This shift is captured in “The Future of the Finance Ministry,” a new global research study from Global Government Finance and Global Government Forum, developed with Microsoft as a Knowledge Partner. Based on candid interviews with 10 senior finance leaders across four continents, the report paints a clear picture: the finance ministry of the future is no longer a back-office function. It is a strategic nerve center of government.

For business decision makers, policymakers, and senior public finance leaders, the message is both sobering and energizing. The pressures are real, and so is the opportunity to reimagine what public finance leadership looks like in an unsettled world.

Beyond the budget: Finance ministries as system stewards

Traditionally, finance ministries have been defined by their role as fiscal gatekeepers: setting budgets, enforcing discipline, and safeguarding public money. That role remains critical, but the research shows it is no longer sufficient.

Across jurisdictions, finance ministries are being pulled into areas where they historically have lacked direct operational responsibility: trade policy, supply chain resilience, national security, climate mitigation, and demographic change. As one interviewee put it, “Disorder is the new norm.”

To respond, leading ministries are shifting from reactive crisis management to system stewardship—providing whole-of-government leadership grounded in foresight, horizon scanning, and scenario planning. In practice, this means:

• Embedding geopolitical and geoeconomic analysis into fiscal decision-making.
• Coordinating cross-government responses to emerging risks.
• Rebuilding fiscal buffers to preserve flexibility for future shocks.

The implication for leaders is clear: predictive intelligence and long-term thinking are becoming core capabilities—not optional extras.

From planning to outcomes: Proving what public money delivers

As fiscal space tightens, the pressure to demonstrate value for money is intensifying. Yet the report highlights a persistent gap between controlling spend and understanding impact.

Budgets and spending reviews remain central mechanisms, but many finance ministries lack timely, consistent data on what public spending actually delivers. Evaluations are often periodic, manual, and backward-looking—misaligned with the pace of modern policy challenges.

The most forward-leaning ministries are beginning to address this by:

• Moving toward shorter budget cycles to improve agility.
• Developing performance and well-being metrics alongside financial measures.
• Building centralized data platforms that link inputs, outputs, and outcomes.

For public finance leaders, this is a strategic inflection point. The future finance ministry must move from insight to action—using real-time data to reallocate resources, justify trade-offs, and build public trust through transparency.

Digital transformation and AI: Cautious optimism, clear imperatives

Digital transformation runs through every chapter of the research, and with it, a consistent note of cautious optimism.

Finance leaders see significant potential in data, analytics, and AI to improve efficiency, resilience, and decision quality. Yet progress is uneven: many ministries remain constrained by legacy systems dating back decades, fragmented data architectures, and legitimate concerns around security, privacy, and sovereignty.

As a result, most AI adoption today is pragmatic and incremental: summarization, research support, internal productivity, and early pilots in tax and procurement. Transformational use cases remain on the horizon, dependent on stronger data foundations and clearer governance.

Microsoft’s perspective as Knowledge Partner reinforces a critical point: AI is not an IT project—it is a leadership imperative. The next wave is already taking shape in the form of agentic AI, where digital agents help orchestrate end-to-end fiscal workflows while skilled public servants remain firmly in control. Real value is unlocked when digital investment is tied to measurable fiscal outcomes, secure architectures, and clear accountability, with sovereign, secure AI built on trusted data fast becoming non-negotiable for public finance agencies.

The war for talent and trust

Perhaps the most human insight from the research is this: the future of the finance ministry depends as much on people as on technology.

Finance ministries are competing for multi-skilled talent in a global market while facing pay gaps, high turnover, and, in some countries, declining public trust in institutions and expertise. The skills mix is changing fast, blending economics and finance with data literacy, digital fluency, and geopolitical awareness.

What retains talent, the research finds, is not compensation alone but meaningful, intellectually engaging work at the heart of government. Ministries that modernize their tools, embrace innovation, and position themselves as strategic leaders are better placed to attract and keep the people they need.

In this sense, digital transformation is also a talent strategy. Modern platforms and AI-enabled workflows free public servants to focus on higher-value analysis, long-term stewardship, and policy impact—the kind of work that motivates and inspires the next generation of public finance leaders.

From balancing the books to anchoring the economy

Taken together, the findings point to a clear conclusion: the finance ministry of the future is not just a controller of spend, but a steward of national resilience.

As Valentina Ion, Microsoft’s Global Industry Lead for Public Finance and Social Services, sets out in the report’s afterword, the most resilient ministries are making four connected shifts:

1. Agility through data collaboration across government, with shorter cycles that move funds to where they deliver the most value. Shift from annual to quarterly reallocations, link inputs to outcomes through shared data, and tie every spending line to a metric you can re-test in-year.
2. AI as a strategic leadership capability, not a tactical tool, with governance and quantifiable impact at its core. Publish a ministry-wide AI strategy this budget cycle, focusing on two or three agentic use cases (tax processing, budget management, financial market oversight), and measure pilots on fiscal impact, not productivity.
3. Sovereignty, security, and safety as design principles for the technology powering the state. Mandate sovereign cloud for core fiscal systems, build human oversight into every AI workflow touching public money, and stress-test critical infrastructure on a fixed cadence.
4. Innovation as a magnet for talent and trust, with modern tools becoming a defining benefit of public service. Retire damaging legacy systems on a published timeline and give every analyst secure AI tooling as standard.

This is not a theoretical vision—it is already taking shape in finance ministries around the world.

For public finance leaders, policymakers, and partners, the question is no longer whether this transformation is necessary, but how quickly it can be realized.

Read “The Future of the Finance Ministry” to explore the full insights and hear directly from global finance leaders shaping what comes next.

The post The future of the finance ministry: From fiscal control to national resilience appeared first on The Microsoft Cloud Blog.

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.

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.

To maintain velocity and control in 2026, steering committees must answer a fundamental question:

Do we have line-of-sight and control over the AI agents being deployed knowing exactly what they are, what data they touch, and what they are doing?

To help leaders navigate this complexity, we have refreshed our core framework: Grow Your Business with AI You Can Trust. This guide is a practical roadmap for structured decision-making across security and governance, now introducing a critical new pillar for 2026: Observability.

AI committee checkpoint: You cannot govern what you cannot see

As AI spreads across teams and tools, observability becomes the prerequisite for scaling. Without a centralized view, “shadow AI” and unmanaged agents may create significant risks, from security vulnerabilities to sensitive data leakage.

To achieve enterprise readiness, your AI steering committee should be able to answer four foundational questions:

• Inventory: What agents currently exist across our environment?
• Identity: Who is using these agents and for what purpose?
• Access: What systems and specific data sets do they have permission to touch?
• Outcomes: What workloads are they driving and what results are they producing?

Four capabilities for AI platform visibility

In our updated guide, we frame observability through four technical capabilities every enterprise platform should support:

1. Registry: A single source of truth to track every AI asset in the organization.
2. Agent analytics: Real-time data on performance, usage patterns, and costs.
3. Agent map: A visualization of the connections between agents, users, and data.
4. Role-specific oversight: Tailored dashboards that give IT, security, and business leaders the specific metrics they need.

The strategic impact: Accenture

Accenture saw innovation stall at the pilot stage as fragmented tools slowed their path to production. By implementing a centralized platform with built-in observability, they unified monitoring across development and deployment.

Accenture has already deployed more than 75 use cases across industries, with 16 in production, reducing AI app build time by 50%.

Executive checklist: Scaling with control

Your AI steering committee can use the refreshed guide as a checklist to support a secure foundation for AI scaling:

• 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.
• Achieve sustainability: Build systems with socio-economic and environmental impact in mind.
• Address digital sovereignty requirements: Understand common sovereignty scenarios and core principles to help your organization address them.
• Ensure visibility: Confirm your platform supports the 4 capabilities for agent observability.

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

This is where the frontier is forming. While copilots help individuals be more effective, agents act on behalf of people. They carry out tasks, orchestrate multi-step workflows, and operate across systems continuously. And they’re moving quickly from experimentation to mainstream use. An IDC InfoBrief, sponsored by Microsoft, shows that 37% of organizations surveyed use agentic AI, another 25% are experimenting with it, and 24% are planning to use it the next 24 months.¹ Organizations that have already invested in people, skills, and responsible practices may be better prepared to operationalize agents at scale—and convert AI’s promise into real business performance.

Five strategic moves for introducing agents responsibly

The new Agents in the Workforce Handbook builds on those earlier foundations. Where the first blog in this series focused on empowering your people, and the second explored how to pair human judgment with AI systems, this third chapter looks ahead: How do you introduce agents into your workforce responsibly and intentionally? Below are five strategic moves leaders should consider. These are high-level guideposts; the Handbook goes much deeper with templates, examples, and decision frameworks to support implementation.

1. Start with your most persistent pain points

When organizations begin exploring agentic AI, a common challenge is prioritization. Imagining use cases is easy. Choosing where to start is harder. Successful organizations don’t begin with futuristic ideas—they begin with the familiar, recurring friction points that quietly drain time and introduce risk.

These are often the workflows teams have learned to “live with”: manual triage, routine follow-up, coordination across systems, repeated reporting steps, or tasks with high error potential. Leaders should observe how work truly happens—shadowing teams, reviewing process maps, and asking simple but revealing questions:

• Where do we lose time?
• What gets done manually that shouldn’t be?
• What feels broken—but no one owns?

These pain points typically offer the clearest path to early value. Addressing them not only frees capacity but also demonstrates to teams how agents can meaningfully improve the day-to-day. The Agents in the Workforce Handbook includes a readiness assessment and real-world patterns to help leaders identify and sequence the right opportunities.

2. Define your AI goal—and lead the change yourself

Introducing agents isn’t only a technical shift—it’s a leadership shift. Frontier Firms choose to align their early agent initiatives around bold, measurable goals: reducing manual work, accelerating cycle times, improving customer responsiveness, or expanding sales capacity. These goals create alignment and momentum, helping teams understand why agents matter and what success looks like.

But goals alone don’t change culture—leaders do. The organizations that move fastest are those whose executives personally model new ways of working. They use agents in their own workflows, talk openly about learnings, and recognize early adopters who demonstrate impact. They also acknowledge that change requires habit‑building. Experimenting with agents for even 20 to 30 minutes a day can materially improve adoption and confidence.

Skilling plays a central role. As Jeana Jorgensen, Corporate Vice President of Global Skilling, notes:

We’re hearing from many of our customers and partners that they expect employees across different roles to spend about 15 to 20% of their week learning and integrating AI into their daily work.

The Handbook offers guidance for identifying the roles, skills, and operating rhythms needed to support agent adoption.

3. Measure what works—and double down where it does

As with any transformative technology, early wins with agents need to be measurable and repeatable. Leaders should ensure visibility into how agents behave, how frequently they’re used, and the outcomes they produce. This isn’t about policing technology—it’s about giving teams the insights needed to improve and scale what’s working.

Effective organizations treat agent adoption like an operational discipline:

• They log and monitor agent activity.
• They measure time saved and business impact generated.
• They expand agents that demonstrate clear value.
• They refine or retire agents that don’t.

These data-driven insights help organizations move from experimentation to a consistent, enterprise-wide model for agent development—one where new ideas become shared services rather than isolated automations. The Handbook goes deeper into measurement strategies, including examples of what high-performing organizations track.

4. As agents become teammates, optimize continuously

Once an organization begins deploying agents across teams, a new challenge emerges: coordination. Agents that start out as individual productivity tools often become shared digital teammates—relied upon by multiple people, processes, and business functions. With that shift comes the need for thoughtful ownership, governance, and communication.

Successful organizations establish clear roles and responsibilities:

• Who owns each agent?
• Who can modify or update it?
• How are changes communicated to the people who rely on it?
• What happens when an agent’s behavior needs tuning?

Agents also require continuous improvement. As they’re used, they encounter edge cases, nuanced team preferences, and shifting processes. Over time, agents become more capable, and employees naturally evolve into “AI managers”—guiding digital apprentices the way they onboard and develop human teammates.

The Handbook provides deeper recommendations for governance models, centers of excellence, and cross-team alignment mechanisms that help organizations scale responsibly.

5. Reinvest the time saved—and push into innovation

While early value often shows up as efficiency, the long-term impact of agentic AI is much bigger: it creates renewed capacity for innovation. Frontier Firms understand that the goal isn’t to simply do the same work faster—it’s to free teams to pursue higher-value ideas, explore new business models, and elevate customer experiences.

Across industries, leading organizations are already demonstrating what this reinvestment looks like:

• L’Oréal is using AI agents to deliver personalized, at-scale beauty guidance.
• AT&T accelerated customer care resolution by 33%, saving millions while improving experience.

These examples highlight a crucial point: agents are not just workflow optimizers. They’re catalysts for reimagining how organizations deliver value. And the companies that begin investing now are positioning themselves for meaningful advantage.

Treat agents like teammates, not tools

The organizations achieving the strongest results view agents not as automations but as digital collaborators—systems that require feedback, tuning, and iteration. They integrate agents into team rhythms, treat them like growing contributors, and help their people evolve into confident AI managers.

This marks the natural third step in the Frontier journey: after empowering employees and strengthening the partnership between human expertise and AI (as explored in the first two blogs), organizations are now ready to bring digital teammates into the workflow in a structured, scalable way.

If your organization is ready to move from experimentation to scaled impact, the Agents in the Workforce Handbook offers the detailed guidance, examples, and templates to support your next phase of Frontier Transformation.

¹ IDC InfoBrief: sponsored by Microsoft, What Every Company Can Learn From Frontier Firms Leading the AI Revolution, IDC # US53838325, November 2025.

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Microsoft Global Skilling helps people and organizations build the skills they need to thrive in an AI‑powered world. Within Global Skilling, the Learning Lab is the innovation engine—a team focused on designing, testing, and evolving modern learning experiences to continuously improve how skills are developed, validated, and applied in the flow of work. 

AI is reshaping how organizations work. Teams aren’t just adopting new tools—they’re also figuring out how those tools fit into existing workflows, roles, and expectations, all while trying to keep pace with business demands in a rapidly changing landscape. It’s a heavy lift. As the leader of the Learning Lab team, I’m navigating these same pressures, along with my team members, as we balance day-to-day delivery with the need to evolve our processes in real time. That’s why we’re embedding AI assistants and agentic workflows into internal processes—using them not only to work differently but also to learn differently. Through experimentation, we’re uncovering new ways to streamline operations and improve the learner experience for our global audience.  

This blog highlights three of our team’s most impactful AI initiatives that could also benefit your organization. Inspired by these projects, we developed A Practical Guide for Bringing AI into Your Business Processes, featuring real-world examples and actionable ideas for integrating AI and human expertise across your organization. 

A Practical Guide for Bringing AI into Your Business Processes

Download the guide

3 impactful AI initiatives leading the way

1. Reducing time-intensive coordination to optimize research 

The challenge of coordinating teams for research  

Before any learning materials can be built, our team conducts extensive research to understand new technologies, identify required skills, and validate what learners need. This early-stage analysis requires input from multiple stakeholders and a deep review of internal documentation, product roadmaps, and existing training materials.  

How AI is helping accelerate our research tasks and optimize cross-team input 

One of the biggest bottlenecks for our research workflows has been the time it takes to synthesize information and align teams around what a course should achieve. To improve this, we began experimenting with Researcher in Microsoft 365 Copilot and persona-based agents to support our research and planning stages. Our new process looks like this: 

• Researcher synthesizes internal documentation, product roadmaps, and existing training materials to surface emerging themes and identify knowledge gaps. With the ability to process thousands of pages in minutes, it flags potential course objectives the team might have missed.
• In parallel, persona-based agents simulate the perspectives of stakeholders from varying teams to help validate ideas before bringing them to the key decision-makers.
• Throughout this process, our team members guide these AI tools through every step—providing the business context, analyzing AI outputs to identify gaps or inconsistencies, refining direction, and ensuring consideration of broader business objectives.  

In our experience with AI handling synthesis and early-stage validation, we’ve reduced the time required for core research processes from two weeks to just one day. This significant time savings extends to every course developed with this method, enabling us to redirect focus toward shaping stronger strategies, aligning content with business impact, and accelerating decision-making across teams.

Applying this approach in your organization 

AI-supported research and planning can help you make sense of complex information faster and build alignment earlier in your decision cycles. By using AI to synthesize documents, surface patterns, and validate assumptions, you can reduce the effort required to get teams on the same page. Your team members can then focus on refining strategy, confirming business priorities, and shaping higher-impact decisions. This combination improves speed and clarity throughout cross-functional work.  

Explore A Practical Guide for Bringing AI into Your Business Processes to learn more about how you can apply this in processes like: 

• Drafting onboarding plans that human resources (HR) leaders can tailor to company culture.
• Developing quarterly sales plays informed by shifting buyer behavior and competitor activity.
• Creating campaign briefs rooted in audience insights, market trends, and performance data.
• Developing forecasting assumptions by synthesizing inputs from sales, operations, and historical data. 

2. Transitioning from manual maintenance to continuous quality improvements 

The challenge of shorter content lifecycles  

We maintain thousands of courses and lab environments as part of our skilling initiatives for Microsoft technologies. With the fast pace of product evolution, it can be challenging to keep learning content accurate and functional.  

How GitHub Copilot became the maintenance partner for the team 

We recognized that the demands for maintaining learning content were increasing beyond our capacity to manage effectively. So we integrated GitHub Copilot into the content maintenance workflow like this: 

• GitHub Copilot tools analyze content repositories—flagging inconsistencies, identifying outdated examples, and recommending updates based on current documentation.
• Throughout this process, our team reviews and refines the AI-generated recommendations. When GitHub Copilot flags an issue, we evaluate how those changes might apply to other training courses. We also ensure that all revisions align with learning objectives and verify that security and accessibility standards are met.
• Then GitHub Copilot helps implement some of the suggested updates, like generating new code samples or suggesting environmental configurations that align with the latest product releases. 

As a result, our team has reduced the time we spend on routine content maintenance by up to 25%. And with these time savings, team members can shift from reactive updates to proactive innovation—evaluating emerging skills, shaping next-generation modules, and exploring how agents, simulations, and personalized learning could improve outcomes. 

Applying this approach in your organization 

AI-assisted maintenance can help you keep large, fast-changing content ecosystems accurate and up to date without overwhelming your teams. By using AI to surface inconsistencies, flag outdated material, and recommend updates, you can dramatically reduce time spent on routine fixes. Your experts can then focus on reviewing changes for accuracy, regulatory needs, and strategic intent. This balance enables you to maintain quality at scale while freeing your teams to invest in higher-value innovation.  

Explore A Practical Guide for Bringing AI into Your Business Processes to learn more about how you can apply this in processes like: 

• Maintaining and updating sales enablement content as product and service offerings evolve.
• Keeping product messaging frameworks and campaign assets consistent and up to date.
• Updating help center articles and support workflows after feature releases.
• Updating contract templates and clause libraries to align with new regulatory guidance.

3. Delivering inclusive learning at scale through diverse content formats 

The challenge of content relevance and engagement  

Our learners span every continent, speak dozens of languages, and have their own preferred learning methods. Creating multimodal, accessible, and inclusive learning experiences while managing constant content updates was stretching the team thin.  

How AI helps scale and translate content for global learners  

To support different learning styles and languages, we’re piloting how to create immersive, inclusive learning through two experiments with AI: 

1. We’re using AI tools to turn a single source of training content, like a session transcript or recording, into multiple formats, such as videos, podcasts, and recap summaries. This multimodal output lets us update learning materials at the pace required by our global audience and helps ensure that we’re reaching learners in their preferred formats.
2. We’re piloting an AI-powered tool that not only translates content but also generates avatars that deliver multilingual voiceovers with more natural lip-sync, eliminating one of the most distracting elements of dubbed content. 

Early results show that we can now recover up to 15 hours per course we develop—time our team can spend on more nuanced work that AI can’t do, like adapting cultural references, verifying that tone and pacing match learning objectives, and maintaining brand voice. 

Applying this approach in your organization 

AI-powered localization can help you deliver content that feels native to every audience you service, no matter the language or market. By pairing AI’s speed in translation, voiceover, and prompt generation with your team’s expertise in cultural nuance and brand standards, you can scale global engagement without diluting quality. This combination lets you reach more learners, customers, and employees while keeping your message consistent and relevant across regions.  

Explore A Practical Guide for Bringing AI into Your Business Processes to learn more about how you can apply this in processes like: 

• Localizing campaign assets for regional markets across languages and cultural norms.
• Tailoring pitch decks and demos for industry-specific or region-specific buyers.
• Creating multilingual chatbot responses and support scripts for global customers.
• Adapting standard operating procedure and process documentation for different facilities or regional regulations. 

Building skills and strengthening our AI strategy

As AI becomes an extension to the Learning Lab, we’ve discovered that it’s much more than just implementing new tools—it’s also a journey of building technical and human skills across the team. Our experiments require every team member to stretch into new capabilities, from process optimization and innovation to strengthening collaboration and creative problem-solving. As a result, we’ve been able to spend less time on repetitive tasks and to dedicate more energy to the kind of creative, relationship-driven work that leads to exceptional learning experiences. 

Looking to build skills for you and your teams? Explore AI Skills Navigator, the agentic learning space that brings together AI-powered skilling experiences and credentials that help individuals build career skills and organizations worldwide accelerate their business.

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