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.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.Verifying private network paths before you deploy: a what-if for Azure private endpoints
A common Azure scenario: public access is disabled on a PaaS resource, a private endpoint is created, and the connection shows as Approved. The application still times out. Resource-level checks such as what-if report nothing wrong, because every resource is configured. What they do not verify is whether the name the application uses resolves to the private address from the application's VNet. In my experience, most private connectivity failures come from DNS configuration, not from firewall rules. What must be true For a workload in a VNet to reach a PaaS service privately: The private endpoint connection is Approved. The matching privatelink private DNS zone exists. The zone is linked to the VNet that performs the lookup. The zone contains an A record for the resource. If the VNet uses custom DNS servers, they forward privatelink zones to Azure DNS (168.63.129.16) or to an Azure DNS Private Resolver. The third point is the one that is easy to miss: the zone and the record exist, but the VNet performing the lookup cannot see the zone, so the name resolves to the public address. An approach: check the whole path in one command I wrote a small, read-only script that takes a target resource and a source VNet, then reports PASS, WARN, or FAIL for each requirement above, with a suggested fix. It uses the Azure CLI and needs only Reader access. Checks performed: publicNetworkAccess on the target resource Private endpoint connections and their approval state Expected privatelink zone for the endpoint's group id Whether the zone is linked to the source VNet Whether the zone has an A record for the resource Whether the source VNet uses custom DNS servers (reported as a warning for manual review) It exits with code 1 on any failure, so it can run as a pipeline gate after a Dev deployment. Limitations It does not test the network path itself (NSGs, firewalls, routing, ports). Azure Network Watcher covers that. It reads configuration and does not resolve DNS from inside the VNet. A real lookup from a VM in the VNet remains the final proof. In hub-and-spoke designs, the hub VNet may need to be passed as the source. The built-in zone table covers a few services (storage, Key Vault, SQL, Databricks) and needs extending for others. Why I think this matters for AI-assisted infrastructure Script and sample pipeline: Ping me or message me When infrastructure code is generated by an AI assistant, a private endpoint without its DNS zone link is a plausible output: the code is valid and the deployment succeeds. Independent, read-only checks like this one are the kind of gate that lets teams review generated changes with confidence. Questions for the community Which private endpoint failure costs your team the most troubleshooting time? How do you manage DNS in hub-and-spoke networks: Private Resolver, DNS forwarders, or another pattern? Would a check like this be more useful as an Azure Policy, a Network Watcher feature, or a pipeline step? Which services should be added to the DNS zone table first? Shivanna GundanavarAI-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.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.Building human-centric security skills for AI
AI is reshaping the workplace, and the organizations thriving in this new era know it takes more than just cutting-edge tech to succeed. A new kind of company is emerging—the so-called Frontier Firm. These organizations are building their business models around on-demand intelligence and hybrid human-AI collaboration. They understand that no matter how advanced technology is, it’s people—armed with the right skills, judgment, and foresight—who make the difference. Through targeted skilling and a culture of shared responsibility, Frontier Firms are ensuring their people are prepared to meet the security demands of an AI-driven world. This growing need for cross-functional security skilling is a central theme in our new e-book Skilling for Secure AI: How Frontier Firms Lead the Way, and we’ve pulled together three takeaways across all roles that preview how leading organizations are approaching this era. Let’s take a closer look at the key insights shaping secure AI skilling today: 1. Human expertise is critical when securing your organization in the age of AI AI may be transforming how to approach daily work, but it doesn’t replace the need for human oversight, judgment, or creativity. As AI tools become more powerful, so does the opportunity for human decisions to drive meaningful impact. That’s why Frontier Firms are strengthening their commitment to human expertise. These companies are equipping their people with the skills to use AI responsibly, securely, and collaboratively. For example, whether it’s reskilling IT teams to manage system permissions or upskilling data specialists to evaluate which datasets are appropriate for use, Frontier Firms are investing in targeted security skilling. By focusing on building expertise, these organizations empower their teams to move faster, innovate more confidently, and safeguard outcomes at every step. 2. Everyday use of AI makes security skilling fundamental across the organization With AI extending into more roles, understanding where human expertise intersects with security risk is becoming essential across the organization. For instance, business leaders can use AI to analyze performance data and line-of-business users can leverage AI to generate content. Each of these AI interactions underscores the need for widespread skilling that helps employees make informed, secure, and ethical choices when working with AI. That’s why security skilling can’t be siloed. Frontier Firms are addressing this shift by making security skilling a shared, organization-wide priority. They’re focused on making secure habits a familiar and consistent part of everyday work. By doing so, they’re building a workforce that understands how to use AI tools effectively and securely, reinforcing security as a core part of productivity. 3. Continuous learning is how to stay agile in a changing world Building security skilling across the organization is an important step, but lasting impact comes from making that learning continuous and adaptive. That’s why Microsoft offers resources like Microsoft Learn for Organizations, designed to help teams build and sustain skills across roles. As the landscape evolves, organizations need to weave security skilling into their culture, so every employee feels empowered to grow their capabilities alongside the technologies they use. Frontier Firms understand this nuance and are developing ongoing strategies to build future-ready security resilience across their workforce. From hosting annual hackathons to creating quarterly forums where teams can exchange ideas, Frontier Firms empower employees to adapt, respond, and lead through change. Showcasing expertise in action: Microsoft Credentials for Security As organizations work to stay ahead in AI, proving relevant skills has become increasingly important. Employers want candidates who can show they know how to use secure AI, while job seekers aim to stand out in a highly competitive market. Microsoft, with over thirty years of experience in credentialing, offers a broad range of Certifications tailored to security roles such as Security Operations Analyst and Cybersecurity Architect, as well as Applied Skills credentials for practical scenarios involving tools like Microsoft Sentinel or Microsoft Defender. Explore Microsoft Credentials for Security. Empowering people is the strategy for securing AI By building skilling strategies that reach across functions, Frontier Firms are prioritizing trust, accountability, and long-term resilience. Their success reinforces that equipping people with the right skills to securely use AI can give organizations a lasting competitive edge. Looking to identify and contextualize AI and security learning opportunities across your organization? Explore our new e-book, Skilling for Secure AI: How Frontier Firms Lead the Way—and be sure to check out the readiness tool at the end to help assess how prepared your teams are for today’s security needs.
