Building on that foundation, a growing community of customers and partners are fueling rapid innovation by fine-tuning new models, deploying AI applications, and developing specialized agents that expand what’s possible across the diagnostic imaging ecosystem. This momentum is shaping the next era of radiology—one defined by continuous innovation, open collaboration, and powerful new ways to connect insights from image to action.

Listening first: How customer feedback shapes every innovation

For decades, PowerScribe has been built alongside radiologists, grounded in real-world workflows and shaped by continuous feedback and close clinical partnerships with healthcare organizations across the country. This approach, building with radiologists and grounding innovation in real-world use, is fundamental to how we design and evolve our solutions, especially when it comes to performant AI. Those insights directly shaped how we evolved to PowerScribe One, where preserving the workflows and integrations that teams rely on while introducing a more modern, cloud-enabled experience designed for what comes next.

We’ve invested in dedicated voice-of-customer programs and teams whose sole focus is to continuously gather feedback. From advisory boards, clinical partnerships, and real-world usage, we translate those insights directly into our roadmap. This isn’t a one-time input; it’s an ongoing loop that ensures the capabilities we deliver reflect the evolving needs of radiologists across a wide range of environments.

That’s why we partner closely with organizations like University of Rochester Medical Center (URMC), St. Luke’s University Health Network (St. Luke’s), along with many others, through early preview programs ahead of general availability, so they can guide how innovation needs to be integrated. By embedding structured preview and validation stages into our development cycle, we align our releases with customer readiness, continuously refining based on real-world feedback. The result: technology that not only pushes boundaries, but prioritizes the workflow and overall customer experience.

Ultimately, it’s this approach, continuous collaboration grounded in the day-to-day realities of radiology, that gives us confidence in how we are shaping the future of the reporting workflow. This foundation makes these customer stories not just possible, but repeatable at scale.

PowerScribe One serves as the foundation for what’s next

At URMC and St. Luke’s, trust in PowerScribe One began with confidence in a cloud-based foundation designed to scale and integrate seamlessly into the radiologist’s workflow. For URMC, moving to the cloud was essential to unlock advanced AI capabilities that improve efficiency and provider satisfaction amid rising volumes and increasing cognitive demands. At St. Luke’s, modernization with cloud capabilities was equally strategic, enabling innovation while maintaining continuity and trust across the enterprise.

Our partnership and deep engagement model with URMC and St. Luke’s are reinforced at scale: today, more than 10,000 radiologists across 250+ organizations have migrated to PowerScribe One, generating millions of reports every week, across environments ranging from large Integrated Delivery Networks (IDNs) and academic medical centers to independent reading groups. That experience shaped a clear understanding of how to bring AI into the reporting workflow—not as a separate tool, but as a capability embedded directly where radiologists work, without introducing additional steps or fragmentation.

Both organizations are realizing real outcomes through PowerScribe One and its AI features, including generated draft impressions personalized to each radiologist that support improved efficiency and report quality.

“We chose PowerScribe One so we could really take advantage of cloud-based reporting. It gives our radiologists built‑in AI, excellent speech recognition and personalized impressions, making it easier to keep up with increasing demands while continuing to deliver great patient care. Microsoft has been with us every step of the way, staying responsive and supportive through implementation, go‑live and ongoing adoption. We will continue this partnership to continue to improve our workflows and efficiency.”

Robert Fournier, MD, Chairman of Radiology, St. Luke’s University Health Network 

At URMC and St. Luke’s, generated draft impressions were widely adopted because the feature works natively inside the reporting workflow—helping ensure key findings are pulled from the report and summarized in the impression section, reinforcing radiologists’ confidence in their report quality.

The ongoing adoption of PowerScribe One and its draft impression capabilities reflects a broader principle: when AI is fully integrated into the workflow, it enables radiologists to deliver more consistent, efficient, and high-quality reports without disrupting how they work.

Extending AI in the reporting workflow with Dragon Copilot

Now, URMC and St. Luke’s are extending these capabilities with Dragon Copilot, building on PowerScribe One to introduce intelligent summarization and automation directly within the reporting experience. Both organizations are actively leveraging prior report summarization, a feature within Dragon Copilot, to surface essential patient context from relevant prior reports, helping radiologists interpret studies with greater clarity and focus. At URMC, this capability is already delivering value by improving visibility into patient history.

“It works amazingly…it provides a great interface for seeing so much about the patient you otherwise might not see.”

Sean Cleary, MD, Vice Chair of Informatics for Imaging Sciences, University of Rochester Medical Center

Looking ahead, both organizations see significant potential as Dragon Copilot continues to evolve. As it gains access to richer patient context and connects to a broader ecosystem of first- and third-party AI applications and agents, Dragon Copilot can help to further reduce cognitive load and enable continuous innovation without disrupting the radiologist’s workflow.

Meeting customers where they are: From deploying off-the-shelf AI to fine-tuning models

Increasingly, innovation in radiology is shaped not just by what Microsoft delivers, but by how customers and partners extend AI within real-world workflows—helping radiologists work more efficiently, surface critical insights faster, and support better patient care.

As AI adoption expands across radiology, organizations aren’t moving along a single path; they’re navigating a wide range of needs simultaneously. Some are focused on deploying trusted, ready-to-use AI solutions directly into clinical workflows, while others are exploring how to build, customize, and push the boundaries of what’s possible with AI. At Microsoft, we’re designing with this range in mind to meet customers where they are and support multiple approaches to innovation.

For organizations looking to quickly operationalize AI, we provide a streamlined path forward with centralized access to a curated set of FDA-cleared third-party imaging AI applications from our ecosystem of partners—helping simplify how they are evaluated, deployed, and integrated. These applications integrate with our reporting workflows, enabling radiologists to access AI-powered insights within PowerScribe One and helping simplify the adoption of new capabilities.

For St. Luke’s, this approach enabled the rapid deployment of a fracture detection model from Gleamer, delivering immediate impact across its geographically distributed network and helping ensure more consistent diagnostic support regardless of where patients entered the system.

In addition to bringing FDA-cleared imaging AI into practice today, we provide the flexibility for customers and partners to build, customize, and extend AI capabilities as their needs evolve. Our premium medical imaging foundation models, MedImageInsight Premium and CXRReportGen Premium, can be requested for preview through Microsoft Foundry, and are designed for fine-tuning across modalities and workflows. These models are not medical devices, but they enable teams to build and fine-tune models that can complement clinically validated imaging AI solutions.

Delivered as fully managed endpoints, our premium models are continuously improved with curated data and enable AI builders, health systems, and partners to develop institution-specific solutions tailored to local data, specialty use cases, and evolving clinical needs. Models derived from CXRReportGen Premium can be integrated into experiences like Dragon Copilot, bringing high-performing AI directly into the radiologist’s workflow for summarization and report generation.

Together, this approach allows organizations to combine production-grade, regulated AI with ongoing innovation on a single platform, bridging standardized diagnostics and bespoke AI development. Companies like Milvue, a radiology-focused AI developer, are already using our models to accelerate development of solutions tailored to real-world clinical workflows.

“Milvue is building a radiology-native VLM. By working with Microsoft and leveraging CXRReportGen, we could start from a strong foundation allowing our team to focus on what matters most: turning foundation-model capability into clinically validated, workflow-ready radiology solutions.”

Alexandre Parpaleix Co-Founder/CEO, Milvue

No matter where customers and partners are in their journey with generative, multimodal, and agentic AI, we’re here to support them. From clinical applications like PowerScribe One and Dragon Copilot to customizable models from Microsoft Foundry, we provide a trusted, scalable foundation for innovation—enabling organizations to advance at their own pace while keeping workflows, performance, and outcomes at the center.

We’re excited to bring this next wave of radiology innovation to life at the SIIM26 Annual Meeting + InformaticsTECH Expo. Join us in Pittsburgh, PA to experience it firsthand. Visit us at the SIIM 2026 Booth #630–632 where customers and partners can explore our solutions, see live demos, and engage with our models in an interactive learning lab. See what’s possible when AI is truly embedded in the workflow.

See how AI fits into your radiology workflow

• Connect with our team
• Read more about migrating to PowerScribe One
• See demo of how Dragon Copilot supports radiologists and other members of the care team
• Learn how Microsoft applies AI for better health outcomes

The post How collaboration advances workflow-native AI appeared first on The Microsoft Cloud Blog.

The answer isn’t more tools. It’s what people are enabled to do with them.

The friction we see is that many people are unsure how to use AI to their greatest benefit. Companies often struggle to measure the impact of their AI investments because they likely haven’t yet demonstrated return on investment for their employees.

Progress comes when employees actively adopt AI and see meaningful impact on their work—when they’re confident about questioning outputs, applying judgment, and integrating it into their real work.

But there’s another layer to that friction.

Alongside the industry’s excitement and expectations, there’s real hesitation. AI still feels uncertain: Where do I start? Am I already behind? What if I get this wrong?

That hesitation is a signal that access alone isn’t enough; people need to feel confident that AI will elevate their work, not detract from it, or worse, make them irrelevant.

You aren’t behind; you just need to get started. And you do that by learning one new skill at a time. Even skeptics can become strong advocates if they start by learning how to use AI to do the traditional task they dislike most. Once they feel the inevitable benefit, they’re highly likely to try the next task they don’t like doing. From there, we often see a path of continuous learning.

Here’s what too few people realize: technology alone isn’t going to elevate their performance. When everyone knows how to use the tools, the differentiator will be their uniquely human skills that no AI tool can replace.

Human skills aren’t “soft”—they’re foundational

In the New York Times bestselling book Open to Work: How to Get Ahead in the Age of AI, the authors describe five human capabilities that no machine can replace: curiosity, compassion, creativity, courage, and communication.

That same idea extends beyond the individual—organizations aren’t abstract systems; they’re made of people.

What we often call “organizational skills” are simply human skills, practiced consistently and scaled intentionally.

From human potential to organizational capability

A new IDC InfoBrief sponsored by Microsoft, Powering Up: Human Skills for the AI Era,¹ highlights a familiar gap: organizations are investing heavily in AI tools but far less in the capabilities needed to turn them into value.

These capabilities span cognitive, collaborative, leadership, ethical, and business domains.

How do these skills scale? They come together across three levels:

1. Individual. How people think, decide, take risks, and act—especially when working with AI.
2. Teams. How those capabilities show up in collaboration and workflows.
3. Organization. What leaders reinforce through culture, systems, and governance.

This is where personal capability becomes organizational advantage.

How human skills scale in the AI era

The human skills explored in Open to Work don’t disappear at the organizational level; they show up differently at scale.

1. Curiosity: Cognitive and collaborative capability

At the individual level, curiosity starts with a desire to explore and learn what’s possible. At scale, this shows up as:

• Asking better questions to challenge assumptions.
• Exploring different approaches beyond the first answer.
• Sharing learnings across teams.

2. Compassion: Ethical and leadership capability

Compassion is empathy and awareness of impact. At scale, this shows up as:

• Applying ethical judgment and accountability.
• Identifying and addressing bias.
• Practicing responsible data use.

3. Creativity: Cognitive and business capability

Creativity isn’t about aesthetics. It’s about imagining what doesn’t yet exist. At scale, this shows up as:

• Framing problems more effectively.
• Creating new sources of value.
• Driving innovation beyond efficiency.

AI can optimize what exists. Humans decide what’s worth building next.

4. Courage: Cognitive and leadership capability

Courage starts with acting even when outcomes aren’t certain. At scale, this shows up as:

• Applying critical thinking and judgment.
• Making decisions in complex environments.
• Leading change without guaranteed outcomes.

5. Communication: Leadership and business capability

Communication starts with clarity and listening. At scale, this shows up as:

• Setting a clear vision for AI transformation.
• Translating technical capability into business meaning.
• Aligning teams across functions.

What leaders should consider next

Taken together, these examples point to a clear pattern: personal strengths become organizational advantage when they’re built at scale.

If human skills are the differentiator, how do we design for them intentionally? Three mindset adjustments matter most—especially in a moment where excitement about AI is often matched by hesitation about where to begin:

1. Focus on the work, not just the training
   • Human skills develop through real decisions, real collaboration, and real accountability—not one-off courses.
2. Model the behaviors consistently
   • What leaders practice signals what’s safe. Judgment, curiosity, empathy, and learning must be seen, not just stated.
3. Measure what actually changes outcomes
   • Beyond adoption, organizations need to track decision quality, trust and confidence, and cross-functional outcomes.

The real opportunity of AI

AI won’t make organizations less human—but it will raise expectations for how people think, decide, and work.

The organizations that succeed won’t be the most automated. They’ll be the ones that invest in people as intentionally as they invest in technology.

That’s the opportunity—and the work—in front of us.

Continue learning at Microsoft AI Skills Fest

If you’re looking for a practical way to build AI and human skills, no matter your role, join us for Microsoft AI Skills Fest, June 8–12, 2026. It’s a week of free, guided, digital learning designed to make skilling more approachable and relevant, with options for leaders, business users, technical roles, and developers.

On the AI Skills Fest Mainstage, human skills will be a prominent theme. I’ll be hosting a conversation with Aneesh Raman, co-author of Open to Work, and Gina Smith, PhD, co-author of Powering Up: Human Skills for the AI Era. Together we’ll unpack what it takes to build human capability alongside AI—from individual habits to team practices to organization-wide norms.

To go deeper, we’ll also have a dedicated session with Dr. Michael Gervais, sport performance psychologist and founder of Finding Mastery, to help you develop the mindsets and human skills that will help you thrive as AI reshapes how we work.

We hope to see you there.

¹IDC InfoBrief, sponsored by Microsoft, Powering Up: Human Skills for the AI Era, Doc. US54451326-IB, May 2026.

The post AI needs more than intelligence—it needs humanity appeared first on The Microsoft Cloud Blog.

Financial service institutions have long been among the sectors requiring the greatest levels of security, compliance, and reliability. Today, in the age of AI, organizations in the financial sector are looking to apply AI to alleviate some of these burdens, while also unlocking meaningful competitive advantage through AI applications.

The good news: If you’re in this industry you will likely have decades of sensitive data you can use for learning and insights that can lead to real customer solutions.

The bad news: Yesterday’s data infrastructure might not be up to the task. Delivering the scale, speed, predictive maintenance, access, and performance that today’s financial data platforms need—along with the standard security and compliance—requires rethinking your database solution for the modern era.

The stakes are higher with sensitive data

Maintaining always-on services and meeting stringent regulatory requirements have been baseline expectations in finance for years. Now, with surging digital transactions and AI-powered projects, the pressure has only intensified. In some financial organizations, even a few minutes of downtime can be disastrous, given the reliance on every day availability. Aging, self-managed databases struggle to keep up with high-volume transactions and real-time analytics demands. The operational overhead of managing such systems (like patching, scaling hardware, and manual failovers) drains resources that could be better spent on innovation.

It’s telling that predictive maintenance and infrastructure automation have become focal points for banks to avoid costly outages. Yet, acting too fast also carries risk: one fintech leader recently cautioned that “in financial services, harm historically scales faster than benefit,” underscoring that if you scale up without a solid foundation, problems can amplify rapidly. In other words, bad data or brittle systems will only wreak havoc faster when accelerated by AI. This reality makes it clear that simply layering AI on top of old infrastructure isn’t viable—the core data platform needs modernization.

An investment in PostgreSQL on Microsoft Azure

Azure PostgreSQL managed services, including Microsoft Azure Database for PostgreSQL, address these modern challenges, combining PostgreSQL’s versatility with Azure’s cloud resilience and ecosystem. It’s a fully managed service, meaning Azure handles the heavy lifting of maintenance, updates, and scaling behind the scenes, so teams can focus on value-add work instead of upkeep. Crucially for financial institutions, Azure Database for PostgreSQL offers flexible performance scaling. You can start with a small instance and scale up to large multi-vCore servers or even scale out across elastic clusters to distribute heavy workloads across multiple nodes. This elasticity ensures that sudden surges in trading volume or customer usage won’t degrade application performance.

Enterprise-grade resilience and availability

Downtime isn’t an option for these critical applications, so continuous availability is baked into Azure PostgreSQL services. With a few clicks, you can enable zone-redundant high availability, deploying a fully synchronized standby server in a different Azure availability zone. In the event of an outage or even an entire datacenter zone failure, the service triggers an automatic failover to the standby typically within 60 to 120 seconds with zero data loss. This architecture delivers up to a 99.99% availability service level agreement (SLA) for mission-critical workloads, which is a key assurance for financial apps that cannot go down.

For read-intensive scenarios, Azure Database for PostgreSQL supports read replicas which asynchronously replicate data and allow you to offload analytics or reporting queries without impacting the primary database’s performance. These replicas can even be in different Azure regions, doubling as a disaster recovery option to keep services running through regional disruptions. The bottom line: whether it’s handling a hardware failure or scaling out reads, the service preserves uptime and consistency so your customers and applications see uninterrupted service.

Security, compliance, and an integrated ecosystem

Azure Database for PostgreSQL helps simplify compliance for sensitive and highly regulated data by providing layered security controls out of the box. All data is encrypted at rest by default, and you have the option to use customer-managed keys for encryption if you need full control over key rotation and access. Network isolation is straightforward: you can deploy your PostgreSQL server into an Azure Virtual Network with private endpoints, so that database access stays entirely on your private Azure network with no exposure to the public internet.

For identity and access management, Azure Database for PostgreSQL supports Microsoft Entra ID authentication, allowing you to manage database users and permissions through centralized Entra ID identities instead of static credentials. This means you can use existing corporate security policies and easily onboard and offboard users per compliance needs. Together, these features help meet strict standards like payment card industry data security standard (PCI DSS) and Security Operations Center (SOC) compliance by controlling who has access to what data and ensuring data is protected at rest and in motion.

Because it’s an Azure service, PostgreSQL integrates naturally with the broader Microsoft ecosystem. You can connect your data to analytics and AI services (such as Microsoft Fabric and Azure AI) without complex Extract, Transform, and Load (ETL), accelerating the development of AI-powered apps on top of your operational data.

In fact, after modernizing its platform, BNY Mellon reported that its teams could “innovate faster in areas such as data management, analytics, AI, and machine learning” once they were running PostgreSQL on Azure. Developers also retain the full power of PostgreSQL’s extensibility. Azure’s managed service supports a wide range of popular Postgres extensions (from PostGIS for geospatial analysis to pg_cron for scheduling), so developers can continue to use specialized plugins for financial calculations, time-series analysis, or even graph queries as needed.

A transformation with returns in nine months

To see these benefits in action, consider BNY Mellon, a global financial services company that modernized a critical data platform by migrating to Azure Database for PostgreSQL. BNY Mellon’s Data Vault system ingests and manages mission-critical, multitenant data for clients—it demanded high resilience, scalability, and agility that their legacy self-managed database couldn’t easily provide. Working closely with Microsoft, BNY Mellon moved this workload to Azure Database for PostgreSQL, completing the migration in just nine months.

By adopting Azure’s fully managed Postgres, the company achieved simplified data storage and analytics and built a “cohesive, customized solution” aligned with their microservices architecture. Resiliency improved immediately, with Azure’s high availability and backup capabilities, and BNY Mellon’s engineering teams gained more time for innovation now that routine database maintenance is offloaded to Azure. This new foundation is not only handling today’s needs but is flexible enough to evolve with future AI and analytics initiatives, exemplifying how a modern cloud database can empower a venerable financial institution to stay on the cutting edge.

A step toward readiness for the era of AI

Modern financial services requires a database platform that can scale effortlessly, stay secure and compliant by default, and free up your teams to innovate with data. Azure Database for PostgreSQL, with its combination of performance, high availability, advanced security, and rich PostgreSQL compatibility, rises to that challenge. It’s a solution that lets developers and Database Administrators (DBAs) spend less time wrestling with infrastructure limitations and more time building the next generation of financial applications.

Ready to take the next step? Explore our PostgreSQL for Financial Services solution guide for architectural best practices and implementation tips.

The post AI is requiring financial services to modernize their data platforms appeared first on The Microsoft Cloud Blog.

This is a defining moment for healthcare and the decisions made now will shape outcomes for years to come. The question is no longer whether to adopt AI—it’s how to alleviate these pressures with agency, security, and trust.

AI for Better Health is our ambition to enable every person on the planet to live healthier by transforming how care is discovered, delivered, and accessed at scale. We are redefining how AI works alongside people to transform health across three priorities: 

1. Accelerating lifesaving breakthroughs.
   
2. Transforming the healthcare experience.
   
3. Advancing global health equity.
   

Accelerating lifesaving breakthroughs

Across healthcare and life sciences, organizations are bringing human judgement together with AI to enable discovery—helping researchers identify patterns that may support earlier insights and accelerate innovation.

In practice

• Novo Nordisk aimed to scale a pipeline of drug discovery, development, and data science capabilities with AI and machine learning. The teams built a Novo Nordisk AI platform and amplified its culture of innovation across many use cases, including predictive AI models for advanced risk detection in cardiovascular diseases. The algorithm may be able to predict patients’ cardiovascular risk better than current clinical standards. 
  
• In clinical settings, AI is helping clinicians act sooner and with greater precision. For example, AI-supported tumor boards are helping organizations like Providence surface potential data insights for clinician review, such as identifying biomarkers or matching patients to clinical trials, to support decision making.
  

These innovations are helping advance research so it can reach more patients, more communities, and more health systems worldwide.

Transforming the healthcare experience

As new discoveries move into practice, the way care is delivered is beginning to change. Increasingly, AI is being embedded directly into workflows with tools designed to reduce friction, connect information, and provide insights in context. As routine work is streamlined, people gain the time and clarity to focus on deeper impact—strengthening care, discovery and operations in service of better health for all.

In practice

• At Piedmont Healthcare in Atlanta, for example, an OB‑GYN is using Microsoft Dragon Copilot to capture and structure clinical conversations in real time—freeing her to listen more deeply, build trust, and apply more informed judgment during sensitive moments like pregnancy and postpartum. This shift toward more empathetic, patient-centered care improves reproductive health outcomes for women of color at her clinic.
  
• These capabilities extend beyond documentation. AI is beginning to surface new signals within the flow of care. For example, identifying patterns in vocal characteristics may provide additional context for clinician evaluation, subject to appropriate validation and oversight. Baptist Health in Kentucky is beginning to apply this approach within an ambient workflow, adopting tools from Canary Speech to support earlier and more informed intervention.
  

What begins as support for individual interactions can scale across teams, specialties, and health systems. And as some of the constraints on their time and attention are lifted, providers can focus more fully on their patients—bringing the human element back to care.

Advancing global health equity

Ensuring that better health is accessible to everyone remains one of the most important and complex challenges in healthcare.

AI has the potential to extend the reach of clinicians and care teams—bringing health information to remote communities, supporting resource-constrained environments, and providing individuals with more direct access to trusted health information.

As these capabilities scale, they can help reduce barriers tied to geography, infrastructure, and access to specialized care—supporting more equitable access to care.

In practice

• Through innovations like Microsoft Copilot Health, AI-powered health companions are helping individuals make sense of complex health information. By bringing together clinical records, wearable data, and medical knowledge, supported by Microsoft security and privacy technologies that help protect data, individuals can better understand their health. This can help them feel more empowered in discussions with their care teams.
  
• Partnerships are helping redesign care models and address long-standing inequities. For example, Microsoft is collaborating with Kearney to mobilize a global community of innovators through the Women’s Health Tech Manifesto—using data and technology to help close gaps in women’s health.
  
• Through the Rural Health Transformation (RHT) Collaborative, a multi-sector public and private partnership, co-chaired by Microsoft and others, we are uniting technology providers, health systems, payers, and non-profits to help states deploy CMS’s rural health funding into ready-to-adopt AI-enabled care models. Together, we are supporting efforts to expand access to primary care, telehealth, and remote monitoring for rural communities at risk of losing critical health services, while strengthening the cybersecurity and interoperability foundations more than 700 rural hospitals already rely on.
  

Ultimately, advancing health equity will depend not only on leveraging AI, but also on the responsible design, deployment, and use of these technologies with appropriate human oversight, transparency, and accountability.

Building the future of health—together

The decisions being made today will shape how AI is used in healthcare for years to come.

Progress will not be uniform. Every worker, leader and organization is learning how to harness AI-enabled workflows that amplify what we as humans can do to make meaningful change. Each step forward unlocks new possibilities for organizations and communities.

Taken together, these outcomes move us toward a broader vision for the future where every person on the planet can live healthier. That’s the goal of AI for Better Health.

Our ambition of AI for Better Health is grounded in use cases that create impact today.

• Get our e-book, AI for Better Health: Enabling transformation in healthcare, to explore how organizations are applying this approach in practice.
  
• Explore Microsoft for Healthcare to see how to drive innovation and improve healthcare experiences with trusted, AI-powered solutions.

The post AI for Better Health: Enabling every person on the planet to live healthier appeared first on The Microsoft Cloud Blog.

That era is over. Brands and retailers have entered a new phase of AI adoption. Agentic AI is where systems don’t just analyze or recommend; they execute. And the conversation has finally moved from hype to economics. Technology budgets across retail and consumer goods are projected to hit $113B in 2026, but the mandate from the C‑suite is no longer “innovate.” It’s “prove the ROI and scale.”

The research is here. A recent Forrester Total Economic Impact™ study of Microsoft AI solutions for retail and consumer goods organizations projects 124% to 282% ROI over three years, with $7.7M to $17.6M in net present value for a composite $5B enterprise.¹ That’s not aspirational. That’s P&L math.

What matters even more than the topline number is where the value is showing up. Across brands and retailers, three lines of business—marketing, supply chain, and store operations—are delivering scaled, measurable impact through proven, named Agentic AI use cases. Not pilots. Not experiments. Production.

Marketing: AI shopping assistants and AI‑assisted campaign execution

Marketing is where many brands and retailers first touched AI, but now it’s where the economics are clearest.

The Forrester TEI highlights three core marketing use cases driving ROI today:

1. AI shopping assistants embedded in digital commerce.
   
2. AI‑assisted content creation and campaign execution.
   
3. AI‑driven marketing performance optimization.
   

AI shopping assistants, built to support product discovery, evaluation, and conversion, delivered up to a 4% improvement in conversion rate, generating $1.5M to $3.4M in incremental digital revenue over three years for the composite organization. Among brands and retailers deploying these assistants at scale, Forrester observed reductions in cart abandonment and increases in average order value that materially changed digital revenue trajectories.

Behind the scenes, AI‑assisted marketing execution is where productivity compounds. According to the study, the composite organization reclaimed 7–13 hours per month per person, shifting time away from mechanical production and toward creative judgment, experimentation, and optimization. By automating research synthesis, content drafting, summarization, and performance analysis, the composite organization is on track to realize $4.5M to $6.7M in labor productivity gains over three years.

Brands and retailers also reduced dependency on external agencies. Early‑stage creative development and campaign prep moved in‑house, cutting outsourced marketing spend by an expected $433K to $881K over three years, while preserving agencies for high‑value strategic work.

The 2026 Work Trend Index reinforces what we’re seeing operationally: 66% of AI users say using it allows them spend more time on high‑value work, and 58% say they’re producing work they couldn’t have created a year ago. This isn’t automation replacing marketers; it’s agentic AI upgrading the role from execution to orchestration.

Supply chain: AI demand forecasting, inventory optimization, and autonomous planning

If marketing proves AI can grow revenue, supply chain proves it can protect margin for brands and retailers that live or die by forecast accuracy.

The TEI study identifies three supply‑chain use cases driving the majority of value:

1. AI‑driven demand forecasting.
   
2. Inventory and allocation optimization.
   
3. Exception‑based planning with agentic execution.
   

AI‑driven demand forecasting and inventory optimization delivered $3M to $6.3M in three‑year benefits, driven by higher forecast accuracy, better buy decisions, and earlier detection of demand shifts. One consumer goods leader cited a 10‑point improvement in forecast accuracy versus traditional statistical models, which is enough to materially reduce both stockouts and excess inventory.

On the labor side, AI automated routine planning tasks like data pulls, reconciliation, and reporting, freeing 6–12 hours per month per planner across hundreds of planning FTEs. One retailer reduced its planning workforce from 50–60 planners to 40–50 while maintaining performance, as AI took over SKU‑store allocation and replenishment decisions.

The real inflection point is agentic execution. Instead of analysts identifying issues and manually implementing changes, planners now work in exception‑based workflows, where AI flags anomalies and agents execute adjustments via natural language commands. As one planning leader put it: planners focus on decisions, not spreadsheets.

For brands and retailers, this is the shift from AI as insight to AI as operator. And the economics follow.

Store operations: Digital shelf labels and frontline task automation

Store operations are where brands and retailers have historically struggled to unlock productivity. Agentic AI is changing that.

The Forrester TEI highlights two frontline use cases delivering immediate ROI:

1. AI‑powered digital shelf labels (DSLs).
   
2. Frontline task automation and employee copilots.
   

Digital shelf labels eliminated manual price changes, saving an estimated 200 labor hours per store per year. For large brands and retailers, that translates into thousands of hours redirected from label maintenance to customer engagement and execution.

Frontline task automation covering price updates, inventory checks, and information lookup delivered 9–15 hours of time savings per store per month. More importantly, it improved employee experience. By stripping out repetitive, low‑value work, retailers and brands can reduce burnout and turnover, and aim to drive $1M to $1.3M in reduced frontline attrition costs over three years.

This is where agentic AI, such as those utilizing Azure AI and Copilot Studio, quietly becomes a people strategy. When frontline roles become less tedious and more customer‑centric, retention improves along with execution.

From line‑of‑business wins to institutional advantage

The ROI is real. Across marketing, supply chain, and store operations, Forrester projects $14M to $23.9M in total three‑year benefits for brands and retailers that scale these use cases. But here’s the uncomfortable truth: Most organizations will capture the first wave of gains and then stall.

The 2026 Work Trend Index shows that organizational factors drive more than 2x the AI impact of individual capability. Agents can take on execution. Human agency expands. But only organizations that redesign how work gets done convert those gains into durable advantage.

Microsoft calls the leaders in this shift Frontier Firms. They’re organizations that move beyond deploying tools to rebuilding operating models around agents, workflows, incentives, and decision rights. These firms become learning systems, compounding insight from every transaction, every forecast, and every customer interaction.

Retailers and brands that treat agentic AI as “just another system” will see diminishing returns. Those that treat it as an operating‑model reset will build something harder to copy than any algorithm.

The leadership imperative is clear: ROI is no longer in question. Sustaining it requires redesigning incentives, workflows, and management systems so line‑of‑business gains become institutional advantage. The hype was fun. The economics are better.

For brands and retailers, agentic AI has moved from experimentation to execution; from insight to action; from promise to profit. The organizations that win from here won’t be the ones with the flashiest pilots or the longest vendor lists. They’ll be the ones that redesign incentives, rebuild workflows, and re‑architect management systems so agentic gains in marketing, supply chain, and store operations compound into institutional advantage.

For brands and retailers, the time is now to maximize agentic AI.

¹ New Technology: The Projected Total Economic Impact™ Of Microsoft AI Solutions For Retail And Consumer Goods Organizations is a Forrester Consulting New Technology Projected Total Economic Impact Study Commissioned by Microsoft, April 2026

To understand the benefits, costs, and risks associated with this investment, Forrester interviewed four decision-makers and surveyed 134 global respondents at the director level and above with experience using Microsoft AI solutions. For the purposes of this study, Forrester aggregated the results from these customers into a single composite organization.

The post Agentic AI is reshaping retail and consumer goods economics appeared first on The Microsoft Cloud Blog.

Property and casualty (P&C) insurers face growing challenges, including macro-economic factors and cyber-attacks, but none is bigger than climate risk. Catastrophic events are nothing new, of course. What has changed is the scale and frequency of weather-related losses and the operational strain that follows. Swiss Re estimates global insured losses from weather‑related natural catastrophes have exceeded $135 billion in 2024, marking the fifth consecutive year insured losses topped $100 billion, and underscoring a structural escalation in climate‑related risk.¹

In response, many insurers are rethinking how to best deliver customer value, profitability, and growth. Mutual and cooperative insurers are under sustained pressure to balance financial strength with their purpose of providing protection in an environment marked by increasingly severe risks and closer regulatory scrutiny. It is a challenge that AI is well suited to answer, helping to expand the role of insurers from risk transfer providers to proactive risk partners.

Insurers and AI: early adoption and opportunity

A 2024 survey by the International Cooperative and Mutual Insurance Federation (ICMIF) found that 62% of respondents were already using AI, with a further 19% planning adoption within the next year. In practice, however, most deployments were commonly concentrated in specific functional areas, such as supporting underwriting, claims processing, and customer interactions. About 67% of insurers expect AI to become more central to their operations, even as many cite data quality and talent gaps as key challenges.²

According to a recent BCG study, only about 7% of insurers have successfully scaled initiatives, with 67% engaged in pilots, fragmented across functions. The opportunity now is to move from isolated use cases to AI embedded across end‑to‑end processes, extending to more automated, interconnected workflows and setting the stage for a shift toward risk prevention.³

How AI helps improve efficiency, service, and relationship management

Prevention does not replace excellence in risk transfer. Forward-looking organizations pursue both. They modernize service and core operations across the customer engagement cycle, while investing in prediction and prevention-oriented capabilities that help reduce future risk and strengthen long-term resilience.

One area where AI delivers important benefits is in enabling faster, more consistent client service by helping representatives locate and validate policy information faster. At Unum Group, for example, a new AI-powered application lets representatives search across 1.3 terabytes of policy and related documents and receive highly relevant answers in four to five seconds, with reported accuracy of up to 95%. This reduces time spent on manual lookup and frees representatives to focus on higher-value client interactions.

Likewise, NFU Mutual uses Copilot for Sales with Microsoft Dynamics 365 to establish a centralized “single source of truth” for customer data and interactions. By capturing and summarizing communications in real time, employees can quickly understand customer needs and respond with greater precision, helping to reduce response times and deliver more informed, personalized engagement.

AI can also streamline First Notice of Loss by ingesting call transcriptions, images, and videos, and guiding representatives to capture the right information in the first conversation, helping accelerate remediation.

In claims review, AI can turn static documentation into insights that inform action. Gallagher, for example, built an internal AI platform that summarizes complex claims files in minutes rather than hours, helping adjusters move faster and apply those insights more effectively across claims and client workflows.

In cases of widespread impact, such as a storm that causes power outages that result in many food spoilage claims, AI can route low-complexity claims through specialized AI agents that can help validate coverage, correlate weather data, detect fraud, calculate payouts, and generate audit trails. This increases service representative capacity for higher-impact cases by addressing low-risk claims with autonomous AI.

These innovations use document processing, contextual summarization, natural language interface and workflow automation, all of which can be used to help improve other processes across core insurance capabilities, customer service, and relationship management.

How AI helps with prevention and protection

The impact of prevention‑led approaches, whether applied to customer risk or enterprise risk, is twofold: financial resilience and stronger trust. This positions insurers as partners that mitigate, not just transfer risk for their customers.

Prevention‑led use cases extend well beyond field‑level interventions, such as property risk scoring or event‑readiness outreach. Increasingly, they focus on identifying and reducing risks earlier, before disruptions, security incidents, or service failures occur.

This shift is visible in how organizations are applying AI to support faster, more informed decisions. At Aon, which has an enterprise grade platform that can operate across solution lines, teams use AI-enabled tools to better assess and respond to risk. To enhance decision quality while maintaining strong governance, they built an Azure-based AI platform called AonGPT that securely connects data and supports consistent, governed analysis, especially in fast-moving situations. During recent California wildfires, Aon’s teams combined near real-time satellite imagery with proprietary data to generate timely insights that helped clients assess damage and plan their response.

AI also enables a shift from paying claims to helping customers reduce exposure before losses occur. Zurich Insurance Group deployed more than 200 AI tools to interpret unstructured inputs in the form of images, reports, and emails in multiple languages, and translate them into clear, consistent risk signals for underwriters. This improves the accuracy and timeliness of risk assessments, helping customers anticipate and reduce potential exposures before losses occur, and supports better informed underwriting decisions.

Prevention can also take the form of making dormant risk visible early enough to act. For example, AI can analyze large volumes of historical risk engineering reports to identify patterns, such as construction materials or design features that are associated with higher structural risk. This can distinguish specific higher-risk properties for expert review—in weeks rather than months in some cases—letting insurers engage earlier, prioritize inspections, and reduce the likelihood of disruption.

Emerging external data sources help improve risk prevention

Many prevention types depend on spotting and interpreting early signals, often from outside of core insurance systems. Using generative AI and machine learning, insurers can integrate third-party signals with internal data to help create new ways to refine risk selection, pricing, event readiness, customer outreach, and more. Sources such as external research, disclosures, regulatory filings, sensor data, and geospatial imagery can have immense impact, provided they are reliably accessible.

Initiatives from Microsoft Research and AI for Good highlight advances in third-party data that can significantly enrich the power of predictive solutions:

• First, Aurora is a foundation model of the atmosphere that produces fast, high-resolution forecasts, especially during extreme and fast-moving conditions. For insurers and reinsurers, that means more timely environmental intelligence to support underwriting, catastrophe modeling, claims surge planning, and reinsurance response.
  
• Second, SPARROW uses solar-powered devices with cameras, microphones, and sensors to detect meaningful changes on the ground and send near real-time insights to the cloud. For insurers, it shows how AI and sensor data can enable earlier risk detection, faster intervention, and reduce loss severity.
  

Earlier, more precise forecasting can inform proactive risk alerts, giving customers and commercial clients time to take preventive actions (for example, securing property or adjusting operations) and support coordination among insurers, risk engineers, brokers, and public authorities. The objective is straightforward: Improve analysis, lead time, and decision quality to mitigate large losses.

Priorities for success with AI and risk prevention

For leaders, realizing measurable value from AI across the business, including enhancing prevention, can happen in a matter of months or quarters. Microsoft’s view of industry patterns indicates that successful approaches often prioritize the following:

• Define a clear strategy and start with a small number of high‑value, extendable use cases aligned to core business priorities.
  
• Build strong data foundations and effective governance.
  
• Balance innovation with credibility and responsible adoption.
  
• Pursue business-led process re-architecture, change management, and talent skilling.
  
• Commit to stretch goals with active leadership, resourcing, and accountability.
  

Insurers who employ this comprehensive approach and tailor AI to their unique business requirements can improve the most critical aspects of their operations. Critically, they can enhance prevention as an important part of their future growth strategies.

Learn more

• To explore how leading insurers are using agentic AI to transform claims, underwriting, and customer experience, read our ebook.
• To explore solutions and resources for insurers, visit Microsoft for Insurance.
• To learn how frontier firms in financial services are using AI to improve efficiency, innovation, and customer satisfaction, get the e-book.
  
• Visit our blog for stories of how Microsoft for Financial Services helps firms accelerate business value.
  

¹ Swiss Re, “Hurricanes, severe thunderstorms and floods drive insured losses above USD 100 billion for 5th consecutive year, says Swiss Re Institute,” December 2024

² International Cooperative and Mutual Insurance Federation, “Balancing AI innovation with member-driven values at mutual and cooperative insurers,” February 26, 2025

³ BCG, “Insurance Leads in AI Adoption. Now It’s Time to Scale.” September 04, 2025

The post From risk transfer to risk prevention: How AI supports long-term financial resilience in insurance appeared first on The Microsoft Cloud Blog.

AI adoption is accelerating—but adoption alone isn’t transformation. Across industries, leaders are moving beyond experimentation and confronting a deeper challenge: How to reshape the way work gets done, decisions get made, and value gets created in an AI-driven world.

This executive series brings together perspectives from Microsoft leaders who are navigating that shift firsthand. Rather than focusing on tools or technology milestones, these conversations explore the leadership choices that determine whether AI delivers incremental efficiency or lasting impact—how leaders set direction, build culture, redesign work, and guide their organizations through change.

As Corporate Vice President, Business Applications and Agents at Microsoft, Bryan Goode spends his time at the intersection of technology, business process, and leadership, working to turn innovation into outcomes. In conversations with customers and partners across industries, he frequently hears the same underlying concern: Are we already too late to implement AI?

Leaders see headlines about rapid adoption and accelerating innovation, and assume that meaningful advantage now belongs only to early movers. From Goode’s perspective, that assumption misunderstands where real advantage is actually created and what kind of leadership this moment truly requires.

From my perspective, you’re not behind the curve if you haven’t started yet—but the time is now to really act.

Bryan Goode, Corporate Vice President, Business Applications and Agents, Microsoft

AI adoption is not the same as AI transformation

AI usage is undoubtedly increasing. More executives are experimenting with copilots, more employees are testing generative tools, and more organizations are exploring automation. But Goode consistently draws a distinction between adoption and transformation. Adoption reflects individual behavior. Transformation reshapes how workflows and value are created. Leaders who blur this distinction often feel progress without impact.

That distinction is critical. Many organizations feel progress because AI appears in daily routines, yet core business processes remain unchanged. Decisions are still delayed. Work still moves across disconnected systems. Potential value remains unrealized. In Goode’s view, this gap explains why so many leaders feel both excited and unsatisfied at the same time—progress is visible, but impact remains elusive.

Why functions—not tools—are the real starting point

From Goode’s perspective, the most effective starting point isn’t a tool, platform, nor architecture—it’s the function. Sales, marketing, finance, HR: each function contains friction that compounds quietly until performance stalls. When AI is applied directly to those processes, transformation can become tangible. Outcomes may improve, not because AI exists, but because work is redesigned.

Leadership sponsorship turns experimentation into execution

Functional ownership matters as much as technical capability. When senior leaders actively sponsor AI initiatives, teams gain clarity on priorities and permission to change how work gets done. That leadership signal is often what separates experimentation from execution. Without that sponsorship, AI remains an experiment rather than a catalyst.

Assistants and agents: Complementary forces

Goode also points to the role of assistants and agents as complementary, not competing, forces. Assistants improve individual productivity in the flow of work. Agents reduce friction across end‑to‑end processes. Together, they create space for human judgment where it matters most.

That’s really how you transform and how you get business value from AI.

Bryan Goode, Corporate Vice President, Business Applications and Agents, Microsoft

Culture is the hidden multiplier

Technology, however, is only part of the equation. Goode consistently highlights culture as the deciding factor. Organizations that treat AI as a shared learning journey where employees are encouraged to experiment, share insights, and iterate, are more likely to scale what works than those that pursue perfection upfront. In organizations that scale AI successfully, culture doesn’t follow transformation—it enables it.

It actually ends up being about culture more than anything else.

Bryan Goode, Corporate Vice President, Business Applications and Agents, Microsoft

Why starting small is a leadership advantage

Importantly, AI transformation does not require a massive rollout. In Goode’s experience, the organizations that make durable progress start small, focus on one function, learn quickly, and then scale intentionally. Transformation can compound as confidence grows.

For leaders who feel left behind, the reality is reassuring: in most organizations, the work itself has not yet changed. That means the opportunity remains.

The number one priority for every business leader is asking: how is AI changing my industry, how is it changing my company, and how am I going to use it to drive competitive advantage?

Bryan Goode, Corporate Vice President, Business Applications and Agents, Microsoft

The question is not how quickly AI can be adopted—it’s how deliberately leaders are willing to redesign the work that matters most and how ready they are to lead that change.

This is the first post in an executive series exploring how leaders navigate AI transformation—from culture and creativity to functions and outcomes.

The post You’re not late to AI—you’re early to Frontier Transformation appeared first on The Microsoft Cloud Blog.

Despite widespread adoption, many organizations remain early in their AI maturity, constrained by fragmented foundations, unclear governance, and limited organizational alignment. These gaps make it difficult to move from experimentation to repeatable, enterprise‑wide impact.

The difference is not access to technology, but how prepared organizations are to deploy AI at scale—securely, responsibly, and in direct support of business outcomes. New research from Microsoft reveals a clear pattern: AI readiness is the foundation of Frontier Transformation—the next phase of enterprise change, where organizations align AI and human ingenuity to achieve their most ambitious goals.

In this research, AI readiness refers to an organization’s ability to deploy and scale AI in a way that is technically robust and organizationally aligned. It encompasses not only the underlying technology—such as data, cloud platforms, security, and AI models—but also the strategic, cultural, and governance capabilities required to operationalize AI responsibly and at scale. Organizations with high AI readiness can move beyond experimentation, embedding AI into core business processes to drive measurable outcomes.

Frontier Transformation starts with readiness

Frontier Transformation describes how leading organizations are embedding AI across every layer of the business—from employee productivity and customer engagement to core operations and decision-making. These organizations are AI leaders, referred to in the research as Frontier Firms that have moved beyond pilots. AI is not a side initiative; it’s a strategic capability.

The AI Readiness Assessment Whitepaper is based on a global study of 1,000 organizations across 15 countries and eight industries. It connects AI capabilities directly to business performance—and the results are striking.

Organizations with high AI readiness report 47–64% stronger performance across key metrics, including operational efficiency, innovation speed, workforce productivity, customer experience, and revenue growth. Readiness doesn’t just enable progress—it compounds advantage.

The readiness gap is widening

Only 17.7% of organizations qualify as AI leaders, meeting the threshold for both technology and organizational readiness. These Frontier Firms realize 56% higher AI value than organizations earlier in their journey.

This gap matters. While many organizations are investing in AI tools, far fewer are building the foundational capabilities required to scale those tools across the enterprise. As a result, leaders continue to accelerate—while others remain stuck in perpetual experimentation.

Readiness must be balanced, not siloed

One of the clearest insights from the research is that AI readiness must be balanced across both technology and organization. Organizations that overindex on technology often struggle with adoption and trust, while those that focus only on governance lack the platforms needed to scale. Frontier Firms avoid this tradeoff by progressing both dimensions together.

Roughly 30% of organizations reach a strong level of technology readiness. A similar share reaches organizational readiness. But only those that achieve both consistently deliver business impact.

Frontier Firms take a unified approach—aligning strategy, governance, culture, and platforms rather than treating them as separate workstreams.

To make readiness measurable, the Microsoft’s AI Readiness Advisor framework evaluates 10 domains across two dimensions:

Technology readiness

• AI models and generative AI applications
  
• Data and integration
  
• Cloud and hosting
  
• Information security
  

Organizational readiness

• Business and AI strategy
  
• AI experience and skills
  
• Organization and culture
  
• Responsible AI and governance
  

This end‑to‑end view helps organizations understand not just where they’re investing, but where gaps may limit scale.

Four readiness profiles—one clear leader

The research identifies four AI readiness segments:

• Observers are early in their journey, focused on exploration and isolated pilots, with limited operational impact.
  
• Operators excel at execution and governance but lack the modern AI platforms needed to accelerate innovation.
  
• Innovators invest heavily in models and applications but struggle to drive consistent adoption and change at scale.
  
• Frontier Firms lead across both dimensions—enabling secure, scalable AI that is embedded into everyday business operations.
  

Frontier Firms have largely moved from experimentation to optimization. Their focus is on standardization, reuse, and managing AI as a portfolio tied to business KPIs.

Cloud maturity differentiates AI leaders

Cloud strategy is a defining characteristic of Frontier Firms.

Frontier Firms treat the cloud not simply as infrastructure, but as a control plane—where data, models, applications, security, and governance operate together. Approximately 60% of AI leaders run workloads on Azure, reflecting the importance of integrated governance, compliance, and data management for enterprise‑grade AI.

This approach allows AI leaders to standardize security, governance, and data access while enabling teams to innovate faster—without re‑creating foundational capabilities for each new use case.

Leaders also tend to invest platform‑first—building strong cloud, data, and model foundations before scaling applications. That sequencing enables faster innovation and more predictable outcomes over time.

Responsible AI accelerates adoption

Trust is not a barrier for Frontier Firms—it’s a capability.

AI leaders consistently score highest on responsible AI maturity, with formal frameworks, oversight, and monitoring in place. Rather than slowing progress, governance enables scale by building confidence among employees, customers, and regulators.

In Frontier organizations, responsibility and innovation move together—unlocking broader adoption and faster value realization.

AI leadership spans every industry

Frontier Firms appear across every industry studied, from financial services and healthcare to retail, manufacturing, and professional services.

What differs is not ambition—but execution. Leaders report improvements in productivity, accuracy, efficiency, and customer experience tailored to their sector. The takeaway is clear: Frontier Transformation is driven by capability, not industry position.

Turning insight into action

The data is clear: AI value is not unlocked by tools alone, but by readiness across technology, organization, and governance. Frontier Firms don’t wait for transformation—they prepare for it.

Importantly, readiness is not a binary state. Organizations progress through stages as they mature their platforms, operating models, and governance. Understanding where you are today is the first step toward making intentional, high‑impact investments that move the organization forward.

Is your organization ready for AI?

Read the AI Readiness Assessment Whitepaper to understand the research behind AI leadership, then take the AI Readiness Assessment to benchmark your organization and identify the most impactful next steps on your journey to Frontier Transformation.

Download the AI Readiness Whitepaper

Learn how to help your business assess and advance its AI readiness, and unlock Frontier Transformation.

Read the Whitepaper

The post From AI ambition to Frontier Transformation: Readiness defines the leaders appeared first on The Microsoft Cloud Blog.

That tension shows up most clearly when AI systems cross borders—touching sensitive data, operating under different regulations, and supporting teams around the world.

Every four to five days, a new regulation targeting AI, cybersecurity, or data privacy is introduced—with more than 1,000 global policy initiatives across 69 countries, and 100-plus nations enforcing privacy laws.¹

In 2026, digital sovereignty is about managing risk, so you can scale AI using the tools and environments your business depends on as sovereignty requirements evolve. To maintain global velocity while managing risk, your steering committee should answer this fundamental question:

Can we meet localized requirements—controlling where data is processed, who can access systems, and how operations continue during disruptions—without additional complexity as requirements evolve?

To help leaders navigate these challenges, we offer a practical guide: Grow Your Business with AI You Can Trust. This guide provides a grounded approach to navigating sovereignty decisions in real environments, covering governance, operational control, and responsible AI deployment without adding unnecessary complexity.

Navigating the 5 common sovereignty scenarios

Sovereignty rarely shows up as a single requirement. If you’re scaling AI, you’re likely encountering it through a small set of recurring scenarios—often as you expand across regions, partners, and regulatory environments:

1. You operate in markets with evolving regulatory requirements.
   
2. You are scaling AI across regions and need clear governance over data processing.
   
3. You need provable controls over who can access sensitive data—across vendors, operators, and jurisdictions.
   
4. You must meet data residency requirements without fragmenting tools, teams, or operating models.
   
5. You need consistent control across global operations because downtime or loss of control in one region now has immediate impact across your business.
   

One example shows how these scenarios come together in practice.

Sovereignty in practice: Raiffeisen Bank International

Raiffeisen Bank International developed an internal generative AI assistant, using Microsoft Foundry to help employees summarize legal, regulatory, and banking documents and retrieve information more quickly. The platform supports employees across the bank’s operations in multiple European markets, helping staff resolve customer requests faster and focus on higher-value work.

Used by more than 20,000 employees, the solution provides faster access to critical information while supporting the bank’s regulatory and operational requirements across jurisdictions—without compromising safeguards.

Executive checklist: Scaling with resilience

Use the guide to align your AI steering committee on these critical checkpoints:

• Define trust: Establish clear Responsible AI principles for your brand.
  
• Secure by design: Shift to a security-first posture across all AI operations.
  
• Govern the loop: Use the “Map, Measure, Manage” framework to mitigate risks.
  
• Support sustainability: Build systems with socio-economic and environmental impact in mind.
  
• Ensure visibility: Confirm your platform supports the 4 capabilities needed for agent observability.
  
• Address digital sovereignty requirements: Understand common sovereignty scenarios and core principles to help your organization address them.
  

As AI becomes core to how your business operates, sovereignty moves from a technical consideration to a leadership one. Our ebook guide can help you understand sovereignty scenarios and principles to help your steering committee take the next step – clearly, confidently, and at scale.

¹ Footnote includes:

• “The AI regulations that aren’t being talked about,” Deloitte Insights, Deloitte, November 10, 2023
• OECD.AI: The OECD’s Hub for AI Policy, Organization for Economic Co-operation and Development
• “Building a Foundation for AI Success: Governance,” Microsoft Cloud Blog, Microsoft, March 28, 2024
• “2025 AI Index Report,” Stanford Institute for Human-Centered Artificial Intelligence
• “AI Regulations Around the World,” Mind Foundry Blog, Mind Foundry, January 13, 2026
• “Identifying Global Privacy Laws Relevant to DPAs,” IAPP News, International Association of Privacy Professionals, March 19, 2024
• “Data Protection and Privacy Legislation Worldwide,” UNCTAD, United Nations Conference on Trade and Development, February 17, 2026

The post Your AI steering committee’s 2026 checklist: Sovereignty appeared first on The Microsoft Cloud Blog.

• Author: You’re producing the work, calling on AI to help as needed — a line of code, a sentence, a chart.
• Editor: You set the intent and AI creates the first draft for you to edit and approve.
• Director: You create a spec and hand off entire tasks for AI to execute in the background.
• Orchestrator: You design a system where multiple agents run in parallel across a workflow, flagging exceptions and escalations to you.

Every business leader knows the world is changing, but far fewer have a clear picture of what to do about it. These four patterns are the place to start. The real work ahead for leaders is redesigning their firm’s operating model around the collaboration patterns.

As agent use increases, human involvement doesn’t disappear — it changes shape. What declines is the amount of tactical, step-by-step execution work humans do themselves. And what rises is the need for humans to set direction, define standards and evaluate outcomes.

Ultimately, the goal is not to move every task and business process to the fourth pattern. Instead, it’s up to leaders to help their organizations develop clarity around matching workstreams to the right collaboration pattern. That’s the shape of the Frontier Firm: defined by how deliberately leaders design work across functions, matching the level of human involvement to the outcome.

What the data shows

Our 2026 Work Trend Index research reinforces this shift across roles and industries. We analyzed trillions of anonymized Microsoft 365 productivity signals and surveyed 20,000 workers using AI across 10 countries. We also spoke with leading experts in AI, work and organizational psychology to help us unpack the insights from the data and understand where all this is going. The conclusion is consistent: the constraint is no longer what people can do, it is how work is structured around them.

• AI lifts individual potential. A privacy-preserving analysis of more than 100,000 chats in Microsoft 365 Copilot shows that 49% of all conversations support cognitive work — helping workers analyze information, solve problems, evaluate and think creatively. This shift is already visible in output, with 58% of AI users saying they’re producing work they couldn’t have a year ago, rising to 80% among Frontier Professionals, the most advanced AI users in our research. Additionally, when AI users were asked which human skills are most important as AI takes on more work, they said two topped the list: quality control of AI output (50%) and critical thinking — that is, analyzing information objectively and making a reasoned judgment (46%).
• The Transformation Paradox. We are seeing a pressure point emerge within the organization where the pull to perform collides with the push to transform. 65% of AI users surveyed fear falling behind if they don’t use AI to adapt quickly, yet 45% say it feels safer to focus on current goals than to redesign work with AI. And only 13% of workers say they’re rewarded for reinvention of work with AI even if results aren’t met. The same forces accelerating AI adoption are holding it back.
• Every organization is a learning system. Our results show that organizational factors like culture, manager support and talent practices account for more than 2X the AI impact of individual factors like mindset and behavior (67% vs. 32%). Specifically, the findings underscore the importance of an AI-ready environment: a culture that treats AI as a strategic advantage and encourages experimentation, managers who model and incentivize AI use and talent practices that build skills and create space to apply them. The real question isn’t whether people have the right skills, it’s whether the organization is built to unlock them.

The firms that build a new operating model today won’t just move faster in the short term. They’ll build something more durable, setting themselves up to create value in ways that we can’t yet conceive of: an organization that learns faster than its competitors, compounds its own intelligence and gets harder to catch with every cycle.

For deeper analysis, see the 2026 Work Trend Index Report.

Enabling the Frontier Firm with Copilot Cowork — now mobile, extensible and enterprise-ready

None of an organization’s system scales without infrastructure that brings people and agents into the same flow of work with connected data and the ability to manage and govern it all. Microsoft 365 Copilot is built for exactly that.

Today, we’re expanding Copilot Cowork with new capabilities for Frontier customers to help organizations move from isolated AI tasks to coordinated, multistep work. Cowork enables people to define outcomes and delegate work across apps, business systems and data, with execution that stays directed and controlled throughout.

This update introduces Copilot Cowork Mobile for iOS and Android, along with a growing plugin ecosystem for Cowork, bringing more of an organization’s tools and data into these experiences. This includes native plugins across Microsoft services like Dynamics 365 and Fabric, and partner integrations available in the coming weeks like LSEG (London Stock Exchange Group), Miro, monday.com, S&P Global Energy and more. Organizations can also build custom plugins to turn their own workflows and expertise into reusable, scalable processes. Additionally, a first wave of federated Copilot connectors in Researcher and Microsoft 365 Copilot Chat is generally available today from partners like HubSpot, LSEG (London Stock Exchange Group), Moody’s, Notion and more.

Together, these updates extend Copilot Cowork from a task-based assistant into an extensible platform that helps orchestrate work across Microsoft and third-party systems. With management and governance through Microsoft Agent 365, organizations can deploy and scale agents across core business functions like sales, service and operations.

For more on these product innovations: Microsoft 365 blog.

AI is no longer an experiment. It is an execution challenge. Employees are already working across all four patterns. The open question for every leadership team is whether they can catch up. Access to AI won’t be the advantage for much longer. How the work is designed around it will be.

Jared Spataro, CMO, AI at Work at Microsoft, shapes how every organization applies AI and agents to reduce costs, create new value and define the future of work. He leads research, strategy and product across Copilot, Copilot Studio, Microsoft 365, Dynamics 365 and Power Platform.

The post How Frontier Firms are rebuilding the operating model for the age of AI appeared first on The Microsoft Cloud Blog.