New Microsoft Certified: AI Business Professional Certification
Do you use generative AI tools, like Researcher and Analyst in Microsoft 365 Copilot, to enhance your daily work, boost productivity, guide decisions, and drive business outcomes—without writing code or developing AI applications? Are you comfortable with AI fundamentals, prompt creation, and applying AI to real-world tasks? Do you produce professional content, summarize meetings, and collaborate across teams using Microsoft 365 apps? If this is your skill set, we have a new Microsoft Certification for you. The Microsoft Certified: AI Business Professional Certification validates your expertise in these skills. To earn this Certification, you need to pass Exam AB-730: AI Business Professional, currently in beta. This new Certification shows employers that you’re an AI-ready professional who can drive better outcomes in any business role, that you’re fluent in using AI in your day-to-day projects, including how to be more productive and creative as you work across Microsoft 365 apps, and that you understand how to use Copilot and agents to analyze data and automate tasks. Is this the right Certification for you? This Certification is designed for business professionals who want to apply AI tools to real business challenges. Whether you're in marketing, operations, project management, human resources, customer service, or another field, this Certification proves that you can unlock new levels of productivity and insight. As a candidate for the Certification, you should have a basic understanding of Microsoft 365 and should be comfortable navigating core apps, such as Outlook, Word, Microsoft Teams, PowerPoint, and Excel. You also need to be familiar with common business processes, including creating presentations, generating images, and managing documents. If you use generative AI tools, such as Microsoft Copilot, to draft emails, summarize documents, create presentations, or generate creative content, like images and text, this Certification could be a great fit for you. Ready to prove your skills? Take advantage of the discounted beta exam offer. The first 300 people who take Exam AB-730 (beta), on or before December 11, 2025, can get 80% off market price. To receive the discount, when you register for the exam and are prompted for payment, use code AB730Smore25. 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 December 11, 2025. Please note that this beta exam is not available in Turkey, Pakistan, India, or China. Get ready to take Exam AB-730 (beta): Review the Exam AB-730 (beta) exam page for details. The Exam AB-730 study guide explores key topics covered in the exam. Want even more in-depth, instructor-led training? 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? Did you know that you can take any Microsoft Certification exam online? Taking your exam from home or the office can be more convenient and less stressful than traveling to a test center—especially when you know what to expect. To find out more, read Online proctored exams: What to expect and how to prepare. The rescore process starts on the day an exam goes live, and final scores for beta exams are released approximately 10 days after that. For details on the timing of beta exam rescoring and results, check out Creating high-quality exams: The path from beta to live. Ready to get started? Remember, the number of spots is limited to the first 300 candidates taking Exam AB-730 (beta) on or before December 11, 2025. Stay tuned for general availability of this Certification in February 2026. Learn more about Microsoft Credentials. Related announcements We recently migrated our subject matter expert (SME) database to LinkedIn. To be notified of beta exam availability or opportunities to help with the development of exam, assessment, or learning content, sign up today for the Microsoft Worldwide Learning SME Group for Credentials.Azure IoT Operations 2603 is now available: Powering the next era of Physical AI
Industrial AI is entering a new phase. For years, AI innovation has largely lived in dashboards, analytics, and digital decision support. Today, that intelligence is moving into the real world, onto factory floors, oil fields, and production lines, where AI systems don’t just analyze data, but sense, reason, and act in physical environments. This shift is increasingly described as Physical AI: intelligence that operates reliably where safety, latency, and real‑world constraints matter most. With the Azure IoT Operations 2603 (v1.3.38) release, Microsoft is delivering one of its most significant updates to date, strengthening the platform foundation required to build, deploy, and operate Physical AI systems at industrial scale. Why Physical AI needs a new kind of platform Physical AI systems are fundamentally different from digital‑only AI. They require: Real‑time, low‑latency decision‑making at the edge Tight integration across devices, assets, and OT systems End‑to‑end observability, health, and lifecycle management Secure cloud‑to‑edge control planes with governance built in Industry leaders and researchers increasingly agree that success in Physical AI depends less on isolated models, and more on software platforms that orchestrate data, assets, actions, and AI workloads across the physical world. Azure IoT Operations was built for exactly this challenge. What’s new in Azure IoT Operations 2603 The 2603 release delivers major advancements across data pipelines, connectivity, reliability, and operational control, enabling customers to move faster from experimentation to production‑grade Physical AI. Cloud‑to‑edge management actions Cloud‑to‑edge management actions enable teams to securely execute control and configuration operations on on‑premises assets, such as invoking methods, writing values, or adjusting settings, using Azure Resource Manager and Event Grid–based MQTT messaging. This capability extends the Azure control plane beyond the cloud, allowing intent, policy, and actions to be delivered reliably to physical systems while remaining decoupled from protocol and device specifics. For Physical AI, this closes the loop between perception and action: insights and decisions derived from models can be translated into governed, auditable changes in the physical world, even when assets operate in distributed or intermittently connected environments. Built‑in RBAC, managed identity, and activity logs ensure every action is authorized, traceable, and compliant, preserving safety, accountability, and human oversight as intelligence increasingly moves from observation to autonomous execution at the edge. No‑code dataflow graphs Azure IoT Operations makes it easier to build real‑time data pipelines at the edge without writing custom code. No‑code data flow graphs let teams design visual processing pipelines using built‑in transforms, with improved reliability, validation, and observability. Visual Editor – Build multi-stage data processing systems in the Operations Experience canvas. Drag and connect sources, transforms, and destinations visually. Configure map rules, filter conditions, and window durations inline. Deploy directly from the browser or define in Bicep/YAML for GitOps. Composable Transforms, Any Order – Chain map, filter, branch, concatenate, and window transforms in any sequence. Branch splits messages down parallel paths based on conditions. Concatenate merges them back. Route messages to different MQTT topics based on content. No fixed pipeline shape. Expressions, Enrichment, and Aggregation – Unit conversions, math, string operations, regex, conditionals, and last-known-value lookups, all built into the expression language. Enrich messages with external data from a state store. Aggregate high-frequency sensor data over tumbling time windows to compute averages, min/max, and counts. Open and Extensible – Connect to MQTT, Kafka, and OpenTelemetry (OTel) endpoints with built-in security through Azure Key Vault and managed identities. Need logic beyond what no-code covers? Drop a custom Wasm module (even embed and run ONNX AI ML models) into the middle of any graph alongside built-in transforms. You're never locked into declarative configuration. Together, these capabilities allow teams to move from raw telemetry to actionable signals directly at the edge without custom code or fragile glue logic. Expanded, production‑ready connectivity The MQTT connector enables customers to onboard MQTT devices as assets and route data to downstream workloads using familiar MQTT topics, with the flexibility to support unified namespace (UNS) patterns when desired. By leveraging MQTT’s lightweight publish/subscribe model, teams can simplify connectivity and share data across consumers without tight coupling between producers and applications. This is especially important for Physical AI, where intelligent systems must continuously sense state changes in the physical world and react quickly based on a consistent, authoritative operational context rather than fragmented data pipelines. Alongside MQTT, Azure IoT Operations continues to deliver broad, industrial‑grade connectivity across OPC UA, ONVIF, Media, REST/HTTP, and other connectors, with improved asset discovery, payload transformation, and lifecycle stability, providing the dependable connectivity layer Physical AI systems rely on to understand and respond to real‑world conditions. Unified health and observability Physical AI systems must be trustworthy. Azure IoT Operations 2603 introduces unified health status reporting across brokers, dataflows, assets, connectors, and endpoints, using consistent states and surfaced through both Kubernetes and Azure Resource Manager. This enables operators to see—not guess—when systems are ready to act in the physical world. Optional OPC UA connector deployment Azure IoT Operations 2603 introduces optional OPC UA connector deployment, reinforcing a design goal to keep deployments as streamlined as possible for scenarios that don’t require OPC UA from day one. The OPC UA connector is a discrete, native component of Azure IoT Operations that can be included during initial instance creation or added later as needs evolve, allowing teams to avoid unnecessary footprint and complexity in MQTT‑only or non‑OPC deployments. This reflects the broader architectural principle behind Azure IoT Operations: a platform built for composability and decomposability, where capabilities are assembled based on scenario requirements rather than assumed defaults, supporting faster onboarding, lower resource consumption, and cleaner production rollouts without limiting future expansion. Broker reliability and platform hardening The 2603 release significantly improves broker reliability through graceful upgrades, idempotent replication, persistence correctness, and backpressure isolation—capabilities essential for always‑on Physical AI systems operating in production environments. Physical AI in action: What customers are achieving today Azure IoT Operations is already powering real‑world Physical AI across industries, helping customers move beyond pilots to repeatable, scalable execution. Procter & Gamble Consumer goods leader P&G continually looks for ways to drive manufacturing efficiency and improve overall equipment effectiveness—a KPI encompassing availability, performance, and quality that’s tracked in P&G facilities around the world. P&G deployed Azure IoT Operations, enabled by Azure Arc, to capture real-time data from equipment at the edge, analyze it in the cloud, and deploy predictive models that enhance manufacturing efficiency and reduce unplanned downtime. Using Azure IoT Operations and Azure Arc, P&G is extrapolating insights and correlating them across plants to improve efficiency, reduce loss, and continue to drive global manufacturing technology forward. More info. Husqvarna Husqvarna Group faced increasing pressure to modernize its fragmented global infrastructure, gain real-time operational insights, and improve efficiency across its supply chain to stay competitive in a rapidly evolving digital and manufacturing landscape. Husqvarna Group implemented a suite of Microsoft Azure solutions—including Azure Arc, Azure IoT Operations, and Azure OpenAI—to unify cloud and on-premises systems, enable real-time data insights, and drive innovation across global manufacturing operations. With Azure, Husqvarna Group achieved 98% faster data deployment and 50% lower infrastructure imaging costs, while improving productivity, reducing downtime, and enabling real-time insights across a growing network of smart, connected factories. More info. Chevron With its Facilities and Operations of the Future initiative, Chevron is reimagining the monitoring of its physical operations to support remote and autonomous operations through enhanced capabilities and real-time access to data. Chevron adopted Microsoft Azure IoT Operations, enabled by Azure Arc, to manage and analyze data locally at remote facilities at the edge, while still maintaining a centralized, cloud-based management plane. Real-time insights enhance worker safety while lowering operational costs, empowering staff to focus on complex, higher-value tasks rather than routine inspections. More info. A platform purpose‑built for Physical AI Across manufacturing, energy, and infrastructure, the message is clear: the next wave of AI value will be created where digital intelligence meets the physical world. Azure IoT Operations 2603 strengthens Microsoft’s commitment to that future—providing the secure, observable, cloud‑connected edge platform required to build Physical AI systems that are not only intelligent, but dependable. Get started To explore the full Azure IoT Operations 2603 release, review the public documentation and release notes, and start building Physical AI solutions that operate and scale confidently in the real world.
Strengthen your research workflow with generative AI
Stop guessing at prompts. Use research-ready templates that guide Microsoft Copilot toward clearer reasoning, better drafts, and transparent methods—so your work is faster, sharper, and credible. Darcy Ogden, Ph. D., leads the academic researcher programs for Microsoft Global Skilling. A computational scientist and former professor of geophysics, Dr. Ogden has a passion for teaching and using new technology to accelerate research. Guidance on using generative AI in research often lands at the extremes; it’s either overly optimistic or far too cautious. Most researchers, students, and professionals working with data or analysis know that the reality sits somewhere in between: all models are useful but fallible tools. As researchers, we ask: What was this model designed to do? Where does it perform well? Where does it fall short? What assumptions are we making when we use this model? Those same questions apply to generative AI. Understanding how these systems work and how they shape the outputs you receive can help you decide when to rely on them and when to adjust. We’re all trying to figure out how best to work with generative AI, and there’s no simple, universal answer. But, in many cases, the work of research itself creates opportunities to apply generative AI thoughtfully and effectively. Explore our new guide for researchers As part of the latest Microsoft efforts to support graduate students, postdocs, and faculty aiming to use generative AI for research, we’re happy to share a new learning resource, The Academic Researcher's Guide to Generative AI. In this guide, we bring together recent insights and practical frameworks for considering generative AI as a research instrument. The guide’s purpose is to support researchers in asking well‑formed questions about the tools they use and in reflecting on the role that those tools play in research processes. Bring generative AI into your research methodology This new guide provides research-aligned approaches to prompting in Microsoft 365 Copilot Chat, along with frameworks for prompt development, testing, and documentation. Further, it includes ready-to-adapt prompting use cases for research scenarios. The following brief examples reflect the kinds of tasks that these prompts support: Research synthesis. Summarize the key arguments across these sources and note where the evidence conflicts. Writing support. Rewrite this paragraph for clarity and precision while keeping the original meaning. Data analysis. Explain the assumptions behind this statistical method and list situations where it may fail. We’ve also included guidance on crafting quality prompts in Copilot, with techniques that can help reduce ambiguity and surface the reasoning behind responses. These approaches for prompting can deliver tailored, well-structured outputs suited for research purposes. The following examples highlight the types of instructions that researchers can use to make the most of Copilot prompts: Surface assumptions. State assumptions and show reasoning before providing the final answer. Limit sources. Use only the attached sources and flag any gaps or uncertainties in the evidence. Structure responses. Follow this structure: Context → key points → limitations → questions to be considered next. This guide treats the use of generative AI like other models or tools you use in your research. Like them, generative AI has no native understanding of fields of study, datasets, or research constraints. The guide introduces an approach to using generative AI as a visible, documentable part of academic research. It treats interactions with Copilot as part of your methodology: something to record, review, and refine as you move through your research. Put the guide to work As generative AI becomes more common across academic and professional environments, the question is no longer whether to use it but how to use it well. As the models grow more capable, the challenge is how to use them in ways that support learning, integrity, and transparency. We developed this guide to help researchers and students engage these tools in ways that strengthen, rather than diminish learning and scholarly judgment. We invite you to read The Academic Researcher's Guide to Generative AI. Use it as a starting point, adapt the frameworks to your own discipline and workflow, and contribute feedback about the guide so that we can continue to evolve this resource alongside the field itself.
Driving AI‑Powered Healthcare: A Data & AI Webinar and Workshop Series
Across these sessions, you’ll learn how healthcare organizations are using Microsoft Fabric, advanced analytics, and AI to unify fragmented data, modernize analytics, and enable intelligent, scalable solutions, from enterprise reporting to AI‑powered use cases. Whether you’re just getting started or looking to accelerate adoption, these sessions offer practical guidance, real‑world examples, and hands‑on learning to help you build a strong data foundation for AI in healthcare. Date Topic Details Location Registration Link May 6 Webinar: Microsoft Fabric Foundations - A Simple Path to Modern Analytics and AI Discover how Microsoft Fabric consolidates fragmented analytics into a single integrated data platform, making it easier to deliver trusted insights and adopt AI without added complexity. Virtual Register May 13 Webinar: Reduce BI Sprawl, Cut Cost and Build an AI-Ready Analytics Foundation Learn how Power BI enables enterprise BI consolidation, consistent metrics, and secure, scalable analytics that support both operational reporting and emerging AI use cases. Virtual Register May 19-20 In Person Workshop: Driving AI‑Powered Healthcare: Advanced Analytics, AI, and Real‑World Impact Attend this two‑day, in‑person event to learn how healthcare organizations use Microsoft Fabric to unify data, accelerate AI adoption, and deliver measurable clinical and operational value. Day 1 focuses on strategy, architecture, and real‑world healthcare use cases, while Day 2 offers hands‑on workshops to apply those concepts through guided labs and agent‑powered solutions. Chicago May 27 Webinar: Unified Data Foundation for AI & Analytics - Leveraging OneLake and Microsoft Fabric This session shows how organizations can simplify fragmented data architectures by using Microsoft Fabric and OneLake as a single, governed foundation for analytics and AI. Virtual Register May 27-28 In Person Workshop: Driving AI‑Powered Healthcare: Advanced Analytics, AI, and Real‑World Impact Attend this two‑day, in‑person event to learn how healthcare organizations use Microsoft Fabric to unify data, accelerate AI adoption, and deliver measurable clinical and operational value. Day 1 focuses on strategy, architecture, and real‑world healthcare use cases, while Day 2 offers hands‑on workshops to apply those concepts through guided labs and agent‑powered solutions. Silicon Valley June 2 Webinar: Delivering Personalized Patient Experiences at Scale with Microsoft Fabric and Adobe Learn how healthcare organizations can improve patient engagement by unifying trusted data in Microsoft Fabric and activating it through Adobe’s personalization platform. Virtual Register June 3-4 In Person Workshop: Driving AI‑Powered Healthcare: Advanced Analytics, AI, and Real‑World Impact Attend this two‑day, in‑person event to learn how healthcare organizations use Microsoft Fabric to unify data, accelerate AI adoption, and deliver measurable clinical and operational value. Day 1 focuses on strategy, architecture, and real‑world healthcare use cases, while Day 2 offers hands‑on workshops to apply those concepts through guided labs and agent‑powered solutions. New York June 10 Webinar: From Data to Decisions: How AI Data Agents in Microsoft Fabric Redefine Analytics Join us to learn how Fabric Data Agents enable users to interact with enterprise data through AI‑powered, governed agents that understand both data and business context. Virtual Register June 17 Webinar: Building the Intelligent Factory: A Unified Data and AI Approach to Life Science Manufacturing Discover how life science & MedTech manufacturers use Microsoft Fabric to integrate operational, quality, and enterprise data and apply AI‑powered analytics for smarter, faster manufacturing decisions. Virtual Register June 23-24 In Person Workshop: Driving AI‑Powered Healthcare: Advanced Analytics, AI, and Real‑World Impact Attend this two‑day, in‑person event to learn how healthcare organizations use Microsoft Fabric to unify data, accelerate AI adoption, and deliver measurable clinical and operational value. Day 1 focuses on strategy, architecture, and real‑world healthcare use cases, while Day 2 offers hands‑on workshops to apply those concepts through guided labs and agent‑powered solutions. DallasEp 38 | The New Growth Engine for Microsoft Partners: Business Central, AI, and P2P Collaboration
The Microsoft Partner Ecosystem Is Entering a New Growth Phase The Microsoft partner ecosystem is evolving rapidly as organizations accelerate cloud adoption and digital transformation, creating new opportunities—and challenges—across the SMB and mid-market landscape. Three forces are reshaping the ecosystem: Rapid adoption of Microsoft Business Central and other Business Applications The accelerating push toward AI-enabled services Increasing reliance on partner-to-partner collaboration (P2P) to deliver complex solutions These trends are converging to create a new growth model for Microsoft partners. The key takeaway: Success will rely on integrated solutions, deeper specialization, and stronger ecosystem relationships. In the latest episode of IAMCP Profiles in Partnership, “How Microsoft Partners Can Grow Faster with Business Central, AI, and P2P Collaboration,” hear insights from longtime Microsoft MVP and industry leader Rick McCutcheon as he sits down with Anthony Carrano and Rudy Rodriquez to discuss how these shifts are unfolding and what partners should be doing to stay competitive. Why Business Applications Are Booming in the SMB Market One of the strongest signals in the Microsoft partner ecosystem today is the growth of Business Applications, particularly Microsoft Business Central. The SMB and mid-market segments are experiencing a surge in ERP modernization. Several factors are driving this expansion: Companies migrating from Microsoft Dynamics NAV Organizations moving off Dynamics GP Businesses outgrowing QuickBooks Enterprise The increasing demand for cloud-based ERP platforms Business Central sits at the center of this transition. Microsoft recently announced that the platform surpassed 50,000 customers, and many partners expect continued double-digit growth as migrations accelerate. For Microsoft Dynamics partners, this shift is a significant opportunity. As legacy systems reach end-of-life and SMBs modernize, demand for ERP consulting, implementation, and integration continues to rise, but the opportunity extends beyond ERP deployments alone. Modern ERP Is Becoming an Ecosystem A modern ERP implementation rarely exists in isolation. Instead, it increasingly acts as a platform that integrates multiple solutions from the broader Microsoft ISV ecosystem. In a typical Business Central deployment today, partners often integrate five to ten independent software vendor (ISV) solutions that extend ERP functionality. Common integrations include: Accounts payable automation Marketing automation platforms Warehouse management systems Logistics and shipping solutions CRM integrations Industry-specific vertical applications This shift means ERP deployments are now comprehensive business platforms. The key takeaway is that partners can now deliver end-to-end solutions rather than just single software tools. This shift greatly expands consulting opportunities for partners. A single ERP project can evolve into a broader transformation initiative involving workflow automation, analytics, and operational modernization. AI Is the Next Major Opportunity, But Data Readiness Matters While ERP modernization is driving immediate demand, AI adoption is emerging as the next major wave in the Microsoft ecosystem. Microsoft’s push toward “frontier partners”—organizations capable of delivering AI-enabled solutions—is accelerating across the channel. Tools such as Microsoft Copilot and AI agents are already improving productivity within partner organizations themselves. Examples include: Automating proposal writing Accelerating RFP responses Summarizing meetings and documentation Supporting research and content creation However, deploying AI within customer environments is far more complex. The biggest barrier to AI adoption is not the technology, it is data readiness. Many organizations still operate with fragmented systems, including: CRM platforms disconnected from marketing automation ERP systems that do not fully integrate with customer data Multiple databases containing duplicate or inconsistent records Without strong data governance, AI systems struggle to produce reliable insights. For partners delivering AI solutions, data architecture is becoming a core capability. Before deploying AI tools, partners must often help clients: Consolidate data sources Standardize data models Improve data quality Establish governance policies The main takeaway: Only after data consolidation and governance can AI offer meaningful business results for clients. Why MSPs and BizApps Partners Are Converging Historically, managed service providers (MSPs) and Business Applications partners operated in separate worlds. MSPs focused on infrastructure, security, and cloud services. BizApps partners specialize in ERP, CRM, and operational systems. Today, those boundaries are dissolving. Several factors are driving convergence: The Microsoft cloud integrates Modern Work, Azure, and Business Applications. Customers expect a single partner experience. AI solutions increasingly require data, infrastructure, and application expertise. As a result, MSPs are entering the Business Applications market in several ways: Building internal ERP and CRM practices Acquiring BizApps consulting firms Partnering with specialized implementation partners For MSPs, the opportunity is compelling. Business Applications projects often generate significantly higher service revenue than software licensing. The key takeaway: Due to the complexity of ERP and CRM, MSPs often need to collaborate with experienced BizApps partners to deliver complete solutions. Why Partner-to-Partner Collaboration Is Becoming Essential As the Microsoft ecosystem becomes more complex, no single partner can deliver everything on their own. Modern customer environments often require expertise across multiple domains: Infrastructure and cloud architecture ERP and CRM systems Data platforms and analytics AI deployment and automation Industry-specific solutions This reality is driving a shift toward partner-to-partner collaboration (P2P). Successful collaborations typically share several characteristics: Long-Term Trust: Effective partnerships are built over time, not through one-off project engagements. Complementary Expertise: Partners focus on their strengths while bringing in specialists where needed. Shared Opportunity: Strong P2P relationships create reciprocal value—partners refer opportunities to each other rather than competing. Communities such as the International Association of Microsoft Channel Partners (IAMCP) play an important role in facilitating these relationships. To summarize: As the Microsoft ecosystem becomes more interconnected, building strong partner networks is becoming essential for success. Strategic Moves Microsoft Partners Should Make Now For Microsoft partners navigating these changes, several strategic priorities are emerging. Invest in Business Applications Expertise The demand for ERP and CRM solutions is growing rapidly, particularly in the SMB market. Partners who understand Business Central and related ISV ecosystems will be well-positioned. Build Data and AI Capabilities AI will increasingly shape how businesses operate. Partners must develop expertise in: Data architecture Data governance AI deployment frameworks Develop P2P Partnerships Strategic alliances allow partners to expand capabilities without building every skill internally. Engage in the Partner Community Industry conferences, partner networks, and learning platforms provide invaluable opportunities to build relationships and stay informed. The main takeaway: Active participation in the partner community is vital to thriving in the Microsoft ecosystem. The Future of Microsoft Partner Growth The Microsoft partner ecosystem is entering a new era. ERP modernization, AI adoption, and collaboration are combining to create larger, more complex opportunities. The partners who succeed in this environment will not be those who try to do everything themselves. Instead, the winners will be the firms that: Build deep expertise in key solution areas. Invest in data and AI capabilities. Form strong, trusted partnerships across the ecosystem The key takeaway: Collaboration has become the foundation of partner growth in the increasingly interconnected Microsoft ecosystem. Listen to the Full Episode To hear the full conversation and additional insights on Microsoft partner growth, listen to the complete episode of IAMCP Profiles in Partnership featuring Rick McCutcheon on demand.AI and human potential: Advancing skills, innovation, and outcomes
When empowered employees put AI skills into action, true transformation begins. Across industries and around the world, organizations are teaming up with Microsoft to help their employees learn the skills they need to put AI into practice. Building AI skills today sets up employees and organizations to thrive in the opportunities of tomorrow. At visionary organizations, like Albertsons Companies, Casey’s, Levi Strauss, and Newell Brands, teams are moving beyond theory and weaving AI into everyday work, transforming routine tasks into opportunities for innovation and business growth. This isn’t just about technology—it’s about people reimagining how they make an impact—and, in the process, they’re building resilience, driving growth, and creating success. AI Skills Navigator: Guidance for your AI-powered growth To help more people tap into this potential, we recently announced AI Skills Navigator an agentic learning space, bringing together AI-powered skilling experiences and credentials that help individuals build career skills and organizations worldwide accelerate their business. Forward-thinking companies Icertis, LexisNexis Risk Solutions, MTN, and Vodafone previewed AI Skills Navigator and shared their excitement about the experience. They see this learning space as a way to strengthen their learning cultures, prepare their workforces for the future, and deliver meaningful results—from better customer experiences to innovation that scales. Empowering teams to lead with confidence We believe skilling should always start with people, especially in a fast-changing AI landscape. We’re inspired by what teams can achieve when they feel supported, and we’re proud to work with organizations of all sizes as they help their employees grow and lead with confidence. The examples that follow highlight how enterprises across industries and regions are building future-ready teams with Microsoft skilling—and seeing real impact. The Adecco Group, a leading talent solutions and advisory firm that serves more than 100,000 companies every year, is committed to upskilling its workforce and preparing employees for the future of work. Partnering with Microsoft, The Adecco Group provides skill-building that supports 300 million candidate interactions annually and fosters a learning culture that scales AI expertise across the company. Abu Dhabi National Oil Company (ADNOC), the state-owned energy company of Abu Dhabi, United Arab Emirates, operates across the entire oil and gas value chain. ADNOC and Microsoft have committed to co-develop and deploy agents that enhance efficiency, enable autonomous operations, and reduce emissions. This collaboration includes advanced AI tools and workforce training from Microsoft, in addition to the creation of a joint innovation ecosystem to drive transformative energy solutions. Albertsons Companies, one of the largest food and drug retailers in the United States, is helping its associates be more effective with AI. According to Anuj Dhanda, Executive Vice President (EVP), Chief Technology & Transformation Officer, "At Albertsons Companies, we’re better together, whether in our stores or behind the scenes, and we embrace groundbreaking innovation that empowers each of our team members to earn customers for life. Our partnership with Microsoft is one of many transformative AI initiatives we’re implementing to unlock the potential of AI, helping our associates to be more effective, simplify their work, and build a more capable workforce to serve our 37 million customers each week." The Belgian Ministry of Defense is working with Microsoft to strengthen its capabilities through the practical application of generative AI, supporting a future-ready workforce. With AI coaching, the ministry equips its personnel with the skills to enhance digital resilience and operational effectiveness. Bupa APAC, a global leader in health insurance, health services, and aged care, keeps skilling central to its strategy evolution. The company cites AI as a critical part of its transformation but points out that technology alone isn't enough. It’s focused on its teams—building the right skills to make AI effective across the organization. Casey’s, a Fortune 500 convenience store chain and one of the largest pizza retailers in the United States, is embracing AI throughout the company. As Sanjeev Satturu, Senior Vice President (SVP) and Chief Information Officer (CIO), explains, “At Casey's, we are dedicated to fostering digital dexterity by equipping our teams with innovative AI tools. Through our strategic partnership with Microsoft, we are providing employees with the knowledge and resources to seamlessly integrate AI into their daily work, empowering them to extend their capabilities, boost productivity, and drive our evolution. By embracing AI in everyday actions, we amplify our collective potential and accelerate meaningful progress across the organization.” Commonwealth Bank of Australia (CommBank), one of Australia’s largest banks, is guided by its goal to build a brighter future for all. With Microsoft skilling offerings, the bank is enabling its teams to engage with AI, use the latest tools, and embrace new ways of working, as employees find out how AI can drive real impact. With a structured skilling approach and real-world experimentation, CommBank employees are learning to confidently apply AI to their day-to-day tasks, stay ahead, innovate, and help shape the future of banking. Danone, a world leader in specialized nutrition, is exploring new frontiers in AI transformation. Juergen Esser, Deputy Chief Executive Officer (CEO) in charge of Finance, Technology and Data, observes, “Our collaboration with Microsoft will accelerate our AI transformation, providing us with the tools, technology, and expertise to explore new frontiers in data analysis, operational efficiency, and consumer engagement. Working together is not just about technology; it’s about fostering a culture of continuous learning, innovation, and performance across our organisation." Digital China Cloud Technology Limited partnered with Lenovo, the world’s largest PC company, to create a training program to help Lenovo employees move from casual interaction with AI tools to meaningful, scenario-driven usage. The goal was to onboard 30,000 employees in China—about 40% of the Lenovo workforce—to Microsoft 365 Copilot Chat and Microsoft Copilot Studio. The results included significant gains in productivity, efficiency, innovation, and more. As Lenovo China Operations Management Professional Dan Zhao explains, “Copilot Chat has fundamentally changed how we manage our daily workflow. What used to take hours now happens in minutes. It not only saves us time but also enhances the quality of our communication and strategic thinking." EPAM, a global leader in digital engineering and AI-powered software engineering services, is differentiating itself by focusing on Microsoft Applied Skills, moving beyond academic knowledge to real client impact. This approach demonstrates proficiency, strengthens outcomes, and provides a clear competitive advantage. With Applied Skills, EPAM fast-tracks AI readiness and project outcomes. Icertis, a leader in AI-powered contract intelligence, previewed AI Skills Navigator. Shwetambari Salgar, Learning & Organisational Development, is enthusiastic, emphasizing, “AI Skills Navigator provides an opportunity to upskill Icertis teams for the future through a unified learning platform. As a long-time Microsoft partner, we have a shared drive for innovation and continuous learning.” The International Center for Artificial Intelligence Research and Ethics (ICAIRE), under the auspices of the United Nations Educational, Scientific and Cultural Organization (UNESCO), aims to empower nations to advance ethical AI for the good of humanity. Through its global Women Elevate initiative, ICAIRE is equipping 5,000 women with entry-level skills in AI, data science, and responsible AI use. The program is delivered by Microsoft Training Services Partner Spectrum Networks and features a comprehensive curriculum covering foundational AI and cloud concepts, Azure essentials, and the Microsoft Certified: Azure AI Fundamentals Certification. By combining technical skilling with a strong focus on ethical innovation, Women Elevate is helping shape a new generation of women leaders ready to use AI to increase productivity, shorten time to market, and contribute meaningfully to the global AI ecosystem. Koç Holding is a Fortune Global 500 company and Turkey’s largest multinational conglomerate, operating across energy, automotive, finance, consumer goods, and retail sectors. The company is taking advantage of Microsoft Applied Skills, empowering more than 6,000 employees to build future-ready skills in cloud, AI, and automation—underscoring the company’s belief that digital transformation begins with people. Koç Holding employees demonstrate their technical expertise through real-world tasks with Applied Skills, which focus on targeted, scenario-based learning and validation. These staff members are turning their new skills into impact, solving business challenges and driving agility. Lenovo, a global technology powerhouse focused on delivering smarter technology for all, is expanding its collaboration with Microsoft to upskill an additional 4,200 employees this year, following the successful training of more than 3,000 to date. What began as a small pilot has evolved into one of Lenovo’s largest learning communities, reinforcing the company’s position as a leading force in the AI revolution. Levi Strauss & Co., global apparel company, is building the foundation to apply AI across its operations. As Karen Scholl, Vice President, Office of the Chief Digital & Technology Officer, explains, “We’re empowering our people to tap into the power of AI to unlock new possibilities, reimagine how we work, and accelerate our evolution into a best-in-class, direct-to-consumer retailer. Our partnership with Microsoft isn’t just about technology, but it’s about building the right foundation to embed AI across everything we do. From product design to store operations, we’re building and equipping every team member with the tools to drive innovation and growth, ensuring Levi’s continues to thrive for another 170 years. By leveraging Microsoft’s technological expertise, we’re redefining what’s possible for the future of retail.” LexisNexis Risk Solutions provides data analytics and technology to help organizations assess risk, ensure compliance, and prevent fraud. Sarah Fabius, Technology Optimization Program Manager, notes that the company is dedicated to equipping its workforce “with cutting-edge AI skills that fuel innovation in risk and data analytics technology. Through our partnership with Microsoft and the integration of AI Skills Navigator, we’re ensuring our teams have the tools and expertise to lead in a rapidly evolving industry.” MTN is Africa’s largest mobile network operator. Paul Norman, Group Chief Human Resources Officer, shares his perspective on AI Skills Navigator, noting, “At MTN, we believe in developing an AI-fluent organisation that empowers every employee to adopt and utilise AI responsibly and ethically. Microsoft AI Skills Navigator provides an exciting new learning innovation that can help demystify AI through its learner-centric and human-first approach to grow and develop an agile, resilient, and AI-inspired workforce of the future!" National Australia Bank (NAB), one of Australia’s largest financial institutions, is advancing its strategic commitment to AI, with more than 100 initiatives underway and thousands of employees already applying generative AI to deliver real business value. The bank emphasizes responsible AI, inclusive innovation, and strong leadership. Through a joint effort with Microsoft, 600 women have received AI training, reinforcing NAB’s vision of a diverse, future-ready workforce equipped to lead in the age of AI. Newell Brands, a leading consumer products company with a portfolio of iconic brands, is empowering its employees with Microsoft technology and skilling. As Chris Peterson, President and CEO, points out, “At Newell Brands, we see AI as a catalyst for creativity, productivity, and transformation. Through the Microsoft AI experience training series and access to Microsoft 365 Copilot, we’re empowering our employees with technology that helps them work smarter, boost productivity, and turn ideas into action faster. This is about more than adopting new technology—it’s about fueling innovation, driving efficiency, and creating lasting value across our business.” NTT DATA is a global IT services and consulting company that provides digital, cloud, and business solutions to help organizations innovate and transform. The company is building its workforce skills in generative AI in close collaboration with Microsoft and other leading partners. The company intends to train 200,000 of its employees by fiscal year 2027 and, as of October 2025, had already trained 70,000 of them. This initiative empowers staff members to apply generative AI independently, driving innovation in daily tasks and creating new value across the organization. OP Pohjola, Finland’s largest provider of financial services, is fueling transformation in the financial sector by embedding continuous learning into its culture. In a joint effort with Microsoft, the company is ensuring that every employee has the opportunity to build AI skills and can apply them responsibly every day. This commitment and world-class learning strengthens customer service, fosters innovation, and helps to shape the future of banking. Ricoh, a global technology company that provides digital services, printing solutions, and workplace innovation, believes that real transformation starts with people. The company is advancing AI adoption across its teams in the Asia-Pacific region, focusing on skill-building so team members can use new tools with confidence. Ricoh and Microsoft worked together to deliver AI Learning Week, which gave employees the opportunity to explore ways to apply AI meaningfully and responsibly and to reimagine how they work, enhancing human potential. Swiss Post, Switzerland's national postal service, together with Campana & Schott and Microsoft, launched an ambitious AI skilling and adoption program, delivering measurable results. The organization conducts regular surveys to gather feedback and better understand impact. Teams across logistics, finance, and corporate services are reporting meaningful gains in productivity and efficiency, while employees cite faster access to information, improved collaboration, and higher satisfaction with their digital work environment. These outcomes reflect a clear return on investment—both in operational performance and employee empowerment. Vodafone, a global telecommunications company, looks to the exciting possibilities of AI Skills Navigator. As Steve Garley, Senior Manager Technical and Digital Skills, explains, “AI Skills Navigator learner-first design aligns well with an AI-first approach and Vodafone’s ambition for readiness in the AI era. We see potential in Microsoft’s [skilling experience] to transform traditional training models and deliver role-based, just-in-time upskilling that drives real business outcomes.” From retail to telecom and more, these organizations prove that empowered teams can spark innovation and lasting growth. Their experiences highlight a truth worth remembering—transformation starts with people. When employees feel confident in their skills, they create success that extends well beyond daily tasks. If you’re ready to empower your workforce with the full potential of AI skilling, get your teams started with AI Skills Navigator.Tekoälystä tekoihin – asiakkaan liiketoimintaa ymmärtäen
Johanna Grönroos, CBO & Paula Kujansivu, CEO, Efima Vaikuttava tekoälyn hyödyntäminen lähtee asiakkaan arjen tuntemisesta Parhaat tekoälyratkaisut eivät synny irrallaan liiketoiminnasta. Ne syntyvät ymmärtämällä työn todellisia vaiheita, arjen kitkakohtia ja sitä, missä tekoäly voi aidosti helpottaa tekemistä tai parantaa päätöksentekoa. Tämä näkyy asiakkaidemme arjessa useilla toimialoilla. Attendolla hoitajat tekevät hoivatyöhön liittyvät kirjaukset työn lomassa puheen avulla, ja tekoäly jäsentää tiedon suoraan rakenteiseen muotoon. Näin vapautuu aikaa tärkeimpään, eli kohtaamisiin ja samalla dokumentaation laatu paranee. Attendon CIO Jukka‑Pekka Luhanko kiteyttää asiakastarinassamme tavoitteen näin: “Meidän pitää antaa hoitajille mahdollisuus keskittyä itse hoivaan ja automatisoida kaikki muu.” Jukka-Pekka Luhanko, CIO, Attendo Lue asiakastarina: efima.com/asiakkaat/attendo Tekoälyn avulla voidaan muotoilla toimintatapoja kokonaan uudelleen. on tästä elävä esimerkki, jonka vaikuttavuus huomioitiin myös Grand One -kilpailun Paras käyttökokemus -kategorian voitolla. Myös HKFoodsilla tekoälyä hyödynnetään hyvin konkreettisessa arjen työssä. Yhdessä kehitetty ratkaisu tukee kuluttaja- ja asiakaspalvelua lajittelemalla automaattisesti saapuvat yhteydenotot, hakemalla ja rikastamalla tarvittavat tiedot järjestelmiin sekä tuottamalla valmiin vastausehdotuksen käsittelijän käyttöön organisaation linjauksia noudattaen. Kuten asiakkaan tiimi kuvaa: “Meidän tarpeemme kuunneltiin tarkasti ja keskustelut käytiin aidosti yhteisellä kielellä. Se on tehnyt yhteistyöstä sujuvaa läpi hankkeen – myös meille, jotka emme ole IT-asiantuntijoita.” Meri Kantoniemi, Customer and Sales Process Developer, Helinä Keskinen, Manager of Business and IT Integration, ja Sari Vuolle, Senior IT Architect, HKFoods Lue asiakastarina: efima.com/asiakkaat/hkfoods Efimalla asiakkaan liiketoiminnan ymmärrys on työn lähtökohta niin yksittäisissä prosessien automatisoinneissa kuin laajamittaisissa toiminnanohjausjärjestelmien uudistuksissa. Ratkaisuja rakennetaan hyödyntäen esimerkiksi Microsoft Dynamics 365, Power Platform, Fabric, Azure ja Cloud for Sustainability -alustoja. Muutosmatkalla yhdessä Microsoft kuvaa AI-aikakauden edelläkävijöitä Frontier Firm -mallilla: organisaatioina, jotka hyödyntävät tekoälyä jokaisella liiketoiminnan tasolla ja näkevät muutoksen strategisena ja kulttuurisena, eikä pelkkänä teknologiapäivityksenä. Tämä ajattelu näkyy myös siinä, miten me Efimalla lähestymme asiakkaidemme kehitystä. Usein suurin este tekoälyn vaikuttavalle hyödyntämiselle ei ole teknologian kyvykkyys. Haasteena ovat monimutkaiset prosessit, siiloutunut tieto ja arki on täynnä poikkeuksia sekä aikaa vieviä rutiineja. Myös datan laatu vaihtelee, ja tekoälykokeilujen edistäminen voi olla vaikeaa ilman yhteistä suuntaa tai ymmärrystä tekoälyn hallittavuudesta. Siksi autamme organisaatioita yhdessä Microsoftin kanssa koko muutosmatkalla: suunnan muodostamisessa, agenttisten prosessien kehittämisessä, datan hallinnassa sekä turvallisten ja hallittujen toimintamallien rakentamisessa. Ratkaisut rakennetaan yhdessä asiakkaan ammattilaisten kanssa, jotka tuntevat työn vaiheet ja käytännön haasteet. Kun uuden toimintatavan juurruttamisen eteen tehdään tiivistä yhteistyötä, ratkaisuja voidaan laajentaa ja skaalata kestävästi. Näin tekoäly muuttuu teoiksi ja vaikuttavuudeksi: sujuvammiksi toimintatavoiksi, paremmaksi päätöksenteoksi ja uudenlaiseksi asiakasarvoksi – sekä varmaksi poluksi kokeiluista tuotantoon Microsoft-teknologioilla. Autamme, kun haluatte viedä tekoälyn teoiksi. https://www.efima.com Efima edistää suurten ja keskisuurten yritysten kestävää kasvua tehostamalla asiakkaidensa liiketoiminta- ja talousprosesseja ja luomalla niille kilpailuetua tekoälyn ja datan innovatiivisella hyödyntämisellä. Palveluidemme keskiössä ovat modernit teknologiat, vahva liiketoimintaymmärrys sekä kehittävä ja kasvollinen kumppanuus asiakkaidemme kanssa. Asiakkaitamme auttaa yli 250 Suomessa toimivaa huippuasiantuntijaa.New Certification for machine learning operations (MLOps) engineers
Do your co-workers rely on you to deploy, operationalize, and maintain machine learning and generative AI solutions in production? Are you working at the intersection of data science, DevOps, and generative AI? If so, the Microsoft Certified: Machine Learning Operations (MLOps) Engineer Associate Certification is designed for you. This Certification validates your ability to operationalize not only traditional machine learning but also generative AI solutions on Azure, reflecting how AI roles have evolved from model experimentation to enterprise-scale AI operations. To earn this Certification, you need to pass Exam AI-300: Operationalizing Machine Learning and Generative AI solutions, currently in beta. Key skills validated by this Certification To earn the MLOps engineer Certification, you must demonstrate your ability to: Design and implement secure, scalable MLOps infrastructure. Automate resource provisioning and deployments by using GitHub Actions, Bicep, and Azure CLI. Orchestrate training, manage model registration and versioning, and monitor production models. Deploy and operationalize generative AI solutions by using Microsoft Foundry. Implement quality assurance, observability, and safety evaluations for generative AI systems. Optimize retrieval-augmented generation (RAG) pipelines and fine-tuned models for performance, accuracy, and cost efficiency. This Certification replaces the Microsoft Certified: Azure Data Scientist Associate Certification (Exam DP-100), which is retiring on June 1, 2026, and reflects the evolution of AI in the enterprise. Exam DP-100 focused on validating your ability to design and implement data science solutions, including data exploration, model training, evaluation, and deployment. Exam AI-300 expands the scope significantly. It retains training and evaluation but places much stronger emphasis on validating your knowledge and experience in automation, infrastructure as code (IaC), continuous integration and continuous deployment (CI/CD), lifecycle governance, observability, drift detection, cost control, and the operationalization of generative AI systems. For more details on the retirement of Exam DP-100, and the latest cloud and AI Certification updates, read our recent blog post, The AI job boom is here. Are you ready to showcase your skills? Skill area Exam AI-300 (new) Exam DP-100 (old) MLOps infrastructure Full CI/CD, IaC (Bicep, Azure CLI), GitHub Actions Basic workspace and compute setup Model lifecycle management Core focus, including registration, versioning, rollout/rollback, monitoring Full lifecycle from training to deployment GenAIOps infrastructure End-to-end lifecycle, including security, automation, and model management with Foundry Basic generative AI setup and experimentation QA and observability Generative AI evaluation, tracing, safety metrics, drift detection, cost monitoring Model evaluation and responsible AI principles Generative AI performance optimization RAG optimization, embedding model selection and tuning, advanced fine-tuning, synthetic data management Basic prompt engineering and fine-tuning Ready to prove your skills? Take advantage of the discounted beta exam offer. The first 300 people who take Exam AI-300 (beta) on or before April 2, 2026, can get 80% off market price. To receive the discount, when you register for the exam and are prompted for payment, use code AI300Meridian. 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 April 2, 2026. Please note that this discount is not available in Turkey, Pakistan, India, or China. Get ready to take Exam AI-300 (beta): Review the Exam AI-300 (beta) exam page for details. The Exam AI-300 study guide explores key topics covered in the exam. Want even more in-depth instructor-led training? We’re creating new instructor-led training that will be released in late March 2026. 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? You can take certification exams online, from home or the office. Learn what to expect in Online proctored exams: What to expect and how to prepare. The rescore process starts on the day an exam goes live, and final scores for beta exams are released approximately 10 days after that. For details on the timing of beta exam rescoring and results, check out Creating high-quality exams: The path from beta to live. Ready to get started? Remember, only the first 300 candidates can get 80% off Exam AI-300 (beta) with code AI300Meridian on or before April 2, 2026. Stay tuned for general availability of this Certification in May 2026. Learn more about Microsoft Credentials. Related announcements We recently migrated our subject matter expert (SME) database to LinkedIn. To be notified of beta exam availability or opportunities to help with the development of exam, assessment, or learning content, sign up today for the Microsoft Worldwide Learning SME Group for Credentials.AI prompting tips & tricks for everyday tasks
Simplify your day, with these practical habits that make the most of Microsoft Copilot. In this second blog post in a series of three, Microsoft Senior Learning Manager Ashley Masters Hall shares her practical perspective on the small prompting choices that can make a big difference in Microsoft Copilot results. I’ve been at Microsoft nearly six years now—long enough to see AI go from interesting experiment to everyday tool. In my first post of this series, Bringing AI fluency to every corner of the organization (even yours!), I explored what AI fluency looks like in real life and why it matters for every role. This post picks up where that one left off, as I share my tips & tricks for practical prompting in Microsoft Copilot to make everyday tasks easier. In my experience, most people don’t need more AI. They need fewer weird moments with AI. You know the ones—a confident answer that’s not true, a draft that sounds like a toaster manual, or a summary that technically covers the content but misses the thing you actually care about. So I pulled together a short list of simple habits that can make Copilot more useful and reliable. These tips & tricks help me (and a lot of my colleagues) get better results right away, without spending all day crafting prompts. Five practical prompting tips Five practical prompting tips. Tip #1. Treat Copilot like a teammate, not a vending machine. Begin with this mindset shift: Copilot isn’t necessarily the source of all truth—it’s a brainstorming partner. When I treat Copilot like a collaborator, my results immediately improve. I ask it to think with me, not for me. This is key. A few prompts I use constantly in Copilot include: Give me three options, not one. List your assumptions and what you need to verify. What are the risks or ways this could be misleading? These questions pull the model out of “confident answer” mode and into “help me reason through this” mode. Tip #2. Take prompt templates and edit them like you mean it. I keep a “favorite prompts” doc open all the time—not because prompts are precious, but because “past me” did “future me” a favor. Here are a few templates that work across roles. Copy them, and fill in the brackets to make the prompts your own: Clean summary. Summarize the text below in 4 bullets for [audience]. Include: decisions, risks, open questions, and next steps. Then propose 3 follow‑up questions I should ask. Rewrite with constraints. Rewrite this to be clear and human. Keep it under [number] words. Use a friendly, direct tone. Don’t add new facts. Notes → plan. Turn these notes into a plan with milestones, owners (use placeholders), risks, and dependencies. Output as a table. Brainstorm with trade‑offs. Generate 10 ideas for [goal] tailored to [persona]. For each idea, include a 1-sentence rationale + 1 downside. Decision support. Create a decision matrix comparing [A] vs [B] vs [C] across cost, time, risk, and impact. Before you start, ask me 3 clarifying questions. The magic isn’t the template—it’s the editing. The more specific you make it, the better the output can be. Tip #3. Add a 60‑second quality‑check loop. My rule is “If I’m going to share it, I’m going to check it.” The good news is that you can do that quickly—and you can ask Copilot to help. A few prompts I use for that final pass: Self‑critique. What are 5 ways this could be wrong, incomplete, or misleading? Missing info. What information do you need to be confident in this? Force structure. Put this into a table with columns: claim, evidence, confidence, and what to verify. Sensitivity scan. Flag anything that might be confidential, policy‑sensitive, or risky to share externally. This loop takes just one minute (or even less) and can save hours of cleanup later. Tip #4. Practice on something straightforward. If you’re trying to build confidence in Copilot and in your own skills, don’t start with your highest‑stakes deck. Begin with the things you do all the time—even something not related to work, like planning a meal or a weekend trip. When you start the day with your to‑do list, pick one thing that shows up regularly—the one that makes you think, “There’s got to be a way to spend less time on this.” Then take a few minutes with Copilot to make that task easier. Try this simple routine: Share the task with Copilot and ask, How can I use Copilot to reduce the amount of time I’m spending on this daily task? Tighten the prompt by providing additional clarity and requesting a format. Do the quality-check loop. Save the prompt that worked. That’s it. You’re building practical AI fluency and making your day a little easier. Tip #5. Borrow good prompts from other people. I asked a few colleagues to share their favorite Copilot prompts. Here are some that can change the way you lead with AI: Build my voice. If you’re looking to guide Copilot to reflect your personal voice and style in outputs, try this: Look at the emails and Teams messages I’ve sent in the last two weeks. Use them to create a personal brand voice document I can use to guide Copilot. Triage my inbox. If you’d like help focusing and prioritizing tasks, try this: Summarize my unread emails in a table. Include: Topic | Summary | Action Items | Follow-Up. If I’m directly mentioned, make the topic bold. Daily AI briefing. If you need a quick, reliable snapshot to help you stay current, try this: Compile the key AI news from the last 24 hours into a structured table. Include: Short Topic, Brief Summary, Suggested Impact, Source Name, and Link. Prioritize the entries by potential impact. Include reputable sources across a diverse range of media outlets. Exclude less reliable sources, and avoid overrepresentation of any single outlet. Explain my job simply. If you need details to help colleagues understand your responsibilities, including what you handle, how you contribute, and when to loop you in, try this: Can you summarize my job in layman’s terms? These are great starting points—and they’re even better after you tune them to your role. Four proven prompting tricks Now that you’ve tried those tips, put these tricks to work for clearer, more dependable results from your prompts. Four proven prompting tricks. Trick #1. Iterate and refine. Don’t stop at the first version. Ask for variations, define constraints, and request a fresh angle. Iteration helps you surface better ideas and guide Copilot toward clearer, more dependable output. Trick #2. Ask Copilot what you should have asked. After you get a response that hits the mark, ask Copilot, What should I have prompted you originally to get this in one shot? This one changed everything for me. It’s a fast way to sharpen your prompting habits and learn to guide Copilot with more precision. Trick #3. Let Copilot ask you questions. Sometimes I don’t know which details matter. So I start high level with, Ask me any clarifying questions you need me to answer. It’s a simple way to give Copilot the context to deliver a result that fits your real intent, uncover what matters, and fill in gaps you didn’t even realize were there. Trick #4. Give your prompts the foundation they need. Good prompts aren’t complicated, but they do have a few elements that make them work better. When you’re writing a prompt, include your goal, the context, the source you want Copilot to use, and your expectations for the output. The more of these elements that you include, the better the result can be. Bring your prompts to life The more you prompt, the better you prompt. It’s a skill you build through regular practice each day. Over time, you start to recognize what makes a prompt work—treating Copilot like a teammate, making prompt templates your own, adding a quality-check loop, tightening your instructions, iterating with purpose, and using the right mix of goal, context, source, and expectations. Those small habits add up to clearer drafts, faster cycles, and fewer of those “Why did it write that?” moments. If you want a structured way to practice, check out Craft effective prompts for Microsoft 365 Copilot in AI Skills Navigator. Using real-world scenarios and examples, learn how to craft effective and contextual prompts for different tasks and how to use built-in features in Copilot to get better results faster. And, while you’re in a module in AI Skills Navigator, try the Summarize module or Turn module into podcast feature. Cool, right? Introduction to the “Craft effective prompts for Microsoft 365 Copilot” learning path in AI Skills Navigator. Up next Stay tuned for my third and final post in this series, AI looks different depending on where you are in your career. Let’s talk about that. In the meantime, happy prompting!How equitable AI skilling takes shape inside a global organization
Discover how women across Microsoft are growing their AI fluency. AI is rapidly becoming a baseline skill at work, shaping how we write, analyze, build, collaborate, and lead. Yet access to AI skill-building isn’t always evenly distributed. AI equity research by Randstad found that 71% of AI-skilled talent are men and 29% are women: a 42 percentage-point gap. This same research found that women are 5% less likely than men to be offered AI skilling opportunities. That combination points in the wrong direction, as AI becomes table stakes. Closing this gap is not just an equity imperative; it’s also how organizations build the broad capabilities they need to perform at their best. Skilling at the frontier That’s why the idea of frontier firms matters: these are places where people and AI work together every day and where learning how to work with AI is a core job skill built into normal workflows and reinforced with training. At Microsoft, AI skilling isn’t reserved for a few teams; it’s part of everyday work across roles. People learn by using the tools on real tasks, taking advantage of training opportunities, sharing what works, and helping colleagues build skills, too. When learning is baked into how everyone works, access is broader by default and skilling becomes more equitable. Spotlight on women building with AI To see what this looks like in real life, we invited women across Microsoft to share how AI skilling is changing their work. What came back was a set of more than 50 powerful stories from women who upskilled, worked through real constraints, and built new workflows that made their work better. We’re highlighting three of those impactful stories. Melody Chen Melody Chen, a Senior Finance Manager, shared her experience turning curiosity into real operational gains. As AI accelerated across the industry, she didn’t wait for a perfect “finance AI” playbook but instead started experimenting inside the work she already owned. She built her Microsoft 365 Copilot skills through experience and practice, earned the Microsoft Certified: AI Business Professional Certification, and then translated what she learned into lightweight solutions that remove everyday friction for her team: an onboarding agent in Microsoft Copilot Studio so new hires could self-serve answers, simple Power Automate workflows to reduce manual follow-up, and repeatable Copilot prompts in Excel that clean and format data consistently for recurring reporting. Those small builds added up, saving her hours of work and, more importantly, creating a team habit of asking, “What can we simplify?” Her takeaway for readers is that you don’t need to be highly technical to lead with AI: pick one workflow, make one improvement, and let small wins compound into confidence and momentum. Ramya Gangula Ramya Gangula is a Senior Cloud Solution Architect who works with healthcare customers, where “almost right” isn’t good enough. As AI became more real in day-to-day work, customer conversations moved from exploring possibilities to planning for safe rollout. So she built up her experience, developed her skills through real implementations, and backed them up with multiple Certifications, including GitHub Copilot, Microsoft Certified: DevOps Engineer Expert, and Microsoft Certified: Azure AI Engineer Associate. That work helped her to design secure, enterprise-ready AI architectures and to guide teams past one-off demos into patterns they could actually reuse in production. Her takeaway is simple: pick a real problem, learn by doing, and write down what works so others can move faster. As she put it, “Imposter syndrome is common, especially in fast-moving fields like AI, but confidence grows through action. Invest in skilling, apply what you learn, and trust that your perspective matters. Women are not just adapting to AI—we are shaping how it’s responsibly used.” Aja Hall Aja Hall is an early-career Product Designer at Microsoft who entered tech through the Microsoft Leap program without a college degree. In just a few years, she has become Chair of the Black at Microsoft (BAM) Puget Sound chapter and a driving force behind AI initiatives, building custom agents in Copilot and designing AI toolkits that help her team work faster. Aja contributed to standout projects, including an AI wireframing Figma plugin that speeds Azure design ideation, an Azure accessibility hub that centralizes guidance for inclusive and scalable experiences, and an accessibility-driven Copilot agent tailored for dyslexic and neurodivergent users. Her creativity and leadership earned hackathon accolades three years in a row. Now, she’s spearheading the 2026 BAM AI Innovation Challenge, where she mentors colleagues and fosters a culture of AI innovation and upskilling. Leading by example and actively advocating for women to build AI fluency, Aja is helping to close the tech skills gap and empower more voices in the AI space. The throughline for successful skilling What stands out across these stories is not how advanced anyone was at the start, but how quickly their abilities grew and compounded after they began. Some women pursued structured learning paths and earned Microsoft Certifications. Others learned through practical application, using AI to synthesize information, draft first passes, reduce manual work, and turn ambiguity into next steps. Across roles and tool sets, the throughline is that progress accelerates when learning is practical, supported, and connected to daily work, because that’s where people can test, refine, and build judgment in real time. Let’s invite everyone in The future of AI at work will be shaped by organizations that treat learning as infrastructure and address access as a design principle. In those environments, women are not left to find their way on their own. Everyone is invited in, supported, and sponsored to build, apply, and lead with AI, and their input shapes what responsible adoption looks like. The voices in these stories are a reminder that transformation is rarely one single dramatic leap. More often, it’s a series of supported steps that compound over time. And this is what frontier organizations do. Take the first step toward upskilling yourself or your team with AI Skills Navigator.
