Measuring AI systems

---
title: Measuring AI systems
description: Measure quantifies the productivity value delivered by your AI systems and tracks their costs. It also reports the net return on AI, enabling you to determine where to scale, fund, or retire AI.
locale: en-US
canonical_url: https://www.servicenow.com/docs/r/intelligent-experiences/aict-measuring-ai-impact.html
release: brazil
topic_type: concept
last_updated: "2026-09-10"
reading_time_minutes: 1
keywords: [ServiceNow Otto, AI Agents, generative AI, agentic AI, use]
breadcrumb: [AI Control Tower, Establishing AI governance, Enable AI Experiences]
---

# Measuring AI systems

Measure quantifies the productivity value delivered by your AI systems and tracks their costs. It also reports the net return on AI, enabling you to determine where to scale, fund, or retire AI.

## Measure overview

Measure is one of the AI Control Tower \(AICT\) pillars \(Discover, Observe, Govern, Secure, Measure\). While the other pillars focus on finding, monitoring, governing, and protecting your AI systems, Measure is responsible for reporting the results produced by those systems. It answers three questions:

-   What is AI actually delivering?
-   What is it costing you?
-   What's the net return?

## Components

Measure has three components:

-   **Value**

    Value converts AI usage into a productivity figure.

-   **Cost**

    Cost records the actual expenses of your AI, enabling AI Control Tower to compare this against productivity gains and calculate the net AI return. You set it up by inputting your organization's average hourly rate and specifying prices for both integrated and non-integrated vendors.

-   **Dashboard**

    Dashboards are where value and cost converge. The dashboard presents productivity gains, AI costs, and net AI return together. It breaks usage and costs down by user and department. It shows adoption patterns and flags AI systems that record no usage.


## How Measure helps

As organizations scale AI across teams and providers, it becomes harder to understand whether AI investments are delivering measurable business value. Costs can accumulate across multiple model providers, while productivity gains often remain anecdotal or inconsistent across teams.

As AI adoption grows, Measure helps organizations:

-   **Prove business value**

    Calculate productivity value and net AI return for specific AI systems, instead of relying on estimates or anecdotal feedback.

-   **Control AI spend**

    Monitor AI system costs, including token-level costs across model providers, so spend is visible and predictable.

-   **Make informed investment decisions**

    Identify where AI is creating value, where spend is increasing, and which AI systems to scale, optimize, fund, or retire.

-   **Standardize value measurement**

    Use templates so teams can calculate value consistently, and results can be compared or rolled up across the organization.

-   **Reduce manual reporting**

    Run scheduled value jobs so dashboards stay up to date without teams assembling spreadsheet-based reports.

-   **Attribute accountability**

    Assign value and cost to the departments and users that generate them.