These questions are especially important in Europe and around the world. Organizations want access to leading AI capabilities while maintaining confidence in how sensitive data, security, infrastructure, and operations are governed. Microsoft’s goal is to enable organizations to adopt AI while helping them maintain the level of control each workload requires to give leaders the confidence to move forward with AI.

Microsoft defines sovereign AI as the design, deployment, and operation of AI workloads under defined controls for data, access, governance, infrastructure, and operations. The right controls depend on the workload, which is why our framework is built around four principles: control, choice, flexibility, and resilience.

This framework is the focus of a new white paper we developed in collaboration with NVIDIA. It helps leaders translate the four principles into practical decisions; from the level of control each workload needs to the operating model that best supports it.

Begin with the workload

We see digital sovereignty as a risk-management principle. Start with the workload and ask: What data will it use or generate, and where can that data be processed? Who needs access to the system, including administrative access? What must keep working if connectivity is interrupted? And how easily must the organization be able to change models or infrastructure as its needs evolve? Consider these questions alongside the workload’s mission, regulatory obligations, and threat profile.

For many workloads, those requirements can be met in the public cloud. Others need greater operational control or infrastructure that remains under the organization’s authority. Some need to operate with limited or no connectivity. Microsoft Sovereign Cloud gives organizations options across the spectrum. Sovereign Public Cloud and Sovereign Private Cloud support different operating models based on the level of control a workload demands.

Sovereignty at every layer

Data location still matters, but an AI workload goes beyond stored data. It can include prompts, models, agents, outputs, and the systems that operate them. Leaders need visibility into which models are being used, the power to switch models if the need arises, where they run, who can administer them, and what happens when connectivity or operating conditions change.

Sovereignty can’t be applied at just one layer. Organizations need control over what an AI system can access and do, which models it can use, and where data and processing can occur. Microsoft provides capabilities across identity, governance, encryption, monitoring, confidential computing, and operational control to help organizations apply and maintain workload-specific controls. Building those requirements into the architecture from the outset, while preserving flexibility in model choice, can make it easier to adapt as workloads, technologies, and regulatory expectations evolve.

Microsoft and NVIDIA bring complementary capabilities across the AI stack. NVIDIA accelerated computing provides the performance required for advanced AI workloads, while NVIDIA Confidential Computing helps protect sensitive data, models, and AI workloads during processing. Together, Microsoft and NVIDIA provide security and control across the AI stack, from the underlying cloud infrastructure to the applications running on top of it.

That shared approach also informed the development of the Microsoft Sovereign AI white paper, created with contributions from NVIDIA to provide organizations with a practical framework for evaluating control, choice, flexibility, and resilience across their AI environments.

Learn more about Sovereign AI

Explore how Microsoft helps governments and regulated industries adopt advanced AI ​with NVIDIA technologies.

Download the Sovereign AI white paper

Accelerating innovation with model and infrastructure choice

Model choice should start with the flexibility to use the best models available and adopt new models as capabilities, quality, availability, and access evolve, without having to redesign the underlying AI platform. AI is moving too quickly to assume that one model will remain the right answer for every workload. Decoupling the platform from the model gives organizations the freedom to evaluate and change models as technology advances and requirements evolve.

Microsoft Foundry and Foundry Local support development and deployment across a broad model ecosystem, so organizations can evaluate models against the requirements of the workload while maintaining consistent approaches to governance and operations. NVIDIA extends that choice across models, AI software, and accelerated infrastructure. NVIDIA AI Enterprise provides software, frameworks, and inference capabilities for production AI. NVIDIA’s model ecosystem, including Nemotron and physical AI models, gives organizations additional options for building and deploying AI. NVIDIA RTX PRO infrastructure extends that choice to the compute layer, supporting demanding inference, agentic, and reasoning workloads.

AI workloads will continue to become more capable and more distributed. Organizations need accelerated computing, software, and model choice that can follow those workloads across cloud, data center, and edge environments. Our work with Microsoft is focused on giving them that foundation with security and control built in.

Dave Salvator, Director, Accelerated Computing Product Marketing, NVIDIA

Model choice is only part of the equation. Organizations also need flexibility in the infrastructure, software, and environments that support those models. An organization may decide to adopt a different model, move processing closer to its data, or change where a workload runs as its requirements evolve.

Scaling from cloud to edge

Where AI runs is becoming as important as the models an organization chooses. For many workloads, the public cloud will remain the right environment, with access to scale and advanced AI services. Others need AI closer to sensitive data or physical operations. An industrial system may need low-latency inference on site. A government or critical infrastructure workload may need to continue operating during a network disruption.

Organizations should not have to reinvent their AI operating model for every environment. Sovereign Private Cloud, built on Azure Local and Foundry Local, extends AI capabilities into environments that remain under the organization’s authority, including connected, intermittently connected, and disconnected deployments. NVIDIA accelerated computing and AI software stack provides a common foundation across these deployment models. With NVIDIA infrastructure and AI stack available across Azure and Azure Local, organizations can bring AI training, inference and increasingly demanding agentic, industrial, and physical AI workloads closer to the data and operations they depend on.

The real test of deployment flexibility is when conditions change. Identify which functions must continue during a connectivity disruption, which can pause, and what data or services each depends on. Then test those assumptions under the conditions the workload may actually face. As AI expands into agentic, industrial, and physical systems, more critical decisions will happen close to data, equipment, and users. The infrastructure needs to follow.

Building for inevitable change

No one can predict exactly what AI requirements will look like five years from now. Models will improve, infrastructure will change, and regulations won’t remain static. New workloads will create requirements that are difficult to anticipate today. IT architecture choices should account for this.

Organizations benefit from a consistent foundation that allows workloads to move across sovereign environments while maintaining common approaches to identity, security, governance, and AI development.

Durable AI strategies will be built for change. Organizations need enough control to use AI confidently, paired with enough choice to keep adopting better technology, and an architecture that can adapt when requirements change.

Learn more about digital sovereignty at Microsoft

• Read the Microsoft Sovereign AI white paper to explore a practical framework for building AI around control, choice, flexibility, and resilience.
• Explore Microsoft Sovereign Cloud resources for your region.
• Read the Microsoft Sovereign Cloud in Europe white paper.
• Watch the Sovereignty Sessions webinar.
• Read why sovereign AI starts with sovereign data, and how Sovereign Private Cloud puts AI to work on sensitive data without giving up control from Douglas Phillips.

Learn more about Sovereign AI

Explore how Microsoft helps governments and regulated industries adopt advanced AI ​with NVIDIA technologies.

Download the Sovereign AI white paper

The post Sovereign AI: Accelerate innovation with control and choice appeared first on The Microsoft Cloud Blog.

That commitment continues today, in a technological moment unlike any we have seen. AI can personalize learning, reduce administrative burden, accelerate research and expand access to high-quality instruction. It also brings real risk. Educators and parents are asking whether students are still doing the thinking, and whether the tools their children use are safe. Those are the right questions.

At its core, teaching and inspiring students to become their best selves is a deeply human endeavor. Education is built on a meaningful partnership among educators, students and families that helps unlock human potential and shape brighter futures.

No one company understands entirely how AI will reshape education. But uncertainty calls for more care, not less. That is why Microsoft is taking a holistic approach, grounded in five principles.

Safety, privacy, security and transparency by design
Educators remain in control and at the center of AI use in the classroom
AI designed to support students’ learning, not replace their thinking
AI strengthens education systems, from learning to discovery
Every student prepared for an AI powered future

1. Safety, privacy, security and transparency by design

Every AI system used in education should be designed with robust safeguards that protect student privacy, enhance safety, reduce risk – including safeguards that address bias – and provide the transparency that students, educators and parents deserve.

We believe institutions should never have to choose between innovation and trust.

Security, privacy and transparency are foundational to Microsoft’s approach in education.

That is why last week, we signed a landmark agreement with the American Federation of Teachers (AFT) and introduced a new Privacy & Safety Standard for Schools covering Microsoft Education products. The agreement represents the first time a major technology company and one of America’s largest teachers’ unions have come together to define what trusted AI should look like within these products. The Standard limits how Microsoft Education products can use student and educator data, requires human oversight for consequential decisions, provides meaningful transparency for families and holds Microsoft accountable when we fall short. It also affirms that schools retain ownership of the knowledge and ideas they create.

We did not build this Standard to keep it to ourselves. Students and institutions deserve these protections, whichever technology their school chooses. We invite others across our industry to meet this bar with us.

2. Educators remain in control and at the center of AI use in the classroom

Educators should remain at the center of teaching and learning. AI should augment educator expertise, not replace professional judgment. Educators should help shape the design of AI systems and be equipped with the skills and support needed to use AI in ways that support student outcomes.

Technology alone does not transform education. Educators do. At a school in the Bronx, special education teacher Ashley Hernandez is using Copilot to develop differentiated, sensory-aware materials for students with complex communication and medical needs. Technology helps her adapt learning experiences more efficiently and creates more time to work directly with students. Just as importantly, her experience has helped shape the future of the technology itself.

Educators know best what works in the classroom, and their voices are reflected in the products we create. Through our Education Insiders Program, educators and school leaders participate in private product previews and feedback sessions to help us improve tools before they’re released.

That is why we build products like Teach in Microsoft 365 Copilot. Teach is our education-first AI experience for educators. Designed around real instructional workflows, it helps educators adapt materials for different learners, build lessons that match their standards and spend less time planning. Our goal is not to automate teaching; it is to lift the administrative burden that gets in the way.

Customers tell us this works. Brisbane Catholic Education achieved a 275% increase in learner agency among at-risk cohorts. Since deploying Microsoft 365 Copilot, Miami Dade College has seen a 15% increase in student pass rates and a 12% drop in course dropout rates.

Educators need practical training, communities of practice and in-demand credentials. Through Microsoft Elevate for Educators, we are connecting educators with those resources and pathways. Over the last year, more than 17 million people have completed an in-demand AI skills credential. Through the Elevate for Educators program, we help educators build the understanding and confidence they need to teach about AI effectively and responsibly. We also offer practical materials that help educators introduce generative AI safely and responsibly to students.

3. AI designed to support students’ learning, not replace their thinking

AI systems in education should be grounded in learning science and designed to promote healthy student engagement, critical thinking, student well-being and improved learning outcomes.

According to our recent research on learning science, a significant risk of AI in education is cognitive offloading that creates the appearance of learning without the cognitive work that makes learning durable. This is a real problem. Students think they are learning when they are not.

Many commonly used tools were not designed for education. They prioritize speed over understanding, and many institutions have responded by limiting or blocking access entirely, even as students continue to use AI on their own. Without a purpose-built learning experience, trust becomes the barrier to enabling AI for students at all.

Education needs AI designed to support the purposes of education: helping people build knowledge, critical thinking, judgement and confidence.

Some of the most important learning happens when students are stuck. Difficulty builds understanding that lasts. AI that removes that struggle can quietly move the thinking from the learner to the machine. We must protect the work that helps learners grow.

Microsoft’s AI experiences in education are age-appropriate by design — including default-off access to Copilot Chat for K-12 students with administrator controls for age-based enablement as learners progress. In earlier grades, Minecraft Education builds foundational AI literacy while Learning Accelerators personalize support and provide timely feedback. As students advance, the Study and Learn Agent uses scaffolded questions and interactive practice to help learners work through concepts without doing the work for them. For older learners, Microsoft 365 Copilot introduces the same enterprise-grade tools they will use in their careers.

4. AI strengthens education systems, from learning to discovery

Strong education institutions create opportunity by identifying needs early, acting quickly and directing limited resources where they matter most.

AI can help institutions support students sooner, reduce paperwork, use information more effectively, strengthen security and accelerate research and discovery.

Today, important information is often scattered across different systems and teams. Used responsibly, AI can bring it together so schools, colleges and universities can make better decisions and take coordinated action.

Broward County Public Schools confronted a significant budget shortfall requiring operational efficiency across 235 schools. Despite being data-rich, the district lacked real-time insights. They deployed Microsoft 365 Copilot integrated with its Microsoft 365 ecosystem and identified significant operational inefficiencies that helped them achieve projected facilities savings of $40 to $50 million over five years. That savings could be reinvested in supporting their educators and students.

At University of North Carolina, medical researchers are using a secure research environment built on Microsoft Azure to conduct data-driven clinical research while protecting sensitive patient information. It’s now supporting 25 active studies and expanding access to advanced analytics and AI capabilities within a controlled research environment.

Innovative educational systems do more than adopt new technology. They drive coordinated action that results in better outcomes for learners, educators, researchers and communities.

5. Every student prepared for an AI powered future

Every student should have the opportunity to develop the knowledge and judgment needed to use AI responsibly and the skills to prosper from the emerging AI economy.

The most consequential AI divide of the next decade may not be who has access to AI. It may be who has access to AI skills and the pathways that turn those skills into economic opportunity.

The AI Economy Institute, Microsoft’s flagship think tank, recently released a book exploring how AI technologies shape the education experience and found that AI-related skills command wage premiums up to 40%, surpassing returns from advanced degrees.

AI Literacy is now nonnegotiable across every discipline. The importance of understanding AI fundamentals, limitations and ethical implications — including healthy skepticism toward outputs — is universal.

Offerings like Minecraft Education AI Ready Skills help students build that foundation through a game-based pathway culminating in a credential aligned to CSTA and ISTE standards

Our partnerships with the American Federation of Teachers, the National Education Association, National Applied AI Consortium and North America’s Building Trades Unions help to ensure this work is informed by educators, workers and community members alike. Their insights help guide the tools we build, the skills we teach and the pathways we create so that students can succeed in an AI-powered economy.

Expanding our NABTU partnership, we recently introduced the Jobs Machine to help parents, students and high school counselors explore the skilled trades as a valuable education and career pathway.

In higher education, institutions are turning AI literacy into curriculum. Auburn University created a “Teaching with Artificial Intelligence” course now available to nearly 10,000 faculty at more than 90 institutions. Babson College built an interdisciplinary AI lab — The Generator — integrating Azure OpenAI tools and Copilot assistants across every faculty member and student. And the University of South Florida built a student ambassador program that has driven 55 to 60% time savings on some tasks and now demonstrates practical AI use in real department workflows.

The aim is not to train learners for one fixed set of tools. It is to help them develop the knowledge, agency and judgment to keep adapting throughout their lives.

The future of education will not be defined by technology alone. It will be defined by how effectively we bring together innovation, trust and human potential.

Building the future of education, together

Microsoft will continue investing across all five principles and, more importantly, alongside the educators, learners, institutions, governments, labor organizations and communities shaping education’s next chapter.

AI is already changing how people learn and work. The responsibility and the opportunity before all of us is to ensure it strengthens learning, expands opportunity and keeps people at the center.

That future is already taking shape, and we are committed to building it together.

Justin Spelhaug leads Microsoft Elevate, a global initiative helping education, workforce and nonprofit organizations expand opportunity and help prepare people for the AI economy. During his 28 years at Microsoft, he has held leadership roles advancing technology’s impact for people and communities. He began his career serving in the U.S. Marine Corps.

The post Microsoft’s commitment for AI in education appeared first on The Microsoft Cloud Blog.

AI is moving fast, and so are societal expectations. The boundaries of what people can accomplish with AI are expanding, and communities are asking more questions about how AI systems are designed, built, and used. As AI becomes more integral to how we live and work, confidence that AI systems are operating reliably and securely is becoming an essential prerequisite to their broad and beneficial adoption.

At Microsoft, we have been building a responsible AI program for nearly a decade, rooted in two core beliefs: that trust is foundational to realizing the benefits of AI and that the empowerment of people and organizations must remain at the center of our strategy. As capabilities advance and adoption accelerates, that experience is helping us meet this moment and adapt for what comes next.

Our third annual Responsible AI Transparency Report shares how our program is evolving and the priorities that continue to shape our work. Over the last year, investing in three specific areas has enabled us to embed trust more deeply and at greater scale: adaptive governance and technical risk management, practical tools and capabilities, and shared practices and strong partnerships. These investments cut across five trends shaping the AI landscape, including the rapid expansion of agentic AI.

Taken together, these trends and investments underscore our view that model capability alone will not determine the impact of AI. That will depend on organizations that develop and deploy AI technologies that deliver real value—and govern them with the rigor and adaptability needed to earn and sustain trust.

Adaptive governance and technical risk management
As the frontiers of AI advance, we are making our governance more adaptive and more tightly integrated with engineering workflows. In practice, this means that we have updated our policies to better match the AI tech stack and AI value chain, evolved our risk management practices to address emerging AI capabilities and risks, and strengthened the readiness of our responsible AI community: the people who operationalize our program at enterprise scale.

This year, we re-engineered our Responsible AI Standard to make it more adaptive to evolving technical realities, uses, risks, and regulatory requirements. The new Standard is structured by reference to different components of the tech stack—models, platform services, and applications—and the role that Microsoft plays in developing or deploying those components. It combines core requirements that always apply with more targeted, scenario-specific requirements that can evolve as capabilities and risks change. For example, we apply some of our most rigorous risk management measures to AI systems with the most significant cyber capabilities, helping ensure that advances in AI favor the defenders responsible for securing critical digital infrastructure.

We are also evolving our technical risk management practices. Increasingly capable systems can retain memory, use tools, access data, and take actions on behalf of users. Governing these systems requires us to think beyond the behavior of an individual model or application to interactions among models, agents, applications, tools, data, and people. Our work increasingly focuses on controls such as agent identities, tool permissions, and monitoring of actions.

And governance only works when people can put it into practice. We have continued to build responsible AI capabilities across Microsoft, equipping thousands of engineers and product managers with training on topics such as agentic AI threat modeling and prompt injection defenses.

Together, these investments are helping us move toward a more continuous, lifecycle-based approach to AI governance. With agentic AI, risks can evolve as systems interact with their environments, users, and other systems. Our governance needs to evolve with these agentic capabilities—and incorporate what we learn from their testing and deployment.

Practical tools and capabilities
Effective governance depends on tools that help translate policy goals into action. As developers and organizations navigate a more complex technical and regulatory environment, they need practical ways to identify risks, evaluate systems, establish controls, and monitor how AI behaves in the real world.

We are applying what we learn from governing AI at Microsoft into tools, capabilities, and resources that help developers and organizations beyond Microsoft do just that—whether they build on our platforms or leverage open-source projects.

We have expanded tools to evaluate AI systems across the lifecycle. A new AI Red Teaming Agent helps accelerate the identification and evaluation of risks. Agent evaluators help developers measure the quality, safety, and performance of agentic applications. RAMPART turns red team findings into repeatable tests, enabling more continuous coverage as systems change.

We are also building greater visibility and control into agentic systems. With ASSERT and Agent Control Specification, developers can evaluate agents against their policies, place runtime controls at critical points in an agent’s workflow, and monitor behavior.

These tools and capabilities reflect a shift: as systems become more dynamic, governance needs to become more operational. Organizations need to be able to see what their systems are doing, test how they behave, and intervene when necessary—not just assess them before deployment.

Organizations also need confidence—and increasingly need to demonstrate—that responsible AI practices are being implemented consistently. Microsoft is one of the few companies certified against ISO 42001 across a broad portfolio, including Microsoft 365 Copilot, Foundry, and GitHub Copilot. Over the last year, we have simplified and strengthened our internal processes that support that certification.

Ultimately, responsible AI governance is a shared responsibility across the AI value chain. Our goal is to help make the practices and capabilities needed to meet that responsibility more accessible, practical, and scalable.

Shared practices and strong partnerships
The challenges of governing AI are bigger than any one company, and increasingly interconnected AI systems make collaboration even more essential.

As AI adoption expands across borders and sectors, we need shared expectations for how systems are evaluated, monitored, and governed, as well as interoperable standards that enable visibility into interactions across tools, data, and systems. We also need to keep advancing the underlying science and technical practices so that we can benefit from rigorous, applied insights into what effective governance looks like and where the remaining gaps are.

That starts with research. Over the past year, we advanced our work with the US Center for AI Standards and Innovation and AI Safety and Security Institutes in Australia, Singapore, and the UK to strengthen the science and practice of AI evaluation. We also launched an External Red Team Alliance with 18 universities across six continents to expand understanding of priority risks.

Common technical practices and standards are critical. Through the Frontier Model Forum, OpenTelemetry, and the Appia Foundation, we are helping develop approaches spanning frontier cyber benchmarks, end-to-end observability for increasingly agentic systems, and AI assurance across supply chains and sectors. We are also contributing to efforts that make transparency reporting more interoperable across organizations and jurisdictions, including through an OECD-led informal task force that developed the Hiroshima AI Process Reporting Framework version 2.0.

We also need shared ways to measure progress. We cannot meaningfully assess progress if every organization measures AI risks differently. Through our work with MLCommons, we are helping expand AILuminate into a broader suite of reliability benchmarks, creating common approaches for evaluating areas such as jailbreak resilience, multilingual performance, and psychosocial risk in conversational AI.

Shared learning, shared practices and standards, and shared measurement can help the entire ecosystem develop while raising shared expectations for trust.

Meeting the moment and investing for the future
Our experience over the past year has reinforced that responsible AI cannot be static. It has to be embedded in development processes, supported by practical tools, and continually informed by what we learn. That is why our responsible AI investments extend from the systems we build, to the tools we provide our customers, to the research, practices, and measurement approaches we help develop with the broader ecosystem.

Our 2026 Responsible AI Transparency Report explores this work in more depth—from how we re-engineered our Responsible AI Standard to how we are strengthening governance for agentic AI, advancing evaluation, and addressing AI misuse. We invite you to explore the report to see what we have learned, what we have changed, and how we are putting our priorities into practice.

As AI becomes more powerful and more present in people’s lives, our commitment is to keep listening and learning, to keep strengthening our safeguards, and to keep putting the empowerment of people and organizations at the center of our strategy.

The post Responsible AI in 2026: How we are adapting for what’s ahead appeared first on The Microsoft Cloud Blog.

Understanding the people you serve

The word “customer” can take on different meanings depending on the industry. In retail, it may mean shoppers. In healthcare, patients. In education, students and teachers. In nonprofits, the communities they support.

Despite this difference, the leaders we spoke with at several Microsoft AI Tours around the world described a common challenge with the people they serve: expectations continue to rise. They expect experiences that are timely, relevant, and tailored to their needs. At the same time, organizations are being asked to do more with limited resources.

That tension is why reinventing customer engagement has become one of the clearest opportunities for AI—and one of the key paths toward Frontier Transformation.

Many organizations are using AI to better understand their customers and make engagement more meaningful at scale by bringing together information from across interactions and getting deeper insight into their needs.

At Lifeline Australia, a nonprofit that provides crisis support and suicide prevention services, AI is helping their teams gain deeper insight into the services they provide and the people who rely on them.

We are looking into more specialized, tuned AI […] from a data science point of view, to really tease out some of those insights or look at behaviors. Getting that insight into the actual service that we deliver, not just how we deliver it, has been a key game changer for us.

Mark West, Head of Data and Insights, Lifeline Australia

When organizations have a clearer understanding of the people they serve, engagement becomes more relevant, responsive, and impactful.

AI can help organizations uncover deeper insights, identify emerging needs, and make more informed decisions. That understanding creates the foundation for more personalized and meaningful experiences.

Personalization that scales

Personalization has always been one of the main goals of customer engagement. The challenge has been delivering it consistently across large and diverse audiences.

AI is helping organizations address that challenge.

At Brisbane Catholic Education, which supports approximately 80,000 students across 150 schools in Australia, AI is helping educators tailor learning experiences to individual student needs.

With AI we’ve been able to get to the point of what I would call hyper personalization, where a teacher can write just one curriculum document and then have Copilot come in and personalize that for every single child in the class.

Leigh Williams, CIO and Education Executive, Brisbane Catholic Education

Healthcare organizations are seeing similar opportunities.

At Sciensus, a European life sciences organization that helps healthcare partners bring the right medicines to the right patients faster, leaders believe that when AI helps manage information and surface relevant insights, it creates more room for people to focus on relationships, empathy, and expertise.

Sciensus recently launched the CareTranscribe pilot project, an AI-enabled initiative designed to improve clinical documentation workflows so clinicians can spend more time with patients during home treatments while improving visibility into patient interactions and care journeys.

Behind the scenes, Sciensus is also building knowledge systems and using AI to help teams learn from interactions and prescriptions and make information more accessible across the organization. Patient data stays protected at every step, with privacy, security, and responsible AI practices built into how these systems are designed and used.

We massively spiraled into an area where a lot of ours is production-ready AI. So, we’re building live agents, we’re building knowledge bases that we didn’t have beforehand, using unstructured data that’s available across all our domains. Copilot has really helped in that because of the secure aspect of it.

Joseph Frost, Director of Data Science and AI, Sciensus

Building stronger relationships

For many organizations, the real return on AI is what they can do with the time given back to their people. Freed from documentation and repetitive administrative work, employees can spend that time listening, understanding what someone needs, following up, and building the kind of trust that only comes from being present. Customers, patients, students, and communities all feel the difference.

At Everything Suarve, a nonprofit that supports at-risk youth across Australia, AI takes care of the case notes and reporting, so staff can give the young people they serve their full attention and spend more time with them.

What AI has done is actually enabled us to get a lot of time back to spend it where it’s needed. For us to make a difference it’s about being with the young person and not sitting behind a computer.

Joseph Te Puni-Fromont, Founder, Everything Suarve

Uniting NSW.ACT, a community services organization that provides care and support services for older adults, families, and communities across Australia, is seeing similar benefits. Its AI-powered platform, Buddy, helps frontline workers to retrieve and create content in less time, so they can spend more time supporting communities.

Activities that used to take 10 to 15 minutes are now taking one to two minutes. So, times that by 9,000 frontline workers, it’s an incredible amount of time back in the day for them to actually look after our customers and provide better aged care and community care.

Andrew Dome, Chief Digital Information Officer, Uniting NSW.ACT

Overall, through the conversations with customers, we’re hearing that AI is helping them create experiences that are more relevant, more personal, and more responsive to the people they serve. As engagement becomes more contextual and individualized, organizations can build stronger relationships while extending their reach and impact.

Make three practical moves now

1. Start with understanding. Use AI to bring together signals and context so teams can better understand the people they serve and what they need.
2. Personalize with purpose. Look for opportunities where AI can help tailor services, experiences, and communications at a scale that wasn’t previously possible.
3. Remove friction, not relationships. Apply AI to administrative and repetitive work so employees can focus on meaningful interactions and higher-value conversations.

Learn more

• Visit the Frontier Transformation site to explore how you can put AI to work, build trust, and scale transformation with Microsoft across everyday workflows
• Download the e-book Four Paths to Business Value with AI to learn how you can move from AI experimentation to Frontier Transformation with intelligence and trust.

Next in the Accelerating Frontier Transformation series: Reshape business processes.

The post Accelerating Frontier Transformation: Reinvent customer engagement appeared first on The Microsoft Cloud Blog.

As business leaders enter this new phase of transformation, they are navigating an expanding universe of AI apps and agents available, and the technology landscape has never been more dynamic—highlighted by projections that over a billion AI agents will be in use by 2028.¹ This abundance of choice is changing how organizations discover and evaluate technology—they need intelligent, digital-first experiences to help them move with greater speed and confidence.

For business leaders, the question is no longer whether to adopt AI. It is how to make faster, better-informed decisions about where AI can create business value, and connect business priorities to the solutions that deliver on them.

How Microsoft Marketplace accelerates AI success

As business leaders look to move AI workloads into production—teams need assurances that investments will drive value while maintaining governance and control. By procuring apps and agents through Microsoft Marketplace, organizations can:

• Accelerate AI decision making: Microsoft Marketplace provides a single source to discover, compare, and procure AI solutions, helping organizations identify the right solution at the right time.
  
• Choose with confidence: Solutions in Marketplace are published by Microsoft partners and undergo additional validation processes, helping organizations trust that selected solutions align with their existing Microsoft investments and technology strategy.
  
• Maximize and govern AI investments: Procuring through Marketplace centralizes cloud and AI spending in a single commercial channel, providing greater visibility into investments, simplifying management, and helping organizations drive innovation while maintaining governance.

With the industry’s largest catalog of AI apps and agents, Microsoft Marketplace gives organizations access to thousands of pre-vetted solutions that integrate with their existing Microsoft environment, helping accelerate AI adoption and realize value faster.

The rise of AI and self-service for technology purchasing

Research shows that buying journeys are becoming increasingly self-directed and digitally enabled, with AI playing a growing role in how organizations gather information, explore options, and validate investment decisions. According to Gartner®, “a survey of 646 B2B buyers conducted from August through September 2025 found the shift is already underway, with 45% reporting they used AI during a recent purchase. Buyer journeys are becoming more self-directed and digitally mediated.” Additionally, “Buyers want to engage on their own terms as 67% of B2B buyers state that they prefer a rep‑free experience.”²

As technology ecosystems continue to expand, the challenge facing business leaders is no longer access to innovation, but determining which innovations will create meaningful business value and how quickly they can be adopted at scale. AI is increasingly becoming a decision-support layer in the technology buying journey, helping leaders synthesize information, evaluate alternatives, and navigate more effectively.

Enabling leaders to adopt AI with greater speed and confidence

To make it faster and easier to explore and select technology solutions, Microsoft Marketplace recently introduced intelligent discovery, now generally available worldwide.

Rather than relying solely on keywords, categories, or product names, customers describe business challenges in natural language, such as “Find agents for collaboration,” receive contextual recommendations, compare solutions, and evaluate options through conversational experiences.

The result is a tailored outcome-oriented approach to technology discovery, creating more efficiencies connecting organizations’ business needs to potential solutions.

Early preview results show that intelligent discovery is helping customers find the right solutions faster, making them 68% more likely to find solutions that meet their needs and take the next step toward a purchase.³

How business leaders can benefit from intelligent discovery in Marketplace

For a retail leader, that might mean identifying AI solutions that improve demand forecasting or personalize customer experiences. For a security team, it could mean strengthening threat detection while fitting into existing cloud investments. For operations leaders, it may mean finding agents that automate repetitive workflows without adding new complexity. Across each scenario, the goal is the same: quickly uncovering the most relevant solutions for a specific business need or industry scenario, reducing evaluation time, and accelerating decision-making with confidence.

Business leaders are already benefiting from intelligent discovery in Marketplace in three important areas:

1. Starting with business needs, not products

Natural-language experiences allow businesses to describe goals, challenges, or desired outcomes. AI can then interpret context, surface relevant options, and help narrow choices based on business priorities. Business leaders can now find the right solutions easier and faster.

2. Accelerating organizational alignment

AI-assisted product comparison experiences can help teams evaluate solutions side by side, understand key differentiators, and assess options against shared business criteria. With easier access to curated insights, they can build consensus more quickly and make more informed decisions with greater confidence.

3. Getting answers tailored to your needs

Interactive chat available on the product detail page can help business leaders ask questions, explore capabilities, and better understand potential fit through natural language interactions. For example, they can explore industry-specific use cases, understand integration requirements, and evaluate fit within their existing stack for faster time-to-value.

Looking ahead

AI transformation will not be defined solely by advances in models, applications, or agents. It will also be defined by how effectively organizations adopt the right technology solutions to embed AI into the core of the business.

Microsoft Marketplace continues to evolve in support of this broader shift, helping organizations discover, evaluate, and adopt cloud and AI solutions within a trusted ecosystem. Intelligent discovery is a critical part of that evolution, bringing AI-powered guidance directly into the process of exploring and selecting technology solutions.

New intelligent discovery capabilities in Marketplace

Available globally August 25

Explore Microsoft Marketplace now

¹ IDC, IDC Info Snapshot, sponsored by Microsoft, 1.3 Billion AI Agents by 2028, Enterprise Grade and in Production, US53361825, (May 2025).

² Gartner Press Release, Gartner Sales Survey Finds 67% of B2B Buyers Prefer a Rep-Free Experience (March 2026). [gartner.com]. GARTNER is a trademark of Gartner, Inc. and/or its affiliates.

³ Microsoft Marketplace internal study

The post Accelerating AI adoption with intelligent discovery in Microsoft Marketplace appeared first on The Microsoft Cloud Blog.

As AI systems become more capable—and increasingly autonomous—a consistent theme is emerging across enterprise conversations: trust is what can unlock scale, turning AI from isolated experiments into enterprise-wide execution.

Why now?

What we’re also seeing across our enterprise customers is a more pointed shift than “scale”: the organizations pulling ahead are the ones who treat trust as an accelerator of AI, not a tax on it. Many AI conversations could still default to one of two extremes—speed at the expense of control, or control at the expense of speed. Frontier Firms reject that tradeoff.

To fully support human ambition, AI needs to be built on two critical elements: intelligence and trust. Intelligence is what helps AI understand the organization—it’s your data, your workflows, people, and business context. Trust is what allows that intelligence to be used responsibly, securely, and with confidence. With both, AI becomes a platform for achieving business outcomes that actually do more for humanity.

That framing changes what “scaling AI” actually means. It’s no longer about deploying more models or buying more agents—it’s about building the operational discipline to let AI act on behalf of humans without losing visibility, control, or accountability. Across the customers we work with, this is increasingly a key differentiator in how organizations scale AI.

The five signals below are what we hear from leaders who are closing that gap and what separates AI strategies that scale from those that stall in pilot.

Signal 1: You can’t scale what you can’t see

Observability as a key foundation for controlling agentic AI systems

As organizations move from copilots and chat experiences to agentic systems that can take action, the question moves from “Can we build this?” to “Can we stay in control once it’s deployed?” That’s why observability is emerging as a prerequisite for scale: if leaders can’t see what AI systems are doing, they can’t manage risk, improve performance, or build confidence across the business.

If organizations are not providing the tools, employees will still go out and use AI tools themselves—not because of bad faith, but because they want to do more. That creates the risk of shadow AI. We addressed that early on by creating the guardrails.

Lev Malinin, Head of Enterprise Systems, Data and AI, The Salvation Army UK and Ireland

Insight: Most organizations treat observability as a tooling decision when it’s really an organization-wide responsibility. As mentioned earlier, according to Microsoft’s Cyber Pulse AI Security Report, 29% of employees have already turned to unsanctioned AI agents—meaning the visibility gap isn’t theoretical; it’s already inside the business. Closing it means IT teams operating the agent registry, developers building guardrails into runtime, and security teams detecting agent sprawl—together, not in sequence.

Executive takeaway: Frontier Firms answer four questions about every agent in their environment: what agents exist, who is using them, what systems and data they access, and what outcomes they drive. If your AI steering committee can’t answer all four today, observability is your starting point, and our guide details the four capabilities that make answering them possible.

Signal 2: Security and privacy are architectural as well as policy concerns

Security must be built into the system, not layered on

When AI systems interact with enterprise data at scale, policy alone may not be enough. That’s why Microsoft applies the three principles of our Secure Future Initiative to AI:

1. Secure by design: Security comes first when designing any product or service
2. Secure by default: Protections are enforced by default and aren’t optional
3. Secure in operations: Controls and monitoring continuously improve to meet evolving threats

Leaders we work with are increasingly applying the same posture to their AI estate: treating security and privacy as design requirements, not policies layered on after deployment.

Once we built out the knowledge base, we decided we didn’t want patient details. So we started masking the details before they go to AI—guardrails, curated content, heavily locked down.

Joseph Frost, Director of Data Science and AI, Sciensus

Insight: As AI becomes more agentic, the risk profile changes. Security and privacy controls should be integrated into identity, access, monitoring, and governance applying Zero Trust principles to AI agents just as organizations do for users and devices. This becomes especially important as systems gain the ability to reason and take action.

Executive takeaway: “Secure by design” is now a steering committee posture, not an implementation detail. Microsoft’s guide walks through how to apply Zero Trust principles to AI agents the same way you apply them to users and devices.

Signal 3: Governance is continuous—not a milestone

AI should have real-time oversight, not one-time approval

In many organizations, “governance” historically meant a review gate: approve a system, then monitor it lightly. AI changes that model. As AI systems continue to evolve and expand into new workflows, organizations are increasingly approaching governance as an ongoing capability, built for iteration, accountability, and continuous risk management.

We have an AI council—a multi-disciplinary stakeholder group of trustees and colleagues at all levels, across all departments, to think about governance and create the right guardrails to allow safe agent creation, but also to decide which agents are applicable to just a person, a team, a directorates or organization wide.

Kwesi Afful, Executive Director for Digital, Data, and Technology, Scope

Insight: Microsoft applies a three-stage model to governing AI risk:

1. Map: Running AI impact assessments and privacy and security reviews, including red teaming and stress testing, to inform decisions about safety, reliability, and fitness for purpose.
2. Measure: Developing the metrics to assess identified risks and testing planned mitigation methods to determine how effective they will be.
3. Manage: Donsistently monitoring performance, identifying opportunities for user agency, and educating stakeholders about responsible use, with human review and oversight built into the process.

Executive takeaway: Adopt governance as a loop, not a gate. The Map → Measure → Manage model gives your leadership a shared language for what continuous oversight actually looks like—and our guide details how each stage operates across the full AI lifecycle.

Signal 4: Responsible AI is becoming operational accountability

Trust depends on clarity, explainability, and defined ownership

Responsible AI is becoming an operational discipline: clear accountability, human oversight, and the ability to explain decisions—especially as AI systems take on more responsibility. Responsibility isn’t delivered by tooling alone; it requires alignment across engineering, business, legal, and security teams.

A common-sense approach is to treat AI as a shared responsibility. Where AI supports business process or decision-making, organizations should apply appropriate levels of validation, whether through human oversight, automated controls, or business rules depending on risk…When using for internal or customer-facing chatbots, the business function owner should define the governance model, including where outputs require review and where guardrails are enforced. AI interactions should generate appropriate telemetry to detect prompt abuse, attempted instruction bypass, policy violations, and other indicators of misuse.

Ben Thomas, Senior Microsoft Solution Architect, CDW UK & International

Insight: Many organizations scaling AI responsibly are increasingly defining ownership early: who is accountable for outcomes, who monitors drift, and who has authority to intervene when something changes. Microsoft’s approach is built on six principles—privacy and security, reliability and safety, accountability, inclusiveness, transparency, and fairness—each translated into practical questions leadership teams should be asking before a system goes live.

Executive takeaway: Start by defining trust. Establish Responsible AI principles that match your organization’s values and risk posture—then operationalize them through governance, oversight, and transparency. The guide includes the conversation-starter questions to align your steering committee on each of the six principles before you scale.

Signal 5: Digital sovereignty is shaping system architecture

Deployment decisions vary by the level of control each workload requires

As AI scales across regions, sovereignty is emerging as an important strategic risk and design consideration shaping where workloads run, how data is processed, and how organizations maintain control over data, access, and operations across jurisdictions.

It’s more than just residency; it’s also about making workload-level decisions based on risk, regulatory requirements, and operational realities without fragmenting tools and operations.

Sovereignty is on all of our minds—Advania operates across several different geographies as many of our customers do. The thing that plays on my mind most is the models that our data could find its way into and where data is being processed. That’s why we decided to go all in on Copilot for Microsoft 365 in the UK—within the guardrails of our tenant, I know where our data is, and we can maintain governance and compliance around the way we use AI as the interface to our daily work.

Nick Isherwood, Chief Operating Officer, Advania UK

Insight: Sovereignty influences architecture decisions early. When organizations treat it as an afterthought, they often face rework later—especially as AI expands into sensitive data, cross-border operations, and regulated contexts.

The five scenarios we hear most often from customers:

1. Policy and governance aligning data and operations with local compliance standards
2. Sovereign AI with clear storage and processing boundaries
3. Cloud operator access restrictions with provable controls against unauthorized access
4. Data residency and confidentiality to meet local laws and privacy expectations
5. Cloud resilience and business continuity to keep mission-critical services running during natural or geopolitical disruption

Executive takeaway: Address sovereignty requirements upfront. Align on five common sovereignty scenarios and core principles so your AI strategy can scale across jurisdictions without requiring re-architecture or compromising on security, compliance, or innovation trade-offs.

What leaders should do next

The five signals point to one possible conclusion: we expect the next phase of enterprise AI will be defined not by who deploys the most agents, but by who builds the operating discipline to scale them safely. Observability, security, governance, responsible AI, and sovereignty aren’t five separate workstreams—they’re the core of trust platform that lets intelligence become a durable business capability. This is the core of what we call Frontier Transformation at Microsoft: intelligence and trust platforms enable organizations to use any model in a model-diverse, open, and heterogeneous environment while compounding their own organizational intelligence.

The five signals above describe what leaders should pay attention to. Our guide, Grow Your Business with AI You Can Trust, gives your AI steering committee the checklist, shared language and frameworks to act on them—including practical depth on each signal alongside customer evidence showing what the trust foundation looks like in production.

Ready to lead Frontier Transformation with confidence?

Download the strategic guide Grow Your Business with AI You Can Trust for deeper dives, customer stories and shared language you can use across your AI steering committee.

Download the guide

The post 5 signals of trusted AI: How organizations scale AI with security, governance, and observability appeared first on The Microsoft Cloud Blog.

Empowering people to thrive at work

When thoughtfully integrated into everyday work, AI has the potential to expand what we can do. It can help us explore and solve new kinds of problems, synthesize information to support decisions, and reduce time spent on some repetitive tasks. This can ultimately create more space for us to apply our expertise, develop new skills, contribute ideas, and focus on more meaningful, higher-value work.

The leaders we spoke with at several Microsoft AI Tours around the world described how these changes can give employees greater opportunities to learn, create, and grow. They also shared how a stronger employee experience can contribute to better outcomes for their organizations.

This is one part of what Frontier Transformation looks like in practice.

Providing broader access to AI at work

Two factors can help accelerate AI adoption: broader access and deeper integration into everyday workflows. When AI is embedded in the tools people already use, it becomes part of how work happens rather than a separate initiative.

Leaders at EY, a global professional services organization, describe a shift from experimentation to everyday use. By making AI broadly available across the workforce, they have helped create a culture where employees at every level are finding new ways to work and create value.

The biggest shift in our organization with AI has been moving from curiosity to everyday use.

Jenelle McMaster, EY Regional Deputy CEO and People & Culture Leader, Oceania

There are a couple of things that help you get beyond the experiment. One is democratizing AI for everyone […] As a result of that culture, we don’t have a constraint around AI adoption.

Katherine Boiciuc, EY Regional Chief Technology and Innovation Officer, Oceania

For Scope, a UK disability equality organization, accessibility is central to its approach. Leaders of this organization see AI as a way to put powerful capabilities into the hands of more people regardless of technical expertise, helping more people contribute ideas and participate in problem-solving across the organization.

You do not have to be a coder. You do not need to have studied AI for four years at a college or university. All you need is a desire, a problem you are trying to solve, and the passion to see it through and resolve that problem.

Kwesi Afful, Executive Director for Digital, Data, and Technology, Scope

The Salvation Army UK and Ireland has taken a people-first approach to adoption. Rather than starting with technology, the organization focused on helping employees, officers, and volunteers build confidence using AI so they could better serve communities and further their mission.

When it came to AI, it started off with putting people at the center. Our first major focus was rolling out Microsoft Copilot across the organization and supporting the rollout with structured training to give every officer, employee, and volunteer the confidence to serve our people with compassion and innovation.

Lev Malinin, Head of Enterprise Systems, Data and AI, The Salvation Army UK and Ireland

Measuring returns beyond efficiency

Organizations often begin their AI journey looking for efficiency gains. But the leaders we spoke with described returns that go well beyond time savings. They shared that AI can help reduce repetitive work and administrative burden, giving employees the opportunity to focus more on activities where their expertise creates the greatest value.

At EY, one of the biggest benefits has been enabling people to apply their knowledge and skills in new ways. One of their leaders describes seeing employees become more confident in their ability to create value with AI and contribute in ways that were previously out of reach.

The most meaningful outcome for employees at EY because of AI is the ability to do something new. We sell expertise, we sell knowledge, content and information. We still do that, but AI enables us to do that in a new way.

Mary Elizabeth Porray, EY Global Vice Chair, Client Technology

At The Salvation Army UK and Ireland, value is measured by helping people focus on the work that matters most. As the organization expands its use of AI, leaders have emphasized the importance of implementing it thoughtfully and responsibly so it can deliver lasting impact.

It’s really about maximizing value over everything and doing things right. Starts with caution and thinking about what you need to get right.

Lev Malinin, Head of Enterprise Systems, Data and AI, The Salvation Army UK and Ireland

The same theme emerged at Advania UK, an IT services provider. Leaders there see AI creating value not simply through efficiency, but by helping employees spend less time on routine work so they can focus on applying their expertise to higher value activities.

It’s actually about the experience that we’re creating for our employees in the workplace […] It’s going to help us all to become better versions of ourselves as we work across the businesses, and allow our staff to really enjoy the work that they do because they’re not doing the mundane […] they’re able to apply their skills to higher value activities, whether they’re in creative roles or analytical roles.

Nick Isherwood, Chief Operating Officer, Advania UK

AI that works alongside people

Based on these and other customer conversations, we can see that the most effective leaders use AI to support their people while keeping them in charge of making decisions. Organizations pursuing lasting value view AI not as a replacement for human expertise, but as a tool that helps people achieve more.

Baringa, a global management consultancy that helps organizations navigate AI transformation, encourages clients to focus first on building trust and proving value before scaling their efforts.

Have the bigger picture. Have a vision. Have a strategy. But start from a use case, which is going to be small enough for you to be able to build trust and to build confidence that that use case is going to deliver value.

Stela Bokun, Partner, Baringa

Educators can use AI tools to support learning, while remaining responsible for evaluating outputs and directing the learning experience. 

Teach For America, a nonprofit dedicated to expanding educational opportunity, is using AI for exactly that. Through AI incubation programs and hands-on experimentation, employees and educators are exploring new ways to support learning while keeping teachers at the center of the experience.

We don’t want AI to replace great teachers. We want it to augment that. We want it to help them in the classroom. I need to transform this organization to really reimagine how learning happens through AI. That’s virtual coaches and tutors and things like that. Things that we couldn’t do before that.

Alan Murray, Chief Data & Technology Officer, Teach for America

The organization’s Reinvention Lab illustrates what happens when employees are given both the tools and the freedom to experiment. Educators have been trained through hackathons and hands-on learning experiences, resulting in hundreds of AI-powered tools designed to address real classroom challenges.

We have our Reinvention Lab. They have educated over 1,200 educators through a whole bunch of hackathons to educate these folks on how to use AI. That has then resulted in over 720 tools that these educators have built. And these are all kinds of things built in AI for the classroom.

Alan Murray, Chief Data & Technology Officer, Teach for America

Across these stories, we can see a common pattern: organizations can create value when AI works alongside people, supporting human judgment, creativity, and expertise rather than replacing them.

Make three practical moves now

1. Start with the friction. Find the repetitive or draining tasks that slow people down, and identify appropriate ways AI can help address those moments.
2. Widen who can contribute. Use AI to help more people build skills and take on higher-value work, expanding what the team can achieve together.
3. Build confidence. Keep people in the lead, make the role of AI clear, and show where it adds value and where human expertise should guide decisions.

This is where Frontier Transformation to enrich employee experience begins. 

As Kwesi Afful of Scope put it:

AI is the train that won’t wait for us. If we avoid it, we will probably be doing a disservice to your organization or the community or customers […] so cautiously embrace it, start small and look for the practical value.

Kwesi Afful, Executive Director for Digital, Data, and Technology, Scope

Learn more

• Visit the Frontier Transformation site to explore how you can put AI to work, build trust, and scale transformation with Microsoft across everyday workflows
• Download the e-book Four Paths to Business Value with AI to learn how you can move from AI experimentation to Frontier Transformation with intelligence and trust.

Next in the Accelerating Frontier Transformation series: Reinvent customer engagement.

The post Accelerating Frontier Transformation: Enrich employee experience appeared first on The Microsoft Cloud Blog.

I drink a lot of tea. English breakfast, usually, in a real cup, every day. So, when my team came to me with an idea for a new series where they wanted to provide a platform for AI leaders to share how they are learning the name felt relatable. What’s the tea? captures something I find genuinely energizing right now: the pace at which leaders are transforming their organizations with AI, and how generously and honestly they’re sharing what they’re learning with one another.

That generosity is the whole reason my podcast, What’s the Tea?, exists. As I told Rachel Thornton, Enterprise CMO at Adobe, in our very first conversation, what excites me most about this moment isn’t only the speed of change, it’s “the eagerness for leaders to learn from one another.” This is the most significant technology shift of our careers, and it’s playing out not in years, but in days. I thought there was no better way to make sense of it than to get leaders together over a cup of tea and ask them, plainly, what they’re seeing inside their businesses. We bring in people who are driving real transformation, pour the tea, and drink in what they spill. That’s the tea.

Across five conversations—with CMOs at Adobe, Lumen, and Sitecore, a behavioral scientist who studies how teams perform, and one of the most provocative voices in modern marketing—a clear pattern emerged. Transformation is a question of mindset that supersedes any technology question. This first post is about the shift in how leaders and their people relate to AI, and why that shift is the precondition for everything else.

The fear has flipped and adoption is now an enterprise expectation

A year ago, the dominant emotion around AI in most organizations was apprehension. Rachel named it precisely. She described “the shift from maybe some trepidation around AI and what would it mean,” to people realizing, “Hey, this is really a game changer.” I’ve watched that exact arc play out, not just in marketing but across functions: the move from quietly wondering whether these tools would replace people, to discovering they can compress work that used to take weeks into minutes.

Ryan Asdourian, EVP and Chief Marketing Officer at Lumen Technologies, took the point to its logical conclusion, and his framing has stuck with me. Reflecting on the instinct to apologize for using AI, he said he’d flipped it entirely: “You should almost apologize if you don’t use AI and you don’t use tools.” For a leader running a function inside a large enterprise, that’s a profound reset of the default. Adoption is no longer the exception to be justified; it’s the expectation. The leadership task is to normalize it—to make it, as Ryan put it, “just common practice to say that, of course I use AI.”

How enterprise AI creates collective intelligence across teams

The trepidation faded fastest wherever leaders stopped framing AI as a personal-productivity gadget and started treating it as a way to raise the intelligence of the whole organization. Jon Levy, the behavioral scientist and author of Team Intelligence, made the sharpest case. The earliest phase of AI adoption, he observed, fixated on cost savings and reductions—which is what happens at the start of every technological revolution. But the real prize is bigger. “The ultimate goal of any organization,” he told me, “is to be as smart as possible. How quickly can you and your team solve the problem?”

That reframing matters enormously for transformation, because it moves the unit of value from the individual to the team and ultimately, the enterprise. Ryan gave me the line I now repeat most often: an individual versus an individual with AI, a team versus a team with AI—”the team with AI always wins.” Rachel described the healthy version of the same idea, that AI “amplifies what I can do, supports what I can do. Not necessarily, oh, this is totally replacing me.” Enterprise impact doesn’t come from a thousand people each saving a few minutes. It comes from teams solving harder problems faster, together—across customers, operations, and growth.

Scaling AI successfully requires leadership, culture, and change management

If the fear has flipped and AI genuinely raises collective intelligence, the decisive variable becomes leadership. Specifically, the ability to move an organization from scattered experimentation to scaled adoption. Every one of my guests came back to this.

Ryan made the case for leading visibly and vulnerably. “Vulnerability as a leader is so important,” he said, “to show how we are using these tools to enhance what we can do with our team.” He practices it: in a company town hall with roughly five thousand people on the call, he used Copilot live, in the moment, to verify an answer before giving it. That’s a leader modeling the behavior he wants to scale.

Rachel described the structural side of the same work—building what she calls “a culture of experimentation,” where training, reskilling, and upskilling become “critically important” and happen continuously, in bite-size pieces, every week rather than once a quarter. That cadence is how experimentation becomes an operating model rather than a pilot.

Jon offered the deeper psychology of why scale so often stalls. People, he reminded me, don’t relate to the present—they relate to the future they believe they’re heading toward. “It is the responsibility of leadership,” he said, “to make sure that their people feel the future will be brighter.” I learned that the hard way. I once unveiled an impressive AI-native platform to my team without first painting the picture of where it would take them, and I watched the energy leave the room. All they could see was a tool that might replace what they did every day. I hadn’t given them a future to walk toward—and without that, adoption never scales.

That’s the through-line of this series. The technology is extraordinary, but transformation is dependent on leaders bringing people with them.

In the episodes ahead, I’ll explore how AI is reshaping the way work gets done and the outcomes it produces, and what the agentic enterprise looks like. For now, the tea is simple: the fear is fading, the value compounds at the level of teams and the enterprise, and the leaders who move deliberately from experimentation to scale are the ones whose organizations will transform.

Watch What’s the Tea? episodes

• What’s the Tea? with Rachel Thornton | Why Marketers Aren’t Afraid of AI Anymore
• What’s the Tea? with Ryan Asdourian | The New Expectations of AI
• What’s the Tea? with Jon Levy | The Human Side of AI
• What’s the Tea? with Michelle Boockoff-Bajdek | AI’s Impact on Customer Experience
• What’s the Tea? with Gary Vaynerchuk | What Leaders Are Missing About AI

The post What’s the Tea? Lessons from leaders navigating AI transformation appeared first on The Microsoft Cloud Blog.

Everything we are doing at Microsoft is empowering Frontier Transformation: Copilot enables AI in the flow of human ambition, Microsoft IQ amplifies and protects an organization’s IQ and Agent 365 is the trust platform that enables observability at every layer of the stack.

Businesses are not static, and neither are the AI systems that support them. As organizations evolve, AI systems must continuously learn and improve. Agentic workflows need to be built, observed and tuned against the outcomes organizations seek and the ROI they demand. Microsoft’s open, model-diverse and heterogenous platform powers that improvement loop. We also recently announced Microsoft Frontier Company, bringing our AI engineering approach to customers around the world to help them build these AI systems to accelerate measurable business outcomes.

Throughout the past year, we saw customers put these capabilities to work in powerful ways — embedding AI into core business processes, building agentic systems, strengthening security, accelerating innovation and creating new sources of value. The stories below highlight organizations leading Frontier Transformation, demonstrating how intelligence, trust and human ambition come together across industries.

To advance its journey to become a global AI-powered company, Atos Group deployed Microsoft 365 Copilot to 56,000 employees across 54 countries — from consultants to engineers to frontline workers — and was one of the first organizations globally to adopt Microsoft 365 E7: The Frontier Suite. Using Microsoft Foundry, Microsoft Copilot Studio and Agent 365, Atos is building, operating and governing a growing ecosystem of 19,000 AI agents through a unified operating model that brings together productivity, security, compliance and agent governance. As Atos embeds secure agentic AI across its workforce, the company is creating a repeatable model to continuously improve thousands of agents at scale while applying the same playbook to help customers accelerate adoption across highly regulated industries.

Facing state-sponsored threats and complex global operations, ASM is strengthening cyber resilience with Microsoft Security Copilot, helping protect the intellectual property behind advanced semiconductor manufacturing. By bringing threat investigations into a unified AI-powered experience, ASM enables analysts to investigate incidents faster, apply consistent decision-making across global operations and accelerate the development of cybersecurity talent. The company reduced incident triage time by 68%, cut laptop compromise investigations from 25 minutes to eight and now saves 337 hours each week on investigations while redeploying 20% of its security operations staff to governance, risk and compliance initiatives.

Banco Popular Dominicano, the largest private-sector bank in the Dominican Republic, transformed operational risk management from periodic, sample-based reviews into continuous, AI-powered supervision. Using AURA — an ecosystem of specialized agents built on Microsoft Copilot Studio and Microsoft Power Platform — the bank monitors 100% of its operational risk universe in real time, up from roughly 40% coverage, and can automatically analyze changes, validate controls and surface issues as they occur. The shift has delivered seven times greater analytical capacity, reduced manual operating effort by 70%, achieved 98% methodological accuracy and enabled continuous processing of approximately 80,000 documents per week and more than 300 cases per day. Just as importantly, risk teams have moved from reacting to problems after the fact to anticipating and preventing deviations before they occur. These results demonstrate the power of AI democratization. By enabling business teams to build intelligent solutions themselves through low-code tools, Banco Popular transformed operational risk management while fostering a culture of innovation led by domain experts.

To help reduce the manual burden for employees while meeting the pharmaceutical industry’s strict data security requirements, Cactus Life Sciences modernized scientific workflows with Microsoft 365 Copilot and agents. The company has deployed more than 30 custom automation agents to streamline document review and structure data extraction and information retrieval across scientific writing and project management teams. Supported by a centralized knowledge repository and the Copilot Champions community, the company reports efficiency improvements of approximately 35% to 50% in structured data extraction. By automating labor-intensive tasks and maintaining human review and quality controls, the company is enabling scientific writers to focus on deeper analysis, synthesis and delivering exceptional science to clients.

Chow Tai Fook is redefining luxury retail with Microsoft 365 E5, Microsoft Purview, Microsoft Azure OpenAI Service, Microsoft Fabric and Microsoft Foundry. The company has deployed over 400 customized AI agents supporting more than 24,000 employees, with millions of AI interactions each month and core business-process efficiency gains exceeding 70%. Through its AI Fook super-agent ecosystem, frontline associates can instantly access product expertise, inventory insights and personalized recommendations, helping drive sales conversion improvements of up to 57% while delivering hyper-personalized omnichannel experiences at scale. With hundreds of AI agents operating across the business, Chow Tai Fook is creating a foundation where customer, product and operational intelligence can be applied across every interaction, helping personalize experiences and improve decision-making across its global retail network.

To accelerate AI adoption, EY moved AI from experimentation into enterprise-wide transformation. After deploying Microsoft 365 Copilot to 150,000 employees and realizing a 15% productivity gain, the firm is expanding the Microsoft 365 Frontier Suite across its global workforce of more than 400,000 people, embedding agentic AI capabilities across the enterprise. As Client Zero, EY is applying Microsoft technologies across its own operations, including Microsoft Power Platform, Microsoft Copilot Studio, Microsoft Azure, Microsoft Foundry and Microsoft Fabric. The results include 95% faster lead times, a more than 37% reduction in finance operating costs and up to a 90% reduction in manual workloads across key business processes.

To reimagine the grocery shopping experience, Grandiose Supermarkets created an AI-powered shopping companion — GrandChef — built on Microsoft Foundry and Azure OpenAI Service. By connecting meal inspiration, recipe discovery and product purchasing into a single experience grounded in live product and inventory data, GrandChef helps shoppers move from intent to purchase faster and with greater confidence. This has led to a 31% increase in conversion, a 20% lift in average basket value and shopping journeys that are 40% faster. Together, these gains are helping Grandiose Supermarkets create more personalized customer experiences while driving measurable business growth. By connecting customer intent, product data and purchasing decisions in a single experience, Grandiose is creating a feedback loop that helps continuously improve recommendations and shopping experiences.

To support audits across its global organization, Grupo Bimbo built two agents with Microsoft Copilot Studio and deployed them through Microsoft 365 Copilot and Microsoft Teams: the result was Audit Assist, an idea generated from a Microsoft-supported internal hackathon and Comatrix. By connecting auditors directly to approved guidance, procedures and templates in SharePoint, the company is helping teams work more efficiently across 39 countries while improving consistency and audit quality. The solution/AI agents reduced planning-phase audit time by 20% and accelerated risk and control matrix creation from days to seconds, enabling auditors to spend less time searching for information and more time on analysis and decision-making. By making approved audit knowledge instantly accessible across its global audit organization, Grupo Bimbo is creating a foundation where expertise can scale with the business — improving consistency, reducing rework and enabling auditors to focus on higher-value analysis and decision-making.

To create a unified foundation for data, automation and AI across its operations in 47 countries, Navien built a connected, intelligent operating model with Microsoft Fabric, Microsoft 365 Copilot, Microsoft Foundry and Microsoft Copilot Studio. By connecting fragmented data and processes across procurement, manufacturing, quality and customer service, the company is enabling more consistent, data-driven decision-making across its operations. Navien saved 28,000 hours annually through AI agents and automation, with 32% of employees using self-service analytics to make decisions without IT support. Through its migration to Azure, it also expects more than 1.4 million in total cost-of-ownership savings over five years. By connecting data, insights and workflows across the business, Navien is enabling faster, more consistent decision-making at scale across its global operations.

NHS England is accelerating AI adoption across the healthcare system with the largest implementation of its kind in the healthcare sector. Following a trial involving 30,000 workers across 90 NHS organizations — where users saved an average of 43 minutes of administrative time per day — NHS England is rolling out Microsoft 365 Copilot to over 500,000 clinicians and support staff. Organizations can use Microsoft Copilot Studio to build and deploy AI agents that streamline clinical, operational and administrative workflows. Through Agent 365, NHS England can govern and scale those agents across the healthcare system while enabling individual trusts to build solutions for local needs — creating a secure framework where agentic workflows can be deployed, managed and expanded consistently to improve service delivery, reduce costs and create more time for patient care.

Novo Nordisk is using AI to help researchers make faster, more quantitative decisions in pharmaceutical R&D. Working with Microsoft’s AI Acceleration Studio within the Forward Deployed Engineering team, the company built a governed reasoning agent on Microsoft Azure with its proprietary dataset, including more than 200,000 patient-years of harmonized clinical trial data, while maintaining rigor, oversight and compliance. The system has expanded the team’s capacity to evaluate potential opportunities from 5 to 10 strong ideas per quarter to more than 50, and the company expects it to reduce time to insight for exploratory analyses from weeks to minutes. By grounding AI in its datasets and governed workflows, Novo Nordisk is helping scientists turn decades of institutional expertise into a reusable intelligence layer that can help accelerate discovery across the organization.

As AI becomes part of investment decision-making, SimCorp is helping financial institutions bring AI into investment workflows without compromising governance, auditability or control. By unifying SimCorp One on Microsoft Azure and leveraging Microsoft Foundry, the company is helping portfolio managers, risk analysts and operations teams access insights faster, automate manual processes and spend more time on higher-value decisions. By standardizing how AI is deployed and governed across global investment operations, SimCorp is creating a foundation for trusted AI at scale, helping organizations embed AI into investment workflows while maintaining the controls required in highly regulated markets. One study found SimCorp One customers realized 134% ROI over three years, improved operational efficiency by 45 percent, saved 10 hours per person per week and accelerated time to market by 50 to 60 days.

Stellantis is accelerating AI-led strategy and digital transformation across its global business, co-developing more than 100 AI initiatives across sales, customer care, product development and operations. By applying AI-powered insights across the business, the automaker is streamlining product development and validation, advancing predictive maintenance and bringing new digital features and services to market faster for customers. Stellantis is also deploying an AI-driven global cyberdefense center to help protect vehicles, customers and operations worldwide while modernizing its infrastructure on Microsoft Azure with a targeted 60% reduction in its datacenter footprint by 2029, powering a more scalable and interconnected digital ecosystem for future digital and connected services and resilient operations.

Facing a growing volume of cyberthreats across a complex healthcare environment, St. Luke’s University Health Network is using Microsoft Security Copilot to help protect the systems clinicians and patients depend on every day. Across 15 campuses, 300 outpatient sites and more than 2.5 petabytes of data and patient records, the organization needed a unified view of threats across a complex environment. Security Copilot connects Microsoft Defender, Microsoft Sentinel, Microsoft Entra, Microsoft Purview and other security tools, helping analysts correlate threats faster, eliminate silos and respond with greater precision. St. Luke’s is saving nearly 200 hours each month in phishing alert triage and creating incident reports in minutes instead of hours, helping security teams focus more time on protecting patient care.

University of Kentucky unified more than 150 AI initiatives across classrooms, research labs, healthcare settings and administrative offices through its CATS AI governance framework. Standardizing on Microsoft’s AI portfolio — including Microsoft 365 Copilot, Microsoft Dragon Copilot, GitHub Copilot and Microsoft Azure — the university achieved campus-wide deployment, providing more than 70,000 students and employees with access to AI capabilities. Clinicians are using Dragon Copilot to reduce documentation burdens and spend more time with patients, and students are using GitHub Copilot to become active builders of digital solutions, such as the Socratic Tutor: an AI-powered learning platform aimed at helping medical students master complex curriculum.

Looking back on FY26, I am inspired not only by the continued pace of AI innovation, but by what our customers are achieving. Across every industry and segment, organizations are turning their unique IQ into strategic advantage with continuously improving agentic workflows. The companies leading this next phase are building, observing and tuning agentic workflows against business outcomes. Intelligence compounds, trust scales and AI can work in the flow of human ambition. As Frontier Firms redefine what is possible with AI, we remain focused on helping our customers amplify and protect their intelligence so they can transform how they operate, compete and grow.

The post Looking back on Microsoft’s FY26: From AI experimentation to Frontier Transformation appeared first on The Microsoft Cloud Blog.

These stories don’t have much in common on the surface, but together, they point to a broader shift: AI is helping nonprofit organizations across the globe tackle some of society’s most pressing challenges.  

Animal Protection Denmark, Answer ALS, and Everything Suarve are three nonprofits that turned to Microsoft to solve different problems. Today, AI is helping them expand opportunity, strengthen communities, and create new possibilities for the people, animals, and causes they serve—extending their impact far beyond what their size or resources might otherwise allow.  

Animal Protection Denmark: From guesswork to insight in kitten care

Every year, Animal Protection Denmark prepares for “kitten season,” when shelters see a surge in vulnerable animals needing care. As the organization grew, so did the volume of data flowing across shelters, foster networks, volunteers, and supporters. Teams often spent long hours every month reconciling information from different systems, making it harder to forecast needs and coordinate resources.

Using Microsoft data and AI capabilities, Animal Protection Denmark unified that data into a single source of truth. Teams can identify trends earlier, forecast demand for foster placements, and monitor shelter capacity in real time. Staff can also access complete records for each animal, helping ensure continuity of care and supporting faster adoption decisions.

By combining a unified data foundation with AI-powered insights, Animal Protection Denmark spends less time managing information and more time acting on it. The result is earlier planning, faster decisions, and better outcomes for the animals that depend on them.

Answer ALS: Turning data into discovery

Answer ALS was created to accelerate progress toward treatments and a cure for amyotrophic lateral sclerosis (ALS), a progressive neurodegenerative disease that affects over 450,000 people worldwide. Together with Microsoft, the organization built Neuromine, one of the world’s largest ALS research hubs, bringing together trillions of data points contributed by more than 2,500 people living with the disease. Azure AI Search powers Neuromine’s query function so researchers can quickly learn details about an individual’s disease trajectory or even their DNA, which helps pinpoint cell lines to further study.

Additionally, Answer ALS is developing a chatbot in Microsoft Foundry that uses generative AI to answer user questions and direct researchers to relevant data. Before Neuromine, researchers often spent months, and sometimes more than a year, assembling data and biological samples before meaningful analysis could begin. Today, researchers around the world can access hundreds of patient lines and related clinical data in hours rather than months, helping them move more quickly from questions to insights.

By making high-quality research data easier to explore, share, and build upon, Answer ALS is helping accelerate research by as much as 65% over the next few years. The platform is enabling researchers around the world to collaborate more effectively, uncover new patterns in the data, and advance the search for better treatments and, ultimately, a cure.

Everything Suarve: Scaling second chances with AI 

Everything Suarve helps young people in Australia rebuild their lives through job training, mentorship, mental health support, and practical life skills. Many participants arrive after facing challenges such as housing insecurity, unstable home environments, or trauma. For a small team, the work depends on trust, consistency, and showing up for young people when they need support most.

As demand for its programs grew, so did the administrative burden. Referrals arrived through emails, paper forms, and phone calls, while staff tracked participant progress across disconnected systems. To streamline operations, Everything Suarve used Microsoft data and AI capabilities to build a solution that centralizes enrollment, case management, reporting, and communications. The organization also uses Microsoft 365 Copilot to help with grant writing, document summarization, and other administrative tasks, reducing the time spent on manual work.

Today, Everything Suarve’s staff can manage referrals, case notes, reporting, and participant communications in a single workflow. The new platform saves up to eight hours per participant during enrollment, while Copilot reduces grant application work by as much as two weeks. By automating processes and putting AI to work, Everything Suarve is reducing administrative overhead and creating more time to help young people build confidence, skills, and a path forward toward employment, education, and long-term stability.

What other nonprofits can learn from these stories

These three organizations serve different communities and pursue different goals. Yet together they show that organizations create the most value from AI when they use it to strengthen human expertise and start with a problem worth solving or a mission worth advancing.

At Animal Protection Denmark, staff can spot needs before they peak. At Answer ALS, researchers are compressing years of discovery into months. At Everything Suarve, staff spend less time on forms and more time with young people rebuilding their lives.

Together, these stories reflect a broader shift already underway across organizations of every size. AI is becoming part of how decisions are made, how services get delivered, and how people work. Success is no longer defined by the technology alone. It comes from combining technology with human judgment, experience, and purpose.

We call this Frontier Transformation: to move beyond isolated tools and individual tasks to rethink how they achieve their mission. Sometimes the result is greater efficiency. Often, it creates something more meaningful: the ability to reach more people, respond more quickly, and extend impact in ways that were previously out of reach.

As AI becomes more accessible, these nonprofits are showing that meaningful transformation is not reserved for the largest enterprises. It can begin anywhere people are willing to pair innovation with a clear sense of purpose.

The post Inside nonprofits using AI to extend their impact appeared first on The Microsoft Cloud Blog.