From synthetic enterprise data evaluations to real Microsoft Copilot AI confidence

From synthetic enterprise data evaluations to real Microsoft Copilot AI confidence - Microsoft Research

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At a glance

Evaluating Microsoft Copilot at enterprise scale requires test data that captures the full complexity of real organizations, yet real enterprise data contains sensitive information that cannot be used for evaluation under any circumstances.

To resolve this tension, we built Industry Evaluation Data Generation Platform: full-scale synthetic enterprise environments grounded in publicly available real-world data sources such as O*NET occupational data, online job postings, and SEC filings.

The synthetic environments mimic real business workflows, helping Microsoft find issues that generic benchmarks would miss.

Safety and privacy are built into the platform by design. Every environment is fully synthetic and automated content-safety checks keep the generated data free of harmful or personally identifiable material.

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:

Synthetic enterprise environments : simulated Microsoft Copilot environments with realistic users, meetings, emails, documents, and organizational structures that replicate the complexity of real organizations without using any customer data

Automated query generation pipelines for rapid, reproducible creation of evaluation datasets

Structured quality frameworks to ensure synthetic data meets the bar for meaningful evaluation

Figure 1: Components of Industry Evaluation Data Generation Platform

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.

Synthetic enterprise environments

Evaluating enterprise AI requires test data that captures the full complexity of real organizations: reporting structures, project histories, communication patterns, and domain specific workflows. But real enterprise data contains sensitive information that cannot be used for evaluation under any circumstances. This is the central tension we set out to resolve.

Rather than relying on small, manually curated test fixtures, we generate full scale synthetic environments : simulated enterprise workspaces, each with realistic organizational structures, communication histories, and work artifacts. In Microsoft terminology, each of these simulated workspaces is called a “tenant”. A tenant is an isolated organizational environment with its own users, data, and configurations. These environments are scalable baselines that teams and organizations can build off of however they want. They are designed to exhibit the same structural and behavioral properties that make enterprise AI evaluation meaningful.

Grounding in real world signals

To ensure our synthetic environments are realistic, we grounded them in publicly available real world data sources.

O*NET data from the U.S. Department of Labor’s occupational database was used to make synthetic employee roles and responsibilities realistic, calibrate the mix and frequency of work activities assigned to different roles, and tailor industry specific workflows such as clinical trial reviews or portfolio updates.

Online job postings provide realistic role titles, reporting hierarchies, and departmental compositions.

SEC filings inform organizational scale, business unit structures, and financial contexts. All of these sources are publicly available and have been reviewed and approved by Microsoft’s legal and policy organization.

Figure 2: Real signals from public datasets that are used to ground the evaluation data...