Most enterprises know what they spend on AI. Few can measure what they get back.
Global generative AI investments are racing toward $1.5T in 2025, yet we found that 81% of leaders say AI investments are difficult to quantify (Larridin State of Enterprise AI 2025 report). An AI measurement framework transforms blind, and possibly even counterproductive, spending into competitive advantage by tracking utilization, proficiency, and business value across your organization.
According to the Larridin State of Enterprise AI 2025 report, 81% of leaders find AI investments hard to quantify. Another 79% report that untracked AI budgets are becoming an accounting problem. Without measurement, you cannot optimize spending, prove business value, or scale what works.
The stakes are high. The report also shows that 85% of leaders think they have less than 18 months before falling behind. With GenAI adoption accelerating across software development, business functions, and workflows, measurement separates winners from those left guessing.
Effective AI measurement frameworks track three core dimensions that connect AI usage to business outcomes.
This dimension tracks who uses AI tools and how often. Key metrics include daily active usage across your AI systems, adoption rate by department and by team, session frequency showing engagement patterns, and feature usage, revealing which capabilities employees actually use.
Buying 100 licenses does not guarantee adoption by 100 users. Real-world usage often differs from what procurement has on the books. Measure actual AI-assisted workflows, not just license counts.
Utilization without proficiency wastes potential. So this dimension tracks how well teams use AI tools through AI-generated metrics for the quality of AI-generated code, cycle time improvements in software engineering, throughput increases across pipelines, and reduction in vulnerabilities from AI code.
Research from theMcKinsey State of AI 2025 report shows that top performers have defined processes for validating AI output. They measure proficiency, not just adoption, at scale.
In this dimension you connect AI performance to business impact. Track metrics such as time saved per developer per week, productivity gains, business outcomes from AI initiatives, and KPIs tied to specific use cases.
Impact varies by team and use case. Some workflows speed up with AI support, while others slow down or add rework. Quality of work may improve, stay the same, or diminish. That’s why value realization has to tie AI-assisted workflows to outcomes—time saved, quality, cycle time, and KPI achievement—not assumptions.
A repeatable AI measurement framework needs four key elements.
Define what you want from AI before choosing metrics. Establish baseline measurements for utilization, proficiency, and value. Larridin research shows 84% of organizations discover more AI tools than expected during audits. You cannot measure improvement without knowing your starting point.
Use dashboards that monitor AI usage across the organization. Track performance metrics for AI agents, LLM interactions, and AI-driven automation. Modern platforms from AI vendors such as Microsoft, OpenAI, and others provide APIs for tracking AI impact in real-time, not via quarterly surveys. Internally created apps should include such APIs as well.
Measurement without action wastes effort, so you should build workflows that turn data into decisions. Identify which AI tools drive results, find bottlenecks in AI-powered pipelines, optimize AI model performance based on usage patterns, and scale successful practices across functions.
Metrics only matter when they drive decisions. Harvard Business Review notes that measurement works best when it translates strategy into concrete targets; otherwise, it can distort decisions. Link every metric to a business goal.
AI systems evolve fast. What worked for GitHub Copilot might not capture AI agents or generative AI in new use cases. Review your framework regularly to track short-term wins and long-term transformation through the AI lifecycle.
Organizations that master AI measurement frameworks gain real advantages. The Larridin State of Enterprise AI report shows 88% of leaders believe measurement will determine market winners. Companies with systematic frameworks can make data-driven decisions about AI investments, scale successful AI initiatives across the enterprise, sunset unsuccessful initiatives, prove ROI to boards with transparent metrics, and optimize spending so that waste doesn’t impact profitability.
Your AI measurement framework should answer three questions:
When you answer with data instead of guesses, you transform AI from hope into competitive advantage.
You cannot manage what you do not measure. In AI, measurement is not optional.
With Larridin, you can achieve ongoing excellence in AI measurement almost overnight. Larridin’s AI measurement is engineered to the highest standards, in cooperation with AI industry leaders, and your TTFD (time to first dashboard) is measured in hours. Join the leaders who spend their time and energy on excellent AI implementation and business KPIs, not on building and maintaining internally developed dashboards that fall short of the state of the art. To learn more about Larridin, connect with us for a demo.
Ready to quickly build a measurement framework that drives real business impact?