B (3:17)

Well, that, that's really fascinating to me because I, I'm an educator myself. I, I just got my PhD a couple years ago, started teaching. I'm teaching at Vanderbilt now and so I'm very fascinated with the different styles and things like that on all this go. So I, you know, this could be interesting. I would love to go teach in Europe for a semester and see what that's like. That would be.

A (3:41)

You should try it, especially Aust. You have to go to Austria now.

B (3:45)

I got to do Austria. I think my wife would be totally down for that. I think that would be awesome. All right, so. So with all this education and this international flair. Right, because you've, you've not just, you're not just an eternal student.

A (4:00)

No.

B (4:01)

You actually work too, right?

A (4:02)

Yeah, exactly. So this year actually I have my 40 years of, of work. I started very early when, when I was 17. And I'm 20 plus years as CFO of international companies. I've managed as a CFO on executive level companies, but I also manage business segments and my PhD is not in finance, it's in communication studies. So I managed also public relations in some of the companies. So I'm a total mix.

B (4:34)

That is awesome. I mean, I would think it'd be in like finance. No, no, I'm going to do it in communications. I think that's, I think that's great.

A (4:41)

Yeah, exactly. And it's very helpful because you need communication at every job. Right. And by the way, mergers and acquisitions is one of my other expertise because I've worked On more than 30m and d transactions in my life. And that's where it's always very important to understand the business before you dive into the number. You know, what, what is the business about? How do you win clients? How do you keep the clients? How do you make money? How do you lose money? And then you jump into the financial statement analysis. So that broad perspective to understanding the target companies has always been one of the things that I really enjoyed about mergers and acquisitions.

B (5:22)

I think that's awesome. Do you think that kind of expertise? Because you gain that expertise through doing it. Right. The first time you did an M and A probably wasn't nearly as smooth as the last time you did an M and A.

A (5:39)

Right, exactly. M and A can be very complex. And by the way, when I was the very first time a CFO in an international company, it was also very challenging. Right. Whatever you do the first time in life, you're just not good at it. Which was also one of the reasons by the way, why I created this by michaelhofer.com, my website where I talk about M and A and what is the process for M and A and how can you make it successful? Because I know how easy it is to get lost because there are so many to do lists from legal to do list, financial marketing, the contracting. It's almost a never ending to do list and people get lost in it. Right. So focusing on the investment thesis, on the big idea, why you bought that company is, and, and how do you actually do it is. Is one of the things I, I provide on, on my website.

B (6:34)

Okay, so that brings up a very, a pretty softball question, I guess. Can't AI help help you do a lot of that stuff? Because it can keep track of a lot of things at the same time, probably even better than humans can.

A (6:49)

Yeah, exactly.

B (6:51)

Where do you think AI will play its role in, in these big deals that, that are happening?

A (6:57)

Yeah, and, and by the way, I just recently finished and, and published a course about AI in finance. And I was just reviewing a chapter of my upcoming book about AI transformation or the financial the Finance Transformation with AI and in, in mergers and acquisitions, it, it's a game changer. It's a game changer in men in many departments. But talking about mergers and acquisitions in the past, what, what you've done, you know, there's usually a data room where the target company puts all of the data, but it's just overwhelming, right? It's hundreds or thousands of documents that you have in there to review and sometimes in a very short period, right? And what you did in the past is you picked a few examples and you hope that those examples included the main elements, right? But now with AI, you basically can review everything in a way shorter period. And then what the humans are doing, right. You cannot just take what the AI gives you, right? So you still need to review it, but it's more, you know, the AI looks at everything and gives you the red flags and highlights. You know, those are the things you should look at, right? So it's basically from sampling a few documents where you hope you got everything, to reviewing everything. You get a very first draft of the analysis from an AI tool and you as the human, you review it and you jump on the exceptions.

B (8:36)

Well, all right, so this, this is really interesting because I know during M and as, and also like when someone's filing for the sec, a lot of financial guys will hide information in plain sight, right? By, by just describing things differently that might be harder to understand, you know, for the. Especially if you're scanning and just taking examples, this kind of exposes some of that now where before. And you know what I'm talking about. Am I. Am I saying this? Okay, so, yeah, I'm not making this stuff up, right?

A (9:11)

No, no. It's happening in almost all of the deals. Right. And you do on purpose. You overwhelm the other party with documents. Right. So that it is just very difficult to find the most important things. And AI changes this completely because in the past it was really difficult. Right. And then sometimes during the integration or 10 months after buying the company, you find stuff that you should have found during the due diligence, and AI changes that completely.

B (9:41)

Wow. So that's going to make these M&As go faster or more contentious, maybe even, right?

A (9:47)

Yeah, exactly. Way faster. I still believe it gives you more time for the analysis. Where in the old days you were really rushed through the analysis, now you have more time for the analysis. So I wouldn't shorten the total analysis time too much, but I would focus more on the analysis and really the interpretation of the red flags that are highlighted by the AI tool.

B (10:15)

Okay. All right. So I see one place that AI is a total superpower, totally changing the M and A market completely, probably, right?

A (10:23)

Yes. And it starts at the beginning because very often one of the biggest problems is a company or an owner gets approached and says, hey, I buy an investment bank. Very often I have this one company. I think it's a good fit. Why don't you look at it? And then they only look at that one company. With AI, you can really look what other companies are available in the market, and you really can start the whole process at a different level.

B (10:51)

All right, so this is upending that whole industry, it sounds like.

A (10:55)

Yeah. And it automates, you know, market research like that. What kind of companies are currently on the market? You can automate this with artificial intelligence so you don't have to buy those very, sometimes very expensive reports. You create your AI agent that crawls through the Internet and every day in the morning sends you an email with the interesting targets that you have.

B (11:24)

Oh my goodness. This is going to change quite a bit. All right, so let's move out of M and A. Because you said you're in the process of writing a book on AI and finance.

A (11:33)

Yeah.

B (11:34)

So let's talk a little bit about that, because this is an area that I know in general, Gen AI and numbers don't mesh very well. But finance is a whole lot more than just numbers, right?

A (11:47)

Yeah. Exactly. It's a lot of reporting, it's a lot of management analysis. And in my ideal world, actually, since I started in finance quite a time ago, for me, the goal was always to be a business partner in finance. Right. To use the numbers, but work very closely with the operational people and the CEO to figure out what the numbers actually tell you and how you can improve the company. Because if you want to improve a company, you improve how people work together and the processes and how you develop products that solve a solution for the client. Right. With AI, a lot of the standard work in finance, whether it's just consolidating, reconciling the initial reporting, which is currently 60 or 70%, probably for most of the people in finance that is getting reduced with AI significantly. And then you can focus really on the analysis and setting up those meetings with the business people and becoming more a business partner.

B (12:59)

Okay, so that tells me that's elevating finance's role in the company. Yes, substantially. Instead of doing all the grunt work that goes with. And so this, this could be extremely valuable, especially if. If you've got a very strategic CFO instead of a CFO that's just preparing reports for SEC filings.

A (13:23)

Yeah, exactly. And AI allows this too. Right. So if you can say you reduce the 60 to 70%, which was just the reconciliations and consolidation and the reporting and compliance and auditing and stuff like that, if you can reduce this to 20 or 30%, basically reviewing the red flags and the exceptions, then you have just more, more time to really go into scenario analysis. I mean, in the past in scenarios, you usually worked on three scenarios. Right. There's a base case scenario, a worst case scenario, and a best case scenario. Right. With AI, you can run hundreds scenarios. Right. It's just you probably don't have only one output, but a range of outputs. But that's probably how reality actually is.

B (14:15)

No, no, no, no. This, this is really interesting because I don't know much about the finance side because when I was an executive, I was a cio, so all I knew about finance was that they just kept cutting my budget every year.

A (14:30)

The bad finance people.

B (14:31)

Yeah, yeah, I agree. They were bad finance people cutting my budget every year, so. So I really see some huge opportunities in that part of the business that I never thought of before.

A (14:46)

Yeah, exactly. It's a game changer, as I said. And it starts in accounting where there's just a lot of repetitive work that you do in the closing of the mouth. Right. And the more it is repetitive and more Rule based, the more you can replace it with AI.

B (15:11)

Do you ever see it getting to the point where I can report financials daily instead of closing a quarter? Because closing a quarter always meant four or five weeks. Or in some big companies like intel where I work, Sometimes it takes 12 weeks to close a quarter. If you know crazy things are going on. You know what I mean?

A (15:34)

Yeah. So one of the things that I described in the chapter about FP&A and artificial intelligence is that usually in the past you wait until you have the actuals after the closing and then you work on the forecast, probably with data that is already a few weeks old and then once a month you have an updated forecast. The idea with AI is that a lot of those things come automatically on a daily basis or at least on a weekly basis. Whether it's from the CIM system, whether it's from the ERP system, from the HR system. And in the past where you had to do everything manually. An AI based system actually as soon as new data is here, it can reforecast and then you can take the reforecast and really dig into the details and also try to figure out whether it makes sense or not. But it comes in really as I said, on a daily basis or a weekly basis. It depends on how often you feed the AI with the data.

B (16:38)

Okay, so here's a question just popped in my head about that. Is there a point where the frequency is too much? Yeah, because now I'm going to. There isn't enough. What's the right word dampening in the system that could thrash decisions at the executive level.

A (16:57)

Yeah. So there's certainly, it depends on the company and also how much volume you have in the company, but also on the topic. So for example, for your lease accounting and you have to pay rent and for your offices, that doesn't change every day. Right, Right.

B (17:14)

Yeah, that's very.

A (17:14)

Yeah, yeah. So and, or, or bigger capex investments, you know, where you invest into bigger machinery, it's probably also doesn't change on a daily basis. Right. However, for some of the company with a high, high volume order intake and, and, and sales can make sense on a daily basis.

B (17:38)

All right, that. All right, so it's, I, I've got to be strategic I guess is the right word about when, when to do this. Because I'm, I'm thinking of, you know, big companies like, like intel for example. Right. Projecting where the market's going to be and how many chips to produce and which mix of chips is a very, very complex situation that can lead us into some major problems if we do it wrong. Right?

A (18:10)

Yeah, exactly. And I would say in the old days you waited for the monthly or even quarterly numbers to take decisions. And the idea with AI based and quicker updates and more often the updates is really you get the information earlier and you shouldn't overreact and over manage. Right. If one day is different than another, well, don't change anything. Right. But if you see a trend in the data, then probably you, you should react as an executive.

B (18:42)

All right. So I really like. So this really changes the executive position substantially because I think a lot of executives work on a cadence, right? Especially CFOs. Right? I work on a quarterly cadence. I am doing other things throughout the quarter, but things are very operational. Right. On the first month of the quarter this is what I expect. On the second month and then the third month of the quarter I'm doing this. So this could change that whole landscape substantially then?

A (19:16)

Completely. Right. It becomes more a rolling forecast. And then it really depends on your company and the product and industry and the size of the company, how often you update that rolling forecast and which lines of the rolling forecast. But it really becomes a rolling forecast with way more updates where you can jump in earlier if you see something developing in a different way as planned, which can be good or bad. Right. So.

B (19:45)

Well, it sounds like AI just gave me more work to do, Michael.

A (19:49)

Exactly. But it also summarizes it, right? So one of the things where I use AI is very often is, is when it comes to contract review, for example. Right. It's one of the low hanging fruit with a significant return on investment where many companies, including companies like Microsoft, report 50% cost reduction because you upload a contract into the AI, at least you get a first draft of analysis of the report. And that's already a way better start than just reading it from page one through the last page and I don't know, maybe 425 pages. Right.

B (20:33)

Those lawyers, I have a lot of friends that are lawyers, they like to write a lot of really big stuff and it's all boilerplate that they cut and paste it from somewhere else.

A (20:43)

Yeah, yeah, exactly. It can also find, by the way, because many of the bigger companies have hundreds or thousands of contracts, right?

B (20:53)

Oh yeah.

A (20:53)

And sometimes there are clauses in there where you really have to follow up, Right. It's not that you sign once the contract and then you don't touch it anymore. There are price adjustments, liquidated damages and all kinds of complicated stuff. Right. AI helps you also to keep A track on all of those things where you need to follow up.

B (21:17)

Wow. I mean, yeah, there's a lot going on here all of a sudden. So it sounds to me like AI is providing more work, but it's more strategic work than tactical work. Like you said before, the mix is changing, but it sounds like it's making people have to think at a much more critical thought and a more strategic level than they have in the past.

A (21:44)

Yeah. So I think we as humans can focus on the things that we do really well. And those are the exceptions, those are the one time cases, those are the relationship management, those are the strategic decisions. And AI should not take over of that kind of stuff. Should not take over that kind of stuff. But the really repetitive things and highlighting some things that may be different. Think about auditing. Right. In the past also, you sampled a few topics, but you never went through 100% of the data. AI goes through 100% and highlight some issues that are just different or that are in line with certain requirements that you set with the AI. And then as humans, you still need to review it because sometimes there may be a false issue, red flag. And in reality there is no problem with it. So what I keep repeating here at the company and also in my course on Udemy and in the upcoming book, it's still us humans to decide and to do the most complicated and difficult things. But everything, what you can repeat easily, where there's a lot of standardization, that's where AI is just extremely helpful.

B (23:09)

Okay. I really, I really like how you've positioned this because it's telling me at the executive level and also I think in, in our everyday lives, we need to, we need to change our work habits away from the mundane and into the more critical, more thoughtful types of jobs because the, the mundane can be handled by somebody else. Which reminds me of the, the event, the invention of the tractor for agro business. Think about, think about that. When farmers first saw tractors, I bet they thought, oh my goodness, what am I going to do all day now? Yeah, I don't have to feed the horses. I don't have to get them shoed or shod. I don't know what the right word is to put horseshoes on it. I, you know, there's, there's so much involved around plowing a field before and now with the tractor, it changes everything. So the way I work changes completely. We're seeing that. What you're telling me is we're seeing the same thing with AI if we adopt.

A (24:25)

Exactly. So, so the discussion that something new is coming and there is opportunity in, in that new thing, but there's also a threat in the new thing that has happened all the time. And you mentioned, I mean, one of the things I like repeating is industrial revolutions and machines that came up. Right. Everyone at that time thought, we all going to lose the jobs and we're not going to find other jobs. We created other jobs. The same was with the Internet. It destroyed some jobs, especially for the middlemen, but at the same time it created a lot of jobs. And the same happens with artificial intelligence. It will destroy or it's going to be a threat for everything. But it's very repetitive. But for everything else, it creates opportunities. And there will be new jobs with AI. There will be the data management jobs, there will be the AI governance jobs. There will be somebody in the company, in IT responsible, making sure that the AI tools work together well. So there are other jobs that are being created at the same time.

B (25:38)

Well, I agree with you. In fact, I asked, I teach at Vanderbilt and I asked my students if they'd ever heard of typing pools and they looked at me like, we have no idea what you're talking about. I said, yeah, professional typists. And they go, what? And I go, oh yeah, guys. Big companies used to have, you know, floors of people that all they did was type memos all day from recordings from managers and directors and executives. And they're like, you have got to be kidding me. I go, no. So where are all those people? Did they just curl up and die? No.

A (26:17)

Yeah, exactly. So by the way, I mentioned before that it is 40 years this year for me to work. When I started working, it was actually in accounting in a big bank. There was no computer, there was no cell phone or nothing. We wrote down the journal entries on the paper. We brought the paper then to a data entry department who entered the data and at the end of the day you got the results back. Whether assets and liabilities and equity matched or not. Right. But it does exactly as you say. You know, it was a different world when I started.

B (26:59)

Yeah, but, but all those people got new jobs. Yeah, higher value jobs, right? A lot higher value jobs. So, yeah, it's a, it's a. To me, it's an extraordinary time. This is a big change, probably even bigger than the Internet. But I'm not sure it's right. Those two are like close to each other, maybe up there with electricity. I'm not quite sure yet. We'll find out.

A (27:26)

Yeah, we'll find out. And like the Internet, we are currently in an AI explosion period. And the same happened with the Internet. Right. Everything that had.com was the valuation was huge. Right. The same happens now if you have AI in your company name. Valuation is huge. Right. The bubble burst with the Internet, but the Internet didn't go away. It just changed it a little bit. And the question at that time came up, yeah, can you really make money? And the companies that made money with it survived. Right. And I think the same is going to happen with AI. It's it I see it last as in the second half of 2025 or let me rephrase this. Initially, when I started working with AI, everybody was happy just to use AI. You were the cool people. Yeah, we used AI. Right. Now when I talk with my direct reports, I say, you know, it's for me, at the end of the year, if we just say, yeah, we used AI, but there is no significant return on investment on it, then we failed. So the discussion is already changing and everybody is more looking, okay, what is really the output of AI and, and how can I use it?

B (28:40)

You know, I, I, that's interesting that you said that, because I'm seeing the same sort of thing. And the companies that are still restricting use of AI in their companies are going to fall further and further behind.

A (28:54)

Yeah.

B (28:55)

Because they're going to lose people. Because people are like, I want to go with a company that lets me be augmented with AI to do my job more effectively than having me, you know, blocking me from, from using this incredible tool that could help me get my job done more effectively.

A (29:14)

Yeah. And especially if you can eliminate a work that anyway nobody wants to do. Right. The repetitive daily stuff, I mean, it's not fun. So killing that kind of work, actually a lot of people are happy about it.

B (29:30)

Yeah, that's true. All right. Hey, Michael, this has been great, but we're out of time. So my question to you is, if people want to learn more, where do they go to learn more from you, Michael?

A (29:41)

Yeah, So I have a website by michaelhofer.com I write a lot about mergers and acquisition and artificial intelligence. There's a lot of free things. There are courses, there are books there. So if you go to that website, you find all of the questions, all the good and the bad, about AI and mergers and acquisitions.

B (30:02)

That's awesome, Michael. Thanks so much for coming on the show. I'm going to go check out your website because I got a lot of questions on the finance side because I don't know that area very well. So this is awesome. Thanks again for coming on the show.

A (30:18)

Thanks for organizing. It was great talking with you.

B (30:25)

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