Generative AI on ServiceNow: Why Value Is a Continuous Conversation, Not a One-Time Outcome

Swaroop-Pydy · yesterday

Generative AI on ServiceNow: Why Value Is a Continuous Conversation, Not a One-Time Outcome

This article is part of a series from the AI Center of Excellence Team at ServiceNow.

We've worked with organizations across industries to help them measure and sustain value from their AI deployments. Even though every enterprise is unique, we've started to see clear patterns in what separates AI initiatives that deliver lasting ROI from those that stall after go-live.

This isn't a formal instruction manual. Think of it more like a framework built from real experience helping platform owners answer the hardest question in AI adoption: "Am I getting back more than I invested, and can I prove it?" Whether you're just starting to think about AI value or looking to strengthen your existing measurement approach, these insights are meant to help you move from abstract promises to concrete evidence.

The answer starts with changing how you think about AI performance itself.

In Formula 1, teams don't judge performance by a single pit stop. Every stop is reviewed: the fast ones, the average ones, and the ones that didn't go as planned. Improvement comes from studying patterns over time. This is the right way to think about the value of Generative AI on ServiceNow.

Generative AI is not a one-time deployment or a feature you enable and move on from. It is a living capability embedded in interactions, workflows, and daily decisions. As user behavior, data quality, and business priorities change, so does the value AI delivers. That makes continuous value assessment essential, not optional.

When organizations adopt Generative AI on ServiceNow, success is often measured by what works: faster resolutions, helpful summaries, or deflected requests. While these signals matter, they tell only half the story.

The real value of AI emerges when we examine both successful and unsuccessful interactions and let platform data guide decisions instead of assumptions.

The Problem: Preparing to Fix Instead of Fixing

In traditional service environments, agents spend most of their time not fixing issues, but preparing to fix them: reading long tickets, searching for knowledge, summarizing interactions, and documenting resolutions.

Like a pit crew searching for tools mid-race, this slows everything down and increases errors.

AI should eliminate this friction. But how do you know it's working? And more importantly, how do you keep it working as conditions change?

AI Value Emerges Through Usage, and It's Always Changing

Early AI success is often measured through anecdotes or initial wins. While encouraging, those signals only tell part of the story. Real value shows up in how AI is used, or avoided, over time. And unlike traditional automation, what performs well today may degrade tomorrow as user behavior, content, or processes change.

Every AI-assisted interaction leaves behind signals:

Which skills are frequently used
Which ones are skipped
Where interactions succeed
Where they break down or escalate

These signals allow teams to replace assumptions with evidence. But here's what makes AI different: you can't review performance once and move on.

Even successful interactions deserve scrutiny:

Was AI actually used, or did the agent bypass it?
Did it meaningfully influence the outcome?
Was the response accurate, or merely fast?

Equally important are interactions that fail:

Where did users abandon the interaction?
Did AI provide irrelevant or incomplete guidance?
Was the issue AI quality, data quality, or process design?

Without reviewing both, organizations risk optimizing based on incomplete truth. A pit stop that looks fast on paper may have cost you the race if the driver stalled coming out of the box.

Regularly reviewing AI usage patterns (including both successful and unsuccessful interactions) alongside operational outcomes should be part of how the platform is run. Just like Formula 1 teams review telemetry from every pit stop, platform owners must study the data trail AI leaves behind in every interaction. This makes ongoing value assessment essential, not optional.

What the Data Reveals: Reading the Signals AI Leaves Behind

ServiceNow captures evidence directly through interaction logs and usage signals. This allows platform owners to move from opinions like "AI isn't working" to facts like "AI is being skipped at this step" or "this skill performs well only for certain request types."

High-Usage Skills: Your Strongest Tools

Skills that see consistent use usually address immediate, real needs:

Summarizing long case or incident histories
Helping users find answers faster through conversational guidance
Assisting with repetitive documentation tasks

High adoption tells a clear story: AI is reducing friction and helping people move faster with more confidence. These are strong candidates for refinement, expansion, and deeper integration.

Underused Skills: Diagnostic Insight, Not Failure

Low usage is not a failure. It's insight. Underused skills often indicate:

Poor placement in the workflow
A mismatch between design and how people actually work
Trust issues driven by inconsistent responses
Capabilities that sounded valuable in theory but don't solve a real problem in practice

A common pitfall in AI programs is assuming low value means low demand. In reality, the data often shows something different:

AI is available but not adopted
Skills exist but are poorly positioned in the workflow
Trust erodes due to a small number of bad experiences

Both Success and Failure Reveal the Truth

An interaction that appears successful may still fall short of its potential. Users might engage with AI but escalate anyway. Agents might rely on AI summaries without seeing improvement in downstream outcomes. High success rates can be misleading. You might notice your Virtual Agent maintained a 90% positive sentiment score, but 70% of interactions still escalated to live agents. Reviewing these "successful" interactions might reveal users were getting generic answers that didn't address their specific compliance questions, leading to polite thank-yous followed by immediate escalations.

Interactions where AI doesn't help are not failures. They are diagnostic signals. They highlight gaps in data quality, intent understanding, knowledge coverage, or orchestration. Treating these interactions as feedback (not defects) enables continuous improvement and builds long-term trust in AI.

In Formula 1, the worst pit stops are studied the most. Was it equipment failure? Driver error? Poor communication?Organizations that ignore these signals stall. Those that study them evolve.

Value is not only about deflection. It's about intent fulfillment.

How to Measure Value: Comparison and Pattern Recognition

Measuring AI value does not start with complex ROI models. It starts with comparison and pattern recognition, the same way pit crews compare lap times and tire performance across races.

The most effective approach is to observe:

AI-assisted interactions versus non-AI interactions
Outcomes before and after AI engagement
Where AI meaningfully changes behavior, decisions, or flow

Measurement is less about a single metric and more about understanding direction:

Are interactions improving?
Is friction being reduced?
Are people choosing to use AI and returning to it?

This doesn't require new teams or additional tooling. It's about using the signals the platform already produces and folding them into existing operational reviews.

ServiceNow's AI Value Framework introduces practical constructs like the Self-Service Efficiency Score, which measures the percentage of requests AI handles versus those requiring live support. A 25% score means AI is automating a quarter of potential support cases. As you refine capabilities, that score might climb to 62%, indicating AI now handles nearly two-thirds of what would have been live requests. This kind of directional measurement makes AI value tangible without requiring complex modeling.

Similarly, the Agent Productivity Score quantifies how much work AI completes compared to human agents. If agents typically complete 7.3 work actions per hour, and AI is now handling 3 of those actions while agents handle 4, your score shows 100% productivity with AI contributing 42% of the work. This reframes the conversation from "is AI helping?" to "how much of the workload is AI carrying?"

Make Value Assessment a Repeatable Practice

ServiceNow continues to rapidly introduce new capabilities across both generative and agentic AI. As the platform evolves, yesterday's value signals may no longer apply tomorrow. Championship pit crews don't review performance once a season. They review it after every race. Your AI strategy deserves the same discipline.

ServiceNow's internal AI journey demonstrates this iterative approach. Starting with a single use case (incident summarization for IT agents), the organization now has over 50 live implementations across multiple personas. This didn't happen through a master plan executed perfectly. It happened through continuous measurement across five key dimensions:

Adoption measures Monthly Active User percentage against total potential users. This tells you if AI is even being discovered in the workflow.
Usage tracks triggers and successful generations. High adoption but low usage indicates positioning problems, not capability problems.
Sentiment calculates the proportion of positive feedback. This is your early warning system for trust erosion.
Accuracy examines outputs through metrics suited to the use case: search success rates for AI Search, code acceptance rates for developer tools, resolution quality for agent assist.
Hours Saved translates all of the above into business impact, measuring time saved for both agents and requesters.

Practical Frameworks to Get Started

The shift to continuous value assessment doesn't require starting from scratch. ServiceNow has documented both the measurement methodology and implementation approach based on internal deployment of AI across 500+ use cases. Two resources provide practical frameworks for enterprise AI value assessment:

AI Value Framework: How ServiceNow Measures the Value of AI outlines specific methodologies for quantifying AI impact across five personas: end users, human agents, process owners, developers, and leaders. It introduces metrics like the Self-Service Efficiency Score and Agent Productivity Score that translate usage patterns into measurable business outcomes.

Now on Now: Our Now Assist Implementation Lessons Learned documents ServiceNow's internal journey from one AI use case to over 50 live implementations. It covers practical considerations like governance, change management, and the five metrics to track across every use case: adoption, usage, sentiment, accuracy, and hours saved. This provides the "how to execute" playbook for continuous assessment.

These frameworks are not prescriptive mandates but starting points. Adapt the methodologies to your organization's context, workflows, and priorities. The key is having a consistent approach to measurement that allows you to compare performance over time and make evidence-based decisions about where AI is delivering value and where it needs refinement.

Why Most AI Value Conversations Fail

Most AI value conversations fail because they start with capability instead of accountability.

As an owner, the real question is simple: Am I getting back more than I invested, and can I prove it?"

For enterprise architects and platform owners, the goal is not to prove AI works once. It is to ensure it keeps working as the business changes. Continuous value assessment turns AI from a feature into a managed capability. One that earns trust, justifies investment, and improves over time.

ServiceNow Generative AI makes this possible because it is not a separate tool. It operates inside the same workflows, tickets, knowledge, and logs you already govern. The evidence is already there. You just have to look at it.

Generative AI on ServiceNow does not fail silently. It leaves a trail of evidence in every interaction.

Organizations that listen to that evidence will know when to invest more, when to course-correct, and when to pause, with confidence. Those that don't will rely on assumptions. And assumptions are the fastest way to lose AI value.

What Happens When You Listen to the Data

When organizations treat AI value as a continuous conversation, they don't just avoid waste. They unlock compounding returns.

Investment decisions become strategic. You know which capabilities deliver measurable impact and which need refinement. Budget conversations shift from "should we invest in AI?" to "which AI capabilities should we scale based on proven performance?"
Adoption accelerates through trust. When teams see AI improving based on real feedback, they engage more deeply. The platform team that repositioned their knowledge suggestion skill saw adoption jump from 12% to 68% because agents saw their workflow feedback translated into tangible improvements.
AI evolves with your business. As priorities shift, your value framework shifts with them. The summarization skill that works brilliantly for IT incidents may need adjustment for HR cases. Continuous assessment catches this drift before it becomes a trust problem.

Two Starting Points for Your AI Value Journey

If you're ready to move from abstract AI value to measurable evidence, here are two concrete starting points that don't require new tools or teams:

Starting Point A: Track Your Deflection Rate for One AI Capability

Pick your most visible AI skill (likely AI Search or Virtual Agent if deployed). Measure how many interactions successfully resolve without escalation to live agents. ServiceNow's approach defines deflection based on demonstrated intent to open a ticket: if a user enters a search description, receives an AI response, and doesn't submit a ticket within 24 hours, that's a deflection. Calculate your rate weekly for one month. You'll quickly see whether AI is actually reducing workload or just creating polite interactions that still escalate.

Starting Point B: Measure Agent Adoption and Usage for Case Summarization

If you've deployed any summarization capabilities (incident, case, or chat summarization), track two simple metrics: what percentage of eligible agents are using it (adoption), and how often they're using it when available (usage). High adoption with low usage signals a workflow placement problem. Low adoption with high usage among active users signals a change management or discovery problem. Both are fixable, but you can't fix what you don't measure.

These two starting points give you directional data within 30-60 days. No complex ROI models. No new platforms. Just evidence about whether AI is working and where to focus next.

Making the Case to Leadership

You've got the data. Deflection rates are improving. Agent adoption is climbing. Hours saved are adding up. Then leadership asks: "So what does this mean for our Q3 budget?" or "How does this compare to our automation goals?" Suddenly, raw metrics need to become executive narratives. Usage signals need to translate into strategic conversations.

This article focused on identifying the signals that matter. The logical next step is translating those signals into executive-level dashboards that tell a compelling business story.

The organizations that get AI value right do it through continuous conversation, not just with their platforms, but with their peers. Let's make this one of those conversations.

I'm working on a practical guide: Building Executive AI Value Dashboards. It will cover how to create Performance Analytics dashboards that connect the metrics like (adoption, usage, sentiment, hours saved) to outcomes. Before I finalize it, I'd like to know what challenges you're facing. Are you struggling to visualize hours saved in a way executives understand? Is the challenge positioning AI investment against other platform priorities? Let me know through comments and your input will help ensure the guide addresses the real problems platform owners face.

P.S. Insights shared here are my own and do not reflect those of my team, employer, partners, or customers.