AI now represents the next step in that evolution, not because it simply introduces another technology, but because it can expand the capacity of people across the healthcare ecosystem.

Across healthcare and life sciences, organizations are moving beyond experimentation and embedding AI into everyday workflows. As they do, they are discovering that AI’s value extends beyond efficiency gains. It can help clinicians spend more time with patients, enable employees to make decisions faster, accelerate scientific discovery, and create new opportunities for innovation.

The shift is no longer theoretical. Providers, payors, life sciences organizations, and MedTech companies are already embracing new ways of working that combine human expertise with AI-powered capabilities, helping employees focus more of their time and energy on the work that matters most.

Helping clinicians spend more time where it matters most

Healthcare providers continue to face a persistent challenge: clinicians spend significant portions of their day on documentation and administrative tasks. For teams already navigating sustained burnout, these burdens compound daily pressure and take time away from meaningful patient interactions.

AI can help ease that burden by helping streamline documentation, improve access to information, and support workflows directly within the systems clinicians already use every day.

Intermountain Health uses Microsoft Dragon Copilot within its Epic electronic health record (EHR) workflow to capture patient-clinician conversations and automatically draft clinical notes. The organization has scaled adoption to more than 2,500 active Dragon Copilot users, helping reduce time spent on documentation and enabling more focused patient interactions.

By reducing administrative work, AI can help clinicians devote more attention to patient care, conversations, and clinical decision-making. In doing so, it creates more capacity for the expertise, empathy, and human connection that sit at the heart of healthcare.

Helping health plans deliver faster answers and better experiences

Payor organizations manage increasingly complex processes across claims, care management, compliance, provider engagement, and member services. Employees often need to navigate large volumes of information while responding to member needs quickly and accurately.

AI can help employees find information faster, surface relevant insights, and make more informed decisions—improving both operational efficiency and customer experiences.

Premera Blue Cross strengthened the digital foundation of its business by migrating Facets, its mission-critical payer administration platform, to Microsoft Azure. The migration was completed with zero data loss and no rollback required, improved batch processing by 38%, and created a more scalable, resilient foundation for future initiatives in claims, AI, and automation.

Expanding the capacity for scientific discovery

Healthcare and life sciences organizations generate enormous volumes of scientific knowledge and research data. Finding meaningful insights within that information has traditionally required significant time and effort.

AI is helping researchers analyze information more efficiently, uncover patterns across large datasets, explore emerging opportunities, and accelerate discovery.

Pierre Fabre built PLA.I.GROUND, a secure, contextualized, multi-model enterprise generative AI platform built on Microsoft Azure and Azure OpenAI. Today, more than half of the company’s office-based employees use PLA.I.GROUND daily to summarize documents, generate content, and accelerate code development, helping employees spend less time on low-value tasks and freeing time for innovation.

As AI becomes more deeply integrated into research workflows, organizations can amplify scientific expertise and create greater capacity to pursue the next breakthrough. When researchers spend less time searching, gathering, and synthesizing information, they gain more time to explore ideas, test hypotheses, and advance discovery.

Accelerating innovation across MedTech

MedTech organizations operate at the intersection of research, manufacturing, healthcare delivery, commercial operations, and regulatory requirements. Employees often need to access information from across the organization while collaborating with teams that span functions and geographies.

AI can help simplify this complexity by making it easier to retrieve information, summarize key discussions, create content, and collaborate more effectively.

KARL STORZ implemented Microsoft 365 Copilot for meeting summaries, drafting, and information retrieval. The organization achieved 97% active usage across approximately 2,000 licensed users, with 93% returning month over month.

These results demonstrate how AI can help organizations accelerate execution, improve collaboration, and empower employees to focus more of their time on innovation and strategic work. By reducing friction in everyday processes, AI enables teams to move from information to action more quickly and bring new ideas forward faster.

Innovation built on trust

As healthcare organizations expand the use of AI, trust remains essential.

Leaders must balance innovation with security, privacy, compliance, and responsible AI practices while ensuring employees feel confident using new technologies in their daily work. The organizations seeing the greatest success are treating trust not as a one-time initiative, but as an ongoing commitment that supports adoption and long-term value.

Ultimately, the value of AI in healthcare will not be measured by how quickly it is deployed, but by how confidently people can use it to make a difference. When innovation is grounded in trust, AI can empower the people delivering, supporting, and advancing care while protecting what matters most.

Put trusted AI to work

Trust is what turns AI adoption into durable value. The next question is where to apply it and how to move from a promising use case to measurable impact.

• Explore From Vision to Value: AI Use Cases Transforming Healthcare to learn how healthcare and life sciences organizations are applying AI today to drive measurable impact.
• Explore the AI in Action infographic for a concise look at how organizations are putting AI to work today and the outcomes they are achieving.
• Join Microsoft and healthcare leaders at MedTech Frontier AI Day during The MedTech Conference to examine what it takes to move from promising use cases to meaningful impact.

The next era of healthcare will be defined by how trusted AI expands the capacity to deliver care, advance discovery, and bring innovation to market.

The post Accelerating human capacity across healthcare with AI appeared first on The Microsoft Cloud Blog.

That advantage will come from three reinforcing moats:

• Reputation
• Alpha
• Distribution

Reputation is the permission to operate in consequential financial workflows. Alpha comes from the proprietary data, institutional knowledge, judgment, and insight an institution can compound over time. Distribution is the ability to put that intelligence into the hands of employees, advisors, clients, and partners at the moments where decisions are made.

What agentic AI means for banks and financial institutions

A financial institution of the agentic era uses governed AI agents, trusted data, and human oversight to connect work across banking operations, risk, compliance, and client service. Rather than deploying isolated AI tools, banks can build reusable intelligence into workflows such as onboarding, lending, payments, treasury, and service while maintaining identity, permissions, auditability, and accountability.

The next phase connects context, execution, and control with intelligence. Context consists of institutional data, market intelligence, relationship history, risk signals, documents, and operating knowledge. Execution means enabling agents and applications to help coordinate work across workflows, not just answer questions in isolation. Control is about ensuring that every action is governed by identity, permissions, observability, auditability, resilience, and human accountability.

Microsoft’s role is to provide an integrated foundation that helps financial institutions retain control of their data and compound their intelligence across cloud, data, AI, security, and collaboration—bringing governed capabilities into the applications and workflows where work happens.

Modernize banking operations with trusted intelligence

Connect onboarding, lending, payments, treasury, and service

Banking value chains have been organized around products and functions:

• Onboarding
• Lending
• Payments
• Treasury
• Service

Trusted intelligence can connect these activities while preserving their controls. A commercial client’s onboarding information could inform treasury setup. Approved cash-flow insights could support a liquidity conversation. Trade-finance documentation could be checked and routed for expert review. By reducing handoffs and carrying permissioned context across workflows, institutions can create a more coherent client relationship.

Capital markets face a related shift. Research, pre-trade analysis, execution, post-trade processing, and custody can become connected services rather than isolated steps. For example, an appropriately authorized agent could assemble approved research and portfolio exposures for an investor, while another could investigate a settlement exception and prepare an escalation. These illustrative scenarios do not justify giving every workflow the same level of autonomy.

Build on a governed data foundation

Modernizing the operating core is about more than technology; it is about improving client outcomes. Institutions need adaptable platforms, governed data foundations that make intelligence reusable, and systems that enable people and agents to collaborate across traditional boundaries.

BNY demonstrates how a global financial institution can embed intelligence into its operating model. Its Eliza platform brings together governance, agent-building capabilities, learning resources, and AI tools in one environment, now supporting more than 300 solutions across the institution. The impact is already showing up in core workflows: AI-assisted onboarding is 20% faster, more than 10% of client settlement inquiries are resolved or assisted by AI with 80% faster processing, and digital employees now handle more than 10% of payment-repair activities globally.

The opportunity is not just to automate work, but to also deepen collaboration through our partner ecosystem and embed Azure intelligence directly into the processes that drive operational performance.

—Deanna Lanier, Global Head of Strategy, Data & Analytics, BNY

None of this works without a governed data foundation. LSEG is using Microsoft Fabric to unify its systems and datasets in a governed platform, improving data quality, and bringing new-product development timelines down from years to months. Genworth is a second illustration, closer to the desk: Microsoft 365 Copilot in investment operations has reduced the time needed to construct complex portfolio trades from days to hours, inside the workflows the team already uses, with the decision itself still resting with people. Neither example creates a competitive moat on its own. The advantage comes from turning trusted intelligence into better decisions across workflows.

Strengthen AI risk management, compliance, and resilience

In financial services, trust cannot be an assurance attached to a technology. It must shape how that technology operates. To operate with trust, every agent needs an identity, a defined purpose, authorized data access, and limits on action. Institutions need to know where data resides, how an answer was grounded, what changed, and who remains accountable.

Security, sovereignty, resilience, recovery, model governance, and auditability belong in the design. Identity, permissioning, auditability, data protection, lifecycle management, and policy controls must travel with the information itself rather than be added later as separate layers, so they still hold when work crosses teams, partners, and differing licensing or confidentiality contexts. Suitability and explainability belong in that design by default, not as a review bolted on at the end.

Keep consequential decisions human-led

The direction is human-led and agent-operated, not autonomous everything. An agent can gather evidence, reconcile information, or propose a response. A person keeps authority where professional judgment, fiduciary obligations, or consequential decisions require it. Escalation paths, approval thresholds, and the ability to stop an action belong in the design before scale, not after an incident.

Bradesco’s use of AILA to support audit planning illustrates how AI can help professionals focus more attention on analytical depth and important business risks. The broader opportunity is to make controls more informed and timely without confusing automation with assurance. A generated explanation is not evidence of correctness, and a faster process is not necessarily a safer one.

The solution has transformed the dynamics of auditing. We’ve evolved toward a more strategic performance, based on analytical intelligence and delivering outcomes that generate direct value for the organization.

Vivian Gentille, Senior Manager, Bradesco

This is how the reputation moat is renewed in everyday operations. Better intelligence surfaces exceptions and informs oversight. Stronger controls make responsible use of intelligence possible in more consequential workflows. Trust and intelligence reinforce each other when institutions test that relationship in practice, measuring decision quality, control effectiveness, and client outcomes alongside productivity.

Empower banking advisors and client-facing teams

The distribution moat is not a larger set of channels. It is the ability to win every client moment: to show up with the right context, at the right moment, in a way that earns the next conversation. For a corporate banker, that means connecting a client’s operating needs with treasury expertise. For a wealth advisor, it means bringing relevant research and approved product information into a discussion of long-term goals.

Morningstar uses Microsoft 365 Copilot and Copilot Studio to bring its trusted research and investment intelligence into advisors’ existing workflows, helping them create research-backed portfolio insights and client-ready materials faster.

Microsoft Copilot and Copilot Studio create a powerful ecosystem. It’s plug-and-play access. You can connect tools that fuel the full advisor lifecycle with AI, creating agents to support discovery and research, portfolio construction, monitoring, and client communication, while grounding AI workflows in trusted data, research, and investment IP.

—Thomas Aviles, Head of Advisor Software, Morningstar

As clients move between digital interactions and human advice, consistency matters as much as personalization. A service agent, a relationship manager, and a specialist should work from compatible, permissioned context rather than ask the client to reconstruct their story. Connecting experiences must not dissolve confidentiality barriers or become indiscriminate cross-selling. Distribution strengthens reputation only when clients can see that their interests, choices, and privacy remain central.

How banks can prepare for the agentic era

For leaders, the implication is straightforward: do not begin with the model. Begin with the outcome. Identify the client, market, operating, or risk outcome that matters most. Determine what proprietary intelligence the institution needs to own. Map the value chain where that intelligence must move. Define which decisions remain human-led. Then build the data, governance, security, and workflow foundation required to scale.

At Sibos 2026, where the industry is focused on trust, cross-border value, AI-powered markets, and the future of financial infrastructure, this conversation is especially timely. The opportunity is no longer to prove that AI can help with individual tasks. It is to build financial institutions that can operationalize intelligence safely, responsibly, and at scale.

The winners will be those that bank on intelligence—not as a feature, but as a trusted institutional capability.

For more on building the banking and capital markets institution of the agentic era, watch my FinTech Futures Sibos interview.

Watch this video for a look back at Microsoft’s week at Sibos—and a look ahead at how financial institutions can turn AI ambition into real business impact.

The post Bank on intelligence: Building a trusted financial institution for the agentic era appeared first on The Microsoft Cloud Blog.

An AI-enabled investment platform connects governed data, institutional knowledge, investment workflows, and AI agents under human oversight so capital markets teams can make faster, better-informed decisions. Rather than adding isolated AI tools, firms can use this platform approach to modernize work across research, due diligence, portfolio analysis, risk, compliance, and client service while preserving accountability and control.

Most AI initiatives in investment management begin with point solutions for research, due diligence, reporting, document review, portfolio analysis, or client service. However, these applications represent only the first stage. The larger opportunity is to redesign the investment lifecycle itself so that information, decisions, controls, and execution can flow continuously across the full ecosystem, built around persistent context.

Nowhere is this truer than in capital markets, where the explosive growth of private markets and the convergence of public and private asset distribution have created a complex, multi-stakeholder ecosystem characterized by intense data dependencies, operational complexity, and regulatory scrutiny.

Major global capital markets participants—from large asset managers to investment banks, sovereign wealth funds, and wealth managers serving high-net-worth clients—all seek integrated technology solutions that can unify data, accelerate AI-powered decision support, and help collapse fragmented workflows while ensuring regulatory compliance. Agentic AI can help orchestrate work across these stages, reducing manual handoffs and enabling more intelligent, responsive business processes.

As policymakers and market participants explore broader access to private-market investments, the industry faces a significant scale challenge. Legacy approaches to capital formation, allocation, and distribution will not support a global alternatives market projected to reach $32 trillion in assets under management (AUM) by 2030 unless firms transition to integrated, scalable operating models.¹

Why capital markets firms need more than isolated AI use cases

As AI becomes embedded across capital markets, value increasingly comes from connecting people, data, and processes across the broader ecosystem. This is why the strategic challenge is not deploying more AI tools, but rather creating an end-to-end, integrated platform that enables firms to leverage shared value while protecting core intellectual property.

The firms that succeed will treat AI as a platform for coordination, rather than isolated intelligence. By enabling secure collaboration across participants while preserving governance and accountability, institutional knowledge compounds, which strengthens each insight and decision over time.

What an AI-enabled investment platform should support

At its core, an AI-enabled investment platform should do three things:

1. Connect governed data and work context across portfolio data, market intelligence, research, operating metrics, client information, risk analysis, legal documentation, and internal knowledge. Bringing this context together in a governed environment can reduce fragmentation and create a more consistent foundation for decision support. It can also help teams understand how a conclusion was reached, what sources informed the work, and where human review remains necessary—an approach LSEG is putting into practice by using Microsoft Fabric to unify 30 systems and 1,200 datasets in a governed platform, improving data quality and reducing new-product development timelines from years to months.
   
2. Support reusable expertise and workflows. Organizations develop advantage through their investment processes, institutional knowledge, relationships, and accumulated experience. Reusable AI-assisted capabilities can help teams scale that expertise more consistently across research, portfolio management, investor engagement, compliance, and distribution activities, while keeping people responsible for material decisions.
   
3. Maintain trust, accountability, and control. As AI becomes more deeply embedded in investment workflows, identity, permissioning, auditability, data protection, lifecycle management, and policy controls need to move alongside information rather than being added later as separate layers. Suitability and explainability of investment decisions and advice must be built in by default.
   

This is how Microsoft’s AI platform approach becomes relevant. Microsoft IQ can provide governed context that agents can readily access within applicable permissions, while Microsoft 365 Copilot brings intelligence into familiar workflows. Microsoft Foundry and Copilot Studio help organizations build and manage reusable AI agents, matching each task with an appropriate model and cost profile. For regulated industries, that flexibility must be matched by strong controls. Microsoft Purview, Microsoft Entra, and Agent 365 support the governance, identity, security, and observability requirements needed to deploy agents responsibly at scale.

How agentic AI can redesign investment operations

1. From origination to investment committee

Investment context is often recreated as opportunities move through sourcing, diligence, risk and legal reviews, and investment committee preparation. An intelligence layer helps maintain a governed, permissioned view of information across that lifecycle, bringing together investment data, research and diligence materials, proprietary firm knowledge, and approved external market data as shared context rather than isolated artifacts.

AI-assisted capabilities surface in the flow of work such as in Microsoft 365 Word or Excel, rather than compelling users to switch applications. Copilot then becomes the orchestration surface: a deal-screening agent can pull private-company signals from PitchBook, while a valuation-review agent can cross-check against prior assessments, identify supporting evidence, and prepare materials for review. Investment decisions remain with people, but teams spend less time assembling information and more time evaluating it.

Genworth demonstrates the potential in investment operations, using Microsoft 365 Copilot to reduce the time required to construct complex portfolio trades from days to hours while strengthening decision support within existing workflows. The result is better decision-readiness, fewer manual handoffs, and a clearer institutional record that can be reused when similar questions, risks, or opportunities arise.

2. Creating a stronger organizational learning loop

The investment lifecycle does not end when a decision is made. Portfolio performance, advisor feedback, investor demand, compliance reviews, operating results, and market developments all generate valuable intelligence, but many firms struggle to make those signals reusable across future decisions. Important insights often remain buried within meeting notes, presentations, email threads, and individual workflows.

With a foundation of intelligence and shared context, decisions that traditionally took weeks to propagate through investment, risk, operations, and distribution functions can become immediately available. Over time, that can make the firm’s experience more durable: patterns from prior investments, recurring client questions, risk signals, and operational lessons can become part of the next workflow rather than remaining isolated in the last one. Morningstar illustrates this model by extending trusted research into reusable, governed agents within advisor workflows, reducing hours of preparation to minutes while keeping intelligence grounded in approved data.

As Martina Cheung, CEO of S&P Global, noted in her recent LinkedIn post, with S&P Global’s data, insights and analytics in Microsoft 365 Copilot workflows, an analyst, for example, can ask Microsoft Copilot for a company’s latest EBITDA trends, market capitalization or peer comparisons, with answers grounded in cited S&P Global intelligence. From there, that data can flow into everyday workflows, whether that’s building a comps table in Excel or preparing a competitive landscape deck in PowerPoint.

3. Collaborating without compromising confidentiality

Investment due diligence relies on coordination across teams. That collaboration requires access to information, but it also requires strong controls around confidentiality, entitlements, licensing requirements, and regulatory obligations. In practice, AI-assisted workflows can help drive scale while respecting organizational policies and access boundaries. That is especially important when workflows span internal teams, external partners, and data from different licensing or confidentiality contexts. As firms scale AI adoption, this balance between collaboration and control becomes increasingly important.

Making collaboration possible without weakening control depends on governance that travels with the information. Identity, permissioning, data protection, auditing, and policy enforcement should ensure that people and AI systems can access only the data they are authorized to use. Nasdaq Boardvantage demonstrates this principle in a particularly sensitive context: it uses AI to support confidential board workflows while preserving established security, access, and governance controls. The approach also delivers measurable efficiency gains, reducing directors’ reading time by up to 60% and overall board preparation time by 25%.

How governed data and human oversight build trust

Capital markets organizations have always competed through expertise, relationships, judgment, and privileged information. In the AI era, those advantages will increasingly depend on how effectively firms connect proprietary data, institutional memory, governance, and human expertise. These characteristics make them distinctive while creating value across the broader ecosystem.

Achieving that requires a platform that connects data, workflows, controls, and institutional knowledge without compromising accountability or trust. The goal is not to replace investment professionals, but to give them better context, stronger governance, and greater leverage across increasingly complex public and private markets. The organizations that lead in the AI era will be those that combine trusted data, human expertise, agentic workflows, and strong governance to improve decision-making across the enterprise.

In this future, the AI-enabled investment platform is more than infrastructure. It is the operating foundation through which investment knowledge is created, governed, and applied at scale.

The next competitive advantage in capital markets

• For a practical roadmap on how to turn isolated AI initiatives into enterprise-wide impact, explore Frontier Transformation in Capital Markets.

• For insights on bringing context into flow of work, read our blog, “AI in financial services: Bringing trusted data into the flow of work.”

• To learn more about scaling agentic AI in asset management with “Trust as infrastructure: How agentic AI is rearchitecting asset management at scale.”

Frequently asked questions

What is an AI-enabled investment platform?

An AI-enabled investment platform connects governed data, institutional knowledge, investment workflows, controls, and AI agents in a unified environment. It gives teams trusted context and reusable capabilities across the investment lifecycle while keeping people accountable for material decisions.

How is agentic AI used in capital markets?

Agentic AI can orchestrate multi-step workflows across research, due diligence, investment committee preparation, risk, compliance, and client service. Agents can gather approved information, analyze evidence, prepare materials, and coordinate handoffs, while investment professionals retain responsibility for review and decisions.

How can capital markets firms use AI while protecting confidential data?

Capital markets firms can protect confidential data by applying identity, permissions, data protection, auditing, and policy controls wherever information and AI agents are used. These safeguards limit access to authorized people and systems, while explainability and human oversight help ensure accountable decisions.

What should firms prioritize when modernizing investment operations with AI?

Firms should prioritize governed data, reusable workflows, clear accountability, and measurable business outcomes rather than deploying disconnected tools. A staged approach can begin with high-value use cases, establish controls and success measures, and then scale proven capabilities across investment operations.

¹ Preqin, “Private Markets in 2030: Data Driving the Future of Alternatives,” October 16, 2025.

The post The AI-enabled investment platform: How capital markets firms can redesign the operating model 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.

The next evolution of Microsoft 365 for government brings productivity, AI, agents, security, identity, compliance, and governance together for organizations operating in Government Community Cloud (GCC) environments.

Across government, leaders are being asked to modernize services, address evolving cybersecurity needs, and help their teams meet growing demands simultaneously. AI tools can play an important role, but technology alone is not enough. Agencies need intelligence grounded in how they work, agents that extend that intelligence, and a control plane to help govern AI adoption securely. Innovation and trust must advance together.

Today, we are announcing the launch of Microsoft 365 G7, the Frontier Suite for government, to Government Community Cloud (GCC) environments. G7 extends the Intelligence + Trust vision introduced with Microsoft 365 E7 to government agencies, bringing together productivity, AI, agents, security, identity, compliance, and governance to support government organizations as they help accelerate mission outcomes and further mature their governed approach to adopting AI at organizational scale.

Both Microsoft 365 G7 and Agent 365 will be available for GCC customers to purchase beginning October 1, with additional purchasing options available November 1, and capabilities expanding in phases. Customers can begin licensing, planning, and adopting available experiences at launch, with additional workloads rolling out as they complete required GCC authorization milestones. This phased approach advances innovation with the rigor government missions demand.

Built on the trusted foundation of Microsoft 365 for government

Microsoft 365 G7 extends the Microsoft 365 foundation agencies know, starting with Microsoft 365 G5 solutions that bring together productivity and collaboration with information protection, security, and compliance capabilities. Microsoft Entra Suite builds on this with advanced identity, access governance, and network security capabilities for users and agents.

G7 is designed to support Frontier Transformation across the U.S. public sector by providing capabilities that agencies can use as they move from isolated AI experimentation toward governed adoption at organizational scale. It connects intelligence to the flow of everyday work, gives agencies a foundation for creating and using mission-focused agents, and provides the controls that IT and security leaders need to manage people, agents, data, and access with confidence.

AI embedded in the flow of work

AI should make everyday work simpler, not create another destination. G7 brings Microsoft Copilot and AI-powered Microsoft 365 experiences into the applications government employees already use. Work IQ grounds these experiences in organizational data, context, and tools with governance capabilities designed to operate in accordance with applicable permissions, so Copilot can provide assistance informed by an agency’s work context and help users complete mission-related work.

At general availability, G7 will bring the value of Microsoft Copilot into a unified offering designed for government. Government teams can use Copilot to support research and analysis, find and synthesize organizational knowledge, and draft, refine, and collaborate on content in the Microsoft 365 apps and experiences they use every day. 

In the months following GA, G7 will also benefit from a surge of new capabilities rolling out in Microsoft Copilot across our US Government clouds, including the latest commercially available GPT models, Work IQ memory and personalization, Edit with Copilot across Word, Excel, and PowerPoint, and Copilot Cowork for longer-running, multi-step work. To learn more about how these advances can help public servants navigate complex work and support their organizations’ mission priorities, read our companion announcement on the Microsoft Public Sector blog.

Agents at the mission’s edge

Agents can support specialized government work, from research and policy analysis to case management, grants, inspections, and constituent service. G7 provides an agentic foundation for government, with mission-focused agents grounded in Work IQ, and operating in accredited government clouds with policy-governed access for connecting to approved agency systems and data.

Agencies can start with Microsoft agents available in GCC, including Researcher and Analyst, and use Agent Builder to create experiences closer to the work. For specialized missions, teams can build custom agents with Microsoft Copilot Studio. Microsoft 365 Copilot Connectors bring agency knowledge from other systems within reach, while managed connections provide secure access to approved data and line-of-business applications. Natural-language creation broadens who can build, while organizational controls keep deployment aligned to agency standards.

Agent 365: The control plane for AI adoption

The speed of agent adoption creates enormous opportunity, but without the right controls it can also create new blind spots. Leaders need to know which agents exist, who owns them, what they can access, and whether they are operating as intended. Agent 365 provides the control plane for agents in G7, helping organizations discover, register, and manage agents and take governance actions such as deploying, blocking, or removing them.

At general availability, Agent 365 will give organizations a unified foundation to discover, identify, and govern agents, with core controls to manage how they are deployed and used. Over time, we expect Agent 365 capabilities to evolve across security, risk management, and lifecycle governance. For detailed information about GCC feature availability, customers can refer to the Microsoft Agent 365 service description. As additional services complete required GCC authorization and readiness milestones, their status will be updated there.

Advancing the mission with confidence

AI creates tremendous opportunity for government teams to build capacity, strengthen resilience, and respond more effectively to the people they serve. Realizing that opportunity requires leadership, workforce readiness, responsible governance, and technology that unites intelligence and trust.

Microsoft 365 G7 advances Frontier Transformation for government, giving agencies a path to empower employees, scale agents and adopt AI securely as capabilities expand. By combining AI in the flow of work, mission-focused agents, and a unified control plane, agencies can advance modernization with controls designed to support the stringent trust, security, and governance needs of public service and national missions.

Public sector transformation is about more than technology. It is about helping people serve communities and advance the mission. With G7, Microsoft builds on its longstanding government partnership to help public servants apply AI with the intelligence to support the mission and the controls to move forward with confidence.

The post Introducing Microsoft 365 G7: Intelligence + Trust for the mission ahead appeared first on The Microsoft Cloud Blog.

Intelligence lives in the engineer who can hear a spindle drifting out of tolerance before a gauge detects it. It lives in the systems teams open every morning, and in the data that keeps production moving shift after shift. It is the accumulation of knowledge, experience, and context that empowers people to make the right decisions at the right time. For most manufacturers, however, that intelligence is fragmented across people, processes, data, and systems that were never designed to work together.

It is the accumulation of knowledge, experience, and context that empowers people to make the right decisions at the right time.

Ben Grimes, corporate vice president, US Manufacturing & Mobility, Microsoft

Unlocking industrial intelligence means connecting that knowledge and putting AI into the flow of work, so it can understand how the business operates, surface what matters, and help people act with greater speed and confidence. That is the difference between companies experimenting with AI through isolated pilots and those turning intelligence into measurable, competitive advantage across the value chain.

And this is the key conversation taking shape this week at IMTS 2026, the largest manufacturing technology show in North America.

Intelligence is half the equation. Trust is the other half.

In my keynote at IMTS, “Industrial Intelligence Unlocked,” I took these observations one step further: Frontier Firms do not simply buy more AI tools. They connect intelligence that understands how work gets done, how business impact is measured, and how enterprise knowledge fits together. At scale, trust is what sets the pace.

Leaders design trust in from the beginning, on a secure, compliant, and observable foundation beneath every agent. Agents carry identity, operate inside clear boundaries, and are accountable for outcomes. When agents are set in motion, innovation moves quickly, more employees are empowered in the flow of work, and IT keeps control without becoming the bottleneck.

If intelligence and trust are the foundation, the practical question is how they show up in daily operations. What I’ve seen across the industry is that three forms of intelligence must come together:

1. Work intelligence. It starts with understanding work: how people get things done, who they collaborate with, what was discussed and agreed. Present across conversations, meetings, and documents, AI can recognize patterns, carry context forward, and turn individual experience into a shared understanding.
   
2. Data intelligence. Every decision on a factory floor runs on data. To move from insight to action, intelligence has to understand not only the data, but what it means: which metrics matter, how performance is measured, where the tradeoffs are made. Data intelligence means both people and agents can make informed decisions from the same source of truth.
   
3. Operational intelligence. Intelligence is also a holistic understanding of how the business is meant to run: the policies, the standards, the operating procedures. Grounding organizational intelligence in context is what lets AI apply the right guidance, at the right moment, for the right person.

When brought together, these facets reveal the most impactful shift in the industry: intelligence stops being something you consult and becomes something that runs alongside the business, reasoning consistently and acting within context as it moves across teams and systems.

The return on intelligence is already being realized

This is not a forecast. Manufacturers are reporting results now, and that is coming through clearly at IMTS, where conversations with our customers and ecosystem partners are focused on practical AI deployments with measurable operational impact.

Riddell put trusted ERP data directly into the flow of its frontline sellers’ work. Its AI assistant, RIA, connects to SAP and answers natural language questions about pricing, delivery dates, and order status inside Microsoft Teams. By reducing time spent searching across disconnected systems, Riddell has returned more than 21,000 hours to the team each year.

AGCO is taking a governed approach to scaling employee-built AI agents with Microsoft Copilot Studio and Microsoft 365 Copilot. Its maker community has grown to roughly 2,000 people, with several hundred enterprise agents now in production. In some quality workflows, reviews that once took weeks can now be completed in about an hour.

Siemens, Rockwell Automation, PTC, RSM, MCA Connect, and Insight featured Microsoft in demonstrations of their latest AI innovations—spanning the intelligent digital thread and product lifecycle, AI-powered engineering and factory operations, and more adaptive supply chains.

Together, these experiences showed how Microsoft technology and deep industry expertise come together to move AI from promising use cases into practical solutions built for the realities of manufacturing.

When intelligence is shared, innovation spreads

Across IMTS, the conversations on the digital thread, physical AI, and agents at scale pointed to the same shift: industrial intelligence creates the most value when it moves beyond isolated teams and becomes part of how the entire organization works.

Frontier Firms do not centralize innovation in a small group of experts. They put the power to create in every room of the house—closer to the people who know the work, see the opportunity, and can imagine a better way forward. With trusted intelligence in the flow of work, more people can solve problems, improve processes, and turn ideas into measurable outcomes.

Because when intelligence is connected, trusted, and shared, innovation no longer belongs to a few. It spreads, and human ingenuity scales with it.

Step into the Factory of the Future

Explore how intelligence connects every part of the factory

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Some visuals in this article were created using AI-assisted tools.

The post Industrial intelligence unlocked: The next competitive advantage in manufacturing appeared first on The Microsoft Cloud Blog.

Case management is where most people meet their government. It is how a family gets help through a crisis, how a small business gets licensed, and how a permit gets issued. It is also where a great deal of skilled time goes to work that is not the mission; finding the file, reconstructing the history, re-entering what someone already explained on the phone. That gap shows up in the numbers leaders answer for: backlogs, turnover, and repeated calls from people still waiting on a decision.

What IDC sees changing

The IDC MarketScape describes the shift this way:

“Case management software platforms are being reshaped by AI with pretrained agents being embedded in applications to increase caseworker productivity, and agentic AI development, orchestration, and governance capabilities are being expanded to enable governments to deploy AI in mission-specific environments to support critical case decisions.”

One clarification before going further, because the phrase “critical case decisions” carries real weight in our context. AI prepares the work. The decision stays with the public servant accountable for it.

With that established, the finding is straightforward. Much of the routine preparation in case work can now be automated. The IDC MarketScape notes, “Microsoft also continues to expand Copilot case summaries and related summarization experiences to reduce time to context for case workers.” The report also noted, “Microsoft invested heavily in its end-to-end AI stack. For case management, Microsoft provides a Case Management Agent in Dynamics 365 Customer Service that can autonomously create cases from conversations and predict/update case fields, with configuration and simulation capabilities for administrators.”

For context: the wait before a caseworker can actually help someone is becoming something agencies can manage rather than absorb.

Four changes leaders will feel

A category placement is only useful if it maps to something you can see in your own agency. Here is where those pieces show up in practice, roughly in the order leaders tend to notice them.

1. More capacity from the team you already have

More of the file arrives already assembled: cases created from conversations, fields predicted, history summarized. The time a case worker spends getting ready to decide goes into deciding instead.

This compounds on the national scale. The US Small Business Administration runs its case work on Dynamics 365, and its program manager, Brian Quay, put the volume plainly:

Since implementing Dynamics 365, we’ve managed 23 million cases and saved millions in operational costs annually.

Brian Quay, program manager, US Small Business Administration

Experienced judgment is the scarcest resource in most agencies, and this puts more of it back on the mission.

2. Shorter waits and fewer repeat calls

A large share of what people experience as slow government is not the decision itself. It is a case sitting in a queue while someone gets up to speed on it. New Zealand’s Department of Internal Affairs saw the effect on both sides at once:

Having more real-time information available online also reduces calls to our contact center, because application statuses are available to the customer at any time.

Greg Archer, general manager of Te Ara Manaaki

The person waiting stops having to ask, and the team stops having to answer. Compressing that wait improves service without touching policy; the eligibility rules, the statutes, and the thresholds stay exactly as they are. That combination can be rare in this type of work.

3. Work that can be accounted for

In our experience, this is the change that determines whether any of it ships. The IDC MarketScape notes, “Dynamics 365 now includes a Case Management Agent that automates case creation and field updates from conversations and email. Microsoft also provides a governance layer for AI interactions using Purview auditing and communication compliance controls that can analyze prompts/responses and preserve evidence for investigations, which is important for mission-critical services and auditability in government.”

In practice, this means the basis for a decision is recorded as the case is worked rather than reconstructed months later when someone asks how it was reached. The record is of the decision, not the person who made it.

Accountability is what moves AI in government from a pilot to a program.

4. Deployment on your terms

The IDC MarketScape notes, “Microsoft has a wide catalog of sovereignty controls (Sovereign Landing Zone, Data Guardian, EU Data Boundary) and multiple deployment models (including national partner clouds in selected countries) that map to government priorities around data residency, operational transparency, and regulated access.”

That tracks how Microsoft describes sovereignty in practice: data stays in region, operational access is logged in a way the customer can inspect, and encryption keys can be held and managed by the customer. These are configurable, documented controls rather than assurances, which is what makes them verifiable during an authorization review.

For a case management program, the question is scope. An agency is not only placing infrastructure inside those controls. It is placing the case records, the AI working on them, and the audit trail of both, which is a materially different ask than sovereign hosting alone.

What’s next

Case management is rarely described as inspiring technology. It is, though, where a government keeps or breaks its word, one person at a time, and the teams running it have been asked to do more with the same for a long time.

Our commitment stays the same: partnering with agencies so those teams spend less of the day getting ready to decide, and more of it deciding, securely and responsibly, and on terms each government sets for itself.

If you are working through what this looks like in your own environment, the first question is the one worth starting with: which archetype matches your case work and your mission. Everything downstream follows from that answer, and it is a conversation we would welcome.

Learn more

• Read the IDC MarketScape excerpt.

• Read The Forrester Wave: Industry Cloud Solutions for Public Sector, Q1 2026.

• See how Microsoft supports government missions with AI.
  

Source: “IDC MarketScape Worldwide Software Platforms for National Civilian Government AI-Enabled Case Management 2026 Vendor Assessment,” 7/29/2026, IDC #US53717226

©2026 IDC #US53717226e. Reprinted with permission.

The post Microsoft named a Leader in the IDC MarketScape for Software Platforms for national civilian government AI-enabled case management 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.

This challenge is not exclusive to lower-income countries. Gaps in access appear in every health system, from rural counties to underserved populations that have long been overlooked. Equity is less about reaching a destination and more about expanding who benefits from better care. 

For health leaders, the real challenge is speed and scale: reaching more people, sooner, before these gaps grow wider. The stakes are economic, too: a Peterson-KFF analysis found 5% of the United States population accounted for nearly half of all health spending in 2023, much of it tied to conditions where earlier access to care may help improve management and long-term outcomes.²

At Microsoft, our ambition for AI for Better Health is to help every person on the planet live healthier by transforming how care is discovered, delivered, and accessed at scale. Advancing global health equity is central to that ambition, and may be the hardest part to get right, and the most consequential. 

How AI can help advance health equity 

While the challenges are significant, so is the opportunity in front of us. Advances in AI are creating new opportunities to support clinicians, streamline certain administrative workflows, increase access to information and expertise, and help organizations explore new ways to scale services. In practice, that can look like a tool that helps a patient prepare for a visit, a system that flags a rising risk before it becomes a crisis, or a workflow that gives hours back to an overstretched care team. 

But AI is not a solution on its own. Technology cannot erase structural inequities, nor can it replace the human relationships at the heart of care. Deployed responsibly and in partnership with providers, governments, researchers, and community organizations, AI has the potential to support efforts aimed at advancing health equity: helping patients access trusted information and support closer to home, helping care teams identify risk earlier, and helping professionals spend more time with patients instead of paperwork. 

Partnerships driving health equity and healthcare innovation 

Three areas illustrate where AI, when combined with collaboration and responsible implementation, can help expand access to care: women’s health, rural health, and care for people with intellectual and developmental disabilities. 

Closing the gap in women’s health 

Consider women’s health, which has long been under-researched and underfunded. Through efforts such as the Microsoft Women’s Health Global Community, the [w]Health Tech Manifesto with Kearney, and collaborations with the Milken Institute Women’s Health Network, clinicians, researchers, technologists, and advocates are working together to close persistent gaps in care.³

In a new film produced for Microsoft by BBC StoryWorks Commercial Productions, healthcare leaders explore how technology can help address long-standing disparities in women’s health. From AI assistants such as Microsoft Dragon Copilot, which help reduce documentation burdens so clinicians can spend more time with patients, to data-driven research and digital health innovations, organizations are using technology to support more personalized care and improve health outcomes for women around the world.  

Expanding rural healthcare access with AI and digital health tools 

Nowhere are gaps in access starker than in rural and remote communities, where distance, workforce shortages, and thin infrastructure can put even routine care out of reach. Much of that starts with connectivity: reliable, affordable internet is the foundation for telehealth, remote monitoring, AI-powered screening, and secure access to cloud-based tools, yet millions of people still live beyond its reach.^{4 5 6} Closing that digital divide is one of the most direct ways to widen who can participate in modern healthcare; for a family far from the nearest hospital, a stable connection can be the difference between managing a condition at home and going without care altogether. 

It is also where technology can shift the equation most. Through the Rural Health Transformation Collaborative, a multi-sector partnership spanning community health, technology, and nonprofit organizations, leaders are working to expand access and specialist expertise in the places that need them most. The emerging model is intuitive: use AI to help identify diseases earlier and closer to home, extend the reach of a limited care team, and keep patients engaged in their health between visits.⁶ The opportunity is not only to find disease earlier, but to connect those findings to the right level of follow-up, from specialist care for high-risk patients to team-based monitoring and coaching for those who can be safely supported closer to home. It is a blueprint that reaches well beyond rural care, because the approaches designed for the hardest-to-reach communities often become the innovations that improve care for everyone. 

Inclusive care for athletes with intellectual and developmental disabilities 

For decades, Special Olympics has worked to expand access to care and create more inclusive health experiences for athletes with intellectual and developmental disabilities around the world. Their work is a reminder that health equity depends not only on access to care, but on whether systems are designed to understand, support, and include the people they serve. 

Microsoft is proud to support Special Olympics’ mission to make healthcare more equitable and accessible. At the 2026 Special Olympics USA Games in Minnesota, Microsoft technologies supported a more connected and inclusive vision for athlete health. 

The broader opportunity is clear: when cloud, data, and AI are designed in service of inclusion, technology becomes more than infrastructure. It becomes a way to help organizations extend their reach, support more consistent care experiences, and build more equitable models that can scale across communities.  

Responsible AI for healthcare in underserved and low-resource communities 

None of this earns adoption unless the technology is worthy of trust, especially in the communities with the least room for error. That means AI that is tested on local data, monitored after deployment, evaluated for bias and safety, and measured against real clinical workflows—not just validated once on broad benchmarks. Through the Trustworthy and Responsible AI Network (TRAIN), a consortium Microsoft helped form, organizations with limited resources can test, monitor, and govern these tools using shared methods, so responsible AI becomes practice, not just principle.^{7 8}

The future of health equity and AI-powered healthcare 

The reality that billions of people still lack full access to essential health services should motivate all of us to think bigger and move faster.¹ By combining responsible AI innovation with deep cross-sector collaboration, we have an opportunity to help health systems explore ways to reach more people, support healthcare workforces, and work toward improved outcomes. 

The goal is not simply to modernize healthcare. It is to help health systems identify need earlier, extend limited clinical capacity further, and ensure that the benefits of innovation reach people who have too often been last in line. 

• Get our e-book, AI for Better Health: Enabling transformation in healthcare, to explore how organizations are applying this approach in practice.
• Read our previous blog on “AI for Better Health: Enabling every person on the planet to live healthier.”

¹ World Health Organization & World Bank, Tracking universal health coverage: 2023 Global monitoring report, September 18, 2023.

² Peterson-KFF Health System Tracker, How do health expenditures vary across the population? March 2, 2026. 

³ Kearney 100, [w]Health Tech Manifesto: from ambition to impact with data, digital, and AI, January 14, 2026. 

⁴ International Telecommunication Union, Fixed-broadband subscriptions per 100 people, 2025.

5 Microsoft, Celebrating 250 million: Empowering communities to enable the global AI economy, February 24, 2026.

⁶ Microsoft, Microsoft Airband will expand internet access to nearly 40 million people across Latin America and Africa, May 16, 2023.

⁷ JAMA, Embí, P. J., Rhew, D. C., Peterson, E. D., & Pencina, M. J., Launching the Trustworthy and Responsible AI Network (TRAIN): A consortium to facilitate safe and effective AI adoption, 333(17), 1481–1482, 2025.

⁸ Microsoft, New consortium of healthcare leaders announces formation of Trustworthy & Responsible AI Network (TRAIN), making safe and fair AI accessible to every healthcare organization, March 11, 2024.

The post Health equity can’t wait: How AI can expand access to care 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.