Even for experienced ServiceNow professionals, understanding where AI fits into day-to-day work can be challenging. The technology is advancing quickly, and many architects and administrators are asking the same question: how does AI actually apply to the work we do today?
In this article, we’ll explore that question through a practical lens, looking at when to use ServiceNow AI skills, when to deploy AI agents, and how real organizations are combining both to simplify complex workflows without starting from scratch.
To understand where AI fits, it helps to look at how automation in ServiceNow has evolved.
How AI Builds on ServiceNow’s Foundation of Automation
In the early days of automation, every new advancement came with a question: would it replace people or help them do more? When ServiceNow introduced routing rules and early workflow automation, some worried the frontline would disappear. It didn’t. The repetitive work simply moved left, and people moved up into analysis, engineering, and problem prevention.
That same evolution is happening again. ServiceNow has always been about making work faster and easier, and AI builds on that foundation. Instead of just automating individual steps, it connects data across systems, makes context-aware decisions, and even resolves issues automatically, allowing people to focus on the work that truly moves the organization forward.
That’s where ServiceNow’s AI skills and agents come in, providing the capabilities that bring this next phase of automation to life.
ServiceNow just released Zurich, their most recent update and an AI-driven leap forward in workflow intelligence. Read about it here.
How AI Builds on ServiceNow’s Foundation of Automation
In the early days of automation, every new advancement came with a question: would it replace people or help them do more? When ServiceNow introduced routing rules and early workflow automation, some worried the frontline would disappear. It didn’t. The repetitive work simply moved left, and people moved up into analysis, engineering, and problem prevention.
That same evolution is happening again. ServiceNow has always been about making work faster and easier, and AI builds on that foundation. Instead of just automating individual steps, it connects data across systems, makes context-aware decisions, and even resolves issues automatically, allowing people to focus on the work that truly moves the organization forward.
That’s where ServiceNow’s AI skills and agents come in, providing the capabilities that bring this next phase of automation to life.
ServiceNow Skills vs. AI Agents: What’s the Difference?
When organizations begin exploring AI-powered automation in ServiceNow, one of the first questions is when to use a skill versus an agent. They work together but serve very different purposes.
Understanding how these work together is the first step toward scaling AI in ServiceNow effectively.
What Are ServiceNow Skills?
Skills are single, targeted actions that perform a specific task. They’re designed to be quick, reusable, and easy to integrate into other workflows. Think of them as building blocks for AI in ServiceNow.
Examples of ServiceNow AI skills include:
Summarizing a knowledge base article.
Generating a JavaScript snippet to update a record.
Checking the status of a Workday API call.
Anonymizing a user’s phone number or email address.
Because they’re narrow in scope, skills are fast to deploy and test. They also lend themselves to reusability. The same data anonymization skill built for privacy requests can be reused in offboarding or vendor contact cleanup — same action, new context.
What Are ServiceNow AI Agents?
Agents are the orchestrators. They tie multiple skills (and sometimes other automated steps) together into a multi-step, cross-system workflow. While a skill completes one action, an agent manages the bigger picture.
Examples of agent-driven processes include:
Verifying a user’s identity, anonymizing their records, calling an external platform like Workday, and then sending a confirmation email.
Triaging inbound incidents by analyzing the request, summarizing relevant knowledge, and assigning automatically.
Coordinated offboarding across systems: disable access in identity, HR, and finance systems, confirm device return steps, and notify the manager.
Agents are ideal when:
A process spans multiple systems or product domains.
Conditional decision-making is required (for example, branching based on user input).
You want an AI-driven process that runs end to end without human intervention.
Rule of thumb:
If it’s a single, predictable action, use a Skill.
If it requires multiple steps, systems, or decisions, use an Agent.
In practice, most ServiceNow AI use cases need both. Skills handle the discrete actions, while agents orchestrate them into a cohesive workflow.
Why the Difference Between Skills and Agents Matters
Understanding this distinction is critical for any ServiceNow AI strategy. Build everything as an agent, and you’ll reinvent the wheel for each new use case. Focus first on reusable skills, and you’ll create a scalable library of building blocks that agents can call on to deliver more sophisticated automation.
Think of it like this: we didn’t write new routing logic for every queue; we created one great rule and reused it. Skills are those rules, and agents are how you string them together when the path forks.
Let’s look at how this works in practice with a common request many organizations face.
ServiceNow AI Use Case: “Right to Be Forgotten” End to End
Meet Mary. Mary won the lottery, so will be parting ways with her employer. On the way out, she asks that her personal data be removed from company systems. Simple on paper, complex in reality.
Breaking Down the Process
Verify identity.
Remove or anonymize personal data in ServiceNow (without breaking references).
Perform the same action in Workday.
Provide auditable confirmation.
Designing Mary’s Solution
Skills handle the discrete actions: identity verification, targeted anonymization (such as phone or email), Workday API call, and status check.
An agent orchestrates the sequence, branches on responses (“delete vs. anonymize”), and posts real-time updates.
If you already have a privacy process in place, you don’t need to start over. Replace manual case tasks with skills and let an agent orchestrate the flow. You keep your governance, eliminate the swivel-chair work, and gain the same kinds of results Mary did.
The outcome is faster execution (Mary gets a confirmation email in seconds rather than days), fewer handoffs, and a complete audit trail, while reusing the same skills used in related workflows such as privacy requests, employee offboarding, or vendor contact cleanup.
Practical Steps to Begin Your ServiceNow AI Journey
The same approach outlined above applies to nearly any process.
Start small, test, and grow:
Identify AI-ready processes. Start with repetitive, high-volume work such as password resets or data updates.
Try out of the box first. Turn on Now Assist and test prebuilt skills like summarization or content generation.
Tweak before you build. Copy and lightly modify an existing skill to fit your data and rules.
Introduce agents when steps multiply. Use agents once you’re tying skills together or branching across systems.
Grow incrementally. Add one skill or one agent at a time, and measure time saved or case deflection as you go.
Each small win builds momentum and creates the foundation for your broader ServiceNow AI strategy.
The Future of AI in ServiceNow
Today, skills and agents handle tasks and orchestrations. Tomorrow, they’ll form the backbone of how organizations design end-to-end digital services.
Imagine a library of proven, tested AI skills that any business unit can plug into their workflows, with agents acting as the connective tissue across departments and platforms. That’s where AI-powered ServiceNow is heading, focusing less on isolated automation and more on building a unified, adaptable fabric of business processes.
Ready to Get Started?
Ready to explore what AI in ServiceNow could look like for your organization? Get in touch to discuss where to start and how to move quickly toward measurable results.
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