00:14:01 · Heather McLean
[00:14:01] S1: It starts with leadership buy in. It starts with talking to people. And a a great methodology is when you start start talking about the pain points that they're feeling right now. Right? Start with a simple use case where people are feeling pain around it every day. Then you get those people to be part of the solution. You bring them into a room and you start talking about, okay. We need to reenvision this process. We need to reengineer it. Get those people to actually be the ones to do that business process reengineering because people help support what they create. And nobody wants to be handed a new process for something without having any involvement with it before that. Right? Nope. That's like the worst thing is to be handed something that is redone for you. So by making them part of that solution, they're gonna get buy in. Another, another approach is training. Like, in the in the air force, we would call this train the trainer. So you can take a small group of people who are maybe change agents in your organization and train them on the new tools first, and then have them help train people after that. So, you know, it's like train the trainer who's gonna go out and then help sell this to the rest of the organization and get them up to speed. So that's a really good method as well. There's lots of ways around these issues. You you just have to be planful and you have to think through it before you just jump in.
00:15:31 · Ezekiel Iferia
[00:15:31] S0: Alright. So, yeah, where do most companies get it wrong? You know, when they try to balance all three, you the people, the process part, the technology.
00:15:42 · Heather McLean
[00:15:42] S1: Well, I can tell you... Maybe give you some examples of why AI projects fail. So number number one, there's there's about four main reasons. The first one is it's a misalignment problem. So this is where a company will buy a tool, and they just they just tell people to go use it, and they don't change their business processes. The second the second weight... Reason that AI projects fail are because they have the wrong focus area. So a lot of companies will wanna start with some flashy use case, something that may be customer facing. Oh, we're gonna we're gonna put a chatbot out here. But a lot of times, maybe that's not gonna be the place where you're get the highest... The biggest ROI for your investment. We're we're seeing that a lot of the back office processes, that's where a lot of companies are seeing that the real value come out. The number three reason I mentioned this earlier is that do it yourself approach. So they... A company that doesn't have the expertise or the experience, but they're gonna try to build it. And like I said earlier, that companies that try this often fail at twice the rate as others. And then and then a big one here is the lack of a strategic foundation. And what I mean by that is they don't have the data infrastructure or the governance to make AI projects succeed. So, you know, it's kinda like the old saying, data... Garbage in, garbage out. It's it's the same thing with AI. Right? If you if you have garbage data, then you're you're not gonna get great results. So those are the the four primary reasons that that that we're seeing AI projects fail.
00:17:19 · Ezekiel Iferia
[00:17:19] S0: Alright. Alright. So, you know, let's cycle back a bit. You know, your your competitors aren't really waiting. So what happens to organizations that are seeing analysis paralysis?
00:17:34 · Heather McLean
[00:17:34] S1: Right. Well, their their competitors are moving ahead. Right? They're they're getting those competitive advantages. They're learning how to do things better, faster, cheaper, which makes them more competitive in the marketplace. Eventually, you know, people are going to choose the competitors and their offerings and their products and services over the companies that aren't doing this. I read a great article, and I can't remember the the author right now, but it was called the seven economies of AI. And it talked about how companies are gonna find themselves in one of seven groupings as they go forward. Right? The the very first grouping would be, like, people who... You know, they're saying, like, oh, I'm just growing blueberries. I don't need AI, so they're not even gonna think about it. Then there's another group that... The second group up that's aware of AI and what it can do, but they refuse to get involved. And these are the people that are gonna lose market share. And then there's the companies that understand the value of AI and they're leaping in and they're they're building with it and they are gonna have that competitive advantage. And then from there, it kinda goes up to people who, you know, all the way up to people who will be using agentic AI and, you know, AI is running everything in their company. But I love the the way this author described these seven economies because I do think that you're gonna see companies kind of falling into these groups over time, and it will predict how well they do.
00:19:05 · Ezekiel Iferia, Heather McLean
[00:19:05] S0: Alright. Alright. So, you know, Heather, you're a woman veteran, you're running a technology and AI firm. So how has that shaped the way you lead, you know, and the problems you choose to solve? [00:19:19] S1: I would say our whole focus is on people and clients and being a people first company. And what that means is we're gonna make decisions that that how... That are best for our clients and our people above profit. So it it does affect every decision that we make. It... You know, we have... Our motto is do the right thing and do the thing right. And so we are very integrity and core value focused company, and that plays into every decision we make. So I think it's just being able to come back to those values and come back to the way we want to be... The things we wanna be prioritizing, and then letting that guide our daily decisions in how we do things. [00:20:08] S0: Alright. So what's something you learned from being in this space that, you know, doesn't have a lot of people who look just like you?