Teradata Corporation (TDC) Earnings Call Transcript & Summary

Thanks for joining us for day 2 of the Citi Global TMT Conference here. Today, my name is YC Wong. I'm part of the software analyst team at Citi. We are excited to have Teradata CEO, Steve McMean. Steve, welcome back. I know you've been a couple of years since you joined us.

It's great to be here, YC. Looking forward to the discussion and telling you everything that's been going on at Teradata.

No, that's awesome. I mean this year, definitely a lot has happened since the beginning of the year. Maybe you can just start off with the -- your background, what have you been doing and the company.

Yes. I joined Teradata in June of 2020, really with a mission to look at how do we modernize the company and make it relevant in the cloud space. And so really taking Teradata's fantastic on-premise technology and making it available to customers in the cloud as they modernize their data estates and started using cloud technologies to really support their data platform. And so when I joined Teradata, I said, look, at our core, we're a technology company. We've been doing a lot of services up until that point. But we had -- we've got so much intellectual property in our Teradata software and the platform that we have that I think that exploiting that for the benefit of our customers is really the core for us. And over that period of time, we actually transformed the company. Now almost half of our recurring revenues are in the cloud. So we made tremendous progress there and developed a really open and connected data platform for our customers. But I think what's been really interesting is if you think of that as Teradata 2.0, we've actually moved into a new phase of Teradata 3.0 when we're looking at AI now. And so that's driven a number of changes for us recently.

Yes. I mean that's an exciting announcement back in May, I believe, when we guys have a big festival out at MISE. So what is the autonomous knowledge platform? Like is it just a repackage of what Teradata has been doing? There's been a lot of changes. Can you kind of help us break down what it is?

Yes. I think really addressing this new world, what we did, we brought in new talent at all layers in the organization. So a refreshed management team to look at the world of AI. We have a new Chief Product Officer that's been with us for 14 months now. And really, the whole team came together not just to repackage what we're doing, but to really think about what is the platform of the future where AI agents and humans can work together and get the most out of their data to really cause business impact. And so that autonomous knowledge platform is essentially a complete architecture and framework where we've launched new products and capabilities at every single layer of the stack. And by the word autonomous, we're really identifying our technology as well. So not just using agents to code, but actually identifying the entire product stack. And I think nothing symbolizes that more than the very top of the stack, which we think of our workspaces where humans and agents work together. We have a technology there called Terra, which is a harness that we've developed for AI agents. It means that our customers can use whatever language model that they want. They can use ChatGPT or Claude. But what's becoming more interesting is using smaller language models and working with customers in Europe, looking at some of their requirements around regulatory compliance, use models like Mistral. And what this harness enables the Teradata platform to do is use the right agent at the right time for the right outcome. And you can really optimize the cost of running your overall environment. And that's just one example of some of the innovation right at the top of our stack. On the very bottom of the stack, if you look at our infrastructure, -- we actually announced a new Teradata factory offering new on-prem technology, GPU accelerated using the NVIDIA product stack built into the data platform so that on-prem, you can run your AI solution and your data platform right next to each other without moving data around and still have all of that great data and financial governance that Teradata provides. So new innovations at every single layer of the stack. The context work that we've been doing is super exciting in terms of letting AI agents really understand what enterprise data is all about. You can do something like define what a customer is, so you can ask interactive questions around the customer. So it's not just a repackaging. It's a completely new innovative technology set.

Yes. No, it sounds like the whole team has definitely been hard at work over the past few quarters here. But zooming out a little bit more, we would love to talk about like some of how is the enterprise AI agent adoption has been going, like why Teradata like decide to go on this path into the autonomous platform?

Yes. I think we recently did a study across 1,000 or so data leaders in large enterprises across the world. And I think what we're finding is that, that kind of headlong rush to the cloud is kind of slowing down. So we've been thinking much more about how do we grow our overall business, both on-premise and cloud. How do we respond to our customers' requirements? Because it's not just about data modernization anymore, it's about getting value from AI. And I think what we've proven using our forward deployed engineering capability that we've put in over the last 12 months is we can take those initial ideas and turn them into production reality at scale, utilizing our services capability, utilizing the new technologies that we have inside our data platform. I've got a fantastic example of doing some work for the military in a European country. We've actually been working with them. We started as a pilot to look at camouflage design and using AI models around camouflage design. And we've worked with that military organization to take that from an idea to production scale for their entire military operation, which is a super interesting use case.

Yes. I know there's certainly a lot of opportunity on the public sector as well. But you mentioned FDE. -- like can you give us a sense of everyone has been talking about FTE at this point, like Palantir kind of started it a few years ago. Salesforce ServiceNow to talk about. Can you give us some flavor of how is your FTE working? And what is the opportunity that you see with FTE?

Yes. I think the great thing about our forward deployed engineers, we had a super consulting and sales and SE team. And it really just formalized the go-to-market structure around working with customers on what the real business problems are. And I think that manifests nowhere as much as in the context layer. So working with customers to actually look at their data models, their business knowledge that has been imbued into their systems over time and then working on a specific business problem. It may be a customer care problem. It could be a supply chain problem and developing the data products to actually help solve that problem in a very pilot phase. So that's what our forward deployed engineers do. And they're organized by industry, they're organized by solution set, and they bring that knowledge and capability to our customers very quickly. And then we can use our AI services team to actually scale that out and use our technology to deliver those ideas into production in real time. And that is really the challenge that a lot of the data leaders that I talk to every day are -- their core challenge is how do I move from pilot to production at enterprise scale. And that's the problem that Teradata can help them solve.

Okay. No, there's definitely a lot of different levels how FTE is able to help an organization get more ROI. Is there any internal metrics that you track, this is actually like worth my time to invest in because what we heard like FTE costs a lot of money is an expensive services part of it. Like what are you seeing that makes you want to continue to invest in FTE?

Yes. I think -- so our FTEs usually are developed in some form of proof of concept with the customer. So we track how those proof of concepts are moving into real opportunities, how those opportunities are then translating into incremental ARR. They start off by potentially generating a services engagement to do that implementation and then generating technology or product-based ARR as a result of that. So we have a whole pipeline measurement and management system that gives us those leading indicators of moving from that proof of concept right the way through into technology implementation.

Okay. Is there any other like hard numbers that we can, hey, this is improving my sales cycle, improving my delivery time?

Yes. I think if you looked at our earnings comments over the past 12 to 18 months, we've seen a continuing increase in number of proof of concepts that we've been doing. I gave some of those numbers in our earnings calls. But the really interesting thing is now seeing those turn into fruition. with major automakers, governments around the world, financial services organizations, telcos really taking advantage of the technology now and implementing.

Okay. On -- with FTE, do you see -- like what is the conversion cycle for you to make going services upfront, like what's the return that you're seeing? Is it 6 months out, a year out?

Yes. I think what we see is an enterprise software sales cycle emerge, right? So it starts off with that thought and then it runs through a sales cycle. So 6 to 9 months is a pretty good indicator of that kind of sales cycle. And it's why we knew that we -- as we came into the year, we would have a tremendous amount of innovation. And you can just see that in terms of the press releases and the capabilities that are going into general availability for us over the first part of the year and our Teradata factory going live, our new dynamic compute engine going live, some of the new context offers becoming available in the marketplace. But it takes time for those to monetize. So as we came into the year, we knew that we returned the company to ARR growth last year. We knew that we would accelerate that this year. But we didn't bake into our number any large upside from the new products for this year. So as we look out into the future, we see a real opportunity to continue that growth acceleration into 2027.

No, that is definitely takes some time. May we look forward to seeing that trajectory improving. Maybe going towards the product side instead of driving too much finance. Maybe Sovereign AI, there's kind of one thing that has been like being a bigger topic with Teradata factory that you mentioned earlier. Can you kind of give us a sense what is the opportunity with sovereign AI? And what are your -- how is your customer conversation with?

Yes. We see it very clearly, especially in regulated or highly regulated industries or in governments around the world. And I think the important thing is to think about sovereign AI, you can break it down. So there's sovereign infrastructure, so making sure that you have control over your infrastructure. And we've seen a number of customers choose to deploy workloads on-prem rather than deploy in the cloud. That's one of the reasons why we oriented our investors to look at what's our total ARR growth, not just thinking about how well we're doing in the cloud as an indication of how well we're going to do in the future. cloud is always going to be important and be a key part of our driver. But looking at the total ARR growth for the company, that's really what's going to drive our company forward. So that -- there's an infrastructure choice there. And Teradata factory gives our customers the opportunity to run AI workloads in their own data center right next to their data platform. And then there's AI sovereignty from a data perspective, enabling customers to choose where they put their data. They can put their data in a cloud, in a private cloud, they can put their data on-prem. And then there's AI sovereignty. So where does the AI model run? Does it run -- do you run in a general purpose model like Claude on the public web? Or do you run that in a cloud environment? Or do you run those models on-prem? And we've developed our platform to be able to have sovereignty at all layers in the stack and give that capability to our customers.

Yes. Is sovereign AI just kind of mainly a compliance discussion that you're having? Or does it also involve customer wants to make based on better performances, the cost involved or the data gravity of a certain?

Yes. I think the initial discussions are certainly being driven from a compliance perspective. We see a lot of our customers in Europe and in Asia really thinking about that sovereign AI infrastructure and not running on public cloud. However, what we do find is that all of the benefits of the Teradata architecture come to life when we see very high volumes of queries, very high volumes of users, query complexity being very high. That's where the Teradata engine really starts to shine in terms of executing this workload. So it's not just a regulated industry. It starts to get into the cost of owning and operating these platforms, how much does it cost to run? Because Teradata, we solve complexity with great software rather than scaling out our compute as some of our competitors do.

Yes. Trying to tie into what you're seeing on sovereign AI, especially outside of Americas, how do you see the opportunity going to be potentially impact your ARR number longer term?

Yes. I think as we've looked at it in the past, really our on-prem business, I think, was kind of flat to decline. But I think what we're now seeing is actually, there's -- we can see growth coming from our on-premise instantiations. In fact, just an interesting statistic. Half of our business is on-prem, half is in the cloud, as I said before. But for the 50% of our ARR that's in the cloud, half of our customers that are in the cloud with us have also retained on-prem environments. And they're creating a fabric across their entire data platform, across the cloud and on-prem to have an integrated data environment no matter where their data is stored. And that's a real advantage that our customers are taking and utilizing to have the best possible data platform for their particular use cases.

Okay. Does that kind of impact how you're thinking? Because historically, we think about cloud transformation project, there's a certain uplift to it. How do you think about balancing customer wants to remain hybrid at this point versus moving to the cloud?

Yes. I think that's the great thing with the Teradata offer. We offer our customers choice. And so when they want to keep that data sovereignty, when they want to keep that data inside a highly controlled environment inside their own 4 walls of a data center, we offer them the capability to do that. If they want to run in a VPC in cloud, their virtual private cloud environment, we can run inside that VPC with them. If they want a fully managed Teradata SaaS solution, we can run it as SaaS for them. So we offer all of those different types of deployment models. And a lot of our customers are responding really well to that because they see it as a real advantage from a flexibility perspective in terms of if rules and regulations change, how can they dynamically respond to that.

Okay. Yes. That sounds like there's a lot more opportunity like people are definitely talking about hybrid. Maybe just moving -- pivoting a little bit with some of the transition that we are seeing with AI coding too, right? We have Astra launching last week, definitely causing a little bit of still within the software industry. Like curious to see how are you seeing -- because you have Terra coding, you have like a different Terra code, a lot of new product coming out. What are the opportunity on the AI coding, either just helping with modernization use cases or day-to-day work within your customers?

Look, the challenge that I've given to Sumeet, our Chief Product Officer, is to use AI and the identification of the entire Teradata platform as an opportunity to leapfrog the competition. And so Tera transforms the way that agents and humans can interact with the Teradata platform. You can use natural language interface. You don't need to learn how to code in SQL anymore. You can use natural language interface to do administration tasks of the platform and manage the control plane. But one of the really interesting things about our Teradata harness is it actually optimizes and governs the use of agents. So a lot of our competition is essentially passing through token cost in terms of the revenue model to their customer. We don't do that. We allow our customers to use the right model at the right time, which has been particularly beneficial in countries like France, where the French government are promoting the use of Mistral as an example, as a language model. You can plug that right into the Terra harness and utilize that as your core language model rather than using a Claude or a ChatGPT. And we're seeing customers create really interesting use cases using that technology and deploying to massive numbers of users inside their environment because it takes away that skill requirement to understand how the data is constructed, what the table schema looks like. It completely leapfrogs the way that you access your data platform. So we think about Terra really as the claude for data, if you can think about it like that.