Great. Good morning, everyone. Thank you for joining the UBS Tech Conference. I am David Vogt. I am the UBS hardware analyst, and we are excited to have with us today IBM. With us is Dinesh Nirmal, SVP of products on IBM Software. Dinesh, thank you for coming again. He was here last year. I think the conversation went so well last year, everybody wanted you back. Thank you again for coming. Thank you, David. Really happy to be here. Obviously, under your purview, IBM Software has done exceptionally well over the last three to five years. Maybe we can start there. If we look at the IBM portfolio, last quarter, you grew software high single digits. The team is talking about double-digit growth as the sustainable strategy for IBM Software. How do we think about the portfolio today versus what the portfolio looked like several years ago? It has been sort of transformative in terms of addition, adding great assets, and leading to where we are today from a growth perspective. First of all, happy to be here. When we talk about IBM Software, if you look at 5 years ago, IBM Software was about 25% of total IBM revenue. If you look today, we are about north of 45%. We are transforming IBM more into a software company, one. Two, if you look at 5 years ago, like you said, it was a 2% growth we were looking at. Now, if you look at it, 8% to 9%, aspiring to be consistent double-digit growth. From a software perspective, now your question on what are we excited about looking into the future, right? If you look at generative AI, I think there is two core benefits of generative AI. One is automation, and the second is productivity. Those are the two things why generative AI is really taking off. On the automation side, as you know very well, David, over the last three, four years, we have really invested in automation. Acquisitions, organic partnership, all those things. On the automation side, if you look at it, right, I mean, the HashiCorp acquisition. How do we really make deployment of applications completely automated? That has really helped from a Terraform perspective. On automation side, we talked about resiliency last year, and if you look at AWS re:Invent, resiliency is becoming a center stage for a lot of vendors and customers because as more and more applications come online, how do I make sure my infrastructure and my application landscape is resilient? Cost of an application, we have Apptio. The whole ecosystem around application automation is a place where we have invested over the last three, four years. That's the IT aspect of it. When you look at automation, there's also a line of business aspect to it. How do I really automate my RPA bots? How do I really take my deterministic workflows and completely automate it from a line of business perspective? This is where organically we talked about watsonx Orchestrate to really take these siloed elements of a line of business, whether it's workflow, whether it's process mining, whether it's RPA bots, document processing. How do we really bring it as skills and really automate it end to end? That's the automation side. Both of them are really driving good growth for us. If you look at the productivity side, right, every enterprise is focused on making their developers more productive. How do I write more code to create more applications? On that side, in October, we announced something called Project Bob, which was all focused on how to make your developers more productive. We got tremendous reception, positive reception from folks there and our customers on Project Bob. We focused productivity from a couple of elements, which is we are not talking about code generation, because if you look at every other vendor out there, they're talking about code generation. Code generation is given, right? Just because you generate 30,000 lines of code doesn't mean that that is good code. We focused on task completion, which is, how do I really take a federal compliance, for example, that used to take 30 days. Can you use generative AI to do it in two days, right? Those kind of things is what we focused from a productivity perspective. There's other elements in IBM Software that we are looking at. For example, when you look at applications and data, there's what I call the middleware of the lifeline, APIs, Kafka, MQ, all those things. This is an area we have been investing. If you look at webMethods, we acquired webMethods to add to our API Connect portfolio on API management, because we think as AI middleware becomes more prevalent, we got to invest in it. Data, unstructured data is another place we are investing. A lot of folks are talking about quantum, right? We are obviously investing heavily in quantum, but there is an element of quantum that's relevant today called quantum safe, which is, how do I make sure that my applications are ready for quantum when it comes? That's another area that we are investing. We announced a product. We are seeing really good uptake in that one. Last but not least is security. How do we make sure that That also came with HashiCorp. We have Vault, which is secrets management, credential management. Today, most of the access in an enterprise is non-human identity, NHI. How do we really use secrets management to enable that? Those kind of things. We're really excited about the IBM Software portfolio because we have multiple elements of growth that enterprise needs, whether it's automation, whether it's productivity, whether it's data, whether it's security, and last but not least, quantum. Really excited about the future of Watson. Lots to unpack there. If I think about the portfolio five years ago, where we are today, is everything today on a go-forward basis infused or common thread tied to what you're doing in AI? When you look at future case capabilities that you're looking to add to a solution for an enterprise customer down the road, I would imagine it's infused with how you're thinking about how do I leverage what we've built from an AI perspective? Since you mentioned Quantum, I'll ask at the same time, does that factor into the decision in terms of how you're thinking about the software portfolio going forward, in the sense that you have an incredibly robust Quantum portfolio today? Over the next 10 years, I would imagine that Quantum is going to be a much bigger part of your business. Does that inform your view on software in that everything that you're looking at today, you're looking through also through a Quantum lens, if you will? Yeah. There's multiple questions there. Let me unpack the first one, which is the AI one. I'll give you one example of AI, how AI is getting infused. Actually, a couple of examples, right? When we announced Project Bob, we became the client zero for Project Bob, meaning I have 18,000 developers worldwide. How many vendors can really stand up and say 18,000 developers are using a product that we are delivering to a customer. Right. That gives us productivity. When you talk about AI infusion, we are infusing AI into the way we develop product. One, that gave us a really clarity on what we want to deliver in the. We could also talk about productivity, right? That's why we said we are not going to talk about code generation. We are going to talk about task completion. For us, we realized that just because you generate code doesn't mean that that's productive, because somebody has to look through the code to make sure that it is really good code, all those things. That's one way from an AI infusion perspective. The second one is, we have a lot of products that thousands of customers are using today. I'll give you an example of Db2. Db2 is widely used by customers, but we are infusing AI into Db2 to say, "Can we make a DBA's life a much more easier?" A task like reorg that used to take 35 minutes can be reduced to two minutes using AI. We are infusing generative AI into the product itself to make it more autonomous. A problem that happens in Db2, how do I debug it? Can I do predictive maintenance into Db2? We are infusing AI into the products, but we are also using AI for productivity ourselves before we deliver the product. Right. Those two. From a quantum perspective, right, obviously, if you look at quantum, we are heavily investing, like you said. If you look at Qiskit, our SDK, 70-plus% of all open source developers use Qiskit. Right. We are taking an NVIDIA-based approach, to be totally transparent, which is they used CUDA, which is software, to drive the adoption. We are doing the same thing with Qiskit to drive that. We're also looking to say, "Okay, how do we make our software quantum safe going forward?" That's how Quantum Safe and Quantum Explorer was born. We want to look at the vulnerabilities of our products to say which vulnerabilities from a quantum perspective you need to worry about, same vulnerabilities our customers have to worry about. Quantum Safe was born, we delivered it, and customers are using it. We have multiple customers who are using it, and we're preparing for the quantum world going forward. I'll come back to Quantum later. When I think about what you just said about AI kind of infused or threaded through your products, are you able to capture value from maybe more traditional IBM software products? That, like in the case of Db2 right now, obviously, a user or developer can be much more efficient. Is there a way that you can leverage AI to create more economic value for IBM, not just from a productivity side internally, but now that Db2 is more valuable, I guess, product? 100%. I think this is what we are working in the labs. Okay. Db2 is one product. You know, you have tens of thousands of customers on Db2. You take another product like MQ. There are countries who push $trillions through MQ every day. These are mission-critical products. Now, on top of the value that brings, can we add generative AI lens into it, right? That adds even more value. If I'm a Db2 customer and I have 20 or 30 DBAs that's doing everyday task, can I really simplify or optimize or make them more productive? Right. That value add on Db2 will excite our customers, and that will add economic value, right? Because if I have obviously taken 35% of time off a DBA to go focus on high-value task. Right. Obviously, from an enterprise perspective, I made the enterprise much more productive. Of course, two, when a problem hits, the immediate thing that an enterprise needs to look at is, where is the source of the problem? That's like looking for a needle in a haystack. Can we really make help from the identification of the problem using AI, right? Can we really deliver predictive maintenance? That's another not so quantifiable, because it could be millions of dollars that a customer has to go through once a problem hits. Can we prevent it from happening? Db2 is one example. MQ is another example. Cognos is another example. We have multiple products within our portfolio that's very much entrenched in enterprises where we can infuse AI- Come back. Right Really drive economic benefits for both us and them. Got it. No, that's helpful. Hadn't appreciated that sort of opportunity. Since you talked about productivity and automation, maybe we'll just touch on HashiCorp a little bit more specifically. That acquisition, I think, has been a strong acquisition in terms of acceleration from the growth perspective through the IBM sort of business model and go to market channel. How do you think about what else can you do with HashiCorp within automation? I think automation was up north of 20%- Yep last quarter. Can you elaborate on what's driving that, what you're doing differently with those assets under your stewardship versus when it was a standalone asset, and how should investors think about sort of the continuity of that growth vector going forward? Jointly, I think it unlocks a lot of value. I'll give you a couple of examples, right? Internally, being client zero is great because now we can use Terraform to really do all of our deployments, all those things. That's one element of it. Externally, right, now I go deploy an environment using Terraform. I can bring Turbonomic to really optimize that resource. I can really bring Apptio to give, based on that optimization, cost elements of it. I can bring Concert to take action. You see the multiplication effect that happens. A standalone company, it would be just deployment element of it. Now we are not just talking about deployment, we are talking about a platform that brings Terraform with Turbonomic, with Concert, with Apptio to say, "I can really optimize your complete deployment on a hybrid cloud." Right? If you look at hyperscalers, you're looking at one cloud, and most customers are looking at a hybrid environment to say, "How do I really make sure that my hybrid environment is optimized?" That's where a together story comes together. Also from innovation perspective, right? Today we just announced at the HashiConf in September, a product called InfraGraph. What does it do is, if you look at a customer, they have thousands, if not hundreds of thousands of deployments that happens with Terraform. Each Terraform deployment creates a state file. Those state files gives you a view of your environment. We have taken that and shown it as a knowledge graph, whereby which you can see where the choke points are, where the red, where the green is, what actions you need to take, all those things. I think joint innovation is happening. A collaborative effort between HashiCorp or Terraform with what we can bring through some of the other products that we have that really benefits customers. Maybe if I can maybe paraphrase. Should investors think about Terraform as sort of a platform or sort of the entry point into the customer where then you can then add on incremental IBM solutions that maybe historically they were not natural customers for IBM because they were siloed with HashiCorp as a standalone entity. Is that the right way to frame it, so when we think about the next couple of years, Terraform is the platform that opens doors for those other software solutions within the IBM portfolio? Yeah, I would look at it as more as an entry point, like you said. Okay. I'm a customer who's using Terraform to deploy. Now deployment is one element of it. Once you deploy it, I want to see where do I need to optimize. I could be using VMs or I could be using resources that I can shut down, right? How do I take those actions? Then IBM. Also look from a cost perspective, because as generative AI becomes more relevant and prevalent at enterprises, cost is going to become a very important element of it. How do I really take actions based on those costs? Last but not least, right? I always talk about it, which is the resiliency of an applications. That is going to be something that will become more and more and more prevalent. How do I really take actions to make sure that my applications and my environment, infrastructure, all that is resilient. Got it. Maybe pivoting in the interest of time to, let's see, data. Within your software portfolio, automation's been growing relatively quickly. The Red Hat business has been growing relatively quickly. Data, it's a little bit more modest growth. How do we think about sort of the opportunity set for that portion of the software vertical, particularly given the success that you've had using really strategic acquisitions in Red Hat, clearly a massive victory. from a long time ago now. It's crazy. Within automation with HashiCorp. How do we think about data going forward? Yeah. What's the strategic vision for that part of the solution set? Yeah. I think if you look at the data journey from 40, 50 years ago, data used to be hierarchical data, then it became relational data, then the data lake was formed with Hadoop, now it's more about lakehouse, right? The data has gone through its own transformations. Today, 90% of growth in data in enterprises is all about unstructured data. That's where we delivered watsonx.data to say, "Look, you have structured data, you have semi-structured data, and you have totally unstructured data. How do you really bring the value of all three together? Because otherwise it's all sitting separately. How do you bring the value of all three together and then really use the new generative AI like RAG, Vector DB, all those things to really drive the value out of the unstructured and combine it with structured data?" That has been the strategy, that is really working well, that's the seven plus% growth that you're seeing. Customers are also on the journey, right? Just because unstructured data is there, it's going to take them some time to say, "How do I combine with this? How do I make sure it's trusted data? How do I really take the metadata out of it? How do I really create value out of it?" It takes a little time for us to see that tick of the growth that's going to happen in the data side of things. I think watsonx.data is really the tool that we have provided for customers to bring all three elements of data together to really get trusted data available for enterprises. Much like IBM is client zero in a lot of these automation efforts, how are you using these tools internally to extract value from data within IBM? That's a really good question. In all these new tools, that software that we deliver to our customers- Whether it's watsonx Orchestrate, whether it's Project Bob, whether it's Concert, we are becoming the client zero by which we can test the scale- Right at an enterprise. That really helps because that becomes a testing bed for the most part before we deliver it to our customers. Same thing on that data, right? How do we really bring the unstructured data within IBM into watsonx.data to really, for customers to do enterprise search, for example. If I'm an internal IBMer, can I do an enterprise search, a search to say, "Okay, when is the next paycheck coming?" Or whatever. Right? To really go and do the search, index that data, and really provide it the value. Our focus is becoming the client zero and really delivering value on top of it. Same thing happens with that data and the product that we are delivering to our customers. Is it more of an outcome-based solution for data as opposed to automation is more of productivity enhancement, then leading to potential revenue enhancement? Is that how we should think about the data use cases going forward with large- Yeah unstructured data sets right now? How do we extract value from that? It's incremental revenue opportunity. I would imagine it's not a productivity-driven sort of solution that your customers are looking at. Right. I think, David, if you look at any enterprise, I always say there's only two things that matter. One is applications, which is where the automation, productivity, all those things. Second is data. You are a data-driven enterprise, or you're taking the data and creating data products. For that, what do you need? Like you said, the value creation. For that value creation to happen, you need to make sure you clean that data or you extract that data. I think that's where we can really add value, is that how do you extract that data? How do you do document understanding? How do you do document processing? How do you take that data, take the metadata, put it into a catalog for easy enterprise search? How do you index that data, right? I think for us, the focus is on value creation through trust and governance of that data. That is the focus that we have. Just the storing of the data is just one element of it. Extraction of that data, governance of the data, making that data trustable and making it available to data scientists and others is where the value is. Got it. If I take automation, data, Red Hat, that is largely the driver of that high single-digit growth vector along with M&A. I think most investors here in the room are familiar with what you're doing with Red Hat, with OpenShift, with Ansible. When you think about that other part of the strategy that requires M&A, where are we focused today? Is it multi-cloud centric, hybrid cloud centric solutions? Does it bolster what is happening within automation and HashiCorp and what Terraform allows you to do with customers? Yeah. What are we thinking about as we go forward over a multi-year period in terms of where investors should focus their attention in terms of what your strategy looks like in software? Yeah. You called out all the core elements of software that is driving growth, but there's one piece that I should also call out, which is TP. TP. Yeah. What we are seeing is that with Z17 launch and with generative AI, customers are doing a lot more transactions, right? Where does the transactions go? The transactions go into the back end. We are also seeing the growth because customers are looking at the applications on mainframe to say, "How do I modernize it?" Modernize doesn't mean migrating the applications. Modernizing meaning, how do I take an application that's running on COBOL Four and how do I seamlessly run it on COBOL Five? In that process, can I optimize it, right? That is also driving. We delivered watsonx Code Assistant for Z. We have watsonx Assistant for Z. We have Concert for Z. We are really driving new innovations onto our TP portfolio that you're seeing the growth that's happening there, customers are really taking it on to say, "Okay, now I can run more transactions there. I have generative AI available there, all those things. I can modernize my applications there." That, we are seeing a lot. Now, from an M&A perspective, right? Always the strategy on M&A has been, look, if we can build organically, we will build it. Resiliency on Concert, Project Bob for productivity, watsonx Orchestrate. Organically, we continue to drive that arm. There are pieces where we feel like it's better to go buy to really bolster our portfolio. HashiCorp is a good example. We don't have anything in the application deployment, we see as more and more applications come online, you need a deployment tool to really go. Two, the fit. The set of products that HashiCorp brought in, like Terraform, is a perfect fit. Right into Turbonomic, Instana, our portfolio, right? We look at the fit, we look at where we have the holes, and how do we take. A couple of areas that I will talk about. When you look at our strategy, we always said it's hybrid cloud and AI. AI. What are the holes that we have on hybrid cloud? I think Red Hat really helped us, right? HashiCorp helped us from a deployment, all those things. I think we have a pretty good portfolio there. From a generative AI, the challenge customers are going to face is not creation of an agent or creation of assistant. It's about infusion of generative AI into enterprises. That's where we will look at opportunity to say, how do you infuse generative AI into enterprise? What are the tools you need, right? Just because an agent is there doesn't mean that agent is calling multiple tools. How do we make that seamless? How do we look at those things? Data is another one, right? We obviously want to continue the growth that we are seeing in the data because we think data will play a prevalent role in enterprises. That's an area. We just acquired DataStax, as you know. That's another area that we will look at. I think connection between applications and data, like how do we make that middleware more prominent is another area we will look. We will look at multiple places to see. It's all based on one fit, a strategic need, and from a strategic perspective, the areas that we called out, hybrid cloud and generative AI, where do we need to fill that to quickly infuse that into the enterprise? I know consulting doesn't fall under your purview, does the opportunity in software stem from the massive book of business that you have on the consulting side in AI? Are you hearing from your customers what their pain points are, what their use cases are, what their needs are, so you get this positive feedback loop in terms of what else can we bring to bear besides consulting on an AI level? Does that help inform your view in terms of what you should be focused on going forward? Yeah. If you look at generative AI, you could ask the question, what differentiates IBM? We have worked with enterprises for decades, obviously we know the enterprises really well. I'll give you a couple of things that differentiates us, and then I will answer your question on consulting as part of it, which is a differentiator. Why is IBM different when it comes to generative AI? Because you hear so many different names in relevance to generative AI. One, hybrid cloud. Meaning, doesn't matter what cloud you want to run, we will help you run. Our services and products run on any cloud, including hyperscale. That's one. Two, If I were to take the productivity tool, Project Bob, that I talked about. Just like other vendors, we did not talk about code generation at all. We only talked about task completion because we knew what is an enterprise focused on. Enterprise is focused on code generation, but they're also focused on, "I do have 30,000 machines that's running Java 7. I want to migrate that to Java 17." It takes 30 days per machine or two weeks per machine. You need that faster. If I could reduce it to one week, look at the productivity and optimization I have driven, right? We know the enterprises so well, we can articulate what we need to do. I'll give you one more example on that. watsonx Orchestrate. Everybody talks about you can come and create agents. Okay, we can also do that. But we have moved from that to say, "Let's not only help you create agents, we'll help you govern and manage those agents," which we call AgentOps. We are looking at from an enterprise lens to say, how can we help enterprises scale it securely, govern it? That's the differentiation we bring because we understand enterprises so well. That is also the differentiation consulting brings. We have a software arm that goes with the consulting arm to say, not only we can deploy this software, but when you look at agents and tool calling and all those things, we will have a consultant who is knowledgeable in this area, who understands your enterprise, who can sit with you and help you drive that. Understood. That's a huge differentiation that we, from an IBM perspective, consulting and software coming together. I wanted to touch on quantum. I don't know if two minutes is enough, but maybe we'll start there real quickly. You touched on it briefly. You touched on it earlier about how quantum is going to be sort of a common thread in terms of your software portfolio, how do you prevent vulnerabilities, et cetera, and why that matters to the enterprise going forward. Give us your best estimate in terms of timetable on this. This is a multi-year, long-term. How are you thinking about positioning your portfolio and your opportunity set in this particular technological vertical? Because I think we had a panel the other day, I think people are talking like five years, six years, seven years for quantum advantage, if not longer. Just maybe what's your perspective from your seat, which I know it's not the quantum seat, but would love to get your view. It's a multi-year view. More than that, David, I think what I'm really excited about is the opportunity it presents, right? You saw what HSBC could do with quantum. I think in multiple industries, multiple opportunities. From an IBM perspective, we have been playing a role, an aggressive role actually, to drive more quantum into partnerships. We announced the AMD partnership. We are the only vendor who has 25 plus deployments out there from a quantum perspective out there. Qiskit, how do we really make it enabled for open source adoption from that perspective? Today, from a quantum perspective, how can we really help our current customers be ready for quantum adoption when it comes? We are looking at, let's start with today. Here's Quantum Safe, here's Quantum Explorer. Use it. Really be ready for quantum. When it comes, you have it all set and ready to go. From an open source perspective, from an adoption perspective, let's help. From a hardware perspective, let's go do this. From a software perspective, we have Qiskit. I think we're looking at in a multiple angles, but we are also looking at quantum today to help our customers be ready. This is a very quick follow-up. Is there cross-functional teams between what falls under your purview in software and what has historically been sort of a siloed quantum business within IBM? Have you kind of torn down the wall, so to speak, and make it more integrated in terms of quantum permeating everything that IBM is thinking about at this point? Well, we do have a big research team who's focused on quantum. Like I said, my focus is today's quantum problems. Okay. How do we really help customers from a and quantum security, all those perspective, can we really make customers ready for when it comes time? I think we're out of time. Dinesh, thank you very much. Thank you everyone for joining. Thank you.