Making Cents with Charlie and Justin Merritt

Let's Make Sense with Charlie. Serious business on serious hosts. Hi everyone, welcome back to MCC, Making Sense with Charlie. I'm really honored and excited to have Justin Mayer with me today. He's a longtime friend and a co-founder and CEO of TwinSix. A leading finance transformation implementation consultancy. He spent over a decade on the practitioner side of finance, using, evaluating, implementing planning systems from the inside of finance functions before moving to the other vendor side in leadership roles at Venna and Planful. He founded 26 to bring all 3 perspectives to bear for finance leaders, modernizing off legacy systems, building scalable, high-performing finance functions through consulting, implementation, change management. Justin, it is a pleasure to have you here with me today. Thanks, Charlie. It's good to be here. It's a long time coming. You and I have known each other a long time and excited, excited for it. Let's do it. It's, it's fun. I mean, we started our business around the same time as well. Very similar journeys. Yeah. You've been in this industry for a long time. You sat on the other side. You give a whole different perspective. I've only been on the other side a little bit, but I think you've spent, what, 5, 10 years and actually been the user loser kind of seeded leadership of finance position, right?

Yeah. And it was, it was at, I think, the perfect time, you know, when I, when I finished university and went out into the working world, I started at IBM and it was a, it was kind of this experimental group that IBM had going on and we were termed mini CFOs. So we got handed these baskets of loser contracts that were losing money and told, you got to figure out how to make us money on these. And so it was wild because, you know, right out of school, You know, a lot of knowledge from school, but not a lot of common sense in terms of how things work. And then, you know, working with some really experienced veteran IBM, you know, delivery folks and things, because these were a lot of big heavy contracts for like managing SAP environments and stuff. And so, it was wild because we got to, I guess, to take like all those case studies and theories and things that I'd learned in school and get to try applying those. And then the complexity of like— Yeah. big corporate enterprise agreements and all the terms and conditions. And then, yeah, moved to Verizon again, kind of right, right place, right time. It was this massive work migration from one center Verizon had to a different center right after the MCI purchase of Verizon out of, out of bankruptcy. Uh, I started in Zaxby's, you know, all that, all that whole stuff. Um, but got to work in like internal audit systems and, and some intense stuff about like how data flows and really shaped kind of a lot of that, that view. And then, Went over to Oshkosh Truck Corporation and really started there kind of right as the economy fully tanked into the gutter.

Perfect timing.

So, you know, in heavy equipment manufacturing and yeah, I mean, the, we had just completed an acquisition that was a $3 billion acquisition. It doubled the size of the company overnight and then the markets disappeared. 98% of the market's gone basically overnight. And because of my background with systems and things like that, I to be part of this really cool team that essentially just worked inside the company on how do we move work, how do we insource from suppliers, how do we reactivate, you know, different facilities in different ways, and then how do we take and, um, capitalize on any opportunity there is to basically keep people employed, uh, across all of our facilities worldwide. Yeah, it was an incredible 7 years at Oshkosh doing all kinds of crazy stuff and working on crazy projects, everything from climbing inside of garbage trucks to, you know, setting up remanufacture lines to, um, you know, selling off some businesses that weren't performing. But yeah, in that I got a lot of experience with financial technology. Um, and that's what really led me over and how we met, because I came over to Venna early days and, uh, you were there and, uh, we worked on the same team together. You then decided to leave, I guess, 'cause you didn't like me or something. And then, yeah, here we are. So, you know, a few, I guess almost 10 years later, here we are both with our own, uh, our own Consulting and implementation shops. Kind of crazy.

You came up through a lot of tech. I actually didn't know that. I didn't know you were that heavily involved with systems, process, and tech from the beginning. Uh, what I found is a good— I wouldn't suggest consulting because, uh, you can— you don't have to do this. You don't have to just get consultants. But I think a big skill that's needed today is just systems thinking, being able to actually think about data, process, and technology within. I think it was Paul Barhers I was talking to earlier this year where I think with AI, a lot of things are getting easier. It's getting emphasized the better you are at systems thinking, moving data between different processes, different systems, actually deploying processes, skills, checks, QA validation, figuring out how to actually incorporate that into real-day life with actual businesses' process. Like, it requires a lot of that ability to think about process and system thinking. I actually think of it as probably the most useful skill these days because a lot of things, even if you don't know accounting too Well, if you don't know how to code in SQL, AI can take care of a lot of that for you. And I know you and I, it's been interesting. We started a consultancy around FP&A systems. You focus on Pigment. I focus on Vena, different systems. I do think they're all great tools and there's a time and location for everything, but a lot has happened last, it hasn't even been 3 years, 2 and a half years for us where AI just exploded. How are you seeing this impact how you think about building a consultancy and how are you seeing it with clients?

Yeah, it's a great question. I think back to when AI first kind of started popping up in this space or in the tech space, and there was a statement, it's been said on every meme possible, right? But it's like, AI won't replace you, but someone who can use AI will. And I think for the first time, especially in the last couple of months, I'm actually really starting to understand what that means and that There's a lot of people who, you know, are just kind of just blindly putting stuff in and you get an output. And that's what AI is designed to do, right? You put something in, it's going to give you an output. And then you have people who have truly taken and embraced AI to make it an actual multiplier to the volume and even to the quality of work that you can produce. And I think that's an incredible thing that I've seen. I myself, you know, I've really focused on trying to figure out how to do that well. It's very interesting. And I, one of the things that drives me crazy is like, you know, the AI outputs, the format, you can always tell right away, right? Like you can see, oh, that was AI. And it's like, I wish there was some kind of flag that says like, hey, but this guy put actually a lot of work into this AI output. He just didn't waste all the time putting, you know, the formatting in the spreadsheet or the slides to get there. Because I think that's the tough part, right? And then from a client perspective, I can only imagine how difficult that is for them. You know, you're now seeing potentially like multiple consultants, very similar presentations, very similar formats. Which one is good? Which, which one actually understands our business or is going to lead to a successful result? And that's what I'm kind of seeing, you know, the direction it's heading. It's like the differentiation that used to come from like yourself and me and our deep knowledge and how we can talk about and present that to clients is still there. But now because we want to do things more efficiently, the way it looks and feels as an output sometimes can be very similar. And then how do you as a client tell the difference between 2 outputs that look and kind of smell the same? And you see some of that too with like coding, right? And like some of the software programs in the market, you can put 'em side by side and they're like almost identical. Like which, if you took the brand, like which software is which? I have no idea. And it's that under the COVID stuff that really matters, right?

It's funny 'cause I went to Orangetheory yesterday and I noticed all the They have little apps that you can track your performance, your heart rate, all that stuff. I noticed I've been going for probably 2 years now. It's the only thing that dragged myself to the gym. And all their UI changed after 2 years. And I can clearly tell this was from Claude. It's the standard Claude font. For a firm that big, I was surprised. Like nothing changed for 2 years and suddenly all you have brand new UI/UX across all the apps and everything. And it's all done by Claude. You mentioned a piece about kind of putting in that work. For us, we know for a fact one of the reasons we do well is just having that deeper understanding for clients. I know it's true for you as well. I actually recently did analysis for our past sales. It was the number one reason for us winning our deals. The number 2 reason was just people like Excel for Venom. Yeah. But you mentioned in the last few months something changed. What happened?

I think what I've seen is that companies, especially in the last few months, have just hugely put their chips in on AI, but without any plan. And so they've got like, you know, like one of our clients, they're a very large enterprise client. They, we've worked with them for over 2 years. Like they're very resistant to AI, very careful, but now I was on a call last week, and it's like all of a sudden they've got all the AI tools, they're doing test user groups, but again, there's no real like guidance or plan. It's more just like they're activating accounts across users, and I'm seeing that a lot. And that's, I would say, like that big change is I feel like it's almost that bandwagon effect where some of the companies have been a bit more careful are either getting pressure, you know, from employees internally or from, you know, the market or just the advertising, right? And it's like, oh, We better do something. We better turn this on for people. Otherwise we're going to miss it.

I've seen that as well. I've seen a lot of companies being slow at it last year, primarily because of security and data reasons. But now I'm noticing the same as you were. They now have this huge FOMO, fear of missing out, that if they don't do this, it's too late. And now they're just, instead of a slow strategy to implement, they're just letting the gates flood open, just open. And I talked to them and I asked, what happened last year? You did not want the AI companies to touching your data, suddenly you're just letting them touch everything. You're going from nothing to everything. And the constant answer I've gotten is basically, if you read the fine print from Claude and you go— if you are on the enterprise license, they do not use your data for training. And if that's what they say, we'll agree to it. And suddenly everyone now is extremely comfortable giving up full control to their data, their database access, everything. And Just deploying everywhere.

Because that's never backfired ever for anyone.

Yes, exactly. But at the same time, I haven't really seen a lot of companies. There are some companies, I find the smaller ones, change management is always easier with smaller companies, but a lot of companies actually haven't had great adoption. Forget all the headless agentic, forget all the really complicated and big multiplier stuff, just general adoption. I found it really poor, even for us. We were at 10% this time last year and we thought we're a tech company, we're consultancy, we're, we know data, we can get adoption really quickly. I think after 9 months we were at 10% adoption. We're now just at 65%. I see other companies having an even more difficult time just getting users to actually use it. How are you seeing it? I know you have more SaaS and tech clients. How are you seeing it for both you at Twin6 and your clients?

I would say the same. Um, you know, we actually at Twin6, we scaled way back, uh, actually on our licensing. Because of exactly that, because we saw, you know, there were people using it and using it very well. And then there were people basically like using it just for Google stuff and it's like, okay, well, that's not helpful. Google still exists. It's still very free. Yeah. And then, yeah. And then, you know, for, for the client side, you kind of see the same thing. You know, we have several clients who are very AI forward But it's that same thing. They have kind of those pockets where you have 1 or 2 folks who are kind of extreme power users. They're creating like the markdown files, they're creating the skills, and then they're propagating those out. And then you have, you know, kind of limited or forced adoption on some of those skills across the team. And I haven't heard any like resistance statements or like, why not? I think it's just people, it's like anything, right? It's that change management and how do I integrate that? into my workflow. The thing that cracks me up, the Google statement, like we have one of our clients is publicly traded. Somebody's working through some preliminary data validation and they were like, hey, they asked me like, hey, do you know if we have anywhere in the client files like this revenue number? And I'm like, I shared my screen. I'm like, Google revenue for this company quarter. And I'm like, here you go. They're like, whoa, you can just do this in Google? It's like, yeah, they're public. Like they were trying Claude and all this stuff. And it's like, yeah, something. Yeah, it's that path, right? And I think it speaks to that adoption component. Like you have some people who just kind of, they've shifted their paradigm and it's like, now I'm just going to go to Claude and I'm going to ask everything to Claude versus, you know, the toolbox approach that you and I have been fans of and talked about forever. It's like it's a tool in the toolbox and like you need to know when to use it. And I think right now nobody— it's the tool in the toolbox that people just don't even know What is it even for? What can it do for me?

We did the exact same thing. We scaled back licenses. Um, we had licenses for everyone. We took all of it away. Then we tried Gemini because it was covered just by our Google account. Now we're testing with Claude as well. Similar case, we're now scaling back a little bit. Initially, we just let it loose, let everyone get access and see how the team performed. We did actually pick a person to kind of spearhead it, to really own the adoption across the organization. That made things a easier for adoption. We started actually pushing training sessions, show and tell. That was actually a big game changer for us. And I think it was 2 months ago, I got this from a client of ours. CFO was explaining to me, well, how he diff— well, how he got adoption across the team was made it a limited resource, just like your budget that you have for your department every year, every quarter. It's another budget. We only have so many licenses. You have to show that you're using it, you're getting output from it. Can't just be token usage, because we've seen this, people can fake that easily. And then you basically allocate that resource to the team. So we did the same thing as well. There's a limited number of users you only get if you're actually showing output and usage and learnings from it every week. And then from there, that's how we were able to push our utilization or adoption from 10% to roughly 60%. Anything on your side that's worked?

Yeah, I would say the biggest thing is very similar. It's kind of that center of excellence approach, and then the training becomes the big piece. And that's, it's the hardest because that's where you get into the change management. The show and tell, you know, has been really good, but then it kind of comes back to that reinforcement of taking that and actually running with it. I think one of the gaps from a product standpoint that we've seen with AI is just that ability to propagate things across. That collaboration, you know, is really difficult. Say, you know, you have a client and you create a project for that client, I mean, it's insane to me, but like you can share that project, but it's not truly shared. It's not a collaborative workspace for that project. You see your stuff, they see their stuff. And that to us at least is still a pretty major holdback because when you have multiple people kind of trying to collaborate and work on something, it causes them to drift away from actually using AI, even if you've got some good skills and stuff for them to use. Because they're still having to manually kind of exchange some information back and forth. Yeah. I'm curious if you've done anything around that.

That's a great point you've— so I went into a deep rabbit hole this year. Basically for weeks I was trying to figure out how to build a scalable collaborative kind of Claude setup so that everyone can benefit your markdowns, your references. Um, I was talking to engineers, I was learning how to I've concluded as of right now, AI is largely an individualistic tool. It's not great for collaboration. I've long given up. I've easily put in probably 100 hours plus sleepless nights trying to figure this out. It's weird. It makes you really strong individually.

Yep.

But if you try to share your markdowns, your skills, because it doesn't work the way that someone else likes to work, I found that the option is is poor. And if they're good at what they do with AI, then they would change the references and skills to suit themselves. And most people just leave it. And then finally, it's not a great collaboration tool, but it makes you yourself 2, 3, 5, 6, 10 times stronger than if you didn't have it. And you and I have talked about this multiple times where we're seeing kind of the labor market, the resources that we hire, both the younger and kind of senior folks that we hire. It's a little bit different from what it used to be 10 years ago. Some of it may be impacted by AI. I'm seeing a little bit of that as well, but what have you seen?

Yeah, I think what I've seen, it's not new, but it's, I would say, kind of this thing that's crept up, you know, like, and it's just that shift in priorities, like, to be constructive about it and not, like, say terrible, nasty things about people. I think it's a shift in priorities, and I don't, while I don't identify with that, That shift in priorities, like, I think it's interesting to watch happen because I don't even think it's necessarily generational. I have people, you know, our age who also I'm seeing this kind of thing where they just really don't have their kind of heart and soul in like work anymore. And I know like not everyone finds jobs they love and stuff like that, And generally, like if you rewind the clock, like generally you always had, if you use the G model, right? Like the Jack Welch ABC player concept. But generally people understood, like if you're working at a company, you're going to show up, you need to perform or you're probably not going to have a job. And it's really interesting because even with the labor market and these horrible stories you hear about people struggling to find jobs and stuff, I'm seeing not just for us sometimes, but also at clients where they're just really having a hard time. Identifying candidates who are going to come in and want to, I think of it as like, want to leave your mark. You know, they want to be a part, they want to learn, they want to help improve the operations, improve the business, leave their mark on what they do there. And I think more and more it's crept up on us, but I think it's very prevalent now cross-generationally. Like, people just, it's just a job, right? And so, even in that professional sphere, it's kind of like the, I think of like that American Beauty movie with Kevin, I think Kevin Spacey, and he like goes and interviews to work at the burger place. Like, yeah, I just basically want to show up, get a paycheck, and have zero responsibility, you know? Like, and yeah, I don't know. It's an interesting thing. I think

there's lots of reasons behind it. We see it as well, for sure. See it with clients as well. Getting Strong talent. That's always been difficult. I think AI is a little bit of that. I don't think it's the whole thing. I think from what I've seen, especially since I live in different places, the kind of rising cost of living sometimes makes it so difficult where you don't see a way out. I mean, I was an immigrant. I knew I came in. I was like, I knew if I worked my ass off with a little bit of luck, things can get easier. And I do hear sometimes from folks that. The cost of living, real estate, things like that. It's so high now that even if they pull the hours, things, they don't see a path out of it. Yeah. I also think AI's made things easier. I don't think people are going through the trenches. You went through the accounting, I think it's more the accounting route earlier, right? There is accounting is difficult early on. I went through the data front. What used to take 5, what used to, what takes 5 minutes now used to take me a week, 2 weeks to code and debug, and you kind of go through that grind, for lack of better words, and you build the grit for it. But now things are getting a lot easier, right? You don't have to struggle. You don't have to— I don't know if eating glass is the right word for it, but you kind of— and I think you lose that muscle for it as well. If things become easier when the tough times come, In your example, kind of leaving a mark, that takes work, that takes dedication. And it's easy not to put in the work now, especially with AI. It's easy to just be average. And I think what people are not realizing is these tools are available for everyone. The outputs, the expected outputs across the board should be going up. And if you don't change your benchmark and the way that you work, you're going to be left behind. I spent a lot of time thinking about this kind of for MCC because I define my job as we need to make sure we survive the next 6 months or the next 2 years. That's how I think about my role. And a big piece is training the team and mentoring them to try to push them, basically realize that the benchmark's changing. I think for the last 20 years, it's been maybe growing a little bit, Maybe when you move from paper to Excel, there was a big improvement. Database macro things came in. I think that has some improvements, but it's nothing like what we're seeing now. And if you don't put in the work, you're going to be left behind. Yeah. I found some luck in studying how people mentor and coach. A lot of this actually came from when I was studying how people train sales. Because that is a difficult place to be, right? If you're especially doing BDR outbound work.

It's the worst.

That is the worst, right? It's difficult, right? So it's not something you want to do day in, day

out. There's 2 jobs that I think everyone should have to have, like no matter what you should, everyone should be forced to work in a restaurant and wait tables. Yes. And not like bartend or work in the kitchen, fun stuff. Literally wait tables and get cut after making $4 and also have 1,000 tables all at once and get screamed and yelled at. It does something to your soul that is important. And that is one. And then the other is the BDR function. You know, like I, even for me, like I have had to do that. I used to have, I worked in jewelry. That was my first, like in university I worked in a jewelry store. Yeah. And we had to do cold calls. We had these call-down lists for, you know, special events and stuff like that. And so, you know, you're working in the store in between somebody coming in, you literally pick up the phone and you get a call, you get the script and you gotta try to get people to commit to coming into the event. And you had to make, you know, anywhere 100, 150, 200 dials a day while you're also working the floor inside jewelry. And what a soul-crushing experience, but in a positive way. Like you come out the other side and it's like, you really appreciate how difficult it is to call to action somebody to, to make a purchase that's a nice to have versus something that is Mm-hmm. forced requirement, like from a government regulation or something. Gives you a better perspective, I think, on just people in general, those 2 types of jobs.

I was a dishwasher for 2 years. I realized very quickly I wasn't a great student at that point. After that, I was like, I need to get my shit together. I need to make it to university and college. I can't keep doing this. She's just rough. Sales are difficult as well, right? Like, how long have you done it? Like, what I've noticed is, so we have a head of BD here, Jacob, and I watched him coach his team, which are these like fresh grads who are excited coming out of university, college, and now they realize they have to make $200, $300, $400 a day. And there's a level of coaching there that's been helpful in terms of what I found to push the team to step up, for lack of better words. How long did you do sales for, for that? And have you found anything that's changed how you approach hiring, coaching, mentoring to help them step up?

I think the biggest thing is trying to identify— it helps you learn how to identify people who can have that self-start conversation, because especially in what we do, you know, in our businesses, Being able to raise your hand and ask a client a clarifying question or anything like that is so important. And part of that soul crushing is realizing that you don't know all the answers and you can just ask people questions. Like, it's okay to just pick up the phone and ask people questions. And I think that is one of the biggest things that I learned is like trying to, like, I'm still trying to figure out the best way to identify it, but that those kind of little tiny skills can really balloon or blossom right into a really great employee.

You bring up the self-starter. I was having a conversation with my team earlier this week, and we were mentioning that there are a lot of things we can coach, but what makes a really good employee— I don't want to just say employee because I think these are characteristics that can help you for life— is If you break down what self-starter actually means, so I think what we came down was one is curiosity and the second one is challenging their beliefs. So I think a lot of folks, they just believe like it's not possible. It's not possible. And they just won't do it. If you combine those 2, and because things are so easy, information is so easily accessible now. If you're curious and you believe it's possible to change how you work, I think it was Bill Gates's favorite word, hire the lazy ones. Yeah. I'm butchering it, but find people who are willing to put in the work to improve the process, willing to put in 4 hours so that you can shave 5 minutes every day. That is a really good characteristic to have for life as well. Do you think this is something that can be mentored and learned or something kind of innate?

In all, all the years, makes me feel really old to say it that way. In all the years, like managing people, hiring, firing, like, I don't know that I've ever successfully trained someone that skill. I've unlocked it in people, right? Where maybe they didn't realize that they had it, or it wasn't something that they saw that value in. But I've def— I don't know that I could say I've ever been able to successfully Yeah. teach someone that, you know, it's, it's like you said, I mean, it's from my experience, like, it's kind of you have it or you don't. And if sometimes though you might have it and it needs to be unlocked, but, but yeah, if you don't have it, you, no matter how you try, how hard I've tried, how hard you've gone after it, like with those folks, like they just, they always revert back to their same you know, bad habits, um, and it becomes a lost cause.

It's interesting you said the word unlocked, um, because I was just thinking about my history. I have unlocked it in people. I didn't train it, I didn't coach it into them. They had it in some other aspects or different parts of life before, maybe not for work, but they had it. I think combining that self-starter curiosity and self-studying with AI these days, you can be quite powerful. How do you look Because you do a lot of recruiting as well. I mean, we're a consultancy, a big thing we have to think about is pipeline for people.

Yeah, unfortunately, all day, every day. Yeah, no, the things I look for are really, and this goes back to Steve Welsh, you know, that you know as well. You know, he put it in words for the first time for me. And that is, you know, somebody who you'd want to sit down and have a beer with, right? So like what we're doing right now, you know, somebody who you could see yourself just voluntarily spending time with and you think you'd have like an enriching conversation or dialogue. You know, I think that's first and foremost because as someone who has those traits, right, who likes those things, you're kind of, I find, naturally drawn toward other people who also have those traits. And so if you feel that kind of personal bond or connection quickly, that's usually a good sign. I think the other one is, especially in today's world, people love to say that they read, right? Like everybody's reading, but I'm pretty sure nobody other than like me and you is reading anything.

Exactly. For me, it's a lot of video consumption, but thank you for believing I do.

Like, I, like our interns last week were showing me this, uh, I'm going to butcher it. It's like Learn LLM or something, or Notebook, Notebook LLM.

Notebook LLM.

Yeah. And, and I made fun of them because like they were showing it to me and all this stuff. And I was like, well, in all the time that you spent setting this up, you could have just read the article that I sent you and then we could have a discussion about it. But instead, You have this like generated podcast from the content. Like, yeah, I mean, it's cool, but like, couldn't you have just read the article and then we could be talking about it? And that's, that's kind of why I get to think the second part is like, you know, does that person have interests, right, that they can just talk about and they are consuming information because, because those interests right outside of work, those are things that you have to be that self-starter. to want to pursue or go after. You know, it could be like literally reading fiction or something or reading the Jack Reacher series, right? It doesn't have to be anything crazy, but it's just that dedication to, I'm really interested in this. And even though nobody is telling me or forcing me to go do it, I'm choosing to go expand and do it. And I can talk to you very coherently and engage in a good conversation about it, because that means you're retaining it, you're processing it, you're thinking about it. Those 2 things to me are kind of the 2 things that, you know, again, it's hard. It's hard to identify, especially in virtual setting versus sitting down face to face. But I think those 2 things are the biggest things I've noticed and seen.

When you talk about that, I hear the word passion. I think that if you can sense passion in someone, it doesn't matter what it is, They're generally putting time over and over again. They're putting reps to get better at something, and that translates quite well. And most things aren't easy the whole time if you're getting good at it. We found that hiring like athletes helps. They've done something over and over again. They put in their time. They're able to kind of build the grit around, around it. They're also constantly looking to improve. You bring up a great point about being able to have a conversation about it as well. They're passionate, meaning they're happy to talk about it. They can nerd out, they can get really into it. Regardless if the other party cares for it. I think that helps as well. And I think to your second point, it requires work. Now, I'm a big fan of NotebookLM, so I think it's a great tool.

It is a great tool. It is a great tool. There's no doubt about it.

I think also for you and I, maybe we're a little bit older, you have to think about the generations are also a little bit different. People don't read as much now. People are listening, podcasts, things like that. Yeah. But I think what you're trying to say is sometimes you have to just do the work. There's no easy way out. I learned this in the last 6 months where it really improved my AI usage is you have to sit there and read the output and it's some of the driest content you can get. But if you don't read it and you just let AI keep going, there are— because the thing with AI is it's the fastest way to produce garbage now, it's the fastest way to produce If you don't read it, you don't read what it's planning to do, you're going to get a lot of garbage. So it takes— you have to sit there, right? Do the hard work.

So it's funny when you say that, and why I'm laughing is because I get made fun of sometimes because I literally expand out all of the things. And then because I have the coding background and stuff as well, even when it's writing the Python scripts and stuff,

Yeah.

I read all of that as it's going because to me, and I'll tell you, I mean, maybe 60%, 70% of the time I end up hitting the stop button, providing a correction before it's even gotten to the output because it's misinterpreted something I've asked or it's misinterpreted a piece of data like in a model that I've put into it. And so that's why I'm laughing because I couldn't agree more. Like, it is, it's a huge accelerator and a huge value-add component. But reading that output and even reading the process it's going through can produce, help you produce such better results in the first pass. You know,

it's day and night. No one told me this, that a big part of AI, good AI usage is just reading. So now I print it out actually. I do a lot of planning mode, do the whole thing. Yeah. And I'll print it. Something's like 10 pages. I'll grab coffee, basically leave my computer so I don't get distracted, and then just sit there and read the whole thing, write my notes out by hand. Then I go back and type it out. It's made such a big difference. And I think that goes back to what you're saying, right? You have to do the hard work. There's no easy way out. I mean, like, AI is going to— yeah, AI has made it easier than ever, but you still have to put in the work.

Yep. And I've I gotta tell you, man, I'm a handwriting person through and through. I always have a notebook, but shameless plug, the reMarkable products are awesome. I got for Christmas the little, the little one that's like the size of a Moleskine, like notebook or whatever. Um, oh man, it's great. You can load in that document and then you can hand mark it up and then their handwriting, my handwriting is terrible. Like, even I can't read it most of the time. Yeah. It has gotten so good, like it can translate that boom right into text and then you can take and actually work with it directly. That to me is like where I get really excited about AI.

I was looking at them. I was trying to justify the price. I started actually looking at an iPad Mini. I'll have to check it out again.

To me, it's that segregation. I was a I'm always an early adopter. And like, I got the iPad when it first came out with the pencil and I was the guy in the meeting, you know, taking notes and manufacturing. Everybody's making fun of me. Like, what is this guy doing? And but it's the distraction component. Like, that's what reMarkable has solved. Just like the Kindle, right? It's like, why, why do you read so much more on a Kindle versus on your iPad? Well, because there's no distraction, because you literally— it's just for reading. The reMarkable, same thing. Yeah. Why do I use the— why do I now naturally pull out the reMarkable to take notes? Because it's for note-taking. And so it has truly supplemented that corner of my existence. And the fact they're searchable, I mean, I have stacks of notebooks that are just filled with notes. You know, you can never go back to them and find anything. And I can never let them go because I have such an emotional attachment to them. Now that is all digitized and it's searchable, which is unbelievable in my own handwriting. So shameless plug, do it. Just pull the trigger.

I'm going to look into it because that was the primary reason I started doing this research a few weeks ago because I have all these notebooks with notes everywhere and I never go through them. I can't find

anything.

So I'll look into it. We have a few minutes left. Justin, this was really great. It's such a changing time right now, and I know we're also seeing it with clients as well. I— what I've seen with a lot of clients is actually they— many of them don't realize getting good at AI is actually becoming like mini dev shops. You have to think like a product manager. What are your requirements? If you do sprints, um, if you're good at it, you start producing tools that serve specific niche needs of yours. But at the end of the day, you're kind of building products sometimes, and that requires maintenance. People don't think about that. And I've noticed a lot of clients in finance, especially with what you mentioned earlier, there's a big push to use AI, AI. And I've noticed a lot of folks kind of pause traditional strategies, data warehousing, building infrastructure, getting tools to help them with better reporting, budgeting consolidation, things like that. I'm actually a firm believer that a lot of it's going to slow down soon. Because I've very rarely actually seen clients get results from it and they're realizing there's still a place for kind of your standard SaaS tools, but they're better with AI. Have you seen anything like this? I know in Pigment there's a big kind of product release in the last 6, 9 months, a year around AI. Have you seen anything like this?

Yeah, I mean, we've seen it's, I would say that typical curve, right? There's the excitement. You know, at launch and then the use and it's like, okay, we're doing some stuff, but then it tapers off just like we talked about with the internal usage of various tools. And I think, like you said, it's understanding that it's probably the hardest change management that an organization will ever face is how to actually integrate AI into what they're doing because you're not just having a new tool. You're truly trying to change or break habit structures that people have, and that's the hardest thing to ever do. And then especially in the realm that you and I are in, where you're mostly working with finance and accounting people, there's a natural tendency to not trust, and I think rightfully so. And so when you have AI, like, I mean, literally doesn't understand debit versus credit, you know, And just like a lot of people says, oh, credit is a negative number. Well, it's not actually a negative number. That's just how it looks in a database because you have to assign a negative to it. And so that creates that mistrust. And I think those pieces, right, are where that slowdown, I couldn't agree more. Like, I think there's a lot of hype. I think it's a lot like what we saw with cloud, you know, and when cloud first became a thing, it was like this huge, are we in the cloud? Are we getting into the cloud? And then now You know, and then it really slowed down. People took a step back, like, what do we need to have in the cloud? Right. And I think that's, that's where we're going to get to. But I think much quicker than with cloud because like you said, the pace is so much faster with AI and you can do so much rapid prototyping. I think people are getting to that conclusion much faster.

I love what you said about prototyping as well. I look at it as We're able to implement a lot faster now. Well, the build partially is getting faster for sure. Any sort of data transformation, things like that. But I think the biggest thing is the prototyping. And I want to emphasize the word prototype because literally last week I was building a model in Excel, and if I did not read the formulas one by one, I did not catch that it decided to just hardcode some figures because I asked for a model. I didn't have the greatest documentation. I should have. It's not me, but, um, it just started hard coding certain drivers for me. And if you didn't do the work, if you didn't actually read the formulas one by one, you would notice that the model doesn't fully work. And some of the driver assumptions are made, which is incorrect. Um, which goes back to what you're saying. I think it's a great way to prototype what you're looking for, but you still need someone that's gone through the trenches, that knows how to actually build it and architect it properly to really set it up so that it's scalable, reusable. I think that's the other word as well. A lot of these prototypes look great and they might work right now, but they're not designed to build and reuse for the long term. And maybe that's not where we're going with the future because it's so fast to build. But a lot of accountants and finance still work that way. They like a model, they like a report, they like data moving from A to B the same way day in, day out. And I think when I talk to a lot of CFOs, there's a big question now is what should be AI-driven and what should be mechanical. I think people need to think about certain things that still needs to be mechanical. I need A plus B to equal C every day. I can't have it just vibe out its feelings and think of a different math every time.

Yeah, it's a really interesting thing when you get into stuff that is strictly rule-based, you know, and sure, we could debate like IFRS versus GAAP, but what I'm talking about is the core, you know, of business, which is always still debits equal credits, assets equal liabilities plus equity. And then all the permutations that come off of that, you know, from the various rules and situations and depreciation schedules. And, you know, you get into those things where, where there isn't flexibility. Right. And, and to your point about like hardcoded numbers, that's— I've seen some of those same things where, you know, the AI makes certain assumptions or builds those things in. And then you, you, if you don't review now, hey, next time you go to add a new asset to this model, it's going to actually calculate the depreciation wrong because it hardcoded 60 and it didn't create a table with the asset types and the acceptable depreciation lives because it doesn't know. Right. And that's, that, that's the thing. It's almost like, it's like the dumbest, smartest, hardest, best, fastest, sloppiest worker that you have. And you have to constantly be like, molding it like clay to get to what you actually need.

And for now, there's limitations on context windows, right? Because we're designing systems that needs to work for different modules, different parts of the business. Um, right now it's really good at building individual components really quickly. Um, like you said, smartest, dumbest, fastest method to do it, but it's not— it oftentimes doesn't have the whole picture in

Yeah. And that's the challenge. And I think to your point about the trenches, one of the things I've seen is because that trench component has become smaller and smaller, especially with folks who have less work experience, they don't have that ability as much to contextualize the full picture because they never had to sit and do horrific month-end journal entries and stuff all the way up through building models, thinking about context, how will this roll forward year to year? You lose that contextualization skill. And, you know, I don't know how you teach that to AI or to a person. It's really hard.

I don't have an answer for it. I know, so a lot of our audience are kind of younger folks. I get a lot of folks actually in accounting asking how to switch to finance, and I don't have an answer for that. What I often say is like that those trenches still exist today. Take advantage of it. I know it's the difficult part. I'm telling you to go enjoy the worst part because it pays dividends later because at some point it's going to disappear. Justin, we're at the end of this. This is a lot of fun. Definitely excited to have you back again. We like to end the session basically just asking what your kind of 2-cent advice is for the audience. What guidance or 2 cents advice do you have for the audience?

I always like to tell people that if you can't teach it, then you don't own it. And what I mean by that is, you have, and perfect for what we're talking about right now, right? There's a lot of, how many times have you worked with a client or for those listening, how many times have you gone in to try to take something over from somebody or learn something from somebody and they can't explain it to you? That you ask a question, well, how does that formula work? Or how does that happen? And they can't explain it. And I think that everything we've talked about today kind of actually goes right into that. And it's, you know, if you can't teach it, like you don't own it because that means you truly don't actually understand it. And if you don't understand it, how can you own it?

Absolutely. Absolutely. You need to understand it in and out. You need to explain it to someone else. You need to be able to teach it. A big piece that we spoke about is this AI. If you don't know what it's actually doing, if you don't actually go through what it's actually built, you won't understand it enough to teach it as well. Well, Justin, this was a pleasure. Thank you for so— thank you so much for being on with me.

Yeah, thanks, Charlie.

Really grateful for this.

Yeah, it's been a lot of fun and looking forward to chatting soon. Thanks again for having me.

Appreciate it.

Take care. See ya.

Thanks. Stay tuned for the next episode of Making Sense with Charlie.