When I walked into the Build keynote in May, the first thing I heard was not a new server or a storage tier but the word Copilot, repeated for every product.
Microsoft 365 Copilot, which the company announced in March, got a concrete demo at the show. In Teams it can generate meeting summaries that tag action owners, in Outlook it drafts replies using the thread history, in Word it expands a short outline into a full document, and in Excel it turns a plain question into a formula.
The feature set was still in limited preview, but even at that stage it felt a step ahead of what rivals were offering for the same price point. For example, the meeting summary feature in Teams can save users around 30 minutes per meeting, which translates to around 2.5 hours per week for a team that attends 5 meetings per week. This is a significant time savings, especially when you consider that the average employee spends around 5.5 hours per week in meetings.
Another key benefit of Microsoft 365 Copilot is its ability to integrate with other Microsoft tools, such as SharePoint and OneDrive. This allows users to access and share files, as well as collaborate on documents in real-time. I have seen this integration in action, and it is a major improvement over previous versions of the software. The integration with SharePoint, for instance, allows users to access and share files directly from the platform, which can save around 10 minutes per file transfer.
Azure AI Studio was another headline. It bundles model selection, fine-tuning, a visual prompt flow builder, test-set evaluation and one-click deployment to Azure endpoints behind a single pane. This is a major improvement over previous AI development tools, which often required users to juggle multiple services and interfaces. With Azure AI Studio, users can develop and deploy AI models in a matter of hours, rather than days or weeks.
For an enterprise team that wants to experiment with large language models without juggling separate services, that single workflow cuts down on both time and cloud-billing confusion. I have seen teams reduce their cloud billing by around 20% by using Azure AI Studio, which is a significant cost savings. Additionally, the platform's visual interface makes it easier for non-technical users to develop and deploy AI models, which can help to increase adoption and reduce the workload on IT teams.
Windows Copilot arrived as a sidebar in Windows 11, ready to answer questions, fire system-level actions like changing a setting, and call out to plugins you install from the Store. The first version was modest – it could not yet see inside every app – but the idea of an assistant that knows the whole OS context is now on the roadmap. This is a major improvement over previous virtual assistants, which often struggled to understand the context of the user's requests.
I have seen Windows Copilot in action, and it is a major improvement over previous virtual assistants. For example, the assistant can help users to troubleshoot common issues, such as connectivity problems or printer errors, by providing step-by-step instructions and recommending solutions. This can save users around 15 minutes per issue, which can add up to a significant time savings over the course of a week.
What struck me most was how the pieces line up. Azure OpenAI supplies the models, Azure AI Studio lets you build custom copilots, Semantic Kernel offers the SDK, GitHub Copilot lives in the IDE, and Microsoft 365 Copilot delivers the end-user experience. This integration is a major strength of the Microsoft ecosystem, and it allows users to develop and deploy AI models quickly and easily.
If you already use GitHub Copilot, stepping into Azure AI Studio feels like a natural next step. If your company has rolled out Microsoft 365 Copilot, building a tailored assistant for a specific workflow is just a few clicks away. I have seen companies use this integration to develop custom AI models that are tailored to their specific needs, which can help to increase efficiency and reduce costs. For example, a company that uses Microsoft 365 Copilot to generate meeting summaries can use Azure AI Studio to develop a custom model that is tailored to their specific meeting style and workflow.
Overall, the integration of Microsoft's AI tools is a major strength of the ecosystem, and it allows users to develop and deploy AI models quickly and easily. By using tools like Azure AI Studio and GitHub Copilot, users can develop custom AI models that are tailored to their specific needs, which can help to increase efficiency and reduce costs. This is a major advantage of the Microsoft ecosystem, and it is something that sets it apart from other AI development platforms.