Become a Creator today!Start creating today - Share your story with the world!
Start for free
00:00:00
00:00:01
Balancing the Future Ep. 34 - Accounting in the AI Era: What CPAs Need to Know with Dr. Sean Stein Smith
162 Plays21 hours ago
AI isn’t just a tech upgrade—it’s rewriting budgets, internal controls, and even how long leadership has to make decisions. In this episode, Dr. Sean Stein Smith breaks down what’s changed fast, why traditional GAAP reporting struggles with AI’s intangible commitments, and how investors and executives alike can cut through hype by focusing on cash flow and footnotes. Join us for a practical, finance-first roadmap for navigating what’s coming next—and where the real ROI will (and won’t) show up.
Earn CPE by listening to this podcast through a Becker Prime CPE subscription.
Listen to this episode through your Becker LMS platform to complete practice questions, pass the final exam, and earn CPE credit.
Already a Becker Prime CPE customer? Login here.
Have access to Becker CPE through your employer? Earn CPE credit for this podcast however you consume Becker CPE, either through your company’s LMS or via the Becker platform. Not sure where to log in? Check with your CPE admin.
Learn more about CPE Podcasts from Becker: https://www.becker.com/cpe/becker-podcasts
Recommended
Transcript
Introduction to AI and Business Economics
00:00:02
Speaker
Music
00:00:09
Speaker
Welcome to another episode Balancing the Future. And I am excited to be here today. We're going to spend some time talking about artificial intelligence. And I have the pleasure of being with Dr.
Guest Introduction: Dr. Sean Stein Smith
00:00:21
Speaker
Sean Stein Smith. And we're going to spend time talking about not only, you know, what's changed in the last six months, but what's on the horizon when we think about the economics of artificial intelligence and what it means, frankly, for every business, whether you're a small business, large business,
00:00:37
Speaker
There's some complexities and what you need to be on the lookout for as we continue to account for this. We spent time in the past talking through strategy and talking through how to use AI to be successful in your current position, as well as what to be on the lookout for if you're a college student. So I welcome back again, Sean Sine-Smith. And we're going to spend, like I said, some time going through this. But I want you to introduce yourself because some may not know you, even though yours is one of the more popular or probably the most popular podcast. I put that out there. I need to check the stats, but I'll go ahead and and go out on a limb and
What are the Costs of AI for Companies?
00:01:12
Speaker
say that. But welcome, sir. So please share something about yourself.
00:01:16
Speaker
Thank you so much, Chris. and And it's an absolute privilege to be back here again. talking to you on AI and all of the impacts that it's already having in every aspect of business, including the accounting world.
00:01:30
Speaker
I'm Sean Steinsmith. I'm on the faculty at Lehman College, where at the college, we're doing a ton of stuff around AI. We have multiple projects in the pipeline that are going to be coming out to marketplace within the next half year or so. I also have a column at Forbes every week.
00:01:46
Speaker
if anybody is ah interested in more tidbits from me. And I also do a whole bunch of trainings, advisory work around AI, agentic AI. So I'm really excited for our conversation here today.
00:02:02
Speaker
Awesome. Awesome. Awesome. You know, one question that comes to mind and I have to share this off the cuff. And I know that We always go through a runner show for those that don't know, but because we want to be organized and and making sure that we message things. But I have a question for you that you're not prepared for.
00:02:18
Speaker
What is this costing? What is this costing companies? And I think it's good to understand what those dollars look like. We don't have to you know make sure that we tailor it for small versus medium and large. But what on typically on average, what is it costing an organization to prep for AI?
High Cash Burn Rates in AI Projects
00:02:35
Speaker
So Chris, that's an excellent question. And but as it turns out, I was actually listening to a podcast earlier in the week where the CEO of NVIDIA came out publicly saying that if you have a software engineer at your company earning, let's say, half a million dollars annually, he or she should be spending half that amount on token use.
00:03:02
Speaker
And in token use, that means actually using AI applications. So if we take that and amplify that across the Fortune 1000,
00:03:14
Speaker
It's now in the hundreds of millions, if not billions of dollars annually spent just on the software side of firms trying to upskill and trying to figure out where to use AI.
00:03:27
Speaker
And to echo a phrase that was popular back during during the dot-com bubble, these AI projects, to a large extent, are are still pre-earnings or pre-revenue at these companies. So in other words, the the cash burn rate of AI being onboarded at most companies, that wouldn't say all, it is a tremendous burn rate in in cash.
00:03:52
Speaker
And there isn't a pathway yet that has been widely identified for how these projects are going to pay back those investments.
GAAP Limitations and AI Costs
00:04:03
Speaker
So when I think about just your sharing that and I think about the the principles that we use as accountants like Gap, and I think about when it was designed and how it was designed and for what economies it was designed for.
00:04:18
Speaker
Does it make sense now the way that it is? and and And I'm thinking about the economy of things and AI and like you said, the burn rate and the and the cash allocations and so forth and so on. so Do we need to tweak it or is it still OK in its current form?
00:04:35
Speaker
On the one hand, it's it's always easy to poke holes and to say, oh, X, y Z framework is out of date. It's obsolete. This time it's different.
00:04:45
Speaker
But all of that being said, Chris, there are some gaps in gap. That the whole Gap framework was designed, originated, and intended for an industrial economy.
00:05:00
Speaker
A rare rose manufacturing, agriculture. mean, that's what Gap was built for. That's the era it was built for. And it's been an ongoing conversation. pretty much ever since the internet came out. Going back now 25, 35 years, there has been this ongoing dialogue where how do we capture the intangible nature of companies? How do we capture correctly ah revenue that is recurring?
00:05:26
Speaker
Revenue that, on the other hand, can be canceled on a month-to-month basis. How do we build out models? How do we value that revenue alongside more traditional forms of revenue?
00:05:39
Speaker
And also, there's a lot of cost and a lot of commitments and a lot of obligations around AI, which... I know we're going to be diving into that aren't really on balance sheet per se.
00:05:51
Speaker
And GAAP, as it's currently constituted, just is not built for capturing all of that information. And so, yes, i mean, GAAP is what it is. And it does definitely have value and it brings good information out to
Investor Challenges with AI Financial Reporting
00:06:07
Speaker
the marketplace. But I would say that there are some areas in which it definitely has to be updated. And I would say modified going past tweaking it.
00:06:17
Speaker
You know, I start thinking about Gap and I think about organizations and those that look good on paper. And you you know what organizations I'm talking about and those that don't.
00:06:28
Speaker
And if I'm an investor, if I'm a shareholder and I'm trying to figure out that equation, I mean, how do I understand or how do would I know that a company is really legit and it's a promising investment because it'd be hard to determine. So what what what do you think when i when i' when I toss that out there? What comes to mind to you right away?
00:06:47
Speaker
My first gut reaction, Chris, is that even though the two of us were just saying um that there are gaps in gap,
00:06:58
Speaker
It really comes back to going through the individual financial statements because ultimately, i don't care what what business you're in, it all comes back to actual cash flow.
00:07:09
Speaker
Income, I can change that up and down. The balance sheet, I can move stuff off balance sheet, on balance sheet. There can be all kinds of contingent obligations. There can be handshake deals.
00:07:21
Speaker
But ultimately, every business ah lives or dies by its cash flow. Cash in, cash out. And cash is hard to fake over time. Adding on to that, I'm always a big advocate for going through the footnotes. And i know going through the footnotes isn't always fun, but there's a lot of good information in there that companies have to legally put in there, but on the other hand, don't have to really talk about.
AI's Impact on Decision-Making
00:07:50
Speaker
And in a in a twist of, I'd say, irony, it's a lot easier to actually do those deep dives on these 100-page filings using AI to help you parse through these dense filings and to and really tease out if all the information on the financial statements, of one, makes sense, and two, actually ties into each other.
00:08:14
Speaker
And I know it sounds pretty basic, but actually going back good back to the financials and especially cash flow can really help focus in on, well, is this company actually making any money? Are they profitable? And is there any cash actually coming into them? Or is it all going out via partnerships or advertised acquisitions of certain assets?
Is AI Investment Worth the Risk?
00:08:40
Speaker
You know, we've talked in the past about AI and the influence it has had on the on the economy and and how we work. I mean, should we continue to bet on AI?
00:08:50
Speaker
I mean, I'm just curious, because what if we're wrong? You know, what if we have made a mistake and we're making all these investments as organizations and building out this infrastructure and it's wrong. Now, i won't toss anything out there, ESG, that you know we thought might pop in a different way, but it hasn't.
00:09:09
Speaker
So what do what do you say to that? if are we Could we make a mistake and would it be catastrophic you know if if if we were wrong? So.
00:09:21
Speaker
What i would say there first is that it's definitely possible that there's some capital being, let's say, inappropriately allocated or improperly allocated. Some valuations are overextended.
00:09:34
Speaker
But I will also issue here, Chris, that during that dot-com bubble, where there was a lot of capital being wasted, a lot of capital being misallocated, you had the biggest goodwill impairment of all time out of that era.
00:09:49
Speaker
Many of the internet companies that all of us use right now, Amazon, Facebook, X.com, Uber, and all that infrastructure was laid during that dot-com bubble.
00:10:01
Speaker
So at the time, there was definitely damage. There were wrong choices made. Capital was inappropriately inappropriately allocated. But 20 years later, that helped breathe life into other new economic areas.
00:10:15
Speaker
And at the time, that was considered impossible. So one, there's definitely that risk. But two, should we keep betting on AI? Chris, I don't think we really have a a choice in the matter, right?
00:10:30
Speaker
if it And if we view it from outside of just our conversation here, talking about the accounting impacts of it, The upside of AI in terms of economic benefits, in terms of healthcare, in terms of research in any number of of areas, those upsides are, I think, too large to give up on.
00:10:54
Speaker
Now, I will say that we're probably getting a bit out in front of ourselves in terms of the current capabilities of AI, in terms of the current investments into AI.
00:11:04
Speaker
But I would say that from a business point of view, and frankly, from a geopolitical point of view, it's going to be awfully tough for one country, be it us or others, to back off AI while everybody else is...
00:11:18
Speaker
ah going ahead with it So I do think we might be over investing in certain areas, but I don't think we should give up. And I don't think that overall it's a bad bet or a. Incorrect choice.
AI Industry Gold Rush and Economic Growth
00:11:34
Speaker
You know, when I think about all that's happened in our history and I think about the railroads and I think about you just mentioned the Internet. And I think about what we learned during those eras, because that was a major shift in how we operated as an as an as a company and as a as a country.
00:11:51
Speaker
um What did we learn from that that we can apply to the situation that we're in right now with A.I.? Is there anything we can draw from that to make us better as we move forward?
00:12:02
Speaker
What I would say there, Chris, is there are always going to be lessons that can be applied, right? It can be railroads, it can be pets.com, or it can be any number of these AI-based firms.
00:12:16
Speaker
But the ultimate thing is is, does the business, one, earn a profit? Now, obviously, there are been plenty of examples of companies that that did take a while to earn a profit. That's fine in and of itself.
00:12:31
Speaker
Two, is there a real pathway for this company to turn a profit? And three, how is the cash flow? at these companies, right? Because Amazon famously was unprofitable for years and is now the undisputed king of e-commerce, web hosting to a large extent, and numerous other areas.
00:12:54
Speaker
So the real thing, and it always comes back to this, Chris, uh, And you and have had conversations about crypto and AI also. It all comes back to, does the business at a fundamental level make sense?
00:13:09
Speaker
Is there a pathway for it to be profitable? And is the product or service offered by that company different enough to actually make it stand out in an increasingly crowded marketplace?
00:13:23
Speaker
Otherwise, and I know there is one CEO, ah Sam Altman said that his vision is to treat AI as a utility, which I know might make sense from his own personal point of view. But in terms of earning a profitability and earning a rate of return on all of this capital being allocated, that's not going to be...
00:13:46
Speaker
awesome news for those investors. So it's all going to come down to how this industry evolves. But I do think there are always lessons and they usually come back to the business fundamentals of the industry and of the company being analyzed.
00:14:00
Speaker
Hey, Sean, we've been talking about you know commitment and obligations. And I'm thinking about a rise that I'm seeing or a shift that I'm seeing on a quarterly basis as it relates to company obligations and trying to figure out how AI is impacting that. What are your thoughts as it relates to that?
00:14:19
Speaker
I think AI is currently in the middle of the equivalent of the California Gold Rush, Chris, that every company out there and there are people at the manager level, director level who are having pressure brought onto them by the CFOs, CIOs, boards who want to implement AI and they want to implement it yesterday.
00:14:40
Speaker
if not sooner. And so right now, there are billions being allocated to building out data centers, water rights, land purchases, and just building out all that backend hardware infrastructure to power the AI applications, which on their own, take billions to train, test, and actually use.
00:15:03
Speaker
Just anecdotally, there was a story, and i believe it was in Eastern Kansas, where a firm was trying to buy up land from farmers to build their ai data center and ended up offering those people in excess of $20 million per person.
00:15:22
Speaker
her per owner to purchase that land. So right now, there's a huge influx of capital into this space to build out all of this infrastructure, all the bandwidth, and and all of the capacity to run all of these AI applications.
00:15:39
Speaker
And just to to try to put this into some bigger picture context here, It is estimated that in 2026, the AI build out in yeah data centers is going to add between 1.5 and of economic growth to the entire US economy.
00:16:02
Speaker
So that's a big impact right there. You know, i'm I'm thinking if I was a financial agency and I alluded to this earlier, how do you know that the system that we have in place
Can Financial Systems Handle AI Changes?
00:16:15
Speaker
right now?
00:16:15
Speaker
Okay. Whether it's a gap or whatever protocol we have and that we respond to is enough. Okay. When I think about, you know, the significant change in financial transparency. i'm I'm curious as to what agencies think is happening and what the solution might be.
00:16:36
Speaker
Do you have any alarm or any concern that the agencies may think that, hey, what else should we be doing? Is there enough financial transparency? What are your thoughts? So that's a very interesting and I think very, very timely question there, Chris, because in early 2026, there had been some noise out of the SEC that the agency was actually considering to take away the quarterly reporting obligation and to only mandate companies post out certain information every half year, which
00:17:16
Speaker
I think given the the pace of change and the sheer volume of investment going on, even more so in the AI space, doesn't really make any sense to me from an economic point of view, nor nor from a market transparency point of view.
00:17:34
Speaker
And there have been conversations that have been going on at several ratings agencies. I don't know how official or how comprehensive these these conversations are, but there have been some murmurings that the credit rating agencies are in the midst of trying to answer that exact question.
00:17:56
Speaker
That, okay, if we have AI causing such a massive upheaval, for almost every company in every industry outside of just the income statement effect in terms of the hiring, in terms of the turnover, terms of pricing.
00:18:12
Speaker
How do we capture all of that using our traditional quarterly re reporting model and our periodic ah credit rating system. And the answer is, given the pace of change and the current structure, those two are not going to intersect, that there is going to have to be a change on on either end.
00:18:34
Speaker
And I don't see the private sector pace of and of investment modifying its course anytime soon. So I would not be surprised if over the next, let's say, 12 to 18 months, that there are some pretty significant changes in terms of how of how companies are evaluated and judged, either they're strictly from a SEC sort of markets point of view, or from a external credit ratings point of view, because right now we are talking apples and oranges.
00:19:09
Speaker
You know, I'm thinking about and I know you've done consulting as it relates to this and building out the right structure internal to an organization to have a conversation about AI and implementation.
Successful AI Implementation Strategies
00:19:22
Speaker
And, you know, those that are at the table, which is executive leadership and probably one one line down. um What does that look like? Remind me as to what that structure, a good structure looks like to make sure that we're not missing something, because the reason I'm Going back to that, Sean, is because I think that there's an incredible amount of responsibility on the accountants, on the CFO and those that are tallying the numbers, but also how well the organization is planning for AI and the transformational exercise that comes with it.
00:19:57
Speaker
So what are your thoughts? And again, just reflect on the structure that needs to be in place. it MIDI? Is it, you know, sharing of information in a different format?
00:20:08
Speaker
I mean, what are your thoughts in order to make sure you build out a sound infrastructure to make sure you're planning and your efforts for AI are successful? Chris, that's an excellent question. And it's a question and topic that I believe is getting kind of not overlooked, but kind of put onto the back burner. That implementing AI at at any company is actually...
00:20:34
Speaker
incredibly complicated, that there is no tool as of right now that is a true plug and and play model, that implementing AI is tough, right? Because AI, if you want to implement it correctly, you really does take, in essence, a true cross-functional team from IT to finance to operations to having that upper management support and buy-in, it takes a real comprehensive ah approach to one, analyze the business and analyze underlying business processes to make sure that one, all of those are happening as they are currently documented, which isn't always happening.
00:21:17
Speaker
And to two, go in and actually try to find out, do we have places in our company and I would assume most firms do, where AI can actually bring us an ah ROI.
00:21:30
Speaker
Now, obviously, for any project, there's going to be some negative ah ROI at the beginning, during the training, upscaling, onboarding part of it. That's fine. But is there a pathway where we as the company, I'm not talking about the AI firms, chip makers, all the rest, firms that are using this in agriculture, medicine, education, is there a pathway for us, the the customer of those AI firms, to turn a profit using AI?
00:22:01
Speaker
And Chris, to be honest with you, right now, that is a hard conversation that I think a lot of people don't want to have. that That sort of cross-functional team, and I'm not saying that that on-ramp to turning a profit isn't there.
00:22:17
Speaker
It certainly is. I do think it's just going to take a bit longer than people want to think and want to really consider. And that's a really interesting, i think, psychological angle.
00:22:30
Speaker
And I'll be really brief here, Chris, that you and I and everybody here today is probably implementing AI in their own lives on an everyday basis. I am certainly, and I've documented benefits right now to me as an individual in research, workflows, ideas, brainstorming, all the rest.
00:22:52
Speaker
But at a enterprise level, actually capturing that upside and those quantifiable benefits, aka profits, is going to take longer. And it's really important to keep that in mind because, as we've talked about,
00:23:08
Speaker
AI build out, AI spend, AI investment isn't just contained to a handful of ai companies. It is being... invested in and spread by asset managers for ONK plans have exposure. It's a real economic engine, but all of that has to be balanced out by some measured objective assessment of the of the cost of these tools, how they're being used, and to really make sure that we, as the firm, as the customer of these AI firms, have a pathway to profitability via AI.
00:23:45
Speaker
You know, one thing that comes to mind, I think about all of the organizations out there, and I read an article not too long ago, and it was talking about strategy.
CFO Challenges in AI Budgeting
00:23:52
Speaker
Okay. And organizations having a strategy as it relates to AI and going through that exercise and putting demands on you know, the human capital to make sure that their business is in alignment.
00:24:04
Speaker
You I couldn't help but think about I'm like, what mistakes are we making from an accounting perspective? And what I mean by that is the CFO has the burden of a lift when we talk about financials and the impact on the business and being able to represent that to executive leadership as well as to the board.
00:24:20
Speaker
And I'm and and I know this is off the cuff, ah Sean. but So give me some some latitude here. i mean, what do you think are some of those mistakes? but Okay. And yeah it could be to planning it could be planning. It could be, you know, you know how we go about ah measuring our success. It could be accounting and how we're budgeting for that. I mean, what do you think are some of the mistakes that some of these CFOs ah could be making as it relates to their business?
00:24:53
Speaker
What I would say there is that there are three main buckets of possible errors that I'm seeing talked about more and more. One, to your point, is the budget. That AI, by its nature, does not function like a traditional IT t budget.
00:25:09
Speaker
and that And that handling it like the usual OPEX budget for IT or a CapEx project, that doesn't really match up with how the use of AI actually scales and grows. ai usage is the epitome of that J curve or that hockey stick that as more people use it and they start to experiment, have some benefits, have some upsides, they are going to use it more and more and more. So I do think that
00:25:41
Speaker
that the overall budget number is probably right in most cases in the short to, let's say, medium term. But how that budget is allocated and how that budget is treated and actually thought about could be an error an area where there are some CFOs who just aren't quite grasping how that AI spend is going to increase over time.
00:26:05
Speaker
And two, what I would say there is that how does this impact other areas under the purview of a CFO? Now, obviously, there are some companies that have separate CFO and CIO lines. That's fine. But I would say that most companies that are enterprise size, the Fortune 1000, the Russell 2000, that there is some overlap.
00:26:29
Speaker
And that how does implementing something like Claude or OpenClaw or any of these other agentic models, how does that impact our firm from the cybersecurity, internal control, and a workflow management point of view?
00:26:49
Speaker
And I don't think anybody really has that answer yet. Everybody wants to use agentic AI because that's the newest version of AI. And all of the firms that are making them are also advocating for that.
00:27:05
Speaker
But from budget point of view, cost point of view, and how it impacts the rest of the internal control structure of the company from top to bottom, I'd say those are areas where there could be some errors being made right now.
00:27:22
Speaker
You know, I think about the overall economies and AI, and I can see the spur and the jolt that we're getting right now.
Skilled Worker Shortage in AI
00:27:28
Speaker
And, you know, I have to go back and i have to reflect on, you know all of the contractors and all the suppliers and all of those stepping up and building out these data centers.
00:27:38
Speaker
And, you know, I want to make sure that it's still promising for them, but i'm I'm trying to figure out who's left holding the ball at things. Don't pan out according to plan. And even though it's...
00:27:49
Speaker
They're busy right now. Do we have enough of them? I mean, is there a concern? i don't know if you've read or if you've investigated anything as it relates to those that are playing a different role and have a heavy hand in our success and the transformational exercise, meaning the human capital piece.
00:28:05
Speaker
Do you think there's a shortage in helping us get online and get on board with AI and be successful as we move forward? Excellent question. And the entire conversation around human capital or people, right?
00:28:20
Speaker
People are human capital. People, employees, do we have the right people trained in the right areas to help us achieve the the correct goals? That's a much bigger question than i think we on the Becker podcast can ah can ah you solve here.
00:28:36
Speaker
But I would say that that right now, We are using, i think, every resource possible that is readily available, both physically and intellectually, to get this AI industry, basically, up and running and to operate as advertised. And I would say that right now, there is a shortage in terms of the educated, skilled, and trained workers to help us do that from every aspect, right? From the actual building of the plants, running the water pipes, building out the actual grid that has to be built out to then connect to this data center, to everything else, to the software engineers, to to people who understand the actual economics of these AI agents.
00:29:25
Speaker
We are, I think, pretty much operating at capacity right now. And I'd say that's a direct result of the rapid influx in in investment that's happened, I'd say really starting 2025, but it is well underway twenty twenty six You know, I started thinking about as we continue down this path.
Rapid AI Advancement and Industry Adaptation
00:29:48
Speaker
OK, and I I see a lot of companies making the investment. Some are late to the game. OK, they're trying to find a human capital and resources. they're thinking about strategy. um But, you know, something I want you to leave with the audience, if you don't mind exploring this further.
00:30:03
Speaker
um Just given this landscape, and I think it's necessary to go back and revisit how quickly AI is is taking over. OK, I speak to the point that what if it doesn't go according to plan? Yeah, that could happen. But um talk to how it's changed so much in the last six months. Forget the last year.
00:30:22
Speaker
Speak to how it has truly changed the languages to just how folks are using it to be more successful in our day in day out jobs, whether you're a coder okay or whether you're an accountant or what have you.
00:30:36
Speaker
It has changed. I know because I dial in AI and it gives me a different answer now than it did three months ago. OK, a better answer, a more concise answer. What are your thoughts when I say stuff like that?
00:30:47
Speaker
So what I would say there first is that AI has, I think, accelerated the pace at which companies and people have to make business choices in terms of investment, in terms of a hiring, in terms of allocating capital and time. that I was just listening earlier in the week. There was a great interview with Michael Dell.
00:31:13
Speaker
And his kind of anecdote here is that right now, a quarter is the equivalent to a whole year. if not two years, in terms of how companies have to think, have to act, have to invest, have to allocate time, money, and internal expertise. So I think that has been the one overarching in- in arguable impact of ai on every business is that it has pushed all of us in every industry to accelerate how we analyze data, make choices, and then take action based on that business data.
00:31:53
Speaker
And then two, are we really seeing benefits from AI from an accounting point of view? Yes, that everybody that I talk to, that I work with, that I consult with in the accounting finance world, they are using AI right now to automate their tasks every single day.
00:32:19
Speaker
And to your point, we are far beyond to just uploading a PDF into ChatGPT and then having it output some bullet points that are half right and half out of date.
00:32:33
Speaker
We are at the point where... I can be an expert in any field, which is dangerous, right? And I'm saying that, all kidding aside, that now we're at that, I think, tipping point where there are people who are able to really leverage themselves exponentially, and especially for people that have their own firm or that or that might work at a firm that has five to seven people, or even people that are in a new practice.
00:33:06
Speaker
at a larger firm. You can do with five people what probably last year or in 2022 definitely would have would have taken 50 people or 100 people in terms of research, client service, analytics, content creation, client outreach, client follow-up, investor management,
00:33:29
Speaker
You can do so much more using these tools that the upside really, i don't think we've even i don't think we've even really started to grasp how much upside there is from an individual level to upskill and to level up themselves in the workforce, especially in ah in fields like accounting, finance, wealth management,
00:33:52
Speaker
ah or economics more sort of broadly, that are so data heavy. AI feeds on data, and it gets better the more data you feed it.
00:34:03
Speaker
And so I do think there's a lot of upside left. And obviously, there are some possible risks and some possible pitfalls for sure. But I, for one, and am incredibly ah optimistic.
00:34:14
Speaker
overall on AI and overall, the opportunity that AI is already creating for people in ah the accounting world.
00:34:24
Speaker
Because then I'll end with this, Chris, that forever, for decades, people have been saying at every conference, at every webinar, and every magazine, back when there were magazines, that the accounting function wants to move to be that trusted business advisor.
00:34:43
Speaker
Now, with AI at our fingertips, we actually have the time to bandwidth and the intellectual horsepower freed up to actually be that advisor to our clients, to our colleagues, and to really help them make better business choices, which is ultimately why all of us are here.
00:35:03
Speaker
You know, and I think that's an outstanding message. I think there's so much going on from a transformational perspective. I think it's a scary time. I think we're still trying to figure out exactly what the components look like for success.
00:35:17
Speaker
I think the speed in which we're moving and and have to grasp this information.
Balancing AI Investments and Workforce
00:35:21
Speaker
is through the roof i mean it's just going to take a lot of energy and effort to focus and plan uh to move forward in accounting in it t and wherever you fit within your organizational structure i mean ai will be front and center you know one more thing i want you to share with just my cfos okay because i know there's some cfos that are tuned in i mean what message would you have for them they're on the hook OK, in a big way.
00:35:49
Speaker
OK, what would you have? What would you share with them? That one thing you want to leave behind with them to make them feel some level of comfort as it relates to their position in this overall change.
00:36:03
Speaker
I think CFOs really have a tough. think balancing act right now, that on the one hand, you have everybody asking you to allocate funds to AI build outs.
00:36:15
Speaker
On the other hand, you have people on your team who are objectively, and I think justifiably concerned about all all of this automation, putting them out of work.
00:36:27
Speaker
So how do you balance that? And how do you make sure that on the one hand, the the people that you need to make AI work for you are on board.
00:36:37
Speaker
And you need to have those people on board to have your investments, to have your debt issuances, to have your equity investments pay off, to have that cash flow currently going out the door actually come back in terms of a of a positive cash flow.
00:36:54
Speaker
But the one sort of point here that I always try to hammer home to people who are at that level, I always try to sort of put this in there. You are the CFO, you are the VP of finance for a reason, because you have the intellectual bandwidth and capability to balance people demands, external demands, and to make sure that if you are allocating capital, it's being put to work in projects that make financial sense.
00:37:23
Speaker
AI changes absolutely nothing of that. Your job and your expertise has not changed. All that's changed is the pace at which those choices have to be made.
00:37:35
Speaker
And that's really the whole the whole crux of this argument. And that's a big part of the anxiety and angst around AI. It isn't so much that that people are concerned that, oh, I can do my job faster.
00:37:50
Speaker
Or that, oh, this this new agent came out. It's that every single day there are changes, releases, updates that are being touted as the upheaval of ah business Y or but industry X. But as the CFO, it's up to you to balance those headlines as you do anyway.
00:38:10
Speaker
Right? You're always balancing headlines and all kinds of things against what is your business doing, how to get better at that, and what investments are going to make that possible.
00:38:21
Speaker
You know, Sean, you are an absolute superstar and I appreciate your time, your energy. I know you are a busy man. So I thank you for being on another episode with me.
00:38:31
Speaker
Absolutely, Chris. It's always a ah awesome time talking to you. Awesome. Awesome. I'm going to look into the camera and I'm going to share this.
AI's Transformative Role in Business
00:38:39
Speaker
AI is nothing to be afraid of. And I've said that before. It is changing and it's changing at a very fast pace. As accountants, we have a responsibility, whether we're the CFO or whether we're a staff accountant. I mean, we're going to play a role in this exercise. So upskill, plan.
00:38:55
Speaker
You know, make sure that the structure within the organization, whatever organization it is, brings in all of those who are influential in the equation, whether that's IT, whether that's accounting, whether those are folks on the operational side. Everyone plays a role in this exercise and the dollars are there.
00:39:13
Speaker
Monies are being spent billions and billions of dollars. There are people that are working. you know, day and night to make sure that we are successful as it relates to our AI transformation. So thank you for joining me on this episode of Balancing the Future, and I look forward to future conversations.