3 metrics to help you measure AI’s impact

The useful part

Coding assistants, automated documentation, AI -powered testing, and intelligent development tools are now embedded in the daily workflows of many engineering teams. For CFOs, the question is no longer whether AI is entering the enterprise, but whether the growing investment behind it is translating into measurable business value. This surge in demand is placing substantial pressure on AI development teams.

How it works

• Is AI freeing up capacity for higher-value work and improved decision making?
• Three ways leaders can tell if AI is really working The metrics you choose need to be owned at the leadership level.
• Rather than supporting a handful of pilot projects, developers are now expected to deliver and maintain AI capabilities across multiple business functions simultaneously.
• AI can significantly increase activity and output, but more projects, more code, and more automation do not automatically translate into better business results.

Details worth keeping

The most useful place to start is with three questions. Are teams delivering customer-visible improvements more quickly? Measurement that doesn't connect to a business outcome...