For the past four years, enterprise software conferences have centered on competitive intensity: which company could debut the most AI agents, the boldest automation capabilities, the most compelling demonstrations.
At ServiceNow’s Knowledge 2026, two of the company’s top customer-facing executives are delivering a different message. The era of AI feature competition is winding down, they told Fortune from the conference floor. What follows is something less spectacular—and far more consequential.
The ‘SaaSpocalypse’ That Wasn’t
The context is one of widespread unease. Over the past 18 months, a question has rippled through enterprise software: if AI agents can automate workflows end-to-end, do companies still need the sprawling SaaS platforms they’ve invested years and billions building? Labeled the “SaaSpocalypse” for the market disruption it triggered before valuations stabilized, this question has unsettled investors and pushed valuations across the sector—including ServiceNow’s, which sits around $96 billion.
Paul Fipps, ServiceNow’s president of global customer operations and a former CIO, rejected the narrative. “The fear is that somehow a startup will use a large language model, put a lightweight wrapper around it, and ServiceNow will sit on its hands for the next 10 years… and wait for that company to catch up, and then we’ll go out of business,” he said. “It just makes no sense.”
Customer sentiment appears to back that stance: 25,000 attendees showed up this week, the largest crowd in the conference’s history. “They’re not showing up because they don’t believe in ServiceNow,” Fipps said.
Amit Zavery, ServiceNow’s president, COO and chief product officer, delivered a similar assessment during a Wednesday fireside chat: “The era of sidecar AI is over. Customers don’t want to cobble pieces together—they want outcomes.”
The Governance Crisis in Plain Sight
The concern haunting ServiceNow’s leadership isn’t competitive disruption. It’s a governance crisis that has been accumulating across corporate America: the proliferation of ungoverned AI.
Fipps opened a packed customer panel Tuesday with two cautionary tales. Three weeks prior, he said, an India meeting with a large financial services company’s CTO revealed the executive had deployed 30 production-grade AI agents for the bank—none of which had reached production because basic questions about their access and performance couldn’t be answered. “In a regulated industry, if you can’t answer those questions, you can’t go live,” Fipps said.
The second example ran even sharper. A healthcare and life sciences company’s CIO told Fipps 900 AI pilots were running across the organization. He shut them all down—not due to failure, but governance gaps. “I have a pile of custom software running around that nobody owns,” the CIO told him.
Fipps delivered the line without embellishment, and the room—packed with Gartner and Constellation Research analysts—went silent. “AI chaos,” Fipps said, echoing language CEO Bill McDermott used throughout the week. “At the very large customers, they’re going to have thousands of applications… if you add AI to all those applications, you can imagine an ungoverned nightmare.”
Zavery has collected similar stories. He referenced the widely shared incident of Pocketbook OS, a startup whose entire customer database was wiped in nine seconds by an AI agent that reportedly acknowledged it knew it shouldn’t have. “These [stories] are pretty common,” Zavery said. “But I think the good thing about enterprises, most of the CIOs and CISOs are more thoughtful. They’re not believing this world that everything should just be rewritten with AI from ground up.” ServiceNow often learns of problems only when things break down, “and by that time it might be too late.”
The Context Problem
ServiceNow’s central technical challenge isn’t building smarter AI models. It’s equipping those models with the contextual guardrails they need to operate reliably within an enterprise.
Large language models are inherently probabilistic—they don’t generate identical outputs each run. For consumers, that variability is acceptable. For a Fortune 500 company handling financial reconciliation, it could be catastrophic. “If your AI gives you random things every time, it doesn’t help,” Zavery said. “If you get two different answers for your financial reconciliation you might be doing, you can’t publish your financial report to Wall Street.”
ServiceNow’s solution is what it calls a “Context Engine”—a proprietary layer built on top of its LLM partners (Anthropic, Google’s Gemini, NVIDIA’s NIM) that draws on the company’s accumulated enterprise data: 100 billion workflows annually, 7 trillion transactions per year. “That is not available in public open source,” Zavery said. “It is available only in our platform.”
Guardrails, Not Just Features
The marquee announcement at Knowledge 2026 is AI Control Tower—a governance layer built on ServiceNow’s existing CMDB asset management infrastructure enabling enterprises to discover, monitor and manage every AI agent across their organization. The recurring metaphor from both Zavery and Fipps: air traffic control.
“Imagine if you didn’t have air traffic control and people were just flying around,” Zavery said. “AI agents are not like humans. AI software can be very, very aggressive and very fast because there are no boundaries of their time or limits.”
Fipps described customer response as nearly visceral. He asks executives: how many agents do you have? Where are they in your organization? What do they have access to? Are they performing as intended? The conversation typically ends with a request to see the AI Control Tower. Fipps called customer uptake one of the week’s biggest surprises—”pleasantly surprised” by engagement speed and contracting demand.
Real-world validation arrived via the customer panel. Melinda McKinley, COO of Strategy and Talent at Standard Chartered Bank, described expanding an AI assistant from a 50,000-person pilot in Hong Kong to 85,000 colleagues globally, with case deflection rates climbing from 77% to 90%—triple the industry baseline. “AI is only as good as the data behind it,” she said. “You have to be intentional about keeping that knowledge base live, current, and trusted.”
Oliver de Wilde, head of ServiceNow’s Centre of Excellence at Hitachi Energy, recounted a tenfold spike in employee self-service usage the week AI went live across 70,000 employees, plus a 25% reduction in IT service desk calls. The service desk manager called him in disbelief: “They knew it was coming—but they couldn’t believe the reduction they were actually seeing.” Those reclaimed hours became negotiating leverage in service provider renegotiations. “When you can use it to renegotiate a contract, the savings become very tangible,” de Wilde said.
The Hard Lift Ahead
When pressed on AI buildout progress—an industry debate over whether we’re in the second or fifth inning—Zavery declined to commit to a number, but suggested any of the first three. “It’s definitely nowhere in the middle,” he said. “I think it’s still very early days.” Technology remains probabilistic and not always backward compatible. Societal and regulatory frameworks are still taking shape. Cost structures haven’t stabilized.
Fipps framed the next phase through personal history. His father was a turbine mechanic who spent his career suspended on high-voltage lines maintaining massive generators. “I think the future infrastructure buildout—for our country, but mostly globally—is going to be a renaissance around innovation and opportunity and GDP growth,” Fipps said. “At the power core, the infrastructure core, it’s going to be so much fun. Because we’re going to do it in such a different way.”
For ServiceNow, the work ahead means security, compliance, backward compatibility, governance across regulatory regimes varying by country, industry and agency. “Enterprise software was never sexy,” Zavery told Fortune, citing three decades in the space and contrasting with the recent AI boom. “The amount of time people building software in this space spend—not just building features, but making it secured, compliant, guaranteed performance—all those things are never sexy jobs. They’re very heavy, painful, getting into the nitty-gritty, making sure you’re solving the difficult problems. And when the user is using it, they would never see any of this stuff. It’s all the work you have to do underneath the covers.”
For a $96 billion company whose pitch is being the infrastructure layer enterprises trust most, unglamorous work isn’t a shortcoming. It’s the value proposition.