ServiceNow Zurich Agentic CSM

PostedDecember 17, 2025

UpdatedDecember 17, 2025

ByDawn Christine Simmons

ServiceNow Zurich Agentic CSM accelerates agentic AI customer service. The success is in shifting Customer Service Management focus from answering questions to completing work.

As a result, support leaders can target zero-touch customer resolution and raise Autonomous Resolution Rate (ARR)—but only if they lead with governance, data readiness, and lifecycle planning.

Why the ServiceNow CSM Zurich Upgrade Matters Now

Self-service deflection still hits a ceiling

Even today, self-service rarely finishes the job end-to-end. Gartner reports that only 14% of customer service issues are fully resolved in self-service, and only 36% resolve fully even when customers describe the issue as “very simple.”

Because that gap persists, “just improve the portal” eventually stalls—customers still escalate, and your cost-to-serve rises.

Agentic AI demands governance, not hype

At the same time, agentic AI programs fail fast when controls lag behind ambition. Gartner predicts over 40% of agentic AI projects will be canceled by the end of 2027 due to escalating costs, unclear business value, or inadequate risk controls. Therefore, Zurich becomes a leadership test: with a mandate to operationalize Governance or pay a premium for uncontrolled experimentation.

Strategic Operating Model: “Case Management” to “Autonomous Service Resolution”

High-level process step: Autonomous Service Resolution

Zurich doesn’t merely speed up routing and response. Instead, it enables a closed-loop execution model where the platform can verify → decide → act → document with measurable guardrails.

Where CSM fits: Customer Workflows orchestration

Within Customer Workflows, CSM operates as the engagement command center. Consequently, it can coordinate back-end fulfillment across ITOM/FSM/ERP/CRM and reduce time-to-resolution—especially when automation can validate entitlements and trigger actions using trusted data sources.

North Star metric: Autonomous Resolution Rate (ARR)

ARR measures the percentage of cases closed with zero human touch. However, ARR only creates value when you score it safely by category, risk tier, and confidence thresholds—so autonomy rises without compliance risk.

Zurich Capabilities That Enable Agentic AI Customer Service

AI Agent Studio + AI Agents: Build “doers,” not just “writers”

ServiceNow describes AI Agent Studio as the place to create, manage, and test AI agents and agentic workflows—so you can build self-executing workflows aligned to business goals. ServiceNow
As a result, you can move beyond “draft an email” toward “complete the task”—while still controlling what the agent can execute.

Practical ServiceNow CSM impact

Context-aware triage and routing (history, product, entitlement, customer tier)
Policy-based execution (refund approvals, RMAs, credits) inside approved thresholds
Faster documentation through automated summaries and resolution notes

Now Assist for CSM: Activate measurable productivity, quality, and consistency

ServiceNow’s Zurich documentation for Now Assist for CSM explicitly calls out capabilities such as summarizing case information and generating case resolution notes. Additionally, ServiceNow community guidance highlights click-to-generate case summaries inside CSM workspaces, which improves speed and consistency while collecting feedback for quality improvement.

Now Assist Guardian: Add AI safety guardrails before customers see output

ServiceNow Now Assist Guardian helps monitor and evaluate generative AI content and help protect the user experience. Meanwhile, real-world reporting continues to highlight new risks tied to agentic AI autonomy and governance—especially when multiple agents interact at speed.

What this means for AI governance in CSM

Log risky content for audit and compliance review
Block unsafe outputs before they reach customers
Tighten policies for regulated workflows (PII/PHI, refunds, entitlements)

Quick Adoption Guidance: How to Raise ARR Without Raising Risk

Phase 1: Start “human-in-the-loop,” then expand autonomy

Begin with recommendation mode, require approvals for higher-risk actions, and widen autonomy only after you hit quality gates (accuracy, exception rate, reopen rate).

Phase 2: Define “safe zones” for autonomous customer support workflows

Focus first on low-risk, high-volume intents (status checks, simple returns, basic access help). Next, extend autonomy into regulated workflows only after Guardian policies and role controls mature.

Phase 3: Measure ARR like an executive metric

Track ARR by category, confidence threshold, escalation rate, and reopen rate. That way, you improve “human vs. machine” performance without gaming the number.

Deprecations and replacements table

Lifecycle management: what’s being deprecated and what to do instead

Date / Release reference	What’s going away (deprecated / removed)	What this impacts	What to use or do instead
March 2023 (Utah GA timeframe)	Connect Support (deprecation effective March 2023)	Embedded support experience patterns and older routing/interaction approaches	Migrate to Advanced Work Assignment (AWA) and Agent Chat patterns (and follow the migration guidance).
Utah (deprecation noted “as of Utah”)	ServiceNow Connect Support is deprecated “as of Utah.”	Upgrade planning if you still rely on Connect Support	Prioritize migration planning and testing for AWA + Agent Chat replacement paths.
Zurich (2025 release train; new-customer experience)	ServiceNow-created/published legacy workflows are removed for customers starting on Zurich.	OOTB “legacy workflow” expectations; upgrade surprise risk	Standardize on Flow Designer and modern automation patterns; inventory legacy workflows early and replace intentionally.
Zurich (release notes context)	Legacy Studio is being prepared for future deprecation; hidden / not installed on new instances (per community + deprecation reference).	Teams who still depend on older Studio-first assumptions	Shift dev standards to modern tooling and deprecation-aware governance; avoid net-new investment in legacy patterns.

Roles and accountability: govern “human vs. machine” outcomes
Even with great AI, you still need clean ownership. So, adopt a role-based model that separates execution from control.