When ServiceNow introduced its new AI-native licensing model in April, the company presented it as a simpler way for customers to consume AI capabilities across the platform.
The move replaced the previous five-tier structure with three new tiers – Foundation, Advanced, and Prime – while bringing AI capabilities closer to the centre of platform adoption.
A few weeks later, at ServiceNow Knowledge 2026, the company unveiled Otto as a unified AI experience spanning Now Assist, Moveworks, and AI Experience, expanded AI Control Tower as a governance platform for enterprise AI, and doubled down on its vision of agentic AI operating across business workflows.
ServiceNow pressed home that AI is no longer an add-on, but a part of how the platform is bought, governed, and measured. Customers, however, are still working out what that means in practice.
According to Julie L. Mohr, principal analyst at Forrester, customer reactions have been mixed.
“It is a very different story for customers that I speak to,” she said. “There are real concerns about how they can afford the increases in costs and they are making the hard decision on how to reduce dependency by moving away from ServiceNow, or what tactics they should use during negotiation.”
Even organizations that recognize the value of the platform are finding contract renewals difficult.
“I have spoken to a few clients that understand the value of ServiceNow, can articulate that to executives in a meaningful way to justify the increase in the costs,” she said. “But even though they understand the value, the contract renewal process is still a very negative part of their experience with the company.”
The Readiness Challenge
While customers may initially focus on licensing, partners argue that pricing is rarely the real issue.
Chris Peart, head of ServiceNow advisory at Phoenix Software, said many public sector organizations are still relatively early in their ServiceNow journey, despite having used the platform for years.
“We tend to see two main scenarios,” he said. “Firstly, organizations newly implementing ServiceNow through Phoenix and a delivery partner, often against the older licensing structures with limited or no AI capability.
“Then we have those organizations that have had ServiceNow for seven years or more where it is typically still used as a ticketing system for IT support. But ServiceNow is much more than that today.”
In many cases, customer adoption hasn’t evolved at the same pace as the platform itself.
“The commercial position has moved forward, but customer usage hasn’t necessarily evolved,” he said.
ServiceNow’s strategy increasingly assumes organizations are ready to embrace AI, automation, and consumption-based services. Many customers are still dealing with fragmented data, siloed processes, and legacy operating models.
“The question becomes less about scaling, and more about readiness,” said Peart.
Similarly, Paul Gregory Kriegler, VP of global alliance management for ServiceNow at CGI, believes that while licensing is an important consideration, the most successful organizations are treating this as a broader platform strategy discussion.
Rather than focusing exclusively on licence entitlements, organizations are increasingly reviewing platform utilization, future demand, and how AI capabilities fit into wider transformation plans.
“Many organizations are using this moment as an opportunity to reassess how they leverage ServiceNow across workflows, automation, and employee experiences,” he said.
Are Customers Ready for Agentic AI?
Knowledge 2026 was dominated by discussions around AI specialists, autonomous workflows, and agentic AI. ServiceNow’s message was that AI is moving beyond productivity assistance towards taking action, resolving issues, and completing work across the enterprise.
But according to partners, many customers aren’t there yet. Peart believes ServiceNow’s strategic direction reflects a level of organizational maturity that many public sector organizations have yet to reach.
“ServiceNow’s direction, particularly around AI and consumption, assumes a more mature, integrated environment. But many organizations are still dealing with fragmented data, siloed services, and legacy processes. For many, AI isn’t yet in production, and sometimes not even in scope.”
That doesn’t mean organizations are uninterested in AI. Rather, the conversations have become more practical.
“There is still strong interest in AI, in its many guises, but it’s becoming more grounded,” said Peart. “Organizations are moving beyond ‘what’s possible?’ to more practical questions – where to start, how to control it, and how to make it work in practice.”
A year ago, many organizations were still exploring isolated pilots and proofs of concept. Today, they are increasingly trying to understand what is required to deploy AI at scale.
From Licence Management to Platform Strategy
One theme emerging from partner conversations is that licensing discussions are evolving into conversations about future operating models. Andy Dunbar, managing director of software and security at SCC, believes many customers initially approach the changes defensively.
“The initial response is almost always defensive,” he said. “Procurement, vendor management, and SAM teams lead the conversation, asking what renewal will cost and how to avoid paying more.”
However, organizations that make the most of the changes tend to move beyond that mindset, he said.
“The more mature ones quickly realize it is actually a platform strategy decision.”
Dunbar believes leading organizations are asking fundamentally different questions than they were a year ago.
“Rather than asking how many fulfiller licences they need, they are asking how many tickets humans should handle in three years. Rather than which ITOM modules to buy, they are asking how much operational work can be fully automated.”
Those discussions extend beyond IT service management into wider questions about workforce productivity, employee experience, and enterprise-wide automation.
“Previously, discussions often focused on whether AI could improve a specific process or workflow,” said CGI’s Kriegler. “Today, organizations are increasingly focused on scaling AI across the enterprise while maintaining governance, transparency and cost predictability.”
As a result, customers are asking new questions about AI operating models, adoption strategies, and how success should be measured.
Cost Concerns Haven’t Gone Away
Despite ServiceNow’s focus on outcomes and AI-enabled productivity, cost remains a major concern for many customers. Mohr believes the introduction of the Foundation tier raises interesting questions about ServiceNow’s broader market strategy.
“The new offering of a Foundation package is the one that is interesting to me,” she said.
While ServiceNow increasingly positions itself as a broad enterprise workflow and AI platform, Foundation appears designed to lower the barrier to entry for organizations earlier in their adoption journey.
At the same time, customers remain concerned about contract costs and commercial complexity.
“There are real concerns about how they can afford the increases in costs,” said Mohr.
In some cases, customers are actively looking for ways to reduce their ServiceNow footprint.
“Clients are trying to remove products from the environment that they don’t use or can live without, but the cost goes up rather than reducing.”
That creates a challenge for both ServiceNow and its partners. Organizations need to understand the value AI can deliver, but they also need confidence that costs can be predicted, controlled, and justified.
Why AI FinOps Is Becoming a Priority
The introduction of consumption-based pricing has created a challenge that many organizations haven’t previously faced: managing AI usage as a measurable business resource.
Several partners described growing interest in what some are beginning to call AI FinOps. Customers want to understand how AI consumption will be tracked, who will own budgets, how costs will be allocated, and what governance structures need to be in place before adoption accelerates.
Dunbar believes organizations should start thinking about AI spend in much the same way they approached cloud consumption.
“Treat AI consumption like cloud consumption, before you need to,” he said.
He argued that organizations should establish usage monitoring, forecasting, and budget ownership before spending begins to scale.
“Many enterprises are building a ServiceNow AI FinOps capability from scratch because nothing equivalent existed before.”
These concerns help explain why ServiceNow has placed so much emphasis on AI Control Tower.
AI Control Tower is designed to provide visibility into AI usage, governance, performance, and risk across ServiceNow and third-party AI environments. For organizations exploring agentic AI, that visibility is becoming increasingly important.
Customers are now asking who owns AI, who pays for it, how its decisions are monitored, and how value is measured.
Mohr believes customers’ primary overall concern is governance.
“Clients need transparency on their demand and spend in addition to understanding how it is driving value and outcomes,” she said.
She added that observability is becoming increasingly important as AI becomes more autonomous.
“Observability of the agentic AI is a high priority.”
Governance Moves to the Front of the Queue
Peart said organizations are increasingly recognizing that successful AI adoption depends on having the right foundations in place.
“There are some consistent patterns emerging,” he said. “Multiple pilots running at once, different teams exploring AI in parallel, fragmented data, and no single view of what’s happening across the organization.”
As a result, governance and data quality are becoming priorities much earlier in projects, particularly in highly regulated sectors such as healthcare and policing.
“Clients are asking new questions around AI operating models, value measurement, adoption strategies, and portfolio prioritization,” said Kriegler. “They want to understand which use cases will generate the greatest impact, how success should be measured, and how AI can be integrated into broader transformation initiatives.”
The audience for ServiceNow conversations is also changing. Decisions that once sat largely within IT are increasingly involving governance teams, finance leaders, risk functions, and senior executives.
Start With Outcomes, Not Licences
Despite differences in customer maturity and market sector, all the partners we spoke with offered consistent advice. Their first recommendation? Stop thinking about licensing as the starting point.
“The organizations navigating this well are not asking how many assists they need. They are asking what success looks like in three years and what licensing model best supports that ambition,” said Dunbar.
“We try to move the conversation away from licensing early,” added Peart.
Most organizations already know what they are trying to achieve, whether that is reducing costs, improving service delivery, or increasing efficiency. The challenge is understanding how platform capabilities, governance, and commercial models support those goals.
“Our advice is to start with business objectives rather than licensing mechanics,” said Kriegler.
He recommends assessing current platform utilization, prioritizing high-value AI use cases, establishing governance, and modelling future consumption before making major commercial decisions.
For organizations that do this successfully, the licensing changes become part of a broader transformation journey rather than an isolated procurement exercise.
Final Thoughts
ServiceNow is attempting to reshape how customers think about the platform, moving the conversation away from licences and modules towards AI-enabled work, automation, and outcomes.
Whether customers are ready to make that shift remains to be seen.