Business
In this episode, we take a look at the AI hammer that's looking for a nail. Hey, welcome everybody to another episode of ClickAI Radio. So I was talking with a group of technical people recently. And they talked about a very cool solution that they prepared. So I asked this question I said, so which part of your business will this benefit? And they said, well, we're still trying to figure that out. I said, Oh, you've got a solution looking for a problem. Is that exactly. I said, Well, you know, if you're not careful, AI can turn out to be the same thing, right? One of the biggest mistakes you can do with AI, in fact, this happens quite often is it's the tendency to create AI solutions. without sufficient understanding of the business problem. Everyone gets excited and enamored about the technology, well, maybe not everyone, but some do. And so it turns out that a crisp definition or description of your business problem is of course critical. However, as you know, with AI, you can, you can do a bit of both, in fact, that encouraged both, it kind of looks like this. So start by building your AI solution. And what you do, the reason you do that is you run it against some of your data. And it gives you a sense, without any sort of tutoring or guidance on your part, it gives us a sense of identifying what it can see, right, and what it does is, it lets you understand the potential areas or problems that the AI can start providing answers to. So with those kind of insights, initial insights, you can go back to the drawing board and rethink the kinds of problems which may be addressable with the kind of data that you have, that's actually really critical to do early on. Some groups will spend a lot of time doing lots of data curation, cleaning stuff up over long periods of time. And the whole time, the other hand is saying, Oh, these are the problems, we want to go solve with it. And then the two don't meet, right, you don't have the kind of information that's even needed, that AI would need to dress it. So what you're really trying to do is an interactive iterative, give and take process, as a way to think about it. Now, I've never seen a situation where the first time the AI solution was created, that it was ready to be used by the business. So you should get that out of your mind. Rather adjust your thinking, to consider discovering what the possible problems are, that can be solved with AI by looking at your data, then go back, evaluate the business problems, and continue to iterate and fine tune that right, you can certainly grow and build on your data set after you do that, but save a lot of time by doing that. All right, well, let's talk about what actually is a more significant and critical way to look at this. So in reality, as you know, most of our businesses have many existing systems, right? And our business information, of course, is spread across the systems with lots of redundant data. Well, we of course, rely on our teams and our brains, obviously, to logically connect the information together on these systems, so that we can have the kind of information that we as humans need in order to make decisions. Now, when we use AI, we want to think about recreating that kind of experience. And you can go through the process, certainly of saying let's integrate all of our systems together. But that can be a real challenge. So for example, let's say that you have three systems. This will be a little bit oversimplified, but let's say you have one system for leads and lead management. Then you have another system for ads and ad campaigns to those leads. And maybe you have a third system that's used for closing sales and servicing. All right, a bit oversimplified, but it's sufficient for the example. So it turns out as you know that your customer Numbers journey unknown to them touches each of those systems. And of course, in many cases, those individual systems provide analytics and insights in their respective rights, which is cool and helpful. But your brain ties that all together into an overarching customer journey or customer lifecycle. So this is where AI can work wonders. So even though these systems may not be connected, or even integrated, using AI, along with some data management, the AI can look across that entire data set from those three systems as one data set, right and view the whole customer lifecycle. So then with that the the AI can identify what are the probabilities that a certain lead will translate into a closed sale, even though your own systems aren't integrated to sport that AI can actually do that work and give you those insights. So you don't need to do that integration in order to make that happen. So I encourage you to focus less on getting all those systems connected, or even transfer, you know, transforming or moving your data to one system that does it all. Certainly, that's good. And there's some benefits of doing that. But the key is, don't focus on getting all those connected, but rather focus more on understanding what the predictive golden nuggets are that are already hiding in plain sight across your integrated data, not necessarily integrated systems, but you integrated data and let the I see that. It turns out that this is a nail that the AI hammer can use. Alright, thanks for joining until next time, everybody. Get a holistic view of your disconnected systems with AI. Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your FREE eBook visit ClickAIRadio.com now.