ServiceNow Q2 earnings show AI momentum as company announces 'Agentic Workforce Management' product

ServiceNow reports Q2 2025 subscription revenues of $3.113 billion, representing 22.5% year-over-year growth and beating the high end of guidance - and analyst expectations - as Artificial Intelligence (AI)-powered solutions increasingly drive large customer deals. The results, announced today alongside a new 'Agentic Workforce Management' product, reflect the company's ambition to become the 'central orchestration layer' for enterprise AI.

Current remaining performance obligations (cRPO) reached $10.92 billion, growing 24.5% year-over-year and exceeding expectations. The company sees 89 transactions over $1 million in net new Annual Contract Value (ACV) in Q2, while 528 customers now generate more than $5 million in ACV – up 19.5% year-over-year. The number of customers with more than $20 million in ACV grows over 30%.

Undeniably an excellent set of results. CEO Bill McDermott says:

ServiceNow's outstanding second quarter results continue our long track record of elite level execution. Every business process in every industry is being refactored for agentic AI. ServiceNow has never been more differentiated as a full stack agentic operating system for the enterprise.

The quarterly performance supports the aggressive market positioning McDermott outlines at Knowledge 2025, where he declares that enterprise customers are actively seeking to eliminate underperforming software vendors from their technology stacks.

The strong financial results also reflect growing customer adoption of ServiceNow's AI capabilities. In a pre-earnings interview, Amit Zavery, ServiceNow's President, Chief Product and Operating Officer, attributes the momentum to enterprises seeking proven AI implementations rather than experimental approaches. Zavery explains:

AI is top of mind for pretty much every enterprise company out there. A lot of them are looking for guidance and technology that works. With our experience doing this work for a long time at the enterprise level, a lot of customers appreciate our expertise in this area.

Zavery explains that ServiceNow's cross-functional platform gives it credibility in AI discussions with senior executives:

When we talk to the C-suite, Chief Information Officers (CIOs), technology teams, and line of business, they look at our breadth of capabilities as well as real-world implementation, and that gets us on the top of the list for them to work with.

The company's positioning, with what Zavery calls an "enterprise OS", appears to be resonating:

We have a pretty large business with enterprise companies as well as different organizations and government agencies. In follow-up conversations, when they're thinking of AI, they're reaching out and saying, 'What does ServiceNow think? What should we do? How should we approach this, and what kind of use cases can we work on?'

This consultative approach translates into implementations that demonstrate measurable value. Zavery notes:

You're starting to see a lot of these AI use cases getting live and creating value. We can approach this problem in many ways because of the breadth of things we do across the enterprise – we touch every part of the business and connect many things together as an enterprise OS.

Data integration becomes strategic

A notable development in Q2 is the prominence of ServiceNow's Workflow Data Fabric in 17 of the company's top 20 deals, reflecting how buyers see AI implementation requiring comprehensive data access across enterprise systems (again, a key ServiceNow proposition since the early days). Zavery explains that Workflow Data Fabric addresses a fundamental challenge in AI deployments:

You want to automate business processes and connect various things together, but to get the most value out of AI use cases, especially agentic use cases, you have a lot of dependency on data. Data in every enterprise is fragmented in various data silos.

ServiceNow's approach uses what Zavery describes as a "zero-copy architecture" that avoids the complexity of traditional data integration projects. He adds:

The Workflow Data Fabric message resonates as technology that solves the problem of getting value out of data into business processes and workflows, without having to do a lot of restructuring and moving things around.

The platform connects to major data sources including Snowflake, Databricks, Oracle, Teradata, BigQuery, and Redshift. Zavery explains:

Our ecosystem works with any data, and that helps us get better value from AI. It's becoming more combined in our conversations, and customers are starting to want to use Workflow Data Fabric to solve other problems while also implementing AI capabilities.

Agentic Workforce Management

ServiceNow also announces its new Agentic Workforce Management tool today, which is described as an extension of its AI agent orchestration capabilities, with the aim being to allow employees and AI agents to work together on complex tasks. The product represents the next phase of ServiceNow's AI strategy, moving beyond individual agents to 'coordinated digital workforces'.

Zavery outlines the multi-layered approach ServiceNow takes to agentic AI:

There are multiple things happening with agentic AI. There's the need for an agentic platform, which is part of our AI platform capabilities – not just AI agents, but connectivity between various AI agents, with the ability to orchestrate, reason, and plan with, not just ours, but third-party AI agents as well.

The company's AI Control Tower provides lifecycle management including "security, risk, compliance, evolution, and ensuring agents are doing the right things with expected outcomes and control across your enterprise."

The new Agentic Workforce Management layer addresses what Zavery calls the 'operational complexity' of managing digital workers:

Many companies are also asking, 'Can I use these AI agents to create a digital workforce?' When you bind agents together with the ability to run and operate work instead of humans doing all those tasks, that becomes complicated to manage. How do you manage this hybrid of humans with digital workers in a thoughtful way?

ServiceNow already implements the technology internally with measurable results. The company has automated 97% of software provisioning requests while reducing service desk volume by nearly 40%. Internal IT support sees 85% of routine requests resolved autonomously, helping the department scale by more than 40%.

Customer support operations show similar results, with the agentic workforce resolving 80% of complex instance administration cases and achieving 50% faster resolution times.

Enterprise architecture transformation

The Q2 results come as ServiceNow pushes a broader thesis about enterprise software consolidation driven by AI requirements. Zavery acknowledges that this transformation is in its infancy but sees clear momentum:

Customers are in the early stage of re-evaluating their underlying legacy systems. Enterprises have always wanted to modernize, but they're rarely able to completely transition to something modern over many years. AI is one driving factor making customers think, 'Maybe I should rethink how I build my stack.'

This shift toward what Zavery calls an "agentic AI stack" represents a departure from traditional siloed enterprise architecture:

Many companies we speak to are moving systems into more modern data sources while moving user experiences away from legacy systems. A lot is happening at the business workflow level versus the siloed, stack-to-stack mindset people were using.

ServiceNow's value proposition centers on bridging existing systems during this transformation. Zavery adds:

When we come in and say we'll rewire your business processes, make them flexible and agile, and give you the ability to evolve quickly across existing systems, it resonates. Once it resonates, customers start thinking about whether to shut down some pieces or consolidate parts of their legacy architecture while modernizing.

The approach appears to be generating both operational savings and strategic value:

It doesn't just save money through automation, but they can modernize and make their businesses much more agile than the architectures they've had for many years.

Market understanding and positioning

When asked about market perception of ServiceNow's growth potential, Zavery suggests that investors are still developing their understanding of the company's expanded scope. With a smile on his face, he comments:

The market is still figuring out what ServiceNow does. It's interesting for them to understand because we run enterprise operating systems for business processes and workflows, which are hard to put in a box.

This positioning challenge reflects ServiceNow's evolution beyond its traditional categories.

We're doing many different things to help businesses, versus traditional businesses that fit in specific segments. We're in a much broader segment now.

Zavery expects market understanding to evolve as AI implementations demonstrate measurable business impact:

Agentic AI is real – and you can see the results – people will start realizing there's a transformation happening, and ServiceNow will be a major player in that transformation.

My take

ServiceNow's Q2 results show the company successfully monetizes its AI platform strategy, with strong revenue growth driven by customer adoption of AI products like Now Assist and Workflow Data Fabric. The launch of agentic workforce management demonstrates ServiceNow's evolution from workflow automation to AI orchestration.

The key test will be whether ServiceNow can sustain this momentum while carrying out complex enterprise AI implementations at scale. The company's unified platform architecture and cross-functional reach provide advantages, but translating McDermott's ambitious consolidation vision into sustained market share will require multi-year, ambitious execution. That being said, I think ServiceNow has surprised many with its successful expansion in recent years.

As Zavery notes:

We continue to see momentum and customer interest, and as long as we keep executing, we should see the growth we've seen so far.