ServiceNow says AI is ‘really kicking in’ — but it’s not enough to help the stock
ServiceNow’s leadership has been clear for more than a year: the company’s generative AI investments are moving from pilots to production, and customers are beginning to pay for them. The message on recent earnings calls has consistently highlighted rising attach rates for AI-infused SKUs, stronger expansion on renewals, and tangible workflow outcomes like faster case resolution, higher agent deflection, and developer productivity gains.
Yet the market’s response has been tepid. Despite the “AI is really kicking in” refrain, shares have struggled around print-and-guide moments, reflecting a gap between improving product traction and investors’ demand for clear, near-term revenue acceleration. The disconnect is less about whether AI works on ServiceNow’s platform and more about how quickly that value shows up in headline growth, bookings, and cash flow.
What “kicking in” looks like inside ServiceNow’s model
ServiceNow’s AI strategy is pragmatic: ship generative features where the platform already owns the workflow, price them into familiar SKUs, and sell to a large installed base that understands the ROI of automation.
Key planks include:
– Generative assistants embedded in IT service management, customer service, HR, and development workflows. Think case summarization, next-best action, knowledge article generation, and conversational Virtual Agent upgrades.
– Premium tiers (often branded “Pro” and “Pro Plus”) that bundle AI and automation with measurable value. This lets account teams monetize AI primarily through expansion rather than net-new logos.
– Domain-tuned models and guardrails that operate on customers’ ServiceNow data, which tends to be clean, structured, and operationally critical—prime territory for reliable AI.
Internally, “kicking in” translates to:
– Higher attach rates on renewals as customers step up from base to Pro/Pro Plus.
– Expansion deals that cite AI as the reason to consolidate more workflows on the Now Platform.
– Early but credible customer outcomes: reduced handle times, improved SLA compliance, faster development cycles, and more self-service containment.
Why the stock hasn’t rewarded the progress
Investors are watching a different scoreboard. Several dynamics have made it hard for AI traction to translate into stock upside, at least so far:
– Ratable revenue recognition: Even when customers buy higher-tier SKUs, revenue is recognized over the life of the contract. That means the dollars arrive gradually, while the expectations are immediate.
– Contract and usage ramps: Enterprises often begin with limited-seat deployments, staged rollouts, or credits. AI adoption may be real, but the first few quarters look like a trickle, not a surge.
– CRPO optics: Current remaining performance obligations (a forward-looking bookings proxy) can wobble with timing of large deals, federal seasonality, or macro caution. When CRPO growth decelerates—even modestly—it can overshadow upbeat AI anecdotes.
– Macro and deal scrutiny: Elongated approvals, multi-stakeholder governance, and budget realignments persist in many enterprise IT shops. AI is a priority, but it competes with cost takeout and platform consolidation mandates.
– Valuation air: ServiceNow has long commanded a premium multiple for durable growth and best-in-class margins. To move higher from an already rich base, the market wants unmistakable evidence that AI is accelerating net new ACV, not just protecting existing momentum.
– Competitive narratives: Giants are embedding AI into adjacent platforms. Even if overlap is limited, the presence of credible alternatives raises questions about long-term pricing power and attach rates.
The monetization puzzle
ServiceNow’s choice to bundle AI into premium tiers rather than meter it as pure consumption aligns with how customers buy workflow software. It also creates a lag between “we sold AI” and “you can see it in the P&L.”
– Pricing power vs. usage variability: Packaging AI with automation and analytics helps justify a clear price step-up. But not every department turns on every feature on day one, which blunts immediate revenue impact.
– Seat- and workflow-based licensing: Many SKUs scale with roles and modules more than tokens or compute. That’s good for predictability, less so for capturing spiky AI usage upside.
– Cost-to-serve: While ServiceNow can optimize model choices and inference routing, AI does add variable costs. Gross margin leverage from AI will take time, particularly while the company invests to harden safety, observability, and model orchestration.
Proof points vs. the “needle”
The company has provided case studies where AI-enabled workflows meaningfully reduce backlogs, improve customer satisfaction, and cut ticket resolution time. Those proof points help close expansion deals. But at a company of ServiceNow’s scale, even healthy AI attach rates are initially diluted by the vast base of non-AI seats and long renewal cycles. Investors are effectively asking: how quickly can AI lift growth from the low-to-mid 20s back toward a sustained higher trajectory?
What would change the narrative
To swing sentiment, investors will look for more than qualitative momentum. Watch for:
– Reacceleration in CRPO growth and clearer AI attribution in net new ACV. Explicit disclosure of AI-driven uplift on large renewals or new logos would help.
– Rising proportion of customers on AI-infused tiers across core products (ITSM, CSM, HR, App Engine), and evidence that early cohorts expand faster than historical baselines.
– Quantified ROI benchmarks that become “standard” in the field (for example, consistent deflection rates or cycle-time reductions across industries), tightening the sales motion and shortening time-to-value.
– Signs of margin resilience alongside AI adoption, indicating that model costs are being managed and that the pricing is sticking.
– Federal and large enterprise deal cadence normalizing, reducing volatility in quarterly bookings optics.
Moats and risks
ServiceNow’s moat in an AI world looks durable where:
– It owns the workflow, data, and governance surface area.
– The value is operational and measurable (tickets closed, outages prevented, requests auto-fulfilled).
– AI augments a platform customers already rely on for compliance, security, and audit trails.
Risks persist where:
– Horizontal assistants from hyperscalers encroach on simpler use cases, pressuring price for basic summarization and Q&A.
– Buyers view AI as “included” elsewhere, pushing for discounts on premium tiers.
– Budget austerity slows expansions, extending the timeline for visible AI-driven uplift.
The bottom line
ServiceNow is doing many of the right things: embedding AI where it matters, pricing it coherently, and proving value in production. By management’s own account, AI is indeed “kicking in.” But the stock is a timing instrument as much as a weighing machine. Until AI visibly reaccelerates bookings and revenue—overcoming ratable accounting, deployment ramps, and macro noise—the market is likely to reserve judgment.
For long-term investors, the thesis rests on whether AI deepens ServiceNow’s platform centrality and boosts lifetime value per customer. For the stock, the near-term catalyst will be less about the promise of AI and more about when it unmistakably shows up in the numbers.
