ServiceNow is handling 90% of IT requests from its own employees autonomously, resolving cases 99% faster than human agents. On Thursday it announced the product technology it wants to use for everyone else.
Organizations have spent three years running pilots that stop when AI reaches execution levels. The agent may identify the problem and recommend a solution, then hand it back to a human because it doesn’t have permission to finish the job or because no one trusts it to act autonomously in a governed environment.
The gap most teams are touching is not ability. This is governance and workflow continuity. ServiceNow’s answer is a new framework called Autonomous Workforce; A new employee-facing product, called EmployeeWorks, builds on the December acquisition of MoveWorks; And this is called an implicit architectural approach "Role Automation."
From ticketing systems to AI workforce
ServiceNow has been working in this direction for two decades. The platform started as a ticketing system, evolved into a workflow automation engine, and over the past two years laid AI on that foundation through its Now Assist product.
What’s different is that the new approach stops treating AI as a feature sitting on top of workflows and starts treating it as a worker working inside them. That shift, from AI that assists to AI that executes, is where the broader enterprise market is going. ServiceNow is making a specific architectural bet about how to get there.
The announcement has three parts: ServiceNow EmployeeWorks lets employees describe a problem in simple language and get it fixed without filing a ticket; Autonomous workforce executes tasks from start to finish; And role automation is the architectural layer that controls how those experts work inside existing enterprise permissions. Most enterprise AI assistants, including Microsoft Copilot and Google Gemini, require employees to know which tool handles which problem. Moveworks, which had 5.5 million enterprise users before its December acquisition, was built around a single entry point that automatically bypasses that ambiguity. Bhavin Shah, founder of Moveworks and now SVP of ServiceNow, laid out the problem directly in a briefing with press and analysts following the acquisition.
"Over the past two years, organizations have rushed to adopt AI, but in many cases that rush has created fragmented devices, disconnected AI experiences and employees bouncing between systems just to get simple tasks done." He said.
Why is role automation different from a regular agent?
ServiceNow is proposing a new architectural layer called Role Automation, and it’s different than the agents most enterprises already run.
Traditional AI agents are task-oriented: they are given a goal, they reason about it, and in doing so they figure out what they are allowed to do at runtime. This creates problems in enterprise environments where governance, audit trails, and permission limits are not optional.
with rOle Automation, an AI expert doesn’t make its way into permissions. He has inherited this. The same access control framework, CMDB (configuration management database) context, SLA (service level agreement) logic, and entitlement rules that govern human workers on the ServiceNow platform governs the AI Expert from the moment it is deployed. It cannot go beyond its prescribed limits. It cannot escalate privileges on its own based on what it learns mid-task.
The company makes a three-tier distinction: task agents handle individual automation steps, agent workflows mix deterministic and probabilistic execution, and role automation sits above both as a fully virtualized employee role with defined responsibilities and pre-inheritance governance.
The first product built on this architecture, Level 1 Service Desk AI Specialist, handles common IT requests end-to-end – password resets, software access provisioning and network troubleshooting – documenting each resolution and reaching out to a human agent only when it hits something outside its defined scope.
‘Don’t chase butterflies’
Alan Rosa has seen what happens when AI governance fails in health care. As CISO and SVP of infrastructure and operations at CVS Health, he manages AI deployments across 300,000 employees where compliance is not optional.
Speaking at the same briefing, their framework for scaling AI maps is based directly on what ServiceNow is claiming architecturally. CVS Health was already a customer of both ServiceNow and Moveworks before the December acquisition. Rosa said the combination of the two platforms is exciting and has potential "coming to life," However, CVS Health has not publicly committed to deploying an autonomous workforce.
"boring is beautiful" Roza said. "Predictable. steady. You have to start with responsible, explainable AI. No bias, no hallucination, clear guardrails. Everyone understands the rules."
On the temptation to pursue the latest AI capabilities before governance is established, he was straightforward: "Don’t chase butterflies. Focus on gritty, non-functional, operational use cases. Ones with real ROI that have an impact on people’s lives."
Rosa’s approach treats AI as an ever-evolving set of capabilities that requires dynamic rather than static testing. CVS Health runs each AI use case through clinical, legal, privacy and security review before it touches production.
"Static review doesn’t cut it when AI is learning and adapting," He said. "Rinse, rinse, repeat."
Rosa’s framework requires governance to be built into the deployment architecture from the beginning, rather than having to be re-implemented after a problem emerges. That’s exactly what ServiceNow is claiming about role automation. AI experts who receive existing enterprise permissions and workflow logic are structurally less likely to break governance boundaries than agents who set their scope at runtime.
What does this mean for enterprises
For any organization evaluating agent AI, regardless of vendor, the practical question is simple: Does your AI governance live inside your execution layer, or does it sit on top of it as a policy document that agents can understand in hindsight?
This is what ServiceNow is trying to solve with Autonomous Workforce and EmployeeWorks, baking governance and workflow context directly into the agentic layer rather than implementing it later. For practitioners, the starting point is governance architecture, not capacity. Before deploying any agentic AI, map out where exactly your permissions, workflow logic, and audit requirements live. If that foundation is not in place, no agent framework will survive at enterprise scale.
"Scale and trust go together," Roza said. "If you lose trust, you lose the right to scale."
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