The AI Accountant | Peter McCarroll | S1E17

Utsav Bhatt: Welcome to Alt Consulting, where we do AI adoption conversations. I am Utsav Bhatt founder of Stratoff. We help companies drive AI adoption and innovation-led growth. Over the last few years, I have spent a lot of time studying how AI is reshaping professional services.

I wrote a book on the future of consulting called Alt Consulting, exploring how AI is going to fundamentally shift the work we do in the strategy consulting area. But consulting isn't the only profession which is facing these tough questions. Accounting is another industry built on Specialized expertise, trusted advisors, established business model, and decades of accumulated professional knowledge. So today, accounting firms are beginning to confront many of the same forces that consulting firms are starting to grapple with.

To explore those questions, I am joined today by Peter. Peter brings nearly three decades of experience across the accounting profession. He started his career in his family's accounting practice before spending more than a decade with Deloitte across New Zealand, Canada, and the US. He later Founded fuel accountants, built a successful accounting practice focused on helping small and medium-sized businesses, became a certified master profit first business coach, and has also built software businesses of his own.

More recently, Peter has become widely known as the AI Accountant, helping firms understand how AI is changing not only accounting workflows, but future of the accounting profession itself. Peter, welcome to the podcast. Peter McCarroll: Thank you. It's nice to be here.

Utsav Bhatt: Right. So Peter, you have seen multiple waves of ⁓ how technology has transformed accounting profession over the course of your career. ⁓ there was cloud, then automation ⁓ which reduced manual work, digital tools transformed how firms ⁓ operated, but AI feels a bit different. So was there a specific moment when you realized AI wasn't just another technology which is going to bring in productivity benefits, but fundamentally reshape how accounting profession ⁓ is is run right now?

Peter McCarroll: Yeah. So my ⁓ you know my my moment, as you said, ⁓ was in May of 2024. And you know, I've always been tech forward. I was on the forefront of cloud technology.

I actually even helped my dad's firm implement, you know, the first computerized ledger systems. ⁓ so I've always been forward on the technology. And we proud yeah, we were very proud of the fact that we were a a technology first company. And in May of 24, I'd been listening to podcasts about the accounting profession and AI.

And I'd never even opened Chat GPT. And so I was starting to hear about it. And of course, you know, I I know where we've come from. I've seen all these technological changes and I know, you know, what's smoke and what's not smoke.

And I realized that what they were talking about and the risks they were talking about were real. that if if the technology continues to progress at at the rate that that they were suggesting, then I think, you know, it absolutely is, that this was going to have a huge impact on the accounting profession in particular. And I so that so it was it was May of 24. I just finished my busy season.

I was listening to these podcasts and I was sitting down one day reflecting on the future of of our of my firm and what I want to do in the next year and things like that. And I just had this image in my brain. of me sitting on the side of the road with my head in my hands crying. And in front of me was what looked like a desk and papers and a computer smashed and just scattered everywhere.

And off in the distance, this truck is driving away, having run over my business. And of course the metaphor is that I sometimes say it's a tank, but you know the same idea. That that the idea is that AI was the truck. And it literally just ran over my business.

And I remember thinking about that image in my brain. And I said, do I want to be the guy sitting on the side of the road crying because my business just got destroyed? Or do I want to be the guy driving the truck? And I said, I'd rather be the guy driving the So that's when the AI accountant was born.

At least the seeds the seeds of it. So I decided at that point that I needed to learn about AI. We needed to make ⁓ make sure that that our firm was on the leading edge of this and that we would be, you know, prepared for when these things came to pass. I thought at that point we had three years.

I'm yeah, ma maybe we'll give it an extra six months. Utsav Bhatt: Wow. Peter McCarroll: But I think I was pretty I was pretty pretty right ⁓ that in three years time ⁓ the market would be would be unrecognizable from what it was then. That was two years ago, so I've got one year and I'm now saying to other firms, we've got one season, one busy season, maybe two before everything has changed.

And that would be exactly in line with that three year mark that I thought about back then. So yeah. ⁓ Yeah, so so that's that's how it was born. And then then I started learning about AI and I I I I you know, of course back then it was all prompt engineering.

⁓ so you learn I learned that, I taught my team, I started teaching my clients. you know, of course everything's changed in the last six months, but but that that's how it was born. Utsav Bhatt: So I I think we so I Yeah, no, I think the image that you have shared ⁓ of, you know, sort of a truck running over your business, when I do my AI adoption work, the first step is to reimagine your business model. And it's very hard for people to take that ⁓ you know, contradictory position and say, hey, whether this business will e exist or not.

Peter McCarroll: Mm-hmm. Utsav Bhatt: It's very tough for people to imagine that world. So the way you have ⁓ pictured it, I think it's it's quite powerful. It gives you a lot of courage and motivation to do something different and and ⁓ change the way of working.

But before we go into what you ended up doing, let's ⁓ pause and sort of for those who don't understand how accounting firms have traditionally operated, if you could just unpack the business model and say what is the traditional accounting business model which has been well established, successfully run for decades. Then we can talk about how it's being threatened just so that everyone understands the baseline and the business model that you tr saw the truck run over Peter McCarroll: Mm-hmm.

Yeah. So, you know, tr a and and the word traditional accounting firms, you know, could be one of two things. It could mean, you know, what we might call old school, you know, accounting firms, firms that are using QuickBooks desktop or or, you know, desktop based tax software, things like that. ⁓ you know, very, very much still manual processes.

Or it could be a firm, you know, even today, you know, that that's into cloud technologies and Xero and QuickBooks Online. ⁓ you know, we all it doesn't matter. Utsav Bhatt: Yeah. Peter McCarroll: how you know how current your technology stack is, there's there's there's still a problem.

⁓ but how do they operate today? ⁓ it's still largely manual. ⁓ you know the the the text tools that we use are only just starting to get things like auto reconciliation in place. And, you know, a modern accounting firm, you know, class two, if you will, of those two options I gave you, you know, will have automated as much as possible using third party tools like Dexed or Auto Entry or HubDoc.

And they'll have a a digital pipeline for documents to flow in to, you know, into a pipeline for the bookkeeper to review and post to the accounting system. And those tools do a reasonably good job of, you know, doing all the OCR and coming up with extracting the relevant data and maybe even choosing the account codes to post to based on a rule or two. Okay? And and that's that's been in place for 10 years now.

You know, and that that was the the the route that that my firm took. And so while I wouldn't call OUS a a traditional firm, that's now become normal in the accounting industry. Rather than ⁓ you know, ⁓ the older school where everything is still paper based and and you've got a clerk sitting there typing things in to the computer, you know, ⁓ like it was twenty years ago. Utsav Bhatt: So ⁓ you know one thing which ⁓ professional service industry in general is facing and I think accounting is also ⁓ not immune to that is growing pricing pressure.

And that is because of various reasons. ⁓ could you just talk us about those pricing pressure and then sort of how people are looking at AI as a way to ⁓ sort of come to the rescue? Peter McCarroll: Yeah. So I talk about four areas of pricing pressure and I actually don't think these are unique to accounting because there'll there'll be an element of all of these points that apply to any professional service.

Just different names, but same idea. So the first pricing pressure is it f coming from the tools that we use on a daily basis. So I mentioned that QuickBooks and Zero are just starting to get their AI act together. And say just doing the same thing.

You pick an accounting platform out there, they're working on AI. Okay. And they are trying to improve the automation. And so that's putting pressure on what we can charge for our fees.

And especially, you know, the accounting industry is well known for our hourly billing. And there are still pockets of the accounting firm that still operate this way as hourly billers rather than value billers or outcome billers. And so, you know, if you're billing by the hour. And all of a sudden the firm can't the the software comes in and says, ⁓ we can we can reduce your time from ten hours a month to five hours a month.

Well, you've just dropped your fees because you're hourly billing. And even if you're if you're ⁓ billing based on value or based on you know a fixed fee, there's a point at which your client says, That's taking less time. I want a lower fee. So we're getting some pressure from the tools we already use.

And of course, anyone, any accountant that uses Intuit's products already knows full well Intuit's out to get your business as well. So we're getting pressure from our vendors, the products we use every single day. Okay, building tools into the products. And and you know, I went through the whole cloud adoption.

I was one of the early adopters of Xero, ⁓ you know, well before QuickBooks Online was on the scene. ⁓ you know, I've been using Xero for 16 years. And you know, when we adopted cloud technology from desktop-based packages, we were able, as a profession, we were able to keep that extra margin. Most firms were 20 to 30% more efficient and our profitability went up.

We're not going to be able to keep that with AI. Okay, so we got pressure from our vendors. We've also got pressure from new entrants coming into the market. So again, to just use the accounting analogy, we've got zero and QuickBooks.

Well, are the elephants in the room? They may not be viable forever. Okay, we've got new products coming onto the market trying to do what they do, but only better. So in the accounting space, there's digits, there's kick, there's a new one called synthetic.

⁓ they are all working very hard to eliminate the human in the loop. Well, who's the human in the loop? It's me, my staff, my business. Okay.

⁓ synthetic, still in the VC stage, not live yet. ⁓ but they have a stated goal. of been able to ⁓ do the bookkeeping at forty nine dollars a month. Like not that's not the software fee.

That's that's not the quick books replacement. That is will you know the system will do the bookkeeping. No human required, no bookkeeper required. That is their stated goal.

So we're getting pressure not just from our current vendors but these new entrants coming into the market. And what that's doing ⁓ is that is setting perception from our customers who now think that this is cheaper. And the problem today is that's not actually reality. Those products are not able to deliver on that promise yet.

But our customers think it should be. And we all know that you can't, you know, someone's perception is their reality. And it's very hard to convince them that they're wrong. When a client comes to you and and they're seeing these ideas of, you know, ⁓ you know, ⁓ digits, $95 a month, or or or or you know, this idea from Synthetic at forty nine dollars a month, or even, you know, Intuit say, well we can do it for you at three hundred dollars a month.

And here I am as a professional accounting firm saying that's going to be nine ninety five. It's like they they just laugh. It's like, but but but that's not what it's worth. You know, because they think their perception is it can be done cheaper.

Utsav Bhatt: So Peter, that's something similar in consulting industry as well. There are companies which are saying we are McKenzie killers, we are BCG killers. ⁓ you know, we can do the entire consulting work on a click of a button, all your reports, all your strategy decks are would be ready. We can make slides for you, do all the analysis, and that comes for $200, $2,000 per month license, you know, fraction of the cost of an analyst of Mackenzie.

I have used those systems, and the argument that ⁓ consultants in the loop make is You know, they will do a few things really well, but you need to have human in the loop. Now ⁓ you have seen other waves of productivity improvement in the accounting world with you know zero coming in and other so manual book bookkeeping became digital, then lot of things were done automated, and then you could increase the portfolio of your work. What do you think would happen to that argument on human in the loop?

Is that something which is under threat for accountants or do you fe because you feel ⁓ the softwares would be good enough to take that role up? ⁓ where is the boundary line, you know, where would you draw that? Peter McCarroll: Yeah, so th this this is where any knowledge profession has to be able to prove its value. Okay?

Because otherwise we just become software vendors. Okay, and script writers, which is great. I've got nothing against that, but that can't be all we do. ⁓ The professional judgment issue is, you know, we all hang our hat on the fact that, well, that's you know, what you really want is professional judgment.

The problem is that 95% of what most of us call professional judgment is just internalized rule following. And especially the case in the accounting world. And the problem is that AI is really good at internalized rule following. It learns that stuff faster than we can.

Someone said to me last week, ⁓ yeah, but the the This the the tax rules, well, they keep changing. And I'm like, yeah, an AI can learn them faster than you can. So that's not a moat. That's not going to protect you.

So yeah, to your point about, you know, how do we keep that human in the loop? From the accounting perspective, the way I see bookkeeping in particular changing. So we're gonna go from data entry clerks. We're almost past that already.

and right now we're more sort of we're managing. ⁓ the system and you know, we're we're doing the the last 20% of transactions and the reconciliations and all that kind of stuff. The system will do all that eventually. Okay.

We're gonna move into what I call quality control. Okay, so a bookkeeper that today might serve 10 to 15 clients will ultimately be managing 50 clients. Okay, but their role will be quite different. Their role is going to be to find the flags.

That the systems generate to say, hey, not sure about this transaction, and go and research it and come up with a decision. ⁓ it'll be monitoring the overall health of the system and saying, hey, something looks a little bit off in these numbers. The you know the system's coded this real in real time, but something's feeling a bit bit funny here, a bit fishy. And so they're gonna be that's gonna be their job.

Their job is to look for things that break, look for things that aren't working, basically keep the machine working like a mechanic does. Problem is the airplane is flying and you have to maintain it while it's in the air. Okay? Not just build it, but maintain it while it's in the air.

So that's what I think the role of the bookkeeper is going to become. And then the other area we have to as an accounting profession, and again, this is you know your bread and butter, is we've got to take the work we do away from the compliance. And when I say away from, I don't mean instead of, I mean in addition to the value add. What do our businesses want?

Actually, you know what? No one ever comes to me and says, I want a tax return. They come to me and they say, I have to do this tax return. Can it scares me?

Can you help me? And of course we say yes, and we've been valued for years because we can take that pain away. But that's not what they want. They want the pain to go away.

But what they really want is a better business. Accountants are really well positioned because we know your numbers, we see inside the transactions, we see lots of businesses of different types and stripes, and we can give you advice on how to run a better business. And a lot of clients don't think that their accountant can do that, but most can. And so what we have to do is we have to really move up the value chain in terms of you know the the bookkeeping or the compliance work has to be the base that empowers more time spent helping business owners achieve their goals rather than doing a tax return or doing the books or you know filing the sales tax or whatever those compliance markers are.

They still have to happen. You know, AI is not going to replace them tomorrow. It probably will eventually. But you know, they still have to happen.

Someone has to be monitoring those things and making sure that everything is done right. But the real value is the consultative value. being able to see through the fog and all the false flags that AI raises and be able to say to the client, you know what? That's a really good starting point.

We can do better. We can make this apply to you. Utsav Bhatt: So I think ⁓ two interesting points. One is with the time that you're saving, ⁓ on one end you can cater to more clients.

So a lot of clients who did not have access to high quality support now have that. The other is you for the clients who value your work, you can use that extra time to add more value to the relationship and help them actually solve their business problems and not just file tax returns. Now when you do that, you're sort of I I call it retreating to the top, like you know, you're going to the top. And trying to be the best in the game and trying to advise them on what other things they should look at, that naturally raises questions about talent.

So historically, you'll hire a junior, train the junior, work with a few clients, grow up, manage more clients, and that's how your career path would look like. Similar in consulting. So When it comes to giving high ⁓ end advice when the new talent might not necessarily be trained on on the sort of mechanics of how do you do bookkeeping and they're using AI to do that. ⁓ wha what's your opinion on that?

What's gonna happen to the accounting talent going forward? Peter McCarroll: That is a huge challenge. ⁓ you know, as you said, we, you know, we all grew up doing the grunt work, doing the bank reconciliations, doing the bookkeeping, entering the account codes, you know, all that kind of that's how we learned the trade, right? That's how we got good.

⁓ and and I'm not sure there's any shortcut to that. And so in a world where the transactional work is effectively free. I I wrote an article the other week and I I said ⁓ you know what happens when the when when the the bookkeeping costs cents to complete? Okay, what is what what do we have to do?

And so this so from a staffing perspective, how do we get someone from being a raw recruit coming out of university who still basically knows nothing ⁓ and get them to a point where they can be an expert or a or a good advisor? That is that is the the the half million dollar question now, okay. Now, there's some suggestions that it's the quality control function that lets you accelerate. You don't have to do the work in order to get the experience.

Your experience is gained by doing the quality control. And you get that experience in a compressed time frame because you're looking at more clients than, you know, you're getting more reps in in a shorter time frame, basically. And to a point, I agree with that. You know, yes, you're you're got you're going to be working on on 50 clients at once instead of 10 or 15.

but do you have the depth? You you might you might be serving more clients, but is that still developing the depth you need to be able to offer insights? And you know, I've got staff working for me that have been with me for five, 10 years, you know, almost 10 years, who sometimes can't still see the obvious problem on the face of the balance sheet. No matter how many times I've said, hey, that smells fishy.

I can sell tell by looking at it. You know, now I have to remind myself, I have 30 years of experience. I've seen thousands of balance sheets. I know what a bad one smells like when I see one.

You know, I know I can look at a number and go, that feels off. What's happening here? And they can't do that yet. And if we take away the transaction level work, does that make it easier for them to see these things or harder for them to see these things?

I'm not convinced that I have the answer on that. Utsav Bhatt: W what do you think about you know, I have a hypothesis that ⁓ even education as a profession is under stress because the way people were taught. ⁓ now like you know, I was just talking to ⁓ some B school graduates recently and they asked me a question, why am I spending you know a few weeks doing a case study? I can just upload it on chat GPT and ask all the questions and get done with it.

So why am I wasting my time paying so much money to the college to do something which I could do in a day, right? You know, sitting by myself with some guidance. So that brought me to a point, how about education institutes start training ⁓ entry level sort of talent on the grunt work. So you actually get s you know simulation of what it fe what it felt like to do that work in in the older days in the college so that you understand the nitty gritties and and and you know mechanics of how the tool works and what happens.

Any any sort of ⁓ thoughts on on this particular point? Peter McCarroll: ⁓ yeah, so a couple of things. So absolutely I think we need to be sort of training people on how to use tools like this much sooner. ⁓ it's still brand new, so of course education actually moves very slow ⁓ as a as an industry.

⁓ the other thing though is particularly with where we are today, you know, language model hallucination, ⁓ you know, errors that creep in, you know, we all we all know the horror stories of of results that get churned out of out of an ⁓ an L L ⁓ that that just are full of holes. And and in the accounting space, you can't tolerate that. Ninety-five percent good enough is not good enough. You know, you you can't allow a you can't allow something to go out to a client with a tax liability attached to it.

Okay? So ⁓ the the the the the reason for still doing the case studies, just to use your example here, the reason for the case studies is if you don't know what good looks like. How do you evaluate the output? You can't.

You take the output at face value and you believe it to be correct. Okay, that's not good enough. At least not today. Now, in a year's time, will that be better?

Maybe. We'll wait and see. But until then, someone has to know what good looks like. And someone has to be able to say, that smells off.

I did some was doing some tax research the other day, and it's very good at research. But it wrote this paper for me and I was reading through it and like, hmm, this doesn't feel right. This section here, I think they've got something wrong. And so I because I knew what good looked like, I have enough experience to go, yeah, that doesn't feel right.

Or I went and read the section you're referring to, and I think you've misinterpreted that section. Okay? And I pushed back on it. And we got it resolved.

And we actually ended up with a better product of research than I would have written by myself. Because the yeah, because the AI could go and do all the deep research and go and pull 15 documents off the web and analyze them. Whereas if I had to do that, that was a whole day's work. And it did it in about 20 minutes.

Okay. So we got a better result than I would have done by myself. But if I'd let the AI write it and just gave that to the client, I would have had an insurance claim on my hands. And that's another question will insurance cover you if AI gives bad advice.

Utsav Bhatt: Yeah. I think so there's something ⁓ which education institutions can do to train the incoming talent better ⁓ by by exposing them with what good looks like, how work used to be done, so that they are much more prepared as they join the profession. So one thing which I have consistently observed when studying transformation is that understanding ⁓ change and acting on change are two very different things. Everyone understands AI is important.

Everyone is saying yes, it's the next big thing, we have to change our business model, but doing it is a whole different Thing so, in your experience, ⁓ what are some of the biggest blockers that you're seeing in organizations that you work with when it comes to sort of driving AI adoption? Is it technology mindset? And I look at it from two perspectives: one are structural brockers, which are metrics, your reporting structure, governance, rules in the company, and the other is cultural, which is your mindset perceptions, what you have been seeing in the organization.

for a long time. So what what have you seen as the biggest ⁓ blockers for driving AI adoption in companies? Peter McCarroll: So there is a perception that AI should make things easy and fast. And it's not there yet, especially not in the accounting space.

And so one of the blockers we see is that, you know, people, accountants, and this again goes for any business. They give their team access to the tools. They may, if they're good, they'll give them some training and then they expect that someone found the easy button and just pushed it. And magically everything gets better.

And what we typically see is it gets worse, not better, ⁓ for a season. So so we have an expectation problem. Now, that expectation problem is a symptom, not the problem. The problem is leadership.

We actually and I think as you know, as I look at that truck, going back to my original metaphor, that truck that's coming, as I look at that truck, as a profession, as a business owner. I have to do the hard work of imagining what my business needs to look like once the truck is driven past and then start reverse engineering my business so I can survive that traffic. Okay, it's not enough to throw a chat GPT or a Claude subscription at my team and say, hey, here's a video, go watch it, you're good.

The other Challenge, you know, it might surprise people to know, accountants are actually very resistant to change. We like doing things the same way. And if you give an accountant, most accountants, especially us older guys, the choice of changing it or doing it the same as we did it last year, we don't even have to think about it. We'll just do it the same as last year.

That's an inbuilt hesitancy within our profession. Because we have this inbuilt nature to not want to change unless there's a reason to change. And so that's one of the biggest blockers. Again, this is a leadership question, not a technology question.

And then the third area, maybe for bigger firms, and this again applies to other businesses, if you have an IT department, is that this question of AI is seen as an IT problem and given to the technologists. Most technologists, ⁓ and especially if you involve legal. If you're a bigger company where you have a legal department, if you involve legal in it, nothing will ever happen. And so we often see this getting blocked because accountants are resistant to change.

We are very legally risk averse. And if we have lawyers on staff, they say, uh-uh, you can't do that. Even the IT team, and then remember their job is to protect the company from risk. Well, IT has ⁓ AI has some risks.

So all of these factors actually prevent, they but they block. Implementation of these new ideas. And so we see that a lot with sort of especially with larger firms, is that these these this these energy suckers, whatever you want to call them, I'm not trying to be pejorative towards them, they have their roles and they do them very, very well. ⁓ but the result of that is they stifle innovation.

And so one of the biggest changes a firm has to make is they have to decide that the risk is worth the effort. Then they have to work out how do we put the right boundaries so that we don't have uncontrolled risk. But if we let our natural instincts as accountants, we're conservative, we don't like change, we're risk averse from a legal perspective and a professional perspective, and our IT folks are just trying to protect our business, those three forces often mean nothing happens.

Utsav Bhatt: Yeah. I I'll ⁓ end with a last question around if you zoom out and look at all these blockers that you're seeing, what advice would you give someone who understands that this industry is risk-averse, who understands the cost of you know sharing ⁓ AI generated data which is not properly validated, verified? ⁓ but even with all those difficulties, what practical advice would you give them beyond you know that yes you should have governance, yes you you should have training, but more on how How leaders should think differently, what kind of mindset they should get in to ensure these blockers can be overcome.

Peter McCarroll: Yeah. The number one piece of advice, the mindset shift, if you will, is that the cost of doing nothing is far greater than the cost of ⁓ of making change. So all of these factors that are, you know, l you know, pushing us towards being slow or not making change, that the those costs could ruin the firm. Those things that are trying to protect the firm from risk.

Are actually creating more risk than adoption. Okay? And the moment that light switch changes for a professional, it's like, okay, we have to deal with this. You guys over there, be quiet.

Tell me how we get this done, not the reasons why it shouldn't be done. And all of a sudden you've got momentum. Okay, so that's that's that's the the number one thing. Understand that the cost of not moving is greater than the cost of moving or the risk of moving.

Utsav Bhatt: Yeah, no, I think that's that's very well summarized. I think we covered a lot of ground. I to start with the analogy that you gave around how you saw the ⁓ business model being run over by a drug grade analogy, and I think that is something which everyone should do who is seriously considering about what their business might be ⁓ in a in an AI era. ⁓ then expectation management.

⁓ they were saying that people generally ⁓ overestimate in the short term and they underestimate in the long term. I think that's what might be happening. We are overestimating Saying hey, the tool is there, clot skills is there, now this can be done, you know. Automated, you know, things would happen, but it's not actually there yet.

There are a lot of quality gaps right now which need to be worked on. So proper expectation management. ⁓ then the time that we save. ⁓ the the the insight that I had from this is serve more clients at the same time, those who are quite close to you have been your clients for a long time, you get more time to add value into that relationship.

⁓ knowing what is good enough for all the incoming ⁓ accountants. is a is a great ⁓ thing to focus on so do that training up front and and this ⁓ point on ⁓ the last point around cost of doing nothing I think that's brilliant because when I sort of was looking ⁓ into consulting world three years back in the same way I ended up writing a book and just thought about okay this is going to happen come what may so either you can shy away from it or take it head on and do something so great ⁓ Peter thank you so much for ⁓ your your input it was a wonderful conversation ⁓ thanks for joining Peter McCarroll: Yes, okay.

Utsav Bhatt: Yeah. Peter McCarroll: Thank you so much. Utsav Bhatt: Right, thank you. So if you enjoyed this conversation, please subscribe, share it with others, and join us for future conversations on AI adoption, transformation, and ⁓ changing of the work.

Thank you for listening.