Why Most AI Transformations Fail, Digital Strategy and Scalable AI with Dan Morrison

Speaker A: Built to Scale with Reem Benjar. This is Built to Scale, the podcast for B2B founders who are playing the long game. I'm your host, Reem Benshahr, and every week we are exploring the mechanics of, uh, sustainable growth with the founders and executives who are defining the industry. No fluff, no growth hacking gimmicks, just the blueprints for building a solid and scalable tech company. Welcome to the show. Today we have a special guest joining us, Dan Morrison, founder and CEO at MoreVault. Dan, thank you so much for joining us today.

Speaker B: Yeah, it's a pleasure and I'm really excited to get going and have uh, a good conversation about, uh, all things, uh, in the technology spectrum and business spectrum.

Speaker A: Me too. Really excited for this conversation. So give me a quick overview of who you are and um, More Evolved.

Speaker B: Well, like you said, my name is Dan Morrison and I am the CEO and founder of, uh, More Evolved. And we are a digital transformation and AI implementation company in the Sacramento or Northern California area. We have clients all over the United States, but, uh, that's where we mostly work from. My background is in the US military. I spent 16 years traveling the world doing different things in there. I have a background in cybersecurity, digital transformation, uh, and manufacturing, which I know is a little different than uh, most of the folks that are out there. So heavy in operations, heavy in technology. And uh, I'm sure we're going to talk a little bit more about AI here in a little bit.

Speaker A: Thank you for, for the introduction and, and thank you for your service.

Speaker B: Thank you.

Speaker A: So, uh, what was the real moment that pushed you to leave Fortune 500 life and step into entrepreneurship knowing that it would be harder.

Speaker B: So that's a funny one, right? I was sitting in uh, my present job in my, in my previous position. And uh, I knew that I wanted to do kind of something more. I was working with uh, some of the technology. I was actually working in operations at the time. I wasn't working fully in the technology spectrum, but I knew I want to do something more. And I was helping one of, one of our engineers in one of the facilities go through and fix uh, an issue that was in there. And I just sat there one day and I'm like, well, I'm not really, you know, this isn't right my position, but this is really kind of what I'm good at. And I decided, I believe it was, um, March M 2021 is when I kind of said, okay, this is something I want to begin pursuing. Uh, and then you know, from there I kind of got my first couple of clients, uh, that were out there. And then I took a leap into consulting right after that, uh, that initial, uh, period, consulted for a little bit, got flown back and forth to, uh, to Texas, uh, to help open a facility and really kind of dive into the operations and technology piece, uh, that goes in there. And in June of uh, 2022, I took the full leap and, uh, I haven't looked back since. I'm sure we'll get into the conversation of what that looks like.

Speaker A: Yeah, absolutely. Thank you for kind of telling us a, uh, about your, your transition from Fortune 500 companies to, to what you're doing today. Um, I'd love to know a little bit more about your, your experience at least in your first year as a founder. Right. What was the biggest. I did not expect this lesson, especially compared to what usually online advice looks like or sounds like.

Speaker B: Can I say everything? Does that count? The. I think one of the biggest things, you look online, right, and you look at YouTube and you look at all of the different, uh, founders that are out there and they make it seem very like they've been doing it for a very short amount of time, uh, that, you know, they've, they've gone through and they've done all of this and they did it in a month. And you can too, right? And it's super exciting when it's all said and done. I learned very early on that that is not the case now. When you work, when you go from, uh, from military to, you know, Fortune 500 or large startups, you have multiple different departments that do different things, while as a founder, you are all of those things. You are the HR department. You are. Even if you're an army of one, you're still the HR department for yourself, which is not as easy as everyone makes it out to be. There are no goals. There are no KPIs, there are no metrics, uh, that, that are out there. You have to build and establish all of those on your own. And if you're trying to build something unique, you know, maybe you're trying to build a software, uh, platform, maybe you're trying to build a drone system or, I don't know, whatever it is, if you're building something unique that's out there, you don't necessarily have those, those metrics. So you have to figure those things out too. I think the biggest thing that I learned is that it's hard. Um, um, and I'm not talking about, you know, going to the Gym and, you know, lifting hard. This is reps. You know, you are not going to be perfect on time one, you're not going to be perfect on time 100. You're never going to be perfect. And I think the biggest thing I learned is that perfectionism itself and the things that we start out sometimes as entrepreneurs actually stops us from accelerating the growth within our companies. That's probably the biggest thing I learned.

Speaker A: Love this. And I can painfully resonate with what you're saying about the. This not being at all similar to going to the gym and doing the reps. You. It doesn't necessarily get necessarily better at it. And perfectionism is essentially the enemy. So, yes, I completely understand where you're coming from, and I'm sure a lot of our listeners will definitely resonate with this part. Thank you for kind of, um, being very honest about that. So let's talk a little bit about AI. Right. Uh, you had some really interesting insights when we had our prep call. And I want to kind of go back to some of the things you said you had mentioned, that bolting AI into broken processes just creates a supercharged broken process. What are, in your opinion, one or two things founders should audit before they actually try to automate anything?

Speaker B: Well, you should audit your procedures, and you should audit your systems, and we'll start with procedures. Right. A lot of. So when you start out a company, right, it's just you, or it's just you and a partner, or it's just a. It's a very small cadre or group of people as you have employees that come in. You know, maybe you have one employee and then you have five employees, and you have 10 employees, and you have a hundred employees. Your understanding of how your business operates no longer exists in the way that you think it does. And I'm not saying that's a bad thing. I'm just saying it's a fact. So when you look at procedures, standard operating procedures and systems, any person they introduce into your overall business is going to change it slightly. Maybe they found a quicker way to do a step, or maybe they found a slower way to do a step, but increase quality. Like, there's a. There's a myriad of different reasons why an employee would change a little bit of your process that, that you have there. Now, you put that at scale. So let's say you have, uh, ten employees or you have a hundred employees, and each one of them tweaks a little bit about what they're doing in their job. Well, now you no longer know what what is going on completely within your organization. You have managers, the managers are managing. You know, there's all these different things, but you have this system that then becomes more of a tribal knowledge. You'll have that one person that's really good at that one job and they've been doing it for X amount of years or X amount of days and they really got it figured out. But nothing that they're doing is documented. So when you're trying to put AI onto something that's undocumented, unscripted, by all means unavailable to the system that you've been building, it's going to infer something or it's not going to work at all. So if you try to automate all of those types of things that are out there, you end up with chaos because the process that you thought was actually the process isn't actually the process at all. So in order to be successful in that, you have to really understand your process and then you have to understand the systems. What's your digital system? Data is messy, uh, I think we talked about that on, on the prep call. Data is absolutely messy. When you first start your business or uh, when you scale your business, you don't know, uh, we were talking about it just a second ago. What are your KPIs? You have to decide those. And when you're deciding those by yourself or with a small group of people, those might not actually be the KPIs that actually scale your business. So now you're going to automate based off of a system that wasn't ever designed to scale to begin with. So now what do you have, you have now 10 times the speed of something that is completely broken. And that's why AI is failing 95% of the time on the implementation side because people are not doing the core foundational work. We are working under assumptions and the procedures and the systems are not there to be able to handle, uh, AI scalability or what AI can bring.

Speaker A: I think you nailed this. You explained it in a such an easy, logical way way that it's really easy to understand how, how AI is not necessarily a real solution. If we're just sticking AI on top of our current systems or processes, if these are somewhat broken or not really aligned with the longer term vision. Thank you for that because I think there's obviously a lot of hype around AI. There's a lot of companies pushing for more adoption, but you know, I mean, overall I can't think of a single company that has fully found efficiencies and even return on investment from all the investments they've made in AI. And I do think this part that you just talked about is not something we hear often, but it is incredibly important. So, so again, thank you for that. And I think it's going to be easy for everybody listening to really understand how they can also change their current data infrastructure or processes or systems prior to making those big investments and implementation of AI. So I appreciate that. Um, so you also said on our prep call that digital transformation has to come before AI implementation. Can you tell us what that sequence looks like from inside a company?

Speaker B: So, not getting into the part where we talk about what we do, but we actually developed kind of a sequence that helps, uh, with that digital transformation. We call it Mosaic. And that's literally what we do, uh, as a company, like we were talking about, uh, in the, in the intro. So in order for digital transformation to work, you have to understand how your organization operates. And that starts with mapping. So M. And Mosaic. Mosaic is an acronym. It's also the, you know, putting broken pieces together into something. That's beautiful. Don't ask me how we came up with that. It just ended up flowing and it worked out really well because that's the, that's the whole point of implementing, uh, AI and emerging technologies on the systems. But you have to map it. You have to really understand it and not understand it from the perspective of what you think your systems are. Because like I said before, as executives, we don't know our systems as well as we think we do. I am sitting in a room right now with a podcast and I have a team that is out there doing different things. And I'm, you know, obviously I have a pulse on em, but they're at work 8 to 10 hours a day doing their thing. We don't know what's going on, so we have to go out there and map that process. So the first kind of step in digital transformation is really understanding what can be improved. You can't improve everything. There are certain systems out there that are going to need a human to operate and you need to identify what those are. You can't just go in and assume that, uh, the other part is the data collection piece. When it comes to digital transformation, that's out there. So if you're trying to build AI on top of broken data, like we said before, it's not going to work. So you need to build the foundation, you need to build the architecture ahead of time. So you can then implement AI based off of real, real data. So One of the things I am also is I'm a lean six Sigma black belt, so I'm really good at process, process improvement, data statistics, those types of things. And during the DMAIC process, there's a process called measurement systems analysis. And when you do this, you're actually measuring how well you collect data and whether or not that data is actually useful. And in order to do a digital transformation and why it's important before you get to AI is you have to be able to go through and understand what your, what it is that you're measuring is actually going to work later on down the road. So the reason why you do digital transformation first is to set the foundation to really dig in, to really understand, because you cannot, cannot audit automate something. And automate automation is still kind of the precursor to AI. I think there's a disparity there that people don't quite understand that just because you automate something does not make it intelligent, it just makes it automated. So hopefully that helps with answering that question.

Speaker A: Totally. Thank you for, for the insights. Um, so moving on to the next question. You do have a military background. What do you think leadership or what leadership mistakes do you think see founders make when introducing major changes like AI into the team? And you might have mentioned some of these mistakes, but can you kind of think of other things that are more obvious from your perspective that people should keep in mind?

Speaker B: I like to call it like a top down, thou shall do type thing. Now obviously in the military, if the orders come down for us to do something, that's something that we have to do, like there's, there's not a, we're, we're trained to do that. We understand that from that perspective, that, but that doesn't mean that you can't necessarily include the people that are going to be doing the job in that decision making. Uh, so when it comes to doing a plan, right, who are, uh, who's in your planning cadre? Who is helping you go through that? Do you have representation as a leader from the ground floor of whoever's going to actually be doing the implementation side? AI is scary to a lot of people. And I know we're going to probably get into this and maybe I'll just kind of segue into that. Right now. People are afraid that AI is coming for their jobs. Uh, and in truth, in certain jobs it is. And we'll get to that here in a minute. You have to include people into that. You have to have them believe that part of what it is that you're changing is they're included in it. You know, they're, if you're a founder, you're a CEO or um, or leader of any kind, the people that are working with you are buying into your dream. And I, I mean that literally. They are taking their time. They're not doing anything on their own necessarily, but they are taking their time and they are buying into your dream. And you need to recognize that, that when it comes to implementing something like, like AI into there, they have good ideas. Your people that are out there on the floors do have good ideas. And uh, they're the peoples that you should, that you should be out there asking these questions to. If you have a room of a dozen people sitting around you that are all, pardon the expression, yes, people, you're, you're not going to get very far on an AI implementation. I mean look at, like I said, 95% failure rate. But if you go down to the ground floor, say if we did this, what would this do? Ask them the question and then figure out a solution to that problem and then figure out the solution to the next problem. And guess what the crazy part about it is, is you can use AI to do that. You can bring an AI recorder and just sit there and have a conversation no different than what we're having right now. It takes notes and you can run it through um, a pattern analytics engine and figure out based off of what they said, how to fix something. And that's, I think that gets lost a lot with you know, kind of these top down initiatives that are driven by the market. You talked about it earlier. There's a lot of hype out there. So everyone feels this pressure to innovate and get like, we need this AI powered this, we need this AI powered that. So you're going to do this and you're going to do that. You know, we spent thousands of years determining how to improve process, how to, how to standard operating procedures. It's like even on the cyber side I have this conversation and uh, other things, it's like because AI has come around, is coming around so fast, everyone has kind of forgotten what it means to be a leader, what it means to be successful, and what it means to bring the people that are running your organization along with you. Like I said, they bought into it.

Speaker A: I love that. I think this is probably, this has been the most honest conversation we've had on the podcast around AI. Not necessarily looking at it only as this positive new technology that's going to help, but also having like the tough conversation around like, you know, why does it fail most of the time and what needs to change and all the kind of more negative or real impact that comes with it. So I appreciate you really diving into it and being candid. I know we kind of, uh, talked. You kind of briefly mentioned that AI essentially will replace some jobs, uh, and that a lot of people are scared to lose theirs. How do you think founders can implement AI In a way that improves performance while keeping trust and morale, uh, inside the company with employees and tech?

Speaker B: So I want to, to answer that question, I want to give a little bit of a history lesson, uh, when it comes to that, uh, and just to answer, kind of go for the viewers as well. We are an AI M implementation company and we have our own AI system that runs 40 to 45 different AI systems together. So we're not against AI technology at all. We just know what works and what doesn't work. Uh, um, that's available out there. So let's go back to the 1980s. So the 1980s was a big time in the world. Another kind of technological revolution. The dawn really of the early Internet. Different networks that are being put together. You know, the Macintosh from Apple was coming out. Personal computing was beginning to slowly start to become a thing. It didn't really become a thing until like the mid to late 90s to early 2000s. But you know, you started to see this dynamic shift, uh, inside of, of the, um, the way that, uh, business is operated now. If you look back in history, if you read history, there are people then that were afraid of the computer taking their job. Uh, and the truth is the computer did take some jobs. And that's going to happen with A.I. no, there's no way of, there's no two ways about that. But what computers also did was create 100 million jobs, new jobs. It created five times more jobs than what it took. The thing about people is people don't like to change. So you have to give them an environment where change is welcome. And that's how you begin to do this. You give people the ability to cross skill into something that is AI related. There are going to be jobs that AI Is going to take hands down, bar none. But there are also going to be new jobs. There's over 7 billion people on this planet. There is no shortage of problems to solve. AI is going to help us get to some of those problems and it's going to create new ones. Look at the, you know, look at the deep fake problems that, that we're having that are online. But the point is, is you have to, to give people an environment that is safe and secure. That security first is there where people can thrive and understand that you are there to support their journey. Uh, in AI. If somebody wants to use uh, ChatGPT or Claude or Gemini or Grok or whatever, uh, whatever LLM that's out there to you know, refine something in particular, once again, write a policy, make it here, uh, so you're not putting your company information onto the Internet like we learned for the last 30 years. You know, give them that opportunity to say, hey, I can, I can do this. You watch the super bowl, all the commercials were all about that same thing. So if the big tech companies that are writing these things are actually saying, hey, like, learn how to do this, learn how to do this, it's probably time to start listening. Those, those companies that are out there, give them the ability to do that, create an upskilling program. Your people don't want to lose their jobs and they have the skills, they have the skill sets that are out there to be able to make your organization better and do it for less cost. That's what you use people for. That's how you begin to get people less afraid. You bring them into the conversation and not throw them to the wolves.

Speaker A: This is great. Thank you. I um, really appreciate your, your insights. I love how you know, you're bringing up the history which, which you know, you're right about right there, there. We went through a lot of different phases in humanity where people were genuinely scared for their jobs and then yes, a lot of them lost them. But we were able to create more um, more jobs based uh, of, of of the changes in the new technology. So I think you're looking at it a very positive way and I appreciate also your actionable, uh, insights for you know, founders and executives who are looking to implement AI in a way that's going to keep trust and morale intact internally. So definitely appreciate that. I think one of the last questions here is around technology shifts. So technology shifts that founders outside of AI are maybe underestimating right now, like quantum, um, quantum robotics or even space based infrastructure. Can you share which ones you feel are going to essentially be unmissable? Uh, um, in the Future and why.

Speaker B: November 2022, ChatGPT launched to the public. It's been available natural language processing, which is, you know, the pre, the precursor of the transformer that's inside of ChatGPT has been around for a while and I was training my computer to talk to it back in the early 2000s, uh, so it's been around for a while. So November 2022, the ChatGPT released, you know, revolutionized everything. It was within a month had been the fastest SaaS, uh, platform with the most downloads, with the most views in the history of technology basically. And AI has now gotten, I uh, don't want to say more intelligent but you know, it's gotten better, less quirky, less issues than that first version that's uh, out there. The next thing that is coming is something that we may or may not see. Nobody saw that coming because everyone even back then was kind of focusing more on quantum. Maybe they were focusing on nanotech, biotech, those types of things that are out there. And then AI releases and now people are like, oh, we can build anything with, with this system and you know, it's really focused on coding. The next big thing is going to be something that takes our current AI or our current technology and compounds it. So if it's quantum, it may be quantum. If it's space based technology figuring out different ways to pull in energy, that's there, Those are, those are different things that we can look at. I know we had talked about, um, there are companies out there that are trying to put data centers in space so we can reduce our energy footprint here on, on Earth. Data centers not just only power AI, but they power everything. They're powering this conversation, uh, that we're having right now. So those types of technologies are going to begin to accelerate. Like we had mentioned, uh before, you have Quantum as well. Quantum is not in a state just yet where it's going to be as useful as we think. But when quantum comes out, it's not an AI based technology but it's going to revolutionize things like communications, it's going to revolutionize sensing, it's going to revolution and it already has in a lot of ways. The next kind of call it Quantum Leap is kind of the, the pun on a movie that's there. We don't know what it's going to be. We don't know when quantum's gonna take over. We don't know when we're gonna have injectable cells that can go into our body and target specific types of cancer just because they're small and they're controlled by uh, by a computer system. But those are the types of things that we need to be keep an eye out for. Uh, and those are the types of things that are gonna be impressive later, uh, on down the road. AI is impressive, but AI is just the catalyst to a greater, a safer, a more productive and honestly just kind of a more fun world that we, uh, that we currently live in. There are caveats. You know, there's always the other. There's always the dark side of everything, uh, that we do. But the, the folks that are really in this, they also think that way. They're like, okay, so before I release this to the public, how do we secure it? How do we make it to where people don't use this for negative? They're going to figure it out. But the less people figure it out, the better.

Speaker A: Absolutely. The pros outweigh the outweigh the cons. So, um, yeah, this is exciting. I can kind of can't wait to see what's, what's next in the future. Thank you so much, Dan, for, for coming to, to the podcast and sharing your, your really, really interesting insights. This was a great conversation. Um, and I'm sure everyone who, who listens to this episode is going to appreciate it as well. If anybody wants to connect with you or learn more about your, your company more involved, what would be.

Speaker B: So you can go to our website@more evolved.com you can spell it with two E's or one E that both goes the same place. You can email me dan more evolved.com or you can get in touch with our team through any one of our different websites that are out there. And yeah, that's. We work in digital transformation, we work in AI and that's kind of where the insights come from. So super excited and I'm very thankful for having it, me being on the, on the show here.

Speaker A: Thank you, Dan. For anybody else who listened to the podcast episode, if you enjoyed it, make sure you're giving us a follow, give us a review or share this episode with someone else who may benefit from hearing it. Thanks Dan again for coming to the podcast and thanks everybody else for listening and see you next time.