AI Strategy for CIOs & CTOs: From Hype to Business Impact with Shawn Mills

AI is here—are you ready to leverage it for real business impact?

In this episode of The IT Masters Podcast, host Rob DeVita sits down with Shawn Mills, CEO of Pisteyo, to cut through the AI hype and get straight to what CIOs, CTOs, and business leaders need to know about AI adoption. From real-world use cases to overcoming internal resistance, this conversation is packed with actionable insights on how AI is saving companies time, cutting costs, and driving revenue.

🔹 Why AI adoption is being driven by business leaders—not IT
🔹 How companies can move from AI experiments to ROI-driven strategies
🔹 The biggest mistakes companies make when implementing AI
🔹 How AI is reshaping IT and the role of technology leaders
🔹 Shawn’s expert take on Copilot, ChatGPT, Gemini, and more

About Our Guest:

Shawn Mills is a tech entrepreneur and AI innovator with a track record of leading digital transformation. From founding Green House Data and Lunavi, where he built cutting-edge cloud solutions, to now spearheading AI-driven business strategies at Pisteyo, Shawn has spent his career at the forefront of emerging technologies. His mission? Helping businesses bridge the gap between AI potential and practical implementation.

If you’re a CIO, CTO, or IT leader wondering how to integrate AI without wasting time and money, this episode is a must-listen.

Listen now and stay ahead in the AI revolution!

Available on Spotify, Apple Podcasts, and YouTube. Don't forget to subscribe and leave a review! 🎧 

Transcript

Welcome to the I. T. Masters podcast, where technology leaders share their strategy, shaping the future of I. T. Hosted by Robert DeVita, CEO of Magetix. We bring you candid conversations with CIOs, CTOs, and enterprise architects who are driving digital information from AI driven security to cloud innovation and IT modernization.

We cut through the noise to bring you real insights from the brightest minds in tech, no sales pitches, no fluff, just thought provoking discussions with the masters of it. Subscribe now and join the conversation. The it masters podcast where the best in it share their journey. 

Rob: Please welcome Sean Mills to CEO Pisteyo.

Sean, welcome to the show. With your extensive experience as a tech entrepreneur and leader from building innovative cloud solutions at Greenhouse Data and Lunavi to now [00:01:00] driving AI adoption at Pisteyo, you've been at the forefront of digital transformation. I'm excited to dive into your journey, the impact of AI on businesses and the trends you're seeing in the market today.

So first, let's start off with where did Pisteyo come from? The name? 

Shawn: Well, Rob, thanks for having me. Um, it's kind of an interesting story. The true story, we can start with the true story and we'll give the fake story later. Uh, the true story was I was skiing backcountry with a friend in Jackson Hole. And the term for skiing backcountry is off piste.

And somebody said, yelled out, piste, yo. And I thought, oh, that's interesting, maybe I'll turn that into a clothing brand. So I registered the domain name, I don't know, 15 years ago. And then when I decided to start this company, I was like, oh, I own that. Maybe maybe I can use that name in the new company. Um, and so, you know, magically names come from all sorts of places.

Um, and then I had to do research to find out, does this mean [00:02:00] something incredibly inappropriate in some other language? Unfortunately, the answer was no. And then in doing the research, there was no other pisteo out there. But if you do some, uh, if you do Google Pisteyo, something that will come up is Pisteou.

It's a, um, Greek word for belief. And so, uh, it, it ended up resonating that, look, the reality is that AI is coming, and it's here, and there's gonna be a lot of opportunities for organizations to adopt AI, and it's gonna probably change the way we operate moving forward forever. 

Rob: So, not that it was gonna be Greek to people when it first came out.

Shawn: Well, that could be another way to look at it. 

Rob: So, Pisteou. Magetix. com was obviously available, similar to how Magetix. com happened to be available. Because it's weird. No, no one else picked it. 

Shawn (2): It's weird. Those, those letters together mean nothing. 

Rob: Exactly. To anybody else. Right. Except for us. Right. Um, can you share the story of how you began your career in IT?

I mean, you [00:03:00] came out of, um, You know, UT, McCombs School of Business, which, by the way, is impossible to get into now. Ninety one, I'm assuming it was a little bit easier, but 

Shawn (2): Wait, wait. I'm pretty certain it was way harder then, even, because that's how smart I am. 

Rob: Ninety six thousand applicants at UT this year, up thirty percent from last year.

It's crazy. It's 

Shawn: incredible. You know, you think back about, it was, we'll call it challenging to get in then, but now it's Basically impossible. It's incredible. 

Rob: So you t how'd you get from there? How'd you get into, you know, it and technology? What was the journey like, 

Shawn: you know, so I graduated from the University of Texas as a finance major.

And so I kind of had had the knowledge and understanding around What it takes to make a company run from a finance perspective and, and really never, I didn't take many marketing classes or sales classes back in the day. And after I graduated, [00:04:00] that became a huge passion of mine is, is what, how does, what does the intersection of sales and marketing look like from a finance perspective?

And so I really took a super analytical approach to, um, running businesses, starting businesses, but how I got the opportunity to start my first business, which was in, uh, voice over IP. This is back before the Dialpads and, uh, before the Vonages, uh, of the world. I started a, a voice over IP company with a good friend of mine, who actually, we are back together again inside of Pisteyo, um, on our next venture.

Um, so we started, uh, in the day, back in the day it was called Call Rewards. We actually started it here in Dallas, Texas. Um, down in Deep Ellum and, uh, was our first office and had such a great experience, um, raising capital, learning what it was, what it took to run the company, then ultimately, um, being acquired, moving to New York.

And that really started my career. 

Rob: Yeah, so there, like, in the Deep Ellum lore of things here, you got overshadowed by my, [00:05:00] by Mark Cuban by just a little bit, right? 

Shawn: Well, so literally they moved in right after we started. So broadcast. com days, um, that was, I think, gosh, what was that? So that's probably nine.

No, it's like 99, 2000 ish era. Uh, broadcast. com moved in and then. Well, now Mark Cuban is what Mark Cuban is. Yep, 

Rob: exactly. Um, how has your vision for Pisteyo evolved since the founding of it? Like, what was the original, um, goal of the company? What were you helping to achieve in the marketplace? And has that changed from your initial idea to today.

Shawn: You know, it's interesting when you think about, um, A. I. When, when I first started considering, you know, what, what do I want to do next? And I started thinking about opportunities. It really kind of was born out of at Luna V. We acquired a company that was focused on digital transformation, helping build a big model, big enterprise applications for organizations around the country.

And the big challenge we saw in that, [00:06:00] um, effort was that these were generally I. T. Led projects and we're really struggling to have bring the business into the conversation. And, you know, it was overcome with the adoption of agile and bringing in stakeholders from the business onto the delivery teams.

So it started to improve. But the big trend we started to see as I was happening. It was this buzzword and the business kept looking to I. T. To help lead it. And it's like You have to tell me what you want or I can't help you at all. And the business didn't understand it. And so they didn't understand what the opportunities were.

And so as we were thinking about building this business, I wanted to move from selling and living and supporting the software development community, the cloud community, inside an organization to, all right, business, let's get you tightly aligned with, um, the tech that is coming that is going to change, uh, the way businesses run.

So as we started [00:07:00] to think about how is this even going to move from an idea of a company to actually execution, we really focused on bringing in people inside of our organization that have run companies. So, you know, I've run several companies. My partner, Zach, who we started the first business with, uh, has run several companies.

He's like the innovators. He's like, he comes, probably comes up with an idea, five ideas a week. And the challenge for me with him is, Hey, let's keep you focused on the next thing. That's going to move the needle. And he is coming up with some very cool things I shared with you, our podcast and, and, and report generation automation.

That's incredible. He created that. He's built some other, um, HR automations, and he's kind of the, the, the. innovation engine that we bring in with us when we go into organizations. And so, you know, it's been a super fun journey. Um, it's still playing out the way I mean, it almost never does, but it's still on directionally relevant the way we thought.

But again, [00:08:00] this was six months ago that we really started this company. So I just read a statistic that AI is moving at 4X Moore's law. Which is like, Moore's Law's compute doubles every 18, uh, months. And it's moving four times faster than that. So like every 3. 6 months or whatever that is, we are seeing massive changes inside the AI industry.

Rob: Um, you know, when we usually deal with, you know, technology, um, opportunities or projects, they're almost always started by the I. T. team. And I don't know that one deal that we've worked together has been even talking to the I. T. team. Right? Everything is business driven. Um, some of it's education, right?

How do I learn more about A. I.? What can I use it for? But everything is, hey, I've got all these tasks. How can you make my life easier? And then I. T. falls in the line. 

Shawn: You're, you're exactly right. And, and so it's really interesting, uh, being in this space now. Like, I've been selling inside of The tech sphere for a very long time and the the [00:09:00] reality of what's really occurring right now.

It's really being a top. It's like a top down lead reality that's happening, but there's a lot of simmering in in in the trenches of the organization and you know that it's marrying those and seeing those two things come together. That's that's the really exciting thing. But just like you said, the cool thing is, is that We're bringing in leaders from HR, we're bringing in leaders, the CEOs, we're bringing in the chief innovation officer, like the person that's out there trying to figure out what's next.

And it's cool that it's really being driven throughout the whole business side of the organization. 

Rob: Um, so PASTEO positions itself as a bridge between Gen AI potential and practical implementation across organizations. How do you achieve this? What specific services do you guys offer to, um, Enable that outcome.

Shawn: So when we, when we started like at Lunavi, we were a custom, everything custom delivery of everything. And [00:10:00] so it made it hard, right? So every single client that we worked with was very bespoke when we really, when we focused on, uh, at Pisteyo is there's a, there's a formula here for success of how to bring gen AI into organization.

And it really starts, as you just mentioned with education. So When we come in, we come in a pretty formulaic way, but hyper customized to their industry. And the formula is this, it's, it's super straightforward. Help the organization understand what is even the art of possible with Gen AI, because it's big hype, I mean, we're at dinner and The table next to us is talking about Chad GPT and they're talking about how to, you know, build a recipe for this, that or the other, but people just don't even know what it can do.

So we always start with education inside of an organization in so much so that people will come to say, Hey, can you build us this thing? We're like, we can. But AI projects have a massive failure rate if you don't follow this formula that [00:11:00] we think works best, which is educate, prepare for change management, like prepare through change management, prepare your team, and you have to bring them along with you.

If you don't bring your team along with you. There's going to be pushback through the process, et cetera. So educate, uh, and then, um, understand what change management is going to mean to the organization and then start thinking about the I tools. And then it's once you start thinking through that process, getting to a I tools that you can really Make sure that the leadership team has buy in and keep going from there.

Rob: Is it safe to say that the majority of people that are using AI right now, just use it as a Google on steroids and aren't even touching, you know, the they're at the top of the iceberg on this stuff. Right. Um, you know, when we, did our session together. It was, hey, you've got to know how to talk to, um, chat GPT or copilot to massage it to get what you need to do.

Um, are you seeing some of the [00:12:00] same stuff? 

Shawn: It's still that reality. You know, people, I mean, we've been trained a long time. I mean, Google's been around for a very long time and it trained us to put our keywords in and then you'll get thousands of websites that you can go sit through yourself. But really, at the end of the day, what you want is an answer.

And so in order to get a great answer out of generative AI at this point. You need to ask it a great question. And so when you think about, you know, and you're going to hear this in this podcast and all other podcasts, you know, but this 

Rob: one's unique, so you're not going to hear it anywhere else. 

Shawn: Well, you're not going to hear it from two really cool people.

That's fair enough. You know, I've been around the block a time or two. Um, but like AI is not going to take your job. You know, another human that really understands AI is and I mean, so much so that as I'm hiring people now and all the people we're talking to are kind of changing their tune to their hiring practice that says, Hey, you're in marketing.

Tell me about your AI skills because you can be super smart. out of, you know, some Ivy [00:13:00] League school. And you can come in here and you can do a lot for us. But you're not going to be able to do it as fast as somebody that knows AI. Yep. And can leverage AI, and can bring that same level of intelligence with these prompting skills that will allow you to move much, much quicker.

Rob: Yeah, we, we're looking for a marketing intern this summer and um, we gave them a project to do. You know, some of the office like, well, what if they use, you know, AI for this? I'm like, I hope they use AI for this, right? Cause it took them two minutes instead of them sweating over this for four hours. Um, that's the type of person we want.

I mean, we talk about, um, you know, schools with kids now, and they don't want them to use AI for certain things. Like that's the same way when we were in school, they don't want you to use a calculator and you're like, what? Have we not learned from our mistakes before? Like, let's teach these kids how to use it, and how to use it efficiently.

Um, and maybe they don't need to write the paper. They've just got to be able to show how they're doing the research on it. Right. Um, there's a whole separate class for writing, and that still has a place for it. [00:14:00] Um, yeah, it's just, it's, it's crazy that, um, you know, as even as early On, we are in the adoption phase of it right now.

It's been around for enough time where people should be able to embrace it. And, you know, especially teaching these high school kids how to use it correctly. 

Shawn: Well, I mean, you asked how, how do we get started and maybe a little bit about the origin story. I mean, part of the reason I decided to start this business is I wanted to know this.

This is going to be the most disruptive technology that has been deployed. And so knowing where it's going, understanding the opportunities that present itself, I mean, it is incredible. We just gave a, um, you know, we, we use Upwork sometimes to hire people. And so we wanted to do a challenge and so we, we did a post out there for 800 bucks.

So we said, all right, look, here's the deal. Here's an image. You have eight hours. We're going to pay you eight hours. I want you to create an app. [00:15:00] This app, an app of this image. So we created a, an image of like just this, um, this like Teams app that we're thinking about building. And I wanted to know, can you build it in eight hours?

They got incredibly close to production ready in eight hours from, from idea. And so the reality was, I need to know that you can prompt. That was the challenge. Like, could you code this? Yes, you could 100 percent code it. And it would take you weeks to get it all done. But if you know how to cook to prompt.

To ask the right questions, you can get super far very, very fast. And that's going to be the thing that as you're hiring people, you're, those are going to be the changing ways of the world, right? So you're great at sales. Okay, great. That's awesome. Show me how you're going to build a prospect list and reach out to 400 people in two hours.

It's not possible without AI. 

Rob: Yep, you were talking about prompts, right? And it sort of goes to my next question. Um, you know, you guys are dealing with so many different industries, right? And [00:16:00] different ways that people want to use it. Um, is the baseline essentially the same from a prompt perspective? And you just sort of put the pillars in there for the different industries?

I can't tell 

Shawn: you all my secrets. 

Rob: I don't I use you because I don't need to know how the sausage is made. But some people here want one a little bit. Like, is there any meat in there? 

Shawn: So, you know, the interesting thing is, is like, I mean, we do. We work across industries. Industries from commercial flooring to multi billion dollar construction firm to top 20, um, legal firms.

Like, these are industries I know A lot about a little. 

Rob: Yep. 

Shawn: But with AI, I can know a lot about a lot. So if you can bring the templates and if you can bring the knowledge of what you need to present, AI can help me get there. So we're able to go into organizations and hyper customize this only because AI is what AI is.

And so we can come in and immediately [00:17:00] speak Customer speak. And that's why I think it ultimately resonates. So well, what we do with our clients resonates so well, they know their business inside and out. We just need to unleash it. And if we can speak the same language with them because we just quickly get up to speed on their business, their industry, they open up, they understand there that we're here to help.

And then between their business expertise and our ability to understand how a I can help them. A lot of very cool stuff happens. 

Rob: What are their differences? in the prompts from Copilot to ChatGPT or any of the other engines out there? 

Shawn: So, um, I have a philosophy, for sure, um, about that question. The short answer is yes.

There, there, some, some, um, LLMs are better at some things versus others. The reality is, they're, you know, for the general populace, they're all very close. It really comes down to how do you use it the best or get the most Uh, out of your [00:18:00] workflows. Um, and so in general, our philosophy, because we have experts in Gemini, we have experts in Claude, we have experts, um, in ChatGPT, we have experts in CoPilot.

It really comes down to what, where does your business work the most? And then we're gonna help you take advantage of that. Like, if you need to go code, we're gonna tell you something different than if you're trying to get most personal productivity out of using Microsoft, right? So, There are such nuances at this point that you need to just understand how to use any of them.

to get a ton of value. 

Rob: Um, you know, some of the things that we saw in the AI offerings that we were given, you know, to from our distributors, a lot of them were around contact center and agent replacements. Um, and it was very hard to justify those costs. to the business, right? Um, because we were looking at very long proof of concept times, you know, six months to get a proof of [00:19:00] concept upped and tweaked to where it actually works.

And hundreds of thousands of dollars to get just the proof of concept up. Um, so it was really hard to justify a lot of these to the business of, Hey, I think we're going to get this out on the other end, but you know, we're not really sure. Um, and. You know, you could look at similar industries and similar data sets, but those aren't pulling from the same data stack that other people are pulling from from their information.

So, um, how have you seen in the work that you're doing? Project approvals. 

Shawn: So you bring up such a such a great point. So part of our process, as I mentioned, we start with education, but that gets you in. And what we're seeing is like 4. 5 4. 5 hours a week of productivity gain, like That's 10%. That's like over 10%.

So when you think about the amount of productivity gain, you get just out of leveraging ChatGPT or Copilot, Gemini in your personal productivity, the [00:20:00] next phase is exactly what you're talking about, which is like AI automation, bringing in tools that are specific to helping you run your business better and or building automations.

So when we come into an organization, and that was kind of the other thing, like Lunavi builds amazing tech, right? But it is pro code, and it's going to be bulletproof, and it's going to be amazing. What organizations need right this split second is, prove to me this works. Yep. Prove to me this works as fast as you humanly can, so that then I can know, what is my return on investment going to look like?

Okay, you just proved to me that I was able to automate the hiring of, uh, any role in my company. From, from job description, to showing up on a call. In my calendar, that's 50 hours saved. That's not like a tiny amount, per roll. So that's not a tiny amount, that's a big amount. And so, if you can get to that proof of value very quickly, [00:21:00] well under 100, 000.

I mean, we're, generally speaking, we like to come into organizations, uh, and figure out, here are the possible use cases. Let's pick one that costs, I don't know, 35. To 50, 000. I mean, 10, 000. I mean, it could be very low depending, but we want to pick something that's significant enough, but can get you there fast enough, like month, one month to meaningful impact on your business and not breaking the bank.

If it needs to be pro code after that, because, hey, you know, it needs to land in this enterprise application and do this, that, or the other, you'll then know, okay, is it worth 200, 000? Yes, it was because it got this return. Now we need to build and spend the right money on it. 

Rob: We were talking before about the rate of change in the AR space and in the model specifically How frustrating is that when a model changes and you've just built a giant process behind it?

And then you've got to go back and keep tweaking them because I would have to assume a lot of [00:22:00] these are Essentially custom models that you're building with custom prompts and now well, you know a new iteration of chat GPT command We've got to go back and retest these they're not giving you a lot of notice of when they're rolling these out Right these upgrades 

Shawn: So it's, um, it's interesting, kind of two, two kind of things pop into my brain from your, your comment there, which is, Hey, should I get started?

Because it might change, right? Like, that's one of the common questions we get. Hey, it's, the pace of change is happening so quickly, you know, should we even start yet? And the answer to that is absolutely yes, because you're going to get value. Is it incrementally better for your, the, the, the activity you're trying to solve for?

Maybe, 10%. But if you waited a year, your competitors could have stomped on all over your process and could have changed the industry completely, but you're starting down the process of building A. I. N. T. Organization. So on the should we get started? [00:23:00] The answer is Absolutely. Yes. Do not wait for the model to get just better, 

Rob: sort of like a firewall, right?

You have to keep patching it because there's new vulnerabilities out there. These are not vulnerabilities. These are enhancements, 

Shawn: right? And so to the second part of your question, which was like, well, what happens when the new model rolls out? I mean, the nice thing is, is that you're building and so you're building automation and you're building in, um, some agentic concept inside of an organization to get things done when the model gets better.

You just Test. It's only a testing exercise at this point. You already built the knowledge, the workflow, and now it's just, it's a smarter person doing, it's a smarter AI doing it versus a less smart AI. So it's actually not super hard to bring in a new model, as long as you build in the testing to make sure that it keeps working the way you wanted it to work.

Rob: Got it. Um, there was a lot of noise about DeepSeek, um, you know, a month ago. It seemed to have died down a little bit. I mean, you must have [00:24:00] got inundated with questions around it, um, and, you know, affects sort of the other side, which is, you know, the amount of just sheer data, data center capacity that's been eaten up here in the United States and around the world by, um, by AI companies.

And, you know, that announcement, I think, shook a lot of people. Um, just curious about that. Your thoughts on it, um, around, is it a viable, um, model to use and do you really think it's going to change the way that, um, these AI models are developed going forward? 

Shawn: So, um, it was a big shakeup and it's really funny because you see this happen a lot of times and it's usually driven in Wall Street, right?

So, somebody finally hears something that moves the needle in one of their models, right? So, there's a financial analyst everywhere and, and they have a model for, um, NVIDIA. And it says, NVIDIA needs to sell this number of GPUs in order for us to justify this. And so, when DeepSeek came out and the news came out [00:25:00] and they're like, Hey, we built this model on 5 million bucks or 6 million bucks, whatever it was, some small amount of money.

Everybody freaked. They're like, Oh my gosh, this is going to crush NVIDIA. And they used old GPUs that, you know, nobody cares about anymore. They failed to say they used Open AI as the basis for the knowledge to create the, um, the models that they created. So it took somebody investing hundreds of millions of dollars to get them to get to that spot.

What open AI is proving and other of these large language models is that it still is a capacity game. You have to have. More data. You have to have more GPUs to be able to be successful in this. And the models are proving it out. And that's why you now see, Oh, in my model when I switch this from NVIDIA is going to sell 700 percent more GPUs over the next to [00:26:00] 4, it crushed it.

Oh, I was wrong. They put it back and they're like, okay, we're good again. 

Rob: Yep. Um, what are the biggest challenges organizations are facing when they try to adopt AI? Um, and how do they overcome them? Um, and. When I'm, this question is really more towards, is it easier for newer companies to adopt AI versus, you know, your 50 or 60 year old companies that have legacy tech stack, um, and legacy, um, technology in there, right?

I would think that the newer tech stack is much easier for adoption than an older tech stack. 

Shawn: So it's interesting. It is not, it's not really a tech issue right now. It is a people issue and it's a, um, A chain, uh, a fear of change challenge more than anything. So is a legacy company harder to adopt AI? 100%.

Because they've been doing what they've been doing for however many years they've been doing it and the mindset is a hard thing to break, right? So, [00:27:00] it's much easier for early stage companies to adopt it They don't have any legacy, right? They don't have like, well, they definitely don't have legacy systems.

They also don't have legacy mindset generally because the processes are new. They're building just everything from scratch. And so it's much easier to adopt change inside those organizations. And so, which is actually one of the super fun things that I get to do. I get to go in mid sized companies that are competing against behemoths, right?

And these behemoths have the ability to stroke a check to McKinsey, have ability to stroke a check to BCG to solve a problem. Now A mid sized company or a small company has the same intelligence available to them to go solve very, very challenging problems at just 30 a month. And so, you know, when organizations, mid sized organizations see this, they see a huge opportunity because they know their bigger competitor is not going to be able to move as fast as them for them to capture market share.

And so it's really fun inside organizations that are seeing this as an opportunity. to disrupt their, their industry. [00:28:00] 

Rob: When we did our, again, when we did our session, it, my, my eyes were bright, wide open. I'm like, this is, we've got to use this in our everyday. Right. Um, it's just going to make us so much more efficient than anyone else out there who might be two, three, four times the size of us that either is afraid to do it, can't do it, or doesn't know how to do it.

Right. Um, when you're working on these projects, what are the mistakes that you're seeing that are being made, um, on the business side, on the customer side? 

Shawn: We see all kinds of stuff. Um, the, probably, the two things, probably the two things that I, that have happened the most recently that pop into my mind are, again, when, I mean, no offense to IT, but, um, IT is scared, right?

Like, they need to be prepared for it because it does propose a higher, if something goes wrong, it goes wrong way faster because of AI at [00:29:00] this point. So, I get it. However, you know, we are seeing, when things don't work out or why companies are failing to adopt it, it's, they're still scared of the security risk.

And that's when you talk to the business. They're like, I'm scared of the existential risk that we're going to fail as a company greater than I am of the security risk. So you need to keep keep moving. Um, that's probably the biggest thing that we see out there right now. 

Rob: Yeah, I mean, when we looked at copilot initially, right, and we had our training on it, you know, the laundry list of stuff you needed to do to segregate data and protect data.

I mean, it was a it was a An Excel sheet, shockingly, you know, 150 lines long, just stuff you need to do. And I looked at it, I'm like, we don't have the resources to be able to do this. Right. Um, it was much easier for us to go. Uh, to chat GPT and turn off a lot of those just just buttons and get it rolling, [00:30:00] um, and not have to worry about the inner company sharing of documentation, right?

I think that's a big difference from chat GPT and Copilot, right? Copilot is more of an ecosystem of your entire environment if you want to enable it to do that. Um, and it's got a lot of power, um, inside of that engine, um, where I, my point of view is chat GPT is just really more straight 

Shawn: AI. Right. Right.

Well, and there's use cases for both. I mean, and we, today we were talking about this with a couple of companies that it really comes down to your use case and what you're trying to accomplish. Um, they're like, the nice thing about copilot is if, if you do set up the things that you're like golden, everything that's in your environment is safe and secure.

Copilot does not have one single security feature in and of itself for copilot. It's like, There's not even the tick box of don't train on my data. It can't train on your data because it's inside your Azure environment. And so that is one of those, like, very [00:31:00] comforting realities of I know that my data is safe and secure, especially for larger enterprises that can trust and lean on Microsoft from that perspective.

But ChatGPT moves a little bit faster, right? Like, it's bringing out the models faster, and there's a lot of value to being able to access the latest reasoning model from, uh, ChatGPT. Like, I just saw, for us in our, uh, OpenAI, or our ChatGPT, the, the 4. 5 preview version just showed up in the last couple days for us.

Um, and so, You're getting the latest, greatest in chat GBT, whereas, you know, copilot is a little bit slower to adopt, but they're doing it in a super safe way, and they're embedding it into a ton of apps, which is a double edged sword because you get lost inside of there. But once you learn how to bring copilot into your workflow, it's super beneficial in and of itself.

Rob: Um, well, the question says what industries have you seen, um, you know, the most adoption, but that's probably not fair because it goes [00:32:00] across industries. Maybe talk more about the Applications or the business uses that you're seeing that are, you know, what are the easiest ones to adopt? What are, what's, you know, If someone says this, you're like, this is a home run for us, or this is a home run for you.

Shawn: Right, right. So the home runs are definitely industry specific. The home runs, the ones that are like We just showed you how you're gonna make money for the next 10 years. Those are industry specific. The common use cases are across like the we think about a I in a very, um, black and white way. You either need to use this to save money or you need to use this to make money.

There's like Don't do it for the glitz and the glamour. Don't do it to say you have A. I. Um, do it because you're going to either make more money or you're going to save more money. So when you think about the saving more money, it's definitely, um, on the personal productivity. People are able to do way more.

So when we think [00:33:00] about, uh, the real opportunities for organizations in the save money or make money category, you know, the personal productivity is generally on the save money. So how do we optimize? Or how do we optimize a workflow? Like, you know, the one we just talked about, um, in HR, hiring is time consuming.

And if you can optimize that and automate it, like from hiring manager conversation with HR to people showing up, that's a big needle mover inside organizations that are growing quickly in organizations that want to make money. Like, you know, we work with a bank and we're focused on helping them close more loans.

That's a needle mover. When you can decrease abandonment rate for loan applications by 1%, that is a big, big dollar figure. And it's not super hard to do that, um, with AI, because you're bringing in the intelligence and the automation required to make it easier for us to get a loan when we need a loan. [00:34:00] 

Rob: Is the IT guy who says, I can't go chasing the bright, shiny object, and this is the bright, shiny object.

How relevant is this, is that statement compared to Other technologies before where you may have been chasing a bright, shiny object. 

Shawn: Right. So it's interesting, you know, we talk about how we don't really, um, target I. T. But actually I. T. is usually the biggest cheerleader behind these initiatives because they've been seeing it coming and they understand kind of what the art of the possible is there.

The challenge in some organizations is that I. T. doesn't have the voice to be heard. Yep. And so, um, So usually when we're working inside an organization, IT comes running up right with us and wants it to be successful. But to your point, the bright shiny object challenge for IT is just like many places inside the organization, they're overburdened with things that they need to get done.

And so they are very excited when somebody [00:35:00] can come help. They know it's going to be beneficial that somebody can come help bring it into their organization. 

Rob: Um, we talked before about, you know, what are the, you know, the best use cases. Um, What's the coolest one that you've worked on? 

Shawn: Hmm. Coolest one.

So we, I can't tell you a ton about this company , because that's how cool it was. Well, so the, the cool, the cool part about it is when it is the re on the revenue generation side is like the super fun side for me. It is changing that company's future. Like when you think about on the cost saving side, it's going to help them invest to grow, but they're still going to grow incrementally.

But when it's, when it's changing your go to market, or when it's changing how your product is perceived in the marketplace and, and you're embedding it into the actual application, that is super fun because they, you know that that company is now going to make a lot more money than they did before. [00:36:00] And that's like.

The most satisfying thing for me personally. 

Rob: Yep Um, I know that when we did our session with you, you know, the first question you asked was what are you using? What what was your last prompt in Chat GPT or copilot? What is the best answer you've gotten has has the one in our session, uh been beat Was this, was this 

Shawn (2): the one about the guns?

Rob: It is, uh, what is the best gun to use for varmint hunting? 

Shawn (2): I'm not sure if 

Shawn: that's been beat at this point. Um, but, we get to see some, some amazing ideas that come out of people, like when we're in these sessions, all kidding aside. Like, the ideas that people are seeing the light bulbs going off are fantastic, it is so much fun to watch when, you know, somebody that's like literally will come in.

And we sat in with this one CEO. He's like, I'm 98 percent against AI, just so you know, 98 percent like, all right, we got 2 percent to work with. That [00:37:00] CEO is like one of our biggest clients now. So it's pretty amazing. 

Rob: Um, on that note, if you could give one piece of advice to CEOs who are hesitant about adopting AI, what would it be?

Shawn: So I, for, for a CEO that's hesitant at this point of thinking about what does it mean to AI, I would say, get your team together. Go through a session to learn what does it mean for AI to be, um, adopted inside your organization because inside, like, no kidding, we, we were with a, a large, uh, health insurance company and literally the very next day, the CFO called me and said, Sean, I just got three weeks worth of work done in two hours.

So the CEOs that are asking that question, rally your team together, learn what this can do for your organization and your eyes will be open pretty quickly. 

Rob: At that point, I was looking at a 400 page contract on a Sunday and I'm like, I've got to extrapolate all this data out of here. And I'm like, wait a minute, [00:38:00] ChatGPT can do this.

And like, I figured out the prompts with, you know, the coaching that you guys gave us and I was able to figure it out. And what took me an hour could have taken me two weeks. It would have been a project that we would not have been able to even start for a customer because we don't have the manpower to get through it.

Um, just get through the 400 page contract. Um, The good thing is that I know not to use that vendor who gave the customer a 400 page contract. So there's some ancillary learning there, but it literally, it's, it gave us an opportunity that we would not have been able to work before because we would never have been able to get through the data.

Um, you know, some of the other use cases that we've used it for, um, we had a customer that was looking to do a, wanted the vendor to do a proof of concept. And the vendor, You know, pretty much didn't want to do is I said, Hey, give me some, some contractual language around, um, a proof of concept. And the customer looks at me and I go, I know we can do this, right?

Cause I built a custom GPT, an [00:39:00] MIT lawyer, uh, an MIT undergrad, a Harvard educated lawyer. And we went in there and gave the parameters of what the proof of concept was and the deal. And, you know, I hit enter. It's got a two page document with signature blocks on it. Um, the caveat that we use is like, Hey, run this past your lawyers because we're not lawyers, right?

Um, but here's your framework that you didn't have before and you would have had to start from scratch. Right, you don't have to go to outside counsel now to do this and, you know, now your inside guys can just, can just take a look at it. Um, you know, and then How do we scope opportunities? Um, it's really helped us, uh, you know, really uncover Every stone when we're going through an opportunity because we've got a list of things that a checklist now, right?

Um by just putting in the parameters of what the deal The specialties are of the deal, so it's really changed the way that we scope deals, that we help customers, um, and one of the other, uh, use cases I'll share is we had, um, a [00:40:00] vice president of IT going in to get an approval from a CFO on a big deal, and I built a prompt that says I am the VP of IT, uh, from, and his LinkedIn profile, going in to speak to the CFO and his LinkedIn profile, the customer's URL in there, give the parameters of the deal, and I need to know six or seven things that a CFO would ask in order to say yes to this project.

And we created two slides, gave it to the VP, and he's like, you know, this is amazing. Um, so we're, able to empower our customers more than we ever were before, um, by just having more knowledge at our fingertips. 

Shawn: Well, it's fun because you, you were just giving examples on the make more money side, right? So, it, it's not like you have to go reinvent everything about your organization to be able to find use cases that allow you to make more money or to close, close more deals, quite frankly.

And, you know, that's the selfish [00:41:00] side of it, but it helped your customer, right? Yeah. At the end of the day, it got the deal that your customer needed. to get done through the process more quickly and without friction. 

Rob: Yep. Well, good stuff, Sean. I appreciate you taking some time with us today. Um, this is a really good session.

Um, thank you again, and we'll see you next time. 

Shawn: Awesome. Thanks. Thanks, Rob.

Thanks for tuning in to the IT Masters Podcast, where technology's top leaders share their winning strategies. Subscribe now and stay ahead in the ever evolving world of IT. Until next time, keep innovating and leading the way.