Episode  123 – How a Pioneer from the SaaS Era is Jumping on the AI Wave to Re-invent his Firm – Member Case by Jeff Pedowitz

Jeff Pedowitz, CEO of The Pedowitz Group, was one of the pioneers of the SaaS era by driving adoption of marketing automation technology from Eloqua, Marketo and others. This allowed his firm, The Pedowitz Group, to dominate his niche for almost two decades. Now, Jeff sees the next big wave, AI, and he shares with Collective 54 how to ride it all the way to the bank.


Greg Alexander [00:00:10] Welcome to the ProServe Podcast, a podcast with leaders of thriving boutique professional services firms. For those that are not familiar with us, Collective 54 is the first mastermind community focused entirely on the unique needs of ProServe firms. My name is Greg Alexander. I’m the Founder and I’ll be your host today. On this episode, we’re going to talk about A.I. artificial intelligence and its impact, in particular around B2B sales and marketing and overall revenue generation. And we have a fantastic role model today, and his name is Jeff Pedowitz and he’s fantastic for a reason, and are many reasons, I should say. But the one that is relevant to today’s topic is the last time we had a major tech wave was the SaaS wave, and Jeff was a pioneer in that space. He and a very small number of people I believe, can claim attribution for the mass adoption of marketing automation. And having gone through that entire journey all the way from a nascent industry to maturity, which it is today, his perspective is profound, and I think he can take those lessons and apply them to AI because it’s early, early days there, and he maybe more than most, can probably share with us where this might be headed. And what we hope to accomplish today is by listening to that story and applying past lessons to new tech, maybe we can get ahead of the curve, learn to learn a few things, and maybe profit from them. So, Jeff, it’s great to see you. Would you mind introducing yourself and your firm to the broader audience, please?

Jeff Pedowitz [00:01:59] Sure thing. Greg, good to see you too and thank you for having me back. So I own the Pedowitz Group and we are a sales and marketing consulting company. We work with sales and marketing leaders who want to drive more revenue and we specialize in digital channels. And of course, AI is probably the best emerging digital channel we’ve seen in quite some time.

Greg Alexander [00:02:21] Yeah. And I understand that you just did a bunch of homework on a new book that you got coming out in just a couple of weeks. What’s the title of the book?

Jeff Pedowitz [00:02:27] It’s called The AI Revenue Architect. Great.

Greg Alexander [00:02:33] So why don’t you kind of give us the outline of what’s in the book and maybe we can use that as a framework for our talk today?

Jeff Pedowitz [00:02:39] Yeah, absolutely. So. Well, my job, my company’s job is to follow technology because that’s what our customers want us to do, is to implement technology so they can scale their sales and marketing engine. So AI and various components of AI have been around for several years now. It’s just this whole emergence now generated by AI and what that Open AI platform are doing are bringing it into the mainstream and really starting to help a lot more people visualize the tremendous possibilities. As I start to think about this and the problems that my company and I have been solving for the last 16 years. There are still systems are still siloed. There’s data that’s everywhere. And people spend more and more and more on technology and data, but they still can’t run sales and marketing any more effectively than they could 15 years ago. They just have a lot more tech now to deal with it. So as I started thinking about the potential of AI, the first thing I wanted to do was really just help companies solve their problems better. And so the book introduces a concept called Rain, and that name was chosen intentionally because in sales, of course, we’re always trying to make it rain. But in this case I took Matt and it really stands for a Revenue Artificial Intelligence Network. And what it does is it connects all your systems and processes both inside and outside through AI. So you can actually, through one single interface, actually start to direct and manage your revenue engine. So it controls scale.

Greg Alexander [00:04:15] Mm hmm. I love the acronym. So let me make sure I understand that. So Revenue Artificial Intelligence Network.

Jeff Pedowitz [00:04:23] Yes.

Greg Alexander [00:04:24] Okay. And the way that you just described it to me, I find myself wanting to apply past frameworks to it. So is it is it middleware in your perspective or is that an incorrect analogy?

Jeff Pedowitz [00:04:39] Well, in some ways, yes. Right. So it’s it could be taken into something like a Boomi or Mule Sox or automatic or any of these integration in all areas. But combining it with AI so that you can train the systems that you have. So even some of the routine, mundane tasks can be done quickly. But as that starts to supplant, you can actually be a lot more productive. So just some typical use cases we scoring, which is something that we had Eloqua pioneer back in 2004 for the first time, largely has not changed dramatically. Most of the input for sales marketing is made, scoring is manually derived. Well, I think we should get ten points for a website, visit it five points about an email or 17 points of view or a demo. And then the models are relatively rigid, and then we send over what I suppose to be qualified ways to scale sales based on this framework, grade spec and prioritize. But with A.I., you don’t actually need an artificial or an arbitrary model. It can actually analyze the real activities, the demographic data that customers really did to come up with a scientific data, factual-based model that will continue to sell, learn, and even more importantly, become more predictive. Wow. So that’s just one example of where AI can play a major role. There isn’t a sales and marketing person I know that loves cleaning up data. We love getting more data, but we don’t actually like going in and cleaning out fields and systems and building new segments and doing all that well. That’s another way that AI can actually do that, because once you train it on what data standards you want for your company, you can start doing that automatically. Content creation and response. No matter what sales methodology is in today’s modern B2B selling environment, our customers are 90% and 95% of the way through the sales cycle. This is not like what, Greg, when you and I are personally, I wish we could control everything. So that inherently puts us at a disadvantage. So if you can use AI to do more informed research on your prospect customer, write better correspondence, look at their content, come up with unique differentiators, anticipate possible objections your buyer might have, and be ready with response to be more proactive. You’re now starting to get ahead of the game.

Greg Alexander [00:07:09] Those are fantastic use cases. It’s causing me to creatively think about how to get them implemented. What I want I would like to do speak to you about a question I have here in my notepad is, you know, selfishly, I’m trying to help the members of collective 54. I know what you’re doing is much bigger than that. But in this particular case, about 85% of a proserve income statement. The expenses are labor. And so if you can replace labor with tech in theory anyways, you can significantly increase profit margins. Now, some people view that as a negative, you know, and a lot of the stuff you read about AI right now is all these scare tactics. But as a capitalist, I view that as a huge plus. I mean, if my members could take their workforce from 110 and keep the revenue the same, I mean they’re going to make a lot more money and scale a lot faster. So is that hype? Is that real? I mean, do you see the tech replacing humans?

Jeff Pedowitz [00:08:05] Well, it’s a little bit of both. I mean, it doesn’t outright replace humans. And it should be noted that we talk about AI as a general category. AI in its truest sense, means artificial intelligence, sentience, self-awareness, emotional awareness, what we’re all talking about, this general of AI, ChatGPT. It’s not that, it’s machine learning. Now it can take large amounts of data and it can learn quickly in a process to make decisions. But it’s not self-aware and has no emotional understanding. It doesn’t understand context. It doesn’t understand nuance. It is still just a tool in the hands of a skilled practitioner. So I view this as the third major generational change since I’ve been in the workforce. The first, of course, being the rise of the Internet. The second, the introduction of the smartphone. And now this. Now, when the Internet first came out and I got my first marketing job in college, I did catalog marketing the bank. There was no email. There was no Internet. There was no nothing. Did catalog marketing go away? No. Did two new digital channels come into play? Yes. Some people that were very skilled in direct mail moved over into email, digital channels and developed new skill sets. When the smartphone came out, it also introduced the whole new apps and mobile advertising and all new ways of doing things. So I think if you’re just doing simple, repetitive tasks and you’re not willing to adapt like any other moment in human history, if you don’t evolve, sure you will get left behind by an unknown space. AI doesn’t replace the human. It can’t because it’s not a human. It can make us a lot more productive. It can make us a lot smarter. And it can process things faster. So sure, it will introduce new margin providing that professional service owners can really think about how to apply it in the best way for their business. So let’s talk about some immediate practicalities. Almost all of us in professional services are doing research with our clients. We’re doing interviews, we have transcripts, We provide some kind of report or presentation that, well, today that takes a lot of manual activity. It requires our senior and junior people to crunch data and do that. Those tasks can be replaced by AI and done in seconds, which will free up more value added time for those professional people to add more quality insights based upon that data back to the client. Mm hmm. And you use AI. to automate the data gathering. So if you’re doing a subjective in-person interview today with your client and you have them, just go to a site, they fill out a survey,  AI processes all that information in real-time, speeds up that discovery period, adds more value. Yes. So there are a lot of different ways that AI can enhance it, but I think it gets a little overhyped to say that it will replace.

Greg Alexander [00:10:55] Okay. So if I’m listening to this, my first thought is I need an AI strategy for my firm. I’m intimidated by that because it’s evolving. I mean, just just in the last 10 minutes, you’ve dropped more things on me that I knew were possible and that I’m imagining that pace of change is going to continue. So what do I do? I mean, how do I develop a strategy for myself and how do I keep it up to date?

Jeff Pedowitz [00:11:24] Well. Try not to make it bigger than it is. Right, because it’s going to keep evolving and changing. So if you can appreciate that this is a way of streamlining and take improving analytical capability processing. Make a list of your business today. Look at your operational things that you do, your sales things, your marketing, and then look at whatever your professional service on or whether you’re on an architecture firm or a law firm or you are on a consulting firm. What are the things that you’re delivering to your clients? Go back and look at your recipes, your statements of work, and say, okay, if I was going to just add AI to my things, what would that look like? How can I just improve my offering? If I was just to AI enable. Many of us have some type of maturity level, some type of tiered offering with our clients, could you take your top tier and introduce AI to it bigger and more advanced? Or conversely, could you introduce AI to your basic tier and make it more palatable for your prospects and clients and thereby lowering your cost of delivery and acquisition? And I would just start there. So build a simple spreadsheet and go through and that’s how you start to frame out a strategy. Don’t sweat about whether or not you got the right tools or not. Start off with something simple like ChatGPT or Bard, which are conversational and generational. Don’t get some. I mean, there are literally since the time you and I talked about doing this podcast, there’s been 500 applications that have hit the market, but only in the last month or so. But a lot of them are crap, you know, and a lot of them are just small little widgets. Don’t get to fall into that trap of getting consumed. Like you’ve got to go out and buy all this stuff. That’s not necessary. Use the free stuff.

Greg Alexander [00:13:15] If you think back to the two previous key changes of your career, the Internet, the smartphone, and now this. What did you with retrospection now, what did you learn from those two previous major moments that you think you can apply to this moment and allow you therefore to take advantage of this moment, maybe more than you did the previous two?

Jeff Pedowitz [00:13:36] Well, as an investor, I definitely wish I would have added that on some of those .coms, not the ones that last, but the ones that I really I, you know I think that I would have gotten involved sooner and incorporated it even much faster in the business. Well, the benefit of hindsight, I think, always makes us all more prescient. But in light of that. I reflect back on the earliest part of my career, I did not understand truly what the Internet was going to become. I had no. I mean, again, this is we’re talking early nineties, mid nineties here. There was no Google, no SEO. We had dialing with AOL and.

Greg Alexander [00:14:18] No one had any.

Jeff Pedowitz [00:14:19] I decided to hear that we got mail. So I’m certainly not going to claim I mean, certainly with the revision I could be a futurist, but at the time, no, I didn’t know. But I think I would have embraced it more and seen seeing where it’s gone. Same thing with a smartphone. I mean, when I first came out, I was I love my BlackBerry like everybody else. I was just like I was reluctant to switch over and what actually got me to do it is a good friend of mine, Dave Lewis, owned a rival firm. We were at some conference up in Toronto and he was showing me all the stock prices of his clients, his public clients that he was helping since he got involved on his smartphone. And I thought that was just the coolest thing, you know what I mean? Basically saying, Hey, what’s going on? Since we got involved this is what my clients are doing. So I went out and got the phone the next day, haven’t looked back. Yeah, but even then, you know, this first couple of years, you think about us here in the States, we would not even think about using it for banking. I know. And working out. I’m not going to have my information out there. I still got to go to the bank like everybody else and deposit my checks. But today, do any of us think twice about just aiming our phone somewhere? Those of you that are listening, I’m like aiming my virtual phone here. Now. I mean, so it’s changed, you know, we get it and it’s proven over and over again that as consumers, we will trade privacy for convenience. Yeah. So at first, what we’re reluctant to until we realize what we’re ever afraid of. So, yes, I mean, the concerns out there right now are real. And I don’t mean and I don’t want to minimize it in any way. I mean, there are definitely ethical concerns. There’s definitely a built in bias to some of these systems and tools. But that doesn’t mean that they still can’t be highly productive. And you just you know, you exercise with some common sense and some caution but today’s fears will be abated by tomorrow’s gains and productivity and the things that we’re going to be able to do because of AI are going to be mind-blowing. In fact, just like I mean, even though I’ve thought about a lot of things, there are so many things that we haven’t even possibly contemplated yet that are going to happen in the next 2 to 5 years because of this change in technology. And that’s the great thing about the human race, is our endless ability to create and to innovate.

Greg Alexander [00:16:33] Yeah, I agree. I mean, if you just think about the health implications of what we’re going to be able to do medically because of these tools, I mean, it’s amazing. And I’m with you. I think the the pros outweigh the cons tremendously. Okay. Well, we’re out of time here. So, Jeff, thanks for being here. Give us the name of the book again, because by the time this airs, we should be able to buy it. And I’m assuming you’re going to sell it on Amazon.

Jeff Pedowitz [00:16:55] You got it. The AI Revenue Architect.

Greg Alexander [00:16:58] Okay, very good. So I encourage everybody that’s listening to this to pick up a copy of that. Jeff is a qualified author, to say the least, so I’m sure it’s well-researched and well-written. Couple other things for you. Obviously, members, you should make sure you attend the Q&A session we’ll have with Jeff when that gets scheduled. You can ask your AI-specific questions to him at that point. If you’re not a member, of course I encourage you to do so. Go to Collective 54.com and apply and one of our reps will get in contact with you. If you want some more content, check out our newsletter Collected 54 Insights. You can find that on the website. And of course our book is called The Boutique: How to Start Scale and Sell a Professional Services Firm. You can find that on Amazon. But until next time, I wish you the best of luck as you try to grow, scale and exit your firm. Take care.

Episode  121 – Data Strategy: How Boutiques Can Get a 360 Degree View of Their Business  – Member Case by Aron Clymer

The average boutique pro-serv firm is using 10-15 SaaS applications yet none of them talk to each other. This makes it hard to get a true 360-degree view of your firm.

On this episode, data warehousing expert and member Aron Clymer, Founder & CEO at Data Clymer, will show members how to solve this problem easily, and cost effectively.


Greg Alexander [00:00:15] Welcome to the Pro Serv podcast, a podcast for leaders of thriving boutique professional services firms. For those that are not familiar with us, we are collective 54 and which is the first mastermind community focused entirely on the unique needs of scaling professional services firms. My name is Greg Alexander. I’m the founder and I’ll be your host today. And on this episode we’re going to talk about running your firm on data and the importance of having a 360 degree view of your business. Many of our members are struggling with this. We’re creating more and more data because we’re all using a ton of SAS tools, but unfortunately sometimes they’re not well connected and we wind up one day with a mess. So we’ll try to talk about that a little bit. And we have a great role model with us, Aaron Clymer. And in my opinion, Aaron is a unique individual in that his background has solved this problem problem for many, many years. And he is a kind of data warehousing expert, if you will. So we’re lucky to have him. So, Aaron, it’s good to see you. Thanks for being here. And would you introduce yourself in your firm, please? 

Aron Clymer [00:01:30] Yeah. Thanks, Greg. It’s great to be here, too, with you. Thanks for inviting me to the podcast. So, yeah, I’m Aaron Clymer, founder and CEO of Data Climber. We are a company that helps and helps our clients, mid-sized clients, usually some small size as well. Solve that problem of not being able to make decisions quickly based on data. So just being able to have all our data at their fingertips to answer all of their questions quickly. We do that by implementing modern cloud data systems, which entails a series of vendor solutions that we put together to work in concert to enable this this capability. And the idea is to have self-service analytics at everybody’s fingertips in the organization. 

Greg Alexander [00:02:13] Okay. So let’s talk about the realities of our community. So I’ll describe a use case and then you can kind of take us through data warehousing one on one, if you will. So it’s very common for our members to be running a lot of their business office spreadsheets. The financials is typically QuickBooks. They usually don’t have a PSA tool installed. Some might have some type of CRM tool, HubSpot, Salesforce or something along those lines. They all have kind of a, I don’t know, advanced use of Microsoft tools, you know, some shared drives, things of that nature. And they’re very frustrated by this. I mean, like I’ll ask the question, what’s your most profitable clients? And their answer is a guess. Or if they answer it definitively, I double click on the answer. And the underlying process upon which they calculated profitability probably wasn’t accurate. And when I say, you know, why are you running your firm this way, they say, well, you know, I’m just overwhelmed by this. I don’t know what to do. And I can’t truly get a 360 degree view of my business. One additional wrinkle that I’ll throw into the mix is many of them often use fractional resources. So a fractional CFO or fractional technology MSP, something along those lines. And those firms have their own systems that they need to get access to data to. So if that’s the starting point and I hate to be so grim, but let’s start there if that is the starting point, you know, how do I get myself out of this mess? 

Aron Clymer [00:03:51] Yeah, yeah. And believe me, I’ve been there as I’ve grown the company myself. Of course, you know, it it just takes a little bit of education on, you know, some of the solutions that are out there and ways to do it. The good news, it’s night and day, much, much easier relative to ten years ago, I would say even five years ago. The other thing I like is that most of the the tooling out there that we use is, you know, the pricing is based on usage to a large extent. So if you’re not if you’re a small company, you’re not using much, you know, your bill isn’t isn’t so big. And as you grow, you you scale and your costs follow you as you grow. So that’s kind of a nice model even for a small business. But at a really high level, there’s three components to getting this done. You know, you’re running your company, all of us are running our companies, like you said, on multiple SAS tools. I’m probably running on at least 15. And you you can’t analyze data from one system to another system. Very rapidly right now if you don’t have a data warehouse. So you need to get a data warehouse, and that’s a central place where you’re going to put all this data and be able to then get answers quickly and join it together in in ways that make sense for your business. So you get a data warehouse to do that, you have to build some data pipes that pipe data into that data warehouse, and then you’ll need some sort of data visualization exploration tool that allows you to easily interface with this data, ask questions. You don’t have to be technical at all to use these tools. That’s the beauty of it. And any business user with just a little bit of training should be able to ask at least some of the simple questions, like you said, like profitability of of clients. So those are the three pieces a data warehouse, some piping in and then a visualization tool to be able to to ask questions. 

Greg Alexander [00:05:36] Okay. So Professional Services has had a long history of owners asking their employees to enter data. It could be timesheets, it could be forecasting sales opportunities, any number of things. And the employees absolutely hate doing it because non billable administrative time. So they either don’t do it or they pencil whip it, so to speak. And it’s garbage in garbage out issue. So I guess what are your thoughts on that and how do you get employees motivated and compliant with entering data into a centralized data warehouse? 

Aron Clymer [00:06:13] Are you referring to data that can’t be gotten from any other means, or is this a sort of duplication effort of data that already exists? 

Greg Alexander [00:06:22] Well, I mean, the most obvious one is timesheets. So in professional services, people build for their time, so they have to issue timesheets internally. And that’s the way that many firms are run and they don’t want to track their times. That’s just one example of many. But so either don’t do it, so therefore there’s no pipe, there’s no data to go to the data warehouse or they do it and it’s sporadic or inaccurate. And then this data warehouse is populated with junk. 

Aron Clymer [00:06:47] Got it. Yeah. Well, first of all, in that case, I highly recommend going ahead and buying some technology just to solve that problem if you can. You know, they’re not they’re affordable and they’re accurate. And then you have all of this wonderful data to then calculate in the case of time tracking that’s critical or calculating some KPIs that we all want to look at, like utilization, for instance. Right? So to get that right and to get it to be able to look at utilization every day, if you’d like to trend it over any timeframe, you want to, you know, sliced by any number of employees you want to or, you know, there’s lots of ways to look at utilization. And if you have all that in a time tracking tool and you get that in your data warehouse, you know, it’s just effortless almost to then start analyzing your utilization and seeing trends. 

Greg Alexander [00:07:34] Okay. And this data warehouse that sits, I guess, in between all of these disconnected SAS tools and my understanding that correctly. 

Aron Clymer [00:07:41] Yeah, that’s exactly right. And let me extend the example just to make it clear why why you need a data warehouse versus the SAS tool itself. So first of all, you could calculate utilization in your SAS time tracking tool, right? And all these tools come with some sort of analytics. But 99% of the time, those analytic capabilities are actually not great. They’re really hard to understand and they’re not intuitive, they’re very limited. You can’t just calculate any KPI you want often, or you might not be able to calculate it in the way you want to calculate it using your formula. So that’s one thing, getting it into the data warehouse and then having a tool where you can calculate anything and do anything with your data is one thing. But even more importantly is that let’s say you want to create a customer or a client dashboard for all of your clients and just look at, you know, everything you want to see in a nutshell with the client. Well, utilization will be obviously one KPI you probably want to put on there and maybe some more information. Maybe average hourly rate might come out of your time cards as well. But as soon as you need information from your CRM system about what industry is that client and you know, other firms, graphics, other, how many statements work have you had with that client? What’s the history of that that’s in your CRM, right? So that’s somewhere else. And so it’s, you know, having it all together and showing it on a single dashboard, which you can do once you have the data from both those systems and your data warehouse, that’s where you get the real power. You can start just adding, you know, all of your data and that’s how you get that, quote, 360 degree view of your client in this case. 

Greg Alexander [00:09:10] That is a good visual for us to think about that. When you mentioned these affordable, easy to use data warehousing solutions, any particular applications you recommend our community to check out? 

Aron Clymer [00:09:22] Yeah. Yeah. So that’s another thing I love about being in services is I feel like we can just pick and choose the best technology out there and go with those, those vendors and we can enlist. Honestly, come to our clients and say, Look, we’re going to choose the best in class technology for you and your circumstance, for your, you know, for your use cases. We have chosen to partner with Snowflake and Databricks or to to nice one snowflake really leading the pack. They’ve really just exploded in the last five years across the market. And pretty much any any company looking at a data warehouse these days will know of Snowflake because they’ve become so popular and that’s because they’ve really solved I mean it was a it was a huge leap forward in innovation when they came on the scene when I was doing data warehousing ten years ago at Salesforce on an antiquated Oracle data warehouse that frankly was kind of a nightmare to maintain. And there’s all sorts of limitations. I mean, that wasn’t that long ago, right? But five, five, four, three years ago, all of sudden, Snowflake came along and they solved all of the technical headaches with doing data warehousing. So now you just focus on your business, like scaling is indefinite computers and you know, you don’t run out of resources, so you can throw as little or as many people on top of the system and you know, it’ll run fine. And so you can just focus on, okay, what they you want, what are your KPIs, you know, what sort of stuff do you need to analyze? What kind of questions are you asking? And it just moves forward. So Snowflake Databricks and then there are a lot of popular BI tools that a lot of the listeners probably aren’t using today to some extent, like maybe Tableau Power BI is extremely cost effective at first, so Microsoft can really get you with their BI tool, that’s their their analytics tool. But there’s some really nice modern, very cutting edge for cloud, which we would always recommend tools like Sigma computing. They have a spreadsheet interface BI tool. So if you know how to use a spreadsheet, you know the interface is familiar, but yet you can be querying, you know, billion or multi million road tables under the covers and it just works just fine. And actually and I’m not, not, we’re not promoting them in all necessarily but just last week they came out with this amazing feature I’ve never seen in any BI tool in my entire career and that is get back to your original question entering data. You know, all these tools for decades have been read only they’re just to consume your data and visualize it. And, you know, look for look for interesting information that you can then action on. Well, there’s always use cases where you want to enter data. I mean, sales forecasting is a very typical one, right? You may want to see your sales trends for the past two years monthly, and then you want to enter your forecast for the next two quarters, you know, next six, six months maybe. Well, you can never do that in one tool with by a tool. But but Sigma computing, it just recently made that available as a feature. You can literally be looking at your spreadsheet with your report and then just type in your predictions and it’ll save that back in your data warehouse and then you can analyze all that together. So I think that’s actually revolutionary and it just shows how this is space is becoming more and more something that drives your company and you operate your company on top of this data rather than just internal reporting. 

Greg Alexander [00:12:36] And what would you say to a member that’s listening to this right now saying, I get it, you know, I wish I was there, I’m growing at 30% a year. I’ve got bigger problems than I can just limp by on my kind of bootstrapped approach to data. What would you say to that person? 

Aron Clymer [00:12:56] Now I’d start with just to try to add up the cost of all the time you spend doing that and start to get into essentially an idea of how much this is costing you. I think that you’ll find that it’s worth the effort to try to migrate, if not now, soon, because it just adds up quickly. And as you get to a certain size, you then you realize you have a pretty big problem on your hands and it’s even more costly to get off of these manual processes down the road. So it’s it’s easy to get started. These tools are up. You can install it in a day and get going. You know, not saying it takes a day because it takes months to do it to it, to really do it right. The technology is simple. It’s more just the methodology and they approach that most of most founders will need help with. 

Greg Alexander [00:13:43] You know, and at the time of this recording, which is April 20, 23, you know, we’re all kind of awestruck by the power of artificial intelligence, in particular chat. Maybe share with us where you think the future of all this is going and how I might play a role for us. 

Aron Clymer [00:14:02] Yeah, that’s that’s a super fascinating. I’ve been thinking a lot about that. I’m actually going to speak at a couple of conferences this year with the title of How A.I. is Impacting Data in My World, and it’ll probably change dramatically between now and three months when I’m giving the talk. Right. But but what I what I’ve seen is that, you know, things like chatbots are, of course, super helpful and impactful right now. They can do a lot to help us. Our data engineer is actually just check some check some code, actually figure out how to do some complicated things with code if they don’t know how to do it right away. The probably the biggest challenge though, with AI in general and data is that data just like maybe it’s analogies to human language, but data needs to be very specific. And so often these models, these element models like chatbots won’t get it right. And if they can’t get it right, even, I mean, 100% of the time, you don’t want to rely on that for your business necessarily, Right? So there’s going to be it’s going to be a while before all of this stuff can be fully automated with A.I., But A.I. now is doing some really helpful things. It’s dramatically speeding up the time to implement some of these systems because it can give you a first cut. Yeah, what I would suggest for like a data model or how you’d want to organize all this stuff, and then you go through there and you make sure it’s, you know, it’s got your I’s and cross your TS and make sure it’s all correct. Then you deploy it business. Yeah. So it’s expediting some things. Yeah. 

Greg Alexander [00:15:33] It might take years. It’s just going to continue to get better and better. So some of the things we talked about today, which would be the building blocks for something like that, I mean, that should be creating urgency in all of us to get going on having, you know, better data running our business on data because the advancements that are coming are going to be exponential. All right. Well, listen, I’m really looking forward to our upcoming Friday Q&A session with the members. You know, I’ve got like 100 more questions I want to ask you, but we try to keep these things tight to 15 minutes or so. So for those that are listening to this members in particular, I encourage you to register for that event. When you get scheduled with Aaron, then you can ask your questions directly. But until then, Aaron, I just wanted to thank you for being here on behalf of the members. We learned a lot today. We’re very lucky to have you in the community. This is an important thing for all of us to get correct. So thank you for giving us your wisdom today. 

Aron Clymer [00:16:26] Well, thank you. Appreciate it. Great to talk to you. 

Greg Alexander [00:16:28] Okay. All right. And a couple of calls to action for listeners. If you are interested in meeting great people like Aron, consider joining Collective 54. You can find that on our website. Fill out a contact us form and a rep will get in contact with you if you want more content. Maybe not ready to join, consider subscribing to collective 54 insights. There you’ll get three things every week. A blog on Monday, a podcast I’m sorry, a video on Wednesday and a chart of the week. Speaking of data on Fridays. And if you want to get more longform content, check out our book, The Boutique. How to Start Scaling Sell a Professional services Firm. You can find that on Amazon. But thanks for listening today. And until next time, we wish you the best of luck to you. Try to grow, scale and someday exit your boutique pro serve firm.