ServiceNow Autonomous Workforce launches with AI specialists handling 90%+ of IT requests autonomously

ServiceNow's Paul Fipps on enterprise AI - 'The LLM reasons, but it's the platform that executes'

Last week, ServiceNow launched its Autonomous Workforce and EmployeeWorks - a product release that felt like a culmination of a lot of groundwork on the vendor's part, which also gave enterprise buyers a taste of what's possible with agentic AI. ServiceNow itself has deployed the new capabilities, with more than 90% of its employee IT requests now handled autonomously internally, with the L1 Service Desk AI Specialist resolving assigned cases 99% faster than human agents. As we've highlighted previously, the company is hoping that whoever controls the execution and governance layer in an agentic AI future captures most of the long-term value. The question I ended that piece with was whether customers and enterprise buyers are actually ready for such substantial change. Paul Fipps, President of Global Customer Operations at ServiceNow, is the person whose job it is to answer that question, given his direct relationship with ServiceNow's install base. Customers are his bread and butter and we got the chance to speak with him about the practical implications for how buyers should be thinking about an 'autonomous workforce' and what this indicates for ServiceNow's future strategy. The frustration Fipps starts with is one our CIO network has been pointing to consistently. Enterprises are excited about AI, are investing heavily - but are not getting to value quickly enough. Our January 2026 AI Projects micro-pulse found 40% negative sentiment among respondents reflecting on their AI projects in 2025, with the dominant theme being the struggle from proof of concept to production. Our November 2025 research with 35 CIOs and CTOs found only 21.4% reporting AI initiative success rates above 80%. ServiceNow's answer is to cut the time to first use case from six months to three - using AI tools embedded in its services organisation to compress what used to take months. The thinking being that if ServiceNow can be a partner to buyers that delivers AI value quickly, that bodes well for future investment. Fipps said: We're using a lot of AI tools - both embedded in the product, but also in services. We're getting our services team trained in these tools we're developing to take everything from statement of works into project plans, into design specifications, into configuration. What would take weeks or months is now taking a week. Whether three months is achievable beyond early adopters with high technology maturity is a question the next wave of deployments will need to answer. But for enterprises already sceptical about pace of returns, reducing the runway to a demonstrable ROI is one of the most validating things a vendor can offer right now. The internal ServiceNow deployment for Autonomous Workforce is the reference point Fipps returns to most often. And Fipps is keen to point out that whilst work has been replaced, the company's people haven't. ServiceNow is trying to actively practice what it preaches, by putting people who were working on now automated work into higher value roles. He said: We no longer have a level one Help Desk at ServiceNow. All of that level one help desk activity is now handled autonomously using agentic AI. And you might ask: what happened to all the level one people? Many of them are now working in cloud operations, or at level two, or in data centers, doing much higher-level work. At ServiceNow we're just going to grow and get operating leverage - so it wasn't a big layoff. It's really allowing those people to move up the stack in terms of what they work on. That's the narrative ServiceNow needs to get across to customers, in order to counter any resistance to AI due to fear of displacement. Whether enterprises that aren't growing as rapidly as ServiceNow can make the same argument for their own level one workers is harder to answer, but I've noted in recent months that the emotional response to this technology is more intense than any previous wave. I'm not just hearing "I'm used to doing it this way," but something closer to an identity-level threat. Fipps engaged with this observation genuinely and said: We have to remember - these are tools. Very unique ones, and the people developing these models are remarkable, but at the end of the day they are technological tools, however real it seems when you're engaging with them. I agree, but I also think that the enterprises that figure out the human change management side of autonomous AI deployment alongside the technical side will have a meaningful advantage over those that don't. The technical architecture underpinning ServiceNow's latest announcement - the Autonomous Workforce - is also worth outlining, as it speaks to the concerns around predictability and governance that often are raised by end users. As I wrote last week, ServiceNow's architecture now is as such: agents handle task automation, agentic workflows mix deterministic and probabilistic elements, and then there is role automation (the Autonomous Workforce level) - which introduces a virtualized employee representative that follows organizational policies, regulations, and auditability requirements. And as I noted then, this approach is essentially a category claim, not just a product description. Fipps pointed to how ServiceNow is delivering the best of both approaches - probabilistic reasoning and deterministic governance - to its customers. He said: We fully want to take advantage of Large Language Models - these are amazing innovations - but we do it in a way where we abstract the intelligence layer from ServiceNow. We use LLMs for decisioning and reasoning. That part is probabilistic, and you can use any LLM: Gemini, ChatGPT, Claude, whichever you want to bring. But the action part in ServiceNow - the workflow part - is deterministic. No guessing. End-to-end compliance, governance, everything. When the LLMs get better, that part of the process gets better, but the execution part is where we innovate. The 'no guessing' comment will speak to the concerns of buyers looking to introduce these capabilities into their production environments. By and large, enterprises don't want probabilistic AI in mission-critical workflows. ServiceNow's answer is that it has no place in the execution layer either. The LLM reasons; the platform acts. The model is interchangeable, which also insulates ServiceNow from model obsolescence in a way that vendors who have bet on a single provider have not. The practical stakes of this distinction came through in something Fipps recounted from a customer meeting the morning we spoke: A lot of companies want to go fast, put things into production, then manage the risk. I flip that. I want to manage the risk first, then go as fast as possible relative to that risk, measure again, then put into production. Because it's just too critical. And the notion of taking a large language model, developing code rapidly, and putting it into a system like that doesn't even cross their mind. This is framing risk governance as the accelerator rather than the brake and frames the thinking for how ServiceNow has been building toward its AI Control Tower and the Veza identity security acquisition. The application consolidation argument that ServiceNow has been making with increasing confidence sits underneath all of this. Large enterprises often operate hundreds or thousands of applications and the opportunity ServiceNow is pursuing most aggressively is in Global Business Services: the horizontal processes that cut across hire-to-retire, procure-to-pay, order-to-cash, and IT operations, where point solutions and custom applications have accumulated over decades. What happens to those applications once the workflow moves onto ServiceNow? Fipps is clear: They take the data out and then they just decommission. That's where the ROI comes from. He then added: Some software companies - you go through M&A cycles, divestiture cycles - you see it happen all the time across different parts of the space, and then immediately there tends to be a lack of innovation. That's where they really lose ground. Whilst ServiceNow has ambitions to be the 'control' and governance layer for AI, it would be remiss to point out that Salesforce, SAP, Workday, etc- each is making some version of the "we are the control layer" argument. I pressed Fipps on what actually differentiates ServiceNow's version for customers. His response is grounded in history as much as product. Nobody, he says, has ever successfully used a CRM or an HCM system as a genuine cross-functional orchestration layer - and as a former practitioner, he says he wouldn't have tried. ServiceNow has always been the platform the CIO reaches for when managing complexity across a fragmented landscape, Fipps argued. The AI Control Tower is a logical evolution of that role, not a new claim. Fipps said: The CIO - or whoever is responsible for governance, risk, compliance, and security - is going to be accountable for the millions of agents that will be running inside most corporations within the next few years, and ServiceNow is the natural place to manage that. On OpenAI and Anthropic moving into enterprise workflow territory, Fipps is added: If you want to play around the edges with point solution use cases, that's much easier - but when you actually get to the core of what's happening in a business, that's a vastly different challenge. That is vastly different from vibe-coding a point solution. He pushes this further on the platform replacement anxiety - the ongoing 'SaaSpocalypse' chatter - that every enterprise software vendor is being asked to address right now: Let's assume you could rewrite and replace any platform at the core of your business - which I don't actually think you can, but let's assume. The question then is, how do you have the domain expertise to keep the R&D process going? Your customers and your user base are going to expect you to keep doing the next thing - the next innovation, the next feature, the next capability. That's not in the DNA of a common enterprise. All the fear, uncertainty, and doubt in the marketplace around this just hasn't caught up to that reality yet. It's a strong argument. And our CIO network data also broadly supports it - this month's survey showed approximately 80% of respondents either sustaining or increasing SaaS spend, with no definitive view emerging on AI replacing the platform itself. There is also ongoing speculation in the market about how SaaS vendors are going to continue pricing their products, given that customers are likely going to argue that they need fewer 'seats' if agents are doing the work. It's not as easy as a shift to consumption based pricing, given that enterprises need reliable financial forecasts. The most consistent assumption is outcome based pricing, but that's been being discussed for decades - well before the onslaught of AI. This is why the market is skittish about SaaS vendors' future prospects, as the adoption of agentic AI essentially cannibalizes their seat-based pricing. And Fipps doesn't pretend the question is resolved: On the SaaS versus AI question - honestly, I can't tell the difference anymore. The same players you'd describe as AI companies now have seat-based models. OpenAI and Anthropic both have seat-based models. So what are they? The reason is that customers need pricing predictability. Right now, most of the revenue in the coding application space is consumption-based, token-based - the more you consume, the more you pay. The value is enormous, but for any development organisation at scale, it's also unpredictable. ServiceNow's current answer is seat base plus consumption - predictability upfront, with additional cost linked to additional value. Fipps is explicit this is provisional: We'll probably have to think about more innovative pricing models as our customers pull us in that direction. As I noted, when the Autonomous Workforce launched, I asked whether enterprises are ready. I was grateful for the time with Fipps, as his assessment was clearly grounded in conversations and I didn't get the sense that he was trying to portray a vision that wasn't grounded in reality of where customers are today. The time to value compression story is savvy, given the ongoing battle in the market to gain share of AI spend (those proving value quickly will win, in my opinion). And the competitive positioning is grounded in genuine workflow heritage rather than marketing repositioning. ServiceNow's 'in' with CIOs and the technology department, who are going to be the ones ultimately responsible for AI deployments, coupled with its contextual workflow knowledge, put it in a solid position. What I keep coming back to is the scale of what's being asked of enterprises. The Autonomous Workforce is a compelling destination. Getting there safely - with the governance maturity, clean data, organizational alignment, and change management capability that safe deployment requires - remains the hard part. Closing that gap is the work that will define whether Fipps' time to value ambitions hold beyond the early adopters. Knowledge 2026 is a few months away. We'll be testing these claims on the ground.

ServiceNow debuts Autonomous Workforce, EmployeeWorks automation tools - SiliconANGLE

ServiceNow debuts Autonomous Workforce, EmployeeWorks automation tools ServiceNow Inc. today debuted two new cloud services that use artificial intelligence to automate manual work for users. The first offering, Autonomous Workforce, is a suite of specialized AI tools that each focus on speeding up a different set of chores. On launch, the bundle features a single automation tool called Level 1 Service Desk AI Specialist. It performs relatively simple technical support tasks such as provisioning application access for employees and fixing network issues. A ServiceNow demo video showed the tool helping a worker who couldn't log into a virtual private network, or VPN, tool. Level 1 Service Desk AI determined that the issue was with the worker's VPN token, a one-time password used to log into the network. It refreshed the token and confirmed that the fix was implemented correctly. Level 1 Service Desk AI identifies the root cause of technical issues by analyzing telemetry about a company's infrastructure. It collects that data from observability tools, cybersecurity software and other applications. ServiceNow plans to expand Autonomous Workforce with several other AI tools down the line. One will focus on helping information technology teams maintain their CMDB, a database that contains information about the configuration of important systems. ServiceNow says that the tool will automatically remove duplicate and outdated information. Another planned AI tool will condense network error logs to speed up troubleshooting. Over time, ServiceNow will extend Autonomous Workforce beyond IT use cases. The company plans to launch a tool called Project Coordinator that will automate tasks such as detecting project cost overruns. "Autonomous Workforce augments human teams with AI specialists that operate with the scope, authority, and governance enterprise work demands," said ServiceNow chief product and operating officer Amit Zavery. Autonomous Workforce is rolling out alongside a second new offering called EmployeeWorks. It's based on technology from Moveworks Inc., an AI task automation startup that ServiceNow bought last year for $2.85 billion. EmployeeWorks enables workers to search their company's applications for business data using natural language prompts. Additionally, it can visualize that data in graphs. A store manager, for example, could request a chart that shows how customer traffic changed over the past month. EmployeeWorks can also automate certain repetitive business tasks. The tool notifies users about important new information, such as a business forecast that indicates store traffic will increase significantly in the coming week. Additionally, it can review business documents, sync their contents to the relevant application and perform other chores.

ServiceNow's Autonomous Workforce is here and it's impressive - are enterprises ready for it?

ServiceNow today launched Autonomous Workforce and ServiceNow EmployeeWorks, bringing together AI specialists and the Moveworks acquisition into a platform play designed to shift enterprise AI from experimentation to execution. ServiceNow today announced two significant additions to its AI platform: Autonomous Workforce, a framework for deploying AI specialists that execute work end-to-end rather than assisting with individual tasks, and ServiceNow EmployeeWorks, which brings Moveworks' conversational AI and enterprise search into the ServiceNow platform as a unified front door for employees. I've been writing about ServiceNow's platform strategy for close to a decade, and this feels like the moment where a lot of the groundwork comes together. The "platform of platforms" approach, which I've argued is a competitive edge and is something that I've outlined time and time again, is once again proving itself valuable in these enterprise AI advancements, where ServiceNow is betting that whoever controls the execution and governance layer in an agentic AI future captures most of the long-term value. The question, as is always the case, is whether customers can actually get there. The headline announcement today is the Autonomous Workforce, and specifically the first AI specialist available out of the box - an L1 Service Desk AI Specialist (apt, given ServiceNow's ITSM history). And the vendor is keen to not minimize the capabilities of these new AI workers. Nenshad Bardoliwalla, Group Vice President of Product Management, said during the press briefing: It is not a bot. Bots were built to follow scripts. You define every branch, you define every condition... Our specialists are fundamentally different. They are not following a script. They are designed to actually do the job. They understand context, they can reason across various systems, they handle exceptions and they get better over time. The taxonomy Bardoliwalla introduced is worth paying attention to too. He explained that agents handle task automation, agentic workflows mix deterministic and probabilistic elements, and then there is role automation - the Autonomous Workforce level - which introduces a virtualized employee representative that follows organizational policies, regulations, and auditability requirements. I want to be really clear that this is essentially a category claim, not just a product description. The practical difference, as demonstrated in the briefing, is that the AI specialist doesn't hand off to a human when it resolves an IT incident - it diagnoses, executes, documents, notifies the affected employee, and updates the knowledge base. Start to finish. John Aisien, SVP of Central Product Management, said it right during the press briefing: Answers are not business outcomes. In other words, a bot synthesizing a bunch of information is not really changing anything at a fundamental level. Whereas, an Autonomous Workforce actually gets work done. The L1 Service Desk AI Specialist is expected to be generally available in Q2 2026, but ServiceNow has already adopted it internally and is using the results of its own internal deployment as the reference point. The vendor claims that its Autonomous Workforce is handling more than 90% of employee IT requests, with the L1 specialist resolving assigned cases 99% faster than human agents. That's quite something. ServiceNow EmployeeWorks, which is the culmination of its Moveworks acquisition, by contrast, is generally available today. It combines Moveworks' conversational AI and enterprise search with ServiceNow's unified portal and autonomous workflows, and is available across Teams, Slack, browser, etc. Bhavin Shah, SVP and General Manager of Moveworks and AI, who joined ServiceNow as founder and CEO of Moveworks, described the core proposition: ServiceNow EmployeeWorks is one of the first AI front doors that doesn't just summarize, it completes the work. Moveworks was already used by over 5.5 million employees worldwide at the point of acquisition, which gives EmployeeWorks an unusually credible production foundation from day one. The strategic framing that ran through the briefing is what Aisien described as the hub-and-spokes architecture. I think this is important too. ServiceNow, to hold on to its value proposition, is declaring itself as the enterprise hub - a governed, deterministic execution layer. Important in enterprise contexts. AI models from OpenAI, Anthropic, Google, and now Moveworks are the intelligence spokes. In Aisien's words: Think of Moveworks as a spoke - our own spoke with differentiated access to our hub - but we will continue to develop and bring additional spokes to market: spokes powered by OpenAI, spokes powered by Anthropic, spokes powered by Google, and so on. This hub is really where work truly gets done. It's a strong position and one which aims to protect ServiceNow from any advancements in AI models (see: SaaSpocalypse narrative keeping everyone entertained/distracted). I've written previously about how ServiceNow's two decades of workflow data, CMDB infrastructure, and cross-functional integration give it a foundation that departmental application vendors genuinely can't replicate quickly. The same capabilities that let ServiceNow orchestrate workflows across fragmented systems are now being used to orchestrate AI agents across those same systems. The main risk in the hub-and-spokes framing, which is worth acknowledging, is whether the spokes stay spokes. OpenAI is building enterprise products directly. Anthropic is extending into workflow contexts. If a model provider decides the execution and governance layer is where they want to play, ServiceNow's defenses will be tested. The vendor's counter argument is that twenty years of workflow patterns, a unified data model, and enterprise context accumulated through the CMDB is not something you replicate quickly - and that's probably right, but it's a position that should receive regular scrutiny. I'd argue, more than anything, where ServiceNow is now positioning itself - where buyers can audit and effectively govern their AI deployments; AI deployments that make use of both probabilistic and deterministic tooling - is one that will be harder for the 'spokes' companies to replicate. One of the most interesting parts of the virtual pre-briefing came from Alan Rosa, Chief Information Security Officer and SVP of Infrastructure and Operations at CVS Health - notably, a pre-existing customer of both ServiceNow and Moveworks before the acquisition. The CVS Health reference is strong precisely because it's not a pilot and because of the highly regulated industry the company operates in. Rosa was clear about what makes AI implementation work in a regulated, high-stakes environment: Boring is beautiful. Focus on value, not novelty. Don't chase butterflies. Focus on gritty, unsexy operational use cases - the ones with real ROI and a real impact on people's lives. He said that every AI use case at CVS Health goes through a clinical, legal, privacy, and security review before touching production, with dynamic testing on top because, as he put it, a static review doesn't cut it when AI is learning and adapting. This is the sort of thing that resonates with serious enterprise buyers. On the relationship between security and delivery speed, his framing resonated: When security is not a gate but a partner, velocity goes up and risk goes down. That will be music to ServiceNow's ears. And he continued: Lose trust, and you lose the right to scale. The consistent themes: boring, value, trust, guardrails, repeatability. That's the sort of disciplined AI implementation most enterprises will be pursuing, and it maps directly onto the governance-as-bottleneck argument that ServiceNow has been making since the AI Control Tower positioning took hold. The diginomica CIO network data from the past few months doesn't make for comfortable reading, however. Our January 2026 AI Projects micro-pulse, drawing on 124 respondents, found 40% negative sentiment, 35% neutral, and 25% positive when CIOs reflected on their AI projects in 2025. The February SaaS vs AI pulse found "Future SaaS Disruption and Evolution" scoring 10 out of 10 on impact - the highest of any theme - with CIOs increasingly questioning legacy SaaS spend. But they're not necessarily replacing it with platforms; they're questioning whether the economic model holds at all. Our November 2025 research report with 35 CIOs and CTOs found that only 21.4% report AI success rates above 80% for their initiatives. Governance and risk management scored highest on both urgency and impact as barriers - which is precisely the bottleneck ServiceNow claims to solve. But the primary blockers weren't technical: poor data quality, disconnected systems, and difficulty aligning AI with business priorities consistently came up. Those are organizational problems, and no amount of platform capability solves them directly. Shah made a comment in the briefing that I think is genuinely useful here, where he outlined the difference between what he called weak ROI and strong ROI. He said: A lot of the fast-moving AI tools out there are focused on improving employee productivity at a fairly superficial level. Those productivity gains come and go with the employee. When that employee moves on, the gains go with them. Strong ROI, in Shah's view, is business process transformation that persists regardless of individual employee turnover - a mission-critical workflow that runs, delivers value, and compounds over time. The harder question is how many customers are genuinely reaching it, and what the realistic implementation journey looks like to get there. The product news today is genuinely impressive. What I keep coming back to is the gap between the disciplined implementation that makes Autonomous Workforce valuable and the organizational reality most enterprises are actually operating in. The platform requires a level of governance maturity, clean data, and aligned leadership that our CIO research consistently shows is the exception rather than the rule. ServiceNow's answer - the AI Control Tower governance layer, the EmployeeWorks front door that reduces friction - is a solid response to that gap. Whether it's sufficient will be tested over the next 12 months with customers - and we look forward to hearing those stories. The hub-and-spokes architecture is a smart strategy if the execution and governance layer remain the battleground. I think it probably will be, for longer than many market observers suggest. ServiceNow's core pitch is that the era of AI experimentation is over and we are now getting serious about AI implementation, where we offload serious work and tasks to autonomous agents - that's a compelling prospect, but one which many organizations will feel ill prepared for. We've got a long road ahead of us and what I'd push ServiceNow to consider is how the platform itself can help manage the change. As we see time and time again, technology is an important enabler, but without people being brought along on the journey, returns will be minimal. ServiceNow's annual user event in Vegas in a couple of months is shaping up to be a very interesting event indeed, and we will be on the ground testing these claims with execs and customers alike.

ServiceNow launches Autonomous Workforce that thinks and acts; adds Moveworks to the ServiceNow AI Platform

AI specialists execute work with the scope, authority, and governance required for business New ServiceNow EmployeeWorks solution connects conversational AI chat and enterprise search from Moveworks with autonomous workflows for nearly 200 million employees ServiceNow today launched Autonomous Workforce, AI specialists that can execute jobs with the scope, authority, and governance required for enterprise work - freeing people to focus on strategic problem solving and personalized service. Just two months after the Moveworks acquisition close, the company also introduced ServiceNow EmployeeWorks, which combines Moveworks' conversational AI and enterprise search with ServiceNow's unified portal and autonomous workflows to turn natural language requests into governed, end-to-end execution for nearly 200 million employees. As enterprises evaluate AI platforms, two competing paradigms have emerged: feature-function AI bolted onto disconnected SaaS apps, and unified platforms that execute work through proven enterprise workflows with AI built in. The difference is fundamental: the feature approach requires enterprises to maintain, integrate, and manage the complexity themselves. ServiceNow eliminates the complexity by unifying conversational AI, workflows, enterprise data, security, and governance on a platform purpose-built for mission-critical operations. "Businesses don't need more pilots or promises. They need AI that gets work done," said Amit Zavery, president, chief product officer, and chief operating officer, ServiceNow. "The leaders realizing value from AI are investing in platforms where intelligence, execution, and trust work as one system. Our platform was purpose-built for this moment. Autonomous Workforce augments human teams with AI specialists that operate with the scope, authority, and governance enterprise work demands. This is a new era of productivity and ROI, at scale." Autonomous Workforce: AI teammates execute jobs in partnership with people ServiceNow's Autonomous Workforce deploys AI specialists with defined roles to augment teams. Unlike AI agents that complete individual tasks, the ServiceNow Autonomous Workforce orchestrates teams of AI specialists with roles such as a Level 1 Service Desk AI Specialist, Employee Service Agent, or Security Operations Analyst to execute work from start to finish. They work alongside humans, follow established processes and policies set by the organization, learn from outcomes and employee feedback, and importantly, improve over time. Today, ServiceNow is introducing the first AI specialist available out-of-the-box for customers, a Level 1 Service Desk AI Specialist. This AI specialist autonomously diagnoses and resolves common IT support requests end-to-end -- password resets, software access provisioning, network troubleshooting -- using enterprise knowledge bases, historical incident data, and proactive remediation workflows. It is designed to operate 24/7 with assignments aligned to specific skillsets and deliverables and escalate issues when human intervention is needed. At ServiceNow, our Autonomous Workforce is handling 90%+ of employee IT requests. Early results show our newest AI specialist, the L1 Service Desk AI Specialist, is already resolving assigned IT cases autonomously, and it's 99% faster than when these cases are handled by human agents. AI models without workflows are probabilistic -- they see patterns, form ideas, and give different answers for the same questions. The enterprise, however, needs deterministic outcomes -- governance, security, auditability, and operations that don't hallucinate. Because ServiceNow combines probabilistic intelligence with deterministic workflow orchestration, AI specialists can interpret a request, decide the right action using business context, and execute autonomously across systems with governance built in through the ServiceNow AI Control Tower. Every action is traceable and governed by policies embedded in the workflow layer itself. ServiceNow EmployeeWorks: Consumer AI experiences meet enterprise-grade execution ServiceNow is bringing the power of Moveworks to the ServiceNow AI Platform and delivering immediate value to customers with ServiceNow EmployeeWorks, a conversational front door for the enterprise. Available where employees already work and collaborate - whether in Teams, Slack, or on any browser - ServiceNow EmployeeWorks connects Moveworks' conversational AI chat and deep enterprise search with ServiceNow's unified portal and autonomous workflows, turning intent into coordinated action across systems. The platform understands organizational structure, approvals, and authorization -- executing tasks that require multi-system coordination while maintaining governance and audit trails. "ServiceNow EmployeeWorks is one of the first AI front doors that doesn't just summarize, it completes the work," said Bhavin Shah, senior vice president and general manager of Moveworks and AI for ServiceNow. "Moveworks proves that when AI solves real problems elegantly, people use it. Combined with ServiceNow's 20+-year foundation in workflow automation, we deliver consumer simplicity with enterprise reliability, including the operational guarantees that mission-critical work demands." What customers are saying "We need AI that can handle the complexity of health care while maintaining compliance and security for our 300,000 colleagues," said Alan Rosa, chief information security officer and senior vice president of infrastructure and operations, CVS Health. "CVS Health builds strong relationships with partners whose platforms allow us to support our colleagues across IT, HR, and procurement. The goal is to automate repetitive tasks so our teams can focus on what matters most -- delivering outstanding care and experiences to the 185 million people we serve." "Raleigh is a smart city built on innovation. We're laser focused on using AI to handle routine tasks so employees can focus on higher-level thinking and delivering the best possible services across the city," said Mark Wittenburg, chief information officer, City of Raleigh. "ServiceNow Now Assist is already resolving 98% of initial touchpoints by intelligently routing requests to the right destination, and we're excited about the potential for Autonomous Workforce to further transform how we deliver IT support, setting a new standard for a responsible, AI-powered government." "At Siemens Healthineers, our 74,000 employees are pushing the boundaries of healthcare to deliver faster, better outcomes -- and they need technology that keeps pace," said Nicole Hulst, head of digital workflows tooling, Siemens Healthineers. "Our AI Assistant 'Ada', built on Moveworks, saves them 5,000 hours monthly with 91% satisfaction, elevating the employee experience. ServiceNow EmployeeWorks takes this further with autonomous workflows that complete tasks fully, giving our teams time back to innovate." "Our top priority is a frictionless digital experience so our employees can focus on what matters most: taking care of our customers," said Lakshman Ramamurthy, Sr. Director, Platform Engineering & Enterprise Operations, UKG. "That meant simplifying duplicative systems and transforming IT operations with the ServiceNow AI Platform -- moving from patchworked data and reactive processes to a data-driven, proactive, and predictive model. Moveworks extends that reach to 15,000 employees with dozens of agentic use cases already live. Now we're building toward a future where AI specialists orchestrate work across our entire enterprise." Availability * ServiceNow EmployeeWorks is generally available to customers today. * The first AI specialist for Autonomous Workforce, a Level 1 Service Desk AI Specialist, is in controlled availability today and expected to be generally available Q2 2026.