A Practical, Technical Guide to Bringing AI Into Everyday Nonprofit Workflows
Nonprofits face increasing pressure to improve efficiency, strengthen reporting, and communicate more frequently—often with limited staff and resources. Microsoft Copilot for Microsoft 365 embeds AI directly into familiar tools like Word, Excel, Outlook, Teams, and PowerPoint, allowing nonprofits to automate knowledge work without adopting entirely new systems or hiring specialized AI teams. How Copilot Works Under the Hood Copilot is built on three core components: 1. Large Language Models (LLMs) These AI models generate, summarize, and transform text and other content. 2. Microsoft Graph Microsoft Graph connects Copilot to your organization’s data—including: Emails Files (SharePoint, OneDrive) Meetings and calendars Teams chats This provides context-aware responses based on your organization’s existing content. 3. Microsoft 365 Apps Copilot is embedded directly inside: Word Excel Outlook Teams PowerPoint Together, these components allow Copilot to generate insights and content grounded in your organization’s data. [learn.microsoft.com] 📌 Important: Copilot does not create new data silos. It respects existing permissions, so users only see data they are already authorized to access. 👉 Learn more: Microsoft 365 Copilot overview Requirements to Enable Copilot To use Copilot in Microsoft 365, organizations generally need: A supported Microsoft 365 plan (e.g., Business Standard, Business Premium, E3, or E5) A Copilot add-on license Proper data stored in Microsoft 365 (e.g., OneDrive, SharePoint, Teams) 📌 Copilot’s effectiveness depends heavily on how well your data is organized and accessible. Technical Use Cases for Nonprofits 1. Grant Writing & Reporting Automation Copilot can: Summarize program outcomes from documents (Word, SharePoint) Generate draft grant narratives Rewrite content to align with funder tone Extract insights from structured data (Excel, reports) ⚠️ Clarification: In many standard Microsoft 365 Copilot scenarios, Copilot does not directly query Power BI datasets. Instead, it relies on data embedded in documents, emails, or exported reports. However, newer integrations (e.g., Microsoft Fabric and Copilot Power BI integration) allow Copilot to access and answer questions using Power BI reports and semantic models, depending on licensing, environment, and configuration. Technical Advantage Copilot uses Microsoft Graph to pull relevant context from your organization’s documents, reducing manual copy-paste work. How to Use Copilot in Word for Grant Writing Open Word Select Copilot icon to open the Copilot pane Choose Draft with Copilot (or start typing a prompt) Enter a prompt such as: “Draft a 2-page grant narrative using the attached program summary and last year’s outcomes.” (Optional) Reference or attach relevant files from OneDrive or SharePoint Click Generate Review the draft Refine using Copilot commands such as: Rewrite (improve clarity) Expand (add detail) Adjust tone (formal, persuasive, etc.) 2. Outlook + Copilot for Donor Communications Copilot can: Draft personalized donor emails Summarize long email threads Rewrite messages for tone and clarity Suggest follow-ups Technical Note Copilot can use: Previous email threads Attached documents Calendar context to generate more relevant responses. How to Use Copilot in Outlook Open a new email Click Copilot icon in the tool bar Select Draft with Copilot Enter a prompt such as: “Write a warm thank-you email to a donor who contributed $500 to our youth program.” Click Generate Review the drafted email Edit directly or use Copilot to: Rewrite Adjust tone (formal, friendly, etc.) Change length (shorter or longer) Select Keep it, then send when ready 3. Teams Meeting Summaries Copilot in Teams can: Generate meeting summaries Identify decisions and key points Extract action items Suggest follow-ups 📌 Copilot works from meeting transcripts and chat logs. How to Use Copilot in Teams Start or join a Teams meeting Enable Transcription (recommended for full Copilot functionality) After the meeting, open the meeting chat or calendar event Select the Recap tab Click Copilot Select or enter a prompt (e.g., "Recap the meeting") Review: key discussion points Decision Action items 4. PowerPoint Storytelling Copilot can transform content into presentations: Word documents → slide decks Meeting summaries → presentations Reports → visual narratives Technical Advantage Copilot uses semantic understanding to: Structure slides Generate speaker notes Suggest layouts and visuals How to Use Copilot in PowerPoint Open PowerPoint Select the Copilot icon to open the Copilot pane Provide a prompt or upload a document Copilot generates slides and notes Refine using design and layout suggestions Security & Compliance Copilot inherits Microsoft 365’s enterprise-grade security model, including: Role-based access control (RBAC) Data residency and compliance controls Existing permissions enforcement Zero Trust principles 📌 Important clarification: Microsoft states that customer data is not used to train foundation models. Data remains within your organization’s tenant boundary. 👉 Learn more: Data, Privacy, and Security for Microsoft 365 Copilot | Microsoft Learn Important Implementation Considerations 1. Data Readiness Copilot’s quality depends on your data: Organized SharePoint libraries Consistent file naming Structured documents 2. Access Control Ensure proper permissions before rollout: Avoid overexposure of sensitive data Audit SharePoint and Teams access 3. Human Oversight Copilot generates drafts—not final outputs. Always review grant narratives Validate donor messaging Confirm factual accuracy Final Thought Microsoft Copilot is not a replacement for nonprofit expertise—it is a force multiplier. By embedding AI into everyday tools, nonprofits can: Reduce administrative workload Accelerate writing and reporting Improve internal communication Focus more time on mission-driven work When implemented thoughtfully—with strong data practices, governance, and human oversight—Copilot can help organizations move faster, communicate more effectively, and make better-informed decisions. Ultimately, the goal is not just efficiency, but greater impact—freeing teams to spend less time on repetitive tasks and more time advancing the mission they serve.
Orchestrating human-AI collaboration in Microsoft Planner
How Microsoft Planner is shaping the future of work management Today at Microsoft Ignite, we announced the next step for Microsoft Planner in the era of AI: Project Manager agent. Project Manager agent is an end-to-end work orchestration experience in Planner that leverages generative AI to streamline the journey from idea, to plan, to done. This is the first time the groundbreaking automation capabilities of Microsoft AutoGen will be available to customers at scale. The new Project Manager agent will be rolling out to public preview in the Planner app in Teams in the coming weeks. To explore these capabilities, customers are required to have a Microsoft 365 Copilot license and also need to ensure their current Microsoft 365 licensing allows them access to Microsoft Loop. Introducing Project Manager agent A team of agents working together Project Manager agent in Planner orchestrates multiple agents like a project manager. Working directly with task-specific execution agents, Project Manager agent assures tasks are completed efficiently. This GenAI-powered system uses task decomposition, planning, and a multi-agent orchestration to enhance team productivity. The system brings together specialized agents, each with their own expertise, to work collaboratively on various tasks with humans in the loop to review and guide outcomes. These agents communicate and cross-check each other's work, much like a team of experts refining each other's contributions. At Microsoft Research, pioneering work on AutoGen has demonstrated the benefits of multi-agent systems. Project Manager agent puts these ideas into practice, streamlining completion of complex tasks: Specialized Expertise: Each agent is a specialist, ensuring high-quality outputs tailored to specific tasks. Specialized agents make output more reliable and predictable while preserving the dynamic nature of agentic systems. Team Collaboration: Agents communicate and cross-check each other's work, much like a team of experts refining each other's contributions. Adversarial agents are specifically designed to challenge each other's work to produce more refined and detailed results. Problem Solving: AutoGen research demonstrates that multi-agent systems significantly enhance problem-solving capabilities, enabling them to tackle more complex tasks effectively. For instance, the integration of multiple agents improves performance in math problem-solving (MATH), real-world reasoning (AFLWorld), and sensitive coding tasks (OptiGuide). Contextual Awareness: Certain agents provide context from your team's existing content, integrating historical data and insights into the current plan. Research on AutoGen shows that multi-agent systems facilitate improved contextual awareness and understanding by integrating multiple retrieval mechanisms, enabling a more interactive and dynamic approach to information retrieval. While in preview, Project Manager agent has access to a small set of "built-in agents" that are tuned to complete common information work tasks. Project Manager agent is scalable, and we are excited to expand the set of available agents and capabilities over time. Human-AI collaboration Whether asking Copilot a simple question or automating a complex workflow, teams in every industry are accelerating work with Generative AI. Collaboration is already a pain-point for many teams and the introduction of AI into workflows can further confound the problem. We believe that Planner’s familiar and accessible collaborative work management concepts and tools can effectively streamline human AI collaboration. Project Manager agent leverages Planner to plan, execute, and iterate agentically in an environment where the human team maintains control. Work Breakdown – When presented with a complex goal or task, Project Manager agent identifies a set of steps to achieve it. This structured approach not only breaks tasks into manageable pieces but also distinguishes work suitable for autonomous agents from higher-value or complex tasks requiring human input. Under the hood, Project Manager agent will be collaborating with multiple agents to deliver the desired outcomes. Complex task execution requires creating a plan, intelligently sequencing actions and dependencies, and maintaining a memory system to store the status of individual steps and their outcomes. Assign to Agent – The user is always in control of exactly what and how much work is assigned to Agents. Project Manager agent is flexible and can handle single tasks or orchestrate longer flows. At any time, team members can pause agent work and make manual adjustments as needed. Human in The Loop – During task execution, Project Manager agent can identify situations where more information or context is required from the team. In such situations, Project Manager agent will compile a list of questions that, when answered, will unblock the Agents working on the task. If the system lacks the tools to complete a task altogether, Project Manager agent can provide advice and assistance to the team member working on the task. Integrating Feedback – Project Manager agent won’t always complete tasks perfectly on the first attempt. Once initial agent work is complete, the team can review the task and leave comments on the content. When the review is complete, Project Manager agent will orchestrate iteration on the content, incorporating the feedback. Safety and privacy Generative AI systems have unprecedented capabilities, which introduces novel risks into our products and services. The Planner team is committed to proactive and continuous mitigation of these risks. We employ a handful of key strategies to ensure that we responsibly deploy generative AI: Impact Assessment: We audit new generative AI products in accordance with Microsoft’s responsible AI standard, identifying harms the system could potentially cause. Redteaming: We thoroughly evaluate new products for risks identified in the impact assessment throughout the development process. Ahead of release, our redteaming results are reviewed by a joint Microsoft-OpenAI Deployment Safety Board, as outlined in Microsoft’s AI Safety Policies. Harm Mitigation: Project Manager agent has multiple redundant checks to prevent harmful content, including harm-specific classifiers and a dedicated responsible AI agent. Additionally, your data remains private when using Project Manager agent. Customer data is governed by the commitments we make in the Microsoft’s Data Protection Addendum, Microsoft’s Product Terms, and the Microsoft Privacy Statement. The future of work management As AI technology continues to evolve, we can expect even more advanced features and capabilities to be integrated into Planner. The vision is to create a truly intelligent work management platform that not only supports project managers but also empowers teams to achieve their best work. The orchestration of human-AI collaboration with AutoGen in Planner represents a significant leap forward in the field of work management. Project Manager agent is a testament to the potential of AI to transform the way we work, making it more efficient, productive, and collaborative. As we look to the future, we are excited about the possibilities that AI holds for work management and beyond.Copilot, Microsoft 365 & Power Platform Community call
💡 Copilot, Microsoft 365 & Power Platform weekly community call focuses on different use cases and features within the Microsoft 365 and Power Platform - across Microsoft 365 Copilot, Copilot Studio, SharePoint, Power Apps and more. Demos in this call are presented by the community members. 👏 Looking to catch up on the latest news and updates, including cool community demos, this call is for you! 📅 On 20th of August we'll have following agenda: Latest on SharePoint Framework (SPFx) Latest on Copilot prompt of the week PnPjs CLI for Microsoft 365 Dev Proxy Reusable Controls for SPFx SPFx Toolkit VS Code extension PnP Search Solution Demos this time OluwaMayowa Ogbeide – Building a Domain-Specific AI Advisor with Power Automate + SharePoint + External LLM APIs Simon Doy – Cowork Plugins and Timesheets - Almost Never have to fill out a timesheet again 📅 Download recurrent invite from https://aka.ms/community/m365-powerplat-dev-call-invite 📞 & 📺 Join the Microsoft Teams meeting live at https://aka.ms/community/m365-powerplat-dev-call-join 💡 Building something cool for Microsoft 365 or Power Platform (Copilot, SharePoint, Power Apps, etc)? We are always looking for presenters - Volunteer for a community call demo at https://aka.ms/community/request/demo 👋 See you in the call! 📖 Resources: Previous community call recordings and demos from the Microsoft Community Learning YouTube channel at https://aka.ms/community/youtube Microsoft 365 & Power Platform samples from Microsoft and community - https://aka.ms/community/samples Microsoft 365 & Power Platform community details - https://aka.ms/community/home 🧡 Sharing is caring!Microsoft Power Platform community call - August 2026
💡 Power Platform monthly community call focuses on different extensibility options for builders, makers and developers within the Power Platform. Typically demos are from our awesome community members who showcase the art of possible within the Power Platform capabilities. 👏 Looking to catch up on the latest news and updates, including cool community demos, this call is for you! 📅 On 19th of August we'll have following agenda: Power Platform Updates & Events Latest on Power Platform samples John Liu - How to easily to convert markdown documents to PDF with Power Automate Wario Wario - Building Copilot Studio Agents with GitHub Copilot or Claude Code Seena Khan - Build an Enterprise AI Document Summarizer with Azure Blob Storage + Copilot Studio 📅 Download recurrent invite from https://aka.ms/powerplatformcommunitycall 📞 & 📺 Join the Microsoft Teams meeting live at https://aka.ms/PowerPlatformMonthlyCall 💡 Building something cool for Microsoft 365 or Power Platform (Copilot, SharePoint, Power Apps, etc)? We are always looking for presenters - Volunteer for a community call demo at https://aka.ms/community/request/demo 👋 See you in the call! 📖 Resources: Previous community call recordings and demos from the Microsoft 365 & Power Platform community YouTube channel at https://aka.ms/community/videos Microsoft 365 & Power Platform samples from Microsoft and community - https://aka.ms/community/samples Microsoft 365 & Power Platform community details - https://aka.ms/community/homeCopilot, Microsoft 365 & Power Platform product updates call
💡Copilot, Microsoft 365 & Power Platform product updates call concentrates on the different use cases and features within the Microsoft 365 and in Power Platform. Call includes topics like Microsoft 365 Copilot, Copilot Studio, Microsoft Teams, Power Platform, Microsoft Graph, Microsoft Viva, Microsoft Search, Microsoft Lists, SharePoint, Power Automate, Power Apps and more. 👏 Weekly Tuesday call is for all community members to see Microsoft PMs, engineering and Cloud Advocates showcasing the art of possible with Microsoft 365 and Power Platform. 📅 On the 18th of August we'll have following agenda: News and updates from Microsoft Together mode group photo Muhammad Raifq – Creating a Copilot Entra ID management agent Albert-Jan Schot – Creating a secure document handoff solution with SPFx and SharePoint Embedded Steve Pucelik & Marc Windle – SharePoint Embedded Monthly booking 📞 & 📺 Join the Microsoft Teams meeting live at https://aka.ms/community/ms-speakers-call-join 🗓️ Download recurrent invite for this weekly call from https://aka.ms/community/ms-speakers-call-invite 👋 See you in the call! 💡 Building something cool for Microsoft 365 or Power Platform (Copilot, SharePoint, Power Apps, etc)? We are always looking for presenters - Volunteer for a community call demo at https://aka.ms/community/request/demo 📖 Resources: Previous community call recordings and demos from the Microsoft Community Learning YouTube channel at https://aka.ms/community/youtube Microsoft 365 & Power Platform samples from Microsoft and community - https://aka.ms/community/samples Microsoft 365 & Power Platform community details - https://aka.ms/community/home 🧡 Sharing is caring!
August 20 Federal Event: Accelerating Enterprise Modernization, AI & Cybersecurity
Overview Federal agencies are under increasing pressure to modernize mission systems, strengthen cybersecurity, and responsibly adopt artificial intelligence. Executive Order 14409, record federal technology investments, and Microsoft's OneGov initiative have created a unique opportunity to accelerate secure digital transformation. What Attendees Will Learn How agencies are translating AI strategy into operational outcomes. Microsoft's latest AI, cybersecurity, and modernization capabilities. How Microsoft OneGov is accelerating secure AI adoption across federal agencies. Real-world modernization strategies and success stories from CDC and USGS. Practical approaches to modernizing legacy systems while improving mission outcomes. Plus: Every attendee receives a complimentary 30-minute FY27 AI Readiness Consultation with a MERP Solutions Architect. Why Attend Join MERP Systems and Microsoft to discover the latest AI and cybersecurity innovations and hear directly from CDC and USGS as they share real-world enterprise modernization strategies and success stories. Attendees will leave with actionable insights, proven approaches, and a better understanding of how to accelerate secure AI adoption across their organizations. Event Link- https://events.teams.microsoft.com/event/b58ebc1e-e927-4e66-b73c-f25d7f43be7f@c33861f9-4a57-4bc4-bc88-d6e0c7b92c37?source=copyLinkOneEventsShareDialogGitHub Admin UI + Billing API: Better together for smarter spend decisions
As a GitHub administrator, you already have a strong place to start when somebody asks, “Why did our AI spend go up?” In Metered usage, you can see the change, choose the period, and group the data by organization or cost center. That first investigation often leads to questions that are specific to your company. Finance may want a month-end report based on its own reporting calendar. An engineering leader may want to see whether an increase is spread across a team or concentrated among a few people. Answering those questions once is useful; answering them repeatedly calls for a reusable approach. Use each surface for what it does best The GitHub admin UI shows you where to look and gives you the controls to respond. The Billing Usage API helps you answer the recurring questions that are specific to your company. Neither replaces the other. Together, they give administrators a practical loop: spot the change in Metered usage, understand it through a reusable API-powered view, and act with a targeted budget. That means better cost control without treating every user or team as the problem. Let’s walk through this better-together approach using a common example: AI spend starts to rise, but the reason is not yet clear. The question: Spend is up, but what is driving it? Imagine that finance notices an increase in AI spend before the next close. It could be a sign that more developers are getting value from Copilot. It could also be one workload using far more than expected. At this point, nobody knows, and a broad restriction would be premature. The GitHub administrator needs to help finance and engineering answer three practical questions: - Which part of the business is driving the increase? - Is the spend concentrated among a few users or broadly distributed? - Which control should change without disrupting everyone else? The goal is not simply to reduce a number. It is to understand the increase well enough to protect useful work while addressing anything unexpected. 1. Start in the admin UI: Find the increase The admin UI is the natural place to begin because it lets you explore the data before you decide what kind of report or control you need. Open **Billing and licensing > Metered usage** and select the relevant reporting period. This first check matters. It confirms that the increase is real, shows when it happened, and gives you a shared starting point for the conversation with finance and engineering. Fig 01: Metered usage establishes the increase and the period that needs investigation. Narrow the increase by organization An enterprise total tells you that spend changed, but not where to look next. Group the usage by organization to see which part of the enterprise contributed most to the increase. Fig 02: Organization grouping narrows an enterprise-wide increase to an accountable business area. Suppose the octodemo organization stands out. You now know where to continue the investigation and which leaders can add context. You do not yet know whether the spend is justified, and that distinction matters. The increase could come from successful Copilot adoption, a migration, a seasonal workload, or an automated process that needs attention. Connect the increase to a cost center An organization can contain several teams, programs, and budgets. Grouping by **cost center** takes the investigation one step closer to the people who understand the work behind the spend. Fig 03: Cost-center grouping identifies the financial owner of the increase. In this scenario, octodemo-org-cc has the largest increase. In only a few clicks, the admin UI has taken us from an enterprise-wide signal to the cost center that needs a closer look. For a one-time question, this may be enough. Now imagine that finance asks for the same analysis every month, with a fixed reporting period and a ranking of spend by user. That is the point where the API adds value. It does not replace the investigation you just completed; it helps you repeat and extend it. 2. Continue with the API: Answer the repeatable question The Billing Usage API gives you access to the data behind a more tailored report. You can use filters to match the period finance cares about, focus on the cost center you found in the UI, and build a view that can run again tomorrow or next month. Fig 04: Billing usage endpoints and time filters provide the inputs for a reusable report. Define the reporting question first Before writing code, state the question the report needs to answer. In this example, it is: > Which users in the selected cost center account for the most net spend during this reporting period? That one question keeps the report focused. It also determines the workflow: 1. List the organization's members to establish the candidate users. 2. Resolve which members belong to the selected cost center. 3. Query organization AI credit and premium-request usage for those users and the selected period. 4. Combine the results into a per-user total. 5. Rank users and aggregate the result by cost center. The prototype uses year, month, and optional day filters so the output matches the finance period. It also accepts a cost-center filter. Because the admin UI has already pointed us to `octodemo-org-cc`, there is no reason to start with every member of the enterprise. Understand the per-user query pattern There is one API behavior to understand before building the report. The organization billing endpoints return an aggregate when the user filter is omitted. To create a spend-by-user ranking, the workflow makes a filtered request for each selected user and usage type. For example, this request asks for Eve's AI credit usage in July 2026: curl -L \ -H "Accept: application/vnd.github+json" \ -H "Authorization: Bearer $GITHUB_TOKEN" \ -H "X-GitHub-Api-Version: 2026-03-10" \ "https://api.github.com/organizations/octodemo/settings/billing/ai_credit/usage?year=2026&month=7&user=eve" The response contains one or more usage items, with amounts such as `grossAmount`, `discountAmount`, and `netAmount`. The prototype adds the `netAmount` values to calculate Eve's AI credit total for the period. It then runs the equivalent premium-request query and combines the two totals. We can now see one user's contribution during the same period we investigated in the UI. Repeating the request for the members of the selected cost center gives us the ranking that finance asked for. For a production workflow, a few practical details matter: - Limit the candidate list to the cost center under investigation. - Paginate organization membership and cost-center results. - Use bounded concurrency instead of sending every request at once. - Record partial failures rather than silently treating them as zero spend. - Keep an audit record of when the data was pulled and transformed. For a daily check, the report can use a narrow period and write a timestamped output. At finance close, the same workflow can produce the month-end rollup. The question stays the same; only the reporting window changes. Reveal concentration that totals can hide The result is a custom Spend by User view that brings the organization, cost center, reporting period, AI credit usage, premium-request usage, and total net spend into one place. Fig 05: A company-specific dashboard exposes per-user concentration inside the selected cost center. In the illustrative data, the octodemo organization has 22 users and $3,651 in total net spend for July 2026. The octodemo-org-cc cost center accounts for $2,700 of that amount. Two users stand out: User AI credit net spend Premium-request net spend Total net spend Eve $900 $600 $1500 Adam $600 $400 $1000 Together, Adam and Eve account for $2,500 of the $2,700 attributed to that cost center. That is approximately 93% of its total in this example. These figures are demonstration data, but they show why the extra view is useful. Instead of reacting to a $2,700 cost-center total, the administrator can talk to the owners of two workloads and understand what the spend supported. Concentration does not automatically mean waste. Adam and Eve may be doing approved, high-value work. The dashboard tells the business where to ask the next question; the people involved provide the context needed to answer it. 3. Return to the admin UI: Choose the right control The API has helped us understand the increase, but it does not make the decision for us. Return to Billing and licensing > Budgets and alerts to review the available controls and choose the narrowest one that fits what you learned. Fig 06: Budget scopes turn the investigation into a targeted governance decision. Set a cost-center user-level baseline A cost-center user-level budget applies the same per-user amount to every current and future member of that cost center. This is useful when the group needs a different baseline from the rest of the enterprise. For example, the administrator might give octodemo-org-cc additional per-user headroom because its work legitimately uses more AI credits. This avoids raising the universal user-level budget for everyone. A user-level budget counts both included and paid AI credit usage. It is always a hard stop for the individual. It does not reserve part of the shared pool, and it does not replace the cost center's paid-usage budget. Preserve justified exceptions If Adam or Eve has an approved role that requires more capacity, an individual user-level budget can replace the cost-center baseline for that person. The exception stays limited to the person who needs it instead of increasing the budget for the whole cost center. Fig 07: Cost-center baselines and individual overrides preserve useful work without widening access for everyone. The precedence is straightforward: 1. An individual user-level budget overrides the cost-center user-level budget. 2. The cost-center user-level budget overrides the universal user-level budget. In practice, you can set a universal baseline, add more headroom for a cost center with a clear business need, and use individual overrides for documented exceptions. Why the UI and API work better together At this point, the better-together pattern becomes clear: - Metered usage supports interactive discovery. - Billing Usage API supports repeatable, company-specific analysis. - Budgets and alerts supports targeted policy decisions. Each surface does the job it is best suited to do. The UI makes it easy to explore and manage GitHub. The API lets you repeat a company-specific analysis without rebuilding it by hand. Used together, they give finance, engineering, and administrators the same evidence before a control changes. Make it part of the operating rhythm A useful dashboard should lead to a useful conversation. Decide who receives the report, how often they review it, and what happens when a user or cost center stands out. For example: - Run a daily pull to detect unusual changes early. - Produce a month-end rollup aligned to finance close. - Route cost-center summaries to the relevant business owner. - Review high-consumption users with engineering before changing limits. - Record approved individual overrides and revisit them regularly. Over time, the conversation can move from “Who spent this?” to “What outcome did this spend support, and does the current policy still fit?” When the same users repeatedly appear at the top, leaders can inspect the workload, remove waste, validate business value, or approve more capacity. When usage becomes broadly distributed, the cost-center baseline may need adjustment instead. The report makes those patterns visible over time. The better-together workflow at a glance The story above introduces each surface when it becomes useful. This table summarizes their roles. Surface Primary role Best used for Important limitation Metered usage Interactive investigation Finding the affected period, organization, and cost center Manual exploration is not a reusable company report Billing Usage API Programmatic usage retrieveal Scheduled reporting, time-sliced analysis, and per-user views Per-user attribution requires filtered requests and careful handling of pagination and failures Custom spend by user view Company-specific interpretation Ranking users and aligning usage to internal ownership Concentration is evidence to investigate, not proof of waste Budgets and alerts Governance controls Cost-center baselines and individual overrides A broader budget cannot override a user who has reached their ULB The practical takeaway is simple: begin with exploration, automate only the question worth repeating, and adjust policy after the data has context. That sequence keeps governance precise while preserving useful AI work. Learn more - REST API endpoints for billing usage - List organization members] - Budgets for usage-based billing] - Using cost centers to allocate costsFeedback : building the foundations for AI adoption with AI itself
I would like to share some feedback on the recent SharePoint Copilot improvements. As a consultant, trainer, and Microsoft MVP working daily with Microsoft 365 Copilot customers, I am genuinely impressed by the direction SharePoint is taking. For years, we have told customers that successful AI adoption requires strong information architecture, metadata, governance, content organization, and naming conventions. What I find particularly interesting today is that SharePoint Copilot is starting to help organizations build these foundations instead of simply depending on them. Features that assist with site creation, page creation, content organization, metadata generation, metadata enrichment, and automation significantly reduce the effort required to establish a well-structured SharePoint environment. This is a meaningful shift. AI is no longer only consuming well-organized knowledge. It is increasingly helping users create and maintain that organization. From my perspective, this may become one of the most important accelerators for Microsoft 365 Copilot adoption because many organizations struggle more with content structure than with AI itself.
