Track 3: Does your Firm Have BDE (Big Data Empowerment)?

Transcription:

Bruce Ditman (00:08):

Start. I want to play a quick game. I want to see, we can play a little bit of Liar's Poker. Who traveled the furthest for today? You have to. Dallas. Dallas, Texas. Boston. Boston is further than Dallas. Who can beat it? Paris. Paris. Yes. Yeah. Paris. All right. We have a Paris. France. Yeah. Paris, Texas.



(00:30):

Paris. France. Paris. Paris. Paris. Paris. Can anyone beat Paris? No. Okay. We we're going to get started. One of the things I want everyone to know about today's session, which is short, is that there are prizes and we have our first prize winner today. So I'll keep setting this up while I tell you guys who I am and then we can jump in. But I usually use plastic bottles. I like to throw them. I'm like all second place, all New Haven cornhole player, but I'm not going to throw them. These are glass. The last time I did this, I did. I sent a perfect one to the back row and it just bonked off someone's head. But when it's free booze, they don't seem to mind as much. That was easy. All right guys,



(01:15):

Thank you. By the way, we're all adults. If you drink them in here, I won't tell, but probably before the flight. So let's jump in. Thanks everybody for joining me today. We've got like 45, 50 minutes or something to talk about your firm, AI automation, and most importantly, what I'd like to suggest is sort of an attitude shift about it. So I'll start with a story. I'm a notorious tinker in my family. Very, very good at breaking things. My wife likes to say that I'm very good at breaking them, and she's very good at fixing them. I agree with her, by the way. Thank God for her. But one of my favorite things to do is buy tools, right? Anyone else here do this. Get excited by a tool. And then you get home and you think, how can I use this goddamn tool? What can I do?



(02:07):

I got this new sauce. Something needs cutting, right? I got this new thing, something right? And it is totally normal behavior, but you know who never does that? A real mechanic, a real carpenter, a real technician. Everyone in this room is a qualified technician of what you do. And what I want to accomplish today is try and change the way we think about ai from the way that I treat tools to the way that a real mechanic treats tools. We don't go into the shop and say, what am I going to use this wrench on today? But so much of our conversations about AI and automation, machine learning, large language models, everything else, it's like, how can I use it? It's exciting. How can I use it? They are exciting. I don't want to come off as negative about it because they're incredible, but that's not the important conversation.



(02:52):

So that's what we're going to talk about today. Changing the way that we think today is interactive. I'm glad that the group is the size that it is. It is because I'm going to be asking you all to participate in meaningless and meaningful ways. So we'll start as I'm saying, this is about mindset. With all due respect to my peers, I'm also a vendor, I guess to my colleagues and peers out there selling, promoting, trying to drive growth with their AI tools, which are miraculous. Nevertheless, we as firm managers, as business people and individuals, have to maintain the rigorous discernment about what we need to use and when the mindset and vision. So to start, we're going to play a game. And the game is, can you tell me what these people did? These are all names of trades that don't really exist anymore, and I am giving out prizes as I've demonstrated. Who can tell me what a Fletcher, by the way, you may notice these are last names for people. There's a reason for that if they're more commonly last names now than trade descriptions, but who knows what Fletcher did? Go on. All right. Yeah, that's right. It's essentially an error repair person. So if you know anyone and last name Fletcher, including Irwin Fletcher, that's where they got it. Tell me about a Thatcher.



Audience Member 1 (04:18):

Roofer.



Bruce Ditman (04:19):

You can't get two. Oh, there you go. Okay. Alright, good. I thought we had a problem on our hands. All right, Keith, let's keep going. What does a fuller do? Everybody knows the fuller brush, man. What did a fuller do?



(04:36):

That's good. No one knows what a fuller did. I didn't also, but they beat and cleaned woolen cloth. That's why they're vacuums. Okay, what about a knacker? Any Brits here? Maybe our friend from Paris is continental enough to know this. If a British person ever said to you that they're knackered, no, a knacker. When a horse was no longer of use, he was too tired. They took him apart and put him to better use, which I think oftentimes was steak. How about a courier? Not a courier, but a courier. Another name you've seen that's a leather worker. Here's a really common one. How about a Cooper? Anyone related or know a cooper? Barrels? Barrels? Yes. Here you go. Here's a exactly. Here's a tiny barrel of booze for you. And lastly, a milliner, which actually do still exist. Anyone gone to the Kentucky Derby? A very fancy affair. Milliner hats in the back. Very good. So why Bruce? Are we talking about all of these completely out of date and now obsolete careers? Well, there's a reason.



(05:44):

Because we are staring down a future that's going to change and it's going to fundamentally change the way we do things. And it is up to us whether we want to plant our feet and say, I fix barrels, God damnit, okay? Or we can think about what we will become in the future as opposed to just a last name, and that's one of our goals for today. So these guys, monks do quite a great work. One of my favorite things that they do is they make really good beer and less and less booze. If anyone here is a chart, true drinker, but they are very good at making beer, but they're also incredibly important in the development of most of your all's business. This one in particular, aside from teaching math to DaVinci, which is kind of a big deal already was, is really, and you probably learned this in school, but the father of accounting, the first to publish a double entry system of accounting beer, taught Da Vinci math and did that. Can you believe it? His mother must have been so proud. Who knows what this is?



(06:50):

Wedgewood, you got a Wedgewood? Very good. There are more prizes, but you can only get one a day. This is Wedgewood. Do you guys know that Josiah Wedgewood invented cost accounting? I'm driving to a point here, stick with me. Also an abolitionist, which is pretty cool. Who can tell me who this person is? It was you got a Capone. Here you go. What does Al Capone have to do with our business right now? Well, the guy who caught him basically invented forensic accounting. So innovation comes from all kinds of places and where AI will take our business. We don't yet know, but we're fitting to figure out. So from early chess machines to now we are as so and said this morning in the process of evolution, which I should note, always feels impossibly slow when you're in the middle of it and in retrospect always seems like it happened overnight. So be patient and diligent. What will our role be in that evolution? My encouragement today is like, let's get out of the cave together. Of course, that is an AI generated picture, which is an old gag at this point. So big data empowerment, what does it mean to your firm? Okay, at its core, lemme just fix something here.



(08:18):

At its core, big data empowerment is about finding ways to leverage technology as a tool. Does anybody know the John Henry story? This is one of my favorites. This is part of American folklore. John Henry, a black American who worked driving stakes into the ground and then a challenged a steam engine to a stake driving competition, right? The greatest man to ever swing a sledge hammer, John Henry. And they brought in this machine and he said, that's it. I can beat it. There's no way that a machine can beat a human at this, not at my level. And they had had it and they went at it all day, back and forth, hammer and not tongs, hammer and spikes, and at the end of the day, John Henry won and then he promptly dropped dead. The lesson that we take from this is let's not challenge robots to an automation fight.



(09:18):

Okay, let's stop thinking that the AI is going to replace us or the robots are going to replace us. One of the beautiful things about being human is that we use tools. So these are not threats to us. These are tools for us. So part of being empowered when it comes to data is understanding that and remembering the first thing that I admitted to you all, which is that we don't look at our tools and figure out what can we apply them to. We look at our problems and then determine what tools we need to fix them.



(09:51):

So we're going to do more q and a in a minute and more game show nonsense. But let's start by asking ourselves some questions because I think the first part of any real growth is always a honest and forgive the pun, sober evaluation of where you are. Okay? So at your firm, at my firm, at your business or my business, excuse me, are we actively automating manual tasks, right? Not are we looking for a tool one ring to rule them all, but rather are we aggressively every day saying, ah, here's another task and we're going to do an exercise for this in a minute. Is there a way, is there a tool that exists that can help us to automate that? Are we using what we have? You all, there's a reason in a courtroom, there are two people that always get oohs when they approach the stand to testify. The first is the housekeeper, the second is the accountant because you both deal in dirty laundry. So we as accountants have a tremendous access to information which is given with permission to us, not just to process, but also to analyze and certainly appropriately to aggregate as thinking creatures and as an extension of being thinking creatures to use machines to inform that aggregation, to drive ways to deliver better service or more service. So are you thinking that way? Maybe it's in the future happening. Are you thinking that way?



(11:25):

This is a tough one. Has anyone here used any predictive modeling? I bet you have. Okay. Yeah. Has anyone here used Waze? Has anyone ever here ever used a map device to see how long it'll take to get to the airport tomorrow morning? Okay, so you've all used predictive modeling, historical data about travel times applied to your scenario like much innovation. And because we are who we are on the surface, we resist things that are very simple. Applications invade our lives, and before we know it, we're this deep in it. The difference is, and this is really important I think to us as an industry deciding and litigating whether or not we should accept AI versus to the person who has got to get to the plane on time, the use the value is obvious to the MapQuest user, MapQuest to the Apple Maps or the Waze user, the value is obvious.



(12:26):

We need to again, look at the value of these tools, not decide philosophically if we're cool with it, who cares if we're cool with it? This is businessman. Alright, I'm going to forego some of these. Okay Bruce, so you just said it's easy to do change when you immediately feel the value. Well, let's talk about that. One of the things that I'm obsessed with is punching above your weight. If you don't believe me, you'll meet my wife someday, hopefully. And you'll see I also, I try to punch above my weight. I'm nowhere good looking enough for her or smart enough or wonderful enough. But how can your firm punch above its weight by leveraging these and other tools, whether it's AI automation, data, we've been doing it with Excel for a long time, by the way. Okay? It's just a tool. It's just a tool. How can we punch above our weight and how can we avoid the totally foreseeable problems? The accounting industry is unique in a lot of wonderful ways and in some, what I find kind of funny ways, some of the things that I find funny are, here's an example.



(13:32):

We are a business that feels we can grow our sales without increasing our inventory. If one of your clients came to you with that forecast, you would never accept that answer or that philosophy, right? But we're out of inventory, okay? Inventory meaning people, inventory meaning 15 minute increments and two, often frankly, what we decide to do is to rightsize our growth strategy to our capacity, which again, none of you would recommend to your clients all. So how can we increase inventory so that we can drive growth in sales with data, AI and automation? The first step again, isn't to decide whether or not I'm going to become a cyborg and bio hack my body with implants of microchips and et cetera. We don't need to go that deep. The first thing we need to do is play a game and the game is called the what if game. And generally it goes like this. What if I could do X without Y? What if I could do more of Z without X or with more X? What if I could make more money without hiring more people? What if I could get my life back? What if I could make my people happy without making myself super sad and poor? Alright, this is the game we're about to play right now. Now the prizes are back active. I don't have a long presentation folks, so I need you to participate.



(14:59):

Let's play what if We're going to start with variables and we're going to focus on these as the sort of general area of our conversation time. Can someone in the room again, we are now, whether you like it or not, we're all playing the what if game when it comes to time. Can someone tell me a real what if then we can look for the tools to solve it. Somebody show me. I'm right about up in the back.



Audience Member 2 (15:26):

What if all of my scheduling meetings internally and with clients could be done by a virtual assistant?



Bruce Ditman (15:35):

That's brilliant. You're not in the virtual assistant business, are you? It would be awesome if you were.



(15:40):

Leslie, thank you for being a good support. Someone give me another What if about time? So that was what if all my scheduling, my calendaring could be handled by a virtual assistant, which is something that is out there, which is smart. Why by the, wait one second. One second. Ms. Paris Men. Perry, why? What's the value that you're gaining?



Audience Member 2 (16:04):

Efficiency. What? Matching inventory of available diet without having to apply brain power.



Bruce Ditman (16:11):

I love it.



Audience Member 2 (16:12):

Back and forth.



Bruce Ditman (16:13):

Yeah, I will break my brain on a calendar. I can't do it. It does not compute for me. And that is I'm going to do what you're suggesting. Let's go. I honestly give me a time problem.



Audience Member 3 (16:27):

I think, if someone can take notes of all the meetings and allocate them to the respective people who are supposed to do that job and also take follow ups.



Bruce Ditman (16:37):

I love it. You already have one. Do you want to nominate someone else to get.



Audience Member 3 (16:42):

You can give it to me. I'll figure it out.



Bruce Ditman (16:46):

All right. I can't hate the hustle on that. So what is the problem we're solving?



Audience Member 2 (16:54):

I think a lot of time. And it's repetitive.



Bruce Ditman (17:00):

Also. Yes,



(17:04):

A hundred percent. The hardest thing to do with those meetings is to remember what everyone's supposed to do. One, what was supposed to be done, two, who was supposed to do it, and then three follow up on it. There are some really good from a sales perspective, I've seen some tools that are blowing my mind. I'm blanking on the name right now. My friend Chris Smith at Quick Fee, who's like an automation genius, uses one records, the call breaks it out like a script, attributes the language to each person on it and will summarize it any different ways and then suggests follow up from a sales perspective. Absolutely amazing. What's that?



Audience Member 2 (17:40):

Otter.



Bruce Ditman (17:40):

Otter. Otter does it now.



Audience Member 2 (17:42):

But does it?



Bruce Ditman (17:46):

Yeah.



Audience Member 2 (17:48):

Synchronous. Why have a virtual assistant?



Bruce Ditman (17:51):

Yeah. So there, right? Instead of us going to the Otter booth or the other booth, let's do one more. We're going to move on. Someone else. Give me a time problem. Yes.



Audience Member 4 (18:04):

Or if I could get a job done in less time.



Bruce Ditman (18:06):

What if you could get a, can I ask a follow up question before we talk about it? What would you do with the rest of the hours?



Audience Member 2 (18:13):

I would go get more money.



Bruce Ditman (18:13):

Go get more money. I heard this story, I'll share it. This happened at a conference and the conversation was about AI ethics, which is a legitimate conversation, right? Ethics are not my area of specialty, but nevertheless for most of my career I could defer to Leslie for that. But the question, so someone raised their hand and they said, yes, I have an ethics question. I discovered that someone on my team, they were an auditor, I think figured out a way to do eight hours of work in two hours with AI and then they build eight hours to the client, took the rest of the day off and played golf and I was on the floor. But not because that's unethical, which it is, but because that was a firm that clearly valued eight hours over innovation or growth. So your attitude is awesome. We should be using these tools to do more of what we intend to do. Not use them necessarily to figure out how to do less of what we intended to do. A lot of people have been talking about intentionality at this conference and I'm here for it. That's a great one. Alright, heavy lifting. Who's got a heavy lifting problem?



Audience Member 2 (19:27):

Related to information systems type. Oh, we got the data from here, how can we make over there? And there's a lot of business in the middle tax things, different way, audit consultants, but that's a lot of heavy lifting just to get people to agree on something.



Bruce Ditman (19:46):

Yeah. Yeah, I mean, and again, here you go. Thank you for participating. So there's a lot of data, a lot of places, and it's a lot of lifting to get it into a one place where it could be used. And even before you do that, you have to make rules for that. So of course there are robotic process automation, there are APIs, there's optical character recognition, all kinds of nerdy stuff that we could talk about about how to accomplish that. It doesn't really matter. You have a problem, which I've got heaps of data in disparate places doing kind of slightly different things, maybe in slightly different dialects and I need to put it together and if I did it myself, it takes forever. Who's got another heavy lifting go?



Audience Member 2 (20:33):

How can I imagine properly all the technology?



Bruce Ditman (20:39):

Interesting chickens, eating chickens. Can I get a technology that manages my technology? Who here's done a tech audit at their firm? How'd it go? What'd you find.



Audience Member 5 (20:51):

That it probably had more than two things. Did the same bank?



Bruce Ditman (20:56):

Here you go bud. Anybody else do a tech audit that had an interesting experience? 23 apps. 23 apps, how many were in use?



Audience Member 6 (21:06):

All of them unfortunately.



Bruce Ditman (21:06):

Wow, I was talking to someone earlier, I'll give you, here's a tech problem that tech could solve. There should be an app for businesses that automatically, right? It's an automated process. Alerts it when something hasn't been used by license holder in X amount of time. Unfortunately or fortunately as humans, we are susceptible to all kinds of influence and people who do sales, and I don't mean this maliciously because I'm a salesperson. I've been a salesperson, but there are sales skills and it's not the same thing as charity skills. Sales skills are ways of making a very compelling case and making somebody want something. So making someone need it and there are really good ways to do it. I think the highest one, if you do education, you never have to do any sales. You educate someone on why they ought to be doing something. However, we are susceptible to being overloaded with information, overloaded with opportunity or perceived opportunity overloaded by the complexity of something, by secret knowledge, by black boxes.



(22:20):

One tool to fix all problems and we buy stuff. We've all probably heard the ads or seen the ads in our email or on podcasts for services that now go in your inbox and audit your subscriptions, right? We are subscriber buyers and the line between B2C sales tactics and B2B sales tactics has become ephemeral. It is really being smudged down. If you don't believe me, half of us are talking about subscription models for accounting services right now, okay? We are subscription buyers. We like to buy it, collect it right, collect it literally, and then sometimes we use it, sometimes we don't. A tool that could do that would be incredibly valuable. I have one more vodka left. I mean here. Yeah, we have two. No, the last thing I want to hear from the group, and this is by the way, I do a lot of calls, consultative calls and sales calls. I guess for our data AI and automation services. This always comes up, so I'm not going to believe you guys if you don't have a bunch of these. Okay, who here has a paper heavy manual entry problem.



Audience Member 6 (23:38):

Fan last year in to being virtual?



Bruce Ditman (23:47):

How'd it go?



Audience Member 6 (23:48):

Yeah, we're working on it.



Bruce Ditman (23:50):

What's the hardest part? The client. The client or the firm?



Audience Member 6 (23:55):

The clients are resistant.



Bruce Ditman (23:56):

Really? That's interesting.



Audience Member 6 (23:59):

A lot of them are restaurants and they're just writing their stuff down on Piper.



Bruce Ditman (24:03):

So we have a client, thank you for sharing that. We have a client, believe it or not, we have a really cool tool. Probably no one here can use it because what it does is it counts cattle from a drone. Anyone here in the livestock business on a now really complicated powered by machine learning, it's absolutely miraculous and incredibly specific. I won't go too far into it, but it's super fun, right? This idiot counted 90,000 head of cattle in two and a half hours at 99.9% accuracy. So it's incredibly cool. So I'm talking about this incredibly cool product and I'm talking about another one that from the sky can measure piles of anything. In this case, it's silage, it's piles of food, it uses math, which I can't do. I mean I can do basic math, but I mean I cannot do what is required here to figure out.



(24:55):

Did you all know that There's a mathematical equation that will tell you when something will tumble in a pile. It's a factor. There's a pitch and this is how they do it. Whether it's coal or corn or Lego bricks, it doesn't matter. You can calculate when it settles and therefore then you can start to set parameters and calculate the volume. Anyways, really complicated, cool stuff. But what happened was we were talking about how the feed gets delivered that we were going to measure from a drone and they said, yeah, well we bought a trucking company and on Mondays they come in all day and we're like, okay, and they turn in a slip of paper to a person who works for us, two people, and then that Monday those people spend a total of 16 hours keying in that information. That's a problem we solved in three hours. Not complicated, not fancy, not ai by the way. It does use technology, but everything is like a hammer is technology. It uses OCR to read it and then sort it and et cetera, but not important. This is a problem they didn't even know they had. Okay, so now who's got another paper problem? I'm going to put a caveat, not cloud related. Does anyone here work with paper? Who has a messy desk? Give them up. Okay. Right.



(26:12):

You have a messy desk, Sarah. So what do you do with your paper?



Audience Member 6 (26:16):

Just move around.



Bruce Ditman (26:16):

Just push it around. How do you know what's important is not important? Do you have a secret system that like I do? Yeah. Or does anyone here pay another person to type in stuff from one piece of paper to another or to copy from this and put to here? Yeah, everyone has that. Okay, we all do that. Those things should be when you go back, you should fix those immediately, okay? They are simple. This is a simple tool. Again, this isn't the skill saw, this is the hammer, but we should fix this right away and every single day with the big data mindset, we should be looking for those kinds of ways to improve. Instead of saying, huh, there's this technology called OCR. Bruce says that stands for I think optical character recognition and it can read documents. By the way, we all do that.



(27:11):

Anyone ever enter their credit card into an app on their phone by holding it over? That's exactly what it's doing. So forget about that. Identify the problem. Thank you all for participating in that. I love these. You guys might've noticed I tell stories. So if anyone has stories or problems, come see me right after whatever the rest of the time we're having a drink, we can share your vodka, we can kill you to share with me. Alright, so that's us. There's a phenomenon that I've experienced when we talk about data, AI and automation in the accounting space and it's totally natural, but this is what it's okay. First panic. First we lock up. Then we say, okay, because you're all smart people.



(27:59):

How can I contextualize this in my knowledge base? Okay? There's a rule of thumb that I discovered frankly, doing m and a, which is explains why people struggle and where they choose during a merger or an integration where people choose to pick a hil to die on. It's very, very simple and it goes like this. Everyone at the accounting firm is incredibly smart, okay? Smart people hate to feel dumb. New stuff makes smart people feel dumb. Problem solved. So going back when in my experience, a lot of the accounting industry, when they're first faced with let's just say ai, the first thought is how can I apply this to my work? Which is normal. How does this affect me? How does this better me? Great. How can I apply this to my work phase two, how can I apply this to my client deliverable?



(28:58):

I think that's where we are right now in our revolution. Phase three is the one that gets me up in the morning. How can I apply this to my client's success? So to go back to the beginning and to approximate a point here, what will we be in the future? What is the evolution of our business? What will our deliverables be when the steam hammer is doing our tax returns, right? It will be leveraging this and much other knowledge to optimize our client success. So frankly, as soon as we can get through the housekeeping part of it, our internal stuff, I think the better because that is when everyone will see the value, we'll be making money on it. That may mean automating our processes, but really what it means is instead of saying, how can I get your tax? Just how can I get your tax return to you on time?



(29:51):

You go to your client instead of that's your problem, by the way, not their problem. So we're showing off by telling them that we had a problem. We have no brought it back to zero. It's not a great look. Instead, what if we went to people and said, what if I could predict fraud at your company? Hey, I've been looking at your books and I've noticed that your warranty returns are through the roof and you are losing X amount of money. People are sending back a product. What if we looked at that data? What if we solve these problems? What if there was a machine, a tool that would flag those for me? Right? In the data we already have, we already hold. Now we are adding value because we are adding dollars to their pockets or employees to the shop floor. What if who here?



(30:39):

So I used to work in marketing as a CMO at Marcum and another firm called UHY, and we would go through and categorize our clients by industry. Everybody's done this exercise probably I can do a little magic trick here. 50% of all your clients fall under manufacturing distribution. I've never seen a list pull that did anything different. Okay? So you've got manufacturing distribution clients. That means part of them are in the D part of that m and d. What if you could go to your trucking clients and say, Hey, I'm looking here, or you can do it, make it into a corny parlor trick. Look I do and say, what if we could take the expenses and the lost opportunity of having your trucks in the repair shop? What if we could replace that with preventative maintenance? And not only could we do that, but I'm going to use machine learning to tell you when, right?



(31:24):

Give me your repair logs on all of your trucks. I'm going to feed it through a machine and it's going to tell you things you can do to mitigate that. Not just expense, but opportunity loss. That's real. That's now guys. Okay? Think about how unlikely it would be for a client to fire you after you pulled that magic trick out. Alright? And so forth and so on. So I'm out of vodka, but I'm going to challenge you guys because you're very smart to give me a value prop to a client that you could potentially fix with data, AI and automation and dream big. You didn't have to do it.



(32:00):

Yeah, but that's cheating. I want to hear a real one. Who here works in a specific industry? What's your industry, sir? Dental. Dental. Great. Is there a way that you could help your client optimize their business using this? What's a problem? A common problem they have. You look at their books, their dirty laundry.



Audience Member 7 (32:24):

It sounds being cheap.



Bruce Ditman (32:26):

You can't fix cheap



Audience Member 7 (32:29):

Typically cash.



Bruce Ditman (32:32):

Okay, so could you say what if I think I don't know the answer, I'm not an account, but I imagine you could take a look at their cashflow and see if there were patterns of behavior or at least predictable expenses that they're having year after year they might be able to treat differently to improve cashflow, right? This is all stuff, by the way, my friend Tim Keith has a company called Propenx.ai. It's this incredibly sophisticated AI tool that uses, that digests all your data and suggest cross-selling. It's very, very cool. But more importantly, he talks about Moneyball. Anyone here seen Moneyball, for those of you who haven't Moneyball is but a revolution that happened. I think Michael Lewis wrote the book. It was a movie and it's about using data to field the team, not based on star power, but based on exactly what you need in the right proportion. Singles and doubles, people who can play hurt, people who it is sort of unromantic and agnostic and really did change the sport.



(33:31):

That is the approach that we need to take to our client's problems and to highlight one of the critical frictions in the movie is that there was a whole team of recruiters who have spent their whole lives, not like anyone in this room. They've spent their whole lives in their more advanced than years and they relied on their nose. I can spot talent. I know no machine can tell me something. I don't know. I spend my whole time in the field and I pick winners. Can we get out of the ego cycle with that? Because you all can do this. You just can't do it at the volume or the accuracy or the frequency that a machine can, so it's not replacing you. It's enhancing you.



(34:17):

Again, I recognize I'm an impatient person, but I do recognize that we should fix our houses before we go out to market and we've done the exercise already, but just to highlight what could happen with a very important caveat. We could anticipate problems. We can be more efficient, we can make more money. More importantly to me, we can have happier people. They're not doing shitty work. Okay? Unrewarding work. Perhaps that is rewarding. The first a hundred times you do it, but not the next 10,000 times you do it. Alright? People who aren't freed up to be innovative, people who aren't mentally freed up to lead because they're buried under the stack of paper. Like that guy in my graphic here. Again, this tool isn't replacing workers, it's liberating workers to be thinking people who are doing valuable work.



(35:18):

Likewise, one thing I do want to point out, that's something we're working on, we're very close on to launching, is the technology is a hundred percent here to de-risk. Nothing can be entirely. I had a fun conversation last night where we decided that business without risk is employment. So we can't totally de-risk our businesses, but we can de-risk our assurance risk using machine learning using large language models and et cetera. Okay? We can go in and instead of taking a sample like my drone, that doesn't take a sample, it looks at everything. You can look at all of the data and now you can really start talking about management overrides and fraud risks and anomalous behaviors. You can confidently say that, and now we're really moving away from our nose to data. By the way, we've all done this and I'm sure you all do, but when you're on the phone with a client and you say something really smart, how much do they pay you extra? Nothing. Right? That's a value add. You can charge for your intelligence when you back it up with data. Alright? Now it's quantified, okay? It's provable, it's repeatable and it's valuable.



(36:38):

I'm not going to read all of these use cases to you, but just a couple other fun areas. Anyone here ever had a reputation issue at their firm? I'll raise my hand. Yeah. Has anyone here ever heard of web scraping? Yep. Okay. You can send a little buggy out into the internet. It can crawl around and collect anything, including comments about your firm despite what someone's going to try and sell you. You really can't wash the internet. Okay? That's something like maybe I think Chris Rock did it successfully. You have to be up here. Okay? I'm going to put on my CMO hat. If there's something bad on the internet about your firm or about something else, you can't make it disappear. All you can do is bury it in good stuff. So stop putting energy into trying to erase it or stifle people or attacking them and just pile on top.



(37:26):

Because the nice thing with the internet is it has almost no memory. It's a goldfish down it goes, okay, that's an aside. No charge, but you can go out and scrape, continuously scrape for reputational risk. That's really valuable for anyone who's had exposure to that. You want to see it coming. You can also do the same thing internally on client behavior. So here's a problem. Instead of talking technology, because said, I told you all we shouldn't do that, Bruce, last week, it's my fault because maybe I don't talk to my people enough, but last week our largest client let us go and I asked the partner what happened and they said, I have no idea what came out of the blue. That's not true. Things feel out of the blue, but nothing comes out of the blue. What if we could go in and study that, study their behavior for the past 24 months? Did they start paying slower? Did we have less profitable jobs with them? Where's the root of this problem? We can start preventing not just detecting detection is the first thing, right? Prevention is what you want because prevention saves you money. Prevention makes you money. Can we look at client behavior to anticipate



(38:41):

Threats? Has anyone here ever done a client experience survey? Client satisfaction survey? Clearly rated. There are other companies that do it. Maybe I'm unromantic. But here's the real value of that. Real value is it's a scale of one to 10, right? Like eight and up, is that right? Is what's the language that they use?



Audience Member 8 (39:04):

Nine and 10 is a promoter.



Bruce Ditman (39:04):

Promoter. And then you've got detractors below. But the real value here is that you, anyone who's done a merger or started a new company in a high level, you may have stepped on a landmine before a problem that was just waiting for you and no one told you about it. You didn't know it was there. That will detect landmines. Okay? So it's really an extension of that. I mentioned cross-selling and upselling opportunities. This is a fun application, okay? Here's the problem that it solves. I'm looking to hire, make a high level hire. I'm running, by the way, I say none of this is me. I run an accounting firm and I'm going to make a million dollar hire for a partner and this partner does a specific thing. Okay? Maybe it's RD tax credits, maybe it's an international tax, maybe it doesn't matter what it's specs, whatever it's, but it's a big ticket person and they tell me they've got a great big Rolodex and a pipeline and et cetera. I'm going to make a big investment.



(40:08):

What if I could know that I had enough extant business sitting dormant that I could pay for that person's salary that year, even if they brought in nothing? How much missed opportunity is laying dormant in my current client set so that I could hire them with comfort and by the way, not ruin that relationship by standing on their neck after month six saying You haven't done anything yet. There are tools. This one in particular, it's called prop say high, but there are tools that will do that. Say literally you're leaving $2.2 million on the table in this particular thing. Maybe I should go hire someone for $800,000 to go mine that and make just money from nothing, right? Incredible, incredible problem solving ability. Obviously you can personalize marketing. That's no big deal. I did it today. I sent out a constant contact and it put all your names, trigger all the trigger emails. Start with the word, how's it? How South African say hi, but put your first name there. We've also had experience. That ain't AI by the way, okay? But that's a series of automations working together to solve a very simple problem. Alright?



(41:12):

This all sounds super easy, right?



(41:18):

I think as an industry we have a little bit of trauma because we have been sold, bought and tanked on a lot of stuff, a lot of change, a lot of game changing technology that just didn't do it. Let's set aside the fact that just didn't do it. I didn't use it, but we'll set that aside for a minute. How do we overcome the resistance to change? Alright? One of the first things that I've heard when we talk to people is that I am not a tech person. I can barely work my phone. I can't talk about this stuff. We all now have the tool to get past that, which is forget about the technology, tell me about the problem, tell me about the problem. And I know some good people, whoever they are, and we're going to talk to them about it. And if they can solve it, they will solve it. If they can't, we, nothing changes, okay? Don't talk about the technology, talk about the value, talk about the problem and think about it that way. Alright? The next one is, Bruce, this sounds great. I'd love to be more efficient, but if I get more business, who's going to do all that work? Forget the fact that I work at an outsourcing company as a lifelong salesperson that makes me want to run through a wall.



(42:33):

We are business people. I think it's a very progressive event where we're acting like business people. We should have that problem. We should have that problem. Yes, we can make more money and yes, we can even do more work with ai, but if it creates opportunities and that makes us mad, I don't know, go into not-for-profit work. I don't know what to tell you. We must, however, increase inventory somehow. So whether that's through technology, whether that's through in-country hiring, whether it's through outsourcing, merger acquisition, whatever it is you do need, I promise you, if you want to do more sales, you have to have more inventory.



(43:15):

And the last one, the most retrograde one, which I'm sure no one in the room feels, I hope at this point is it's just not what I do for my clients. It's not the relationship that they want. You're wrong. I love you, but you're wrong. Your clients want their jobs to be easier. They want to make more money, they want to hire more people. They want to create something to pass on down or they want to sell it for more money. Your clients do want this. What they don't want is to tell them how hard your job is and now how much needer it is because you bought a toy or how smart your firm is about ai. They don't want that. I'll go further.



(43:51):

Your clients don't care about accounting. That's why they hired you, okay? They care about their financial health, they care about their risks, they care about their compliance issues in their fascinated when you do something really cool with their taxes. But do they want to know how advanced your firm is when it comes to AI? No. How does that help them be value first, be interested in their industry. Be interested in, geez, Bruce, that sounds crazy. You use drones? Is that common actually? Yeah. In that industry. Okay. Be interested in their industry for please. Your clients do want their lives to be easier, more profitable. I promise you that.



(44:33):

This I put up here, I really, again, I really don't want to talk about specific technologies. I put this up as a primer. My background, you may find it hard to believe, is not in accounting. It isn't languages, and I tend to define the whole, define the world through language. I think we need vocabulary in order to have reasoned conversations about things, which is another great thing about this conference and other things like it. The more we talk about it, the better the smarter conversations we can have. So I did include a glossary here. Okay.



(45:07):

Chief data officer top Datadog, that could also potentially be a chief technology officer, maybe chief information officer, maybe chief innovation officer. There's a lot of fluidity in accounting firms around c-suite except for CFO and CEO. But remember that What's a data scientist? Data scientists, which can also be used for generally, these are your data detectives, data analysts, okay? If you hear this in conversation, I had to dumb it down for me. I hope it's helpful for you. Data engineers, they're the plumbers, alright? It's just that simple. Data architects, they're the draftsmen and data analysts, they're the storytellers. Ultimately, we want to get to a story and the story is again, hey, I noticed you've had a lot of, your revenue is down and your expenses are up. And I'm looking at it, and it appears to be because you're doing a ton of repairs on your trucks, on your fleet, and you've had 25% of your fleet off the road for average of 15 days a month, right? That's a story that data analysts can tell you. Now, how can we solve it? So in summation, whatever you do, what I hope that you get from today is that it is about a mindset. It's about problem solving and it is not about tools. Okay? I love Home Depot. I love going and looking at the tools, but that does not make me a craftsman. Alright? We're going to focus, say, ask ourselves, rigorously interrogate it. Where's my pain? Where's my frustration? What's holding us back? What's holding me back? And then we're going to ask someone who might know a bit more about this, here's my problem, can you solve it? Okay. Thank you all very much for your time today. Enjoy the vodka and enjoy the rest of your time here.