Building a Human-AI Collaboration Index

What if you could measure how well AI actually helps your team think, decide, and deliver?

Over this past year, I’ve been working with more and more clients who are piloting Copilot, getting general AI training, and thinking hard about how AI fits into their M365 modernization and digital transformation strategies. In addition, I’ve interviewed dozens of leaders of product and consulting companies, learning from their experiences and customer stories. One example in particular stood out.

This client’s sales team had been experimenting with generative AI tools to speed up client proposals and pitch decks (a problem that many of us want to automate). What started as a test turned into a quiet revolution. Within a few weeks, they saw proposal approval times drop by 40 percent. Strategy meetings began yielding more ideas, faster. Compliance errors in final documents were down by a quarter. And maybe most important, team sentiment about the value of AI jumped from lukewarm to enthusiastic. One of my podcast guests put it this way: “It’s like having a second brain that never gets tired.”

That shift didn’t happen by accident. The client had started tracking a new kind of metric: a Human-AI Collaboration Index. Unlike simple usage stats, this index is designed to measure how well AI tools like Copilot actually enhance human decisions and outcomes. Think of it as a composite score that tells you whether your investment in AI is making people smarter, faster, and more effective — or just busier.

Why does this matter? Because “adoption” doesn’t mean value. We saw this in the early days of Office 365, when Microsoft realized that enterprises were not renewing enterprise agreements (EAs). They had been measuring adoption as licenses activated instead of looking at actual usage and engagement. Similarly, a Collaboration Index gives leaders a way to quantify what AI is doing beyond time saved. It surfaces where AI is working, where it isn’t, and what needs adjusting. It also provides the data to justify continued investment, identify best practices, and direct training where it counts.

So what goes into this index?

Start with the right metrics. Most dashboards stop at usage: who opened Copilot, how often, for how long. That’s not enough. We want to know how AI is augmenting real human capabilities. We measure things like idea generation rate, error catch rate, and decision velocity. Is Copilot helping your team generate more options in a brainstorm? Is it helping you catch mistakes before they leave the building? Are decisions happening faster — and staying aligned with quality standards?

These are the signals that tell us AI is pulling its weight.

Next, look at your data sources. It’s not just about tracking sessions. You want to tie usage to real outcomes. That means linking Copilot activity to CRM milestones, ERP records, or whatever operational system reflects results in your business. If a forecast was written with AI and led to faster executive buy-in, that’s worth capturing.

But don’t stop at the quantitative. Survey your users. Ask them what Copilot is helping with — or where it gets in the way. Are they getting better ideas? Making stronger decisions? Or just saving time on boilerplate? You can’t capture the full picture without user sentiment.

With these inputs in place, you can build a dashboard that reflects the whole story. That means KPIs across teams, heatmaps that show where AI is delivering the most value, and trend lines to highlight improvement (or stagnation) over time. A good dashboard helps you spot not just usage, but impact.

Of course, numbers without context don’t mean much. You need to interpret what you see. A dip in decision velocity might not be bad news — it could mean teams are being more thoughtful. But a drop in error catch rate or idea output could signal training gaps or process friction. The goal isn’t to chase a number, it’s to understand the story behind the trend.

And here’s where it gets even more useful: benchmarking. When you can compare your Collaboration Index to anonymized peers in your industry, you get real perspective. Maybe your marketing team is leading the pack, but your product managers are behind the curve. Maybe your error detection is world-class, but idea generation lags. Knowing where you stand helps you focus your efforts.

That client didn’t get it all right on day one. They started with basic tracking, then layered in sentiment data, then began linking outcomes. But once they had the full picture, they were able to prove to leadership not just that AI was being used, but that it was making their people better.

That’s the story your Collaboration Index should tell. It’s not about dashboards for their own sake. It’s about making AI real, measurable, and accountable — not as a replacement for human work, but as a partner to it.