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 calledIndustry 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. 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 generatefull 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. To ensure our synthetic environments are realistic, we grounded them in publicly available real world data sources. Importantly, these synthetic environments are not static snapshots. They simulate dynamic, time-evolving organizations with meeting histories, project timelines, email threads that develop over weeks, and organizational changes that unfold naturally. This temporal dimension is essential because a large class of Copilot queries are inherently time aware. Questions like “What did the team decide last Thursday?” or “What’s changed since the last quarterly review?” only produce meaningful evaluations if the underlying data has a genuine temporal structure. We generated three synthetic environments across multiple industries, each with distinct characteristics that mirror real world usage: This industry specificity ensures that our evaluation queries test Copilot against the kinds of questions real users in those domains actually ask, rather than relying on generic test cases that may not surface domain specific failure modes. While synthetic environments provide the substrate for evaluation, they face a key limitation: we still need realistic evaluation queries. These are the questions that test whether Copilot can surface the right information, synthesize it correctly, and present it helpfully. Manually writing thousands of grounded, industry specific queries is prohibitively expensive and slow. That’s where our automated pipeline comes in. We built an automated pipeline that turns simple, generic query ideas into realistic, industry-specific questions for Copilot. It starts with a large library of common workplace tasks, narrows them down to what’s relevant for a given industry, and adapts them using domain-specific language and context. The system then uses AI to generate queries grounded in the synthetic environment’s data, rewrites them to sound more natural and human, and validates each one for accuracy and uniqueness. The result is a rich set of high-quality, lifelike queries that enable scalable and reliable evaluation of Copilot across real-world scenarios. Of course, generating synthetic data is only part of the challenge. We also need to ensure the data is good enough to produce meaningful evaluations. We assess synthetic environment quality using our internal semantic evaluation framework which measures quality across three dimensions: While our synthetic environments and query pipelines provide the methodology, they face several practical limitations without proper automation, including: This automation reduced evaluation dataset creation from a multi-day manual process to automated runs. We estimate savings of approximately $100,000 per evaluation dataset and $300,000 in total project savings. But the more important outcome is that any team can now regenerate an evaluation dataset from the same configuration and get identical results. Join Microsoft’s Peter Lee on a journey to discover how AI is impacting healthcare and what it means for the future of medicine. Enterprise grade evaluation demands more than good methodology. It requires a privacy posture that allows teams to move fast without compromising user trust. A non-negotiable design principle of our industry evaluation datasets is thatno real customer data is used at any stage of the pipeline. Every element, from organizational structures to email content to meeting transcripts, is synthetically generated, with no customer data inputs. Because the data is both fully synthetic and safety-screened, teams can share and reuse it widely without introducing reputational, compliance, orResponsible AIrisk. Our safeguards include privacy preserving aggregation techniques where real-world statistical distributions are referenced, expert legal review and approval of all public data sources, and clear data provenance and lineage tracking throughout the pipeline. This means teams across Microsoft can use these evaluation datasets freely for experimentation, benchmarking, and regression testing without privacy review overhead or data handling restrictions that would otherwise slow down iteration. The privacy design is not a constraint on the system. It is what makes broad adoption possible. Since starting this project, we have observed significant improvements across multiple dimensions. Onsynthetic data quality, our internal scoring system showed significant quality gains in the structural, contextual, and statistical fidelity of the generated data. Across our composite quality metric (which measures how closely synthetic data mirrors the complexity and patterns of real enterprise environments), scores improved 39% compared to our initial generation approach. Some industry-specific environments reached quality scores above 0.9 out of 1.0, demonstrating that domain focused generation produces highly realistic evaluation environments. The improvements were obtained in part by incorporating realistic, industry signals and in part by automating quality improvements via key composite quality metrics that help improve the structural integrity, contextual coherence and statistical diversity of our synthetic industry environments. Onscale and efficiency, each synthetic environment now contains hundreds of users with complete communication