Loading
Good day, everyone. My name is Megan, and I will be your conference operator today. At this time, I would like to welcome you to the UiPath's First Quarter 2027 Earnings Conference Call. [Operator Instructions] At this time, I would like to turn the call over to Allise Furlani, Vice President of Investor Relations.
Good afternoon, and thank you for joining us today to review UiPath's First Quarter Fiscal 2027 Financial Results, which we announced in our earnings press release issued after the close of the market today. On the call with me are Daniel Dines, Founder and Chief Executive Officer; and Ashim Gupta, Chief Operating and Financial Officer, to deliver our prepared comments and answer questions. Our earnings press release and financial supplemental materials are posted on the UiPath Investor Relations website. These materials include GAAP to non-GAAP reconciliations.
We will be discussing non-GAAP metrics on today's call. This afternoon's call includes forward-looking statements regarding our financial guidance for the second quarter and full year fiscal 2027 and our ability to drive and accelerate future growth and operational efficiency and grow our platform, product offerings and market opportunity. Actual results may differ materially from those expressed in the forward-looking statements due to many factors, and therefore, investors should not place undue reliance on these statements. For a discussion of the material risks and uncertainties that could affect our actual results, please refer to our annual report on Form 10-K for the year ended January 31, 2026, and our subsequent reports filed with the SEC.
Forward-looking statements made on this call reflect our views as of today. We undertake no obligation to update them. I would like to highlight that this webcast is being accompanied by slides. We will post the slides and a copy of our prepared remarks to our Investor Relations website and immediately following the conclusion of this call.
In addition, please note that all comparisons are year-over-year unless otherwise indicated. Now I'd like to hand the call over to Daniel.
Thank you, Allise. Good afternoon, everyone. Thanks for joining us. We delivered a strong start to fiscal 2027, once again exceeding our guidance across all key financial metrics.
Before I dive into the results, I want to take a moment to reflect on our progress over the last year, In May of last year, we launched our agentic and business process orchestration products into general availability. One year in, adoption has moved from early experimentation to production deployment. . We are seeing this play out across 3 areas in particular: installed base expansion, process orchestration adoption and vertical AI workflows. A great example is one of the largest health care distribution companies in the U.S.
One end-to-end workflow, combining UiPath agents and deterministic automation is expected to drive multimillion-dollar annual savings, which led to a 7-figure expansion in the quarter. One of the world's largest construction companies adopted our purchase-to-pay vertical solution and told us they chose UiPath not as a software vendor, but as a strategic codevelopment partner for their enterprise AI transformation. And the Fortune 500 energy company placed UiPath at the center of a $70 million cost reduction initiative made possible by our ability to bring deterministic agentic and process orchestration together as a single platform.
Turning to the quarter. First quarter ARR reached $1.901 billion, up 12% year-over-year, driven by $49 million of net new ARR and revenue of $418 million, up 17% year-over-year. We grew first quarter non-GAAP operating income to $92 million a 22% margin, driven by improved operational efficiency and disciplined execution across the business. And we delivered first quarter GAAP profitability for the first time in company history.
This quarter's performance is built on the strength of our enterprise automation installed base thousands of customers with deep platform adoption, proven ROI and a track record of expanding with us over time. And it reflects continued momentum with our AI products. In the quarter, 16 out of top 20 deals including AI and expansion deals that included AI were 6x larger than those that did not. The drivers behind these results are the same core differentiators we outlined last quarter our platform that brings together deterministic and agentic automation with enterprise-grade process orchestration, our installed base flywheel, our governance foundation and our ability to combine a horizontal automation platform with deep vertical solutions.
I saw that momentum firsthand across our global events including in India at our annual Fusion event and DevCon developer conference. Across customers, developers and partners, the message was consistent, enterprises increasingly need the platform that can cover and orchestrate humans, agents, workflows, automations and systems, an area where UiPath has a structural advantage. At DevCon, we launched UiPath for coding agents, enabling developers to connect their coding agent of choice, to create, test, deploy and manage automations across the full life cycle on the UiPath platform with enterprise-grade governance and reliability built in.
This matters because nearly every customer conversation surfaces the same constraint, and automation backlog that outpaces their capacity to build and maintain. Implementation is often the hardest part, particularly in complex enterprise environment, where upstream system changes can drive maintenance costs over time. By combining coding agents with the governance, orchestration and self-healing capabilities built into our platform, we can dramatically reduce that operational burden and compress deployment time lines from quarters to weeks. We expect this to accelerate time to value for our customers. drive deeper adoption and strengthen long-term retention across our customer base.
Our internal teams and customers are also seeing great results with coding agents, including one of the world's largest consumer electronics companies, which reduced a 4-week project built to 3 hours and one of the world's largest chip manufacturers reduced a 2-month project build to a few days. What stood out most this quarter is how clearly customer priorities have evolved with the focus consistently centered on process orchestration. As one customer put it during DevCon, models are easy, orchestration is not. That directly reflects what we hear across our customer base.
Customers are no longer asking us simply to deploy more agents or generate more code. They are asking us to transform our entire business functions operate through end-to-end workflows that span departments, connect systems and deliver measurable operational outcomes. And delivering that kind of transformation requires more than individual AI agents. It requires a platform that can orchestrate agents, automation, APIs, systems and people together within secure, governed enterprise workflows. A great example is one of the world's largest telecommunications companies, with nearly 2,000 processes already automated and all the $30 million in annual cost savings, they are now expanding their deterministic base further and moving into agentic workflow building a pipeline of more than 200 additional deterministic automations and over 20 agentic use cases.
That same process orchestration capability also drove a competitive displacement with the Fortune Global 500 electronics manufacturer where we were the only platform that could take them from task-based automation to enterprise-wide business process orchestration. Building on a strong deterministic foundation, they are now expanding across manufacturing and supply chain workflows using Maestro to coordinate automation, agent, systems and human decisioning globally. Maestro already excels at structured workflows like invoice approvals and deployment pipelines where the process itself is clearly defined.
But increasingly, enterprise work is nonlinear and its dynamic, exception driven and centered around decisions that move across teams and systems. This is why at DevCon, we launched Maestro Case into public preview, extending beyond traditional process orchestration into the orchestration of unstructured enterprise work. The breadth is what makes UiPath the most complete process orchestration and automation platform in the market, and it's already driving broader customer adoption, including Sonic Automotive, an early adopter of our agentic product. They initially deployed UiPath to automate vehicle stocking and sales lead follow up.
They are now standardizing their agentic automation strategy on the UiPath platform under a broader C-suite initiative and expanding into workflow such month-end close and employee onboarding. A key driver of the expansion was Maestro Case's ability to orchestrate complex multistage workflows across agents, automations and people. Beyond process orchestration, documents remain one of the biggest sources of friction in enterprise work. And customers are increasingly turning to UiPath IXP to automate document-intensive workflows at enterprise scale.
In May, we were named the leader in The Forrester Wave, Document Mining and Analytics Platforms Q2 2026. We are seeing that momentum translate directly into largest enterprise deployments and competitive wins. A great example is the leading medical technology company that is standardizing on UiPath IXP to automate high-volume unstructured documents like invoices and purchase orders. The customer is already realizing approximately $5 million in annual savings and expects that to grow to $10 million as they scale. Demand for industry-specific governed workflows continues to grow as enterprises increasingly adopt purpose-built AI solution tailored to their business.
What differentiates UiPath is our ability to combine those deep domain-specific solutions with the same process orchestration, automation and governance platform. This quarter, we expanded our portfolio across financial services, retail and manufacturing and the office of the CFO. We are already seeing momentum in health care in a 7-figure new logo win, a leading Latin American health care provider, selected our vertical solutions to support revenue cycle management, medical record summarization and claim denial management and expect $12 million in cumulative benefits. Customers are also realizing meaningful operational benefits from these vertical solutions, a leading health care technology company produced clinical summary review times by 90% using our Medical Record Summarization solution.
We are seeing similar momentum in financial services. A regional bank is now automating 61% of sanctions hit reviews with our transaction screening alert review solution, processing roughly 14,000 alerts per month. AI is accelerating software creation, but it is also accelerating the need to validate it, as code volume grows, so does the testing burden, Independent research firms have consistently recognized UiPath as a leader in this space, and we believe that validation reflects the real and growing structural advantage. Test Cloud is at the center of that, helping customers move testing from a downstream bottleneck to a continuous intelligent function embedded across the delivery life cycle.
One example this quarter is the leading U.S. utility provider that adopted UiPath Test Cloud for agentic testing to streamline customer platform support launch. The solution is expected to significantly reduce manual testing while generating nearly $3 million in savings. During the quarter, we continue to deepen our partnerships across both go-to-market and technical integrations. This included our expanded collaborations with Deloitte. Embedding UiPath Test Cloud into their Ascend delivery platform, bringing agentic testing capabilities to Deloitte's global client base.
We are seeing similar momentum with Accenture, a life sciences customer, we highlighted last quarter, worked with Accenture to deploy global agentic sales entry solution and has now scaled based across 70 countries. Building on that success, they signed a 7-figure expansion and are now partnering with us to design an office of the CIO intake solution built on our process orchestration platform . On the technical side, we continue to broaden our reach across key enterprise ecosystem. With Microsoft, we integrated UiPath with their security suite to help automate threat detection and response.
With Salesforce, we launched a new AgentExchange offering that extend Maestro process orchestration across Salesforce and back office systems. With Google Cloud, we brought our IXP solution to their marketplace. And with Databricks, we connected their data intelligence platform directly with UiPath process orchestration to help enterprises move from data insights to automated action within governed workflows. In summary, this quarter reflected disciplined execution across the business, continued AI adoption and growing momentum across our platform.
No other vendor can bring together deterministic automation, agentic AI, document intelligence and business process orchestration on a single platform. And that completeness is what customers are standardizing on. We believe we are uniquely positioned for this next phase of enterprise AI adoption and our strong start to fiscal 2027 reinforces both the durability of our business and the scale of the opportunity ahead. Before I turn it over to Ashim, I want to take a moment to acknowledge the loss of our dear friend and board member, Soma Somasegar.
Soma was the long-time investor in UiPath and rejoined our Board just 8 months ago. His impact on UiPath was immediate and profound. He was the mentor, a trusted adviser and someone I deeply admire both professionally and personally. I will miss him greatly and I know our entire Board and leadership team share that feeling.
Our hearts are with his family. With that, I'll turn the call over to Ashim.
Thank you, Daniel, and good afternoon, everyone. Before turning to the financials, I'd like to provide a quick operational update. We continue to make meaningful progress across the key priorities we outlined last year. Our partner ecosystem is becoming more deeply integrated with both our go-to-market motion and customer adoption efforts, helping us scale larger enterprise deployments across industries.
As Daniel mentioned, partners like Deloitte and Accenture are increasingly instrumental, not just in selling, but in helping customers operationalize and scale AI-driven workflows. and we are seeing that play out across financial services, health care and other key verticals. At the same time, our internal focus on customer adoption remains a central operating priority. We continue to invest in our services organization and industry expertise to help customers accelerate deployment and expand platform usage. A key part of that effort is our forward deployed engineering program, which we launched 6 months ago.
FDEs are proving to be an effective bridge between product innovation and customer deployment, shaping vertical workflows directly in customer environments and accelerating time to value. In addition to adoption, our go-to-market teams are executing with discipline and customer interest. AI is now part of virtually every strategic customer conversation. And those discussions are increasingly expanded into platform, orchestration and vertical solutions.
The deal data Daniel mentioned reflects that. AI was included in 16 of our top 20 deals and expansion deals that include AI were 6x larger than those that did not. Finally, on operational efficiency, AI is changing how we run the business internally. We are seeing increased operating leverage across the organization while continuing to invest deliberately in R&D, vertical solutions and customer-facing functions.
Turning to the quarter. Unless otherwise indicated, I will be discussing results on a non-GAAP basis, and all growth rates are year-over-year. I also want to note that since we price and sell in local currency, fluctuations in FX rates impact results. Since the time of our last earnings call through the end of the first quarter, rates remained largely stable and resulted in an incremental tailwind to our first quarter ARR and revenue results of less than $1 million.
First quarter revenue grew to $418 million, an increase of 17%. Normalizing for the year-over-year FX tailwind of approximately $7 million, revenue grew 15%. ARR totaled $1.901 billion, an increase of 11%. This included a $9 million year-over-year FX tailwind.
Net new ARR was $49 million. Normalized for foreign exchange and the impact of M&A, net new ARR improved on a year-over-year basis. Our dollar-based gross retention -- gross retention rates remained best-in-class at 97% and our dollar-based net retention rate was 109%, underscoring the durability of our customer base as they embrace our agentic automation solutions. Adjusting for FX, dollar-based net retention rate was 108%, demonstrating stabilization across our business.
We ended the quarter with approximately 10,550 customers. Attrition continues to be concentrated amongst our smallest customers, while customers generating more than $30,000 in ARR grew 7% year-over-year. That dynamic is also reflected in our cohort performance. Customers with $100,000 or more in ARR increased 11% to 2,624 and customers with $1 million or more in ARR, increased 18% to 374. Our customer strategy has continued to focus on deepening our presence within the world's most complex enterprises, where we see the greatest opportunity for long-term expansion.
Consistent with that strategy, we continue to add new enterprise customers with significant long-term expansion potential, including new logos like Candela Medical, Tire Rack, Shoprite Holdings and a global semiconductor company. who is replacing a legacy RPA vendor with UiPath as their strategic automation platform. Our cross-system integration and end-to-end process orchestration capabilities, given them a scalable foundation they need to migrate their existing automation program beyond task-based automation into broader agentic workflows. Remaining performance obligations increased to $1.413 billion, up 15%.
Normalizing for the FX headwind, which was approximately $9 million, RPO grew 16%. Current RPO increased to $908 million, up 17%. Turning to expenses. We delivered first quarter overall gross margin of 83% and software gross margin was 90%.
First quarter operating expenses were $256 million. For the first time in company history, we delivered a GAAP profitable first quarter with GAAP operating income of $28 million, up from the prior year GAAP operating loss of $16 million. GAAP operating income included $53 million of stock-based compensation expense. First quarter non-GAAP operating income was $92 million, representing a 22% margin, up over 250 basis points year-over-year and driven by our continued focus on operational efficiency.
First quarter non-GAAP adjusted free cash flow was $130 million. We ended the quarter with a healthy balance sheet of $1.4 billion in cash, cash equivalents and marketable securities and no debt. During the first quarter, we repurchased 20 million shares at an average price of $11.47. Since April 30, under our 10b5-1 plan, we have repurchased an additional 2 million shares at an average price of $9.63 through May 27, 2026. Now turning to guidance. We are pleased with the team's execution in what continues to be a variable macroeconomic environment.
We continue to maintain a prudent outlook and guide to what we see in front of us. Since we provided guidance on our last call, the euro has remained largely stable while other currencies such as INR and Romanian Lei have experienced volatility. As a result, for the second quarter and full year we expect a nominal incremental FX headwind to ARR and revenue. Despite the incremental FX headwind, we are raising guidance for the progress we've made on our operating priorities.
Turning to the specifics of our guide. For the second fiscal quarter 2027, we expect revenue in the range of $395 million to $400 million. ARR in the range of $1.929 billion to $1.934 billion, non-GAAP operating income of approximately $75 million, and we expect second quarter basic share count to be approximately 518 million shares. For the fiscal full year 2027, we expect revenue in the range of $1.776 billion to $1.781 billion.
ARR in the range of $2.058 billion to $2.063 billion, non-GAAP operating income of approximately $430 million. And finally, we continue to expect fiscal year 2027 non-GAAP adjusted free cash flow of approximately $425 million and non-GAAP gross margin of approximately 84%. Thank you for joining us today, and we look forward to speaking to with many of you during the quarter. With that, I will now turn the call over to the operator.
Operator, please poll for questions.
[Operator Instructions] Our first question will come from Bryan Bergin with TD Cowen.
Ashim, maybe just to start on the overall demand environment, any interesting changes in the underlying demand trends and pipeline conversion, anything as it relates to deal timing, sales cycles, things like that, just as this conflict has been extended?
No. We actually feel like the environment has stayed relatively stable versus what we saw in the first quarter -- sorry, when we guided into the first quarter earlier this year, Bryan, I think we actually feel very positive about the momentum in the business, the health of our pipeline and the conversion rates and the predictability. The customer conversations are going really well. A lot of the pilots are beginning to now starting to convert, which we feel really positive about.
So overall, we're actually very positive overall on our pipeline and the environment remains variable as it has been, it feels like a new normal is the way we've got to think about it.
Okay. And then on AI product ARR levels, any sizing you can update us there? And how the pricing conversation across those solutions is evolving?
Yes. We'll disclose the product ARR periodically here. We feel really good about the momentum. I think we pointed to it in terms of 16 of the top 20 deals for the quarter involved AI.
I think agentic and our AI products in general have really good, strong momentum. And our vertical solutions are also starting to really get traction both from customer interest and pipeline, particularly in health care and financial services. And then lastly, I think test, which is our agentic testing solutions that has really good traction as well. We look forward to update the numbers here in the coming periods.
But right now, we feel really good momentum. And I think the deal traction kind of speaks to the overall trajectory for the AI products.
Your next question will come from Scott Berg with Needham.
Hi, everyone. Nice quarter. Daniel, you spoke extensively about orchestration, and it's a key topic that comes up in our work on the space consistently over the last probably year or 2. When you think about Maestro and the deals that you have out there, is there any reason why Maestro isn't a part of basically every deal that has AI? Or is there some combination that would suggest that, that's not going to be a part of every deal going forward?
I don't think Maestro can be part of every deal. The way we are looking at our business, it's -- we have an entire platform that can address the whole spectrum of task and process orchestration. Maestro is a solution that comes into play when customers are doing process orchestration and automation and end-to-end process orchestration and automation. But we have customers out there that are happy to start with the task automation product.
And task automation can also be deterministic and cognitive. I would say that RPA and API automation plays into deterministic task automation, while we have agents that can be applied to task level. Maestro comes into place when you need more complex orchestration of work that involves humans, task automations, enterprise workflows systems and agents. So it's naturally more for our more involved customers.
Maestro helps us lending bigger deals, makes our installed base stickier to the customers, but I cannot say it can be deployed in every single deal.
Got it. Helpful. And then Ashim, a follow-up to the last questions that were out there. I think what we're all trying to understand is the impact of, obviously, some of your AI modules on the business and the bookings and what the general trajectory is.
I understand that you don't want to necessarily report that AI metric every quarter. But if I ask a question a slightly different way is if I think about those 16 deals in the top 20 that had an AI component of them., How significant are those transactions is coming from some of the AI functionality. I think we're all trying to understand is it still traditional RPA heavy in those transactions or if we're seeing a bigger impact from some of the AI functionality?
No, we're seeing a bigger impact. I think the way I look at it is I kind of would divide it into 3 areas, like our top customers and our top deals, the majority of our transactions have a significant AI, if not a majority, AI component. Scott, that's driving it. They're not piecemeal where it's kind of like 1 or 2 SKUs that get moved in or small quantities.
They are materially what we are selling, right, to our customers. I think there is a mid-tier of customers where you see actually a continued demand in traditional RPA and deterministic automation. And those are companies that are not -- that either are -- have embraced agentic and AI in a major way, and they are actually pulling forward more deterministic automations as they weigh both the cost and the trust and governance, that agents versus deterministic automations give you. And then really, the kind of some of the drag that we talked about is really from the low end of the market, smaller customers and personal productivity.
That's kind of the way I would divide up the quarter. So we're actually really pleased with the pull that we're getting on the agentic side and its contribution to our growth.
Your next question will come from Sanjit Singh with Morgan Stanley.
This is Abhishek Murli on for Sanjit Singh. I'd love to hear a little more on the beat and kind of dig into -- given Q1 revenue upside was strong, but the beat was largely driven by license revenue and the ARR was relatively in line. So can you kind of help us understand the quality of that revenue beat? Was there anything unusual in license timing or customer behavior that we should be aware of?
And then how should we think about the relationship between license performance and ARR trajectory for the rest of the year?
Yes. I mean, I would say 2 things. One is we feel really good about the quality of that revenue, both in terms of the products as well as the deal quality and structures. I would say, it's -- our quarters have been very clean.
And we feel very good about the overall deal quality and construction. Remember, revenue is a quarterly performance metric when you're looking at the growth rates, and we are on ASC 606 versus ARR, which is a 12-month metric, right? So if I break down the question, you look at revenue growth at 17%. When you look at a trailing 12-month period, the revenue growth rate is 15%.
So actually, which makes me feel very good about 15% growth on a trailing 12-month basis. And it's relatively in line with the ARR growth at 12%. In terms of ARR beat versus revenue beat, it's really just the mix of deals with 606 timing. And the license revenue being a factor in that is a sign actually of really good quality revenue overall.
And then as a follow-up, anything you can share in terms of the mix between consumption-based revenue and per seat?
We don't -- consumption-based revenue is a very small part of what we do. We still have -- the subscription really dominates our pricing model and per seat pricing as well, that is not the majority of what we do. We are really selling executions as well as kind of our typical server-based pricing that we have for unintended robots in particular. I just -- I'd really emphasize again, personal productivity is a very small part of our portfolio, simple task-based automation.
So what we sell is the larger complex use cases now. And that really mix us higher towards both server-based and subscription-based pricing.
Your next question will come from Sanika Merchant with RBC.
Guys, this is Sanika Merchant on Matthew Hedberg from RBC. Could you talk a little bit about the broader competitive environment for orchestration and any changes or trends you're seeing and there's also been a lot of developments around frontier model capabilities. Could you talk to how you see these developments impacting the broader competitive landscape and the company specifically?
Yes, sure. I would like to start by saying that we have a really unique platform in the market. So -- and it's based on 3 major pillars. We have a very modern process orchestration technology that is built on a very innovative workflow engine capabilities.
We have proven of 10 years deployment of scales of automations in a secure and governed environment with some of the largest companies in the world. And we have a unique ability to connect to both modern API-based systems and legacy systems. This 3 pillars make our platform quite unique in the market. In terms of the new development that we have seen, I think we all recognized the huge impact of the coding agents of the entire ecosystem.
And I want to point to you to an interesting phenomenon that it's something that we spot with our customers and within our own UiPath operations. It's becoming increasingly easier to build deterministic automation. You are using coding agents to build deterministic automations and deploy them at scale. It's becoming really easier to address the long tail of opportunities of work.
And it was not economically feasible before coding agents to get to this level of automation. And building automations, it's really creating the substrate for deploying agentic AI later on. I would point to why coding agents are so successful nowadays because they really combined model, the strength of the models with the strength of deterministic automations. Claude Code is so good because there is this deterministic harness around it.
So Claude generates code but then it uses a compiler, which is a deterministic piece of technology to compile the code, and then it's using testing, which are another deterministic piece of code to validate the code that is generated. So I think it's becoming more clear to everyone that the combination of deterministic automations and models are what makes the real deployments in production. And I would say that in this regard, we do have tremendous advantage. Our platform is already enabled for coding agents, and we showed at our DevCon in India, we showed that we can reduce significantly the implementation times.
Think for a second, weeks to hours, that really means a lot when you go and deploy automation to the long tail of possible work.
Thanks. Appreciate the color there. And as a quick follow-up, so you've talked a lot about sort of profitability. And last quarter, you also updated your long-term non-GAAP operating margin target to 30%. And keeping in mind the fact that growth remain a priority for the company, what are some keys to margin expansion in fiscal year '27? And is there any seasonality you would point out on that?
Look, I think from a cost seasonality, nothing except for, obviously, there are later parts of the year, we have sales compensation. There's just normal SaaS seasonality from an expense standpoint. Otherwise, I think we're pretty -- there's no real seasonality to mention. From my standpoint, I think we're looking at as growth is our first priority.
So we are investing in FDEs. We are investing in test. We're investing in vertical solutions. We are investing in coding agents as evidenced by the speed of the launch by which we're moving through things.
And so from our standpoint, that investment is our first priority. At the same time, we updated our long-term models because we are able to find increasing levels of efficiency both through continued discipline and scrutiny and then also from implementing both our platform as well as broader AI tools within the company. And so I would say we're an invest-first mindset, and a waste nothing mindset. And that combination, I think, gives us the ability to both grow and drive the strategic initiatives while expanding operating margins.
Your next question will come from Pat McIlwee with William Blair.
My first question, I thought the AI summit you put on earlier this year was very helpful in envisioning how customers can evolve from your traditional RPA workflows towards more agentic-enabled workflows. And specifically how they can choose their own autonomy level and then kind of use a feedback loop to evolve the level of automation in that process over time. So I know it's early on, but for your existing customers, where are they in that autonomy, evolution right now, are a lot of them content with the value they're getting from current RPA workflows and leveraging AI within newer workflows?
Or are they really racing towards these agentic solutions to maximize the ROI they're getting from the platform, both existing and new workflows alike?
Yes. I would like to point out that despite the technology being very new, it is held by our customers with a lot of enthusiasm. Even when we were in like close review, we got a lot of requirements from the customers. They -- some of the customers even went to find on like some of the skills that we publish and use them with the coding agents. And also, I would like to point out to the fact that basically coding agents solved 2 of the biggest hurdles in deployment of automation.
Number one was always the implementation leading time to when -- until an enterprise would get value from automation. So that's been already proven internally by our own forward-deployed engineers and externally by a few advanced customers that can be shrink in many cases, from weeks to hours, which is very significant. The second way that coding agents unlocks a bottleneck of automation is in maintenance. One of the apparent flaws of automations was always the fact that they are fragile and they break.
If there is an upstream modification in an enterprise system that automations are not aware they might break and that will require human intervention and many days of reviewing and understanding. Now we offer both a heating agent and the diagnosed agent. So the heating agent can do a lot of the work during run time, during execution. And in many cases, the healing agent can fix the execution in itself and the processes are unaffected.
When there is an exception, we help tremendously developers with these diagnosed agents to gather all the context around automation, and they can publish a fix much faster than before. So yes, I would conclude that for us, this is a really big unlock. And we see the potential for a huge acceleration of customer adoption.
Right. Okay. And to kind of continue on it, it sounds like AI agents are largely extending, not replacing deterministic automation within your platform. But as we think about that, I wanted to ask, is there any sort of dynamic where you're seeing customers leverage agentic AI to somewhat cannibalize some of the traditional bot monetization?
Or is it largely building incremental automation and therefore, incremental monetization on top of those workflows?
Yes. I would like to say that perhaps this is one of the biggest confusion that AI brings into the table. The idea that nondeterministic probabilistic technology can replace deterministic automation. This is not true.
It's not true from the capability perspective, and it's not true from an economical standpoint. And let me elaborate a bit on both. A probabilistic technology is not architecturally meant to follow a dozen of steps and sometimes hundreds of steps in the same order, in the same sequence. Every step will have a probability.
When you multiply these probabilities, you will end up with something that is not reliable end of the day. And there are many regulated industries that cannot tolerate anything that is not 100% reliable. They will prefer an automation to favor an exception rather than produce an unexpected result. So deterministic bots cannot be replaced by nondeterministic AI agents.
Again, this is the architecture that is proven over and over again by all the AI agents that are out there, the most sophisticated agents like Claude Codes or OpenAI Codex are built on the foundation of deterministic tools that they quote. So it's a hardness around the model and deterministic tools. This is exactly how they work. This is exactly what we are proposing to our customers, hey, reuse your investment in your existing deterministic automation and surround it with process orchestration, which is also deterministic that can orchestrate models, agents in the context of determinism.
That's really the only way to deploy effectively into an enterprise context. And now to the second point about the economical aspect, even if in certain cases, an agent can replicate some steps that are deterministic. Why would you do something that is costly and it's going to consume tokens at every step in the process rather than generate a script that works. It costs nothing in order to run.
So to my previous answer, this is the best combination between AI and deterministic. AI creates automation, sometimes maybe even on the fly. AI will run those automations. It's very cheap to run, very deterministic, reliable, auditable and only when this scripts break, you can invoke again AI to fix the scripts, but that's basically the right model to run agentic AI and automation into enterprise context.
Your next question will come from Raimo Lenschow with Barclays.
Daniel, could you stay on that subject because that's obviously where a lot of the investor questions are coming around. So how do you -- how does the world work then going forward? If you do the deterministic part, and you have all the experience in the world, so you should do that, who is doing then the probabilistic part. What are you seeing there in terms of where customers thinking and how they think about you in that context?
And then I had 1 follow-up for Ashim.
Well, I think the answer varies in we are model agnostic in terms of how we see the world. We provide deterministic orchestration and we can infuse that deterministic orchestration at any steps with agentic AI.That agentic AI used behind the scene frontier lab models can use, open weight models. We have to bring your own model policy. So we will accommodate every spectrum of requirements from our customers.
But again, I think what's important to note that even on the frontier lab model, the offering, it's a combination between deterministic and the model itself, which is purely cognitive. We extend in a way that model into the enterprise work itself. And when you go and I think very important distinction to understand the enterprise work is to think of who initiates an agent where process automation. It's a big difference if it's initiated by a person and the agent work on a person desktop versus an automation is triggered by an event or buy an enterprise workforce where you will need to have a different degree of auditability and reliability.
And again, this is where we really shine. We have this 10 years of experience in running a large-scale unattended automation that work on event triggers. And we are involving agentic AI and models into these workflows that can run unattended.
Yes. Okay. Makes sense. That's very clear.
And then Ashim, the one other question I get from investors a lot at the moment is you're doing really well on the revenue side. ARR is very steady. But at some point, they kind of need to kind of start lining up. So revenue at the moment keeps growing faster than ARR.
How do we need to think about that dynamic? Because in theory, you would think that they should line up, I think.
Yes. I think, Raimo, the first piece is, again, like when you look at it on a trailing 12-month basis, the revenue growth rate is 15% versus the ARR growth rate of 12% that you see. The second piece is within the revenue growth rate, there's obviously the license revenue growth rate and then there's services, et cetera. You can see we actually had good services revenue as FDEs, et cetera, are in demand from our customers.
So that's a second piece, that is there. Over time, this has moved in different directions. There's been times where with 606, revenue has trailed as certain duration and mix has moved the growth rate and where it's exceeded but when you look at it like on an overall average over a longer period of time, it's together. I don't really see any major disconnect at this moment that is driven by a business-specific area.
It's really just a mix of 606 impacts on the business. And again, I would emphasize to look at it on a trailing 12-month basis, versus looking at it where it is just in a particular quarter because ARR is obviously an annual metric.
Your next question will come from Michael Turrin with Wells Fargo.
I'll just ask 2 upfront, and you can take them in whatever sequence you like. I guess the first is just in terms of public sector, as we roll into midyear, maybe just remind us how you're thinking about public sector this year. Any updates in terms of progress or deal progression from that vertical specifically and maybe Ashim, just on the net retention rate, just what you're seeing currently in the uptick there and how to think about the trend line obviously, without guidance, but just thinking through the drivers there.
Yes, I can answer both questions. I'll start with the net dollar retention rate. I'm actually super excited with the net dollar retention rate and the progress we've made on it. As you can see, we have a 2-point increase quarter-over-quarter.
That's one of the first times we've had an increase as we've stabilized net new ARR and beginning to point the trajectory up towards that reacceleration mark. One, when you normalize for foreign exchange and the impact of M&A, that is 1 point, but it's still a reacceleration of net dollar retention rate that we're actually very encouraged by. And as I said, as we start to stabilize net new ARR, the next step is reacceleration. So we're moving into that territory.
And I think that's really great progress by the teams and speaks to the strategy and the operational execution that we -- that you see. In terms of public sector, I actually was at the public sector, a FUSION event that we had. The energy was very strong. I think public sector in terms of disruption of budgets, et cetera, we feel pretty -- we feel like there is good stability.
Obviously, as funding moves with different defense initiatives and awards, et cetera, that are there, we stay on top of it. But within many agencies, we actually have a very good presence, strong relationships with really good use cases, whether that is audit compliance within the government, which we have a very strong set of solutions and partners that we're working with or other transactional areas we actually feel like our relationships are very good. In terms of what's in for -- as we talk about guidance, we continue to guide what's in front of us there, which is we're pretty measured and prudent.
We know what projects are generally funded and we're looking to execute against that.
Your next question will come from Radi Sultan with UBS.
First for Daniel, just on the UiPath for coding agents, you mentioned this will be targeted at the full software development life cycle. But I guess -- are there like 1 or 2 areas where you see the biggest pain points where you can add the most value. And then you also mentioned this could strengthen retention. Maybe just how you imagine monetizing or bundling these agents into the broader suite?
So we plan to bring agents across the entire development life cycle. We are starting with an agent that helps with planning for an automation. So you can have a business analyst that came with the help of the agent interview different subject matter experts, gather all the information, create a process documentation document and then we will have like a solution architect agent that will take this design document and convert it into code. And we will have different agents for different types of code.
We have an agent that can create enterprise user interface. We have an agent that will create RPA, another agent that can create API workflows, an agent that will create process orchestration based on Maestro. This can be deployed and tested, again, it's fully agentic. Once they are in production, we have agents that monitor the entire execution and can fix proactively the errors that are coming.
And once there is an exception, we have also agents that help our developers to diagnose faster, the exception and fixed them faster. So the entire life cycle, there is no single point in the life cycle that is not touched by agents. In fact, we believe that the entire offering surface of our platform is basically agentic first. Humans, we think, are mostly going to do validation.
They will inject goals to the agents, and they will do the validation and supervision of the work. But most of the work itself is going to be created by agents.
Got it. Maybe just one follow-up for Ashim. Last quarter, we talked about core RPA still growing and becoming increasingly strategic to the AI product offering. Can you just talk through how you think about how pricing should evolve for the RPA, deterministic automation side of the business, given that it's becoming increasingly more strategic to customer AI initiatives to kind of capture that incremental value?
Yes. Look, I think that there's a lot of discussions around outcome-based pricing that are real and active more than they ever have been before. So I think like that is one tier of pricing to our top customers that I think is -- it's a real evolution. We see real line of path.
And we see people, especially with their fears of -- about getting ROI with AI, really looking for that. The second piece I would say is, I think where we're looking through is we also see like use case or process-based pricing where people are looking for restricted use cases so they can solve problems and have -- be able to use different parts of the platform that enable them to do so. Those are 2 evolutions that are there in terms of where we are with the deterministic side and overall.
This now concludes the Q&A session. I'd like to turn the call back over to management for closing remarks.
Thank you so much for the questions. And as usual, we would like to speak directly with many of you over the next few days. Thank you so much.