Organizations are deploying AI faster than they can evaluate its impact. While 55% of organizations have deployed more than 100 AI use cases, only 19% report realizing significant business outcomes, according to ServiceNow’s 2025 Enterprise AI Maturity Index.
The issue? Many IT leaders still evaluate AI one initiative at a time. Individual teams measure adoption, usage, or operational performance, but those measures don’t provide a consistent view across the enterprise. This siloed approach makes it difficult to understand which initiatives truly contribute to strategic goals, and which don’t.
Evaluating AI as a “portfolio of investments” creates a more complete view of enterprise AI. With a single source of truth for AI in the enterprise, CIOs can confidently expand the AI initiatives delivering the greatest value. This model starts with understanding what’s running across the organization. From there, CIOs can connect operational insights with business outcomes for better AI investment decisions.
Start with an inventory of AI investments
An AI inventory should include every asset running in the organization — from agents and models to hyperscalers and development platforms. Those assets should then be mapped to the applications and processes they interact with. Lifecycle phase and status, as well as risk and ownership data, also provide important context. These details help teams manage dependencies and understand the potential impact of changes or incidents.
For example, when identifying an AI agent that automates password resets, the inventory should show that it’s connected to the IT service desk, the employee services it supports, and the business outcome it was created to achieve. Linking AI assets to business services and responsibilities integrates AI into the broader enterprise architecture instead of treating it as a separate technology layer.
Track performance and measure business value
To understand whether or not AI investments are delivering value, leaders need real-time visibility into performance and value metrics. Effectively tracking the performance of AI agents requires continuous monitoring and evaluation. Teams should be able to see if AI systems continue to perform as intended and how their behavior changes over time.
CIOs also need to understand which investments provide business value, and which aren’t meeting expectations. This means looking at measures that cover adoption, productivity, and ROI.
Imagine an AI agent that resolves routine service desk incidents. Performance monitoring shows the agent continues to operate reliably, with high quality and safety scores. However, value metrics show fewer hours saved and low employee adoption. Comprehensive measures like these give CIOs the evidence to decide if the initiative is still worth the investment.
Build a stronger investment strategy
Using the information above, IT and business leaders can develop a strategy for keeping AI investments focused on value. Experts recommend these best practices:
- Prioritize AI initiatives based on business value and organizational readiness.
- Align funding and resources with each initiative’s strategic outcome.
- Monitor progress and business value across all AI initiatives.
Learn how ServiceNow’s AI Control Tower helps organizations connect AI investments to business outcomes and make more informed investment decisions.