bit more about that. Look, the only thing I wanted to add, then, thank you, Sean, for mentioning data that we happy to share. And of course, for those interested to connect. But you're very right that one of the meaningful opportunities from AI, for example, in the area of investor relations, but of course, in other areas as well as to act as a debiasing agent, if you want, or support in preparing for what the market could be asking, it could be challenging. To a certain extent, that is something that leaders have been doing all the time in normal preparations. But one of the advantages of AI is that you can effectively load all your previous interactions with the market public data, as well as your peers, other companies interactions with the market, and get a very crisp and performing agent that asks you hard-eat-his-questions in a way. And what could investors ask? Now, they don't need to be questions that have actually been asked to appear. But rather an evolution that takes into account what you are going through as a company and what others are going through, and really tries to help prepare an offer at the biasing at this biasing angle. And not only that, this becomes also a competitive opportunity, competitive intelligence opportunities, to hear what are the themes in the market. Again, this is something that can be accessible to people that read and go through. But AI can be a meaningful accelerator and also a debiasing mechanism. Maybe one other question about board meetings and preparing.
for board meetings. When you talk about the equity story, what are you seeing in your work with clients in terms of the effect and the benefits that AI can have on the time that executives need to spend preparing for board meetings as well as investor road shows? Yeah, look, one thing I'll say, and of course my colleagues may have more to add is that not all it's two aspects. One, it's a meaningful time saving. You can get a lot of data processed, that's one of the major advantages of language models, the ability to deal with the unstructured data, but as well you can also get an opportunity similar to the one we were discussing when looking at performance management to create a common base of understanding that is accessible through natural language, which for many board members that are coming from different, let's call them walks of life in terms of professional exposure, can become a way for them to get a lot more into the details in a way that is productive for the conversation. Thank you and Kevin, anything you'd like to add here? The only thing I'd say is what we've heard from CFOs is that by leveraging AI, you're seeing what has typically been a team of analysts spending weeks of man-hour time, researching for board meetings, understanding what they need to say in earnings release, looking at what their pair of groups are doing, really basically doing the manual work around quarterly earnings releases, analyst reports, etc. And a lot of that can be automated right now. So they're going from literally man hours of weeks to days of time to get ready for their quarterly earnings. You trees up so much time in the finance organization to think about the value they can create within the firewalls of their company and that has changed rapidly. That is really impressive. And now let's turn to the other side of this. The hard part, David, what are some of the barriers to scaling AI? Yeah, I mean, number one is waiting for the perfect data. Right? I've got a bunch of clients say, okay, I will not do AI until I have the perfect data platform, the perfect data quality. When it might experience you'll never get there. I've got clients that have invested hundreds of millions in building the perfect data platform not getting there. So start with the data you have and then build your data foundations along the way. Second, trying to transform all at once. Right? You've got a bunch of clients saying, okay, I'm going to do a lot of use, gave a lot of pilots falling into what we call the pilot Trump. What we suggest, pick one domain, you've seen some examples, strategic planning or cost or countpables and receivable investors, pick one and transform it at when. The third barrier is jumping in without a clear roadmap. What would recommend a star by saying, okay, how will the finance function of the future look like? Have a clear north star, align your stakeholders both internally and the rest of the organization, the business leaders, the CEOs on how the finance function of the future look like and then reversely reverse engineering, what are the initiatives, what are the use cases to get there as opposed to moving randomly. The fourth would be the neglect and change management. This is super important. And technology is becoming more and more sort of commodities, not the biggest barrier. The most important thing is change management. You can have the best AI models. You can have the best technologies, but if people don't use it, if people don't change the way they work, you're not going to get the value. So invest in change management. And the fourth is not fixing the process first. Right, there are a bunch of things you can do in the processes and we describe the order to cash, the procurement to pay before embedding it. So simplify the process and work around the out very client to really rethink the budget process and plan. There's a bunch of things you can do to simplify it before adopting a technology. And last, again, I repeat, the obsessed with bag creation. Eventually you need to create value in your AI transformation. Think about where the barrier is and then start from there. Indeed, it is all about the value. And maybe, David, now you can comment on which of these that you just brought us through are the biggest or most common barriers. Is it waiting for that perfect data? And what's the barrier that you would recommend that organizations tackle first? Well, I think the first thing I would do is to have a road, a clear road map and a vision. Right. So build a vision, share where your stakeholders understand where the value is and then a large implementation of starting from the most impactful domains. So don't think about data, don't think about technology, don't think about governance. Governance is not a typical barrier or you're going to need to understand how to govern the process. So you're going into politics, right? And you're going to get stuck. Start with a clear vision. Well, not to do is don't start from technology, don't start from governance, don't start from random pilots. Right. So start with a clear strategy and vision and one domain where to create value. Thank you, David. And Kevin, let's wrap this up. What are the core themes CFOs and finance should be thinking about to be successful in their approach to AI implementation and their function? First one, David, you just mentioned setting a clear vision from the perspective of the CFO. We call that the CFO 23 vision, but not beginning with technology. But rather, where do you want to take the business in a very clear and thoughtful way that has some detail behind it. And then second, what does it take to align the executive team around that vision? Once you get that figured out, the next question is how do you harness technology to make that happen at scale and at the speed that fully capitalizes on what technology provides to the company? The second point is really translating that vision into a concrete domain approach. So really thinking about the use cases that generate value across the company from a pit on L perspective, but also from an enterprise value perspective and how technology can help your company get to that landing spot. The third point is a bit around adopting a domain by domain-driven approach to build the data and the technological foundations in a thoughtful way. And that really relates to page 0.4, which is you can't do everything at once. So thinking about how you sequence this work over time to harness technological change, executing it in waves. So you're generating real value throughout the entire journey without losing time or making mistakes is really important. So invest in that time and really putting forth the roadmap that you can execute against. That is really robust and gets the buy-in from the broader company is important. And the fifth one is something around change management. We've talked a little bit about this throughout the conversation, but what we think is for every dollar that you're spending as you adopt or implement new technology. Another five really deals with the upskilling that is required throughout your organization to change management that needs to happen to ensure that not only executive but all the way through line management that folks understand how to utilize technology. So you're getting the most that you can out of the technological offerings to really drive value to help your company reach full potential. So that change management aspect is one of the most important elements that we see as a key driver at success. These are super useful and I think provide a great set of principles and starting points for CFOs right now. I have just a couple more quick questions before we conclude. Kevin, I mentioned earlier that you helped found our transformation practice. If you're a CFO looking at implementing AI, but you're also about to embark on an organization-wide transformation, is there anything you do differently? I think the CEOs that are really bold in thinking about how they want to reinvent their companies through transformation really start with the strategy and I think they feed it through four performance pillars which start with growth. It's not just cost-op and visualization. Third is your team. So the organization, the upskilling we talked about and the fourth one has always been digital or technology. But I think technology in the context of an enterprise-wide transformation is an accelerant. So I think it feeds directly into how a company will think about their path to reach full potential, how they leverage data better and really the speed at which you can get there. So I think you can find the transformations that are thoughtful about how they harness the technology and AI in particular can shorten the amount of time it takes to early drive performance and change the business in a fundamental fashion. I think it's exciting and relatively new. Indeed it is, Kevin. Thank you. And for my last question, finance teams can often be viewed as cost centers, even by the finance function itself. So what is the finance team's value creation story that builds excitement around implementing AI in the finance function for value creation aside from just removing cost? Kevin, maybe you'd like to take this one to bring us home? I'll start quickly. I think it goes back where we started the conversation. The quicker the finance organization beginning with the CFO and then a team can be accepted as a true partner of the business, the better it becomes. Because I think what happens then is the finance organization is seen as part of the solution more forward looking than historic. And we talked about challenging sessions, things like for example, are these assumptions right? It's not just a finance question in general, it's how do they partner more effectively with the business? Thank you. Kevin, David A. Andre.
I really appreciate you sharing these insights with us today. Thank you again. Thank you. Thank you. And thank you to all of our listeners for joining us today. We hope you enjoyed the conversation, and we welcome your feedback and ideas for future podcasts. Just email us at
[email protected], which stands for Inside the Strategy Room. You can also share your ratings and reviews on any podcast player with many thanks to all who've already done so. We really appreciate the comments and feedback we receive every week and encourage you to keep them coming. If you enjoyed the episode and you'd like to subscribe, just follow our weekly stories on any podcast player where you can also access our entire library of more than 300 episodes. We also offer an Inside the Strategy Room Podcast Collection page available at McKenzie.com/iTSR where you can easily browse our prior podcasts across nine major themes and access written transcripts of all those conversations. And finally, if you'd like to automatically receive our latest publications and insights, we encourage you to sign up for email alerts on our insights page at McKenzie.com/scf or to connect with us on LinkedIn at the McKenzie Strategy and Corporate Finance Practice page. Thanks again for listening. We look forward to having you join us again next week Inside the Strategy Room.
PodcastTranscript AI