Speaker 1: So, it's just a key component to keep in mind is it's not static. It's not something that you simply do once and you're done and you walk away from and then come back 20 years later and reevaluate. It's something that is constantly needing to be looked at, updated, and thought about within the company, the business, the economy, where are we going, what are we doing, and what technology is available. With this comes a key aspect of this entire thing, right? When you look at this and say, what can we digress this entire presentation down to, here are the top seven steps, right? And when you look at this, I would say, we're talking about creating this alignment of AI business goals and the business objectives. These are the key areas. We're going to go through each one of these, but here's the questions that you should be asking yourself or comments you should have, right? So when we look at even, for example, the first one, define clear business objectives, you have to say, what is the purpose of our business, right? And I've had so many conversations with different technology leaders, and I sometimes have to stop them and say, okay, what does your company do? Okay, you understand the high level. Now, can you tell me how it's done, right? Can you tell me why this is occurring, how this happens, whether you're building something, you're creating something, you're offering your product or service, it requires this entire ecosystem of looking at the business. And sometimes it's good to take a step back and just have the conversation of like, do we know what we're actually doing? We're manufacturing something, right? We're building parts and components. How does that work? What does the factory look like? How does this work in terms of business ecosystem? All these other steps we'll be diving into, right? Like identifying the AI opportunities. This is a big one. So let's start. You don't want to boil the ocean, but you definitely want to consider everything that's in the ocean ecosystem and figure out what's the best fish to catch, right? What is the low hanging fruit? What are the quick wins? Of course, coming from the business value background, as well as that quantifying value. And I always think this is one of the most important aspects, mainly because it's expensive. It's a time commitment. And we have to say, how can we help impact the business? At the end of the day, when you come from a technical background in IT, and you go to someone in finance, everything is looked at as a cost center. Everything is a line item on a business sheet saying, what's the cost to this? What do we need? Why do we need this? And how do we justify that? And that's a big component of this, is why do we want to do this as a business? Does it make sense? Is this going to help us make money? Is it going to help reduce costs, improve asset efficiency, top line and bottom line impacts? Four is developing the strategic AI roadmap. This is a good aspect to have, right? And we talk about an AI roadmap. It's basically project management. It's that very long and detailed approach saying, what are we doing today? Where are we going to be? How are we going to get there? It can't just be pie in the sky, and we're just going to start integrating LLM models into a certain application. It has to be detailed out. Why are we doing this? We have to ask ourselves, where are we doing this? When are we going to do this? And this ties in hand-in-hand with that quantification of the value. Next one is integrate this AI and data strategies. You will probably already have a data strategy, or your company has some semblance of a data strategy. These things go hand-in-hand. And we say AI strategy, oftentimes I say AI strategy and data strategy, they're interchangeable. You can have both of these together and say, what are we doing today in our data strategy? How can we augment this with our data strategy? Because a lot of these principles, you can just replace AI with data, and a lot of these principles and implementation hold the same. They hold true. The last is building this AI data literacy and culture. This is really kind of asking, we have all these users, and they're not machine learning scientists, right? They're not data engineers. They're not going to understand the complexities of all this math. They hated math in school. They don't even want to talk about statistics. They don't want to know it. They just want to use it. How do you implement that to the general user community? How do you implement this business where you have the most effective use case? So there's always that joke, right? Especially in the analytics world. We built all these wonderful analytics dashboards and nobody used them, right? We have a thousand of them and nobody uses them. You have to do the same thing with AI. If you create something that's very expensive, time-consuming, and requires a huge commitment to do, and you implement it in the community and it's never used, the value's dead, your roadmap is dead, and there's constant improvement to any future projects that's going to be looked at with a negative side eye from the business.