Claude + GPT | Multi-model intelligence in Copilot
Generate briefing documents, presentations, and Excel files from a single prompt with Copilot Cowork, pulling from your emails, calendar, and SharePoint through Work IQ — and fold in new tasks mid-run without stopping. Using Copilot Cowork, you can use the same platform that powers Claude Cowork. It’s designed for long-running, multi-step task automation. Use Critique in Researcher to pair a generation model with a dedicated review model, applying source reliability and evidence grounding before the report lands. Run model Council to submit one prompt to GPT and Claude simultaneously and compare their full reasoning side-by-side. These experiences with Copilot Cowork and Researcher are available now if your organization has the Frontier Program enabled. Jeremy Chapman, Microsoft 365 Director, shares how to choose, direct, and compare the right AI model for every task, all from within Microsoft 365. One prompt. Three files. Copilot Cowork generates your briefing doc, presentation, and Excel output — grounded in Work IQ data and saved directly to OneDrive. Try it now. Copilot Cowork handles new requests mid-run. Add meeting scheduling or an email update partway through and it integrates them into the active plan. Check it out. No more copy/paste into unmanaged AI sites. Work IQ automatically supplies Cowork and Researcher with your emails, calendar, Teams transcripts, and SharePoint files. Every output is grounded in your actual data. See how it works. QUICK LINKS: 00:00 — Copilot capabilities 01:06 — Copilot Cowork 02:32 — Mid-Run Task Injection 03:05 — Output 04:17 — Researcher Critique: Dual-Model Pipeline 05:58 — Work IQ Auto-Retrieval 06:58 — Model Council 08:50 — Wrap up Link References Try it at https://microsoft365.com/copilot Unfamiliar with Microsoft Mechanics? As Microsoft’s official video series for IT, you can watch and share valuable content and demos of current and upcoming tech from the people who build it at Microsoft. Subscribe to our YouTube: https://www.youtube.com/c/MicrosoftMechanicsSeries Talk with other IT Pros, join us on the Microsoft Tech Community: https://techcommunity.microsoft.com/t5/microsoft-mechanics-blog/bg-p/MicrosoftMechanicsBlog Watch or listen from anywhere, subscribe to our podcast: https://microsoftmechanics.libsyn.com/podcast Keep getting this insider knowledge, join us on social: Follow us on Twitter: https://twitter.com/MSFTMechanics Share knowledge on LinkedIn: https://www.linkedin.com/company/microsoft-mechanics/ Enjoy us on Instagram: https://www.instagram.com/msftmechanics/ Loosen up with us on TikTok: https://www.tiktok.com/@msftmechanics Video Transcript: -Now you don’t need to switch between AI model providers for the best models for work. Copilot has options from Anthropic and OpenAI available directly from Microsoft 365. Using Copilot Cowork, you can use the same platform that powers Claude Cowork. It’s designed for long-running, multi-step task automation and it’s grounded by Work IQ, so you don’t need to move files and data outside of Microsoft 365 to other potentially unprotected services. Researcher has also been expanded with multi-model intelligence, where the new Critique capability separates the models, with one used to generate and another to refine its research outputs. And the new Council capability lets you submit a single prompt and view a side-by-side comparison across multiple model outputs. -Now, these experiences with Copilot Cowork and Researcher are available now if your organization has the Frontier program enabled, and today I’ll go hands-on with each while explaining the mechanics of how they work. Let’s start with Copilot Cowork. So in this example, I need to prepare for a customer meeting, and I want Cowork to build me a briefing document in Word, a PowerPoint presentation, and an Excel file with customer insights. I already have Copilot pinned with my agents and it’s opened. -Before I start, I’ll show you what’s set up in the knowledge sources. I can access information on the web, from people, and from Work IQ, so it doesn’t rely on connectors to access my work files, calendar, or previous meetings. Now I’ll paste in my prompt with links to reference files so it can help me then prepare for my meeting, and I want Copilot to pull in details from relevant emails and my calendar. I’ve also referenced an existing briefing document template as an example to follow, as well as an Excel overview with customer-specific metrics and visuals. And I want it to create a new briefing document as well as a client-ready PowerPoint presentation with our differentiators and recommended next steps. -So now I’m going to kick off the process and Cowork will show its progress, its inputs and outputs on the upper right-hand side of the screen. Cowork will then reason through all of the inputs and tasks from my prompt, then systematically work through everything until it generates the files that I requested. And it’s not only using the files referenced, but also searching across my Work IQ information. As it works, I can even request more tasks while it’s running. -For example, I can ask it to schedule prep time with people on my team and send an email status update to the account team. Cowork just folds that into the plan and keeps going. It checks schedules, and here’s the meeting it proposes for me and Riley on my team to review, and I’ll create that right from here. Then it authors an email to Ellis from the account team that I can choose to edit manually if I want. I’ll go ahead and add a thank you in line and then hit send. This can process for several minutes, so to save a little time, I’ll move on to when everything is complete. You’ll see that on the right in the output folder, it’s created a Zava client presentation, a customer briefing doc, and also a customer overview Excel file. -Now, I’ll open up the briefing document first, and it has everything relevant to the meeting and it uses our standard briefing template. In fact, if I open up the original one, you can see just how close the formatting is. Now I’ll open the presentation it generated. It explains our work at a glance, with key metrics from Work IQ and referenced files, as well as revenue and growth highlights. Now if I move on to the generated Excel file and open that, it’s laid out our year-over-year performance and used it to create forecasts for this year. We can also see the growth trends over time, and if I click into Sales by Category, we can even see a detailed breakdown across different product lines with comparisons for the last two years. And as it worked on my behalf, everything was saved directly into OneDrive, so it’s protected and can be shared with my team like any other Microsoft 365 file. -Next, one of the most powerful experiences in Copilot, Researcher, has also added new multi-model intelligence capabilities in addition to its options for using Claude from Anthropic or GPT from OpenAI. Researcher now takes us a step further with Critique by using a combination of models to separate generation from evaluation tasks, where one model leads the generation phase, planning the task, iterating through retrieval steps, and producing an initial draft, while the second model then focuses on review and refinement, acting like an expert reviewer before the final report is presented to you. This is now the default experience, and having these models work together helps ensure higher-quality outputs. Let me show you. -From Copilot and Microsoft 365, I already have Researcher open. At the top right, I’ll expand the model picker and explain the options. Choosing Auto will automatically generate responses using Critique with the two models working together. Under that is an option for Model Council that I’ll walk through in a moment. Then there are also options to choose GPT and Claude as standalone models. So I’m going to keep Auto in this case, and then I’ll paste in my prompt to generate an executive brief about the competition in our industry and where there might be expansion opportunities. Now, this is a very research-intensive request that will need to retrieve, evaluate, and analyze many resources via Work IQ and the web. -Now I’ll submit my prompt to get it started. Researcher can take several minutes to research and reason over a topic and generate its response, so to save a little time, I’ll move to its output. On the top I can see the content was generated by GPT and refined by Claude. First, there’s an executive summary about the market-related conditions. As I scroll down, you can see it’s assessed source reliability, where it focuses on reputable, authoritative, and domain-appropriate sources. Then as I continue scrolling, it’s also assessed report completeness, where the reviewer model ensures that the final report satisfies the request, along with relevant insights. -As you can see with the rest of the citations, it’s enforced strict evidence grounding, making sure that every key claim is anchored to a reliable source. So for example, here you can see that it’s pulled in structured data from an Excel file with detailed financials and several relevant Word documents from our internal SharePoint sites. And it’s done all of this research automatically without me having to manually reference or upload files into my prompt. Both models work together in this case to improve the generated output. Next, let’s move on to Model Council in Researcher. Now, this lets you compare responses from different models side by side so that you can see where they agree, where they don’t, as well as what differentiates each model. -So I’m back in Researcher, and this time from the model picker, I’ll choose Model Council. From there, I’ll just paste in my detailed prompt, in this case to review our latest customer feedback interviews to find the top themes and give recommendations based on our current plans in motion. Again, this is going to leverage Work IQ to find and analyze recent Teams meeting transcripts, our product plans from files and SharePoint and more as research sources, and it’s a lot to process. Everything looks good here, so I’ll go ahead and send it. And in this case, Researcher asks clarifying questions to better understand my goal. -So I’ll choose a short one-to-five-page report length. Then below that I’ll type “Go ahead” and it gets to work. I only need to submit my prompt one time for both models to process it simultaneously. Again, this process can run 10 or more minutes, so I’ll skip to the output. You can see that each model has its own tile on top, and you can click into any of them to view their outputs. Below that is a summary for how each model did, comparing their responses. And I can also view a full output for each model. So I’m going to drill into the GPT output, and that shows me a split-screen view with the GPT tab open on the right, and I can scroll its results and I can look at its structured reasoning and its response and all the details. -Now moving to the Claude tab, I can also look at its detailed response and reasoning and everything that it performed to derive the output. I don’t need to run separate prompts to find the model that I prefer. Now Model Council helps do that work for me. So now Copilot and Microsoft 365 gives you direct access to leading models, including Anthropic and OpenAI, with multi-model intelligence and without having to switch between platforms. -To get started, enable the Frontier program in your Microsoft 365 environment. Then go to microsoft365.com/copilot or use the mobile app to try it out. And keep watching Microsoft Mechanics for the latest tech updates, and thanks so much for watching.New Microsoft 365 Copilot Tuning | Create fine-tuned models to write like you do
Fine-tuning adds new skills to foundational models, simulating experience in the tasks you teach the model to do. This complements Retrieval Augmented Generation, which in real-time uses search to find related information, then add that to your prompts for context. Fine-tuning helps ensure that responses meet your quality expectations for specific repeatable tasks, without needing to be prompting expert. It’s great for drafting complex legal agreements, writing technical documentation, authoring medical papers, and more — using detailed, often lengthy precedent files along with what you teach the model. Using Copilot Studio, anyone can create and deploy these fine-tuned models to use with agents without data science or coding expertise. There, you can teach models using data labeling, ground them in your organization’s content — while keeping the information in-place and maintaining data security and access policies. The information contained in the task-specific models that you create stay private to your team and organization. Task-specific models and related information are only accessible to the people and departments you specify — and information is not merged into shared large language models or used for model training. Jeremy Chapman, Director on the Microsoft 365 product team, shows how this simple, zero-code approach helps the agents you build write and reason like your experts — delivering high-quality, detailed responses. Keep information permissions as-is. Use your organization’s knowledge and sharing controls. See how Copilot Tuning works. Guide Copilot with labeled examples. Copilot learns to reason and write like you are your expert team. Check it out. Build Copilot agents powered by your fine-tuned models. Automate work with your tone, structure, and standards. Take a look at Copilot Chat. QUICK LINKS: 00:00 — Fine-tune Copilot 01:21 — Tailor Copilot for specialized tasks 05:12 — How it works 05:57 — Create a task-specific model 07:43 — Data labeling 08:59 — Build agents that use your fine-tuned model 11:42 — Wrap up Link References Check out https://aka.ms/FineTuningCopilot Unfamiliar with Microsoft Mechanics? As Microsoft’s official video series for IT, you can watch and share valuable content and demos of current and upcoming tech from the people who build it at Microsoft. Subscribe to our YouTube: https://www.youtube.com/c/MicrosoftMechanicsSeries Talk with other IT Pros, join us on the Microsoft Tech Community: https://techcommunity.microsoft.com/t5/microsoft-mechanics-blog/bg-p/MicrosoftMechanicsBlog Watch or listen from anywhere, subscribe to our podcast: https://microsoftmechanics.libsyn.com/podcast Keep getting this insider knowledge, join us on social: Follow us on Twitter: https://twitter.com/MSFTMechanics Share knowledge on LinkedIn: https://www.linkedin.com/company/microsoft-mechanics/ Enjoy us on Instagram: https://www.instagram.com/msftmechanics/ Loosen up with us on TikTok: https://www.tiktok.com/@msftmechanics Video Transcript: -You can now teach or fine-tune your Microsoft 365 Copilot experience by creating your own task-specific fine-tune models that channel your expertise and experience to carry out specialized jobs and tasks accurately and on your behalf. In fact, from Copilot Studio, anyone can use this zero-code approach to teaching Copilot’s underlying model the skills from your organization to produce more usable, high-quality responses that can be as detailed as they need to be, even hundreds of pages long to get the job done. And the model remains exclusive to your organization and only the people and departments you specify. -If you compare this to the traditional way of doing this until now, this level of customization would require data science, machine learning, and coding skills. So this process is a lot simpler. And unlike existing approaches where, as a data scientist, you may be copying data into locations that may not be aware of your protections and access controls, this is enterprise-grade by design. You just focus on the outcome that you want to achieve. And because your data stays in place, your existing data access and protection policies are respected by default. Let me show you the power of this in action by comparing the results of an agent that’s calling a fine-tuned, task-specific model of Copilot versus one that’s just calling the original underlying Copilot model. So both agents are configured to author loan agreement documents. On the left is our agent using the task-specific model, on the right is our SharePoint-based agent using a general model. -Now, both agents are focused on the same exact underlying knowledge. It’s all in a SharePoint location, as you can see here with this precedent file set. And both user prompts are identical with example reference files and the client term sheets containing new information. In fact, this is a precedent file that I’ll use. It’s a long and detailed document with 14 pages and more than 5,000 words. The term sheet is quite a bit shorter as you can see here, but it’s still long and detailed with information about the loan amounts, all the details, and if I scroll all the way down to the bottom, you’ll see signatory information for both parties. -So let’s go back to our side-by-side view and run them. So, I’ll start with the general model agent on the right. And it starts to generate its response. And I’ll let this one respond for a moment until it completes. There we go. And now I’ll move over to the agent on the left. It immediately informs me that it’ll receive an email once it’s finished. Now, this is going to be a longer-form document, so we’ll fast forward in time to see each completed response. -So, starting with the general model, I’ve copied it into a Word document, and the output is solid. You’ll see that the two parties are correct, the loan structure, all the amounts are also correct from the term sheet, but it has a few tells. It’s missing a lot of specificity and nuance that a member of our legal team would typically include in all of the terms. It’s also very summarized and not how our firm would draft an agreement like this. When I scroll down to the bottom, the signatories and addresses are captured correctly and match the term sheet. That said, though, it’s just four pages long and has around 800 words, versus more than 5,000 words in our precedent document. So it kind of follows the 80–20 rule where a good portion of the response could maybe work with some edits, but it’s not reflecting how my firm thinks and how it writes when authoring legal documents like this one. -So let’s go ahead and look at the results of a fine-tuned, task-specific agent. So immediately, you can see this document is verbose. It’s 14 pages long with more than 5,300 words. The word count doesn’t always equate to quality, so let’s look at the document itself. Now, as I scroll down, you’ll see that this agent has been taught our firm-specific patterns and the clauses that we use in existing case files. It is structured and worded things just like the precedent document. It’s reasoning and writing with more precision, like an experienced member of our firm would. And while as with any other AI-generated document, I still need to check it for accuracy, it really captures that extra detail and polish to save us time and effort. So model fine-tuning is a powerful way to tailor state-of-the-art large language models that are used behind Copilot to your specific needs. -And as you saw, it also can significantly improve the handling of specialized tasks. So let me explain how fine-tuning works in this case. Unlike Retrieval Augmented Generation, it doesn’t rely on search and orchestration processes that run external to the large language model. The additional knowledge added as part of the fine-tuning process is a protected container of information that attaches the large language models training set to teach it effectively a new skill. Now, it’s never merged into the LLM or used for future model training, and is temporarily attached to the LLM when it’s needed. Again, the skill and knowledge that it contains is exclusive to you and the people or groups that you’ve shared it with, so it can’t be accessed without the right permissions. -Next, let me show you what it takes to create and fine-tune your own task-specific model. I’m in Microsoft Copilot Studio, which you can reach from your browser by navigating to copilotstudio.microsoft.com. I’m on the task-specific model page and I want to customize a model to generate partner agreements. So I’ll paste in a corresponding name. Then I’ll paste in a description. Then as the task type, I’ll select a customization recipe that reflects what I want it to do. And my options here include expert Q&A, document generation, and document summarization, with more task types coming over time. From there, I can provide additional instructions to tailor the fine-tuning recipe, like how the model should use original files, for example, to inform the structure, formatting, company-specific clauses, and other areas important to your model, like we saw before. -Next, I can define my own knowledge sources. Now, these can use information from SharePoint sites and folders, and soon, you’ll be able to add information external to Microsoft 365 using Microsoft Graph connectors. In this case, I’ll define a SharePoint source. Then browse the sites that I have access to. I’ll choose this folder inside the Agreements library. And from there, I can even drill into specific folders for the precise information that I want to use to teach the model, which I’ll do here with the Agreements folder. -For permissions, this process aligns to the enterprise-grade controls that you already have in your organization backed by your Microsoft Entra account. Now, the next step is to process the data you selected for training or what’s known as data labeling. So here, you’ll be presented with data labeling tasks in small, iterative batches. They’re kind of like questionnaires for you to complete, where the fine-tuning process will generate documents and request assessment of them for clarity, completeness, accuracy, and professionalism. This process requires subject matter expertise to open these documents and rate the quality of the generative output for each. I’m just going to show one question here, but you’d repeat this process for every batch. And once all batches are labeled, I can start model training. Now, this will take some time to process, so I’ll fast forward a little in time. -Now with everything finished, I can publish the model to my Microsoft 365 tenant. And it will be available to anyone we’ve shared it with, like our audit team from before, to build new agents. And the process I just showed is called supervised learning, where the model is trained on label data. And soon, you’ll also have the option to use reinforcement learning to enhance the agent’s reasoning capabilities. Now let me show you how to build an agent from Copilot Chat that can leverage our new task-specific model for partner agreement generation. So I’m going to select Create agent. And for the purpose, I have a new option here to build a task-specific agent. Next, I can choose from the existing task-specific models. So I’m going to choose the one that we just created for new partner agreements. There we go. And with any agent, I just need to give it a name. Now I’ll paste in a description for people on the team to know its purpose and what it can do. -And next, I can specify additional instructions as guidelines to provide more context to the agent, as I’m doing here to ensure the structure aligns with our organizational standards. Because this is a very specific agent to write partner agreements, I’ll just specify one starter prompt with details for referencing a precedent source document to start with and a term sheet to get specific new information from, kind of like we saw before. Now, the preview on the right looks good, and I can create the agent right from here. For sharing, permissions also need to align with whoever my task-specific model was shared with, which, as you’ll remember, again, was our audit team. In this case, for my own validation, I’ll select only you so that I can test it before sharing it out with other auditors on my team. -So let’s go ahead and test it out. So I’m going to use the starter prompt. Then I’ll replace the variable file names here. I’ll use the forward slash reference, starting with the precedent file. Now I’ll look for the term sheet file. There it is. From there I can submit my prompt. This is going to take a moment for the response. You can see the structure with sections based on our task-specific files used with the fine-tuning. It tells me that it’ll send me a Word document and email once it’s finished again. In fact, if I fast forward in time a little, I’ll move over to Outlook. And this is the file the agent sent me with links to the new agreement draft. So I’ll open it using Word in the browser. There’s my agreement. And you’ll see it follows exactly how we wrote the precedent agreement. As I scroll through the document, I can see all the structure and phrasing aligned with how we write these types of agreements. In fact, this Representations and Warranties section is word for word direct from our standard terms that our firm always incorporates. And that’s it. My agent is now backed with my task-specific, fine-tuned knowledge, and it’s ready to go and I’m ready to share it with my team. -So those are just a few examples of how fine-tuning in Microsoft 365 Copilot can give you on-demand expertise, and task-specific models respond more accurately using your specified voice and process so that you and your team can get more done. -To find out more, check out aka.ms/FineTuningCopilot, and keep watching Microsoft Mechanics for the latest tech updates, subscribe to our channel, and thanks for watching.Introducing Copilot in the Microsoft 365 admin centers
Streamline daily admin tasks with AI-powered insights, natural language queries, and automation using Copilot in Microsoft 365 admin centers. Quickly recap key updates, monitor service health, and track important changes — all in one place. No more digging through multiple pages — just ask Copilot for the answers you need, grounded in real-time data from your tenant. From finding users and managing licenses to generating visual insights and automating tasks with PowerShell, use Copilot to simplify complex admin workflows and save valuable time. For Copilot in the admin center to light up, all you need is one active Microsoft 365 Copilot license for any user in your tenant and from the Microsoft 365 admin center, you can get started right away. Jeremy Chapman, Director of Microsoft 365, demonstrates how to leverage Copilot for proactive guidance, whether in the Microsoft 365 admin center or directly within Copilot Chat. Save time with Copilot. Type Recap to instantly see critical admin updates and actions in one view. Check it out in the Microsoft 365 admin center. Stay on top of changes. Copilot summarizes new features & updates from the Message Center, so you never miss an important rollout. Get started. Instant visual insights. Ask Copilot how many Copilot licenses are left and see a breakdown, no manual reports needed. Watch it here. Watch our video here. QUICK LINKS: 00:00 — Copilot in Microsoft 365 admin centers 00:42 — Use Copilot for change management 02:13 — Stay ahead of upcoming changes 03:31 — User and licensing queries 04:21 — Generate Visual Insights for Licensing and Usage 04:50 — Author PowerShell scripts for bulk operations 06:07 — Copilot Chat using Microsoft 365 Admin agent 07:37 — Copilot coming soon to other admin centers 07:51— Wrap up Link References For more information, check out https://aka.ms/CopilotinMAC Start using Copilot in the Microsoft 365 admin center at https://admin.microsoft.com Unfamiliar with Microsoft Mechanics? As Microsoft’s official video series for IT, you can watch and share valuable content and demos of current and upcoming tech from the people who build it at Microsoft. Subscribe to our YouTube: https://www.youtube.com/c/MicrosoftMechanicsSeries Talk with other IT Pros, join us on the Microsoft Tech Community: https://techcommunity.microsoft.com/t5/microsoft-mechanics-blog/bg-p/MicrosoftMechanicsBlog Watch or listen from anywhere, subscribe to our podcast: https://microsoftmechanics.libsyn.com/podcast Keep getting this insider knowledge, join us on social: Follow us on Twitter: https://twitter.com/MSFTMechanics Share knowledge on LinkedIn: https://www.linkedin.com/company/microsoft-mechanics/ Enjoy us on Instagram: https://www.instagram.com/msftmechanics/ Loosen up with us on TikTok: https://www.tiktok.com/@msftmechanics Video Transcript: -If you’re a Microsoft 365 admin, you can now take advantage of Copilot and generative AI to perform tasks across different Microsoft 365 services. In the next few minutes, in fact, I’ll show you how you can interact with it using natural language, get contextual guidance, and find proactive suggestions for common admin tasks. For the experience to light up, all you need is one active Microsoft 365 Copilot license for any user in your tenant. And from the admin center, you can get started right away. That said, before we get started, in case you’re wondering, Copilot Microsoft 365 admin centers does not make configuration changes autonomously on your behalf. As I’ll show you, it’s designed to save you time and many of the things that you do every day as an admin or business owner. -And I’ll start by showing you an example of how you can use it for change management. I’m in the Microsoft 365 admin center, and now Copilot can help you keep track of new capabilities rolling out, as well as changes that you need to action as an admin. In fact, you can use the starter prompt recap, and I’ll add the latest admin info, and you’ll see that Copilot is generating an up-to-date view of important information and key insights across service health, message center, and Microsoft 365 Copilot usage insights. This summary is personalized to you based on your specific admin role, highlighting the parts of the admin center that you use most, and your real-time individual tenant information. So this saves you time looking for information and insights that are typically spread across multiple locations in the Microsoft 365 admin center. And you can click on the see details controls to expand each area, and find out more, as well as where you can go to take any corresponding actions. For example, with these now expanded, I can see my tenant service health status, and a summary of active incidents, issues and advisories. In this case, I have one issue and three total advisories across Microsoft 365 suite, Microsoft Purview, and others. -From here I can even use these as deep links to click into my active issue for the updated attack simulations, and training URL endpoint, in this case, to find out more. Copilot can also help you stay ahead of upcoming changes, along with the items that you need to take care of from the message center. For example, back in my recap, I can see details highlighting three new features, and also three feature updates. So for this new feature, I can see details about Copilot in Edge, new contextual features to find out more about its capabilities and rollout details. I can also use the view in buttons for deep links directly into service health or the message center, like you’re seeing here with all my recent unread messages. -So as we saw, Copilot helps you stay on top of issues with its suggested prompt starters, like recap, and, of course, you can author your own prompts too, and they’ll also be grounded on data from your individual tenant. In this case, I’ll type in “Summarize my announcements for Outlook,” and Copilot generates a full summary with feature updates from the past week for Outlook. For example, here’s a new capability rolling out for the Microsoft 365 app, getting updated to be the Microsoft 365 Copilot app, and corresponding changes to the Outlook apps for iOS and Android. This change will allow more people to experience Copilot chat from their mobile apps. And now you have all the details you need to prepare for the update. -Next, let me show you how Copilot can help you with common admin tasks, like user and licensing queries using natural language. So I’ll prompt Copilot to find users in the marketing department with a Copilot license, and submit. Now, behind the scenes, it’s combining a directory attribute, the marketing department, with a licensing attribute for Copilot, what would’ve previously required advanced filtering or PowerShell. And it finds three people that match the query. And if it’s a larger group of people, you can use the CSV file option to export a list that you might use for a broader email campaign or with PowerShell scripting. -To be clear, everything that you’ve just seen is running under the permissions context of the admin using Copilot, so it can only find information that the individual account specifically has access to. Now, another area where Copilot can help is with generating visualizations for bulk insights into things like usage and licensing. For example, you might want to see how many Copilot licenses in your tenant have been acquired, and how many are available to assign. So for that, I can prompt Copilot, “How many Copilot licenses do I have available to assign?” And it generates an inline bar chart with details about Microsoft 365 Copilot, Copilot Studio, and Sales Copilot licenses available to assign. -And since this is Mechanics, let me show you an early look at a more advanced admin scenario to help author PowerShell scripts for bulk operations. Now, this is useful where performing specific tasks in the admin center at scale might be too manual or in cases where the control is not available in the admin center. For example, as part of my Microsoft 365 Copilot rollout, if I want to enable restricted SharePoint search using a list of allowed sites, which is only possible using PowerShell, I can prompt Copilot with “How do I get the SharePoint online PowerShell module, then enable restricted SharePoint search using a CSV file with a allowed sites using PowerShell?” And Copilot will use the Microsoft 365 admin documentation and PowerShell reference guides so that I can save time by not having to look that information up myself. And notice that everything is formatted so I can easily parse what the commands are doing, and I just need to change the placeholder values for the URL and file path, and I can run what’s presented. -Okay, and just to prove that it will work, let’s test it out. So these are the cmdlets that we just saw from Copilot with updated placeholders. So I’ll go ahead and run it. You’ll see there are no errors. Now, I’ll get the status of the feature. It’s enabled. Then get the list of allowed sites, and there they are. Next, let me show you another early look for performing these tasks in Copilot Chat using a Microsoft 365 admin agent. If your day-to-day Microsoft 365 account is the same account that you use for admin tasks, and you don’t use a separate admin-only account, you’ll be able to access these admin experiences from Copilot Chat. I’m in the Microsoft 365 Copilot app. I just need to type the @ symbol to pull up a list of available agents or I could directly type @Microsoft365Admin. -And from here I can run the same admin recap we saw earlier by typing “recap important info for me” as my prompt. You’ll see that it surfaces the same information that we saw before in the admin center. In fact, when I expand the details under service health, there’s our attack simulation’s URL endpoint update. The view in buttons also link me directly to the Microsoft 365 admin center. And because it’s an agent, you’ll also be able to access the Microsoft 365 admin agent from other app endpoints, like you’re seeing here with Microsoft Word. I can get the same information with my recap from before, and once it completes, I can use that right from Word, for example, if I wanted to write a change management report. Now, it’s worth pointing out that whereas everything I showed from the admin center does not require a Microsoft 365 Copilot license for your admin account, to use the Microsoft 365 admin agent, your admin account would need one. Finally, Copilot in admin center’s experiences will extend to other surface areas, as we presented in November, including the admin centers for Microsoft Teams, as well as SharePoint online, and more details for those are coming soon. -So those are just a few examples of how Copilot can help you as an admin save time with your day-to-day work, and give you proactive suggestions for different admin tasks. Again, all you need is just one active Microsoft 365 Copilot license in your tenant, and you can get started right away. To find out more, check out aka.ms/CopilotinMAC, and start using it today in the Microsoft 365 admin center at admin.microsoft.com. Keep watching Microsoft Mechanics for the latest tech updates. Subscribe to our channel and thanks for watching.
