ADSK Stock | Autodesk Inc. Q1 2027 Earnings Call

Thank you Simon and welcome everyone to the call. We delivered strong Q1 fiscal 27 results today with revenue and earnings per share above the high end of guidance ranges and this outperformance flows through to our full-year guidance. Today we shared in a separate announcement that we have entered into a definitive agreement to acquire MaintainX, a leading modern maintenance and asset operations solutions used by organizations to manage and optimize day-to-day operations. The acquisition reflects Autodesk's commitment to becoming a long-term leader in operations and builds upon Autodesk Operations Solutions (AOS) capabilities in digital twins and factory design. I'm going to devote my opening remarks to operations and we'll return to our other strategic initiatives after Janesh's discussion of our financial performance and guidance. Autodesk's strategy is to converge design, make and operate data and context continuously through the full life cycle. This benefits owners, designers, builders, manufacturers, and operators through increased efficiency and resilience and reduced risk and downtime. Operations is highly complementary to the design and make category, reflecting growing customer demand for more continuous data-driven workflows from concept through to operation and optimization. With this expansion in operations, we plan to unlock higher value system-level AI, extend our duration with assets and systems from years to decades, and meaningfully expand our addressable market. MaintainX helps organizations manage maintenance, assets, and frontline operations with a modern mobile-first platform with pre-built integrations that serve as both a system of record and action for day-to-day operational workflows. It's led by a world-class engineering and AI talent and delivers a scalable go-to-market growth motion for operations and strong expansion potential across customer segments, geographies and adjacent use cases. Its position as a key component of maintenance and operational activity gives Autodesk access to rich data on asset condition and history, inspections, maintenance patterns, and real-world performance. Bringing MaintainX into Autodesk begins to build the data and context loop that enables a new generation of more integrated, data-driven and AI-powered capabilities connecting digital design and make with real-world performance to deliver predictive maintenance, intelligent automation and real-time decision support. In short, we are excited to welcome the MaintainX team to Autodesk and this acquisition will position us to help our customers increase efficiency and resilience and reduce risk and downtime through convergence. Janesh, over to you to discuss our quarterly financial performance and guidance.

Thank you, Janesh. Autodesk is focused on convergence powered by our platform, industry clouds, and AI. Let me give you some examples of our progress in the quarter that demonstrate how this differentiated strategy works for our customers in architecture, engineering, construction, manufacturing, and operations. Convergence increases efficiency and resilience and reduces risk and downtime. All of this is in service of deploying fewer resources to every project so they can bid on and win more projects with the resources they have. As you can see in the performance of make, formerly for construction's revenue growth accelerated again and has strong momentum with owners, designers, GCs, and subcontractors seeking to converge design and construction workflows. Once again, customers are choosing to consolidate fragmented legacy systems onto Autodesk's platform. For example, Dome Construction, an ENR top 400 general contractor, selected Forma for construction to replace disconnected legacy point solutions and standardized workflows across pre-construction, VDC, project execution, cost management, and turnover. In Europe, Essex Services Group, a leading UK building services contractor, signed a multi-year enterprise agreement for Forma Build to consolidate fragmented systems across complex data center and commercial projects. And Germany's largest municipal water utility, Berlin Water, expanded its use of Autodesk solutions, including Forma design collaboration to modernize collaboration across water infrastructure planning and delivery. These stories have a common theme: converging people, processes, and data across the project life cycle to increase efficiency and resilience, decrease risk, and prepare for an agentic AI world. Our comprehensive end-to-end industry clouds and platform drive convergence and extend our footprint further into the larger growth segment. In manufacturing, customers are demanding convergence as they invest in their digital transformation to leverage granular and unified data and embrace AI-driven automation capable of industry transformation. By consolidating on our platform, customers have the flexibility and connectivity across workflows to increase agility, innovation, and resilience. For example, Low Services, the shared services of the Friedhelm Loh Group, a German industrial technology conglomerate, renewed and expanded its enterprise agreement with Autodesk to better connect CAD, product data management, and enterprise systems, reducing fragmentation and accelerating time to market. In the US, a leading automotive manufacturer renewed its enterprise agreement with Autodesk to advance its factory of the future strategy, standardizing on Autodesk solutions across digital factory and AECO workflows to reduce vehicle lead times and scale factory design simulation across 14 factories for more proactive data-driven production planning. In addition, a visual display and fabrication company replaced a legacy design solution with Fusion after a multi-month evaluation, connecting design and manufacturing in the cloud to reduce handoffs and move projects through development faster. And in Europe, Schiedel, a leading manufacturer of chimney systems, implemented an integrated Inventor, Vault, and Fusion workflow to automate product configuration, generating thousands of modular component variants automatically to fast-track assembly and drawing creation. All of this was reflected in Fusion's accelerating growth. Let me finish by talking about AI. As I said last quarter, building agentic AI requires data, context, and expertise. What differentiates Autodesk is that we have all three at scale, and each one is scarce. We have scarce geometric-rich data from real design and make workflows that lets us build frontier 3D foundation models grounded in how the built world is actually designed and made. We have real-world workflow context including design intent, constraints, constructability, coordination and trade-offs that enable industry-specific MCP and agentic-based workflows that work reliably across the life cycle. And we have deep domain and technical expertise that translates data and context into trusted products, defensible IP and knowledge graphs built for professional-grade outcomes. That foundation matters because our customers do not just need AI that can generate. They need AI that produces outputs that are correct in the real world. That is a hard problem and we are using a hybrid approach to solve it. We are combining probabilistic AI generation with deterministic engineering validation using parametric and physics-based engines designed to reason about the physical world in 3D. So AI-generated outcomes can be validated against real-world constraints. Simply put, AI can generate and our engines can validate. Let me unpack that a bit. Frontier models are incredibly capable, but they are still fundamentally vision and language systems. Simply generating drawings is very different from understanding how something performs, behaves, or can actually be manufactured and constructed. For our customers, that accuracy matters. Through assistance and MCP infrastructure, Autodesk provides the harness layer that makes frontier models more controllable, context-aware, and useful across the life cycle. Autodesk Assistant is a good example already in market and there are even more powerful tools on the way, but we are going much further than MCP and agent-based workflows. Autodesk 3D Foundation models use decades of engineering intelligence combined with trusted product engines to directly reason about geometry and physical relationships, enabling workflows like similarity search, drawing dimensions, constraints, building plans, and geometry-aware automation. Auto-constraint infusion and more expansive features like our soon-to-be-launched building layout explorer in Forma are good examples of our progress here. When Autodesk AI generates a design, revision, manufacturing tool path, routing layout, or simulation setup, that output is validated through the same core parametric models from Autodesk that our customers have trusted for decades. These systems perform deterministic checks for geometric integrity, manufacturability, constructability, standards compliance, and performance. Every validation loop also improves the platform itself, continuously strengthening our AI models, validation systems, and quality thresholds over time. With MaintainX, we intend to extend those capabilities to develop digital twins that move beyond descriptive and informative models to high-value predictive and autonomous workflows and systems. This combination of probabilistic AI generation and deterministic engineering validation is why we said last quarter that 3D agentic AI requires high-fidelity geometry-rich data, deep industry context embedded in real-world design and make processes, and specialized 3D AI expertise. Few companies have all these. Autodesk does. It's why we believe Autodesk will define the next generation of industrial AI. Operator, we would now like to open the call up for questions.

Thanks, Sacket.

Yeah, I think that's an excellent place to start, Sacket. So let's start with the broader strategic context. At Autodesk, our goal is to converge the entire built world life cycle from design, make all the way through operate and close that loop. You've already seen us make a lot of great progress into the design and make side of this. You've seen what we've done in manufacturing and construction. Now we're moving more aggressively into operations and this is going to unlock a deeper and broader data and context layer that makes our capabilities more powerful in the agentic world. It's going to unlock a $40 billion TAM for us. And it's also going to advance our digital twin strategy from static to dynamic and ultimately to predictive. Let me tell you a little bit about the scope of our ambition and then how MaintainX plays into it. Our scope spans all the way from product manufacturing through to critical infrastructure like water and data centers, through transportation like airports, into commercial buildings. If you just look narrowly at the product manufacturing piece today, we can build static twins with tandem, dynamic twin capabilities that help define how a factory works with FlexSim, and ultimately look at the operations with Fusion Operations. But what MaintainX brings us is the field execution and data piece, the actual asset data piece. They're the fastest growing company in the space. They're rapidly consolidating a lot of the space onto their platform and there's a reason for that. What they're bringing to us is not only that expertise at the point of work where the assets are functioning, they're bringing to us a rich set of data about asset performance in the real world. This data is going to help us move along that spectrum from static to dynamic to predictive. And midsize and small manufacturers are going to be a big place where we're going to be testing this out. So, it's an exciting growth opportunity and it has a lot of potential for Autodesk and it kind of completes that whole vision of design, make, operate and closing that loop in that data and context layer.

Yeah. All right. Thank you, Janesh. It's a good question. Let me start with this. When we started construction, we took a set of assets that we had in pre-construction planning and in collaboration with 3D models in the cloud and some early assets we had around punch lists and field assets. And then we brought in an experienced leader at Autodesk who knew how to scale a new business. Jim Lynch in particular, he scaled the Revit business inside of Autodesk and was a critical part of that growth. We brought in people like Paul Blandini that were also part of that. And then what we did is we engaged in a series of acquisitions that expanded us in the places where we didn't have coverage. Field execution was a key area in that space. Look at what we're doing with MaintainX. It's field execution, field data, where things are happening on the site. This enabled us to do a lot of automations and capabilities around request for information flows and all the things associated with construction. We then went on to expand that and consolidated into a group. We ran it independently inside the company. Look at the results. Five years ago, we spent about $1.8 billion on acquisitions. Today, looking at this business, going back 12 months, it's worth almost $600 million in revenue and it's growing north of 20%. So, pretty impressive results. So, how does this translate into what we're doing with operations? I think we can actually do a lot more in operations today. We're putting an experienced leader that scaled Fusion in its early days. That's Steven Hooper. We're bringing back Paul Blandini into the operations team. He has a lot of the chops from that. We're bringing in a best-in-class field execution, on-site maintenance and asset-aware solution into our portfolio. MaintainX, a great team that knows how to build this kind of technology, a great team that's already built AI-driven workflows based on some of that data, and then we're going to work to incrementally bring in some smaller acquisitions over time that round out the solution. So follow the same playbook, integrate the things that we need to integrate quickly, hold back on the things that drive the health of the business, run the same playbook, and I think we're going to have even more impressive results than what you've seen with construction.

Thank you.

Yeah. So, as you know, the usage of our APIs and the AEC data model and the manufacturing data model by our customers continues to go up month after month after month. They're using it to provide real solutions. We're actually monetizing some of that API usage today. We continue to create granular solutions of our data and granular capabilities. All of these are tools that our customers will be able to plug into, but that we also plug into to create some of the agentic workflows that we're doing for our customers. So, it's really important to look at the progress we're doing there. When you look at MaintainX, it brings another piece of granular data, asset performance data into the cycle. This is actually going to be valuable for some of our customers to actually build new businesses on from our stack. So our goal is our customers should be doing more projects, bidding on more projects, winning more projects and getting a larger slice of the life cycle of the built world. This is another way we enable this. Our customers are going to be able to expand their offerings to their customers through some of the new types of granular data we'll be able to offer through the asset layer as well. So it's another way of rounding things out not only for our customers but for us as well. So it's going to be pretty exciting.

We absolutely want them to do this, Joe. You know we have a long history of enabling our customers to expand our offerings to build tools that fit for their solutions. Now when you look at the agentic world, remember though that there's this important critical mass around data and context that extends beyond any single customer. No single customer is going to have enough data to fully understand the scope and impact of a project. Nor will they have the entire context of the project. Remember context for a project flows across companies and across silos within a particular project. It is an ecosystem problem and we're well positioned to orchestrate the ecosystem at the data and context layer. So we're going to add a lot of model capability that is really above and beyond what any of our customers can build on their own. But we encourage them and we want to enable them to build vertical capabilities on top of this that make their environments and their practices even more rich and more powerful. But they're going to need a strong platform to do that. And that strong platform is going to be built on the data, context and expertise that we bring to play.

Thank you.

Yeah. So, remember, let's talk about the goals of these changes. The goal is to focus the entire ecosystem on new business generation, capturing and growing new businesses. So we shifted away from renewals as a big part of the partner business to new business as an emerging bigger part of the partners' business and we also moved to automating some of the processes around renewals so that we could get better renewal performance and more focus on the new business. Actually, we saw that in the quarter. We saw strong renewal performance and we saw exactly the kind of weak new performance we expected as people shift over to this. We expect this to all work its way out over time as the channel partners kind of get their hands around how their teams are reconfigured to build out new business. And we're looking forward to seeing the results. Janesh, do you want to comment on anything?

Yeah, I think Janesh said it pretty well. I'll just add on and double down on this notion of the fact that the data that we're getting about asset performance and asset life cycles is incredibly valuable to us. It allows us to move up the spectrum from the operational and the static and dynamic twins into the predictive world. That's going to provide a lot of value to the mid-market customers we serve. It's going to provide high-value AI-driven solutions that do things that most small and midsize players are not able to do without some of these tools. So, look for us to really dig deeply into that layer of this asset because it's a very important component of it for our context and data strategy.

This is the largest deal we've ever done.

Thanks, Brent.

Yeah, we absolutely have overlapping customers in the manufacturing sectors and machine operating sectors that do overlap with us. The core user is the person actually owning the asset who has a high concern about uptime and managing and maintaining that uptime. It allows them to get the maintenance people out into the facility, track what needs to be fixed, track how it needs to be fixed. And the value we're going to bring moving forward is we're going to be able to turn that into a predictive cycle and a closed loop into being able to allow them to optimize facilities. I think the greatest example you can use here is what happens in a small and midsize factory where they're trying to highly automate their processes and try to create a digital twin of the operational capabilities of their factory and reconfigure that factory on a regular basis and keeping it running as much as they possibly can. That's going to be a big opportunity for us with regards to the predictive twin capability.

Thank you.

Yeah. So there are numerous things that we built into the assistant layer that customers are going to be able to do that they haven't done before. Coming out very shortly is what's called the building layout explorer. It's going to be built into the Forma building design suite. It allows people to explore the layout of a building within the framework of the envelope for the building that they're proposing and that they're exploring. These are the kind of capabilities that are built off of models that we've created ourselves that allow customers to do things that they would not be able to do dynamically with some of the other kind of horizontal large language model tools. We're also going to be building in additional capabilities that bring in some of our vertical tools into the assistant workflow so that they can actually dynamically create some user interfaces that solve particular problems for them. Again, going to be unique to the assistant. I don't want to say too much because we're going to have a lot of announcements at AU that kind of tell people where we're going, but you're going to see us add a lot more to the layer between the frontier models and what the assistant does and some of the capability that Autodesk builds on top.

Thank you.

Whenever you meet the customers where they're at, you bring new customers into the ecosystem, right? And part of what we're doing is to meet the customers where they're at. We pull them into the ecosystem and then they're able to explore the capabilities that we're able to help them finish above and beyond what they do in some of these other layers. So this is absolutely a channel for us but it's also meeting the customers where they're at today.

Certainly, we're aware of all those other explorations in this space. One of the things I want to make sure that you understand here is it's not just about the maintenance piece and what we're acquiring today. It's about the whole experience around how a digital twin actually operates for our customers in their real world settings. So we're actually building out capabilities that circle a whole bunch of the problem. And I gave some of those examples in the factory space. But also when you look at some of these other solutions and why MaintainX has managed to grow so fast, a lot of these older solutions required custom solutions. They were dependent on other platforms like Salesforce for instance and they were narrowly focused on an ecosystem of vendors that were supported by the particular players. MaintainX is a complete heterogeneous solution. Every asset that the customer has to manage can be managed within an environment like MaintainX, which I think is a very important differentiator as we move here. But you combine that also with what we're trying to do with the end to end where you're defining the twin, you're running things in the twin and you're moving into optimizing and predictive workflows. It's something that other vendors have not done and have not been able to do successfully. And again, the reason MaintainX has been successful as it is in consolidating the space is because they cracked the code on how to drive some of these solutions at scale.

No, nothing we didn't plan for in the cycle. When you move sales incentives around and move sales roles around, you're obviously going to go through a cycle of absorbing those changes and absorbing those things into your pipeline, you're going to go through some pipeline disruption and all the things associated with that. These are things we factored in. So, we're not seeing anything we didn't expect like we discussed during the new transaction model change. It's all kinds of things we baked into the cycle and into the change management.

Thank you.

Yeah, Michael. That's a really relevant question. So, as you know, we've made a lot of investments in kind of changing the company and preparing it for the future at various levels. One of the things we also did very early on is we prepared for a mixed, blended world of subscription and consumption. It's one of the things I think we're well ahead of a lot of other companies in the application space. So we already have offerings that allow us to provide subscriptions for people that are heavy users, blended consumption models like Flex for people that are occasional users, and we also have ways and means of which to blend these things together. We have capacity models, project-based pricing models. So we have a lot of various models that allow us to meet the customer at the project level and also meet them at the usage of AI in ways that maybe other players in the space don't, which I think is really important, and we built the systems and processes up around that. We've also built the systems and processes that allow us to bring in acquisitions and integrate them into our processes more effectively. Now MaintainX is a subscription model and they're moving into the consumption space moving forward. So they're a natural fit for some of the capabilities that we already have. So we feel very confident about how we can integrate them and move them into our processes. But we've come quite a ways up in the maturity curve and we feel pretty good about where we are with regards to being ready for the new world in terms of some of the pricing models and the packaging models that are associated with that.

Thank you.

Yeah, we can. Josh, go ahead.

Yeah. So you're actually convolving two things there, Joshua. Let me help you with that a little bit. So the rebranding of everything under Forma is to acknowledge that we have an end-to-end solution that's entirely cloud-native from design, pre-construction planning all the way into construction. So everything's branded under the Forma layer. So Forma for construction is now connected directly through Forma data management to Forma site design and Forma building design. The offering you're talking about is Forma Build Essentials. Build Essentials is a version of Build designed to reach the small and midsize general contractors. So, it's an effort to expand our footprint down market at a price point and a capability point that works. So, it's essentially a smaller version of Autodesk Build that fits for the people who are participating in the project more broadly. That's what that price point is. It's basically going to expand our footprint and our reach into the entire ecosystem and it's designed to actually accelerate some of our expansion into other parts of the construction process.

Yeah. So, we're a very disciplined company when it comes to acquisitions. And I want to go back to what we did with construction because again, we're running a very similar playbook that we did with construction. And frankly, over a five-year period, a lot of the pricing dynamics and all the operating dynamics are actually very similar when you adjust for the 5-year difference in time frame. So, we're super disciplined about these things. We do work like this when we know we can absorb it and we know we can execute on it. The timing is right because this is the time when you want to expand the breadth and depth of your data and context layer across entire life cycles. The broader and deeper your context and data layer, the more competitive you are in the future agentic world because the more workflows you can automate, the more value you can bring to your customers. So the timing is right for us. Super disciplined about processes like this and we've been waiting to do this and planning to do this for quite some time.

Thank you.

Yes, we can.

Yeah, our relationship with our customers is one of transparency and choice. We are very transparent about what we're doing with their data and how we're using it and where we're using it and where we're going to give them choice for opting out of how we use their data. That relationship has been highly effective. It's resulted in robust conversations with a lot of customers and I think it's resulted in good outcomes for them and for us. Everybody wants the automations we're able to deliver. A lot of customers realize that they can't deliver some of the things that they really need at the scale that they're at. So, as long as we continue to lean into transparency, be very straightforward with our customers and direct about what we're doing and what we're not doing and offering some level of choice, we've been having a very productive relationship with all of these customers and really good discussions.

Thank you.

Yeah, first off, it's far too early to look at MaintainX through the lens of consumption and things associated with consumption. They're very early in that journey. They've got a bunch of AI-driven capabilities that will evolve over time. Now with regards to the larger question about branching out to those other segments, again following very much what we did in construction, we built an entire go-to-market machinery around construction that allowed us to reach various players within the construction ecosystem. That's why we pull it out separately and that's why we engage the way we do to make these businesses successful is that we want to make sure we're building the ecosystem, not just the product ecosystem but the go-to-market ecosystem that allows us to reach the various customers. You heard us kind of talk pretty vocally about the first pillar being probably in the product manufacturing space but remember we already have a footprint in water operations and we already have a footprint in building operations as well. Our desire is absolutely to get deeper into the owner operator workflow because we feel that converging them and bringing them in closer to the entire process is going to be valuable to us, valuable to them and valuable to the rest of our customers in our ecosystem.

Thank you.