An enterprise-grade approach to AI testing

Enterprises run on predictability. So does traditional software testing. Input A produces output B—today, tomorrow, and next quarter. But AI has changed the game entirely.

A fundamental shift in testing needs

Unlike traditional systems, AI exhibits probabilistic behavior. Ask a large language model (LLM) to summarize a support ticket twice, and you'll get two different responses—both potentially valid. That’s a feature, not a bug.

This variability creates unprecedented challenges for enterprise environments:

1. Model drift without code changes: Performance can shift over time even when no updates are made.

2. Context-dependent performance: The same AI can excel in one customer environment yet struggle in another.

3. Unpredictable risk profiles: When outputs vary, identifying potential failures becomes exponentially more complex.

For ServiceNow customers, this isn't theoretical—it's business-critical. You need to be sure your AI Virtual Agent will resolve incidents consistently in your specific environment.

Our enterprise-grade AI testing approach

We've built a multidimensional framework specifically for probabilistic systems. It features:

• Cross-functional collaboration: AI testing isn't isolated to engineering. Our approach distributes responsibilities across business units, development teams, product management, and quality engineering—ensuring alignment with real business goals.

• Human-in-the-loop evaluation: Automated metrics provide scale, but human evaluators assess subjective qualities such as helpfulness and appropriateness that metrics alone can't capture.

• Rigorous data selection: We ensure testing data represents your specific business context, covering both frequent scenarios and critical edge cases with statistical validity.

Real-world impact you can measure

When we applied this framework to our Virtual Agent skills, we uncovered performance inconsistencies that traditional testing missed. These issues appeared minor in aggregate testing but were critical to affected customers.

By implementing targeted improvements based on our comprehensive approach, we increased resolution rates by 17% across challenging scenarios—improvements that traditional testing could never have identified.

Continuous testing

With AI, testing isn't a one-time gate but an ongoing journey.

Our approach includes:

• Persistent evaluations triggered by model updates, changing user patterns, and scheduled assessments
• Business impact monitoring connecting technical metrics to actual outcomes
• Qualitative feedback loops providing context that automated metrics can’t capture

As AI becomes embedded in increasingly critical workflows, we're investing in next-generation evaluation approaches, from high-risk testing methodologies to automated adversarial testing.

When you deploy ServiceNow AI capabilities across your business, you're entrusting core operations to these systems. Our testing framework transforms the inherent variability of AI from a liability into a strength—delivering solutions that are both powerful and reliably consistent in enterprise environments.

Find out more about ServiceNow’s approach to responsible AI deployment.