← Embracing Digital Transformation
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- [00:00]
A
Welcome to Embracing Digital Transformation. Before we dive in, I wanted to personally thank you for listening. Many of the ideas we discuss on this show inspired my new book, AI Augmented Teams. If you're looking for practical ways to combine human expertise and AI to achieve better outcomes, I think you'll find it valuable. Learn more at Paydar AI Books. That is P A I D A R AI Books. Now let's get started with the show.
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B
The big companies, they just are. You look at the big four consultancies, you know, how are they going to continue to compete at the same level when smaller consultancies are, to your point, much more agile, very quick to innovate and adopt and transform in their business?
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A
Why this matters to you? AI is moving from experimentation to operating model and that changes the questions leaders have to ask. The real risk is not missing a tool. It is automating the wrong process at the wrong cost with the wrong data. Mid sized companies have a real opening here because they can move faster than large enterprises, but only if they start with business friction and not vendor hype. In the next few minutes you will hear a practical way to think about where AI can actually create value. Welcome to Embracing Digital transformation. This is Dr. Darren on this episode, why AI projects fail when Leaders Skip Data and Workflow Readiness with Matt Strippelhoff, founder and CEO of Red Hawk Technologies.
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C
Matt, welcome to the show.
- [01:45]
B
Thanks Darren. Glad to be here.
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C
Hey, I'm really interested in talking about the topic today, which is AI. Oh my goodness, is it about AI? Everything's about AI today, but specifically how do you get started, especially those mid tiers. I think that'd be an interesting topic. But before we do, everyone that listens to my show knows that I only have superheroes on the show and every superhero has a background story. So Matt, what's your background story?
- [02:14]
B
My background story. Wow. Entrepreneurialism is, you know, it's always been fascinating to me. My grandfather traveled here from Germany with when he was 15. His best friend was 16. They could not speak any English and they landed on Ellis island on Black Tuesday.
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C
Oh.
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B
My grandfather went on to be a very successful business owner in the tool and dye industry, which was the trade that he came over as a journeyman and, and watching his journey as a, you know, growing up and, and seeing what he was able to accomplish was inspirational for me. And I think that there's a lot of entrepreneurs in our family in general. And so, I don't know, it was really kind of cool to just watch all of that. And you know, he's he passed many, many years ago. And I like to think that he's probably very proud of what we've been able to accomplish as a family and kind of carrying on the tradition and, you know, all of that good stuff.
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C
So yeah, that's really interesting because I've talked to several entrepreneurs and it is generational. It's almost like it's, it's in your DNA or you see, you see your, your grandparents and your parents go through it and you're like, I want to be part of that. But it's not all, it's all, not all fun.
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B
No, it's not. I mean it's, it is. The hills and valleys are very real. You know, I'm sure everybody listening has probably seen the, you know, the iceberg, the tip of the iceberg above the water and then all of that stuff below. Or maybe the journey, you know, where there's stick figure walking across and you can kind of see the hilltop but you don't see any of the traps along the way. And it's every bit the grind that you read about if you're going to read about entrepreneurism for sure. But it's a journey. I don't think I could have chosen any other way.
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C
Any other way. I just isn't that crazy though that. And so I, I think this is really cool because you actually help mid sized companies. That's where you guys primarily do. Which are probably run by entrepreneurs.
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B
Oh absolutely. They're all privately owned. There's probably some, certainly many are multi generational family owned businesses that we're working with and a lot are entrepreneurs who started and just have the grit to really grow and start to scale and have these different breakthroughs along the way and, and they're transforming digitally as a result of that, trying to get to that next level.
- [04:39]
C
Yeah, that's awesome. So let's talk about that. That's a unique. It's actually one of the largest economic engines in the United States is the mid size and small business. We always think of the big boys. Tesla, Intel, Google, Meta. Right. But they don't employ nearly as many people as mid tier and small. Small businesses.
- [05:04]
B
That's right. And, and quite frankly they're not as stable either because they're beholden to their shareholders. They will make sweeping decisions on a quarterly basis to make sure their numbers look good. And, and it's, you know, corporate America that's a. Working in that environment, it was something that was never really, I was never really cut out for. I have a lot of friends who have done quite well and are quite successful in those types of environments. But I prefer privately owned mid market businesses. I think that's where people can still have really long careers and have some stability.
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C
Well, I'm glad you brought that up because AI has kind of thrown a whole kink into that.
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B
Sure.
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C
And I actually think mid and small businesses, because they're agile enough, I think they're going to be the winners in this whole thing. And big corporations are not going to win because they're bureaucratic, they're slow, they're like moving a Titanic. Right. So I think there's a lot, there's a lot of really cool things going on here. Are you seeing the same sort of thing?
- [06:14]
B
Absolutely, yeah. The, the mid market, fast growing privately owned businesses are disrupting the big companies. They just are. You look at the, the big four consultancies, you know, how are they going to continue to compete at the same level when smaller consultancies can are, to your point, much more agile, very quick to innovate and adopt and transform in their business? We're actually seeing software as a service companies, big platforms, CRM platforms starting to lose market share because companies can build their own now. And so that's really quick actually. Yeah, very quickly. Yeah, we're seeing a lot of that.
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A
Yeah.
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C
Wow, that puts you in a really great spot. Or not because you're a software engineer by trade.
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B
Yes.
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C
Doesn't AI scare you a little bit as far as your job security and sustainability?
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B
Well, I would say that a year ago I was probably more concerned about the future of software engineering and what it would mean for our business than I am today. Now I'm seeing a tremendous amount of upside and opportunity that I couldn't have predicted this time last year.
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C
Okay, so what changed?
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B
Subject matter experts within these businesses that we serve are vibe coding solutions that they think can really help and move their business forward. But they don't really have the software engineering expertise to bring that vision to life and then support and properly maintain it. And so what's interesting is instead of these conversations historically would start with hey Matt, I've got an idea. And then we would kind of put together the roadmap, figure out what it would take. No, now they're coming to us and saying, look at my idea, this is how I want this whole thing to flow. And I've already worked through a lot of the nuances that otherwise might have been disruptive or created a lot of friction at the beginning of these projects. They're working through a lot of key business Decisions as they're vibe coding some of these solutions, and then they're bringing it to companies like ours and saying, help me get this into production. We're so excited about it. And if we do this, then our time and effort in these types of work categories are going to get significantly reduced. We can have people on more meaningful work. You know, there's all these benefits to what they're. What, they're Vibe coding.
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C
So, so this is really interesting because I, I know this. I teach computer science.
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B
Oh, okay, great.
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C
Yeah. So. And I've been telling my students, look, don't write code. Architect. Architect. It's all about architecture. So are you seeing the same thing? I vibe code something. It's. It's great for prototyping, but when it comes to production, reliability, scalability. Yeah, it just doesn't cut it because there's no architecture behind it. It's just fluff.
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B
It's fluff, absolutely. And a lot of these subject matter experts who are embracing vibe coding tools, they're not engineers, they're not software architects. They're not thinking about scalability, sustainability, being able to support it. You know, Darren, you and I are probably always thinking about things like, well, we need a business logic layer that sits between the interface in the database.
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C
That's right.
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B
We need. We need a serverless architecture so that it can really scale. Vibe coding tools aren't thinking about any of that. Vibe coding is only going to generate results based on the expertise you bring to the conversation.
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C
I'm glad you brought that up because that is so important. In fact, I cover it in my book, AI Augmented Teams. We, we have this concept called an AI faker. Right. And what's really interesting about AI fakers is they may be subject matter experts in something, but if they're not in another thing, they end up faking their way through it. And they even think now that they can write software or that they can do, you know, fill in the blank. They can write market copy or, you know, whatever the case is. You still need some subject matter exper. Expertise, that grounding part to use AI to augment yourself. So I, I love that we're seeing this in real life, what you're seeing. I still need those subject matter experts. AI with two subject matter experts from two different perspectives can explode in an organization and make it just. Just skyrocket.
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B
Yes. Yeah, yeah, absolutely right.
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C
Yeah.
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B
And. And so you can't rely on the agent to be the expert. You just cannot do that. You have to bring the expertise. That's just. That's the only way that it's really going to work. And I don't think that's going to change. As intelligent as these large language models are getting and they have a lot of expertise to bring into the conversation. But the thing I think a lot of people don't realize is AI can be the great homogenizer. I love it because it's looking at the whole like all the sum of all things that it has built knowledge on and it's going to give you the mean. Right. It's like based on all this stuff and you know, you might be have some, some. You're going to have various levels of quality.
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C
Yeah, that's true.
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B
It's going to bring you right down the middle. You know, it's, it's, it's just what it does.
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C
So you're always seeing this with your clients, that they are coming to you. You can move a lot faster now with them.
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B
Absolutely. Yeah. We've seen a reduction in software engineering effort when we're building new things of about 30%.
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C
That's incredible.
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B
It is incredible. Absolutely. And then when we look at supporting things that are already in production, we're seeing as much as a 75% reduction in and effort.
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C
Oh, and as, yeah, as a software engineer, I hate support, man. I hated that part of my job. Right, yeah. So yeah, I'll give AI support stuff. Absolutely.
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B
We call it doing the dishes. You know, doing the dishes. That's fresh, you know, so we, we actually have an agentic solution now that we offer our clients under a subscription model that does the dishes. So it maintains the bill of materials, it identifies vulnerabilities, the agentic tools remediate those vulnerabilities and then it sends a notification to the engineering team, like it's time to go review the quality of this before we push this update to production. So it's doing all the dishes and it's saying, hey, come check my work. Wow, that is, that is how we're seeing that reduction. So it's fun, it's exciting.
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C
I'm glad to hear because there's been the doom and gloom and frankly, I blame Elon Musk, I blame Altman. I blame all those big boys, right. Because they're like, we need universal, universal income for everyone because no one's going to have jobs. They're just fear baiting everyone. Right. It sounds to me like we're already seeing this. There are, there is actually more value than there was before.
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B
I would agree. Yeah. Now again, a year ago I was probably a lot More concerned. But now that we're seeing this all actually in practice and we've adopted all these tools ourselves, we understand where the opportunities are and it's an exciting, it is absolutely an exciting time.
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C
Okay, so how do I, if I'm a mid sized company, let's ignore the big boys.
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B
Sure.
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C
They got other issues, right?
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B
Yes.
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C
If I'm a mid sized company, how do I get started?
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B
So we, I ran a workshop for business leaders last week on this very subject and it was awesome. Fundamentally, it's basic business strategy. I encourage people to take AI out of the conversation and you bring your leaders from each business unit into the same room for strategic planning session and you identify where friction is occurring from opportunity to cash. Because every business is, I don't care if it's manufacturing, if it's professional services, consulting, accounting, it doesn't really matter. Business, the purpose of business is to find opportunities that you can serve customers in the marketplace and then service those accounts to generate revenue and then optimize how you're operating so that you can create a gap between your operating cost and your gross margin to create that net. That's the fundamental. Yeah, you're right. And so if you can bring your business leaders all into the same planning session, take AI out of the conversation and identify where those, where there's friction occurring as things are being handed off from opportunity all the way through delivery. Because where you, where you find those points of friction, you can probably optimize your workflows using AI to reduce that time. The idea is to reduce the time from opportunity to cash. And it's amazing. Once you frame it that way and they start thinking about it that way, the ideas start to flow. Then you can look at the AI tooling that's available. Then you can decide which ones are most appropriate based on that specific opportunity. But the first pass really needs to be where is my, where's my greatest opportunity for improvement in my business?
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C
I. It totally makes sense. This is business, you know, MBA School 101.
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B
Sure. Yeah.
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C
Right. So instead of chasing shiny objects, we look at the fundamentals and, and we look at process re engineering. In fact, my, my PhD was in information management systems and a lot of work that I did was on process re engineering in my dissertation and things like that. And this is exactly what we're going through. But now we've got a tool that can actually help make that. Because process re engineering. Oh, it's a slog. Oh, it's so brutal. Right? But now I got a friend that can sit next to me and get through all the minutiae part of it. Right. Because that's the hardest part of process reengineering is all the fine details and all it can, it can get through all that stuff and help me push these things forward. I think this is a great time for mid sized small businesses.
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B
Agreed. You know, if you, so if you run a strategic planning session kind of like what I just described, then you take the artifacts from that conversation, transcripts, standard operating procedures that hopefully they have documented for each of those handoffs. And then you bring all that as context and use AI to help you distill that down and then define where your best opportunities really are for improvement and where I might be able to assist. And then all of a sudden it's, people are excited about their strategy and now they have something meaningful. They actually should, by the end of that strategic planning session, they should be able to know how they're going to manage and track their KPIs.
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C
Yeah, yeah.
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B
You want to measure your return. The other piece I think that people need to be acutely aware of is that AI is a utilization fee. It's incremental cost and the cost at some point will increase. Unlike traditional project based work where you have a capital investment, you probably have some operational investment recurring to support and maintain something.
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C
Right.
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B
It's not, it's truly not a one time project cost. And that's probably the hardest part to figure out right now is, is how to handle that the way tokenization is handled. I don't want to be too technical for the audience here, but.
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C
No, but we should talk about this because cost in AI is not free and you talk about token costs. This really reminds me of the whole shift to the cloud sort of thing. Right. It was a consumption cost, it was very different than what we were used to. And the cloud service providers sold it as, hey, no big upfront costs. Instead you pay per the drink. Well, and then you get kind of fat on the drink, right? And then you're like, you get that first bill from the cloud service provider and you're like what, 10 times more than what I expected. We're going to see the same thing with AI. Are there options there?
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B
There are options and they're constantly changing and evolving. So we use tools, Claude code, as an example. When the engineers are working with Claude code to assist with their planning, because they truly are the architects and the experts, right. And they're using Claude code to help them develop the plan, then as the owner of the plan, the engineers have to review, modify, approve the plan. Then we cut CLAUDE code loose on writing syntax, right? Yeah, yeah, that's the best. That's just really the way that it should flow. However, in that process they're making decisions which model to use in CLAUDE code and those different models have different costs. So Opus for example is a little bit slower on it but. And it's quite a bit more expensive. It's a lot more robust and scientific in its approach. So it's appropriate for certain use but your costs are going to be far greater.
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C
So, so it threw, it throws another element into that. Like a software engineer has to start thinking about cost, which they have. At first they didn't with cloud. Right. They just went hogwart crazy. Because I'm a software engineer too. I did the same thing until I got that first Amazon bill at $500. I went, Holy smokes, I could have just gone out and bought a workstation for $500. But there's another option. I don't know that you know about this. It's private gen AI.
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B
Oh sure, yes, yeah, right.
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C
And I'm starting to see small businesses and mid sized businesses look at that and go wait, I can run generative AI on my laptop. And okay, maybe it doesn't do Shakespearean Japanese haiku of my project plan. Yeah, but who needs that?
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B
Right?
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C
These smaller models that run locally can do a lot of stuff and maybe I can get away with that. So there's options which I think is wonderful in the way that this is working out because it will keep the big boys prices at bay because there's
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B
competition, you have to have competition in the marketplace. And I'm glad you brought up the local models. Yeah, that is absolutely an option. I think a lot of non technical folks in the audience might be a little like what does that mean? How do we make that decision? How do we bring that into our organization? I still think that you start with strategy first and then you got to work with experts like you Darren that can say oh this is perfect for a local model and this will help you control your costs.
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C
Well, the reason I brought it up, I'm glad you said that I want people to know that there's options that they're not holden to these high cost models, that they can change the cost on these models willy nilly just like the cloud service providers have done there. And we've seen it ebb and flow and it's, you know, they, you know, every once in a while they'll try and undercut each other and we're all like saying, yay. You know, because we get cheaper, we get cheaper instances in the cloud. We're going to see the same thing with, with these big public gen AIs as well.
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B
When, when they decide that they've the big boys, when they decide that they have secured enough market share to take risk to raise their prices, that's when we're going to see significant price increases because they're all losing money right now.
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C
Yeah. Well, and they're betting that we have, that we have drank the Kool Aid so much that all of our systems are tightly integrated with their stuff.
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B
Yeah. Then it's, they're super sticky right at that point. Then it's operating costs. It's hard to kind of back out of that.
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C
So as a small business owner, I'm like, that's why maybe I might be a little hesitant in using these new technologies because I've been bit before with cloud service providers.
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B
That's a good point.
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C
So I don't want to do that again. So I love how you say you start with strategy first, then we can look at the tools that are sitting there.
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A
Yeah.
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B
I think a lot of people get distracted by all the hype and they're thinking about what each tool can do based on the case studies and information they're reading. And then they start with trying to bring that tool into their organization to replicate somebody else's case study. And that's not the, that's not the path to success. You know, every business has some subtleties in the way that they operate that make them unique. And if you can find those, what makes you unique and then exploit that, that's how you're going to win.
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C
Do you think that's one of the reasons why so many AI projects have failed? I mean, because MIT came out with that report, right? 95% failure rate.
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B
Yeah, I do. I think there's two reasons why a lot of those AI projects fail. One is they weren't ready with regard to their data. Data governance. Single point of truth. And I think a lot of companies assumed that AI is going to clean up their data. AI is an amplifier.
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C
Yeah, I totally agree. It is a magnifier amplifier. So if you got bad data, it's
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B
going to expedite the, the how quickly you can make more bad decisions
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C
and, and not just how quickly. It's not just an accelerator, it's a magnifier. So those decision, the impact of those decisions could be much larger as well.
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B
Much larger.
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C
Yeah.
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B
Yeah, I think that's one of the big reasons is, is they weren't quite ready. They were, they're, they wanted to be early adopters first to market, transform their business and lost sight of the fundamentals.
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C
Yeah. Where do you see AI being used most effectively?
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B
I see it working most effectively in reducing manual labor in general. So kind of coming back to the strategic perspective here is if you're reducing the time that it takes to go from one step to another step.
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C
Right.
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B
Oftentimes that's because you're processing various information or you're collecting and gathering information and trying to make decisions based on that information. AI can be really, really helpful in those cycles to reduce the cycle time. And fundamentally that's what we see most often is the vibe coded solutions that we're helping bring to production or the solutions we're developing from the beginning with our clients. It's about workflow automation and reducing those cycle times. That's where I see it being most effective. If the data that we need can be properly sourced and that data's properly governed, then it gets even better. Otherwise you might have to take a step back and say, okay, we're not ready for this plan. We have to create our data warehouse. We have to agree on metrics and how we measure specific information, because finance might measure information differently than operations. And so you have to, you have to bring those leaders together and align on what truth is.
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C
Yeah, yeah.
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B
So, but yeah, I think that that's really, it's, it's the, it's streamlining workflows is where we're seeing it being most effective.
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C
Yeah, well, which, which is interesting because that's one of the harder places to, to work. Right. Because you're dealing with culture and process. Yes, Culture is really hard to change. And processes too. Right. Because process reinforces culture.
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B
That's a great point. And there's a lot of fear, a lot of fear about AI taking your job. So if you've defined an opportunity, you want to pursue that opportunity and it's going to reduce effort and cycle times. What does that mean for the people doing that job?
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C
Hopefully it means that they can focus on more valuable things. And this is an argument that I've had with my wife. Just recently, we were traveling in the Nordic countries, in Finland and Denmark, Norway, Sweden. And we noticed something very interesting. They use a lot more automation than we do in the US specifically around travel. So we, when we checked in, in Finland to an airport, we checked our own bags. No one checked. You go up to this thing and it's a kiosk Right. You go to your kiosk, you scan your boarding pass, you scan your passport, it prints out your. Your bag tags. You put your bag tags on your stuff, you put it on the conveyor belt and no one checks you.
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B
Wow.
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C
And there's one person standing there that's managing probably about 15 or 20 kiosks with, with bag things. So if you do have a question, there's someone there, but in the us we've got people that, that do that work, but it can be fully automated. When we get into Denmark, similar sort of thing, we have people saying, go to this line, go to that line. They have these doors. It's hilarious, right? They have these doors that are on your. Your normal line markers that are just that. The ribbon. Yeah. That goes across that says, here's your line. Right. And to. To direct people to go to which line? There's these doors and they open and then people go in and then they close and people go to this other line over here. So I'm like, I. I told my wife, I says, look, this is awesome. And she goes, but what if someone likes doing that job? And I said, yeah, but it's not providing any real value. Right. Maybe that person could be more valuable greeting people or helping people that need real help, like maybe in a wheelchair or an elderly person or something like that. And she made a very interesting point. And the only reason I'm talking about this is because I'm still mulling it in my head, which, which is what if people like to do mundane things?
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B
I'm sure some do. There's some psychological safety in, in doing mundane work.
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C
So what. What are they going to do if AI is going to replace that mundane work? And do I, as an employer, do I keep them around just because.
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B
Yeah.
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C
Or do I find them something more valuable to do that? I. I don't know.
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B
Yeah.
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C
This is a dilemma I'm dealing with myself.
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B
It's interesting. I think we could look back at the Industrial Revolution and see how things changed. History is a great teacher.
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C
Yeah.
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B
You know, people started moving to the cities to work in the factories and, and gave up a lot of rural farming lifestyles. And that was hard work. That was very hard work. Factory work was certainly difficult as well, but it provided so much more opportunity for them than. It was a big transition there in the workforce. I think this one's happening much, much faster.
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C
Oh, yeah, Much faster. Yeah. So. So I. Because I always had. In my. In my head, of course, people want to be more valuable.
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B
Yes.
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C
That's because. Because I'm an entrepreneur as well. I want to be more valuable. I'm always looking for, you know, improvement, and my wife made that point to me. I'm still struggling with, hey, maybe people just want to punch a clock and do something mundane because they don't want to elevate themselves beyond where they're at.
- [31:51]
B
Yeah, they're certainly a segment of the population that would fit that description. I, I have to assume maybe that they need to focus on the trades, that that group, because the trades are not going anywhere. And, and we've got. When it comes to electricians, welders, plumbers, home builders, I mean, that AIs. You're not going to have robots building houses anytime soon. That's not going to happen.
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C
No. Unless. No. Unless you're Elon who's producing an army of robots. Right. That's going to do everything for everyone.
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B
Yeah, yeah, so he says.
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C
Exactly. So, Matt, hey, this has been incredible. If people want to find out more about you and your company, where do they go to find out more?
- [32:40]
B
Well, they can just look me up on LinkedIn. Matt Stripperhoff, do a quick search there. And there's a link newsletter that we're publishing for Red hawk Technologies on LinkedIn as well, our company page. And we're writing about these topics all the time. Once a month there's a new issue that comes out and if they want to visit the website, it's. It's redhawk-tech.com.
- [33:01]
C
that's awesome, Matt. Thanks for coming on the show. This has been. I had a lot of fun today.
- [33:06]
B
Yeah, me too. Darren, it was a pleasure.
- [33:07]
A
Yeah, Matt, thank you for the conversation. This is what I take away from this interview. The first lesson is that AI is not a strategy. It is a force multiplier for a strategy that already exists. If leaders cannot explain where friction lives in their operation, AI will simply accelerate confusion. The second lesson is that expertise still matters. The best results come when subject matter experts bring the problem, define the desired outcome, and then use AI as an assistant, not a substitute. The third lesson is that governance and economics have to be designed in from day one. Data quality, a single source of truth and model selection, are not technical details. They are executive decisions that shape performance, risk and spend. The larger trend here is that AI is pushing organizations back to fundamentals. What process truly matters, what work should be automated, what should remain human, and what metrics prove that the investment is working. Leaders should ask whether they are buying tools or redesigning how the business creates value. They should also decide how they will measure return before they scale usage over the next several years. The companies that win will not be the ones that use the most AI. They will be the ones that use AI with discipline, clarity, and a deep understanding of their own business model. That is the real competitive advantage.
- [34:31]
C
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