International Business Machines Corporation (IBM) Earnings Call Transcript & Summary

Well, let's get started, guys. Thank you very much for joining us. Welcome to day 2 of the flagship TMT Conference. My name is Erik Woodring. I lead the U.S. IT hardware coverage here. I am delighted to be joined by Rob Thomas, IBM's Head of Software and Chief Commercial Officer. But before we get started, I just need to read this disclosure statement. I need to mention that important disclosures can be found at the Morgan Stanley research disclosure website at www.morganstanley.com/researchdisclosures. If you have any questions, please reach out to your Morgan Stanley sales representative. So Rob, thank you very much for joining us today.

Thanks for having me. Great to be with you.

So I think the best place to start maybe is just better understand how to think about the key drivers of growth for IBM. So exiting 2025, 3 of your 4 software subsegments were growing double digits. Mainframe had a record year, helping to offset maybe more tepid growth in services. So taking a step back as we think over kind of the medium term and beyond, what are the key growth drivers as we think about the IBM model? And let's start from there.

So maybe go back to 2020 when Arvind Krishna took over. He had the insight that we can be uniquely differentiated and win in hybrid cloud and AI. And that was actually before AI was, I'd say, such the moment in discussion that it is right now. But everything we've done is about repositioning the company for that. You take software, we kind of moved away from applications, divested health care applications, weather. We really said we want to be an infrastructure software company because we think we can be really good at that. For consulting, we built big partnerships like AWS, Microsoft, expanded what we've done with SAP and really became, I'd say, a partner of choice for key skills and consulting helps our software business a lot. And then for infrastructure, we said we need to continue to innovate, simplified what we're doing in storage. As you said, we've had great growth in mainframe. And a big catalyst has been, I'd say, everything we've done in the go-to-market, and we'll kind of talk about that as we go. But as you look forward, it's not a change in strategy. We have the right strategy. It's about how do we continue to be a high-growth software business. We kind of said we can get to double digit for this year, which we think is a big step. And we have a lot of momentum. So we feel good about that.

Great. So let's first touch on something that obviously was the market responded to, which is AI disruption risk. We've heard it across the software stack. I want you to address IBM disruption risk at IBM because you lead the software business, you lead the sales team, so you perhaps have maybe the broadest purview to the potential impact to IBM. So there's maybe thoughts around disruption risk to mainframe and software more broadly. Let's just start on the software side and say, help us understand how you think about that risk to the portfolio? And could it bring disruption risk, could that bring opportunities as well if we want to maybe flip that question on its head.

Obviously, we think about this a lot. And AI has been a good tailwind for us, which you've seen in the last few years. And it's on us to make sure that AI continues to be a tailwind. And so we think a lot about how are we ensuring that, that is true. I would say now you take a slight step back, when you hear all the discussion of generative AI is -- makes it easy to build new applications. That's kind of the buy/build discussion that has always existed in technology. Yes, clients can build something or they can buy it. We shouldn't forget normally 20% of the effort in deploying software is actually building the application itself. The 80% is how do you do upgrades, how do you bring new features? How do you support it? How do you do security fixes. There's a lot that goes into that. And I'm not sure the buy/build equation will change dramatically for our clients. Let's look at what's happening in software. So think about maybe a software application, which we're less in that business. You've got a user interface, you've got business logic and then you've got a back-end database. I do think AI will become the new UI. So there's probably a lot of risk if your moat is based around a UI. Business logic, I would say some of that can be done with AI, but maybe not all of it. And then you have kind of the back end, the plumbing, that's what we do. When we see things like projections of 1 billion new applications being built based on generative AI, to us, that's a catalyst because every one of those applications is going to need containers. It's going to need real-time data. It's going to need infrastructure automation. It's going to need all the things that we do. So I really view this as an opportunity, but we have to stay sharp here and stay focused. To give you an example, one thing that we previewed at our developer conference last November was Db2, but an AI edition. And I think there's a big opportunity for companies like us where we have incumbency to take a product like Db2 that's used everywhere in the world and say, bring AI capabilities that takes 85% of the labor out of operating Db2, I think that creates a growth catalyst.

Yes. Okay. That's extremely insightful. So let's turn that conversation to mainframe, and we're going to kind of ask a 2-sided question as well, which is it's obviously been a very strong start to the z17 cycle despite the kind of perceived AI disruption risks around COBOL modernization. So I'd love for you to add any color on, first, why this cycle has started strong, but then also maybe understand your long-term view on the mainframe. And maybe the simple question is, is it an AI disruptor? Or will it be disrupted by AI?

So we've been working on mainframe modernization. I think it goes back a decade. And why would we do that if we didn't think it was good. So we actually think it's very good, modernization. And if you look at what happened, call it, 2015 to 2020, I think that's the time where we working with clients, we looked at certain applications. We said, we're not sure this belongs on the mainframe. You don't need a CRM app or a mobile app on the mainframe. Mainframe is really good at processing transactions. So anything that's transactionally intensive, that belongs there. So we did a lot of work on mainframe modernization. And I think we got to the point where clients realized this is the most price-performant way to do transactions. And that's why you've seen the growth that you've seen. I mean, 3x MIPS growth in the last couple of cycles. That's because the right work is happening there and people are willing to lean in and do more. So number one is I think mainframe modernization is good. We will continue to do that. Secondly is I think what gets lost in some of the COBOL discussion is mainframe is truly a platform. COBOL is machine-level code, meaning it does not run unless it's taking advantage of the hardware architecture of the mainframe, storage subsystems, memory optimization, how you're using the compute. The very simple analogy I would use is you could vibe code a version of iOS. That doesn't mean you're going to deploy it to 1 billion phones because the software works because it's tied to the phone. It's actually the same thing with mainframe. COBOL applications built on mainframe are designed to run there. They don't really run that well anywhere else. And so I think this platform point is really critical, which kind of links to the first one. If you have the right apps there, you should keep going, and that's what most clients do. Third comment I'd make is there's a lot in the world right now on sovereign technology. I think it's unlikely people are going to use SaaS applications to do modernization of the mainframe. Clients, governments that are using this the fact that it's their environment, they can control it, they can control who's utilizing it. So I think there's a lot of positives to what AI can do for mainframe. You can see it in a lot of the products that we've built as well around this. And so that's where we are at the moment.

Okay. Okay. Really helpful. I want to now maybe move beyond AI disruption and kind of touch on some of the subsegments. And the first one just being Red Hat because the business has compounded around 12% constant currency over the last 2 years. You hear Arvind or Jim speak, and they sound extremely confident that Red Hat can kind of reaccelerate into -- back into kind of a mid-teens growth rate. So help us understand the specific factors or the specific drivers that give IBM the confidence to say, yes, we can get back to that mid-teens growth rate.

Let's start with the original thesis on the Red Hat acquisition. Our view was that we were at the start of what I think will be probably a 20-year cycle of a new application architecture, which is containers. We had a really good play in IBM, if you go back to, call it, 1999 with J2E and Java. And you get about 15 years of growth and then you get a long tail of this is how applications are built and run, and we had a very strong play then. Our view was the same thing was going to happen again, but this time, the architecture would be containers because that gives you the flexibility to run on multi-cloud or hybrid cloud. It makes applications easier to utilize. And so this is why you see, I would say, the success we've seen with OpenShift. So a couple of billion dollars now, growing 30%. I think we're actually just getting started. If you had to put innings on this, we're probably in third inning on containers and what will happen around application modernization. So I think there's a lot of room to run here. Second is there's been obviously a lot of, I'd say, question marks on virtualization and do people want to stay on VMs or do they want to move to something else? I think we have a great alternative there for companies that are looking to move to containers. We've had really strong bookings there, which has been great. That's a good catalyst, but that takes time to play out. It's a lot of effort to move from VMs even to Red Hat virtualization or Red Hat OpenShift ultimately. And so I think that will continue to go. Ansible is a great product. There's a lot of free use of Ansible. So part of what we've done kind of connecting to our M&A strategy, when we buy something like HashiCorp, the integration we do between Terraform and Ansible requires that you have premium versions of each. So that's a good way to create monetization momentum around Ansible. And the last is Linux. I think probably the most underdiscussed point on Linux was a couple of years ago, we announced a partnership with SAP that said they would be moving from SUSE to Red Hat Enterprise Linux. That takes a long time to play out. But as you probably know, SUSE is kind of what it is because of SAP. And so we think that's a big growth driver for us over time.

Yes. Okay. If we move to transaction processing, obviously, very clearly tied to mainframe. You've guided to low to mid-single-digit growth in 2026 for transaction processing. I'm going to ask maybe kind of in an aggressive way, which is why isn't there upside risk to that? If I consider that there's been 100% growth in installed MIPS capacity with the z17, your long-term TP guidance is for mid-single-digit growth. Are there offsets that maybe wouldn't allow TP to grow in line with that midterm model? Or is there conservatism baked into the 2026 outlook? Would just love -- what are the building blocks there to go underlying that mid-single-digit growth?

There's a few drivers of TP growth, I would say 3. So one is MIPS growth that you alluded to. As I said, as people have modernized, we think the workloads are roughly right. And when people like it and they are using it, they're going to continue to grow MIPS. So that's one. Second is price. We can command price on mainframe. At the same time, we're not going to do that too aggressively. We want to be the gentle in the room that's helping clients be successful. We don't want to push on price. But it's a lever that we can use. And third, and I think this is probably the big swinger is new innovation. If you go back in time, people ask a lot why was there not as much TP growth, call it, 2015 to 2020? We didn't do a lot of innovation then. There wasn't a lot of new product. And you look at since 2020, let's see kind of off the top of my head, we've delivered Code Assistant for Z, watsonx Assistant for Z, IntelliMagic, which is AIOps. We brought HashiCorp Vault on to Z, Terraform on to Z, we brought a ton of new product on to Z. And what we know with Z clients is they will adopt new capability if they see value. They also will not do that nearly as fast as any of us may want because the mainframe is the heart and the lungs of the business. So you're not going to start to do major surgery without making sure everything is lined up, and it's going to go well. And so that does take time. There's also a lot of mainframe tools that are not IBM and many of those clients -- many clients come to IBM now saying, "Hey, we'd like to use IBM mainframe tools." We can help them with that. But that also takes 2 to 3 years to do. So I think kind of the upside, downside to your question, it comes down to adoption of the new innovation, and I think that kind of ebbs and flows.

Okay. That's really helpful. Last question on software, at least specifically to kind of how we think about the segmenting is just talking about your overall strategy on the automation portfolio that you've built, how Hashi fits in with that strategy? And then specifically on Hashi, just an update on integration, how cost synergies are progressing as the Chief Commercial Officer. There's a lot of -- you kind of outlined a lot of benefits that Hashi can benefit from under the IBM umbrella. So let's maybe unpack that.

So I'm super pleased with everything we've done in automation. This wasn't even really a business we were in if you go back to 2020. And our thesis at the time was there's AI models, and those will be important, but I would think of this as applied AI. We thought every company that has technology and operations, which is every company is going to want to apply AI to make that way more productive and efficient. And that was the thesis behind automation. So we started with -- we bought Turbonomic, which is how you optimize your compute and storage. We bought Apptio for FinOps and understanding how you spend money on technology. We organically developed a product called Concert, which is resiliency for your technology and operations. Then we bring in HashiCorp. So we've built the best portfolio for how you apply AI to improve your technology and operations. And I don't think there's a close second at the moment. So that's why you can see the momentum that we've built in the business, which has been pretty consistent. I actually think we're just getting started here because any client you visit, they still have a lot of complexity in what they're doing. So as we think about things like Concert, we will evolve this to become a platform for all of your need for FinOps and AIOps taking advantage of these other capabilities. So I think there's a lot to do here. Now linking it to HashiCorp, I'd say probably the biggest driver here is what I would call the new stack of enterprise software, which is companies want something that's open architecture, open source in many cases, easy to utilize. And think of that as Red Hat HashiCorp for automation, Confluent when it comes for real-time data. I think we have a unique play for a new enterprise stack, which will have sovereign deployments, can run on public cloud, can run hybrid. And to your question on kind of the Hashi integration, I think it's gone incredibly well. I think a few people ask questions when we did it, will there be dilution? I think we've really perfected the playbook on M&A, which is why we are confident to then go after something like Confluent and say, we can do this, and we can absorb the dilution as part of our model.

Yes. And on that point, just using HashiCorp as the example, lessons learned for as Confluent closes, integration, again, the ability to drive cost out of the model. What have you learned from Hashi that you are applying as you think about the closing of Confluent? And then what you would do, obviously, beyond that into the future?

One, when we buy companies, we tend to invest more into R&D because we can get innovation going faster. We'll continue to do that. Two is we can integrate kind of back office quickly. What we've learned from both HashiCorp and hopefully Confluent to come is with companies like this, they tend to have a different way of leveraging technology to run their business. We can learn from that. We learned a ton from HashiCorp on that, and I sense there will be some things for Confluent as well. Third is we can get a lot of synergy and go-to-market quickly. We have broad distribution. We've got great coverage across the top 1,000. We've got a big ecosystem. And so as we bring in new capability, we can distribute quite quickly.

Okay. I want to ask one services question, and that is the market is going through its own kind of perceived risk related to AI disruption. You guys have been able to capture, I think it's over $10 billion of cumulative AI bookings in 2 years. I know you're not necessarily going to share that going forward. But how much of a driver will AI be in reaccelerating this market or maybe more specifically, the IBMs specific services business? And how do we think about maybe the risk of cannibalization between AI engagements as you see them today and the traditional old projects within the services business? Is there cannibalization risk? Or how do we think about all of those together?

So I'll decompose the services market, kind of simplistic. But do you think kind of at the bottom end, there's kind of body shop, just work for hire. Kind of the top end is the McKinsey BCG types. We're kind of more in the middle, which is how do you apply technology to make your business better. I think AI coding tools are very good. So body shop type work, we've tried to move away from that because I'm not sure that's where you want to be as there's more and more AI in the world. We announced Consulting Advantage, which is really our play on services as software. And I think we hit a sweet spot here where clients want assets as part of a consulting engagement. They don't just want labor because, obviously, they want to get more productivity over time. So I think we kind of hit the sweet spot there. We don't really play at the high end. So at this point, we don't really play at the high end. We're not trying to be BCG and McKinsey. We don't really do the body shop work. We're kind of in the middle. I think the partnerships we've built was really all upside for us because we weren't doing much with AWS and Microsoft before. We're doing a good bit with SAP, but there was more we could do. So I think partnerships with Palo Alto, we've built partnerships that kind of catalyze what we're doing. We've got consulting advantage that gives us a unique platform for deploying technology. I think we've found the right balance here.

And I think something that maybe is differentiated for you guys as it relates to this business is, while you do have partnerships and those have been very strong, you also are -- you deliver technologies that can be solutions for what your customers are coming to you on the services side. Just talk about how that integrated portfolio maybe gives you an advantage over peers.

Well, it's certainly a big driver of the AI book of business that you described because any company that we're talking to about AI or applied AI, their next question is, can you help us? And so having an integrated model, we're one of the few companies that does. I think that gives us an advantage. If you think about what we've done, the progress on Red Hat with things like OpenShift and containers, that would not have happened without the role that consulting has played in that. So we kind of pick the key places. It's not we want our consulting to do every IBM product, but it's make the bigger bets. Red Hat was a big bet. Watsonx was a big bet. I'd say the next one is this kind of this new enterprise stack I talked about, which is as we bring in Confluent, HashiCorp, there's an opportunity to do more there with consulting.

Okay. Cool. So I'm going to circle back to software and then maybe get into things like M&A and cost rationalization. As we think about the totality of the software business that you run, what are some of the maybe potential upside you see to that kind of 10% low double-digit targeted software growth over the next 1 to 3 years? What could be some of the downside risk? Maybe just frame the risk-reward as you think about it relative to how you've communicated growth to the market.

So I think maybe the thing we haven't talked about much yet is the change that we've made in go-to-market, which has been dramatic. And go back to 2020, I would say there's 3 main things we did. One was a change in incentives. Two was really, I'd say, opening up the ecosystem. And three was kind of building a technical go-to-market. So let me describe all 3. Incentives, I would say we're largely done with that. We saw the opportunity to create a much more aggressive sales culture where people were incented to go out and deploy technology, make clients successful. I'd say we're largely done with that. For the rest of go-to-market, I think we're actually less than halfway through the transformation. So on partnerships, we've built some great ones. We have now over 100 software products that run on AWS, about half that, that run on Azure, about 1/3 of that, that run on GCP. So we've built big hyperscaler partnerships. We're listed in all the marketplaces. That's a big lift for how we're going to market. We've also built partnerships with SAP. I talked about that a little bit before with -- obviously, with Linux, but they're also using Red Hat OpenShift. They're using HashiCorp. So different dimensions on partnerships. On technical sales, the way I'd describe it is we were too much steak dinner and not enough writing code. And I would just go talk to clients and say, we want you to help us deploy technology. We don't need all the other stuff. And so we've done a pretty dramatic change in the complexion of the sales force to be way more technical. When we show up to a meeting, it's not PowerPoints. We want to roll up our sleeves, demonstrate technology, get it up and running. But I say, for these 2 for ecosystem and kind of technical go to market, I think we're only maybe midway through that. But we've done a lot on the R&D side. We kind of talked about that. I think we have a great portfolio. Go-to-market is the other piece. The combination of those 2, that gives me a lot of confidence.

Okay. Great. Let's maybe shift to M&A. And maybe the question is, how much internal desire is there to quickly get another acquisition? I know you're still even in the process of closing Confluent, but it is a key part of the model and specifically in software. So how much internal desire is there to get that done? And then second, has the criteria for M&A kind of changed at all as you think about what could come beyond Confluent?

Well, priority #1 is get Confluent done, so we should be clear. Not that I call desire. I'd say we are always open to what's possible, and we're always looking. I think it was Benjamin Graham that talked about Mr. Market gives you opportunities. Clearly, with what's happening in the markets right now, there's a lot of opportunity that was maybe less obvious a year ago. So yes, we keep a close eye to that. But in every decision, we're always going through buy/build. To some extent, I think we can build anything, but we tend to buy when we can accelerate time to market or we can acquire category-leading assets, that's kind of the criteria, the process that we go through. It has to be at a reasonable price. So there's no real urgency or desire, but I'd say we're always open to looking at this.

And the criteria in terms of -- I think the way that Arvind has communicated it was, I think it was 75% software, 25% services. It has to be additive if you bring it into the IBM distribution channel. There needs to be a path to becoming free cash flow accretive within kind of, I think it's 1 or 2 years. That criteria has not changed. That is still kind of the building blocks for how you approach those targets.

Yes. Criteria has not changed. It's accretive within 2 years. And I think there's -- as you think about our software portfolio, there's certainly more we can do on automation. There's more we can do in data. I'd say most of what we would do in Red Hat or transaction processing tends to be more tuck-ins. Those tend to be smaller. But I think there's still a lot that we can do, but the criteria hasn't changed.

Okay. Cool. I know not necessarily core to your role at IBM, but something that I think of that is extremely impressive and it ties together with M&A, especially sometimes companies that might be dilutive when you purchase them at first is the cost rationalization we've seen at IBM. You are leaning into R&D. That line is clearly growing. You've talked about innovation. But if you look at SG&A, you've really been able to kind of hold that steady. How are you able to basically keep that spend steady while also absorbing, let's call it, at least one pretty sizable transaction, at least one every year?

So maybe to go back to where you started. I actually do view this as core to my job. And I think that's something Arvind kind of has pushed all of the management team on as we all own productivity for IBM. So it's part of all of our jobs. I think it's critical. To your point, the main thing that we looked at was we're spending a lot of money in what I would call back office. That tends to be repetitive tasks. And our view was AI, specifically watsonx, could do that very well. So we've driven nearly $5 billion of productivity, all coming from back office. And we've taken that -- and a lot of it has been reinvested into R&D to your point. R&D has grown 30%, basically, almost over $2 billion since 2019. And that's the important part of being a technology company is you have to invest more in -- you have to invest at a competitive level in R&D, and I think we're getting to that point now. So that's all been, I'd say, an intentional strategy. Now where do I think we go from here? As I look at things like our go-to-market, I still think we can be more productive, whether it's through leveraging channels or ecosystem. If you become more technical, then your deal sizes can go up because your consumption grows faster over time. So I think there's still more to do on the productivity and the go-to-market, which is why we can kind of, I'd say, stay where we are. But as we grow revenue, we get more leverage out of the business model, which is what we've tended to do well.

Okay. I want to touch on kind of the combination of everything you've talked about plus leaning into R&D, and that is it's clear you are leaning into innovation, you're expanding your portfolio, perhaps in ways that people don't appreciate or perhaps in ways that you didn't necessarily do, as you mentioned, from 2015 to 2020. How would you characterize what the next phase of innovation at IBM looks like? What should we expect to see more of going forward?

So first of all, it will be rate and pace. Because of what we're doing with AI, the productivity of the engineering team is going up dramatically, which is great. So we built a software development tool called Bob. We now have 40,000 people in IBM using it. And it's not just software developers. We have people in our finance organization using it. And the productivity that this drives is immense. And so one is I just look at rate and pace. Can we improve the amount that we're doing and the speed at which we're doing it. Two, kind of my example before on creating AI additions of existing products. That used to be very hard. It's not easy now, but it's actually easier. You can get something out there, you can iterate with the client. And so we'll continue to do those types of things. Next is we made -- we kind of did a technology preview at Davos this year around something called IBM Sovereign Core. And this is our view that technology architectures are going to change again in the next couple of years, which is countries around the world are looking for sovereign deployments of technology. And so again, we went from kind of idea to a technology preview. We'll make it available later this year for what we think is really the only alternative in the market for sovereign AI that can run on-premise that can leverage containers, that can leverage automation. And so we're going into, I'd say, spaces that before we probably didn't have the capacity to do or maybe even the imagination, but I think now we can do that.

Okay. Before we wrap, there's a lot of things to kind of get excited about here. We didn't touch on quantum. I just maybe want to give you the opportunity because this should be a very important year. The goal is to kind of prove quantum advantage. Just your broad thoughts on that before we maybe get to a wrap-up question.

I think quantum is going to be significant, and it will have a decent piece of computing infrastructure going forward. We've kind of said 2029, 2030 roughly is the range. I think probably the question is, is quantum a huge part of computing or a small part? I don't think we know that yet. But I'd say our view is it will be significant because as we demonstrate quantum advantage and as we solve error correction, the use cases are things that cannot be done with classical computing, whether it's going to be drug discovery, material sciences, natural resource discovery, we will go after a set of use cases that economically and in terms of GDP growth are really critical, but are really untapped today. And I think the HSBC example that was shared last year was kind of the first demonstration of this has relevance to even a use case like bond trading and bond pricing, which I think kind of woke people up to say, maybe there's something really here. We open sourced our development kit, Qiskit, a couple of years ago. The momentum there from students, developers, academics, research labs is enormous. I think it's really the default answer for quantum applications right now. So I think we have a lot of momentum here, but we still have a lot of innovation to go.

Yes. Okay. Fair. Last question before we wrap up is just as we think about everything going on at IBM go-to-market changes, what's happening in software, the momentum you're seeing in mainframe, kind of the changes to the market going on in services, quantum, there's a lot obviously clearly going on. What are you -- maybe the question is what are you most personally excited about as you think about the next 3 to 5 years? What do you want investors to kind of take away from that and this broader conversation?

I go back to this notion of there's going to be a new stack for enterprise software because these kind of run in 10- to 15-year cycles. We're kind of coming up on this next moment. And you look at what we've constructed with Red Hat, HashiCorp, Confluent to come, watsonx Orchestrate, which is our agent fabric. I think we're the only company that truly has what customers want out of this new enterprise stack. So that's number one. I think this is a shift in technology, and I think we're leading this. Two is a lot of IBM's business today is in call it the top 2,000 customers in the world, 2,000 companies. We've made a shift in our go-to-market to focus on outside of the top 2,000. And I'd say after a year or so of doing this, the results are encouraging, meaning I think we have the portfolio now that we can cater to a new customer base, people that may not know IBM, that want to start to learn about IBM. So I'm pretty optimistic that there's a lot of TAM expansion for us, just getting outside of the top 2,000. By the way, it's half the IT market. So it's not a small prize.

That's perfect. I think it's a great place to end. Thank you very much, Rob.

Thanks, Erik.

Awesome.