Measuring AI impact

  • Release version: Zurich
  • Updated April 17, 2026
  • 2 minutes to read

    • Track the business value, productivity gains, and user adoption of AI assets across your organization to quantify return on investment and identify opportunities for improvement.

    Why measuring impact matters

    Deploying AI is an investment, and stakeholders need to understand whether that investment is paying off. Without measurement, AI initiatives operate on faith and leadership can't distinguish between AI systems that are transforming workflows and AI systems that are barely used. Measurement also reveals optimization opportunities, including which AI systems could be expanded to more users, which are underperforming, and where adoption barriers must be addressed.

    AI Control Tower provides value and adoption analytics that connect AI system usage to measurable business outcomes — productivity hours saved, cost reductions, and user engagement metrics. These analytics span both ServiceNow AI and external AI assets, providing a unified view of impact across your entire AI portfolio.

    Tracking AI value and productivity

    Value insights in AI Control Tower help product owners and AI stewards quantify the business impact of AI in concrete terms. Productivity gains are measured in hours saved, with breakdowns by AI system, time period, and asset type. Creator skills metrics are aggregated across non-production and production instances to capture the full scope of AI-assisted development work.

    Value calculations are driven by value templates — configurable formulas that define how impact is measured for each AI system type. Default templates are provided, but organizations can create custom templates that reflect their own definitions of value. Templates can be mapped to specific asset types and assigned as defaults, ensuring consistent measurement across the portfolio.

    Analyzing AI adoption and engagement

    Value metrics tell you what AI is delivering; engagement metrics tell you whether people are actually using it. Engagement metrics provide complementary visibility into user adoption patterns, helping you understand not just aggregate numbers but which departments, workflows, and AI system types are driving the most engagement.

    Usage is tracked through daily AI actions, daily unique users, AI system group distribution (interactive trend charts with group-by filtering), and usage comparison by workflow. Adoption is tracked through department-level usage rankings, AI action comparison by department, and user feedback details — including positive feedback percentages against total AI actions.

    Together, these metrics reveal the shape of adoption across your organization. High total actions but low unique users may indicate power-user dependency. High usage in some departments but low usage in others may indicate training or awareness gaps. Declining positive feedback may signal quality issues that need investigation through the monitoring capabilities.

    ServiceNow AI metrics

    For ServiceNow AI assets specifically, AI Control Tower provides dedicated views for value, engagement, creator skills, and evaluation metrics.

    These views consolidate ServiceNow specific data that may be distributed across multiple areas of the workspace. Product owners get a focused view of how ServiceNow AI is performing within their scope.