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.People are the difference: The learning culture behind Investec’s workforce transformation
Can a company of 8,000 support AI and cloud adoption across its workforce? Discover how Investec's learning culture and people-first mindset supported employee upskilling. If you’re leading AI adoption, whether across a team or an entire company, you may be facing challenges, especially when transformation needs to happen at scale. You may even be wondering, “Where do I begin?” In this post, we’ll explore how Investec approached cloud and AI adoption across a workforce of approximately 8,000 employees, along with principles that may help inform your own efforts. First, some background: Investec is an international bank and wealth manager with a regional focus in Southern Africa and the United Kingdom, complemented by a presence in Continental Europe, the Channel Islands, Dubai, India, Mauritius, Switzerland, and the United States. The organization’s culture emphasizes entrepreneurship and the potential of its people, an approach that would also shape its adoption of cloud and AI technology. Supporting the human side of technology change Here’s how things started. Five years ago, Investec made the strategic decision to undertake a significant cloud transformation and, to date, began migrating its digital estate to Azure. But, to begin that process, the company recognized that adopting new technology required helping its workforce understand new tools, concepts, and ways of working. One immediate hurdle the firm faced? Many employees were being asked to operate in a world very different from the one in which they had built their expertise. “There will be some people who are filled with uncertainty at the replacement of familiar technology,” notes Paul Haupt, People and Organization Consultant at Investec, “or fear that skills they mastered over the last ten, twenty, thirty years are no longer relevant." With increasingly complex technologies entering the workplace, Investec remained steadfast in its belief that supporting the human side of change is more important than ever. "The pace at which the stuff is moving, we have to respond to it,” Haupt explains. “And we really need to help people along this journey." Supporting the human side of technology change Here’s how things started. Five years ago, Investec made the strategic decision to undertake a significant cloud transformation and, to date, began migrating its digital estate to Azure. But, to begin that process, the company recognized that adopting new technology required helping its workforce understand new tools, concepts, and ways of working. One immediate hurdle the firm faced? Many employees were being asked to operate in a world very different from the one in which they had built their expertise. “There will be some people who are filled with uncertainty at the replacement of familiar technology,” notes Paul Haupt, People and Organization Consultant at Investec, “or fear that skills they mastered over the last ten, twenty, thirty years are no longer relevant." With increasingly complex technologies entering the workplace, Investec remained steadfast in its belief that supporting the human side of change is more important than ever. "The pace at which the stuff is moving, we have to respond to it,” Haupt explains. “And we really need to help people along this journey." Moving through fear toward growth To address employees’ concerns about how technology may affect their roles, Investec framed the change as an opportunity for growth and encouraged employees to explore new ways of working. This approach again underscores the firm’s core belief: technology does not define Investec; human capability does. “Our people are our proposition,” Haupt states. “We need to equip them so they can use their imaginations to unlock the full potential of technology.” The first step was to create a foundation of technical skills. Investec turned to Microsoft Certifications and required all its Digital & Technology staff, nearly 2,000 people, to earn the Microsoft Certified: Azure Fundamentals certification by passing Exam AZ-900, helping establish shared cloud literacy across the organization. By making learning a visible priority from the top down, Investec reinforced that professional development is not a one-time event but instead an ongoing part of employees’ career development. Providing continual career development Beyond foundational skilling, Investec sought to create opportunities for continued development. The organization expanded access to Microsoft Certifications and other learning opportunities, including the Microsoft Organizational Skilling program. One inspiring example is Lukhanyo Dzedze, Investec Software Engineer in PCT Continuity CLM Onboarding, who describes himself as a lifelong learner. "I wanted to become a leader within the space,” Dzedze notes, “and I wanted to be able to lead innovation and contribute more at a higher level.” Dzedze has earned six certifications, with more in progress, and credits the people-first mentality at Investec in helping him advance his career potential. "One important thing about Investec is the ease of access to people,” Dzedze says. “You can go talk to anybody, and they'll give you their two cents.” Preparing all staff for an AI-powered future It’s important to note that Investec's focus on ongoing professional development isn’t limited to technical staff. In 2026, the company publicly announced a deployment of Microsoft Copilot for its entire workforce, making it available to approximately 8,000 employees. To support adoption, Investec aimed to help people understand what AI can do and build confidence in using it. “You can give everyone a tool,” states Haupt, “but what we’re interested in is people demystifying AI and experiencing its potential.” Investec encouraged training focused on practical use cases, along with short sessions led by employees to help colleagues understand and apply Copilot in their work. Building deeper role-aligned expertise Beyond a common understanding of cloud technologies and AI, Investec remains keen to help all its employees—not just technical staff—build deeper expertise aligned to their roles. "Moving into the next phase, we want to be quite deliberate about role-specific certification,” Haupt explains, “that as you build your craft, you build your skills closer to your day-to-day work.” The road to adopting new technologies at scale is rarely a straight line, but Investec teams are navigating it together. “We can either pontificate about the future and not start, or we can learn as we go. And so, we’re taking the ‘learn as we go’ route,” says Haupt. Investec’s learning culture, combined with skilling resources and employees’ ambition and imagination, is helping its workforce prepare for what comes next. As Haupt says, “That, we believe, is where the magic will happen.” Conclusion and next steps for you and your team Investec’s experience demonstrates the importance of addressing the human side of cloud and AI adoption. By establishing foundational knowledge, supporting continued development, and bringing learning closer to employees’ roles, Investec sought to help its people approach technology change with greater confidence. Investec’s approach illustrates how a people-first learning culture can support transformation efforts. To move your own transformation forward, explore AI Skills Navigator, an AI-powered skilling experience that helps you build the skills you need whether you're investing in your own development or building capability across your organization. If you’re engaged with a Microsoft account team, contact them to explore additional skilling opportunities.Five ways to build early-career readiness in the AI era
AI is changing the work that once helped early-career employees build judgment. Explore five practical ways talent leaders can redesign readiness and development. The question every talent leader is being asked AI can now support many of the tasks that once helped early-career employees build judgment through repetition and feedback. For learning and talent leaders, that raises a practical question: How should development change when producing a first draft is easier, but evaluating it is more important? In one Microsoft business, we addressed this challenge by redesigning the first-year experience for early-career employees around five principles: role-specific mentoring, protected learning time, deliberate practice of human skills, purposeful in-person connection, and evidence-based readiness. The results were encouraging: 91% reported accelerated readiness by week 12, meaning they understood how the business works and what their role owns. 99% felt confident executing their core customer activities independently within six months, cutting the ramp to independent contribution from 12 months to six. Here are the five elements participants and program leaders identified as most important, along with ways to apply each one in your organization. Why the development model needed rethinking Until recently, much of the learning early in a career happened through the work itself: the background research nobody thanked you for or the first-draft deck that came back covered in red. The output was not the point. Judgment was built one unglamorous task at a time. AI now supports or accelerates many of those tasks. Early-career employees may have fewer opportunities to build judgment by producing the work themselves, even as they are increasingly expected to evaluate AI-generated drafts. Recent research reinforces the challenge. McKinsey notes that many of the routine tasks through which early-career professionals traditionally developed judgment are increasingly being automated, creating new challenges for developing the next generation of talent. PwC found that AI-exposed junior roles are seven times more likely to demand traditionally senior skills such as leadership and strategic thinking. The implication is direct: If the work no longer teaches judgment on its own, development must be designed to teach it intentionally. Five actions that can guide your talent strategy 1. Assign role-specific mentors The principle: Task-specific guidance and access to real work matter more than assigning a buddy or general career mentor. Early-career employees need someone who can explain why a customer question was framed a certain way in a specific meeting. How we applied it: Each early-career hire was matched from day one with a mentor on their team, in the same role, with at least two years of experience. Managers selected mentors for their interest in developing others, ability to dedicate time, willingness to open real work for shadowing, and capacity to provide role-specific guidance. In the post-program survey, role mentor impact scored 97%, making it the highest-rated element of the first-year experience. Mentors also reported learning from the experience. Explaining tacit judgment made it more visible, while mentees’ fluency with new AI tools prompted new ways of working. What leaders can do: Select mentors for role proximity and willingness to share real work, not seniority or availability. Protect mentoring time and recognize the experience as development for mentors, too. 2. Protect learning time explicitly The principle: Learning time is easily displaced by immediate business priorities. Unless it is defined and defended, it disappears. How we applied it: Early-career employees received 20 hours of protected learning time per week for the first eight weeks, supported by a clear cadence for coaching and job shadowing. The precise number was specific to our program. The transferable principle is that the protection must be explicit, not assumed. What leaders can do: Define the time, manager expectations, and learning activities in writing, then put them on the calendar from week one. Assign someone to protect that time when business priorities compete. 3. Create space to rehearse human skills The principle: If early-career roles require judgment, empathy, and collaboration from the start, employees need a low-stakes place to practice. Human skills develop through doing, feedback, and reflection, not through content alone. How we applied it: We created a readiness lab with small peer groups where employees practiced skills in a deliberate progression from self, to team, to customer. Each stage paired a concept with live practice and structured peer feedback. What leaders can do: Identify the two or three human skills an early-career role needs first. Create a recurring, low-stakes setting where employees can practice those skills and receive structured feedback before applying them with customers. 4. Use in-person time intentionally The principle: Some outcomes are difficult to achieve through virtual interaction alone. The value of in-person time often appears in relationships, trust, and shared identity. How we applied it: After several weeks of virtual learning, we brought the cohort together in person. The experience strengthened cross-market peer relationships, built trust with senior leaders, and created a shared cohort identity that supported collaboration across teams. What leaders can do: Treat in-person time as a design choice, not a perk. Schedule it after participants have built a foundation virtually, involve senior leaders, and focus the agenda on relationships and shared problem-solving rather than content that could be delivered online. 5. Make readiness an evidence-based decision The principle: Readiness should be demonstrated through evidence, not inferred from time served. The question is not how long someone has been in a role, but what they can do. How we applied it: Beginning in month two, early-career employees and their role mentors shared accountability for real business outcomes. Employees built a readiness portfolio with work examples demonstrating customer-facing competence, independent execution, cross-team orchestration, judgment and risk management, and ownership. Managers and mentors could coach and make readiness decisions using evidence rather than a date on the calendar. What leaders can do: Define the capabilities that signal independence, ask employees to collect real work examples against them, and use that evidence in coaching and readiness decisions. What we learned: readiness has two halves Early-career readiness requires both knowledge and applied judgment. Information, role expectations, foundational skills, and credentials can be organized and scaled. Judgment develops through real work, observation, practice, feedback, and reflection. The five actions in this article help create those conditions. AI may change the tasks through which early-career employees once developed judgment, but it does not remove the need for judgment. Leaders now have a responsibility to create those experiences intentionally and strengthen the leadership bench for the future. Start with one early-career role in your organization. Identify a task AI now supports, name the judgment that task once helped build, and decide where employees will practice that judgment instead. Ready to start learning? Explore Microsoft AI Skills Navigator for AI-powered learning experiences that helps you build the skills you need whether you're investing in your own development or building capability across your organization. Sources McKinsey & Company, “Building expertise in the age of AI: Who trains the next generation?” PwC, “2026 Global AI Jobs Barometer,” 15 June 2026.
What’s new in AI Skills Navigator: October 2026
Discover new ways to find relevant learning, stay engaged, prepare for credentials, and build skills in the flow of work with AI Skills Navigator. The way we build skills is changing quickly. Learners want guidance that reflects their roles and goals, fits into busy schedules, and helps them move from learning to practice to validated skills. Team leaders and learning professionals need experiences that connect employees with the right next step without adding complexity. We’re continuing to improve AI Skills Navigator to support those needs. The latest updates make learning more interactive, help you discover relevant content, connect credential preparation, and make progress easier to follow, whether you’re learning independently or helping others build skills. Stay engaged with skilling sessions Sometimes learning takes more than watching a video. You may want to ask a question, test your understanding, or work through a concept before moving on. Skilling sessions combine expert-created video content with AI-powered coaching and knowledge checks, helping you learn something new in a short amount of time and gradually build your knowledge. During a skilling session, an AI-powered coach can answer questions grounded in the material, while chapters and quick knowledge checks help you navigate and reinforce what you’ve learned. Recent updates add voice dictation and identify sessions that use an AI-generated presenter or voice. The catalog spans business and technical topics, from prompting, marketing, and customer service to agent development, DevOps, data, cloud migration, and security. A skilling session with video learning and AI-powered coaching Learn from people you trust AI Skills Navigator now offers expanded ways to explore learning from Microsoft experts and industry leaders. Expert Voices brings their perspectives into the experience, helping you understand not only what to learn, but why it matters. You can also explore technical content around goals such as AI-assisted coding and building or orchestrating agents, making it easier to start with what you want to accomplish and find learning that can help you get there. Follow a path with skilling playlists When there’s a lot to learn, figuring out where to start—and what to tackle next—can be part of the challenge. Skilling playlists are structured paths that help you build skills around a goal or need. You can use AI to create one tailored to your goals, build a custom playlist when you already have specific content in mind, and now start with a featured playlist curated by Microsoft experts. Find a path that fits what you want to learn by exploring Featured playlists, where you can search, sort, filter, and preview paths by role, product, topic, or level. See how skilling is progressing across your organization After you create and assign a skilling playlist, Your playlists gives you a snapshot of learner progress for that individual path. For a broader view, Reporting brings together insights across the organization. Team leaders can see learner onboarding and engagement, playlist adoption and progress, popular learning content, and the skills employees are developing through that learning. Together, these views make it easier to understand how learners are engaging with skilling and where additional support may be useful. We’ll take a closer look at playlists, reporting, and organizational skilling with AI Skills Navigator in upcoming feature posts. Organizational reporting with insights into playlist activity and progress Prepare for credentials with a clearer path Credentials give you a way to validate what you know and can do. Microsoft Certifications and Microsoft Applied Skills can help you demonstrate your proficiency as you build expertise or prepare for what’s next. AI Skills Navigator brings more of that credential journey together. You can now see earned Certifications and Applied Skills in your profile, making it easier to keep track of your achievements in one place. If you’re preparing for a certification, featured playlists, practice assessments, and additional resources bring recommended steps into a more guided experience. For example, the Microsoft Certified: Azure Fundamentals page brings preparation resources together so you can follow a structured path, check your readiness, and identify what to revisit before the exam. As you work toward a credential, the learning along the way counts too. Your learning streak tracks consecutive weeks when you complete at least one module, video, or skilling session. Visit the For you page to see your current streak and personal best, then see how far you can take it. Bring skilling into the flow of work with Learning Agent For people and organizations using Microsoft Copilot, there’s a new way to bring skilling into the flow of work. Learning Agent in Microsoft Copilot is a personalized upskilling experience that uses role information, work context, skills signals, and organizational learning sources to help you find a relevant next step. With AI Skills Navigator now part of the experience, Learning Agent can also recommend modules, learning paths, curated videos, skilling sessions, and Microsoft Credentials. Supported content can open alongside your Copilot conversation, with progress carried into AI Skills Navigator so you can continue later. Start with a question in Copilot, get guidance in the moment, and keep building skills over time. Read the full story: AI Skills Navigator is now available in Microsoft Copilot. Make AI Skills Navigator yours Together, these updates give you more relevant ways to find learning, stay engaged, prepare for credentials, and keep building momentum. Your AI Skills Navigator profile helps shape that experience around you. Add or update details such as your role, goals, and interests so AI Skills Navigator can connect you with learning that’s more relevant to the skills you want to build. For organizations, these updates create stronger ways to guide skilling across roles and goals, with insights to understand how learners are engaging and progressing along the way. Explore AI Skills Navigator to find the next step that fits you or your team. Byline: Vaishali Kartik is a Principal Product Manager at Microsoft, leading the team that develops AI Skills Navigator learning, practice, and credential capabilities. Tanmay Dileep Bhagwat is a Principal Product Manager at Microsoft, leading the team that develops AI-powered and organizational capabilities for AI Skills Navigator.How EQ Bank connects AI learning, innovation, and governance
Explore EQ Bank’s practical framework for turning AI learning into responsible, measurable business impact. If you’re building an AI-ready workforce, employees may be starting with different levels of knowledge and confidence, but the organization still needs to find a shared path forward. In financial services, that challenge comes with an added requirement: Experimentation must coexist with oversight, security, risk management, and regulatory compliance. EQ Bank, a Canadian digital financial institution, addressed this challenge with an enterprise-wide model that connects four elements of AI workforce readiness: role-based learning, practical application, peer support, and responsible governance. You can use the same four-part framework to assess where your organization is strong and where employees may need more support. Build a common foundation for AI growth EQ Bank created its uniquely named AI Belt Programs to give employees clear, role-appropriate pathways for developing AI expertise: White Belt: AI fundamentals, responsible AI, and foundational Microsoft 365 Copilot skills. Blue Belt: AI agents, Copilot Studio, and solution development. Black Belt: Microsoft Foundry, AI operations, GitHub Copilot, and solutions designed to scale securely. Executive Belt: Strategic and responsible AI adoption for leaders. The programs provide multiple entry points and a path for continued growth. Today, 906 colleagues are building foundational AI literacy through White Belt, 242 technical specialists are advancing solution development through Blue Belt, 130 participants are developing advanced solutions through Black Belt, and 67 leaders are shaping AI strategy through Executive Belt. The lesson for other leaders is to meet employees at different levels of readiness while connecting everyone through a common learning framework. Turn AI learning into measurable business impact EQ Bank’s AI Centre of Enablement helps employees apply their skills through hackathons, immersive learning, agent-building opportunities, and an AI Innovation Hall of Fame that recognizes use cases developed through the Belt Programs. An example of AI innovation is HR Bestie, which gives employees and managers access to published HR information. Since launch, it has supported 16,214 conversation sessions and answered 4,433 questions, with 79.5% of sampled answers rated as good quality. By supporting routine inquiries autonomously, the agent can reduce reliance on manual support and help HR focus on more complex work. It also shows learners how trusted organizational knowledge can support everyday tasks. Another example is TestGenie, an implemented AI agent that demonstrates measurable productivity and capacity gains in software quality engineering. The agent turns user stories and acceptance criteria into review-ready test cases, while Quality Engineers retain responsibility for review and approval. With AI assistance, the number of user stories processed per day increased from approximately 8 per Quality Engineer to as many as 40, contributing to an estimated 80% to 90% reduction in manual test-authoring effort. The example shows how Quality Engineering teams can combine AI assistance with human oversight and accountability. Sustain adoption through community and recognition Applying new skills is important, but sustaining adoption also requires opportunities for employees to share their work and learn from their peers. EQ Bank supports that exchange through innovation events, including a pitch-style showcase where colleagues present AI solutions to a panel of leaders. Recognition encourages employees to share what they have built while giving colleagues concrete examples of responsible AI application. For example, EQ Rovo Agent, helps employees find and summarize information stored across EQ Bank’s project-management and knowledge systems. The agent respects employees’ existing access permissions and requires their confirmation before making changes, demonstrating how practical AI innovation can incorporate human oversight. EQ Bank’s AI Champions community helps extend that learning across the organization. Champions receive early exposure to emerging technologies, participate in specialized enablement, share use cases, and help successful practices spread across teams. Govern experimentation responsibly As employees created more agents and solutions, EQ Bank established an AI Governance Board that tracks every use case from submission through impact assessment to completion. Each request moves through an AI Impact Assessment lifecycle, creating a clear, auditable trail. AML Watchtower, an AI-assisted transaction-monitoring solution, shows how EQ Bank’s approach can support speed and accountability. Developed by an AI Champion, the solution helps teams translate anti-money-laundering risk scenarios into explainable, governed detection logic. The agent owner estimates that AML Watchtower reduced development time by approximately 20% to 30% and documentation effort by 70% to 80%, accelerating delivery while improving consistency and readiness for review and approval. The project demonstrates how governance capabilities can develop alongside technical solution-building skills. For regulated organizations, the takeaway is to design enablement and governance together. Clear ownership, centralized visibility, and an established review process can support experimentation while keeping solutions aligned with risk, security, and compliance standards. Partnering with Microsoft to accelerate practical AI readiness Microsoft support helped EQ Bank adapt learning to different roles and levels of technical readiness. That support included specialized training, technical workshops, GitHub Copilot enablement, and hands-on sessions focused on practical workflows and agent development. In less than six months beginning in March 2026, broader AI Centre of Enablement programming supported 1,192 participants across 49 events, including nine customized sessions delivered with Microsoft for 161 participants across executive and business functions. Conclusion and next steps for you and your team EQ Bank’s experience offers a practical framework for connecting AI learning with application, community, and governance. Its integrated approach is already supporting faster solution development, greater employee support capacity, and more efficient delivery of governed AI solutions. For business, HR, learning, and IT leaders, four principles stand out: Build the foundation: Create role-appropriate pathways for employees at different levels of readiness. Connect learning to application: Give employees opportunities to address real business challenges and measure results. Scale through community: Use champions, recognition, peer learning, and employee examples to expand adoption. Govern experimentation responsibly: Establish ownership, visibility, review processes, and safeguards as solutions scale. Use these principles to identify where your organization is strong and where employees need more support. Then connect those needs to relevant learning experiences and opportunities for practical application. Ready to start learning? Explore Microsoft AI Skills Navigator for AI-powered learning experiences and credentials designed to build employee expertise and accelerate organizational transformation.AI-powered + human-focused: Powering workforce transformation
How Microsoft combines the reach and personalization of AI with human expertise to turn workforce learning into business impact. When we think about AI transformation, we have to start with employees. There is simply no way around it. You can deploy the technology and set ambitious goals, but if people do not have the confidence, skills, and support to use AI in their work, transformation will not happen. Employees are the ones who turn AI from an experiment into real business impact. Microsoft’s skilling approach brings together two complementary ideas: AI-powered skilling and human-focused learning. AI-powered experiences extend reach, personalization, and access. Human-focused experiences provide context, trust, dialogue, and connection. The point is not to choose between AI and people. It’s to combine their strengths in a portfolio that can meet different workforce needs, organizational goals, levels of readiness, and learning preferences. AI-powered learning experiences AI can make skilling more personal, relevant, and continuous. It can help employees identify the right place to start, shape learning around their roles and goals, adapt content to different needs and preferences, and provide support as they build and apply new skills. For organizations, AI makes it possible to guide learning at scale, align development with workforce priorities, and understand progress over time. AI Skills Navigator brings these capabilities together in one connected, AI-powered skilling experience for individuals and organizations. Learning shaped around individual and team needs Employees bring different goals and levels of experience to learning. AI Skills Navigator can help meet those differences at scale: employees can use AI to create skilling playlists around their own goals, while team leaders can create playlists tailored to their teams’ needs. In skilling sessions, an AI-powered coach can answer employees’ questions, help them work through concepts, and reinforce understanding with knowledge checks. Together, these experiences help organizations provide trusted, relevant learning while supporting each employee’s progress. Relevant skilling across roles The AI skills needed to lead, market, sell, build, secure, or manage technology are different. AI Skills Navigator supports roles across the organization with ready-to-use featured playlists curated by Microsoft around roles, topics, or outcomes. AI-generated podcasts and summaries also let employees transform written content into new modalities to easily revisit key ideas in a format that fits their preferences and available time—making role-relevant learning easier to access across the workforce. Guiding and measuring workforce skilling Organizations need more than access to learning. AI Skills Navigator enables leaders to assign skilling playlists across teams, business groups, or the entire company and track progress over time. Organizational reporting provides visibility into participation, playlist adoption, progress, popular content, and the skills employees are developing. These insights help leaders understand engagement, reinforce workforce priorities, and focus continued development where it matters most. Human skill building and delivery AI transformation demands more than technical skills. We pair technical and role-based skilling with enduring human capabilities such as curiosity, courage, compassion, creativity, collaboration, and judgment. These capabilities help employees ask better questions, navigate ambiguity, make responsible decisions, collaborate effectively, and adapt as roles evolve. Human-led learning reinforces them through context, dialogue, practice, and connection—experiences that complement the reach and personalization of AI-powered learning and prepare people not only to use AI tools, but to exercise judgment, adapt their work, and thrive as roles continue to evolve. Microsoft offers scalable skilling solutions designed to meet the needs of organizations at every stage of their AI transformation. In select situations where deeper support can accelerate progress, Microsoft works directly with customers on tailored skilling plans and private onsite or virtual delivery, combining role-based learning with human expertise, context, and practice to help teams move from AI intent to confident execution. One portfolio, two complementary approaches AI-powered and human-focused learning solve different parts of the same workforce challenge. AI-powered experiences make learning easier to personalize, discover, and scale. Human-focused experiences add the expertise, trust, context, and connection that people need to turn knowledge into confident action. Organizations can use on-demand, AI-led learning where flexibility and reach matter most, then add human-led delivery where deeper context, practice, or alignment is needed. The mix can vary by role, population, initiative, and stage of readiness. The next step: Explore AI Skills Navigator to find role-based learning, build a path that fits your goals, and start developing the skills to turn AI into impact—one skill at a time.Announcing the Microsoft AI Power Days Agent-a-thon winners
The February 2026 Microsoft AI Power Days Agent-a-thon brought together builders from around the world with one shared goal—to turn ideas into working AI agents that solve real problems. From first-time builders to advanced practitioners, the creativity, ambition, and practical impact on display exceeded all expectations. Over the course of the Agent-a-thon, participants explored what’s possible with agentic AI—learning, building, and iterating using Microsoft agent-building tools. The result? A powerful showcase of agents designed to streamline work, unlock insights, and reimagine how work gets done. Congratulations to the 12 Agent-a-thon grand prize winners! The winning submissions were selected based on innovation, usability, and impact. Innovation. These newly created agents go beyond replicating demos. Each winner tackled a real problem in a novel way—applying agentic AI creatively to scenarios we hadn’t seen before. Usability. Winners demonstrated that their agents are not only functional but also genuinely loved by users. Builders tested their ideas, gathered peer or user feedback, and iterated—showing clear evidence of why people would continue to use these agents in real-world settings. Impact. Every winning agent delivered measurable value. Whether saving meaningful amounts of time, improving efficiency or productivity, increasing revenue potential, or enhancing customer satisfaction, these agents showed clear qualitative or quantitative results. Together, these submissions represent what great agent-building looks like in practice—and how those agents can translate into practical impact right away. Grand prize winners To recognize their achievement and support their continued journey as an AI builder, each grand prize winner will receive a Microsoft Surface Pro. Winners (listed in alphabetical order) Agent names Built with David Díaz Merino LicitaAI Microsoft Foundry Angelo Fabrizio Dileo Aura | Buyer Persona Agent Agent Builder in Microsoft 365 Copilot Steve Glasspool Book Genie Agent Builder in Microsoft 365 Copilot Sunny Hwang NZ Free Course Finder Agent Builder in Microsoft 365 Copilot Rohit Jagadale CR Sentinel Agent Microsoft Copilot Studio Andrejs Karpovs EU-AI-Act-Compliance-Assessment Microsoft Foundry Salma Kudapali Internal Mobility & Skills Opportunity Broker Microsoft Foundry Arianna Manchia PackAgent Microsoft Copilot Studio Pablo Santisteban Fernández LeadMasterAI Microsoft Copilot Studio Sai Sirisha Return-to-Work Agent Builder in Microsoft 365 Copilot Abhishek Tarafdar Governed Compliance Intelligence System (GCIS) Microsoft Foundry Apoorva Vairagi Compliance Intake Orchestrator Microsoft Copilot Studio Whether built with Agent Builder in Microsoft 365 Copilot, Microsoft Copilot Studio, or Microsoft Foundry, each agent reflects a clear job to be done and a strong understanding of how agentic AI can create real-world benefits. Winner spotlight One of the most rewarding aspects of the Agent-a-thon was hearing directly from participants about what they built and the difference that their new agents can make. "I built a four‑agent pipeline on Microsoft Foundry that monitors Spain’s public procurement platform, scores tenders against our company profile, and generates a populated proposal document end‑to‑end in under two minutes... The multi‑agent architecture mapped to how BD [business development] teams actually work: one agent per role." —David Díaz Merino, AI Solution Architect, creator of LicitaAI This sentiment captures what the Agent-a-thon is all about—lowering the barriers to building, empowering experimentation, and helping people to realize that they can be capable AI creators, regardless of their starting point and skill level. Again, congratulations to all of our winners, and thank you to everyone who joined us in exploring what’s possible and driving innovation, one agent at a time. Sign up for the next Agent-a-thon If you missed this one, register for the next Microsoft Agent-a-thon, which begins on May 6, 2026, across three time zones, making it easier for builders everywhere to participate, learn, and create together. Whether you’re new to building agents or ready to deepen your existing expertise, this is your opportunity to take advantage of immersive learning, experiment with Copilot Studio and Foundry, and turn bold ideas into working agents. To get ready for the event, check out the training opportunities in AI Skills Navigator, our agentic learning space that brings together AI, cloud, and security training into one seamless, connected skilling experience to help you build career skills.This Cybersecurity Awareness Month, build skills to secure the future
Build practical cybersecurity skills to help protect yourself and your environment while preparing for an AI-powered future. Cybersecurity is relevant to every role. Whether you build applications, manage data, lead a team, protect systems, or simply use digital tools, the decisions you make can contribute to a stronger security posture. October is Cybersecurity Awareness Month, we’re offering a variety of practical ways to enhance your security skills. Explore role-based learning on AI Skills Navigator, learn from security experts and Microsoft MVPs, join free events around the world, put your knowledge into practice through realistic challenges, and work toward credentials that validate what you can do. New videos and other content will drop throughout October, so keep checking back to see what’s new. Why participate? You can choose experiences that fit your goals and build cybersecurity skills you can put to work right away. Here are a few ways to take part: Find the right curated learning for your role Explore curated playlists designed around the cybersecurity skills most relevant to your role. Security professionals: Secure Your AI Workloads Layer by Layer. Learn to protect AI workloads across identity, data, models, applications, and runtime layers using Microsoft security and AI platforms. IT professionals: Defend Your Tenant Environment. Learn to secure identities, devices, data, and collaboration tools across Microsoft 365 and strengthen your organization’s security posture. Developers: Ship Faster, Break Less with GitHub Code Quality. Learn how GitHub Code Quality helps you find issues earlier, improve confidence in AI-generated code, and ship faster. Data professionals: Assign Ownership and Control Over Your Sensitive Data. Explore best practices for protecting sensitive information, managing access, and preventing unauthorized exposure. Business users: Recognize and Shut Down Every day Digital Threats. Build practical awareness of common digital threats and learn how to make safer decisions at work. Executives and business leaders: Evolve your Security Strategy in the Age of AI. Explore learning that connects cybersecurity strategy with resilience, governance, trust, and AI adoption. Students and career changers: Start your Cybersecurity Career Here. Build foundational cybersecurity knowledge and explore career opportunities to help you prepare to launch a successful cybersecurity career. October spotlight Defend the Tenant Challenge Put your technical skill and strategic judgment to the test in the Defend the Tenant Challenge Competition. Step into a realistic Microsoft 365 environment to harden your tenant and provide business impact. Compete to win a visa gift card – up to $10,000 total prize pool available.* Want to know more before you start? Join our live session on October 19 to learn what to expect, get your questions answered, and explore materials to help you prepare. Register today *Terms and conditions apply, contest rules available here. Cyber Genius Build practical security skills with Cyber Genius, a Microsoft-designed learning experience for security professionals. Microsoft Security Solution Engineers walk you through real-world attack scenarios and show how defenders investigate and respond with Microsoft security tools. Practice hands-on, continue the conversation with your dedicated AI Fellow, and prepare for a Microsoft Applied Skills assessment to earn an industry-recognized credential. Explore Cyber Genius More ways to learn and connect Role-based playlists are only one place to begin. Throughout October, you can also learn from experts, join live events, connect with peers, work toward recognized credentials, and discover new content as it becomes available. Get practical answers through Ask a Cybersecurity Pro. Four new episodes on data classification, data governance, identity security, and AI security will drop throughout October. Learn live at free cybersecurity events delivered by Microsoft Training Services Partners around the world. Connect with the Microsoft Cybersecurity Pros community to ask questions, learn from others, and continue the conversation. Work toward Microsoft Applied Skills and certification pathways that validate what you can do. Ready to get started? Choose the pathway, event, challenge, or credential opportunity that interests you most, and dive in. With new ways to learn, practice, and connect throughout October, you can keep coming back to build skills and explore what’s next. Whether you want to develop safer digital habits, deepen your technical expertise, or help your organization prepare for what’s next, there’s a way for you to build skills during Cybersecurity Awareness Month. Join us, keep learning, and help build a more secure future. Build skills. Earn credentials. Help secure the future.Skill vs. will: What leaders must solve for AI adoption
If you’re leading AI adoption, you may be asking why trained employees aren’t changing how they work. In this post, you’ll learn how to distinguish skill gaps from motivation and system barriers, so you can choose the right response and build an AI-ready workforce. William Nations is a Senior Learning & Skilling Manager at Microsoft. He is also the creator of The AI-Ready Executive, a thought-leadership video series that helps senior leaders translate AI ambition into practical decisions about their organizations, teams, and business value. AI adoption requires capability and the conditions to act Many organizations begin AI adoption by providing access and training. Both are necessary, but neither guarantees that work will change. Training builds individual capability, but whether that capability translates into changed work depends on the organizational conditions that enable employees to apply it. Research supports this distinction. Microsoft’s 2026 Work Trend Index surveyed 20,000 workers across 10 countries and examined AI’s reported impact on outcomes such as creativity, work quality, collaboration, and the ability to do higher-value work. The research found that culture, manager support, and talent practices account for more than twice the AI impact of individual skill. In other words, individual capability matters, but the conditions surrounding employees can have an even greater influence on whether AI improves their work. So, given these findings, where should leaders begin? A useful starting point is to examine two factors: whether people have the skills to use AI effectively and the willingness to apply those skills. I call this the “skill versus will” framework. Leaders should also examine whether the surrounding system enables people to act. These are the three key questions: Skill: Can people perform the work appropriately and effectively? Will: Do people see the value, feel confident, and intend to change? System: Do workflows, incentives, leadership behavior, governance, and access make the change possible Use skill and will as the first diagnostic lens but examine the surrounding system before concluding that an employee lacks motivation. Before applying the framework, confirm both access and accessibility. Access means employees have the necessary AI tools, licenses, devices, and permissions. Accessibility means those tools, learning experiences, and surrounding workflows are designed so that all employees can use them, including those who use assistive technologies. Both are prerequisites across every part of the framework. Consider what Microsoft’s 2026 Work Trend Index calls “blocked agency.” Ten percent of workers surveyed had strong AI skills but lacked the organizational conditions to use them. These employees completed the training. They knew the tools. But no one modeled the behavior, the workflow wasn’t redesigned, or experimentation felt unsafe. That is a system barrier that can suppress employees’ willingness or ability to act, not a skill gap. So, before adding another course, campaign, or mandate, leaders need to understand what is blocking change and choose a response that addresses the actual barrier. Skill gaps require practice tied to real work AI fluency involves more than knowing product features or writing prompts. Employees need to recognize when AI is useful, provide relevant context, evaluate outputs, protect sensitive information, and know when human judgment must lead. Those skills vary by role. A finance leader, seller, developer, and human resources professional may use the same technology, but they make different decisions, work with different information, and manage different risks. Generic learning can build awareness, but role-based practice is what builds capability. The strongest learning experiences start with a real problem that teams care about solving. Practicing on meaningful work gives people a reason to build and apply new skills, while creating an opportunity to improve the workflow itself. Teams can compare results with clear quality standards and identify where AI saves time, strengthens thinking, or requires additional review. This approach builds skill and confidence while generating evidence of real business value. Low motivation often reflects the system, not the employee When employees hesitate to use AI, leaders may interpret the behavior as resistance. However, hesitation can be a rational response to the work environment. Employees notice what managers model, what the organization rewards, which mistakes carry consequences, and whether experimentation feels safe. A capable employee may avoid AI if the process requires duplicate work, the approval path is unclear, or the expected benefit is vague. Employees may also have valid questions about quality, data protection, compliance, accountability, and whether AI improves the work. These concerns should inform the adoption strategy rather than automatically be treated as a lack of enthusiasm. Leaders can strengthen will and remove system barriers by making the purpose practical: Start with the business problem. Name the priority workflow and define what better work should look like. Establish clear boundaries for responsible use while preserving appropriate space for experimentation and creativity. Explain where people remain accountable. Align workflows, incentives, and recognition with the behavior leaders are asking employees to adopt. In Episode 4 of The AI-Ready Executive, Alan Murray discusses why skepticism is not always a skill deficit and why leaders should understand employees’ concerns before prescribing more training. Diagnose the barrier before choosing the response Before diagnosing skill or will, confirm that employees have access to the tools, learning experiences, and workflows they need to participate. Once access is confirmed, leaders can use the framework to identify the primary barrier and select an appropriate response: The purpose is to identify what employees need to succeed, so leaders can avoid prescribing more training when the system is the barrier or increasing pressure when people need practice and support. Five actions to improve AI adoption Start with the business problem. Name a priority workflow where speed, quality, insight, or customer experience matters, and define what better work should look like. Model responsible AI use. Show how leaders use AI, evaluate results, and decide when not to automate. A Microsoft study of 1,800 workers found that manager modeling was associated with a 17-point increase in reported AI value and a 30-point increase in trust in agentic AI. 1 Set permission and appropriate boundaries. Define where teams can experiment, what information they can use, what review is required, and which decisions remain with people. Provide enough governance to support responsible use without unnecessarily limiting experimentation and creativity. Align workflows, incentives, and recognition. Recognize teams that remove low-value steps, improve decisions, strengthen customer outcomes, or responsibly spread an effective practice. Only 13 percent of AI users in the 2026 Work Trend Index said they were rewarded for reinventing how they work. 2 Measure workflow outcomes. Track whether the work changed, quality improved, time was saved, or effective practices spread. Course completion and tool usage can provide useful signals, but they should not be the final measures of success. Start here: one priority workflow Start with one priority workflow. Confirm that employees can participate fully, diagnose whether the primary barrier is skill, will, or the surrounding system, and choose the response that addresses it. Once the needed capabilities are clear, use AI Skills Navigator to find learning aligned with the skills your team needs to build and apply. Sources 1. Microsoft. “Research drop: Empowering managers for an AI-first future.” Microsoft Community Hub. View source 2. Microsoft. 2026 Work Trend Index Annual Report: Agents, Human Agency, and Opportunity, p. 29. View reportAI at work: Learn, build, and grow with Microsoft Copilot and agents
Explore new Copilot modes, learn the basics of AI FinOps, and understand what’s next for agents. Microsoft is reimagining Copilot, bringing familiar tools together with new capabilities that help people and organizations build, customize, and scale AI across work. For a full overview of what’s new, read the Official Microsoft Blog. To help you make the most of what’s new, start with the new AI at Work playlist in AI Skills Navigator. This playlist brings together practical learning to help you choose and use the Copilot mode that best fits your role and your work. AI Skills Navigator brings Microsoft learning into one place, helping individuals and teams find curated learning by role or goal, create custom learning plans, and track team progress. One Copilot, new ways of working with AI The AI at Work playlist starts by helping you understand how the Copilot app’s three new capabilities support connected ways of working with AI: Home is your new starting point, where Chat and Cowork come together. Chat answers questions, explores ideas, and completes everyday tasks. Cowork collaborates on complex, multi-step work. Code extends solution-building to every knowledge worker. Autopilot is a persistent, proactive, and personal agent that keeps working even when you’re not. The playlist helps you recognize when and how to use each mode to move from an idea or need to an outcome. Learn by doing with the AI at Work playlist The new AI at Work playlist in AI Skills Navigator shows you how to use the new Copilot in three practical areas. Navigate the new Copilot modes. Learn how the Copilot app supports different kinds of work, from answering questions and collaborating on deliverables to building solutions and delegating ongoing tasks. Balance usage, cost, and value. Explore AI financial operations (AI FinOps) concepts for business professionals, administrators, and builders, including responsible consumption and managing usage and cost. For a deeper look, read the FinOps for AI blog. Understand how agents are evolving. Build familiarity with Copilot Studio and agent harnesses. Explore how agents can support increasingly connected, multi-step work. For the full news, read the Copilot Studio blog. Put one skill into practice How do you make the most of the new Copilot in your role? Choose a task you know well. Define the outcome, give Copilot useful context, choose whether to collaborate, build, or delegate, and review the result with judgment. As Copilot evolves, that cycle can help your skills evolve with it. Explore the AI at Work playlist in AI Skills Navigator and choose one new skill to put into practice today.
