AI Skills Navigator is now available in Microsoft Copilot
Start with a question for the Learning Agent in Copilot and get recommendations from AI Skills Navigator, helping you find the right next step whenever you need to learn something new. Matt Erni is Product Manager at Microsoft working across Learning Agent and AI Skills Navigator to bring relevant skilling into the flow of work. Learning new AI skills has never been more important for individuals and organizations alike. But when you need to learn something new, finding the right next step isn’t always straightforward. Let’s say you’re preparing a presentation, building your first agent, securing an AI workload, or even planning your next career step. You’re right in the middle of a task and realize there’s a skill you need to learn to do it well. The challenge isn't just finding training. It's finding resources you can trust that are tailored to your role, goals, experience level, and preferred way to learn. That’s why we recently introduced Learning Agent in Microsoft Copilot, a personalized AI upskilling experience that helps you build Copilot and AI skills in the flow of work. Learning Agent uses role information, work context, skills signals, and organizational learning sources to bring personalized recommendations directly into Copilot, helping you discover relevant learning opportunities and build skills when and where you need them most. Today, we're extending that experience through a new integration with AI Skills Navigator, our agentic skilling platform. Learning Agent can now also recommend training, skilling experiences, and credentials from AI Skills Navigator directly within Copilot, making it easier to move from a question to the next step in building a skill. Videoclip: Overview of Learning Agent in Microsoft Copilot. If the player doesn’t load, open the video in a new window. Turn work questions into learning opportunities Whether you're trying to solve a problem, build a new skill, or explore your next learning goal, you can start by asking a question in Copilot using natural language. Here are some examples: How can I use Copilot in new ways? What should I learn before building my first agent? Which videos can help me learn about Copilot Cowork? What courses can help me learn how to secure my AI agent? Which Microsoft credential can help me lead AI adoption in my organization? Learning Agent uses your question along with context from you and your organization to surface personalized skilling recommendations and guidance. With the AI Skills Navigator integration, those recommendations can now also include training modules, learning paths, skilling sessions, videos, and Microsoft Credentials. So, no matter what you're trying to accomplish, Learning Agent is there to guide you to the right next step. The recommendations from AI Skills Navigator are clearly labeled, so you can understand the source and choose the next step that works for you. Getting started is simple: open Learning Agent in Microsoft Copilot and ask a question about a skill, topic, or credential you'd like to learn more about. Keep learning without losing your place When Learning Agent recommends a module, learning path, or curated video from AI Skills Navigator, you keep the context that brought you there. You can open the recommendation alongside your Copilot conversation, explore the content, and use features such as AI-generated summaries and podcasts as you go. Progress is automatically saved in AI Skills Navigator, making it easy to pick up where you left off and continue learning over time. There's no separate sign-in required. The full AI Skills Navigator catalog gives you access to interactive learning experiences like skilling sessions or opportunities to earn Microsoft Credentials. The first time you access these experiences, you'll create an AI Skills Navigator profile, unlocking additional personalized learning, progress tracking, and recommendations in the full experience. Whether you start with a quick recommendation or continue into a skilling session or credential, you can keep learning without losing momentum. Learning for individuals—and entire organizations Learning Agent and AI Skills Navigator are designed to support you in skilling at any level while also helping your organization scale learning more effectively. Learning Agent can surface recommendations from organizational knowledge sources, including SharePoint, LinkedIn Learning, learning management systems, third-party content, role-play providers, and now AI Skills Navigator. This helps employees spend less time searching for learning resources and more time building skills. So, whether you’re building AI fluency, preparing for a new role, earning a credential, or developing technical expertise, Learning Agent and AI Skills Navigator help connect everyday questions with the learning opportunities that matter most. At the organizational level, this creates a stronger connection between employee learning, company knowledge, and the skills needed to support business priorities. Availability: Learning Agent is available to organizations and their employees with Microsoft Copilot. Access to connected learning experiences, including some third-party sources, depends on how an organization has configured and licensed those sources. Check with your organization if you're not sure what’s available to you. Keep building skills over time Learning Agent helps you get started on the right track. AI Skills Navigator helps you go deeper, track progress, and keep building skills over time. Together, they help individuals and organizations connect learning to real work and skilling goals. Get started now. Add Learning Agent to Microsoft Copilot and start asking questions about the skills, topics, and credentials you’d like to learn more about. New to AI Skills Navigator? Read The moment AI skilling stopped being optional—and started being personal.New Microsoft 365 Certified: AI Services Administrator Associate Certification
As AI moves deeper into mission-critical work, organizations need trusted administrators who can govern access, secure data, monitor services, and keep Microsoft 365 AI experiences running at enterprise scale. Show you’re ready with new Exam AB-650 (beta). AI-powered productivity is no longer just about enabling new capabilities, it’s about operating them with the right controls, safeguards, and visibility. Organizations need administrators who can help make Microsoft 365, Microsoft 365 Copilot, agents, and connected AI services secure, compliant, reliable, and scalable across the enterprise. The new Microsoft 365 Certified: AI Services Administrator Associate Certification validates the skills to configure, manage, secure, govern, and continuously optimize Microsoft 365 tenants, workloads, and AI services so organizations can adopt AI with confidence and drive effective outcomes at scale. To earn this Microsoft Certification, you’ll need to pass Exam AB-650: Administering Microsoft 365 and AI Services, currently in beta. Is this the right Certification for you? Candidates for this Certification: Configure, manage, secure, and govern Microsoft 365 tenants, workloads, and AI services, including Copilot, agents, and connected AI capabilities. Govern data access, protect information, and support collaboration between users and agents. Operate and continuously optimize Microsoft 365 and AI services at enterprise scale, helping to ensure reliable and effective outcomes in an AI-powered workplace. Candidates should have experience with Microsoft 365 workloads and Microsoft Entra ID, an understanding of Defender XDR capabilities, and familiarity with Microsoft Graph PowerShell. Ready to prove your skills? Take advantage of the discounted beta exam offer. The first 300 people who take Exam AB-650 (beta) on or before August 18, 2026, can get 80% off. To receive the discount, when you register for the exam and are prompted for payment, use code AB-650SkyClub . This is not a private access code. The seats are offered on a first-come, first-served basis. As noted, you must take the exam on or before August 18, 2026. Please note that this discount is not available in Turkey, Pakistan, India, or China. How to prepare Get ready to take Exam AB-650 (beta): Review the Exam AB-650 (beta) page for training resources, exam registration, and other details. The Exam AB-650 study guide explores key topics covered in the exam. Connect with Microsoft Training Services Partners in your area for in-person offerings. Need other preparation ideas? Check out Just How Does One Prepare for Beta Exams? Ready to get started? You can take Certification exams online, from your home or office. Get the details in Online proctored exams: What to expect and how to prepare. Remember, only the first 300 candidates can get 80% off Exam AB-650 (beta) with code AB-650SkyClub on or before August 18, 2026. Beta exam rescoring begins when the exam goes live, with final results released approximately 10 days later. For more details, read Creating high-quality exams: The path from beta to live. Stay tuned for general availability of this Certification in October 2026. Additional information For more Certification updates, read our recent blog post, Microsoft Credentials roundup: June 2026. Follow our credentials news on The Skills Hub Blog as we roll out additional new Certifications in August and September 2026. Join our Microsoft Worldwide Learning SME Group for Credentials on LinkedIn for beta exam alerts and opportunities to help shape future Microsoft learning and assessments. Explore Microsoft Credentials on AI Skills Navigator.New Microsoft Certified: Multi-Agent AI Solutions Expert Certification
The future of AI isn’t a single model, it’s intelligent systems of agents working together. Are you ready to build them? As organizations move from standalone AI features to complex, multi-agent systems, the skills required are evolving fast. Organizations are no longer just experimenting, they’re deploying production-scale agent ecosystems that must be orchestrated, governed, and optimized. Introducing the Microsoft Certified: Multi-Agent AI Solutions Expert Certification, that validates your ability to design, build, and operate scalable, production-ready multi-agent AI solutions. To earn it, you’ll need to pass Exam AI-500: Designing and Implementing Multi-Agent AI Solutions (currently in beta). Is this the right Certification for you? This Certification focuses on the practical skills needed to architect and develop multi-agent AI solutions by using Microsoft Foundry and Azure. It validates the ability to: Design logical architecture for multi-agent solutions. Build and integrate tool ecosystems. Implement multi-agent orchestration. Evaluate, optimize, and monitor multi-agent solutions. Secure, govern, and deploy multi-agent solutions. Candidates for this Certification are expert-level practitioners with subject matter expertise in designing, building, and optimizing scalable, production-ready, multi-agent AI solutions and workflows. They lead the end-to-end lifecycle of AI solution development from architecture and design through deployment and optimization and are prepared to progress from building individual components to owning complete AI systems architecture and delivery. These professionals collaborate closely with developers, machine learning engineers, platform engineers, data scientists, and business stakeholders to translate complex business and technical requirements into production-ready, multi-agent solutions They should have experience developing AI and machine learning based solutions, deploying agentic systems in production environments, and orchestrating agent logic by using Microsoft Foundry. They should also be proficient in Python. Additionally, they need experience developing solutions that include Azure compute, network, storage, and data services. They should be familiar with open-source frameworks and standards, including Microsoft Agent Framework, Model Context Protocol (MCP), retrieval-augmented generation (RAG), and LangGraph. Certification requirement: To earn the Microsoft Certified: Multi-Agent AI Solutions Expert (AI-500) certification, candidates must also earn the Microsoft Certified: Azure AI Apps and Agents Developer Associate (Exam AI -103) certification. AI-103 provides the foundational Azure AI Foundry and agent development skills that AI-500 extends through advanced multi-agent solution design, orchestration, governance, and optimization. Ready to prove your skills? Take advantage of the discounted beta exam offer. The first 300 people who take Exam AI-500 (beta) on or before August 5, 2026, can get 80% off. To receive the discount, when you register for the exam and are prompted for payment, use code AI500Wabash. This is not a private access code. The seats are offered on a first-come, first-served basis. As noted, you must take the exam on or before August 5, 2026. Please note that this discount is not available in Turkey, Pakistan, India, or China. How to prepare Get ready to take Exam AI-500 (beta): Review the Exam AI-500 (beta) page for training resources, exam registration, and other details. The Exam AI-500 study guide explores key topics covered in the exam. Connect with Microsoft Training Services Partners in your area for in-person offerings. Need other preparation ideas? Check out Just How Does One Prepare for Beta Exams? Ready to get started? You can take Certification exams online, from your home or office. Get the details in Online proctored exams: What to expect and how to prepare. Remember, only the first 300 candidates can get 80% off Exam AI-500 (beta) with code AI500Wabash on or before 8/5/2026. Beta exam rescoring begins when the exam goes live, with final results released approximately 10 days later. For more details, read Creating high-quality exams: The path from beta to live. Stay tuned for general availability of this Certification in October 2026. Additional information For more Certification updates, read our recent blog post, Microsoft Credentials roundup: June 2026. Follow our credentials news on The Skills Hub Blog as we roll out additional new Certifications in July, August, and September 2026. Join our Microsoft Worldwide Learning SME Group for Credentials on LinkedIn for beta exam alerts and opportunities to help shape future Microsoft learning and assessments. Explore Microsoft Credentials on AI Skills Navigator.AI at every career stage (start, grow, lead)
Explore how AI can support your work at every stage of your career. Learn practical ways to build confidence early on, scale your impact midcareer, and strengthen strategy and coaching as you lead. In the final post in this series on AI for business users, Microsoft Senior Learning Manager Ashley Masters Hall shares insights on how AI skills can help you grow your confidence and impact, along with your career. In my first post in this series, Bringing AI fluency to every corner of the organization (even yours!), I highlight why AI fluency matters for every business user and how to get started. In the second, AI prompting tips & tricks for everyday tasks, we look at simple approaches that help non-technical roles work smarter. In this final post, we explore how AI can support you at every stage of your career, from your first role to senior leadership. I hear two questions all the time from people at very different stages: Is it too early for me to care about AI? and Is it too late for me to learn this? My answer to both is simple: no. AI is useful at every career stage. What changes is how you use it to support your work and grow into what’s next. If you’re about to graduate (or new to your role) Early in career can feel like learning a language: acronyms, tools, processes, and all the unwritten rules. AI can help you ramp up so you don’t have to pretend to know everything. I recommend: Translate what’s unfamiliar. Prompt Microsoft Copilot: Explain [this] like I’m new to the team. What are the key terms, and what do they mean? Great for onboarding docs, meeting notes, product overviews, and customer histories. Prepare with confidence. Prompt Copilot: I have a 1:1 with my manager. Offer some smart questions to ask based on [these] goals. Helpful for performance conversations, project kickoffs, and cross‑team introductions. Draft quickly and cleanly. Prompt Copilot: Turn my [notes] into a clear email/status update with a short subject line. Works well for weekly updates, follow‑ups, summaries, and stakeholder recaps. How it looks: After a big meeting, Maya, a new program coordinator, has pages of notes and a calendar of follow‑ups but needs an organized summary: She drops her notes into Copilot and asks for decisions made, action items with placeholder owners, open questions, and a draft summary in her team’s format. Putting AI fluency into action, she prompts: What might I have missed? Maya acts on the response and verifies the final summary before she shares it with the team. Instead of spending hours turning notes into structure, she can spend her time making sure the structure is right. That’s a good trade. If you’re midcareer (more scope, more stakeholders, more “Can you just…?”) In midcareer, your work becomes less about your individual output and more about how work moves across teams. You’re interacting with different roles, finding how best to meet goals, and strengthening relationships. AI can help you scale the tasks that quietly take up your week. For example: Audience‑specific rewrites. Prompt Copilot: Create a version of [this] update for each audience: leaders, teammates, stakeholders. Helpful for keeping your message consistent so you don’t have to write each version from scratch. Decision memos and tradeoffs. Prompt Copilot: Draft a decision memo with risks, options, and what we need to decide, based on [these] details. Useful for product choices, budget requests, vendor evaluations, and roadmap changes. Workflow cleanup. Prompt Copilot: Turn [this] messy intake process into a simple template. Great for streamlining workflows across teams. How it looks: Jordan manages marketing for a product launch and is juggling competing requests: He prompts Copilot: Draft three campaign brief options, including assumptions, target audience, success metrics, and items to confirm. From the results, Jordan picks the best draft and adds details that Copilot didn’t have, such as brand voice and stakeholder nuance. He then prompts Copilot: Generate an FAQ for field teams and a launch timeline. Jordan refines everything into a final draft. The win isn’t that AI “did the work.” It’s that Jordan got to a usable plan fast, freeing him to focus on the judgment calls. If you’re an experienced professional (strategy, coaching, and making the system better) Later in your career, your job shifts from producing artifacts to shaping direction, like spotting patterns, coaching people, and reducing friction so teams can execute. AI can support that work in ways we often overlook, such as: Stress‑test strategy. Prompt Copilot: What would have to be true for [this] plan to fail? Helpful for pressure‑testing assumptions before you commit. Scenario thinking. Prompt Copilot: If [X] happens, what’s the second‑order impact on customers/teams/timelines? Useful for planning cycles, risk reviews, and leadership discussions. Coaching support. Prompt Copilot: How can I help my team members turn [these] rough drafts into clear narratives? Practical for freeing up more time for the coaching only you can do. How it looks: Rina runs business operations for a large organization. Instead of sifting through 40 pages of retrospectives: She uses Copilot to cluster themes, prompting: What slowed teams down? Which patterns repeat? Which decisions keep getting revisited? Rina turns the response into a short pre-read, with top themes, supporting examples, and a proposed set of focus areas. Then she brings leaders together to decide what should change in the coming quarter. How to pick your next AI skill (without spiraling) If you’re wondering where to start, I recommend that you check out AI Skills Navigator, the agentic learning space from Microsoft that brings together AI-powered skilling experiences and credentials to help you build career skills. AI Skills Navigator is structured, creating pathways based on what you do, so you don’t have to bounce between random tutorials. Map Copilot skilling to your career stage: Early in career. Copilot foundations, prompting basics, and quality checks. Try How to Prompt Copilot to Make Presentations in PowerPoint. Midcareer. Role‑based scenarios, decision memos, and stakeholder communication. Check out Copilot in Outlook helps you craft more impactful communications. Experienced. Strategy, responsible adoption, coaching, and scaling patterns. Explore The AI Shift: What Leaders Need to Know About AI Agents.
What’s new in AI Skills Navigator: April 2026
Learn about recent improvements to playlists, skilling sessions, partner discovery, and credentials, based on what learners and team leaders told us they need to build skills with confidence. Priya Vaidyanathan is a Director of Product Management at Microsoft, where she leads the product management teams that are building AI Skills Navigator. AI Skills Navigator exists to help people and teams build confidence with AI skills, through clear paths, practical learning, and guidance that fits real work. It brings together trusted training and credentials from Microsoft, LinkedIn, GitHub, and other sources in one connected experience so you can build skills without bouncing between tools. Since launch, we’ve kept improving the experience, guided by your feedback, based on how people are learning and working. For the deeper story behind why we built AI Skills Navigator, read The moment AI skilling stopped being optional—and started being personal. Today’s post focuses on what’s changed, including recent updates designed to make it easier to guide teams, learn at your own pace, and stay on track. Skilling playlists: From small teams to organization-wide rollouts Skilling playlists in AI Skills Navigator turn priorities into clear, role-aligned paths, helping teams focus on the skills that matter for their day-to-day work. They’re often where people begin, especially when team leaders want learning plans tied to real projects and role-specific responsibilities. The process for creating playlists is now more transparent and efficient. As you draft a playlist with AI support, you can see why specific content is suggested, apply your own judgment, and adjust as needed. You can also select multiple AI-suggested prompts in a single interaction, reducing manual effort while maintaining control of what your teams learn next. We’ve improved visibility for playlist owners and team leaders. Real-time progress tracking helps you see how your learners are moving through a playlist, spot where support may be needed, and adjust plans as priorities change. Progress view in an AI Skills Navigator skilling playlist. We’ve also enhanced skilling playlists to support wider rollouts, whether you’re guiding a small team or coordinating learning across an organization. We made this change based on your feedback, so playlists can scale with your needs—without added complexity. Skilling sessions: Now more flexible for real workdays Skilling sessions in AI Skills Navigator feature content written by human experts and presented by AI, with in-session support from the Skilling Coach agent. You can ask questions at any time, and the coach prompts quick knowledge checks and reflections to reinforce what you’re learning. Recent improvements give learners more control over how they explore the sessions, including the ability to move forward or rewind, adjust playback speed, and save progress to resume later. These updates make it easier for learners to fit deeper learning into their workdays, while continuing to build skills they can apply right away. Pro tip: Find skilling sessions in Explore content, where you can browse and filter by learning type. Microsoft Training Services Partners: Tailored, local training support From individual skill‑building to supporting your team’s goals, Training Services Partners can help. They can tailor instructor-led training to your needs, from role- and project-specific enablement to local language delivery and flexible formats (in-person, virtual, or blended). The new Training Services Partners directory in AI Skills Navigator makes it easier to discover Microsoft partners that can support human‑led training, customized programs, and organization‑wide skilling initiatives. This directory helps organizations move from individual learning to coordinated, supported skilling, without having to search across multiple sites or programs. New content and credentials: All in one place AI Skills Navigator brings training and credentials together so you can follow a clear path and build toward readiness. The Microsoft Certified: AI Transformation Leader and Microsoft Certified: AI Business Professional Certifications are now generally available, giving individuals and organizations a way to validate applied capability—not just knowledge. With training and credential prep in one place, you can focus on getting ready and proving what you can do. Earn an AI Transformation Leader or AI Business Professional Certification. As we continue evolving the content and credentials available in AI Skills Navigator, our focus remains the same: making it easier for you to find relevant learning, build skills that stick, and demonstrate progress over time. We’re listening—and continuing to improve AI Skills Navigator These updates reflect your feedback: you’ve asked for clearer ways to guide teams with playlists, more flexible learning through skilling sessions, and more support when you want it, through partner discovery and credentials in one place. We’ll keep evolving AI Skills Navigator based on how people learn, how teams work, and what we hear from individuals and organizations. And we’ll continue sharing updates as the experience grows. Sign in to get started with AI Skills Navigator. Sign in to AI Skills Navigator to see what’s most relevant for you, and pick up right where you left off. Stay tuned for more updates soon. In our next Inside AI Skills Navigator post, we’ll take a closer look at skilling playlists.
Strengthen your research workflow with generative AI
Stop guessing at prompts. Use research-ready templates that guide Microsoft Copilot toward clearer reasoning, better drafts, and transparent methods—so your work is faster, sharper, and credible. Darcy Ogden, Ph. D., leads the academic researcher programs for Microsoft Global Skilling. A computational scientist and former professor of geophysics, Dr. Ogden has a passion for teaching and using new technology to accelerate research. Guidance on using generative AI in research often lands at the extremes; it’s either overly optimistic or far too cautious. Most researchers, students, and professionals working with data or analysis know that the reality sits somewhere in between: all models are useful but fallible tools. As researchers, we ask: What was this model designed to do? Where does it perform well? Where does it fall short? What assumptions are we making when we use this model? Those same questions apply to generative AI. Understanding how these systems work and how they shape the outputs you receive can help you decide when to rely on them and when to adjust. We’re all trying to figure out how best to work with generative AI, and there’s no simple, universal answer. But, in many cases, the work of research itself creates opportunities to apply generative AI thoughtfully and effectively. Explore our new guide for researchers As part of the latest Microsoft efforts to support graduate students, postdocs, and faculty aiming to use generative AI for research, we’re happy to share a new learning resource, The Academic Researcher's Guide to Generative AI. In this guide, we bring together recent insights and practical frameworks for considering generative AI as a research instrument. The guide’s purpose is to support researchers in asking well‑formed questions about the tools they use and in reflecting on the role that those tools play in research processes. Bring generative AI into your research methodology This new guide provides research-aligned approaches to prompting in Microsoft 365 Copilot Chat, along with frameworks for prompt development, testing, and documentation. Further, it includes ready-to-adapt prompting use cases for research scenarios. The following brief examples reflect the kinds of tasks that these prompts support: Research synthesis. Summarize the key arguments across these sources and note where the evidence conflicts. Writing support. Rewrite this paragraph for clarity and precision while keeping the original meaning. Data analysis. Explain the assumptions behind this statistical method and list situations where it may fail. We’ve also included guidance on crafting quality prompts in Copilot, with techniques that can help reduce ambiguity and surface the reasoning behind responses. These approaches for prompting can deliver tailored, well-structured outputs suited for research purposes. The following examples highlight the types of instructions that researchers can use to make the most of Copilot prompts: Surface assumptions. State assumptions and show reasoning before providing the final answer. Limit sources. Use only the attached sources and flag any gaps or uncertainties in the evidence. Structure responses. Follow this structure: Context → key points → limitations → questions to be considered next. This guide treats the use of generative AI like other models or tools you use in your research. Like them, generative AI has no native understanding of fields of study, datasets, or research constraints. The guide introduces an approach to using generative AI as a visible, documentable part of academic research. It treats interactions with Copilot as part of your methodology: something to record, review, and refine as you move through your research. Put the guide to work As generative AI becomes more common across academic and professional environments, the question is no longer whether to use it but how to use it well. As the models grow more capable, the challenge is how to use them in ways that support learning, integrity, and transparency. We developed this guide to help researchers and students engage these tools in ways that strengthen, rather than diminish learning and scholarly judgment. We invite you to read The Academic Researcher's Guide to Generative AI. Use it as a starting point, adapt the frameworks to your own discipline and workflow, and contribute feedback about the guide so that we can continue to evolve this resource alongside the field itself.
The moment AI skilling stopped being optional—and started being personal
Find out what it takes to build AI skills with confidence, individually and as a team. Kavitha Radhakrishnan is a General Manager in Microsoft Global Skilling, where she leads the teams creating AI‑first, learner‑centered experiences that help people and teams build skills they can apply at work. Sunday night. The week hasn’t started yet, but the questions already have. A leader is scrolling through AI headlines, trying to keep up with the constant changes. Every day there’s a new tool, a new capability, a new prediction about how work is changing. And it is. By Monday morning, the pressure isn’t theoretical; it’s sitting on a packed calendar and a team that’s already running hot. Everyone’s saying, “We should be using AI,” but nobody’s quite sure what that means for this team, this week. Elsewhere, an employee is watching coworkers use AI with speed and confidence. They want to keep up without feeling exposed for what they don’t know yet. The gap isn’t intelligence; it’s psychological safety and a clear starting point. And then there’s the learning leader who’s had the “training participation” conversation a hundred times, but now the question is sharper. It isn’t How many people finished?, but What changed in the way they work? The bar has moved from awareness to application. None of these people are asking for more content. There’s plenty of that. They want a path that respects their time, fits their role, and helps them build confidence, both individually and as part of a team. Enter AI Skills Navigator AI is moving faster than most of us can track. The problem is figuring out what to do next. Leaders don’t want to stitch together five different tools. People don’t want another long course about AI. Teams are looking for skilling that fits into real work. That’s the gap AI Skills Navigator is built to address. AI Skills Navigator brings role‑based, practical skilling into a single experience, so individuals and teams have a clear starting point, a sense of direction, and ways to see progress as they go. Instead of an endless catalog, it offers guided paths that respect time, align to real responsibilities, and make it easier to turn learning into action. At its core, it’s designed to help turn skilling into execution—progress that people can feel and leaders can point to. How AI Skills Navigator fits your flow Alex, a team manager, is trying to set the team up for success. The team is kicking off a new project with clear goals, tight timelines, and a mix of responsibilities across roles. Everyone is expected to use AI more effectively, but “go learn AI” isn’t a plan. Sending people to a long list of links doesn’t help either. So Alex turns to AI Skills Navigator. Instead of gathering content from multiple places, Alex uses AI Skills Navigator to design a skilling playlist for the team. The conversational AI experience helps him identify what his team really needs. The playlist is grounded in what the team is actually working on and intentionally structured around the project goals and role-specific responsibilities. It brings together different content formats on purpose: short sessions for core concepts, practice where it matters, and optional deeper dives for people who want to explore further. It’s not about forcing everyone through the same experience. It’s about giving the team a shared path forward, while respecting different roles, learning needs, and preferences. Sam, an experienced marketing manager on the team, doesn’t have to figure out where to start. A link from Alex lands in their inbox, and the intent is clear: this is what matters for our work right now. As Sam works through the playlist, they move naturally between different ways of learning. The structure makes it easy for them to focus without feeling boxed in. For a topic that matters most to the project, Sam chooses a skilling session. A video sets the context, and the Skilling Session Coach AI agent is there along the way—ready to clarify a concept, answer a quick question, or pause to check understanding. Sometimes there’s a short quiz to help Sam confirm that they’re learning and making progress. People are juggling meetings, messages, deadlines, and more. Attention spans are shorter, and learning often happens in brief moments between tasks. AI Skills Navigator is designed for that reality. Sometimes Sam feels like listening instead of reading. An AI‑generated podcast turns dense material into something easier to absorb. When time is tight, an AI-generated summary helps Sam catch up in minutes, without losing the thread. From Alex’s perspective, there’s a simple view of how the team is progressing—enough to see who’s moving forward, where people might be getting stuck, and when it’s time to adjust the plan. Together, these moments add up. Skilling sessions provide depth when it’s needed. Podcasts and summaries offer flexibility when attention is limited. Skilling playlists keep everything connected, so learning feels purposeful rather than scattered. By combining structured paths with flexible ways to learn, and pairing AI support with human expertise, AI Skills Navigator helps individuals and teams build confidence, apply skills, and make progress together. How we’re building confidence, together AI Skills Navigator is designed to help people and teams build confidence, not by adding more noise, but by providing guidance that fits real work. It brings together training content from sources that many people already know and trust, including Microsoft Learn, LinkedIn, and GitHub, and connects it into structured paths that make it easier to start, go deeper, and keep moving forward. Whether you’re learning on your own or designing skilling for a team, the goal is the same: turn learning into progress that you can feel. And this isn’t static. We’ll continue evolving AI Skills Navigator based on how people learn, how teams work, and the feedback we hear from learners and leaders along the way. We’ll share updates regularly, including new content, new capabilities, and what’s coming next, so you can stay current as the experience grows. After you've signed in, you can get started with these options. (Pro tip: To expand the navigation pane on the left, try selecting it.) Create a skilling playlist for your team. Try the “Explore Microsoft 365 Copilot Chat” skilling session. Access all available AI Skills Navigator training content. Review Microsoft Credentials in AI Skills Navigator.
Four Best Practices for Leading Through AI Adoption
Actionable guidance for building AI leadership skills—drawn from real customer conversations. Chris Henley is a Microsoft Trainer, part of a community of professionals at Microsoft Global Skilling, working with customer and partner leaders to help them build the skills required to drive their organization’s AI strategy. Where AI conversations are shifting for leaders I’ve been working with executives and company leaders for several years, and it feels like the AI conversation is finally shifting from “What can AI do?” to “How do we move forward with AI in a way that creates real value?” That shift often shows up as organizations move beyond isolated pilots and begin integrating AI across the business: into processes, employee experiences, customer engagement, and innovation. Microsoft refers to this broader shift as Frontier Transformation, where AI becomes a strategic priority and changes how intelligence operates inside the organization, not just which tools people use. From what we’re hearing from executives, one thing becomes clear: AI adoption rarely comes down to a single decision. Progress unfolds through small experiments, sharper priorities, and measured results that reveal what’s working and what to do next. What seems to help drive adoption progress isn’t a rigid plan. It’s returning to a few best practices that show up regularly in real business discussions: Reframe: recognizing that AI is not just another tool rollout, but a shift in how work is structured, how decisions are made, and where intelligence shows up across the business. Focus: identifying a specific business priority where AI can create measurable value, rather than spreading experimentation across too many disconnected pilots. Assess: taking an honest look at whether the organization is ready to move forward across data foundations, leadership alignment, team capabilities, and governance. Commit: selecting a defined AI initiative, assigning ownership, and establishing how success will be measured over a clear timeframe. These aren’t meant to be a strict sequence. Leaders often move between them as they clarify strategy, prioritize investments, and decide what to do next. The following sections take a closer look at how each of these best practices show up in real leadership discussions about AI adoption. 1. Reframing how to think about AI in the organization AI often begins framed as a tool rollout, but leaders I work with frequently find that this narrows the discussion too quickly. Many have shared that the most useful shift happens when the question moves beyond “How do we use this?” to something broader: “Where could AI change how our business actually works?” I was recently delivering a training session with one of our customers, a consulting firm that had rolled out Microsoft 365 Copilot. Early wins were familiar: faster emails, cleaner summaries, better documentation, and the team was energized. But in one session we paused and asked a tougher question: if AI is now part of the business, should client reporting and analysis still look the same? The focus shifted from incremental productivity gains to rethinking how insights were created and delivered. You start to see where work should be redesigned—not just sped up. The technology remained the same, but leadership perspectives evolved. 2. Choosing where to focus before moving forward Another common situation we’re seeing is companies trying to use many different AI solutions in hopes of finding an area where AI might have an impact. Pilots are running across the business, but their intended business impact is unclear. Activity isn’t the same as progress. Momentum usually picks up when leaders choose one area where AI clearly connects to a meaningful business outcome. The experience of one of our customers, a global automotive manufacturer, is a good example of this. Rather than trying to use AI everywhere, they pinpointed a bottleneck that was slowing down their accounting workflows. So, they applied AI document intelligence to that problem first. That targeted focus reclaimed thousands of hours of manual work. You can see what’s working and what it tells you about your organization. Investment conversations become easier, because you’re not funding “AI.” You’re funding a business outcome. 3. Assessing organizational readiness as aspiration meets reality One of the most useful shifts happens when leaders pause to examine how ready the organization actually is for AI. It’s easy to assume you’re “AI-ready,” but that closer look often reveals where ambition is moving faster than capability. In one executive discussion, a leader paused and said, “I thought this was an IT implementation. I didn’t realize how much AI would change how my leadership team operates.” That moment shifted the conversation from infrastructure and deployment to the real question: Was the organization ready to operate differently with AI? The team shifted its attention to making better decisions, ownership, and leadership readiness. You can see whether AI is tied to business priorities, whether teams have enough hands-on capability, whether the culture supports experimentation and learning, and whether risk and accountability are clearly defined. 4. Committing to shape your AI strategy through action Here’s something I see a lot: Leadership teams often agree AI is important, but progress stalls when no one defines the next step or who owns it. In one session, an executive team had been reviewing use cases for months. Mid-meeting, someone finally said, “We’ve been talking about this for a while. What have we accomplished?” That simple question immediately shifted the conversation from exploration to ownership. The team aligned on one outcome and how they’d measure it, and that’s when momentum finally started. The goal isn’t to have the perfect strategy upfront. It’s to commit to a focused, informed, effort that starts to transform your business in a meaningful way. The efforts that gain traction are usually clearly defined, have senior leadership behind them, and everyone on the team is aligned on how to measure progress. Time to roll up the sleeves and get to work In the end, leading through AI adoption isn’t about getting everything right from the start. What I see work most often is leaders building the skills to navigate it as they go, and learning how to judge where AI really matters, where to begin, and how to move forward through focused action. If you’d like a practical way to start that process, check out our new Develop AI Leadership Skills guide for a structured starting point. It’ll help you think about your organization's next steps with AI and it’ll give you a clear framework for prioritizing actions and moving ahead confidently.How equitable AI skilling takes shape inside a global organization
Discover how women across Microsoft are growing their AI fluency. AI is rapidly becoming a baseline skill at work, shaping how we write, analyze, build, collaborate, and lead. Yet access to AI skill-building isn’t always evenly distributed. AI equity research by Randstad found that 71% of AI-skilled talent are men and 29% are women: a 42 percentage-point gap. This same research found that women are 5% less likely than men to be offered AI skilling opportunities. That combination points in the wrong direction, as AI becomes table stakes. Closing this gap is not just an equity imperative; it’s also how organizations build the broad capabilities they need to perform at their best. Skilling at the frontier That’s why the idea of frontier firms matters: these are places where people and AI work together every day and where learning how to work with AI is a core job skill built into normal workflows and reinforced with training. At Microsoft, AI skilling isn’t reserved for a few teams; it’s part of everyday work across roles. People learn by using the tools on real tasks, taking advantage of training opportunities, sharing what works, and helping colleagues build skills, too. When learning is baked into how everyone works, access is broader by default and skilling becomes more equitable. Spotlight on women building with AI To see what this looks like in real life, we invited women across Microsoft to share how AI skilling is changing their work. What came back was a set of more than 50 powerful stories from women who upskilled, worked through real constraints, and built new workflows that made their work better. We’re highlighting three of those impactful stories. Melody Chen Melody Chen, a Senior Finance Manager, shared her experience turning curiosity into real operational gains. As AI accelerated across the industry, she didn’t wait for a perfect “finance AI” playbook but instead started experimenting inside the work she already owned. She built her Microsoft 365 Copilot skills through experience and practice, earned the Microsoft Certified: AI Business Professional Certification, and then translated what she learned into lightweight solutions that remove everyday friction for her team: an onboarding agent in Microsoft Copilot Studio so new hires could self-serve answers, simple Power Automate workflows to reduce manual follow-up, and repeatable Copilot prompts in Excel that clean and format data consistently for recurring reporting. Those small builds added up, saving her hours of work and, more importantly, creating a team habit of asking, “What can we simplify?” Her takeaway for readers is that you don’t need to be highly technical to lead with AI: pick one workflow, make one improvement, and let small wins compound into confidence and momentum. Ramya Gangula Ramya Gangula is a Senior Cloud Solution Architect who works with healthcare customers, where “almost right” isn’t good enough. As AI became more real in day-to-day work, customer conversations moved from exploring possibilities to planning for safe rollout. So she built up her experience, developed her skills through real implementations, and backed them up with multiple Certifications, including GitHub Copilot, Microsoft Certified: DevOps Engineer Expert, and Microsoft Certified: Azure AI Engineer Associate. That work helped her to design secure, enterprise-ready AI architectures and to guide teams past one-off demos into patterns they could actually reuse in production. Her takeaway is simple: pick a real problem, learn by doing, and write down what works so others can move faster. As she put it, “Imposter syndrome is common, especially in fast-moving fields like AI, but confidence grows through action. Invest in skilling, apply what you learn, and trust that your perspective matters. Women are not just adapting to AI—we are shaping how it’s responsibly used.” Aja Hall Aja Hall is an early-career Product Designer at Microsoft who entered tech through the Microsoft Leap program without a college degree. In just a few years, she has become Chair of the Black at Microsoft (BAM) Puget Sound chapter and a driving force behind AI initiatives, building custom agents in Copilot and designing AI toolkits that help her team work faster. Aja contributed to standout projects, including an AI wireframing Figma plugin that speeds Azure design ideation, an Azure accessibility hub that centralizes guidance for inclusive and scalable experiences, and an accessibility-driven Copilot agent tailored for dyslexic and neurodivergent users. Her creativity and leadership earned hackathon accolades three years in a row. Now, she’s spearheading the 2026 BAM AI Innovation Challenge, where she mentors colleagues and fosters a culture of AI innovation and upskilling. Leading by example and actively advocating for women to build AI fluency, Aja is helping to close the tech skills gap and empower more voices in the AI space. The throughline for successful skilling What stands out across these stories is not how advanced anyone was at the start, but how quickly their abilities grew and compounded after they began. Some women pursued structured learning paths and earned Microsoft Certifications. Others learned through practical application, using AI to synthesize information, draft first passes, reduce manual work, and turn ambiguity into next steps. Across roles and tool sets, the throughline is that progress accelerates when learning is practical, supported, and connected to daily work, because that’s where people can test, refine, and build judgment in real time. Let’s invite everyone in The future of AI at work will be shaped by organizations that treat learning as infrastructure and address access as a design principle. In those environments, women are not left to find their way on their own. Everyone is invited in, supported, and sponsored to build, apply, and lead with AI, and their input shapes what responsible adoption looks like. The voices in these stories are a reminder that transformation is rarely one single dramatic leap. More often, it’s a series of supported steps that compound over time. And this is what frontier organizations do. Take the first step toward upskilling yourself or your team with AI Skills Navigator.AI prompting tips & tricks for everyday tasks
Simplify your day, with these practical habits that make the most of Microsoft Copilot. In this second blog post in a series of three, Microsoft Senior Learning Manager Ashley Masters Hall shares her practical perspective on the small prompting choices that can make a big difference in Microsoft Copilot results. I’ve been at Microsoft nearly six years now—long enough to see AI go from interesting experiment to everyday tool. In my first post of this series, Bringing AI fluency to every corner of the organization (even yours!), I explored what AI fluency looks like in real life and why it matters for every role. This post picks up where that one left off, as I share my tips & tricks for practical prompting in Microsoft Copilot to make everyday tasks easier. In my experience, most people don’t need more AI. They need fewer weird moments with AI. You know the ones—a confident answer that’s not true, a draft that sounds like a toaster manual, or a summary that technically covers the content but misses the thing you actually care about. So I pulled together a short list of simple habits that can make Copilot more useful and reliable. These tips & tricks help me (and a lot of my colleagues) get better results right away, without spending all day crafting prompts. Five practical prompting tips Five practical prompting tips. Tip #1. Treat Copilot like a teammate, not a vending machine. Begin with this mindset shift: Copilot isn’t necessarily the source of all truth—it’s a brainstorming partner. When I treat Copilot like a collaborator, my results immediately improve. I ask it to think with me, not for me. This is key. A few prompts I use constantly in Copilot include: Give me three options, not one. List your assumptions and what you need to verify. What are the risks or ways this could be misleading? These questions pull the model out of “confident answer” mode and into “help me reason through this” mode. Tip #2. Take prompt templates and edit them like you mean it. I keep a “favorite prompts” doc open all the time—not because prompts are precious, but because “past me” did “future me” a favor. Here are a few templates that work across roles. Copy them, and fill in the brackets to make the prompts your own: Clean summary. Summarize the text below in 4 bullets for [audience]. Include: decisions, risks, open questions, and next steps. Then propose 3 follow‑up questions I should ask. Rewrite with constraints. Rewrite this to be clear and human. Keep it under [number] words. Use a friendly, direct tone. Don’t add new facts. Notes → plan. Turn these notes into a plan with milestones, owners (use placeholders), risks, and dependencies. Output as a table. Brainstorm with trade‑offs. Generate 10 ideas for [goal] tailored to [persona]. For each idea, include a 1-sentence rationale + 1 downside. Decision support. Create a decision matrix comparing [A] vs [B] vs [C] across cost, time, risk, and impact. Before you start, ask me 3 clarifying questions. The magic isn’t the template—it’s the editing. The more specific you make it, the better the output can be. Tip #3. Add a 60‑second quality‑check loop. My rule is “If I’m going to share it, I’m going to check it.” The good news is that you can do that quickly—and you can ask Copilot to help. A few prompts I use for that final pass: Self‑critique. What are 5 ways this could be wrong, incomplete, or misleading? Missing info. What information do you need to be confident in this? Force structure. Put this into a table with columns: claim, evidence, confidence, and what to verify. Sensitivity scan. Flag anything that might be confidential, policy‑sensitive, or risky to share externally. This loop takes just one minute (or even less) and can save hours of cleanup later. Tip #4. Practice on something straightforward. If you’re trying to build confidence in Copilot and in your own skills, don’t start with your highest‑stakes deck. Begin with the things you do all the time—even something not related to work, like planning a meal or a weekend trip. When you start the day with your to‑do list, pick one thing that shows up regularly—the one that makes you think, “There’s got to be a way to spend less time on this.” Then take a few minutes with Copilot to make that task easier. Try this simple routine: Share the task with Copilot and ask, How can I use Copilot to reduce the amount of time I’m spending on this daily task? Tighten the prompt by providing additional clarity and requesting a format. Do the quality-check loop. Save the prompt that worked. That’s it. You’re building practical AI fluency and making your day a little easier. Tip #5. Borrow good prompts from other people. I asked a few colleagues to share their favorite Copilot prompts. Here are some that can change the way you lead with AI: Build my voice. If you’re looking to guide Copilot to reflect your personal voice and style in outputs, try this: Look at the emails and Teams messages I’ve sent in the last two weeks. Use them to create a personal brand voice document I can use to guide Copilot. Triage my inbox. If you’d like help focusing and prioritizing tasks, try this: Summarize my unread emails in a table. Include: Topic | Summary | Action Items | Follow-Up. If I’m directly mentioned, make the topic bold. Daily AI briefing. If you need a quick, reliable snapshot to help you stay current, try this: Compile the key AI news from the last 24 hours into a structured table. Include: Short Topic, Brief Summary, Suggested Impact, Source Name, and Link. Prioritize the entries by potential impact. Include reputable sources across a diverse range of media outlets. Exclude less reliable sources, and avoid overrepresentation of any single outlet. Explain my job simply. If you need details to help colleagues understand your responsibilities, including what you handle, how you contribute, and when to loop you in, try this: Can you summarize my job in layman’s terms? These are great starting points—and they’re even better after you tune them to your role. Four proven prompting tricks Now that you’ve tried those tips, put these tricks to work for clearer, more dependable results from your prompts. Four proven prompting tricks. Trick #1. Iterate and refine. Don’t stop at the first version. Ask for variations, define constraints, and request a fresh angle. Iteration helps you surface better ideas and guide Copilot toward clearer, more dependable output. Trick #2. Ask Copilot what you should have asked. After you get a response that hits the mark, ask Copilot, What should I have prompted you originally to get this in one shot? This one changed everything for me. It’s a fast way to sharpen your prompting habits and learn to guide Copilot with more precision. Trick #3. Let Copilot ask you questions. Sometimes I don’t know which details matter. So I start high level with, Ask me any clarifying questions you need me to answer. It’s a simple way to give Copilot the context to deliver a result that fits your real intent, uncover what matters, and fill in gaps you didn’t even realize were there. Trick #4. Give your prompts the foundation they need. Good prompts aren’t complicated, but they do have a few elements that make them work better. When you’re writing a prompt, include your goal, the context, the source you want Copilot to use, and your expectations for the output. The more of these elements that you include, the better the result can be. Bring your prompts to life The more you prompt, the better you prompt. It’s a skill you build through regular practice each day. Over time, you start to recognize what makes a prompt work—treating Copilot like a teammate, making prompt templates your own, adding a quality-check loop, tightening your instructions, iterating with purpose, and using the right mix of goal, context, source, and expectations. Those small habits add up to clearer drafts, faster cycles, and fewer of those “Why did it write that?” moments. If you want a structured way to practice, check out Craft effective prompts for Microsoft 365 Copilot in AI Skills Navigator. Using real-world scenarios and examples, learn how to craft effective and contextual prompts for different tasks and how to use built-in features in Copilot to get better results faster. And, while you’re in a module in AI Skills Navigator, try the Summarize module or Turn module into podcast feature. Cool, right? Introduction to the “Craft effective prompts for Microsoft 365 Copilot” learning path in AI Skills Navigator. Up next Stay tuned for my third and final post in this series, AI looks different depending on where you are in your career. Let’s talk about that. In the meantime, happy prompting!
