New Microsoft Certified: Azure Databricks Data Engineer Associate Certification
As a data engineer, you understand that AI performance depends directly on the quality of its data. If the data isn’t clean, well-managed, and accessible at scale, even the most sophisticated AI models won’t perform as expected. Introducing the Microsoft Certified: Azure Databricks Data Engineer Associate Certification, designed to prove that you have the skills required to build and operate reliable data systems by using Azure Databricks. To earn the Certification, you need to pass Exam DP-750: Implementing Data Engineering Solutions Using Azure Databricks, currently in beta. Is this Certification right for you? This Certification offers you the opportunity to prove your skills and validate your expertise in the following areas: Core technical skills Ingesting, transforming, and modeling data using SQL and Python Building production data pipelines on Azure Databricks Implementing software development lifecycle (SDLC) practices with Git-based workflows Integrating Azure Databricks with key Microsoft services, such as Azure Storage, Azure Data Factory, Azure Monitor, Azure Key Vault, and Microsoft Entra ID Governance and security Securing and governing data with Unity Catalog and Microsoft Purview Applying workspace, cluster, and data-level security best practices Performance and reliability Optimizing compute, caching, partitioning, and Delta Lake design patterns Troubleshooting and resolving issues with jobs and pipelines Managing workloads across development, staging, and production For engineers already familiar with Azure Databricks, this Certification bridges the gap between general Azure Databricks skills and the Azure‑specific architecture, security, and operational patterns that employers increasingly expect. Ready to prove your skills? The first 300 candidates can save 80% Take advantage of the discounted beta exam offer. The first 300 people who take Exam DP-750 (beta) on or before April 2, 2026, can get 80% off. To receive the discount, when you register for the exam and are prompted for payment, use code DP750Deltona. 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. How to prepare Get ready to take Exam DP-750 (beta): Review the Exam DP-750 (beta) exam page for details. The Exam DP-750 study guide explores key topics covered in the exam. Work through the Plan on Microsoft Learn: Get Exam‑Ready for DP‑750: Azure Databricks Data Engineer Associate Certification. Need other preparation ideas? Check out Just How Does One Prepare for Beta Exams? You can take Certification exams online, from your home or office. Learn what to expect in Online proctored exams: What to expect and how to prepare. Interested in unlocking more Azure Databricks expertise? Grow your skills and take the next step by exploring Databricks credentials and show what you can do with Azure Databricks. Ready to get started? Remember, only the first 300 candidates can get 80% Exam DP-750 (beta) with code DP750Deltona on or before April 2, 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 early May 2026. Get involved: Help shape future Microsoft Credentials 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. Additional information For more cloud and AI Certification updates, read our recent blog post, The AI job boom is here. Are you ready to showcase your skills? Explore Microsoft Credentials on AI Skills Navigator.
What’s new in AI Skills Navigator: April 2026
Learn about recent improvements to playlists, skilling sessions, partner discovery, and credentials, based on what learners and team leaders told us they need to build skills with confidence. Priya Vaidyanathan is a Director of Product Management at Microsoft, where she leads the product management teams that are building AI Skills Navigator. AI Skills Navigator exists to help people and teams build confidence with AI skills, through clear paths, practical learning, and guidance that fits real work. It brings together trusted training and credentials from Microsoft, LinkedIn, GitHub, and other sources in one connected experience so you can build skills without bouncing between tools. Since launch, we’ve kept improving the experience, guided by your feedback, based on how people are learning and working. For the deeper story behind why we built AI Skills Navigator, read The moment AI skilling stopped being optional—and started being personal. Today’s post focuses on what’s changed, including recent updates designed to make it easier to guide teams, learn at your own pace, and stay on track. Skilling playlists: From small teams to organization-wide rollouts Skilling playlists in AI Skills Navigator turn priorities into clear, role-aligned paths, helping teams focus on the skills that matter for their day-to-day work. They’re often where people begin, especially when team leaders want learning plans tied to real projects and role-specific responsibilities. The process for creating playlists is now more transparent and efficient. As you draft a playlist with AI support, you can see why specific content is suggested, apply your own judgment, and adjust as needed. You can also select multiple AI-suggested prompts in a single interaction, reducing manual effort while maintaining control of what your teams learn next. We’ve improved visibility for playlist owners and team leaders. Real-time progress tracking helps you see how your learners are moving through a playlist, spot where support may be needed, and adjust plans as priorities change. Progress view in an AI Skills Navigator skilling playlist. We’ve also enhanced skilling playlists to support wider rollouts, whether you’re guiding a small team or coordinating learning across an organization. We made this change based on your feedback, so playlists can scale with your needs—without added complexity. Skilling sessions: Now more flexible for real workdays Skilling sessions in AI Skills Navigator feature content written by human experts and presented by AI, with in-session support from the Skilling Coach agent. You can ask questions at any time, and the coach prompts quick knowledge checks and reflections to reinforce what you’re learning. Recent improvements give learners more control over how they explore the sessions, including the ability to move forward or rewind, adjust playback speed, and save progress to resume later. These updates make it easier for learners to fit deeper learning into their workdays, while continuing to build skills they can apply right away. Pro tip: Find skilling sessions in Explore content, where you can browse and filter by learning type. Microsoft Training Services Partners: Tailored, local training support From individual skill‑building to supporting your team’s goals, Training Services Partners can help. They can tailor instructor-led training to your needs, from role- and project-specific enablement to local language delivery and flexible formats (in-person, virtual, or blended). The new Training Services Partners directory in AI Skills Navigator makes it easier to discover Microsoft partners that can support human‑led training, customized programs, and organization‑wide skilling initiatives. This directory helps organizations move from individual learning to coordinated, supported skilling, without having to search across multiple sites or programs. New content and credentials: All in one place AI Skills Navigator brings training and credentials together so you can follow a clear path and build toward readiness. The Microsoft Certified: AI Transformation Leader and Microsoft Certified: AI Business Professional Certifications are now generally available, giving individuals and organizations a way to validate applied capability—not just knowledge. With training and credential prep in one place, you can focus on getting ready and proving what you can do. Earn an AI Transformation Leader or AI Business Professional Certification. As we continue evolving the content and credentials available in AI Skills Navigator, our focus remains the same: making it easier for you to find relevant learning, build skills that stick, and demonstrate progress over time. We’re listening—and continuing to improve AI Skills Navigator These updates reflect your feedback: you’ve asked for clearer ways to guide teams with playlists, more flexible learning through skilling sessions, and more support when you want it, through partner discovery and credentials in one place. We’ll keep evolving AI Skills Navigator based on how people learn, how teams work, and what we hear from individuals and organizations. And we’ll continue sharing updates as the experience grows. Sign in to get started with AI Skills Navigator. Sign in to AI Skills Navigator to see what’s most relevant for you, and pick up right where you left off. Stay tuned for more updates soon. In our next Inside AI Skills Navigator post, we’ll take a closer look at skilling playlists.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.
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.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.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.
The moment AI skilling stopped being optional—and started being personal
Find out what it takes to build AI skills with confidence, individually and as a team. Kavitha Radhakrishnan is a General Manager in Microsoft Global Skilling, where she leads the teams creating AI‑first, learner‑centered experiences that help people and teams build skills they can apply at work. Sunday night. The week hasn’t started yet, but the questions already have. A leader is scrolling through AI headlines, trying to keep up with the constant changes. Every day there’s a new tool, a new capability, a new prediction about how work is changing. And it is. By Monday morning, the pressure isn’t theoretical; it’s sitting on a packed calendar and a team that’s already running hot. Everyone’s saying, “We should be using AI,” but nobody’s quite sure what that means for this team, this week. Elsewhere, an employee is watching coworkers use AI with speed and confidence. They want to keep up without feeling exposed for what they don’t know yet. The gap isn’t intelligence; it’s psychological safety and a clear starting point. And then there’s the learning leader who’s had the “training participation” conversation a hundred times, but now the question is sharper. It isn’t How many people finished?, but What changed in the way they work? The bar has moved from awareness to application. None of these people are asking for more content. There’s plenty of that. They want a path that respects their time, fits their role, and helps them build confidence, both individually and as part of a team. Enter AI Skills Navigator AI is moving faster than most of us can track. The problem is figuring out what to do next. Leaders don’t want to stitch together five different tools. People don’t want another long course about AI. Teams are looking for skilling that fits into real work. That’s the gap AI Skills Navigator is built to address. AI Skills Navigator brings role‑based, practical skilling into a single experience, so individuals and teams have a clear starting point, a sense of direction, and ways to see progress as they go. Instead of an endless catalog, it offers guided paths that respect time, align to real responsibilities, and make it easier to turn learning into action. At its core, it’s designed to help turn skilling into execution—progress that people can feel and leaders can point to. How AI Skills Navigator fits your flow Alex, a team manager, is trying to set the team up for success. The team is kicking off a new project with clear goals, tight timelines, and a mix of responsibilities across roles. Everyone is expected to use AI more effectively, but “go learn AI” isn’t a plan. Sending people to a long list of links doesn’t help either. So Alex turns to AI Skills Navigator. Instead of gathering content from multiple places, Alex uses AI Skills Navigator to design a skilling playlist for the team. The conversational AI experience helps him identify what his team really needs. The playlist is grounded in what the team is actually working on and intentionally structured around the project goals and role-specific responsibilities. It brings together different content formats on purpose: short sessions for core concepts, practice where it matters, and optional deeper dives for people who want to explore further. It’s not about forcing everyone through the same experience. It’s about giving the team a shared path forward, while respecting different roles, learning needs, and preferences. Sam, an experienced marketing manager on the team, doesn’t have to figure out where to start. A link from Alex lands in their inbox, and the intent is clear: this is what matters for our work right now. As Sam works through the playlist, they move naturally between different ways of learning. The structure makes it easy for them to focus without feeling boxed in. For a topic that matters most to the project, Sam chooses a skilling session. A video sets the context, and the Skilling Session Coach AI agent is there along the way—ready to clarify a concept, answer a quick question, or pause to check understanding. Sometimes there’s a short quiz to help Sam confirm that they’re learning and making progress. People are juggling meetings, messages, deadlines, and more. Attention spans are shorter, and learning often happens in brief moments between tasks. AI Skills Navigator is designed for that reality. Sometimes Sam feels like listening instead of reading. An AI‑generated podcast turns dense material into something easier to absorb. When time is tight, an AI-generated summary helps Sam catch up in minutes, without losing the thread. From Alex’s perspective, there’s a simple view of how the team is progressing—enough to see who’s moving forward, where people might be getting stuck, and when it’s time to adjust the plan. Together, these moments add up. Skilling sessions provide depth when it’s needed. Podcasts and summaries offer flexibility when attention is limited. Skilling playlists keep everything connected, so learning feels purposeful rather than scattered. By combining structured paths with flexible ways to learn, and pairing AI support with human expertise, AI Skills Navigator helps individuals and teams build confidence, apply skills, and make progress together. How we’re building confidence, together AI Skills Navigator is designed to help people and teams build confidence, not by adding more noise, but by providing guidance that fits real work. It brings together training content from sources that many people already know and trust, including Microsoft Learn, LinkedIn, and GitHub, and connects it into structured paths that make it easier to start, go deeper, and keep moving forward. Whether you’re learning on your own or designing skilling for a team, the goal is the same: turn learning into progress that you can feel. And this isn’t static. We’ll continue evolving AI Skills Navigator based on how people learn, how teams work, and the feedback we hear from learners and leaders along the way. We’ll share updates regularly, including new content, new capabilities, and what’s coming next, so you can stay current as the experience grows. After you've signed in, you can get started with these options. (Pro tip: To expand the navigation pane on the left, try selecting it.) Create a skilling playlist for your team. Try the “Explore Microsoft 365 Copilot Chat” skilling session. Access all available AI Skills Navigator training content. Review Microsoft Credentials in AI Skills Navigator.
Four Best Practices for Leading Through AI Adoption
Actionable guidance for building AI leadership skills—drawn from real customer conversations. Chris Henley is a Microsoft Trainer, part of a community of professionals at Microsoft Global Skilling, working with customer and partner leaders to help them build the skills required to drive their organization’s AI strategy. Where AI conversations are shifting for leaders I’ve been working with executives and company leaders for several years, and it feels like the AI conversation is finally shifting from “What can AI do?” to “How do we move forward with AI in a way that creates real value?” That shift often shows up as organizations move beyond isolated pilots and begin integrating AI across the business: into processes, employee experiences, customer engagement, and innovation. Microsoft refers to this broader shift as Frontier Transformation, where AI becomes a strategic priority and changes how intelligence operates inside the organization, not just which tools people use. From what we’re hearing from executives, one thing becomes clear: AI adoption rarely comes down to a single decision. Progress unfolds through small experiments, sharper priorities, and measured results that reveal what’s working and what to do next. What seems to help drive adoption progress isn’t a rigid plan. It’s returning to a few best practices that show up regularly in real business discussions: Reframe: recognizing that AI is not just another tool rollout, but a shift in how work is structured, how decisions are made, and where intelligence shows up across the business. Focus: identifying a specific business priority where AI can create measurable value, rather than spreading experimentation across too many disconnected pilots. Assess: taking an honest look at whether the organization is ready to move forward across data foundations, leadership alignment, team capabilities, and governance. Commit: selecting a defined AI initiative, assigning ownership, and establishing how success will be measured over a clear timeframe. These aren’t meant to be a strict sequence. Leaders often move between them as they clarify strategy, prioritize investments, and decide what to do next. The following sections take a closer look at how each of these best practices show up in real leadership discussions about AI adoption. 1. Reframing how to think about AI in the organization AI often begins framed as a tool rollout, but leaders I work with frequently find that this narrows the discussion too quickly. Many have shared that the most useful shift happens when the question moves beyond “How do we use this?” to something broader: “Where could AI change how our business actually works?” I was recently delivering a training session with one of our customers, a consulting firm that had rolled out Microsoft 365 Copilot. Early wins were familiar: faster emails, cleaner summaries, better documentation, and the team was energized. But in one session we paused and asked a tougher question: if AI is now part of the business, should client reporting and analysis still look the same? The focus shifted from incremental productivity gains to rethinking how insights were created and delivered. You start to see where work should be redesigned—not just sped up. The technology remained the same, but leadership perspectives evolved. 2. Choosing where to focus before moving forward Another common situation we’re seeing is companies trying to use many different AI solutions in hopes of finding an area where AI might have an impact. Pilots are running across the business, but their intended business impact is unclear. Activity isn’t the same as progress. Momentum usually picks up when leaders choose one area where AI clearly connects to a meaningful business outcome. The experience of one of our customers, a global automotive manufacturer, is a good example of this. Rather than trying to use AI everywhere, they pinpointed a bottleneck that was slowing down their accounting workflows. So, they applied AI document intelligence to that problem first. That targeted focus reclaimed thousands of hours of manual work. You can see what’s working and what it tells you about your organization. Investment conversations become easier, because you’re not funding “AI.” You’re funding a business outcome. 3. Assessing organizational readiness as aspiration meets reality One of the most useful shifts happens when leaders pause to examine how ready the organization actually is for AI. It’s easy to assume you’re “AI-ready,” but that closer look often reveals where ambition is moving faster than capability. In one executive discussion, a leader paused and said, “I thought this was an IT implementation. I didn’t realize how much AI would change how my leadership team operates.” That moment shifted the conversation from infrastructure and deployment to the real question: Was the organization ready to operate differently with AI? The team shifted its attention to making better decisions, ownership, and leadership readiness. You can see whether AI is tied to business priorities, whether teams have enough hands-on capability, whether the culture supports experimentation and learning, and whether risk and accountability are clearly defined. 4. Committing to shape your AI strategy through action Here’s something I see a lot: Leadership teams often agree AI is important, but progress stalls when no one defines the next step or who owns it. In one session, an executive team had been reviewing use cases for months. Mid-meeting, someone finally said, “We’ve been talking about this for a while. What have we accomplished?” That simple question immediately shifted the conversation from exploration to ownership. The team aligned on one outcome and how they’d measure it, and that’s when momentum finally started. The goal isn’t to have the perfect strategy upfront. It’s to commit to a focused, informed, effort that starts to transform your business in a meaningful way. The efforts that gain traction are usually clearly defined, have senior leadership behind them, and everyone on the team is aligned on how to measure progress. Time to roll up the sleeves and get to work In the end, leading through AI adoption isn’t about getting everything right from the start. What I see work most often is leaders building the skills to navigate it as they go, and learning how to judge where AI really matters, where to begin, and how to move forward through focused action. If you’d like a practical way to start that process, check out our new Develop AI Leadership Skills guide for a structured starting point. It’ll help you think about your organization's next steps with AI and it’ll give you a clear framework for prioritizing actions and moving ahead confidently.
