The Innovation Metric: Measuring What Matters In AI And Change Projects

When setting goals for AI, innovation, and change projects, traditional metrics often fall short in capturing progress. What truly matters, especially early in the process, is the journey rather than just the destination.

If you’re running an AI project – or any other innovation or change project – how do you measure progress and success? It’s tricky, because the ways we traditionally set goals and measure progress don’t work.

This topic came up recently when I was working with a group of leaders who are running an AI project this year. One leader asked me:

“We have started this project, and we’re making some progress, but how do we measure whether we’re doing the right things? And how do we know if we’re on track? And how do we know what success looks like?”

This is a great question, not only for AI projects but for any project involving change and innovation. When working on familiar projects, it’s easier to track progress because you can rely on past knowledge and experience. But this approach doesn’t work with projects where the process is unkown and the outcome is uncertain.

The key is to measure progress, not outcomes.

Focus on what you’ve done rather than how far you have to go.

Ask questions like:

  • How much time have we invested this week?
  • What have we learned?
  • How many conversations have we had with customers about the impact?
  • What new ideas have we tried?

There are two reasons for doing it this way – especially early in the process.

First, you might not know the full scope of the project because it’s new. Second, measuring progress facilitates learning, improvement, and evolution. As the project continues, you will get better!

So, in any innovation, change, or AI project where the path isn’t clear, remember to measure progress, not just the final outcome.

If you would like some help, download my worksheet about “keeping them on track”. And if you’d like me to help, please reach out!


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