Adam Larson: 00:20

Welcome back to Count Me In. I'm your host, Adam Larson. And today, we're excited to have Court Watson, a partner in Deloitte's Advisory's controllership services practice. In this episode, we explore the evolving world of accounting and how modern accountants need to adapt to technological advancements. We'll discuss the transition from traditional reporting to data driven methods, the importance of continuous learning, and the impactful role of AI.

Adam Larson: 00:45

Core provides insights on bridging the gap between accountants and IT, leveraging AI and future proofing accounting practices. Join us for real world examples and practical advice to help you stay ahead in the fast paced accounting landscape. But before we get started, we wanted to give a quick shout out to Deloitte's Resilient Controller podcast. It's a fantastic resource for controllers and financial executives. You can find it in your favorite podcast platform, but we'll also include a link in the show notes.

Adam Larson: 01:17

So, Court, just really excited to have you on the podcast today, and we're gonna be covering a lot of different things around the controllership and AI and how it's all it's all being reshaped by everything that's happening within the industry. And I figured we could start off by just talking a little bit about AI and its effect on the controllership within organizations because, accounting and finance teams are having to learn a lot of new skills now with all these new tools coming up.

Court Watson: 01:40

Yeah. I mean, I think if we look at generative AI, we've sort of changed the definite definition of AI to include many things that historically weren't machine learning. And we're sort of all bucketing them into that that category. Generative AI is still in its infancy. And I think, really, the impact on accounting organizations today is more of an inward or internal pressure to adopt AI.

Court Watson: 02:08

And controllership, I think, is a unique space, specifically if you're a public company, right? Because there are requirements around how you use technology that don't necessarily exist in the rest of the organization, and they are a an external requirement or regulatory requirement. So I need to not only know that I have completeness and accuracy around everything that that a system did, but I also need to understand at a pretty good level how the system came to the answer it did. And if you look at a lot of what's out there in AI right now, it's a little bit of a black box in understanding how the output was created. Right?

Court Watson: 02:47

We're sort of relying on it to do that, and I think there's there's many cases where, you know, you've asked some sort of generative AI to do something and came out with an answer that that didn't really align with what you thought. Yeah. But that opaqueness needs to get resolved before I think you're gonna see wide adoption of of AI in the accounting space. Now I still think right now it's a really good sort of overeager assistant who can give you a first pass, but the accountant still needs to know on what what it did to have the domain or subject matter expertise to be able to say, hey. That that makes sense, or it doesn't.

Court Watson: 03:29

Here's why. And so there's 2 components. The accountant is going to gradually have to upscale in that sort of the AI domain and, I think, understand more of an AI model uses linear regression to produce a expected outcome on, let's say, an accrual or a management estimate. The accountant needs to understand the metrics that come out of a linear regression model and whether or not that regression model is a usable model or is not reliable. And that's a kind of a new skill set for a lot of of accountants, so there's going to be that upskilling over time as it comes to AI before you I think you get widespread.

Adam Larson: 04:08

Yeah. Well, and like you said, it's still in its infancy. And when something is in infancy, everybody sees a little infant baby. They're like, oh, how cute. But then a few years down the road, when it's a crazy toddler making a ruckus throughout the restaurant, nobody's saying how cute.

Adam Larson: 04:20

They're like, oh, this is such a nuisance. So it'll be interesting to see what trajectory this new generative AI goes because and when it's in its emphasis, everybody's giving each other high fives. And I think what people aren't really talking about is some of the challenges that it's bringing. But, like, you mentioned the one thing about controllers, you know, having to know why something was, and you don't always see that with the AI. Are there other challenges that are being brought up as their people are getting into it?

Court Watson: 04:44

Yeah. I mean, the skill gap is certainly there. In order to run and maintain any sort of sort of technology, you need to have the requisite knowledge to make sure that it's it's updating. Finance data is still a big gap for a lot of companies. Mhmm.

Court Watson: 05:04

You're often getting transactions that are incorrect, that are incomplete, that aren't all sitting in one location, that aren't all brought together from various source systems, various inbound interfaces in one place that an AI model can sort of access and tell a narrative or tell a story from. Now as, you know, as AI gets more robust, it's going to be able to ping all the different systems and bring it all together. And that's truly, I think, what eventually the strength of these AI models will be. But today, we don't we don't have that. How do you quantify risk?

Court Watson: 05:38

Risk and material misstatement, how do you put a number to that? Like, yes, it might not save time, but it might produce a better quality outcome. How do I justify that in terms of ROI? So that that's still being figured out is what the ROI those use cases are.

Adam Larson: 05:52

Well and you also need still need the human interaction like you mentioned because we all know about the hallucinations. We've all seen the examples of them. We've seen the examples of the lawyer who created this whole speech out of it. Not that we're talking about lawyers, but there's lots of popular examples that are out there, and you still need that human aspect because, like you said, it's a really eager assistant giving a first draft.

Court Watson: 06:14

Correct. Yes. And I I will go every 3 to 5 months into one of the language models that I have access to, and I will ask it the same accounting question each time. And I always I always pick foreign currency because that's a topic that is that is pretty complex but is very rules based. Like, if you know the rules, you can do the calculations.

Court Watson: 06:37

And the generative AI tools sound really, really smart, but they're also really, really incorrect. And that's always my fear is someone is relying on it, but doesn't understand the subject matter, uses that, and then produces something that results in a incorrect or inaccurate filing related to financial statements. And I think that I think, at least the conversations I have, every controllership at every company is skeptical enough that they're not at that point. So I don't I don't think it's a pervasive risk, but I think it could happen.

Adam Larson: 07:13

And should we be worried about, you know, the next generation of accountants that are coming up? Because, you know, my oldest is is is a freshman in college, and, you know, I know she's using a lot of generative AI to kind of help give first drafts or or get ideas for things and stuff like that. And a lot of people are doing in their works. But if people are utilizing those and not necessarily understanding the having a good foundation, will will we have a gap in a few years when those this next generation is coming up and doing those low level jobs in in accounting firms?

Court Watson: 07:43

So I think for future generations, I think there is going to be a greater need for sort of logic, philosophy, ethics. And the reason I say that is I think you're going to see generative AI replace a lot of the menial tasks that are day to day, and the individual is going to spend less time preparing and more time analyzing. And that analysis requires sort of taking a step back, thinking about sort of the macro picture and telling that story, the way the regulatory environment is, it's never going to be there's not people, right? You always need you can't have generative AI sign off on a 3 zero two certification for a financial statement. There's always going to need to be individuals.

Court Watson: 08:31

And if you look like companies today, they don't close the books in a day. They close it in 5 or 6. And if you look at those 5 or 6, right now, there's a lot of manual work. I always say manual journal entries are a defect of your technology. Right?

Court Watson: 08:49

It's just whether or not the cost benefit of resolving that defect is there. If it's something that takes 5 minutes to prepare and book, are you really going to industrialize the solution? Because it could take 6 months and, like, a full software development lifestyle. It's like, well, no, I'm not. I'm going to keep doing the manual entry.

Adam Larson: 09:05

Yeah. And you

Court Watson: 09:05

sort of pick off your large ones and fix it. And those are all processes in the close. You're like, how do we fix? If I look at, like, accounting technology, I wouldn't be surprised, and I don't think it'll happen soon, but if someone sort of thinks of a ground up accounting technology solution around the ERP, Like, could there be a world where the tax provision, instead of booked on day 6 of close after day 4 where you've fully closed the books and consolidated, is being booked on the fly with every journal entry? Because leveraging AI or leveraging technology, it's able to sort of think about, okay, what goes into the provision?

Court Watson: 09:42

How do these transactions typically impact our results? Is the technology able to consolidate on the fly versus now where you have to press a button at the end of the period and bring it all together? Like all those things, I think it'd be really cool. And I would love to be part of something where like blank slate, ground up, ERP, subledgers, closed solution from scratch, embedding current technology. I think you're seeing the large manufacturers bring that in over time.

Court Watson: 10:13

And and I think that's what a lot of control ships waiting for is is the proven industrial solutions to have AI embedded in their solutions because they know they're going to have controls around that. They're going to have a SOC report. The controlship organizations will be able to say, hey, I can rely on this because it's got it's got that right. And the industrial solutions know what accountants need in terms of validating that that what happened makes sense, and they understand it. And so rather than building custom and bespoke solutions like we're seeing in other domains in a company, accounting is sort of like, let's see what the major players do and how they bring it into their value proposition so that we can

Adam Larson: 10:51

That makes a lot of sense. And what you describe would be really cool, but I feel like because of how things move in this industry, it would take a long time for adoption. You know, a lot of times you see, you know, all these cool things going up at rest of the organization, but the accounting team is still using the same software they used 20 years ago. Why do you think that is that it's there's a long adoption period, especially in the accountings, accounting and finance teams?

Court Watson: 11:14

So I will maintain that, like, you cannot have agile software development as it relates to accounting technology. And if if you want an agile solution, well, your first release has to be all the requirements, which yeah. It has to be. Right? Like Yeah.

Court Watson: 11:33

If I build a revenue recognition solution and it meets half of the accounting standard, well, then it's not usable. That that's one piece of why it's slow. The other thing is the other piece is accounting is a cost center. Right? And a, and a business has to ration its IT budget and where is it going to spend, you know, where it thinks is most important and totally makes sense that, you know, customer facing revenue generating parts of the business get that priority.

Court Watson: 12:03

Totally get it. And then the other thing is, you know, there is, and it's funny, I'll tell sort of an anecdotal story. There's a gap between how an accountant describes what they need and how someone in information technology or a developer builds a solution. And one of my colleagues did a presentation, a CPE presentation, so you get, you know, CPE credits for your CPA license. And he had accountants write out the requirements for making a peanut butter and jelly sandwich.

Court Watson: 12:36

He took one and he brought a loaf of bread, peanut butter, jelly, a knife, and acted out the requirements. And he ended up smearing peanut butter on top of the bag of the bread because there was never a requirement to remove the bread from the bag and then just jamming the jar of jelly on top because there was never a requirement to open the jelly and use a knife to spread it on the bread. So it's like in an accountant's head, it's like, well, I need to, you know, I need to book a bad debt estimate. And, okay, you might in your you might verbalize 4 steps. Well, someone who has no idea how that comes about is now in charge of developing it, and they're not gonna come up with a solution that meets the accountant's needs.

Court Watson: 13:23

And that just creates more manual processes on top. And so finding that balance, it's really hard to find a unicorn who knows accounting, knows accounting standards, but also knows how detailed requirements work, how user stories work, how testing works. Yeah. And bringing those together is hard and often leads to unsuccessful outcomes that actually, you know, kind of make kind of make controllerships the organizations go a bit backwards. I've seen instances where the close cycle has lengthened because of new technology, and a lot of it is because companies did not rethink the processes they have and whether or not they could standardize, and then the gap between what the requirements are for the customer, the accounting organization, relationship organization, and the team that implemented the solution.

Adam Larson: 14:07

Well, and I think that really points how important it is to have good internal controls, good to have good processes. And if you're gonna implement any new technology, you should look at all your current processes to see how could you improve upon them when you're when you're looking at those things.

Court Watson: 14:20

Yes. And and that ties back to control ship organizations are already really, really lean. Yeah. How do I spend meaningful time on that when I barely have enough headcount to do the day to day? So I'm trying to do the day to day, but also trying to do this extra incremental thing to spend meaningful time revisiting processes, redesigning processes, redesigning controls, and producing requirements.

Court Watson: 14:44

Like, it's a massive challenge. I

Court Watson: 14:47

have seen some companies go a route that I thought was really interesting where, you know, they baked into the budget of the the initiative or the transformation effort, co sourcing. So they would bring in a third party to backfill their accountants so their accountants could focus on that. And that, to me, makes a ton of sense because then you're leveraging the people with the knowledge and the expertise, and they're focused on it. And sometimes what you see is your existing processes that you're someone else is coming in to do, they'll see some things and bring in a different perspective that potentially makes it better as well.

Adam Larson: 15:18

I sometimes hate, but I also love when somebody new comes in and takes an SOP that was written. And then you you with by all based on the questions they give you, you realize how badly written your SOP was. And I think it's a good practice for all organizations to, at some point, bake in, hey. We need to look at our practices and make sure we're doing things efficiently because otherwise, you'll continue on and maybe never grow Correct. Within your within your team.

Court Watson: 15:43

Yeah. And, you know, that's not only logically makes sense, but human nature is like, this is mine. I'm gonna be defensive if you say it's bad. Right?

Adam Larson: 15:51

Exactly.

Court Watson: 15:52

We can we can make fun of our own family, but if you're on the outside, don't make fun of my family. That that's totally different. And and it's not much difference. If you have any pride when you do, you're like, who is this person telling me I'm doing it wrong or should do it differently? But also like from a controls framework, that's good governance.

Court Watson: 16:07

Like periodically rotating people into different roles helps you detect things that may be amiss. And that's one of the recommendations of a solid controls framework. So it's sort of dual hatting in its purpose.

Adam Larson: 16:20

It really is. Now, do you think that as some of these bigger software companies that are kinda old hats that have been around for a while as they incorporate some of this AI features into their softwares and people start to get into it, do you think we'll start to see an ROI within those systems, or is it just, hey, this is just a new cool, tool, but I'm not really gonna use it very often?

Court Watson: 16:39

I'm gonna give an accounting answer and say it depends. Like like like, I think it depends on how well integrated it is, how clearly explained it is to the customer, and then how willing the customer is to change. And I think with a lot of companies, they do a disservice by not being transparent of what the limitations are or what companies need to have in place for this to work. And I think when you do, especially with accountants, it resonates much better. Like, hey, I understand it's not gonna do it all, but I understand what I need to do to get to that point.

Court Watson: 17:15

The other thing is AI is still quite expensive. Mhmm. And you've seen situations where a license will double in cost for an AI functionality that is kind of an administrative assistant. It's like, well, do I really wanna pay double for that, versus just like an incremental value? So that pricing, I think, needs to get figured out.

Court Watson: 17:37

But, you know, there are a lot of industrial solutions, both in your core finance technology plus what I call edge systems, so your reporting layer, your closed like closed tasks, closed tools. And they're starting to look at it a lot, and they're starting to try and incorporate things. And I think it's going to be in incremental steps because I think they're learning along the way too of what works with the customer, where the feedback is, where the friction points are, where clients, where customers, you know, in controllership are like, Hey, we can't use this because we don't understand what's happening, and we can't get past regulatory questions around that. So it's, it'll happen think it's gonna happen in graduated steps.

Adam Larson: 18:18

Yeah. In graduated steps. That makes sense. Do you think there's steps that we can be taking now to make sure that the, we can get over that communication gap? Because you're mentioning that we're we're talking about how, like, it's hard to have somebody who understands all the steps and accounting and understands how to write the stories and the processes for for things to work properly and and to make sure we get all those steps down, especially for technology work properly.

Adam Larson: 18:40

Are there are there ideas of of or or steps people can take to kinda get over that gap? Because that's probably the hardest part of getting over the the communication gap that's there.

Court Watson: 18:52

Yeah. So there's a very famous technology evangelist, and he recently Mhmm. Posted and said, hey. You know, artificial intelligence is the gas, and regulations are the brake.

Adam Larson: 19:05

Mhmm.

Court Watson: 19:06

And my first thought was, well, the better the brake, the faster your top speed can be. Right? Okay. If you're on a bicycle and you have no brake, are you going to go 40 miles per hour? Are you going to go 5?

Court Watson: 19:19

Because you can't stop. Right? Whereas if you could, you can stop and control. You're gonna, you're gonna be able to go faster. And I think defining what that break is, what the governance around the model is, what you actually need to see from a model to get comfortable with it.

Court Watson: 19:35

I don't think anybody spent meaningful time other than saying this model doesn't give me enough that I can rely on it. It should be like, what do you need to rely on it? I need completeness. I need accuracy. I need to understand the inputs.

Court Watson: 19:48

I need to understand the transformation of the inputs and how it derived it because at the end of the day, I need to be able to back test it. I need to be able to go back and look and say, did it work? Did it do what it was intended to do? So spending time building out that framework and what that looks like, and for right now, it's going to be company by company basis because they are all going to have different measures of risk. They're all going to have different materiality levels, and they're going to have to work on that.

Court Watson: 20:11

The other thing is cleaning your financial data, going back, where do we have gaps? And a lot of companies are doing this as they have to and are required to modernize their finance technology because we're kind of in a cycle of, like, the updates are occurring, but going through that, really thinking with a future sort of focus of, like, yeah, I don't use this dimension today consistently, but I have an opportunity here to clean that populate because I know it's gonna have value in the future. So those are the 2 things that companies can do now for sure.

Adam Larson: 20:40

Definitely. Do you think this is gonna have a greater impact on small to medium sized businesses? Because, obviously, the large organizations might have the capital to put the time in. But small organizations, you know, you've already mentioned the controllership is very lean. A lot of finance and accounting teams have had to cut down and and make it more lean and do the same amount of work.

Adam Larson: 20:56

How are we gonna get these extra skills and upskill and be ready for this in the midst of this fast changing market?

Court Watson: 21:04

It can go both ways on that question. Okay. So it may be easier for small to medium businesses because their systems are going to be relatively cleaner and simpler.

Adam Larson: 21:17

Okay.

Court Watson: 21:19

At the same time, AI is expensive. And for it to be fully built into software and products, I think, you know, the companies that produce finance technology with the capital and resources to embed AI

Court Watson: 21:36

typically don't target the small to medium sized business. Right? That they're gonna go up, you know, they are the the the vendors for the Fortune 500 companies. So there's going to be that cost component.

Court Watson: 21:49

But I can see where the deployment will be much easier, you know, at a small business that uses one sort of web based ledger to track expenses and payroll and everything and really doesn't have a complex structure of a multinational that, over several acquisitions, uses 4 different ERPs and 7 different customer billing system. That just doesn't happen in a small medium business, so the opportunity to get the most, I think, will be an easier lift. It's just in affordability.

Adam Larson: 22:21

Yeah. That makes a lot of sense. Well and and you can go either way to from the from the the software company's perspective because why not test it out with a bunch of small companies, and then you can roll it out the big ones, but then you're not doing anything complicated. So it's a a give and take depending on what you're doing. Yeah.

Court Watson: 22:36

And, you know, a lot a lot of what you do is is proof of concepts where you pick a component of a large business

Court Watson: 22:39

and test it. I think that would be a great idea to find that mid market client that's willing to to be involved in those pilots and provide feedback. That I think that's the best way you're going to build a solution that works for everyone. It's just a totally different beast when you get a multinational that has been around forever. They have processes they've had forever that don't always align nationally.

Court Watson: 23:04

Again, systems are different all over the globe. It's just is a very large effort to standardize and align across multiple geographical jurisdictions.

Adam Larson: 23:17

Yeah. So we've talked a little bit about upskilling and this new skills that finance and accounting teams are gonna need to have. Maybe we can dig a little bit more into that because, you know, obviously, analytics, analyzing, being able to analyze this data that comes out, what are the other skills that they're gonna need to have and and that, you know, accounting and finance leaders are gonna need to say, hey, guys. We need to get these skills. We need to make time for this.

Court Watson: 23:44

If I think about skills that that a typical accountant doesn't have today, there aren't too many that I think an accountant can't be capable of. There needs to be a better understanding of data models and data structure and data tables, sort of that information science component of it. Yeah. Because the accountant, if you look at the applications that are coming out, they're going to be more self-service. But that self-service requires some of that requisite knowledge of, like, how do I want to structure this to get the most out of it?

Court Watson: 24:18

If I want to build something in a visualization layer, I'm not in the underlying table going to create a data structure that's already a table output like the face financials. Think of let's just go back to a basic, like a pivot table. I think everyone knows what a pivot table is. That's already a final report structure. That is not a data table structure, and a lot of accountants think in that final report format.

Court Watson: 24:42

Well, you need to prepare the data in the data table structure so that you can then leverage your other systems and solutions and capabilities to get that. And that's a that is actually a big gap in accountants because they're always looking at it from a balance sheet income statement format, which is a final report structure. So being able to sort of think about the data component is going to be a big piece. But accountants, again, have historically dealt with a ton of data and have worked with it to prepare reports and done it in a way where, like, there's controls around it. They can validate the completeness and accuracy.

Court Watson: 25:15

So they have the skill set, and oftentimes, if you can if you can find the right analogy, it will click in an account's eyes. They will always say, like, when the new revenue standard came out, there was a big concern about the material right. And I was like, well, the material right isn't much different than derivatives in derivative accounting. Right?

Court Watson: 25:35

It's an option in the future to provide, like, some sort of benefit to a customer. And then they will blank on, like, no. I don't know derivatives. I'm scared of those. I'm like, hey.

Court Watson: 25:46

Have you ever, like, had a buddy bought, like, a ticket to a sporting event or a concert and invited you and said you don't have to pay for the ticket till the day of? That's that's a derivative. You basically entered into an agreement. You paid nothing up front, and you're gonna get to go in, you know, you're gonna pay for it at the end. I'm, like, it's it's not much different.

Court Watson: 26:04

It's just, you know, accountants just need that understanding, and then they, like, there's a lot of intellectual capital in in accounting. Lot of smart people. I'm confident that the people who remain curious and continually learn will be successful in this space.

Adam Larson: 26:19

Yeah. I I agree a 100%. It it's it's gonna take us being curious to really, get the generative AI and these tools, these new tools, and get them out of their infancy into something that we can maturely use within our teams. You know? Because you think about things like data analytics, you think about things like, processing automation, and you think about understanding those things in a better way.

Adam Larson: 26:44

We have to be curious and kind of look into that. Do you think that we need to all accounts should be looking and dabbling in a little bit of data science to understand those things?

Court Watson: 26:52

Yes. 100%. And try and use it in day to day life. You know, spend time building your own ledger for your personal finances. So I'm gonna download my credit card statement, download my banking statement, and think about, okay, if I was trying to put this into, you know, some of there's a lot of tools that are free from a from a personal use perspective.

Court Watson: 27:14

That'll teach you those lessons. You know, I an example I did to help understand some of the visualization capabilities of some of the solutions, I used them to track my me and my college teammates' golf trip when we went to Ireland. And I set it up so in my app, I could just enter the scores on my phone, and it would update and produce a graph that tracked, like, who was leading. And we had a complex point system. We had to incorporate handicaps, had to calculate handicaps on the fly, but it really taught me sort of the end to end everything I need in a way that I can then take back.

Court Watson: 27:50

Okay. If I'm going to directly access my ledger and my consolidation ledger, and I need to do some transformations on it, where that would sit, just going out and searching the web on how to do those programming languages that really are more accessible than a lot of accountants think. They're not that hard once you spend a little bit of time with them because accountants are logical, and those languages are logical. Now, you're not going to be doing really complex things or be the most advanced, but you're gonna do enough to be able to add, like, column a to column b or filter out only your balance sheet items in a dataset. Like, you can learn it.

Court Watson: 28:27

You just have to be curious. You have to spend time on your own trying to figure it out. And those are the people who will be the most successful. And back to your point of, like, what is the risk to, like, the next generation, not having that curiosity and teaching those skills on your own is what's going to be the risk to each individual.

Adam Larson: 28:45

Yeah. And for those who've been in the industry for a long time, yeah, it might be a a little bit of a bigger curve, but it's worth your time. And it's also worth it for leaders to say, hey, guys. I want you to spend the time here.

Court Watson: 28:58

Yes. Yes. It totally. And I think, you know, one of the things that think probably needs to be factored into performance evaluations Mhmm. Is formalized and structured learning.

Court Watson: 29:14

So are you spending the time each year to develop your skills? And part of that is not only saying, hey, you know, you need to have 40 hours of learning a year, with the company providing the right learning lead resources. And that's not just, like, courses online that are video based. It is, hey. We're gonna have a sandbox, and we're gonna have case studies.

Court Watson: 29:41

I think case studies are a great way. Right? Because an individual has to follow sort of instructions, use the technologies themselves, and come to the solution. And maybe that's just how I learn, but I learn best by doing. Some of the vendors have really good examples and really good documentation.

Court Watson: 30:01

And if you say, I wanna learn how to bring 2 datasets together, you can go to the reference documentation, hold they have example files that you can use, and learn to do it. And then you apply it to your real scenario.

Adam Larson: 30:15

Yeah. I and yeah. Hands on learning is probably one of the best ways to do it because you make live mistakes and you learn as you're going, and it's the best way to kinda get you going. Now, you know, everybody learns in different ways, but I think it's a it is a really effective way. And, you know, you know, I think and it also goes beyond because a lot of people have in the accounting space have certifications.

Adam Larson: 30:36

You know, IMA offers certification. But those courses are geared toward one thing. They may not get you to the other places you wanna go, so you have to make sure you look beyond that as well.

Court Watson: 30:46

Yes. And a lot of it is more theory based. You know, for a lot of licensing, you need to demonstrate a prerequisite amount of working time. That totally makes sense. But to have your hands in a technology being able to do something, And what's more interesting when you can do something that's personal to you, because then you're like, I'm not only learning, but I'm it's something I'm interested in.

Court Watson: 31:08

That that's the best way. But that doesn't always happen. The other challenge is companies will deploy a solution at a production level. And if they want a sandbox, that's an incremental cost. So they have to evaluate, do we want something like a sandbox that gives our people the ability to go in and learn and kind of like their scratch pad or or somewhere to play with, knowing that it's gonna have an incremental cost.

Court Watson: 31:31

And I totally get their constraints, and you think about that. But I do think if it's something that company believes in, we wanna leverage and we wanna get the most out of, it's worth that investment because, ultimately, you're gonna get more value out of that production solution.

Adam Larson: 31:44

Definitely. Well, Court, I think this has been a great conversation. We've covered a lot of things, and I hope our audience enjoyed it as much as I have. And I just wanna thank you again for coming on.

Court Watson: 31:53

Yeah. Thank you for having me. Have fun.

Announcer: 31:56

This has been Count Me In, IMA's podcast providing you with the latest perspectives of thought leaders from the accounting and finance profession. If you like what you heard and you'd like to be counted in for more relevant accounting and finance education, visit IMA's website at www.ima net.org.