A (2:38)

Where do you see a gap in being able to mine that? Because I know when we chatted before, we talked about this a little bit, and I would just love to set the table for our audience of like, yes, we've got access to all this data, but that doesn't mean we can use it.

B (2:50)

Yeah, I mean, you know, look, for years, what everyone's been really good about is really capturing all that structured data. And so, you know, we have details on, on customers and accounts and, you know, what they purchase with us and their purchasing history. We've done a really good job of curating that data and it's available and customers have it locked up and their enterprise data warehouses all over. But what, what's not being captured today is all of that other information that's available, whether it's from voice or text or video chats. You know, that's the new frontier. And, and you know, the problem with that is one, it's the volume of that information. There is just so much available to, to companies today, to mine. But then the next problem is how do you organize it, how do you structure it, how do you search it, how do you make meaning of it? And that's, that's where it gets really complex. It's not just a, a volume issue, although that's a part of it, but it's a structure issue. How do you mine all of that unstructured data to get true benefit?

A (3:49)

How have people historically been doing that in the last couple of years? And obviously now that's shifted as we have AI as a tool to help us with it.

B (3:57)

Yeah, I mean, what I'd say is over the past couple years, they haven't been doing it well. You know, it really is, you know, in many cases what it's trying to do is take some voice or video and, you know, obviously translating that to text, then putting text into some side search engine and, you know, really trying their best to get what they can out of it. But it's a complex problem. And, you know, obviously, you know, we're going to talk about AI today and what's making that a lot easier is just the capabilities that AI is bringing to the marketplace.

A (4:33)

So, yeah, let's just shift right into that with what AI can do. Now. How are you seeing people not only being able to clean up this unstructured data, but actually use it.

B (4:41)

Well, it's interesting. I think what I see right now is customers are really trying to tap into it. And so maybe it's tapping into texts that they got or chats that they have with customers. And so being able to, to bring that in through into a vector store, which then gives you an inherent capability to search this unstructured text, to make meaning of it and to gain insights from it. You know, that's step one. But I think what I'm seeing in the market is, is people are beginning to fall short because it's not just take chats for an example. It's not just enough to search through those chats and understand what it's those chats say. Now you've got to get real meaning, you've got to get context from them. And the question is, how are you going to go about doing that and.

A (5:26)

How are people going about doing that? Is it just with AI or there are other ways?

B (5:30)

No, I mean, the best thing to do is, is, is what you've got to do is you've got to marry up that unstructured data with your structured data. So if you step back and think of an example like a bank, a bank is going to have all that information about all of their customers. It's going to understand, you know, how valuable they are as a customer. You know, what type of products do they have, what's their annual, you know, with the bank, is the bank making frankly money off them? That's their job. So the value comes in and taking all of that structured data that, you know, and then marrying it up with that unstructured data. So think of it in terms of chat. So if you're a bank, you may have, you know, thousands upon tens of thousands of chats a day coming in through your portal or through a mobile app where customers are asking for things, complaining about things, recognizing things. The, the, where AI is really coming in is to take all of that unstructured data sense of it, but then to combine it with that structured data so you get some real meaning, you get some real insights.

A (6:30)

Yep. How are you guys thinking about this at Teradata?

B (6:33)

So, you know, look, what we know at Teradata is we, you know, we are custodians and our customers, we have that structured data and we have many of our customers for 30 years have been curating that data. It's well organized. It is, they understand what's in, in, in their enterprise data warehouse. You know, really what that's turning into a knowledge platform to marry up with that unstructured data. So at Teradata, one of the things we're doing is we're offering customers the ability to get to that structured data and combine it with that unstructured data through tools like a vector store, an MCP server, an agent builder. And what that's allowing customers to do is to, to take that foundation that they have in that, in that enterprise data warehouse and really turn it into a context engine for AI. And we're having great success at that. We've seen really great examples of customers being able to get tremendous value out of something that they've been building for years, but now they're using it for a new use case. And that new use case is, how do I make my AI better? How do I give it context?

A (7:42)

Do you see your customers that are really excelling or just in, I guess, in general in the market? Are you seeing the people that succeed at these types of AI use cases, the ones that we're spending the time, years ago, cleaning up the data, thinking about how I'm going to maintain this over time. It's something that I've heard across the business world and across our podcast over the years about the importance of cleaning up data, but I don't think it's something that people really felt super strongly till the last couple of years when they've been trying to implement AI solutions and not had proper data to use. So I'm just wondering if you're seeing that where these companies that had started years ago, thinking about this problem, have like a significant advantage today.

B (8:21)

No, they absolutely do. And I mean, that's one of the. It's, it's interesting. We, not only do we see it and, you know, our, our customers, just by the nature of the use of our platform, have been doing that. And so we're seeing our customers have a huge leap ahead when it comes to AI because they have spent the time to, to analyze that data, have it structured, have it clean, have it accessible. And it's interesting, not only are we seeing it, our partners are seeing it because suddenly we've got a lot of partners coming to us with the recognition, hey, Teradata, your customers have this data and they already have it structured and they already have it accessible. And now with AI, we can give it meaning. So how can we help? You know, help you help them get, you know, real value out of AI?

A (9:06)

I'm wondering from your perspective with. You know, we've talked a lot on the show about AI use cases and some that have succeeded and some that have flopped and like sort of the over promise of AI. And I'm wondering if you would say that maybe one of the biggest gaps is this data problem between companies that are able to really see success with AI and those that are not, or is there something else that you're seeing as well that's preventing people from really succeeding at AI use cases?

B (9:33)

Yeah, I think there's, I think there's two things. I think number one is, yes, there is. There is a data problem. And so while you may be able to use AI for some really cool things, if you don't have that data available, that's going to give AI context, it's going to give AI the guardrails, it's going to keep it from hallucinating it. You know, you, you're going to end up with AI that's not going to be really valuable to you. And so one is, yeah, it is a data problem. And the customers that have spent time curating their data and building these data sets and data products, they're going to be ahead. There's no doubt about it. We're seeing that. I think the other case that I'm seeing with use cases, though, is it's really the approach to get to them. And what I mean by that is, you know, the best use cases are the ones that are starting to solve a business problem. And that's a much better use case than the one that's come out as part of a hackathon or some kind of sponsored activity where teams are like, hey, let's figure out what a great use case is for AI. And here it is. You really need to start out with what's the business problem I'm trying to solve, and then start from there and back into a use case. And then if you've got the data ready to go, you're going to be in much better shape.

A (10:49)

It is interesting because I do think we talked to.

B (10:54)

Arora, he's our chief Product officer.

A (10:56)

Yeah, so we talked to Sumit Arora, your chief Product officer on it. Visionary is one of our sister shows about this and he'd mentioned the hackathon thing and I've heard about that a lot. And I feel like I've actually talked to people over the years about this and I'm like, oh, I'm realizing maybe this was just part of their AI hackathon whenever we were discussing it. Right. But that is an interesting thing that I think every company can feel or has felt it's just this, like, we must use AI. So I'm going to think of the thousand different ways I could possibly work it into my current work stream, but there's not the pause and zoom out and think, okay, what's the, as you mentioned, the business outcome or the business problem I'm trying to solve for and then back into how can I solve it with AI? I'm seeing just a lot of people trying to replace steps in their process versus how do we just completely rethink this entire process to get to this certain outcome that we're trying to do?

B (11:47)

No, I mean, you're right. And you know, look, hackathons have a purpose. A lot of times they're great for, you know, education and getting people up to speed on new tools and technology. But to, you know, to use a hackathon to say, okay, we're going to come up with our use cases for AI. Not a great approach.

A (12:05)

Well, and I do like the idea though of having those types of things or at least having that dialogue in your company to encourage imagination and creativity around these new tools. So yeah, to your point, I think that is like a valid use for them, but whether or not we're actually going to take those AI demos and turn them into something for our companies to be determined. So I'm curious, from your perspective, working with all these companies and over the years, have you what's the like top successful AI use cases you've seen come out of this?

B (12:35)

Yeah, let me give you a couple of good, good examples and these are all recent. And I think that the best story behind all of these is they're, they're short term in terms of development and getting to deployment. And again, it goes back to the fact that, hey, you've got all that use case available. So one of them I was really kind of talking a little bit about when we were talking earlier and is a bank coming out of the APJ region. It's a large multinational bank. NPS is, is really, really, really important to them. And so, you know, what they, they were seeing is they were seeing, you know, a decline in their NPS scores, which was really concerning. And so what they did realize in the example I gave before is they had all this wealth of knowledge and chats that they never used. And so what they were able to do is we working with them, we're able to take all that chat data, get it into a vector store so that it can be searched, married up with that structured data so they can understand, you know, their most valuable Customers, what are they saying via chat? Out of that came a number of recommendations and over, you know, a period of three months or so they implemented them. And as they've gone through their next NPS cycle, they've seen a significant increase in NPS Store and that's really hard to do in a short term.

A (13:51)

What they were really only three months. Yeah, it's brilliant.

B (13:55)

And so what they were able to do is with the help of AI, but taking all that chat data is really understanding frankly what was irritating their most valuable customers and put in things to rectify it. So that's a great use case. Another use case is a large European airline that we've got. They also were looking to understand customer sentiment and again around the customer experience point of view and coming out of it, what they determined their biggest problem was their baggage and how their baggage handling was so negatively impacting their overall customer sentiment. And so, you know, I think, you know, they were able then to quickly, you know, start haven't fully rectified it yet, but they got to a very quick understanding of what it was that was, you know, really impacting their brand and so really focused on that, you know, so there's, there's lots of good experiences that I've seen with customers even internally At Teradata, one of the things that we've done is our account planning is now fully agentic and for all of our customers, all of our sales reps have an updated account plan every week. And that account plan takes into account everything we know about a business. It's all of our internal data. And so our data out of Salesforce, our Data out of ServiceNow, our data out of our telemetry system which monitors our system. But it also takes into account all of that public information that customer have. And so, you know, as you talked about earlier, sometimes people look at just let's automate, you know, some step in a process. What we've done is we've rolled out a full end to end new account planning process for our go to market team and every a has delivered an account plan on a weekly basis that really is up to date on everything we know and is publicly available about that customer.

A (15:52)

You know, when we were talking a few weeks ago, you mentioned that you are maybe one of the biggest users of Teradata and Kritik too because you are a user and you're going to your team and you're like, hey, we need this. And so it is so cool hearing you talk about how you guys are using this internally because you guys really are your first customer. You're using all this data, you're matching it up. So yeah, I mean, how do you kind of go about doing that kind of like feedback cycle with your team internally?

B (16:18)

Yeah, so I mean we, we consider ourselves what we always call ourselves as customer zero. And so you know, the efforts that, that my team, you know, our job is, you know, I'm a customer of Teradata from that point of view. And so you know what, we're giving feedback constantly to Sumit, who you had talked to earlier and his team is, hey, here's what we really need. And frankly if we need it, probably our customers need it too in terms of features and capabilities. We're also really making sure that when it comes to AI, one of the important things is scale. And so we do things on massive volume. So we have every teradata system out in the world. We're getting telemetry on that every second. And so we're able to deal with massive volumes of data as well. So not only do we look at it from kind of a feature function point of view, but we also look at, from a practical point of view. And you know, we provide that feedback both to our product management team and our engineering teams really almost on a nearly daily basis. And so, you know, I think Samit would say I'm, I'm his best customer, but he probably also say I'm his noisiest customer. And, and that is fully with intent to be. That.

A (17:28)

That's great. That's great. I was going to say they probably like love and hate that daily note from you.

B (17:34)

Absolutely they do. Absolutely.

A (17:36)

That's great. Well, we've talked a few about a few examples from these large companies like a large bank, a large airline, if I'm a smaller organization or medium sized business. Are these also use cases? Are you seeing companies also execute on these use cases as well even if they're on this smaller, medium size because budgets are tighter, the amount of investment you can make, maybe the amount of data you even have because you're a younger company. I'm just curious how those companies are faring in this.

B (18:03)

Yeah, absolutely. So we've got a number of what we call new logos and those new logos may be a division of a larger company or maybe a small organization themselves. What they're finding is the value in Teradata because Teradata can scale up. We can scale up to and have scaled up to the world's largest companies, but we also have a number of smaller organizations or departments, even that are using Teradata for their own purposes. And so scaling is something that we've done for years, years. And so we can go up to the biggest, we can work with the smallest as well. The other thing that, that's important to a lot of our customers is just where they're going to run this. So we still have a, a number of customers and even new customers that aren't, aren't ready to move some of their most valuable and most protected data to the cloud. And so we offer both an an on prem version of Teradata and a cloud version of teradata across the CSPs. And so it's a flexible tool that allows you to really bring AI to where your data is so you don't have to think about, oh, I've got to move my data to the cloud. No you don't. You can bring the AI tool to wherever your data is and do what makes most sense for you as an.

A (19:18)

Organization from that security lens. Are you guys running into any concerns about that? Besides just like, I don't want my data in the cloud. But there are we. I've heard it so many times now about hallucinations in the data, things being misrepresented, like, is this something you guys are regularly seen and you're helping clean up or how are you kind of censoring that?

B (19:38)

Yeah, I mean, look, there's multiple aspects of data. So yeah, there is an issue with some customers where, you know, for data sovereignty reasons, maybe it's regulatory reasons or maybe it's just, you know, how sensitive their data is, they're choosing it not to move in, into the cloud. I think the way that we're really focused though, on solving the hallucination issue is really you've got to put the guardrails on AI and so being able to tell AI, hey, this is where you're going to get the facts. If, if anything in this realm of questions come to you, here's going to get the facts. And so for us internally, one of those, for example, is I can't have AI guessing what our total ARR or annual recurring revenue is for a company. Right? Yeah, it, we have it clearly outlined. And if you were go, go into, in our, in our enterprise data warehouse, you know where to find it. It's a matter of just telling AI the same thing. Hey, this is the definition of ARR. This is where you, you get this information. We, we guide AI, we, we govern AI. We put guardrails around it. And so you, you've got work to do. You've got to protect yourself from the hallucinations. And, and the way you do that is by just making sure that again, it goes back to having that, that, that knowledge base, you know, and what we call teradata is the knowledge platform to really give AI that context.

A (21:03)

Yeah, okay, that's brilliant. That is smart. And thinking about how to map that. Because there is so many times, I mean, I'll use the consumer example of like, I'm on ChatGPT and then it just makes something up randomly. Right. And it's just pulling. But if I had said, hey, here's my document, only pull information from this document. It's far more effective at maintaining a consistent response.

B (21:22)

Absolutely.

A (21:22)

So, yeah, it is a lot of that and is part of that process for you guys kind of troubleshooting, like, what information do we need to provide? And having some sort of system for flagging. Hey, we're noticing that there is some sort of hallucination or something starting to happen here and we need to fix that.

B (21:37)

Yeah, absolutely. I mean, one of the things you've got to do with AI is you've got to always, you know, you've got to always look at the outcome and, and, you know, make sure that every day it's accurate and every day it's getting better. You need to expect that there are going to be hallucinations. But, you know, the, the more you, the more you tell AI where to go get its answers. You know, let, let AI do the thinking of, of the different, you know, different problems to solve. Let it do the thinking of different scenarios. But when it comes to, okay, now let's go solve this scenario. Making sure it understands, hey, here's where you need to pull this information from. That's where the key comes from.

A (22:16)

Yeah. And this is the, this is the part of AI that's just not the flashy, like, sexy thing. Right. It's like you've got to go, yeah, it's like, oh, we still need to clean up this data. We still actually need to map it properly. We need to think about all this, like where it's coming from, what's accurate. Oh, actually, all this data from three years ago, maybe we don't need to use that. We only want to use the stuff that's from like two years ago because it's more relevant to now. Da, da, da. All that happening in the back end is not the, like, I made a flashy new AI demo that people want. Right.

B (22:47)

Well, you know, I met with a partner of ours and one of the things they told me was they, they, they were, they were using AI for their, to support their products. And, and they, they spent a year and they put in all the documentation around their entire product base. And I said, well, how'd that go? And they said, well, we spent the nine months taking out 70% of it. And again, because you know, they didn't think how important that unstructured data was in terms of documentation. And suddenly you're going, you're giving AI too much to choose from and you know, making sure that it can only look at the relevant material is absolutely key.

A (23:24)

Yeah, that's great, that's great. I'm thinking too about companies that, let's say they are younger or they just haven't been managing their data well, like it's been years and they haven't been listening to what everyone's been saying for, for 10 years now to clean up your data, maintain like a good data integrity. All this stuff. What do you, like, what advice do you have to a company like that that's realizing, oh shoot, we messed up, we don't have this. Is it just like, hey, you need to invest now and get this rolling now or, or if I'm, again, if I'm a young company and I just don't have data to pull from, like how am I going to start to accumulate it so I can really compete?

B (23:59)

Yeah. What I'm going to say is even if you're a young company, you probably do have data out there, right. It just may not be data that's in your four walls and you're thinking about, but you know, if you're, if you're a consumer company, there's tons of data out there because people are talking about you somewhere. And so, you know, being able to grab that data. I think that the beauty that the customers have now that they're starting this though is they can actually use AI to help them with that problem. Help, you know, use AI to actually sort through the data they do have, help them get it organized, help them understand what's relevant. And so there's a new tool set that you know, we didn't have 10 years ago when we were all struggling to get our data organized and we were doing it kind of brute force ways through queries and matching and looking at this. And now, now you can use AI to help you. And so, you know, really I don't think there's an excuse not to get your data in order.

A (24:51)

Absolutely. And what's the response been on the actual customer side? So We've talked about how businesses have been implementing this. Are you seeing any like, oh, you know, I mean, I shared the example of the bank, the NPS increasing, but are you seeing more examples of that where the customers are actually like, ooh, we're feeling this, we don't understand it, we don't know what's going on the back end, but we are feeling like, like some, something's improving here because of this, you know, AI tool or whatever.

B (25:17)

Yep. You know, I, I, you, you, you do hear the antidote stories from customers where they're having an experience. You know, it's really interesting to me is someone was talking the other day about how, you know, they were 10 minutes into a call and it was trying to solve an issue. In this case, it was an, an insurance issue. And they were 10 minutes into a call before they finally realized they were talking to a chat bottle lot. You know, they were talking to an AI agent, but the AI agent was doing such a good job in terms of, you know, understanding what they were calling about, you know, having relevant information. And so we are seeing, you know, we are seeing this improve for the, you know, the ultimate end users, those ultimate customers seeing improve month over month. And, you know, the, the experiences, they're not, not perfect and they won't be perfect for a long time, but they're improving pretty quickly. And you're getting, I see great examples both within our, within our customers that they're providing to their end customers. And I see some great examples personally as well where that's happening.

A (26:24)

So we, we spoke to a woman from Qualtrics. She had thought leadership there, and there was like a new survey that, that they did. And part of the response was that like, people, customers are still hesitant, like they're still not seeing because they, one person might have this great experience with a Jabba and not to be able to tell for 10 minutes that it's a chat or an AI person that they're speaking with. But, like 90% of the other people are having awful experiences with a chatbot or whatever. So it is definitely skewing people's response to this negatively, unfortunately, since there are really great examples of it being successful. But I'm optimistic that over the next year or two, it's going to start to even out and we'll start to see like, more positive response. But are you guys kind of seeing that as well?

B (27:07)

Yeah, I mean, there's always, you know, there's a hesitancy with AI right now because the last thing you want to do is you, you don't want to create a worse customer experience. You don't want to create a bad customer experience where AI is giving a hallucination or giving a wrong answer or, you know, you're, you're on a call with an agent that's, you know, not being helpful whatsoever. So they are being hesitant, but what I'm, I'm seeing with our customers is they're picking areas where they know they can make a significant difference and they're making sure that they do that really well. So instead of like a broad based approach, hey, here's, you know, here's five things that customers call about on a regular basis. Let's make these almost as perfect as we can make them and that'll improve the experience. Let's not try to be that generalist when AI is not ready to be a generalist.

A (27:56)

Yeah, that's, that's really good advice, but it does take a lot of discernment to come up with what is the thing that we're going to focus on that we can actually solve with this? Because I do feel like so much of this has been like, oh, we're in a slash, you know, headcount. We're really, you know, save so many dollars in all these different areas if we just roll it out broadly. And then the response and the feedback from customers has not necessarily been the most positive in those instances.

B (28:19)

Absolutely. I mean, you really, I mean, you know, at this point with the, the capabilities, A.I. i mean, you know, we're looking at it certainly internally and I know a lot of customers that I've talked to, we're looking at it. How does it make our employees better? You know, how can it make better agent? How can it make them a better rep, a better salesperson? We're not looking at it as, okay, this is, this is going to replace our workforce. We're not there yet and frankly, I don't think most of our customers want that. But they do want, you know, they do are looking for that agent or a rep to be as good as they can be. And AI can definitely help with that.

A (28:57)

Yeah, I mean, augment the people that you have, support the people that you have. That's absolutely the right way to think about it. And you feel it. I mean, I was speaking to some, you know, we're shipping things right now this time of year. Right. So let's say no name shipper. But yeah, I was talking to them and it was clear that they, that they, you know, something had gone wrong. But they addressed it immediately. And the woman on the phone was using, like, chat history. She clearly had some sort of integration going on in the back end. And it made it so seamless for me to go from one rep to another. It's quickly handled, super smooth. Absolutely loved it. And I was like, this is what it's supposed to do. This is what AI is supposed to do to support this person that I'm speaking with. Absolutely flipped the other side. I had the same similar experience, you know, with concert tickets, where something went wrong and awful, like, could not. I could not connect. They could not connect the dots. They kept blaming, oh, we have this new AI system that won't let me do, blah, blah, blah. So, like, I was like, this is totally different. This is what it's supposed to do. On this one side is like, augment the person that I'm speaking with. And on this other side, it's becoming an excuse for the sale or for this customer service person not to support me. So it's just definitely getting such a range of experiences. And unfortunately with customers, the way that our brains work over time as humans is if I even have this one negative experience with AI, I'm now wrapping everything up into that, right?

B (30:17)

Absolutely.

A (30:17)

So it makes me think even all the positive ones are negligible because I don't like it. So, yeah, it is just an interesting psychological problem of how do we continue to, like, accept that there will be companies that do this poorly and that AI will make mistakes or there will be hallucinations, but ultimately I'm getting better service, you know, overall, right? With it.

B (30:37)

Yeah, it's, it's, you know, it's, it's, it's certainly a dilemma for companies. And I think, you know, the problem is you, you can really hurt your brand pretty quick, you know, with this and this, you know, and that's what. No one has that goal. And I think, you know, making sure that folks are taking the approach to. That they're taking the best approach for customer experience. They're not doing it as a cost cutting. Getting rid of people at this point in time, you know, things like that may come down the road, right? It may. You, you may. You certainly will be able to save some dollars. But around the customer experience point of view, it's how can you make up those frontline people that much better, that much more effective and actually boost your overall NPS with your, with your end customers? That's what it should be about.

A (31:22)

So we talked a lot about AI and how it impacts the customer today. Let me rephrase that, sorry, brain's not functioning. We've talked a lot about AI and how it's impacting the customer and how businesses can use it to improve the experience today. But I also think there's new interesting use cases on how we can actually predict what's coming down the line for customers. So how are you seeing companies use their data paired with AI to really have predict what customers are going to need and then offer experiences to them before something even happens?

B (31:59)

Yeah, I think we're seeing it a lot, right. And we're seeing it and this is something we're really seeing across industries. We're certainly seeing it in the travel sector as an example with all the major airlines. We're seeing it in the retail side. I think what AI is able to do is of course, everyone's had predictive models in the past. They've had predictive models for load factors and what's going to sell and what's not. But the problem with a lot of those models is they can only have so many parameters, right? And where do you cut it off and say, okay, this is really all we can handle? What we're seeing now with AI though, is that, you know, an unlimited number of parameters to really be thinking about when it, when it comes to those prediction as they're looking forward. And so if you're looking at, you know, in the world of air travel example, you know, you have all that historical, in terms of load and wind and wear and vacations, but now also taking in the seasonality of the weather patterns and what weather patterns are doing and what are they predicted to do and how is that going to impact if it's a La Nina or an El Nino, in terms of how is those weathering patterns going to predict people's desire to, to travel to certain places? And so I think what AI is able to do is, is start really being able to pull, you know, from multiple different kind of multiple different areas of the spectrum, different things that really can affect a customer's ability to sell and, or deliver. And it's being to have expense exponentially more models to be able to look at. And what that's then allowing customers to do is just act that much quicker. And so they can have the eventuality of weather patterns, they can have the eventuality of, of a global conflict. And what does that mean? And they don't have to react when it happens. They can say, okay, we've already, we've already done a scenario that says there's a regional conflict here. What's that going to mean? Or there's a weather pattern here. What's that going to mean? Or. Or there's a economic issue, you know, tariffs as an example. Whatever the case may be, what they're able to do is just have so many models out there today that they couldn't have before and, you know, have those. Have those at their fingertips. And so they're able to react, you know, be, be prepared to react that much quicker for the eventuality of whatever. Whatever it may be that's coming about their way.

A (34:35)

Yeah, that is so smart. We have another podcast we host with Lawrence Livermore National Lab, and we've talked to them before about their modeling, like weather pattern pattern modeling over the last 30 years and everything that they've done. And I've been fascinated by the way that they've been able to put together all these analytics and systems and maps and everything to predict what's going to occur. And it's so cool to hear that we can now do this for businesses much faster and easier than it was for even a national Lab to do 10 years ago. And it's just very, very cool to hear all this data is like at your fingertips that you can now access and have different tools available to you no matter what's going to happen.

B (35:15)

Yeah, I mean, it's, look, it's making the scenario planning for businesses, it's so much easier than it. And it'll continue to get easier because all of these different modelings are going to be, you know, they're available today, more of them are going to come about. And so it's just really for a business to understand what are things that can impact us. And let's go grab a model that's going to take that into account.

A (35:37)

Yeah, yeah. And not be limited by, again, the scope of the model. Right. Like, I can actually just go get that information. I don't need to limit my imagination because it's too hard to get that data or too hard to compile that information. It definitely opens up the door for more imaginative thought of how does my business really interact in the world? I'm curious to hear from you. What, what new skills do you think people need to be having as we get into this next decade of innovation?

B (36:07)

You know, it's funny, last week I just did a call with a bunch of our interns at Teradata and one of the questions was, hey, what are the skill sets that we, you know, what's the skill sets that we need as we, as we come into this world of AI And I Kind of chuckled a little bit because I said, look, you know, I've been around a long time and, you know, when I started my career, the Internet wasn't even a thing. And so, you know, the skill sets really, you know, that as I told them, it's less about the tool and the technology because you're going to learn that. And guess what? It's going to change. You know, what you're going to learn today? You know, in five years, that tool and technology is going to be different. But I think there's some fundamentals that are really important to people. One is, you know, the data science skill is going to continue to be important just to understand, being able to understand the data, understand data lineage, how important that is, being able to get to true, true understanding of both your structured and unstructured data. So, you know, that's going to be a skill that is out. Yes, there's tools that data scientists use every day, but those will change as well. But understanding, you know, really that the. Almost the theology, if you will, of data science. And what does that mean? You know, the most important thing that I think, though, is what I've told this group of interns. Interns, it's, it's a curiosity and being able to win, you know, in this world of AI right now, what I'm encouraging them is go try it. Go try different. Go build your own agents. Go try different things, Learn. You know, that thirst for learning is probably the most important skill that when I look at our interns that I want them to have, we're going to teach them, you know, we're going to teach them the tools of the day. You know, just having that really a solid background in technology is going to be important. Having that curiosity, understanding the, you know, data science is going to be key. You know, if you have that set of skills, you're going to, you're going to be hugely valuable not only today, but for many years to come and.

A (38:13)

Across the board in any business. Yeah, because, I mean, those, those skills are highly relevant no matter where you go.

B (38:19)

Absolutely.

A (38:20)

And I think about that curiosity piece because it's not something that I don't think you can teach that. Right. Like, I can't, I can't teach you to be curious. But it is something I feel like I'm noticing younger people do tend to have, at least the crowd that I, you know, like, I think about my younger brother and his friends and I'm like, I feel like people are leaning more into being like, skeptical or asking questions of things or being curious about the origin of how something works. So I'm hoping it's becoming more of a standard in how people think. Are you seeing that with sort of the younger generation?

B (38:54)

I am, I'm seeing it, you know, definitely with my kids. Right. I mean, they, you know, they want to understand. You know, I guess what I say is they don't accept everything at face value. They want to understand, hey, how did we get here? How do we get to this answer? What does this mean? And then what I'd also say is they're not afraid. I mean, they're not a bit, you know, afraid to go try things, you know, and, and, you know, what I see with them is, you know, in AI, for example, they're using it, you know, and they're all professionals, and two of them are in the medical field, one's, one's a consultant. I mean, they're all using it every day and they're trying things that, that, you know, in their profession may or may not have been tried before, but they're, they're using what's available to them. They're not, they're not afraid of it. You know, they're not necessarily 100 accepting of it out of the gate, but they're going, you know, they really don't understand, you know, the, the, the, the science behind it, the background behind that, the wise behind it. And once they get comfortable with that, then just look out because they're, they're, they're adopting it and moving quickly. Quickly.

A (39:57)

Oh, so quickly. I know. Are you seeing this with even like more established people at the company, people that you've been working with? I mean, you've been working in CX for 35 plus years. You said, like, are you seeing people in your peer group also being ready to adopt it, or are you seeing kind of mixed responses?

B (40:14)

Definitely mixed responses. You know, I was in a, with a group of peers, not folks from Teradata, but some industry folks. And we just happened to be at a roundtable table and, you know, I asked, I, I asked the question, I said, how are, how are you, how are you guys using AI in your work life or your personal life, like, every day? And, you know, one of the people said, I'm trying to avoid it. Which it just startled me to, you know, that someone would, would say that. He's like, I'm not ready to adopt it yet. And so, you know, what, you know, I just, you know, the, you know, as, as I tell my team internally, I, you know, it's not the AI is going to replace you. But. But people that use AI and understand AI and are embracing it, they will replace you for sure.

A (41:03)

Yeah.

B (41:03)

And so I would say, you know, there's some mixed results. There's a lot of folks that are 100% diving in, trying to figure out how does it make them more productive, how does it make them a better employee, how does it make them better on the, you know, the things they have to do at home. But, you know, there's. There's. I think there's a percentage of folks out there, you know, that are still a little reticent and like, okay, this might be a fad. You know, it's the people back in the day that said cloud was going to be a fad as well.

A (41:32)

So everything's a fad. But then it's not.

B (41:36)

Oh, wait, it's not. And I need to learn it now.

A (41:39)

Yeah. Yeah, it is interesting. I mean, I talked to a few people this year specifically that I would ask them, how are you using AI, or when's the last time you used ChatGPT? I haven't. And I'm like, what? You haven't been using? Like, what are we doing? Why are we talking? Yeah, it's very interesting, the diverse range of opinions, especially from creatives, because I work in, obviously in podcasting and writing and scripting, and there's such a mix of people that you overuse it and you're like, okay, this is not helpful. And then the people that are augmenting themselves, I use it as my editor, I use it as my brainstormer, and they accept that. And then the people that are just radically against any type of AI use at all, and I feel that really strongly in the creative space. But I got to imagine when you're talking about data and cx, hopefully people are going to be more open over time to this, and your peer groups will eventually see it from that perspective.

B (42:39)

I could only hope so. And I think they would. They should be. I think it's. It's one of those things that, you know, one of the things I encourage people is use it. Use. You know, if you're reticent to use it at work, you'll get there, but use it in your personal life. And, you know, the example I had as I met with my financial planner last week, and I put my entire portfolio through AI, and I gave him a report card, and he wasn't real happy because. And I told him, I said, look, AI is not already right, but AI is giving you a grade. And it's not that great. Yeah, but it, you know, there's so much that it can help you with personally. And I think once people really start understanding the value, whether it's on the creative side or, you know, just on the work side, I mean, they've got to adopt it. I just can't see any other, any other way.

A (43:28)

What core skills do you think people will continue to need to have? Like, ones that you think like the entire time you've been in your career, even like this skill or this handful of skills are really relevant to people. Is there anything that you think will not change, change that people should still be really investing in themselves?

B (43:44)

Yeah, I mean, I think the, like I said a couple of them, that continuous learning, that curiosity, I think that's important. I think the other thing is just the importance. And, and this is where on the CX side it, it does concern me a little bit. You can't underestimate those, the importance of those human relationships. And so, you know, it, it, it, it's not hard to see in some cases when you get an, you know, an email from, you know, from a vendor, someone that you work with that, oh, they've, they've had AI write their email for me now. And so, you know, you've still got to make sure that you're, you're nurturing that human connection. You know, use AI to its best of its ability, but don't replace those relationships that you have in particular in the CX side. Because I think it, that's, that's going to become a problem. People are, people are going to continually gravitate to people that they have working relationships with and AI can't replace that.

A (44:46)

I mean, I think that's great business advice across the board, not just in cx. Right. Business has always been about relationships and nurturing those authentically as yourself and not as a SMS AI bot that just automatically sends things out to people you've talked to recently. It's just like the company Christmas card that gets sent out that, you know, that your CEO didn't actually look at or approve. That doesn't mean that much.

B (45:14)

Reminds me a Christmas vacation and the holiday gifts.

A (45:17)

I just watched it, which is why I said that.

B (45:19)

Exactly, exactly.

A (45:22)

Oh man, I'm in the Christmas mood apparently. I had one other, oh, one other question for you. This is something that's been kind of on, in debate. You know, on LinkedIn they like to have strong debates about this or I've heard it a couple different sides of this on a show before around expertise Right. So we talked about. You said data science, for example. Like, that's something that's gonna still be important to understand, but I may not need to know how to actually use the tools to do it. Right, right. And I went to school, weirdly enough, for mechanical engineering. Love engineering, love building. And I always felt like, yeah, the tools are gonna change. Right. The tools I was learning in engineering school that they use now are not the same, but if I can understand the ideas behind it and how the physics, even I need to know that stuff. So I could still build with whatever I'm gonna be building with, no matter how. Now we're at the point where I could speak into an AI tool and it would just make what I want. Right. And so then you could argue, okay, does expertise even matter? Because I don't need to know the math or the physics or anything behind any of this, because the AI bot can just do it. My argument is that you can't prompt that tool properly unless you have the expertise and know what to ask of it. So I'm curious where you stand on this. Like, how important is expertise or even, like, years of experience in a domain as we get into this future where, like, AI can pretty much do everything. So why would I need to understand that?

B (46:46)

Well, I mean, I think my analogy is, is just a pilot on an airplane. Airplanes today can land themselves. They're doing testing right now, so they can do auto takeoffs. Do I want to be on a plane without a pilot that actually knows aeronautics and understand how. How to actually operate that plane? That's not a plane I want to be on. So the expertise, even if in the world of airlines, you know, planes can fly themselves, you want someone on there that understands it. You know, I was talking to someone the other day, and they were. We were talking about, you know, coding and that AI, you know, AI is going to replace. You know, are we going to need engineers that write code? And my point to them was, you know, AI is going to write the code. That's great, right? If an engineer can't understand the logic of that code and how it's going to work, how are we ever going to really understand if we're getting to the right outcomes and the right results? So, first of all, being able to tell AI, you know, just, you know, here's the things that we're looking for and here's the design and here's the intended outcome, and here's how I want it to work. Being able to. To convey that to AI so that it can Generate the code is one thing, but someone's got to look that, you know, that output over and say, hey, is this getting to our intended outcomes? So I, I don't, I don't at all think that we're in a position where we can let the expertise go on how to fly a plane or how to write code or, you know, how to build something mechanically. If we don't, if we don't have that, we're not going to be able to direct, you know, the tools to get to the right outcome. And we're certainly not going to be able to validate that we're getting to that right outcome. So. So, yeah, not a world that I'm looking forward to.

A (48:36)

No. I also watched Idiocracy recently and I was thinking about this very thing. It's like, how could you possibly be an engineer if you've not learned engineering and done it? So, yeah, that's fun. Well, Mike, I'm just going to pause really quickly. We're coming up on time. I want to end on a question about trends, but I'm going to tee it up kind of weirdly because we are in the end of 2020 or 2025. This won't be releasing till 2026, so I'm gonna say this year, I mean, 2026. Okay, just wanted to flag that. Okay. So it's the beginning of the year. We've got, you know, 12 months ahead of us. If I were to talk to you at the end of the year, Mike, what's one trend that you think you would be like so excited by or just one thing that you're like, this year, you know, like, like basically your prediction for like the end of the year, something that you think will have happened, happened.

B (49:29)

So I mean, great question. I think by the end year what we will see is we will see companies and folks and companies really developing large scale number of agents to do tasks for the company. Right. And so I think, you know, the tooling's there, in many cases, the data is already there, the AI is there. And so now it's really figuring out what are the agents that are going to have the biggest return. And whether that's on customer experience or growing revenue or answering support questions, whatever it is. I think what we'll see is a year of agent building and getting outcomes and you know, moving more and more to autonomous agents that are, that you have the confidence in and the trust in to actually handle particular interactions. So we're going to get there, but it's, it's not going to be overnight. It's going to be a year of, of, of building and trying and testing and but we will get to some really good outcomes. We're, you know, we're already saw we as we ended last year and I think that momentum is just going to really continue to build.

A (50:36)

Absolutely. With you know, autonomous agents. I want to, would like to hear how you would define them because I feel like for the past year we've talked about them but it's really under the hood just been automations and not an agent. It's just connected automations. So when we say, you know, AI agents that we trust, what does that actually mean?

B (50:59)

Let me give you, let me give you an example. So we, and it's a use case that I talked about, we've done internally here is. So what we've built is we built this account planning agent for our go to market team and what that agent is doing, you know, one of the things that our concept is agents never sleep. Agents are always going to be running. And so our agents are running and what they're doing for all of our customers and our customer base, I mean they're looking at, okay, who filed anything publicly today and what can we learn from it? You know, what's the data that we've been capturing in the last 24 hours from our telemetry systems as we're monitoring our customer systems. What's new happened in our, in our ServiceNow system or our Salesforce system. And that agent is just autonomous and it's making a determination, do I need to spin up a new account plan for my rep? Because there's been a significant enough change in any one of these, you know, different areas that hey, the account, the account plan should change because we've learned something new. And so I think that's a great example of how, you know, this thing is just in the background running autonomously. It's, it's making decisions on, hey, we are going to build a new account plan for company XYZ because they've stated some changes in their, in their last earnings report and we're seeing some different things in terms of their telemetry data. And so you know, that's what we're looking for more and more and more. So it's, you know, the account rep comes in, hey, I got an email, I, I got a new account plan because something's out there. You know, our agent, our account plan agent has said it's time for a new account plan because something's changed in the course and direction of this company.

A (52:35)

Yeah, that's great. That's great. That's. I mean, it's really cool to hear how that's already happening. And so, like, what will that be in 12 months and what will be in 24 months? We can only guess. But I. Yeah, really excited for this world. Yeah, I'm really excited for this world of AI agents being able to operate in the background, truly. We got to speak to Vajoy from Cisco at Outshift by Cisco, and he was talking about the Internet of agents and agents being able to communicate with each other and hire each other. And the future that we have for agentic AI is very promising and interesting. It was definitely overhyped this year, but I think next year will be.

B (53:14)

We'll get there. I mean, one of the ones we're working on a POC right now is how do we have an agent that's a buyer and an agent that's a seller? Negotiated contract contracts. And so how do. How does. How does everyone. How do each agent get the best terms, whether you're a buyer or seller, and how do they get an agreement on. Okay, here's. Here's the deal that we should move forward with.

A (53:32)

So that's such an interesting idea, too, to think about. Like, negotiations of the future are not even going to be done by, you know, you and me. It's just going to be our two agents going back and forth trying to figure out the best terms and then.

B (53:44)

We'Ll be emotional about it.

A (53:45)

No, no, you didn't respond to my email fast enough. So I actually don't like you now, so I don't want to work with you. Well, Mike, this has been fantastic. If anyone's interested in learning more about Teradata, what you guys are up to, where should they go?

B (53:59)

Hey, Teradata.com is the place to go. You can learn all about Teradata. You can actually use what's called our Clearscape experience. There's lots of ways to actually get into the software, see the capabilities, learn about how you can build your own agents. So come on out.

A (54:16)

Awesome. All right, thanks. Watching for Mike.

B (54:18)

All right, thank you. It's been a great day.