Across industries, CMOs are no longer asking whether AI will transform marketing but how fast they can move from experimentation to impact, and how to re‑architect work so AI shows up where decisions are actually made.

That question sat at the center of Microsoft’s CMO AI Innovation Forums, convened at CES and Cannes Lions, and designed for one purpose: helping marketing leaders navigate Frontier Transformation—the shift from tools and pilots to AI embedded in the flow of work, driving measurable business outcomes.

Frontier Transformation starts in the flow of work 

In the months between Cannes Lions last year and CES, it’s incredible to see how much things have changed. Six months ago, the question was “Where can we use AI?”Today, it’s “How do we make it deliver real business value—and prove it?” As we head toward Cannes again, the bar has moved even higher. The era of experimentation is over. Boards and CEOs are no longer interested in pilots—they’re expecting tangible outcomes: monetization, measurable growth, and a clear line from AI investment to business impact.

At the same time, most organizations aren’t set up to deliver that. At least not yet.

CMOs described teams juggling 25–30 disconnected applications, with AI pilots layered on top but rarely integrated end-to-end. The result is predictable: disconnected workflows, inconsistent insights, and limited scale. But the real challenge runs deeper than the tech.

What we’re hearing consistently from marketing leaders is this: AI initiatives fail when they are contained to a single function. You can succeed in marketing, but if your workflows aren’t connected to other functions in the enterprise, you will fail.

That’s why the next phase of transformation isn’t about deploying AI around the business it’s about embedding it through the business. Because ultimately, AI transformation is business transformation.

And let’s face it the stakes are rising fast:

• Monetization is mission-critical. AI investments must tie directly to revenue acceleration, margin expansion, or customer lifetime value not just productivity gains.

• Agentic commerce is reshaping the funnel. Discovery, consideration, and even purchase decisions are increasingly intermediated by AI agents disrupting traditional attribution models and forcing CMOs to rethink influence altogether.

• Trust is becoming a defining brand asset AND competitive advantage. As AI-generated interactions scale, consumer confidence in data usage, content authenticity, and brand integrity becomes a competitive differentiator.

• Measurement needs a reset. Legacy metrics can’t capture AI-driven, non-linear journeys. We need new protocols that reflect intent-based engagement, agent participation, and real-time orchestration.

CMO efforts are accelerating

So, as we think about how these shifts are impacting the role of CMOs, I wanted to bring you inside these CMO forums and share what leading CMOs are doing differently. These leaders aren’t hesitating. In fact, quite the opposite. They’re accelerating the integration and operationalization of AI in an effort to rewire processes and supercharge their people. Four patterns are emerging:

1. Measuring AI value is now non‑negotiable, but still unresolved

Efficiency and time savings are table stakes. CMOs are under pressure to tie AI directly to growth, effectiveness, and enterprise outcomes. To do this, they are moving beyond proxy metrics (time saved, content produced) toward value-based measurement frameworks, including:

• Linking AI-driven personalization to incremental revenue lift and conversion quality.
• Measuring speed-to-market as a competitive advantage, not just an operational KPI.
• Understanding how to measure attribution with agentic commerce increasingly mediating the buying journey.

CMOs are in agreement that measuring productivity and effectiveness end-to-end is a critical, unresolved issue.

2. Cross-functional workflows matter more than functional excellence

Marketing wins alone are no longer enough if sales, commerce, service, and supply chains are not connected. Leading organizations are:

• Embedding AI into end-to-end demand-to-fulfillment processes, not just campaign execution.
• Connecting marketing signals directly into sales prioritization, supply chain planning, and service resolution.
• Using AI to orchestrate real-time decisioning across functions, not just optimize within silos.

We have learned that you can knock it out of the park in marketing and still fail if the other organizations aren’t connected.

3. AI is changing who marketers serve—and how

It’s clear that we are no longer just marketing to consumers. This introduces a profound shift: 

• Brands must optimize not just for human attention, but for machine comprehension and recommendation.
• Content strategies must evolve toward structured, verifiable information that AI systems can trust.
• Influence changes as what the model believes about your brand becomes just as important as what the customer sees.

Customer and consumer engagement is not limited to human audiences, but LLMs and agents shaping discovery, consideration, and purchase in real time.

4. Agentic AI exposes operating model gaps

As teams experiment with agents, undocumented processes, tribal knowledge, and governance gaps surface immediately—forcing a rethinking of roles, incentives, and accountability. Leading companies are taking decisive action:

• Redesigning roles around human + agent collaboration, not task ownership
• Establishing clear governance models for AI decision-making and accountability.
• Creating shared data and process standards to enable agents to operate reliably.
• Investing in trust frameworks—including transparency, explainability, and responsible AI practices.

The fourth bullet is especially important, as this is where trust becomes critical not just externally with customers, but also internally. Can teams trust AI outputs enough to act at speed? And can leaders scale AI without introducing risk to their brand?

The takeaway

Across all of these conversations, one thing is clear: CMOs don’t just need more technology. They need clarity. They need connection. And they need confidence in how to scale. They’re looking for real patterns, proven approaches, and practical pathways from pilots to enterprise value. That’s because the next chapter isn’t about experimenting with AI. It’s about operationalizing it across the business to deliver real, measurable impact.

The post The CMO on the frontier: From AI experimentation to AI at work appeared first on The Microsoft Cloud Blog.

In conversations across marketing organizations—and increasingly with customers—this often surfaces as a creative concern: if everyone is using the same tools, will everything begin to sound the same?

From Tracie Westby’s perspective, the answer has less to do with the technology itself and more to do with how it’s applied. In her role leading integrated marketing for Commercial Cloud and AI, she sees AI not as something that diminishes creativity, but as something that reflects the clarity—or ambiguity—behind the work.

In this moment of change, creativity isn’t being replaced. It’s being reshaped. And the organizations navigating this well are balancing their need for oversight of AI tools with a clear focus on the conditions that allow strong creative work to emerge.

AI amplifies the clarity behind the work

In practice, AI behaves less like a disruptor and more like a mirror.

Westby has observed this across both her own teams and in conversations with customers. When values, messaging frameworks, and creative guardrails are clearly defined, AI tends to reinforce distinctiveness. When direction is less defined, it doesn’t create sameness—it reveals it.

From her experience, the risk of all marketing messages sounding the same is rarely a reflection of the tools themselves. More often, it emerges when teams are operating without shared clarity. AI doesn’t erase voice—it amplifies whatever foundation is already in place.

That’s where leadership matters—helping set direction, align teams, and establish the guardrails that allow creative work to scale without losing its distinctiveness.

AI creates space by removing friction

One of the most immediate impacts Westby has seen isn’t replacing imagination—it’s removing the friction around it.

“In our organization, we’re using AI to help write briefs for campaigns, create content for customers, and manage content workflows,” she explains.

Meetings generate summaries instead of scattered notes. Drafts move more quickly from a blank page to a starting point. Teams spend less time coordinating and more time shaping ideas.

Creativity expands when space is protected

That shift matters because creativity requires time, focus, and energy—resources that are often consumed by repetitive work.

As Westby puts it, when some of that load is removed, people gain the capacity to think more deeply—and to take more intentional risks.

When AI absorbs more of the operational overhead, teams have more room to explore ideas, refine them, and push them further. The opportunity isn’t just to move faster, but to create better work.

At the same time, there’s an important balance. While speed matters, creative work ultimately serves something more enduring: trust and differentiation. Efficiency gains only go so far if the output loses the qualities that make it meaningful and distinctive.

Start small—scale with intention

In practice, this kind of transformation doesn’t begin with sweeping change.

Westby describes an approach that starts with focused experimentation—teams piloting AI in specific workflows, learning what works in their own context, and sharing those outcomes. Over time, those efforts begin to connect, making it easier to scale them more deliberately.

Throughout, responsible AI and security remain foundational. Establishing that trust early allows teams to move forward with greater confidence, rather than introducing friction later.

What accelerates or limits creative momentum

How organizations approach this moment has a direct impact on how creativity evolves.

From what Westby has seen, progress builds when curiosity is visible, experimentation is encouraged, and learning is shared openly. When leaders participate alongside their teams—testing, learning, and iterating—it helps normalize change and build momentum.

At the same time, it’s easy to over‑rotate on efficiency alone. The organizations seeing the most sustainable progress are the ones that balance productivity with thoughtful governance—ensuring that creativity can scale without losing integrity.

Guiding creativity through AI

AI will change how creative work gets done. What isn’t predetermined is whether that change feels constraining or enabling.

In Westby’s view, that outcome depends on the choices organizations make—how clearly direction is set, how intentionally teams are supported, and how much space is created for human insight.

The goal is not to protect creativity from AI. It is to lead creativity through it—ensuring that technology creates more room for thinking, exploration, and originality rather than less.

When teams see progress and small wins are recognized, adoption is more likely to take hold. Momentum builds over time—not through mandate, but through shared confidence.

Frontier transformation isn’t a one‑time event. Even at Microsoft, it’s an ongoing journey. But the direction is clear: AI is here to stay—and how it shapes creative work will depend on how it’s guided over time.

This is the second post in an executive mini‑series exploring how organizations are navigating AI transformation—from culture and creativity to functions and outcomes.

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

The post AI amplifies creativity by removing friction 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 e-book 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.

As Australia looked to extend its winning streak to four straight Ashes on home soil, it was clear that left-handed batter Travis Head was leading the way for Australia as the runs piled up. But just how good was his performance? Fans using the Cricket Australia Live app had an instant answer. 

Thanks to the app’s new AI Insights feature, which provides live insights on player milestones, records and key moments using OpenAI’s GPT-5 within Microsoft Foundry, cricket aficionados and newcomers can now access much-needed context to better engage with the game. They can also dig deeper by asking follow-up questions about the insights provided. It’s an exciting development for Cricket Australia, the governing body of the sport in the country. 

“The recent series where England were here in Australia had a couple of key moments where I saw the insights come to life in real-time,” says Cricket Australia CEO Todd Greenberg. “And you can see the engagement through the analytics and the tracking that when something is delivered in the right time frame, in the right format, into the right hands, it has a huge effect.” 

Indeed, AI Insights showed that Head’s 172 runs for the match were his fifth-highest aggregate total in a test. His only higher efforts were 220 runs against Sri Lanka, 213 against West Indies, 181 against India and 180 against England. Head’s big day earned him Player of the Match honors and helped Australia claim a five-wicket victory in the match and a 4-1 Ashes series victory against its archrivals. 

Going beyond the box score 

“Scores and highlights tell you what happened. But the context tells you why you should care about it,” says Balamurugan P M, chief technology and digital officer at Cricket Australia. 

“It comes down to the storytelling. From my perspective, I thought it was essential for fans to learn more about the story rather than just following the scores or watching highlights. So, we wanted to give a different experience.” 

Cricket Australia had a corker in its arsenal as AI Insights came into focus – an extensive archive of official scorecards that dates to 1886, providing a wealth of historical data that could bridge the gap between the past and present. Those scorecards were carefully integrated over a period of three months to ensure the information would pass muster among the serious cricket experts. 

“We had hundreds of years of data, and when it comes to fans, trust is non-negotiable,” Balamurugan says. “When you’re dealing with records and milestones, you can’t make mistakes. There are some hardcore fans who know these stats like the back of their hand. History is core to cricket’s identity. And instant context turns a scoreboard into a story. 

“Getting that volume of data, integrating it and surfacing greater context for live games required huge data alignment and validation. With our systems and with the skilled team that we’ve got, that was made possible.” 

Creating a solution fans can use in real time 

Cricket Australia joined forces with Microsoft, alongside technical partners Insight Enterprises, HCL Tech and Skewer, to create the new iteration of the app. With the important Ashes and T20 international tournaments on the horizon, time was of the essence to launch the app before the bats were raised on those key fixtures. 

The app is anchored by Microsoft Azure, the cloud foundation that Cricket Australia uses to run and scale its digital platforms and the app experience. AI Insights takes advantage of Azure OpenAI Service in Microsoft Foundry, which generates the real-time, match-aware insights that serve as a companion to what fans are seeing on the field. 

“What we’re talking about is a really good example of solving a fan-facing problem with deep technical capability and a shared vision on delivery,” Greenberg says. “Microsoft brought world-class cloud and AI foundations. Without them, we would not have been able to get as far as we have. And our partners have helped accelerate the build, the integration and, importantly, operational readiness.” 

One of the biggest challenges with AI Insights is ensuring that fans watching a match and using the app can get updates and context within the flow of the game, making it an additional resource for fans at the grounds or watching alongside with commentary. 

Azure Cosmos DB supports Cricket Australia’s ecosystem of apps – including Cricket Australia Live with AI Insights and PlayCricket, which hosts scores for up to 7,000 community matches a weekend. The technology provides a fast, scalable data layer that can update quickly during live play, always keeping fans aware of the latest scores. 

“All live sport has one thing in common. There are no pauses,” Greenberg says. “It’s not like reality television. So, the experience has to be fast, reliable and consistent, especially when it’s under peak demand and when you have millions of people enjoying it at the same time.” 

An experience for every type of fan 

While cricket has its ardent supporters, especially in Australia, it can also be difficult for newcomers to pick up. As Cricket Australia looks to cultivate the next generation of fans, Greenberg realizes that the app can prevent sticky wickets for the sport’s novices. 

“I mean, we play a crazy sport that goes over five days and sometimes at the end of the five days, you still don’t get a result,” Greenberg says. “We can’t expect people to be tuned in at every moment, but what we can do is we can hyper-personalize the way they would like to engage with the sport during the contest.” 

The Seddon Cricket Club in Melbourne has been in existence since the 1920s and is now home to several senior, junior and all abilities sides that compete in associations across Australia. It is also home to a loyal supporters group, featuring fans who love the game in all forms. For them, the AI Insights on the Cricket Live App has been a value add as they go deeper into the game. 

“It’s definitely made it more interesting to follow along and learn more about the players,” says Cassie Gray, a Seddon Club supporter and cricket fan. “You could follow a player, you could see what they’re known for, as well as figure out what’s their next step or what do they need to get an amazing moment next. 

“Cricket is a game of history. It’s been around for a really long time, and the players influence other players, and countries influence other countries. With the insights, it gives me an understanding of not just what’s happening today, but what’s led up to that in the game itself.” 

The next step for AI insights is to create greater personalization within its levels of information for different types of fans. A user can select “newcomer,” “history buff” or “stats guru” and receive insights tailored to their persona. 

“We want to understand every fan and cater to how they want to be served by the app,” Balamurugan says. “We have moved from scores to storytelling, but we want to move from storytelling to fans setting up the narrative themselves. Fans should hear the story how they want to hear it. That is one of our lodestars.” 

With the initial success of the AI Insights feature, Greenberg said other sports organizations have reached out to learn more about how it was developed and the impact on the fanbase. Most people working at Cricket Australia have a deep love of the sport, often having played for many years. Greenberg hopes the app’s success and further innovation can continue the sport’s momentum. 

“The thing we’ll never know until much later on is the impact that we’re having on young kids falling in love and choosing cricket as their preferred sport,” he says. “And if we help them love it, what we can create for a fan on their journey between the ages of 8 and 80 is astronomical for a sport like cricket. And so, we’re very mindful of ensuring kids get the opportunity to engage in cricket so we can form lifelong partnerships.” 

Top Image caption: Supporters at the Seddon Cricket Club in Melbourne love the game in all forms, and the Cricket Live App featuring AI Insights has allowed them to gain further insights into the sport, whether they are a novice fan or stats guru. Photo by Graham Denholm for Microsoft.  

Elliott Smith writes about AI and innovation at Microsoft, from how the Premier League is transforming its online presence to why AI may play a major role in saving the Amazon rainforest. Previously, Smith worked as a sports reporter in Washington, D.C., Washington state and Texas, covering high schools to the pros. You can contact him on LinkedIn. 

The post Cricket Australia uses AI Insights to bring fans closer to the action appeared first on The Microsoft Cloud Blog.

It is a task that is not taken lightly by the Jets’ coaches, front office and scouting department, who will be featured in the team’s draft room as they make their final decisions.

But that gathering is just the tip of the iceberg when it comes to player evaluation, and the Jets are careful to explore every avenue available when assessing the thousands of players who are draft-eligible each season. Utilizing the latest technology to help them make more informed decisions is now a critical part of the team’s overall strategy.

“The draft is one of the primary ways in which we’re able to acquire talent, and it’s an extremely important event for us,” says Dan Zbojovsky, senior director, football operations for the Jets. “It’s a year-long process and oftentimes multi-year process for us to evaluate these players coming out of college and then get the opportunity to select them.”

A ‘Titan’ off the field

In the traditional draft scenario, teams receive dispatches from scouts around the country who file reports on players that range from height and weight measurements to 40-yard dash times to vertical leaps. Colleges will conduct Pro Days, in which scouts are invited to see a team’s top performers run drills and catch passes. And independent scouting services provide roundups of prospects major and minor, with their own evaluation systems.

In short, there’s a lot of information on a lot of potential draftees, and that doesn’t even include each NFL team’s own preferences based on organizational philosophy, coaching schemes and roster needs. While some teams prefer to keep things more analog, the Jets have been at the forefront of embracing technology to help them prepare not only for the draft but also the fast-paced nature of an NFL season.

The team’s proprietary Titan app (winkingly named after the team’s original moniker, The Titans of New York) is the team’s “mothership” for football operations – a custom-built web application that contains essential tools for draft preparation, scouting and personnel strategy.

“Titan is really the hub behind everything we do on the football side,” says Paul Marsh, senior director of application development. “It’s a legacy application of 15 years now through many, many different iterations, but it’s always remained Titan. It is where all of our scouting and football data is housed. It is the view into that data and it enables the powers that be to help make their decisions and come up with their plans to help make the team win.”

Titan is built on Microsoft technology, including Microsoft Azure, GitHub Copilot and GitHub Actions. Marsh’s team relies on GitHub Copilot to speed up coding, prototyping and iteration, helping them gain greater efficiency when time is tight leading up to the draft. GitHub Actions are used to automate, build and deploy pipelines, enabling frequent updates and continuous integration across Titan’s modules.

Another key element of Titan is the team’s draft/trade calculator, a points-based tool the Jets use to evaluate draft-day trade scenarios. In real time, New York’s football brain trust can plug in picks, compare values and determine whether a proposed trade would result in a net gain or loss for the team.

“This is a UI that was designed really by our [general manager] and his close advisors to work the way that they want to work,” says Marsh, who has been with the Jets for 24 seasons. “And it simply allows them to kind of dig into the information and game plan on what they’re going to do going forward into the draft.”

The Jets’ process has paid off with several important contributors being acquired via the draft, including wide receiver and 2022 Offensive Rookie of the Year Garrett Wilson, 2025 No. 7 selection Armand Membou, defensive end Will McDonald IV, running backs Breece Hall and Braelon Allen, and tight end Mason Taylor.

Old school, meet new school

For Zbojovsky, who is entering his 19th season with the franchise, the draft successes reflect the balance the team uses when combining the old-school scouting mentality and the technology and analytics of the new school of player evaluation.

“[Titan] is an extremely important internal website for us, and we’ve made a lot of really cool advancements over the years on it,” he says.

“I think everything has its piece of the puzzle. On certain players, some parts might be a bigger piece of that puzzle. We like to, of course, rely on our film work as the foundation of our reports and our scouting evaluations, and then we can utilize all these other cool tools or data points to help inform those evaluations and really help us, whether it be our stacking of our players or comparing players to each other. And really it adds a little bit of an objective piece into what can be a largely subjective evaluation off film.”

Zbojovsky and Marsh work closely with each other to ensure that any late-bloomers, fast-risers or strategic adjustments are reflected quickly in Titan so that everyone is on the same page.

“We always joke that if the GM wanted to come down to our office and say, ‘I need a button here, here and here, and I need it to do these things,’ we’re working on that right out of the gate as soon as he leaves that office,” Marsh says.

“We’re able to turn around very, very quickly because we’re able to push those changes right into our Microsoft stack and get them in front of him before he hits the end of the hall. It’s the trust that we can get things done very quickly because these guys have deadlines that don’t move. We can’t push back the draft. We can’t push back free agency.”

The fastest agent at the Combine

The Jets recently wrapped their time at the NFL Combine in Indianapolis, where all 32 teams convene to scout draft prospects as they go through a whirlwind of testing and drills. Numbers and measurements are flying fast and furious, so the Jets, along with the league’s other squads, use the NFL Combine App to help surface the official Combine data to coaches and scouts.

A custom Copilot AI agent is built into the NFL Combine App to allow coaches and scouts to surface fast insights and prospect comparisons with natural language questions that allow teams to get information on, for example, the average, highest and lowest linebacker results for each drill since 2015.

“The Copilot feature not only allows us to ask questions and filter through the information that’s present at the time, but also compare that back to previous years,” Zbojovsky says.

“So you start to really be able to stack how this player not only performed against this cohort here, but also against players that are currently in the NFL. And that helps you start to really understand where that player’s performance metrics on the field might fit within the players that he’s going to be joining in the league.”

Let the countdown begin

In a league where every decision matters and every potential advantage could swing the final score, both Marsh and Zbojovsky are thankful that the Jets continue to see technology as an integral part of scouting and preparation.

“We really are in a great spot for what we need to do. It keeps us nimble,” Marsh says. “Talking to other organizations and other technology companies, they are impressed with how quickly we’re able to iterate and move to get those solutions. We’re not bogged down. We’re given a lot of flexibility and the trust to do what we need to do.”

When the Jets turn in their pick, it will be so much more than writing a name on a card to hand to the commissioner. It will be the final product of research, data, scouting and technology all coming together to welcome the next potential superstar to the NFL.

“A lot of work goes in from a lot of people throughout the organization,” Zbojovsky says.

“We incorporate a lot of different data points and different types of evaluations, whether it be analytics or our scouts. We put a lot of work behind that to make sure we get our board right in advance and then we see how things fall on draft day. We look forward to success in April.”

Top photo courtesy of the Jets.

Learn how the NFL is using AI on and off the field to enhance operations and read how technology could help the Minnesota Vikings build next year’s winning edge. 

Elliott Smith writes about AI and innovation at Microsoft, from how the Premier League is transforming its online presence to why AI may play a major role in saving the Amazon rainforest. Previously, Smith worked as a sports reporter in Washington, D.C., Washington state and Texas, covering high schools to the pros. You can contact him on LinkedIn.

The post The New York Jets are happy to have a ‘Titan’ in their corner at the NFL Draft appeared first on The Microsoft Cloud Blog.

Yet despite broad cloud adoption, migration and modernization remain complex. Legacy architectures, fragmented environments, and persistent skills gaps continue to slow progress, pushing organizations to find ways to migrate faster while minimizing operational risk. 

The IDC study highlights agentic AI as a critical unlock. These intelligent systems automate assessments, orchestrate migration and modernization efforts, and optimize operations across hybrid environments—helping organizations shift from periodic, manual initiatives to continuous, adaptive modernization. This momentum is driving unprecedented growth, with IDC forecasting the public cloud services market will reach USD1.9 trillion by 2029. 

While migration frameworks may be horizontal, their real-world impact is industry-specific. Healthcare, financial services, and manufacturing each face unique constraints shaped by regulation, operational risk, and mission-critical systems. 

In this blog, we explore the key migration and modernization challenges across these three industries—healthcare, manufacturing, and financial services—through real customer stories that highlight the tangible impact cloud adoption is delivering today.

Healthcare: Modernizing securely while powering next-generation clinical experiences

Healthcare faces the toughest modernization headwinds: strict regulations (HIPAA/HITECH, HITRUST), fragmented clinical data across electronic health records (EHRs) and imaging systems, aging on-premises infrastructure resulting in high Capex, and heightened exposure to ransomware.¹ Clinical environments also demand extremely low latency and high reliability.

The IDC study notes that these constraints slow modernization—but accelerate the need for it, as organizations push to scale telehealth, imaging workloads, genomics pipelines, and AI-powered clinical workflows.¹ 

What healthcare organizations need, according to the IDC study: 

• Secure, compliant integration across EHRs, picture archiving and communication systems (PACS), genomics systems, and Internet of Things (IoT) medical devices.¹
• Elastic compute for high-throughput imaging and genomics. 
• Stronger disaster recovery and recovery time performance.¹
• Ambient documentation and AI-supported diagnostics.
• Secure clinician collaboration and modern patient digital front doors.

Customer spotlight: Franciscan Health

Facing aging infrastructure and disaster recovery risks, Franciscan adopted a pragmatic workload placement strategy—moving its Epic EHR to Microsoft Azure.

The results included: 

• $45 million in savings over five years after migrating Epic to Azure.
• 90% faster disaster recovery compared to the prior environment.
• Around a 30-minute failover, reduced from hours.
• $10–$12 million per day in potential downtime risk avoided.

Learn more about Franciscan Health’s journey to migrate its Epic EHR to Azure.

Healthcare’s modernization mandate is clear: reduce operational risk, meet regulatory demands, and harness cloud AI to improve patient outcomes. 

Financial services: Enabling real-time intelligence and automated compliance

Financial institutions operate in one of the most regulated environments, including the payment card industry data security standard (PCI DSS), the Sarbanes-Oxley Act (SOX), the Gramm-Leach-Bliley Act (GLBA), Basel capital frameworks, and know your customer (KYC) and anti-money laundering (AML) requirements, and rely heavily on legacy mainframes that are difficult to modernize. Today, regulatory pressure is intensifying further as new frameworks such as the EU’s Digital Operational Resilience Act (DORA) and the EU AI Act raise the bar for operational resilience, third-party risk management, model transparency, and ongoing compliance monitoring. Under DORA, financial services firms must demonstrate continuous information and communication technology (ICT) risk management, advanced incident reporting, and resilience testing across critical systems and cloud service providers. Meanwhile, the EU AI Act introduces governance requirements for high-risk AI systems, including explainability, data lineage, human oversight, and auditability—with direct implications for fraud models, credit scoring, and customer decisioning platforms.

IDC interviews highlight accelerating demand for real-time risk analytics, fraud detection, digital onboarding, and infrastructure elasticity to support peak activity—capabilities that are increasingly mandated, not optional.¹

Key challenges the IDC study identifies: 

• Strict data residency, model risk governance, explainability, and eDiscovery requirements.¹
• Heightened expectations for operational resilience, cyber defense, and third-party risk oversight.
• Legacy systems and common business-oriented language (COBOL)-based batch processes resistant to change.
• Rapidly evolving regulatory mandates requiring continuous compliance rather than point-in-time audits.

Cloud—especially especially platform as a service (PaaS) and managed services—helps financial institutions shift from static, batch-driven compliance to continuous controls and real-time observability. By reducing batch windows from hours to minutes, modern cloud platforms enable real-time insights, automated evidence collection, resilient architectures, and policy-driven compliance workflows aligned with DORA and AI governance requirements.¹ Learn more about how Microsoft can help financial institutions navigate these requirements. 

Customer spotlight: Crediclub

To accelerate product innovation and meet expectations from Mexico’s national banking and securities commission (CNBV), Mexican fintech Crediclub modernized its databases to a serverless platform as a service (PaaS) architecture and adopted microservices.¹

The impact:

• Uptime improved from around 80% to 99.5%.
• 90% reduction in network latency through Multiprotocol Label Switching (MPLS) and dark fiber.
• Rapid deployment of new financial products via Kubernetes and DevSecOps.

For financial institutions, modernization is no longer just about efficiency—it is foundational to resilience, trustworthy AI, and regulatory compliance at scale. 

Manufacturing: Unifying IT and OT for predictive, data-driven industrial operations

Manufacturers operate in one of the most complex operating environments—defined by legacy and proprietary operational technology (OT) protocols, historically air-gapped manufacturing execution systems (MES) and supervisory control and data acquisition (SCADA) systems, and globally distributed supply chains. Stringent low-latency requirements for safety-critical systems, intermittent connectivity at the edge, and the need to protect intellectual property further compound the challenge. The ability to modernize and unify these environments—without compromising safety, reliability, or performance—represents a critical inflection point for industrial transformation.

Unique modernization challenges according to the IDC study:

• Ultra-low latency requirements for safety-critical operations.
• Massive telemetry ingestion and time-series analytics at scale.
• Operational complexity across global, distributed supply chains.
• Secure protection of intellectual property across edge and cloud environments.

Opportunities unlocked by cloud:

• Predictive maintenance with IoT ingestion.¹ 
• Reduced unplanned downtime and improved overall equipment effectiveness (OEE).
• Digital twins for plants, lines, and products.
• Computer vision for real-time quality and safety. 
• High-performance computing (HPC) simulations for engineering and design. 
• Standardized, global data models.

Customer spotlight: ASTEC Industries

ASTEC unified fragmented systems across its rock to road value chain—from aggregate processing through asphalt production and paving—by adopting Azure, modernizing to timeseries databases, and building a universal connectivity platform using Azure IoT Hub, Azure Events Hub, and Power BI.¹

The results:

• Realtime operational visibility across fleets.
• Predictive maintenance for reducing downtime.
• New digital services supported by connected equipment.

Manufacturing’s modernization imperative: unify OT and IT, scale real-time intelligence, and enable global efficiency. 

Microsoft’s approach: Continuous, intelligent, collaborative modernization 

Microsoft’s strategy is grounded in a simple principle: modernization should be continuous, intelligent, and collaborative. The IDC study emphasizes that successful enterprises adopt a balanced, multipath migration strategy, blending rehost, replatform, refactor, and software as a service (SaaS) substitution based on workload criticality.¹

Microsoft enables this approach through a comprehensive set of tools and offerings, including Azure Copilot and GitHub Copilot. Agentic automation enables:

• Discovery and dependency mapping.
• Security assessment and 6R recommendations.
• Application refactoring, code remediation, and modernization. 

Azure Migrate provides unified discovery, assessment, migration execution, and modernization services. Azure Accelerate complements this with a coordinated framework that includes:

• Guided deployments through Cloud Accelerate Factory.¹ 
• Funding and Azure credits for planning, pilot, and rollout. 
• Expert partners and tailored skilling programs.

The IDC study concludes that organizations using Microsoft Azure for migration and modernization achieve lower operational costs, improved resiliency, faster modernization timelines, and stronger security postures—especially in regulated industries.¹

Looking ahead: Agentic modernization as the foundation for AI-ready enterprises

Across all industries, IDC’s findings are consistent: agentic AI is emerging as the new force multiplier for modernization, enabling organizations to keep pace with rising complexity, regulatory demands, and competitive pressure. 

Healthcare, financial services, and manufacturing each face unique constraints—but cloud modernization remains the foundation for innovation, operational excellence, and enterprise AI. 

Microsoft’s approach gives organizations the unified automation, intelligence, and tooling they need to modernize securely and at scale. 

• Explore how Azure Copilot accelerates cloud migration.
• Discover GitHub Copilot for app modernization.
• See how Azure Accelerate supports transformation.

¹ IDC White Paper, Cloud Migration and Modernization Strategies for Healthcare, Financial Services, and Manufacturing, February 2026.

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