 Hello everyone and welcome back to the cubes live coverage of Teradata Possible. I'm your host Rebecca Knight, along with my co-host and analyst Rob Streche. We are joined by William McKnight. He is the president of William McKnight's. monthly consulting group. McKnight Consulting Group, direct from Dallas Texas. Thank you so much for coming on the show, William. It's great to be here. So we want to talk to you about, first of all, the conversations that you're having here at Teradata Possible. But our topic of the day is really preparing the enterprise for AI. And so much of Teradata Possible is focused on, okay, how do we get the technology ready? But that's not, that's not almost often the biggest part of the puzzle. In fact, it's often the people. That's right. I sort of gravitated my projects to those things that nobody's doing, but yet really needs to get done. I mean, I really enjoy the technical architecture challenges and doing all that work. But I find that if you don't bring people along with your project, that the project has low chances of being successful. And so there's a wide range of misconceptions out there about AI. And so I am encouraging my clients and really everybody for the leaderships to understand what they're doing with AI in the organization, craft the right messages for the organization, and deliver those messages to the organization to put aside any of those fears and trepidations that can bring an organization back in terms of what they wanted to be doing with AI. And it's more than just, I agree, it's more than just technology because I've worked with a number of companies and it's how do you have the right people and processes in place so that you don't have the fear or you don't have that or you think you can handle it? Is do you see that a lot of people are trying to reallocate their organization, re-skill people, or how is that, what are some of the suggestions? You're doing it. Yeah, not enough. I think what's happening right now in most enterprises is a lot of what I call accidental AI. Not necessarily top down driven, but that's changing. And I think it's going to change big time next year. This year I think we saw a lot of flat budgets with some polarization at each end where we had some companies that were spending a lot and some companies that were really pulling back. But what I see is a lot of pinup demand for AI and there's going to be a lot of that in the budgets for next year. So if we can hang in there, next year is going to be a big year for AI. You were talking a lot about the trepidation and the fear and I also want to add skepticism to that. Oh, yes. People are worried about holding down their jobs with AI but they are also saying, well, what can AI really do? So what are the kinds of messaging that needs to happen from the leadership down to the rank and file to overcome some of those things? So the misconceptions are at both ends of the spectrum. There are those people that are underestimating AI. Like as if it's a fad, it's going to go away, and it really can't do very much and some of them don't even believe there are driverless cars, there are. And then the other end of the spectrum, there are people who hear AI and they think, oh, IT's going to push a button next week and my job's going to go away. And so some of the messages that leadership needs to craft and get moving in the organization have to do with education. What is AI? People don't know what AI is. And more importantly, what does it mean to this company? And what are we doing about it? Are we considering AI for every project? Are we going back and looking at the projects that we have in place and thinking about re-engineering them with the new capabilities of AI? What are we doing and how is it going to affect our processes, the jobs of the future that we have here? And really, I think they need to be impressing upon everybody that this is big change and get ready for it. Well, that scare people more, though. If you handle it appropriately, I don't think it will. Now consider this, though, that I am not aware of any AI projects being crafted that are actually targeting the elimination of a lot of jobs in an organization. Now, I happen to be on the destruction side of AI in terms of jobs, I think down the road that's going to change. But right now, we sort of have the luxury in the enterprise of that not being overhanging us when it is, this is all going to be a bigger problem. And so if we don't start getting in front of that with organizational change management now, it's going to hit us like a ton of bricks in the future and it really could stop our AI efforts in the future if we don't get in front of that now. No, I totally agree. I think that the fear has gotten somewhat out in front of the actual reality of the capabilities of the actual tech and what's being there. I do agree that longer term, I think one of the places that I have friends in the recruiting industry, for instance, and they've seen a lot, the uptick of AI enabled recruiting and vetting and things of that. And I think that's where a lot of people have the fear of the unknown and the bias and things of that nature. And we see it creeping into other parts of HR as well. When you talk to these organizations, are there particular places you say, hey, do a POC and start with here where it's core, where maybe it doesn't impact your customer base that's spending money with you, but you can start to cut your teeth on and learn how to implement this properly and put the right team around that. Is that kind of approach that you're taking with them? Yeah, absolutely. I mean, I think AI is here to stay, it's a keeper, you got to get in front of it, and I like that approach and I recommend that approach. And so one of the places that we're looking at with our clients is around LLMs, of course, right? That is a very progressive use today of artificial intelligence. And where do you put that in the organization? Well, a lot of it's going to be in the contact center and how you interface with the customers and enhance the customer experience with actual words that make sense to the customer based upon real data, and that's all what's being made possible through LLMs. So I think there's going to be big thrust in that area, but it's also going to be sort of industry dependent. So like in healthcare, a lot of the triage that we have when we enter a medical facility, a lot of that, a lot of the patient recommendations that come back is going to be more English oriented, and I think that's all going to be under supported by LLMs. So what are you hearing at this conference that makes you the most excited in terms of proof of concepts and in terms of the way you're seeing AI leveraged in organizations, but also really industry-wide? Yeah, some of that I was just touching on. I've been hearing some great healthcare studies about how they're using and planning for more AI. That's a vertical that I kind of focus on somewhat, so that's very interesting to me, but I've heard all kinds of things here this week, and the plans are really high out there, and that's why I'm really excited about AI. This whole industry that we're in, and I consider myself in data, it just keeps on giving. It keeps on giving and growing, and next year it's going to be a big step forward for everything it is that we do. Yeah, I think it definitely is. In fact, we have a partner organization, ETR, that does a lot of polling, and in their polling, really right now the only thing that's being invested in is at the number one rate is AI. And I think a lot of it is exactly what they did is they dug down into that and saw that was people had moved, I think, people who said they weren't doing AI this year went and dropped from 50% at the beginning of the year down to 29% now, and they do this quarterly, and I think what, to your point, is most of them are in the experimental stage and trying to figure that out, and I think part of it, what we're hearing from people is we just don't know how to, we're still trying to manage our data no less now. How do we get the data to the AI or the AI to the data, and what are you seeing with organizations about, are they starting to understand data management and get a handle on that problem? So, yeah, that's a big question. I get asked that a lot. What do we need to do to get our data ready for AI? And my answer to that is have a great data architecture. And that means multiple platforms, no one size fits all. It means data warehouses, data lakes, streaming real-time data, probably graph data, probably master data management, and a lot of other things. It's all context-dependent, but those are some of the staples that every organization needs. I know when they have these things in place that they're further along the maturity cycle and more ready for AI. Now, it can't just be any data either in these artifacts. It has to be clean enough so that the AI algorithms produce great results. And so there's that sense of governance that has to overlay all the data that you have under management wherever it is. You've talked a lot about the messaging that leadership should be pressing down to their workforces. What would be your advice to the rank and file, to the people with jobs who are either worried or nervous about AI, in terms of getting started with it, developing the right kinds of mindset around it? How would you talk to people about this? You know, it kind of depends where they're coming from. A lot of people are coming from the point of view of head in the sand. It's not real. I'm going to ignore it until it hits me like a ton of bricks. And that's exactly what it's going to do. This is actually an easier conversation for me to have with people and helping them individually than it is when an organization is going to, you know, do a lot of layoffs and things like that with a project down the road. We're all going to have to deal with that, I think at some point. But my advice to individuals is to learn about AI and look at how you contribute to your company today. And if you are doing things that it takes, oh, about a minute to research, that is in great jeopardy of near term removal from your job responsibilities. Now, do you have enough left? Probably you do. And one of the things about AI in 2024 that is a luxury that we're going to have is one of the things it's going to do is it's going to remove a lot of the baggage, the parts of jobs out there that people don't like to do. So one of the things I help people with is, look, what is it about your job you don't like to do? And then they'll list some things and I'll say, aha, that's probably where AI is targeted in the next year. Now get yourself ready for 2025 and start learning more about AI and learning more about how you can work with this technology. Bring ideas to the table of your organization. And that's one thing that when management starts to create the conversation in the organization about AI, that's one thing that can happen. Is that more of the staff can be bringing ideas to the table? Because management can think of the whole thing. And so this is a great way to establish an ecosystem of ideas by having management get out in front of the issue. Yeah, I think that makes total sense that it's looking across almost, I hate to say it this way, but the low hanging fruit of what really doesn't excite you about your job. Because I think one example that I've seen actually at a company so far was that in their HR organization for managers bringing on new employees, they have an LLM that they've used and trained on here's how you go and set up direct deposit, here's how you go in and they're able to go in and self service. Those questions that frustrate them that they, used to take the HR generalist some amount of time to say okay now I got to find the tab and send them the thing and the link to the page versus them being able to self service. I think and see those things being kind of the first low hanging fruit. Is that where you're seeing and pushing people towards? That's a good one, that's a good one. There are many more like that. Like one that I particularly like is to train new employees or people that are just coming into a particular responsibility. How do I get access to the data of the organization? Do I go to the warehouse or the lake or the this or the that? I mean they're hearing all sorts of things but if we can crystallize that and then create some LLM interface for that around that. What that also gives people is access to the data that they can do their job with. So that's one that I particularly like but there's a lot of possibilities. No and Kara Swisher was on that stage today talking about journalism, my field and how you can use it to write headlines which is a very tedious thing to do particularly because that's to be a certain number of characters. You got to say certain words for SEO purposes and it is, yeah. Certainly, certainly a lot of writing is low hanging fruit as well. Yeah but not all writing. Steve, come on. Not all. Well, William McKnight thank you so much for coming on theCUBE, we appreciate it. Oh it's been great, thank you. I'm Rebecca Knight for Rob Stretcher. Stay tuned for more of theCUBE's live coverage of Teradata Possible.