00:12:26 · Ezekiel Iferia, Heather McLean
[00:12:26] S0: Alright. So, you know, there's a lot of noise about artificial intelligence right now. What's a dangerous myth? You know, you keep hearing from business leaders. You know, what do they believe that's actually holding them back? [00:12:40] S1: I think I think one of... The one is... The one that I'd already mentioned is that it's not a technology problem, it's a people problem. But the other myth, I think, is the we can do it ourselves myth. And for some organizations, they are advanced. They have some of this expertise. But for a lot of organizations that we're talking to, they're trying it themselves, but they don't have the experience or the expertise, and they're sort of floundering. Right? Because they don't really know necessarily what direction to go, and they might not know what good looks like, and they don't have a good road map for how to get there. So this really does hurt companies when they're trying to do it themselves. In fact, companies that do this try to do AI themselves, like, fail at double the rate of companies that that hire this out. So we would always recommend, you know, if you don't have this experience, don't try it yourself. Start with, you know, a good consultant or a good partner who does know how to do this to get you going so you don't fall into some of these traps.
00:12:40 · Heather McLean, Ezekiel Iferia
[00:12:40] S1: I think I think one of... The one is... The one that I'd already mentioned is that it's not a technology problem, it's a people problem. But the other myth, I think, is the we can do it ourselves myth. And for some organizations, they are advanced. They have some of this expertise. But for a lot of organizations that we're talking to, they're trying it themselves, but they don't have the experience or the expertise, and they're sort of floundering. Right? Because they don't really know necessarily what direction to go, and they might not know what good looks like, and they don't have a good road map for how to get there. So this really does hurt companies when they're trying to do it themselves. In fact, companies that do this try to do AI themselves, like, fail at double the rate of companies that that hire this out. So we would always recommend, you know, if you don't have this experience, don't try it yourself. Start with, you know, a good consultant or a good partner who does know how to do this to get you going so you don't fall into some of these traps. [00:13:43] S0: Alright. So, you know, you you... You've said that organizations who successfully adopt technology are those that address the people and, you know, process parts too. So what does that look like in practice? You know, how do you get the human side right?
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?