Every day, millions of knowledge workers around the world rely on Microsoft Copilot to draft documents, summarize meetings, surface relevant information, and accelerate productivity. Behind the scenes, this is powered by a network of AI systems, and ensuring they work as intended requires a thoughtful approach to evaluation. At Microsoft, we know that high performance doesn’t come from guesswork. It comes from testing, measuring, and iterating constantly.

Healthcare organizations are constantly synthesizing safety signals from across teams so that they can make faster, better decisions that directly influence patient outcomes and regulatory confidence.

They may ask Copilot to summarize a meeting and extract action items for their teams. Within seconds, Copilot delivers a structured summary citing specific discussion points from the transcript. But how do we ensure this response is accurate, complete, and properly grounded in the meeting content? This is where our evaluation system comes in.

We’ve developed a structured evaluation strategy called Industry Evaluation Data Generation Platform, which includes industry specific evaluation datasets designed to test AI systems against realistic enterprise scenarios, grounded in three core pillars:

Together, these pillars help us answer two fundamental questions: Is Microsoft Copilot actually doing what our users need it to do, and can we prove it without ever touching their data?

In this blog post, we’ll walk through our evaluation system and processes, from synthetic environment generation to automated query pipelines, and how it all comes together to establish the first standardized evaluation baseline for Microsoft Copilot.

Evaluating enterprise AI requires test data that captures the full complexity of real organizations: reporting structures, project histories, communication patterns, and domain specific workflows.

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