SharePoint Showcase: 10 Custom AI Skills Every SharePoint Site Owner Should Build
In this edition of SharePoint Showcase, we explore how skills work, how to create or install them, and ten practical examples to help SharePoint site owners get started. These examples are not an exhaustive list, but a curated starting point for identifying everyday processes that can become reusable, team-ready skills.
Beyond Benchmarks: The Lifecycle of Measuring Agentic Quality in AI Content Management
By Sujay Kumar Jauhar, Spriha Chandrayan, Natasha Gaitonde, Zhen Lei, Reed Pankhurst, Anush Sankaran, Amrit Shandilya, Ryen W. White Organizations put their most important content in OneDrive and SharePoint, trusting us to deliver AI experiences they can depend on. That trust runs across everything Copilot does with their content — surfacing the right information through retrieval, and, increasingly, driving agentic workflows that reason over documents and take action on their behalf. Living up to that trust is what we care about most, and it's why we put the quality of these experiences — and consequently the ways we measure that quality — at the center of how we build. It turns out that this is a non-trivial problem, because quality in AI is rarely binary. An agent can call exactly the right tools and still produce an answer that's subtly, confidently wrong. A search result can be technically relevant and still miss what the user was really after. An AI-generated page can be well-structured and fluent and still omit the one detail that mattered. Quality here is continuous, context-dependent, and multidimensional — and when these experiences are serving hundreds of millions of enterprise users, measuring it demands scientific rigor across the full lifecycle of building AI products. Across OneDrive and SharePoint — the knowledge platform for Copilot and Agents — we’ve spent the past year building a rigorous practice around evaluation: determining what to measure, developing the right methods to measure it, and learning how to act on those measurements to improve our systems and experiences. In software engineering, tests verify correctness and telemetry monitors health. The boundaries are well-defined: a function returns the right value or it doesn’t; a service meets its service level objective or it doesn’t. AI evaluation requires an analogous discipline, but the problem is structurally harder — there is rarely a single correct answer, and quality depends on factors that shift with every user, task, and context. This post describes the evaluation practice we are building across Copilot capabilities in OneDrive and SharePoint. It follows the arc of what we think of as the evaluation lifecycle — from rapid inner-loop validation, through rigorous offline measurement, to customer-grounded evaluation that evolves with real feedback, and ultimately to understanding whether our metrics predict what users actually experience. Each stage addresses a question the previous ones cannot, but they don’t run in a straight line. What we learn downstream can reshape the data and methodology of upstream measurements. That’s what makes this a lifecycle — and together they form a strategy for building AI systems that aim to earn user trust at enterprise scale. We're on a journey — learning and evolving as we build this practice. Some of the things we're actively working on, omitted from this blog post for the sake of brevity, are: holding ourselves to public, industry-standard benchmarks such as GAIA and GDPval, with more in the pipeline; and investing in the foundations that keep evaluation trustworthy, by generating realistic synthetic evaluation data so our test sets keep pace with new models, and monitoring the health of our benchmarks for saturation. The inner loop: "Did I break anything?" The first requirement of any AI development workflow is a fast feedback signal. Teams are experimenting constantly on internal builds — adjusting prompts, swapping model versions, rearchitecting retrieval pipelines — and they need to know within minutes whether a change helped or hurt. That is the role of our unit-level evaluations: a rapid inner loop for feature crews. A developer modifies a system prompt or adjusts the grounding context, runs it against a targeted set of test cases, and gets immediate feedback on whether key behaviors are preserved. These checks are fast, focused, and designed to catch regressions before they compound. The distinction from traditional unit testing is worth noting. In software, a test asserts an exact expected output. In AI, you are validating behavioral properties — coherence, completeness, tool selection, factual consistency — against approximate expectations. The pass/fail boundary is softer, but the engineering purpose is the same: give teams confidence to iterate without fear of silent degradation. One of the crews building Copilot's agentic features over SharePoint Lists knows this loop well. They keep a large set of prompts that engineers run whenever they change something — a tweak to a tool's description, a different setting passed to the model, a reworded prompt. Many of those questions run over the same synthetic data: lists of bakery orders, each row carrying details like the baker, the delivery agent, the profit on the order, and its status. The same lists come in a range of sizes, from a few thousand rows up to a million. The largest are used to push the system's limits but the inner loop lives at the lower end. There, most questions are ones the system has gotten right for a long time, so when a change quietly breaks one of them, it shows up right away. An engineer can try an idea, run the set quickly, and whether they've introduced a regression. Fast iteration checks are essential, but they only tell you whether something regressed. They cannot tell you how good the system actually is. Offline evaluation: "How good is this, really?" This is the domain of our offline evaluation practice. We maintain curated, scenario-focused benchmarks evaluated in a controlled harness that reflect the tasks and content types our enterprise users encounter, alongside public benchmarks that situate our systems in the broader AI landscape. The goal is to bring each AI scenario to a state of decision-readiness: explicit success criteria, representative evaluation datasets, metrics we attempt to validate against real outcomes, and systematic coverage of known failure modes. We try to do this rigorously by running evals against the real product. Each test case is a plan — a short script of things a user might ask for — paired with criteria (or assertions) describing what a good outcome looks like, sorted by how much each one matters: Critical, Expected, or Aspirational. An LLM plays the user: it drives the live product through the browser, sends the opening request, and then reacts the way a person would, confirming an action card here or answering a clarifying question there, so the system is exercised end to end. When the conversation finishes, the harness gathers the evidence of what actually happened — the responses in the chat, the state of the page, and screenshots where the judgment is visual. That evidence goes to a second model acting as judge — the increasingly standard "LLM-as-a-judge" approach — which scores the run against the original assertions. And because a model grading a model is only as trustworthy as its agreement with people, those scores are calibrated against human raters. Using a high-priority, curated slice of these evals, the team ran big proposed changes side by side to see which ones came out ahead. One of those comparisons looked at how the orchestrator is told about the tools it can use. At one extreme, you hand the model the full description of every tool up front; at the other, you give it only short, tweet-length summaries and let it pull a tool's full description and parameters only when it decides it needs them. Configuration Quality % Latency vs. default Cost vs. default Baseline 83.0 1.00x 1.00x Default tool discovery 90.3 1.11x 0.62x Rearchitected tool discovery 85.6 0.97x 0.73x Semantic tool search 92.8 0.95x 0.74x Hybrid 93.5 0.82x 0.62x Table 1: A comparison of the different approaches for tool selection we experimented with. Based on internal evaluation results using our internal test environment, and test configurations of the product. Quality was measured through LLM judgement of AI responses based on a set of criteria, latency was measured by the harness running the evaluations, and cost is based on publicly disclosed price heuristics for token usage for the LLM API being used for orchestration. Results were calculated from 500 scenario tests based on synthetic data. The table above summarizes our results from an internal offline evaluation on a curated slice. In this evaluation, we found that the baseline which loads everything up front was, predictably, the most expensive. The out-of-the-box tool-discovery method cut token usage sharply — but was a bit slower. Pushing further, a rearchitecting of the orchestrator for tool discovery did speed things up, but at the cost of quality. Meanwhile semantic tool search improved on both quality and latency relative to the baseline but wasn’t as cost-efficient as the default tool discovery method. Overall, in this evaluation, a strong balance of quality, token usage, and latency came from a hybrid — the standard tool-learning architecture with a few targeted semantic-search changes, which still resulted in a more than 35% reduction in token usage when compared with full tool descriptions. Offline benchmarks, however carefully constructed, have an inherent limitation. They represent a fixed snapshot — based often on what product teams believe will be important use cases, particularly for features that have not yet shipped to real customers. As the product evolves and real usage patterns emerge, the gap between what our benchmarks measure and what actually matters can widen. The evals flywheel: "Are we measuring what users care about?" This is the motivation behind what we call the Evals Flywheel: a closed-loop system that translates real user dissatisfaction directly into better evaluations and, ultimately, better systems. In software engineering, there is a well-established discipline of turning production bugs into tests. In the AI analog, user feedback provided with appropriate consent helps produce new evaluation data, systematic failure diagnoses, and targeted improvements. Take for instance a user that consents to share feedback with us (via a thumbs down). At first glance, this seems like a single interaction. But to us, it's an opportunity to learn. We investigate what happened, identify the underlying reason for the user dissatisfaction, and ask a critical question: was this an isolated mistake, or does it reveal a broader weakness in how the system understands documents like this one? Here's a representative example. A user uploaded a 120-page quarterly financial report packed with dense tables, nested headers, footnotes, and appendices. They asked a straightforward question: "What was our actual spending in Q3?" The answer seemed like it should be easy — the relevant numbers were all in the document. The assistant responded confidently with a figure from one of the summary tables; but it cited the budgeted amount, not the actual spend. The mistake stemmed from the document's complexity. The table spanned multiple pages, with a top-level "Q3" header extending across several sub-columns such as Budget, Actual, Forecast, and Variance. On its own, that's one mistake in one document. However, it pointed at something more general: whenever a table uses a heading that spans multiple columns, or stacks two rows of headers, or tucks a small table inside a larger one, then the link between a header label and its value turns fragile. Understanding the text is often not enough — the assistant must also accurately preserve and reason over intricate document structure, table relationships, and context that can span dozens of pages. Hence, instead of patching that single file, we built a family of evaluation documents that deliberately stress that exact structure. Each eval document pairs a question with the single correct answer and a short list of "tempting but wrong" answers which are the nearby values a confused reader would most likely grab. That last part is the key: an eval that only checks for the right answer can be passed by luck, but an eval that includes likely distractors tells you whether the system is genuinely reasoning or just guessing. This example reflects a repeatable method. The first step is resisting the urge to treat a user dissatisfaction as an isolated bug. Instead, we ask why it happened and not "what's the right answer for this question." That root-cause identification is what turns the user feedback into a useful signal. The payoff is a flywheel between real-world experience and offline evaluation. The improvement ships back into the product, where future interactions validate it — and surface the next gap. Each loop widens our coverage and, just as importantly, helps us prioritize the improvements that matter most to the people using the product. But it raises a deeper question: when our benchmarks are representative of user concerns, do improvements actually translate into better experiences in production? Real-world insights: "Does this actually matter to users?" It is possible to improve a benchmark score and have it mean nothing in practice. A model that scores five points higher on an offline evaluation may produce no detectable difference in user satisfaction or task success. Conversely, a change that barely moves the needle offline might transform the user experience in unexpected ways. This gap between offline measurement and real-world impact is the focus of our online evaluation work. Through production telemetry, we study user-AI interaction patterns at scale. The objective is not simply to monitor system health, but to develop an empirical understanding of how users experience our AI features in context. In traditional software, production telemetry provides crisp, well-defined signals: uptime, latency percentiles, error rates. In AI, the equivalent signals — satisfaction, intent alignment, task success — are inherently noisier and harder to define. The central challenge here is alignment: do our offline metrics predict real user value? Answering that depends first on being able to measure value reliably in production at all. Consider a recent experiment on one of our top-of-funnel entry points — the most common way users first reach the assistant. The team suspected that engagement was low (around 1%) simply because users weren't noticing the entry point. So they ran a user-level A/B test across a sample of 900,000 Copilot-licensed users over 21 days, swapping the entry point's icon for a more contextually relevant one. The effect was a statistically significant 42% lift in click-through, alongside a 1.7% increase in active users. That result is encouraging, but it also illustrates a subtle nuance. We moved an engagement metric sharply without touching quality at all — this tells us that we had an education gap, but didn’t inform our understanding of the AI system’s performance. To get closer to that distinction, we looked at kept rate: the share of AI responses where the user does something that signals the answer was worth keeping — copying the result, clicking through to a citation or a suggested follow-up, or providing a thumbs up. And because it's captured passively, from actions users take anyway, it scales to every response rather than the small fraction where someone leaves explicit feedback. Even an indirect, partial signal like kept rate lets us close the offline-online loop. Once we have a read on what users valued, we can start to ask how well it correlates with our offline benchmarks. When offline gains track with a rise in kept rate, for example, it's early evidence the benchmark is steering us toward something real; when they are uncorrelated, it's a warning that we may be hill-climbing in a wrong direction. Beyond quality Quality matters — but in production systems, it is entangled with cost and latency in ways that cannot be evaluated in isolation. A high-quality response that is unacceptably slow degrades the user experience regardless of its correctness. Or a frontier model that is marginally better than a smaller alternative may not justify the cost differential. This is the next frontier of our evaluation work: learning to reason about quality, cost, and latency as a tradeoff surface rather than independent dimensions. The goal is to build the instrumentation and frameworks that make these tradeoffs visible and deliberate, so that teams can make principled decisions about how they ship. To make this concrete, consider some early findings from our internal evaluations. We took a range of system configurations — models, reasoning levels, tool access — and, for a set of representative tasks — QA over lists, the AppWorld public benchmark, and a collection of complex multi-step plans that stress-test the Copilot in SharePoint agent — plotted each one by the quality it achieved against what it cost to run. The result is a Pareto frontier: the curve of the best quality reachable at each price, with everything below it dominated by something strictly better. Two things stand out. First, different tasks live in different performance regions. Simple, factual lookups like those in ListsQA cost fractions of a cent; harder, more agentic tasks like those in AppWorld and Copilot in SharePoint run to cents or dollars; there is no single answer to what "good" costs. Second, the shape of each curve tells you where spending more is actually worth it. Where the frontier rises sharply, additional cost buys real quality; where it flattens, you have hit diminishing returns, paying more for gains a user may not notice. In short, the decision stops being "is the quality good enough to ship?" and becomes "which point on this curve is right for this scenario?" given budget and latency constraints — a question a feature crew can answer, with the tradeoffs in front of them. What we've learned so far We are still early in this work. Models are improving rapidly, user expectations are evolving, and the definition of quality continues to shift. But several principles have become clear to us. No single evaluation approach is sufficient. Inner-loop checks catch regressions but cannot establish absolute quality. Offline benchmarks provide rigor but go stale. Customer feedback grounds evaluation in real needs but covers only the cases where dissatisfaction is expressed. Online metrics reveal real behavioral patterns but cannot always explain the mechanisms behind them. A credible evaluation practice requires all of these, operating in concert and informing each other. Evaluation is not a gate you pass before shipping — it is a continuous discipline that evolves with the product. The teams that build the best AI experiences are not necessarily those with the highest benchmark scores, but those with the clearest understanding of where their systems fall short and the most systematic approach to closing those gaps. And measuring what matters is itself a scientific problem, one that demands the same rigor we apply to the systems being measured. Developing evaluation methods that are reliable, predictive of real user value, and robust to the rapid evolution of AI capabilities is core to our mission across OneDrive and SharePoint. It is how we work to earn the trust of the enterprise users who depend on these experiences, and we are committed to advancing this work and sharing what we learn.SharePoint Showcase: How Microsoft uses Copilot in SharePoint, and how you can get started
For this month's SharePoint showcase, we're zooming in on AI skills in Sharepoint and real Microsoft use cases for Copilot in SharePoint. We're also introducing a new "Product Drops" standing section to recap all the new GA functionality across the platform each month. As always, would love to hear from you in the comments. Let's get into it!Business Applications Built for Microsoft 365 – Cubic Logics – SharePoint Partner Spotlight
Discover how Cubic Logics is helping organizations transform Microsoft 365 into a business application platform. With over 12,100 deployments in 172 countries, their Apps365 solutions bring HR, contract management, help desk, asset management, and AI-powered experiences directly into the flow of work across Microsoft 365.
A new SharePoint Look and Feel: What’s Changing and Why It Matters
SharePoint is getting a refreshed look and feel designed to make the product feel simpler, clearer, and more focused on your content. We’ve recently introduced a new SharePoint experience, organized around helping you discover knowledge, publish content, and build solutions. Along with that new experience, we’re introducing visual updates across key product surfaces that reduce visual noise, improve readability, and bring more consistency to the interface while preserving the branding investments your organization has already made. We call this work the SharePoint visual refresh: a thoughtful update to the product’s visual language that helps SharePoint feel more modern, approachable, and easier to use. Background Over time, SharePoint’s capabilities have continued to grow, which has given us an opportunity to update the look and feel as well. These updates not only make the SharePoint UI fresh, but it ensures that we are consistent across M365. The SharePoint visual refresh, rolling out to general availability now, is part of a broader collaborative effort across Microsoft 365 to modernize the suite and improve usability with this bold, new visual design, while also responding to customer feedback about visual clutter and the need for clearer focus on content. These updates are grounded in core design principles that prioritize usability, coherence, and a sense of delight in everyday interactions. This bold, new design is part of a larger update across many apps (such as Microsoft 365 Copilot) and platforms that family together to create a cohesive, modern look and feel. Goals The Visual Refresh is guided by several key goals: Deliver a more contemporary, polished look aligned with the broader Microsoft 365 experience to existing and new SharePoint experiences Improve consistency, usability, and accessibility across key SharePoint surfaces, including Sites, Pages, and Document Libraries and the new Discover, Publish, Build destinations. Reduce visual clutter to improve focus on what matters most: your content Create a design system that scales, from simple team sites to complex enterprise scenarios Consistency Across Experiences Once adopted, the visual refresh will strengthen alignment between SharePoint, OneDrive, Teams and the Microsoft 365 shell, reducing visual fragmentation across navigation, pages, and components. By aligning how our UI looks and our theming approaches, we aim to create a more predictable experience for users moving between tools and surfaces, helping avoid visual dissonance and reducing cognitive load across day‑to‑day workflows. What’s being updated Canvas elevation: putting content in the spotlight One of the most noticeable changes is the elevation of the SharePoint site canvas. By adding a subtle shadow, refined spacing, and a light gray shade to backgrounds, the refreshed design helps your content stand out, making it easier to scan, read, and interact with pages. Importantly, this is not achieved by changing your content. Instead, the improvements come from thoughtful adjustments to padding, layout spacing, and visual framing, ensuring that the emphasis remains on the information you create and manage. This does not effect existing layouts or how sites reflow. Neutral theming for app surfaces We’re also evolving how themes are applied within SharePoint to improve consistency, accessibility, and clarity across experiences by separating the core SharePoint user interface from customer content. Updated neutral theming of app surfaces provides a more stable visual foundation and establishes a unified set of navigational UI, where the customer brand remains in an anchor position and is in harmony with the new neutral navigation. This approach allows customer branding to be expressed more clearly without competing with structural UI elements, clarifies the distinction between customer branding and the SharePoint app, reduces visual competition, and improves focus on primary content. ual noise, improving usability. Styling updates Additional updates to typography, spacing, and corner rounding introduce a more cohesive and contemporary visual language across SharePoint surfaces. Updated typography and spacing enhance readability and create more consistent rhythm across pages and components, making information easier to scan and interact with. At the same time, increasing corner radius of the UI makes it feel more approachable, and through a flexible system brings greater consistency to our products, helping related components feel more integrated and visually connected. Together, these refinements simplify the overall interface, reduce unnecessary visual noise, and contribute to a lighter, more modern experience that aligns more closely with the broader Microsoft 365 design ecosystem. What’s Not Changing While the visual refresh introduces meaningful visual improvements, core SharePoint concepts and workflows remain familiar. Your content, structure, and brand are preserved Existing site architecture remains unchanged Day‑to‑day workflows continue to work unchanged There is no impact on existing SPFx extensions or solutions with this change This update focuses on evolution, not reinvention, so users can benefit from improved clarity and modern visuals while familiar work flows and patterns remain the same. Evaluated with Research Across our research studies, participants consistently favored the Visual Refresh due to the cleaner and more contemporary look and improved labeling and structure. A calmer, more modern UI that’s easier to scan. Elevation + neutral theming made the page feel cleaner and it is easier to focus on the content. Clearer actions in the command bar. Stronger affordances (like button outlines) make common tasks - edit, undo, save, share - more obvious and easier complete workflows. Less guesswork when navigating. Icons and labelling in the app bar reduced friction and participants spent less time hovering and interpreting icons, especially those less familiar with SharePoint. Overall, the Visual Refresh provides users with a new look they prefer without slowing down their workflow. Looking Ahead The SharePoint Visual Refresh is part of an ongoing journey. We’ll continue refining the experience, learning from customer feedback provided directly in-product, and shipping improvements incrementally, so SharePoint keeps getting better without disrupting how people work. Familiar workflows will remain in place, now enhanced by improved clarity, consistency, and a more modern feel. We welcome all feedback! See this post for more information about SharePoint’s exciting next chapter.SharePoint Showcase: From Chaos to AI-Ready with the SharePoint Admin Agent
By: Sesha Mani and Sophia Peng The way governance gets done continues to evolve. Across organizations, agents are no longer a sidebar conversation; they are showing up in everyday work, reasoning over content, and stretching how IT thinks about permissions, lifecycle, and recovery. At the Microsoft 365 Community Conference, nearly every demo this year landed on the same grounding question from CISOs and admins alike: "What will it reason over, and how do we stay in control?" That question is exactly what the SharePoint Admin Agent was built to answer. In this month's SharePoint Showcase, we're spotlighting Microsoft's first-party AI assistant for managing your digital estate, along with the six-step Content Governance Journey it powers, designed to take a tenant from chaos to AI-ready through a practical governance journey. Meet the SharePoint Admin Agent As the content backbone of Microsoft 365, powering Teams, OneDrive, Loop, Copilot, Copilot Cowork, and a growing ecosystem of agents, SharePoint sits at the center of content governance. Permissions, lifecycle, resilience, and relevance now span users, apps, and AI agents. Managing that surface with portals and PowerShell alone doesn't scale. The SharePoint Admin Agent brings these capabilities together in one simple conversational experience. Admins can ask questions in natural language, gain actionable insights, and take meaningful action without switching portals or writing scripts. Behind the scenes, it's powered by SharePoint Advanced Management (SAM), the foundation for Copilot-native governance, and aligned to the 3Rs framework: Readiness, Relevance, and Resiliency. Because the SharePoint Admin Agent is a declarative agent, you aren't locked into a single surface. You can summon it from the SharePoint admin center, the Microsoft 365 admin center, Microsoft Teams, or directly inside chat, all gated by role-based access so only the right people can see admin-level insights and take admin-level actions. The Content Governance Journey: a practical path to AI-readiness We've mapped governance into a practical, six-step path that helps move a tenant from "I don't know where to start" to AI-readiness: Assess, Structure, Lifecycle, Oversharing, Access, and Resiliency. The agent rides along through every step, surfacing insights, recommending actions, and helping admins execute. 1. Assess content state Status: Generally Available Step one is the easy button. From the Advanced Management page in the SharePoint admin center, a single click kicks off a tenant-wide scan across SharePoint sites OneDrive’s, and tenant settings, with no manual data pulls or cross-referencing reports. The assessment returns a prioritized map of your content risks across Site Lifecycle, Oversharing, and Storage, with recommended next steps for each issue. From there, you can ask the agent to go deeper, for example: “Which sites were last accessed by external users?” or “What policy should I create next?” The agent reasons over your tenant signals and recommends actions you can take in place. 2. Control content structure with Catalog Management Status: Built-in grouping: Generally Available · Custom catalogs: Public Preview Many admins find it hard to answer questions like “which sites belong to Finance, and is Finance oversharing more than Sales?” Catalog Management makes that kind of grouping straightforward. Out of the box, your sites are grouped by region, department, and user type using built-in Microsoft 365 metadata, so you can target policies, reports, access reviews, and Copilot rollout with precision, department by department or region by region. And because organizations are unique, custom catalog creation is rolling out now: build site groups by direct CSV upload, by custom site properties, or by Entra ID extension attributes, for example, an "Executive Leadership" group that's excluded from certain lifecycle notifications. This structure gives the system a stronger foundation for more precise insights and recommendations, and it sets the stage for deeper anomaly detection over time. Storage runways, growth trends, and ranked cleanup opportunities all become more precise the moment you give the agent the coordinates of your organization. 3. Control content lifecycle Status: Generally Available Insights without ongoing automation drift back into chaos. Lifecycle skill turns inactive site management into an always-on system: a five-minute wizard lets you scope a policy (start with North America, or Finance, or just executive sites), set inactivity thresholds, choose who gets notified, and customize the message that lands in site owners' inboxes. Run the policy in simulation mode to preview which sites would be flagged before any notifications go out, then flip to active to let it run automatically every month. Ask the agent "Identify sites with low activity owned by Sales and Marketing" and it returns a ranked table in seconds, with recommendations like "Archive these top 10 sites to free up 5 TB," and a one-step path to create the policy that prevents the same buildup next quarter. 4. Control content oversharing Status: SAM Admin role: Generally Available · EEEU at file/folder: Private Preview Oversharing is one of the first questions organizations want to answer as they prepare content for Copilot. Most enterprise oversharing traces back to five common causes: site privacy set to public, default sharing set to "Everyone," broken permission inheritance, the "Everyone Except External Users" (EEEU) group, and content without sensitivity labels. To make those root causes visible at scale, Data Access Governance (DAG) reports give you a tenant-wide permission view, supporting up to one million sites, with insights into root causes and built-in mitigation actions. New in this wave: file-level reporting, starting with content shared via Everyone groups. Because file-level visibility is sensitive (a report can reveal what an executive is working on), we're introducing a new SharePoint Advanced Management Admin role, the SAM Admin, that grants the right people the right view without expanding broader tenant rights. These reports are available in SAM today, while file-level integration with the agent is still to come. In the meantime, the agent can help admins identify overshared sites and answer broader governance questions, while the new reports provide deeper file-level visibility where needed. 5. Control content access Status: Generally Available Site Access Reviews help you delegate access reviews to the people closest to the content, site owners, while you maintain tenant-level visibility. The agent can help admins identify where reviews are needed and guide next steps. Site owners get a clear, branded email and a focused view that shows only the files and folders presenting an oversharing risk, not the entire site. Pair this with Restricted Access Control (RAC) and Restricted Content Discovery (RCD), both honored by Copilot and both delegable to site admins, to keep sensitive content out of AI reasoning until access is right. 6. Plan for resiliency Status: Microsoft Baseline Security Mode GA · Microsoft 365 Backup GA - Multi-Geo Skill: Private Preview Resiliency is the part of the journey that's easy to skip, and the part that matters most when something goes wrong. The agent is already connected to more than 60 tenant settings spanning sharing, storage, and permissions, so you can ask, "Is Microsoft Baseline Security Mode enabled in my tenant?" or "Where can I optimize sharing?" without digging through admin centers. If recovery is needed, the agent helps you locate restore points across SharePoint and OneDrive from your Microsoft 365 Backup, and because recovery is high-stakes, the agent guides you step by step rather than acting on its own. Sign-up for Recovery Skill Private Preview. New this month: the Multi-Geo Skill (Private Preview), starting with move-status tracking. Ask the agent about the status of user or content moves at the geo or user level, with no more hunting through reports for an update. More Multi-Geo capabilities, including initiating moves, are on the roadmap. Interested? Sign-up for Private Preview. Designed with admins in the loop One of the most important design principles behind the SharePoint Admin Agent is what it won't do. Ask it to delete overshared sites and it will say no. The agent is built to analyze, recommend, and take safe actions, but destructive operations like deleting content or removing sites stay in the admin's hands, with full context. By design, you can stay in control with the agent. That's also why the new SAM Admin role exists: file-level insight is powerful, and the people who use it should be the ones who own it. The combination of conversational reasoning, a layered policy framework (RAC, RCD, inactive site policy, catalog management, and more), and clear role boundaries gives admins a governance posture that matches the pace of agent adoption: discover, decide, and act, without leaving the admin center. What's next The journey doesn't stop here. In the second half of 2026, expect deeper anomaly detection and notifications in the Storage skill, cross-skill queries that chain insights across permissions, lifecycle, and storage, the ability to control the agent's tone and temperament, and voice-driven tasks. A new Assess Progress capability will let admins track tenant readiness over time, and the Multi-Geo skill is set to expand from status tracking into initiating moves. Site owners will get their own governance hub, and we'll continue extending agent governance, including agent access insights, so the agentic estate stays as well-governed as the content beneath it. Governance is the grounding question, and the strategic enabler The real unlock is control. Instead of treating Copilot rollout as a tenant-wide switch, admins can roll out department by department or region by region. Take Finance as an example: scope inactive-site and ownership policies to that catalog, run the DAG permission report for the same set of sites, initiate Site Access Reviews with site owners, apply Restricted Content Discovery where labels or access aren't yet in place, and then enable Copilot for Finance with confidence. Repeat for the next group. The SharePoint Admin Agent, together with the broader SAM portfolio, is how we’re meeting admins where they are: managing a rapidly expanding digital estate with conversational tools that make readiness, relevance, and resiliency something you can act on in real time, not just plan for. Open the SharePoint admin center, start the assessment, and let the agent show you how quickly focused governance can drive progress. Learn more Microsoft 365 Copilot readiness and resiliency with SharePoint and M365 Backup/Archive Introducing SharePoint Admin Agent: Governing and securing SharePoint in the agentic era AI Security & Admin Innovation in Microsoft 365 — Microsoft 365 Community Conference demos SharePoint Advanced Management (SAM) overview Microsoft 365 Backup Stay tuned every month for the SharePoint Showcase, where we share updates, best practices, and real-world examples of how SharePoint helps teams move faster, work smarter, and stay in control as AI reshapes work.Proven intranet framework - Involv Intranet - SharePoint Partner Spotlight
We’re excited to share a new episode in our partner showcase series focused on SharePoint in Microsoft 365. In this episode, we spotlight Involv Intranet and how it brings a modern intranet experience to life using the SharePoint Framework (SPFx).Microsoft named a Leader in the 2026 Gartner® Magic Quadrant™ for Document Management
AI is raising the bar for document management. It’s no longer enough to store and share files— content needs to be governed, structured, and connected to the way teams actually work so AI can reason over it responsibly and help people move from information to action. That’s why we’re excited to share that Microsoft has been named a Leader in the 2026 Gartner® Magic Quadrant™ for Document Management, positioned highest for Ability to Execute. We believe this recognition reflects the investments we’ve made in making SharePoint and OneDrive the unified knowledge platform at the heart of Microsoft 365, keeping content trusted and compliant, while making it more discoverable, more actionable, and more valuable as AI adoption accelerates. In our view, this recognition is also a reflection of the customers and partners who build with us. Every day, users create 2 million new SharePoint sites, add 2 billion files, and run 3 billion automated workflows on SharePoint content each week, a scale that reflects how deeply document management is embedded in daily work. We’re excited to keep building what’s next with you. Grounding Copilot in governed content SharePoint and OneDrive serve as the trusted content layer for Microsoft 365 Copilot, giving AI a single, governed foundation to reason over. Over the past year we’ve deepened that connection across sites, files, and even encrypted content. Reasoning over sites: Copilot reasons across SharePoint sites and libraries, understanding pages, files, and metadata together to answer questions with full site context and governance. Reasoning over files (including encrypted content): Copilot can reason over individual SharePoint files — including image-level understanding in PowerPoint — and it can do so even when content is encrypted, while honoring permissions, sensitivity labels, and security policies. Connect Copilot Studio and Foundry to SharePoint: Use SharePoint and OneDrive as a trusted knowledge source for agents built in Copilot Studio and Foundry, so they can retrieve grounded answers from governed content while respecting the same permissions and compliance controls. AI-powered document insights: Every SharePoint site has an agent that can answer questions about site and library contents. Users can do scoped Q&A over one or many files, summarize, compare, and generate FAQs in one click, or listen to an Audio Overview of a file on the go. AI-assisted work in SharePoint AI in SharePoint is available now in public preview, so you can go beyond answering questions to getting real content work done right where your files live. Turn ideas into execution: Describe the outcome you want and SharePoint proposes a structured plan — spanning sites, pages, libraries, lists, and starter content — so you can review and iterate before anything is created. With a single prompt, you can also kick off multi-step content work across SharePoint assets, staying in control to review, adjust, or interrupt as SharePoint executes. Teach AI how your team works: Capture shared skills and context at the site level so one person can encode standards and everyone benefits. Skills help ensure consistent outputs, applying the same rules regardless of who asks or when. From content chaos to Copilot ready: SharePoint keeps content structured, current, and governed with automated metadata tagging and proactive site health monitoring, so Copilot and agent experiences across Microsoft 365 are more relevant and reliable. A modern, intuitive experience We continue to invest in the fundamentals — fresh, clean, consumer-grade experiences that make content easier to find, author, and act on. New SharePoint experience (rolling out now): A visual refresh and simpler navigation oriented around three core jobs: discovering what’s new, publishing content, and building solutions. Includes new front doors like Discover, Publish, and Build plus a new app bar that makes it faster to move across SharePoint and get back to the content you need. Refreshed document libraries: A modernized UX with built-in AI actions and forms for file intake, bringing insights, actions, and collection into the library experience. Page authoring and news publication: Best-in-class web authoring with flex sections, editorial cards, design ideas, motion, fine-grained image controls, and hero carousels. Viva Connections dashboards are now available on all SharePoint sites, with a new news feed that’s both user-personalized and tenant-programmable. Strengthened governance and trust Scaling AI on content only works if the content layer is governed and secure. This past year we delivered significant advances across governance, lifecycle, and data protection. Copilot-ready content governance: SharePoint Advanced Management (SAM) delivers permission state reports to detect overshared sites and content, and Restricted Content Discovery lets admins exclude sites that aren’t ready for Copilot. Content lifecycle management: SAM’s Inactive Sites Policy and Ownership Policy identify stale or unmanaged sites and drive remediation at scale. Unified data protection with Microsoft Purview: Dynamic watermarking and broader label support extend protection across content types. Records management in Purview: Multi-stage disposition through retention labels (up to five stages) enables multiphase review, relabeling, extension, and defensible deletion across Microsoft 365. Content management and industry solutions at scale As content management needs grow, customers want a single solution that blends secure collaboration with records, compliance, and line-of-business content, built for how specific industries and roles operate. SharePoint and OneDrive provide a flexible, governed foundation for regulated and high-volume content processes. Governance by default: Metadata, retention, sensitivity labels, and permissions stay with content, supporting audit-ready control for regulated industries like financial services, healthcare, and the public sector. Role-based experiences: Build repeatable journeys for HR, legal, and operations, from onboarding and case files to contract review, approvals, and publishing. Secure internal and external collaboration: Enable controlled sharing for customer onboarding, vendor management, and partner workflows while maintaining policy enforcement and admin visibility. AI-ready content: Keep information organized and trusted so Copilot and agents can deliver more relevant, permission-aware answers and next actions, especially for high-stakes roles working with sensitive content. Connected with Microsoft 365 apps: Enter and update SharePoint metadata directly from Word; request and sign documents with eSignature from Word and PDFs in SharePoint; use AI-powered document assembly to generate compliant Word documents from approved templates; and rely on a single “hero link” per file that controls all access — no more juggling multiple sharing links. Learn more about SharePoint, OneDrive, and Microsoft 365 Copilot We remain committed to making content governed, intelligent, and AI-ready — so every document in your organization can become knowledge that moves work forward. Start using Microsoft 365 Copilot today. Visit Microsoft365.com/copilot or download the Microsoft 365 Copilot app. Learn about Microsoft SharePoint: https://www.microsoft.com/en-us/microsoft-365/sharepoint/ Learn about SharePoint’s latest AI capabilities: https://aka.ms/SharePointAI Read more about 2026 Gartner® Magic Quadrant™ for Document Management: https://aka.ms/2026GartnerDocMgmtMQReport Gartner, Magic Quadrant for Document Management, Tim Nelms, Jed Cawthorne, Rachel O’Farrel, Marko Sillanpaa, Stephen Emmott, April 28, 2026. Gartner does not endorse any vendor, product or service depicted in its research publications and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s Research & Advisory organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose. Gartner and Magic Quadrant are trademarks of Gartner, Inc., and/or its affiliates. *This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request here.SharePoint Showcase: AI-Forward Content Creation & Curation for the Modern Intranet
More than 2,700 Microsoft 365 customers joined us at the Microsoft 365 Community Conference in Orlando to learn what’s new across Microsoft 365. In one of our sessions, AI‑Forward Content Creation & Curation for the Modern Intranet, we shared how SharePoint is evolving from a place where employees find information, to a platform that helps teams create and keep it current. New AI capabilities now in public preview can generate and edit pages from natural language, helping you move from an idea to structured, publish-ready content faster. Because SharePoint content helps ground Microsoft 365 Copilot and agents, quality and consistency matter. Below, we’ll walk through the AI features across two areas: creation (drafting and visual storytelling) and curation (keeping your intranet accurate, relevant, and trustworthy over time). AI in SharePoint: An overview - Microsoft Support Create and edit pages with AI First up: creating and refining pages directly in SharePoint. You can engage with SharePoint’s AI capabilities from multiple entry points: select the floating action button in the lower right corner of a site, start from the template gallery, create new pages directly from a site, or navigate to the Publish tab to update recently created pages. Once in Edit mode, AI becomes your coauthor—you can use chat to make page edits like adding, moving, updating, and formatting your content for you. Describe what the page should include, and SharePoint can generate a structured first draft. This experience is intentionally iterative: you can switch back and forth between traditional page building (choosing layouts, adding web parts, and fine-tuning design) and AI-assisted authoring (drafting, rewriting, summarizing, or restructuring) until the page looks and reads exactly the way you want. Visual storytelling with charts Communicating clearly often means communicating visually. With AI‑generated charts in SharePoint, authors can describe what they need in plain language and add rich, interactive charts directly to a page. Charts can be grounded in existing documents and refined through multi‑turn interaction, helping teams turn complex information into content that’s easier to understand and act on, without requiring design expertise. Improve your site Creation is only half the story. AI in SharePoint goes further—delivering intelligent, automated content curation that helps site owners continuously maintain intranet quality at scale with minimal manual effort. Embedded directly into the SharePoint experience, “Improve your site” leverages user behavior, search insights, and page analytics to surface targeted, action‑oriented recommendations—with one‑click actions, bulk updates, and the ability to learn from user decisions over time. Capabilities include: Retiring low‑activity or outdated pages (individually or in bulk) to reduce clutter and improve content relevance and search quality Identifying content gaps based on employee search intent and unresolved queries, with AI‑assisted content creation to fill them Fixing broken links and navigation issues to remove friction and improve user experience Enhancing content quality through recommendations to update, rewrite, or restructure pages for clarity, engagement, and accessibility Improving site discoverability with suggestions for keywords, metadata, navigation, and featured content (e.g., search pinning and best bets) With these insights available on demand, SharePoint helps ensure the content Copilot and agents reason over remains up to date, relevant, and aligned with how employees actually search and work—without introducing additional governance processes or tools. Improve your SharePoint site with AI in SharePoint - Microsoft Support Content Readiness Is the Foundation for Better AI Outcomes AI capabilities in SharePoint will continue to advance, but content quality, structure, freshness, and governance remain the foundation of effective AI experiences. By making it easier to both create and curate content, SharePoint helps organizations strengthen content trust today while enriching AI experiences and Work IQ across Microsoft 365. The result is higher quality answers, better decision making, and greater productivity, for both people and the AI systems that rely on SharePoint content. Learn More Content creation with AI in SharePoint
