ServiceNow delivered a strong fiscal Q1 2026, exceeding guidance across revenue, margins, and bookings. Subscription revenue grew 19% year-over-year in constant currency, remaining performance obligations increased by more than 23%, and large-deal activity remained healthy, with multiple transactions above $10 million. The company raised its full-year outlook, reinforcing confidence in its sustained growth of approximately 20% at scale.
While its earnings could just be seen as an enterprise software vendor riding the AI tailwind driving the industry, I don’t think so. The more interesting story sits beneath the headline numbers.
This earnings call offers a clearer view of how AI is reshaping enterprise software and of ServiceNow’s role in that shift. The implications span pricing architecture, competitive positioning, labor economics, and how enterprises actually spend.
Let’s go to the transcript and look at some of the non-obvious AI takeaways from ServiceNow’s earnings call.
ServiceNow Is Rewriting the SaaS Pricing Model
One of the most consequential shifts in the call had little to do with product features. ServiceNow disclosed that roughly half of new business now uses non-seat-based pricing, including tokens and other consumption metrics.
At the same time, ServiceNow is embedding its AI capabilities across all SKUs rather than selling them as standalone add-ons. This is a structural shift, as enterprise software pricing has historically been tied to user counts.
AI disrupts that model in three ways:
- It reduces human involvement in workflows, shrinking the logic behind per-seat economics
- It introduces machine-driven consumption patterns with no natural headcount ceiling
- It shifts the perceived value from access to outcomes
ServiceNow is not abandoning subscriptions. Instead, it is layering consumption-based pricing on top of them, creating a hybrid model with predictable base revenue from seats and variable upside tied to AI usage:
- For buyers, this introduces flexibility.
- For vendors, it creates a new avenue for expansion.
- For competitors, it raises a harder question: how to monetize AI without eroding existing revenue streams.
Owning the AI Control Plane
ServiceNow has deliberately chosen to operate above the AI model tier rather than competing within it. The company repeatedly emphasized an “AI control tower” framing on the call, highlighting context management, auditability, governance, and workflow execution across systems.
This approach views OpenAI, Anthropic, and Google as customers for the problem ServiceNow is solving, not as rivals in the same market.
ServiceNow’s recent acquisitions reinforce this thesis. Armis provides asset visibility across the enterprise, while Veza provides identity governance.
Together, these components support a clear argument that, as enterprises deploy multiple models across multiple systems, the orchestration and governance layer becomes load-bearing infrastructure.
ServiceNow is building toward ownership of that layer.
AI Adds Operational Complexity Before It Removes It
Management pushed back on the assumption that AI reduces workload by automating tasks. The argument on the call was more nuanced, noting that code volume, AI agent proliferation, ticket volumes, and operational event counts will grow significantly over the next decade, even as AI automates individual tasks. Automation at scale creates its own coordination overhead.
The practical implication is that systems that manage workflows, approvals, dependencies, and remediation become more valuable as AI-generated activity scales.
For ServiceNow, this is a structurally favorable dynamic, as its core business benefits from both the automation of work and the complexity that automation introduces.
That dual exposure is easy to overlook but strategically important.
“Autonomous Workforce” Is a Labor Model Shift
The call included several examples of AI-driven automation at scale, such as 90% of internal requests being resolved without human intervention, dramatic compression of resolution times, and internal productivity gains measured in hundreds of millions of dollars. Read carefully, these figures describe labor substitution, not workflow enhancement.
There’s little question that ServiceNow’s AI capabilities are compressing service delivery timelines, reducing reliance on human operators, and shifting budgets from headcount to software. That shifts how deals are funded and who approves them.
ServiceNow has historically competed for IT budgets. Increasingly, the relevant budget is in operations, support headcount, and process outsourcing, which means different buying stakeholders and ROI expectations entering the conversation.
AI Revenue Is Underreported by Design
ServiceNow raised its AI revenue trajectory from approximately $1 billion to $1.5 billion. At the same time, it noted that it counts only incremental AI contributions, not the full value embedded across the platform. This creates a meaningful gap between the reported AI number and AI’s actual business impact.
The reported figure reflects incremental upsell tied to AI features, but it excludes AI-driven expansion in core workflows, pricing power from AI-native packaging, and increased deal sizes from AI adoption.
Investors looking for a clean AI revenue line will underestimate the effect. The reported number is a conservative accounting choice, and the company said as much.
Enterprises Are Spending on AI Before They Fully Understand It
One of the more candid moments on the call acknowledged that customers are highly interested in AI and actively investing, but they are not always clear on their implementation strategy. Despite that uncertainty, large-deal activity remains strong, new-customer growth is accelerating, and AI adoption is moving into production environments.
The market is in a forced adoption phase. Enterprises are committing budget now, even as architectures continue to evolve.
In this environment, platforms tend to win over point solutions, and flexibility and integration matter more than specialization. ServiceNow’s open, model-agnostic approach aligns with that reality.
The Competitive Risk Is Platform Convergence
ServiceNow’s strategy assumes enterprises will need a distinct orchestration and governance layer – a reasonable assumption, but not a guaranteed one. Platform vendors such as Microsoft are simultaneously integrating AI into productivity tools, embedding identity, security, and data platforms, and expanding into workflow and automation.
If those capabilities converge into a sufficiently cohesive platform, the need for an external control layer diminishes in some environments. ServiceNow’s differentiation depends on remaining the most effective cross-platform orchestration layer and maintaining openness across models, clouds, and systems.
The competitive risk here is less about individual model providers and more about vertically integrated platforms reducing the available surface area for an independent control layer.
Final Thoughts
AI-driven growth is table stakes at this point for nearly every enterprise technology provider, including ServiceNow. The more substantive question from this call is where ServiceNow sees its permanent place in the stack.
The company has made a clear architectural choice: to own how work gets executed, how decisions are governed, and how enterprise systems interact. Model performance is someone else’s problem.
Under that thesis, every AI investment across the enterprise, regardless of source or vendor, creates downstream demand for orchestration, governance, and workflow execution. ServiceNow benefits from the aggregate spend, not just its own.
ServiceNow becomes even more structurally embedded in enterprise AI operations. If enterprises consolidate around fewer, more vertically integrated platforms, then ServiceNow faces a more challenging path.
For now, the data suggests enterprises are moving toward greater complexity, not less. That is the environment ServiceNow is built to operate in.
