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.

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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.

• 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.

Traditionally, return on investment is a financial measure—growth weighed against cost. With AI, returns still include financial impact, but they increasingly span a broader set of outcomes. As organizations internalize this shift, they see a return on intelligence: compounding gains across cost savings, risk mitigation, performance, growth, and innovation. Momentum often begins with efficiency improvements, then builds into innovation and growth outcomes such as more personalized experiences, faster cycles, better decisions, and new products and services.

At Microsoft, we’re seeing this shift play out firsthand as we work with thousands of organizations across various industries. This post is the first in a series on industry Frontier Transformation. In the articles that follow, I’ll further explore the key dimensions of this shift—how AI is driving growth and reshaping innovation across industries.

AI use cases across industries

Explore real-world use cases to help you accelerate your business goals.

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How Frontier Transformation unfolds across industries

According to an IDC study, 68% of organizations are already using AI. Many report measurable gains, with an average 2.3x return on investment. The most advanced among them, Frontier Firms, take AI further, embedding it across functions, roles, and processes to solve high-impact, industry-specific challenges.

Industry context makes AI investment outcomes visible and measurable. Many organizations are already seeing early wins from AI in their industry, such as inventory optimization in retail, fewer safety incidents in manufacturing, and less documentation burden in healthcare. But industry leaders are pushing further, turning those early gains into frontier outcomes across growth and innovation. Examples include personalized engagement and product discovery in retail, higher order-fulfillment rates in manufacturing, and new care pathways or improved patient retention in healthcare. Frontier leaders are proving that when AI is grounded in industry realities, early wins turn into compounding impact across the value chain.

Frontier customers in action

In my role at Microsoft, I get to see this shift up close as we work with thousands of organizations across industries. What I’m seeing is that Frontier Transformation is already taking shape in the flow of work: processes are redesigned, decisions accelerate, and teams move with greater clarity and speed. From there, that momentum scales into new sources of growth and innovation across the business. Below are some great examples of what I see as customers driving true frontier outcomes.

Financial services: Transforming legal research with AI-powered insights

UBS’s in-house legal team must find very specific information, like a clause or regulation, across a library of 26 million legal documents in multiple languages.

Finding specific knowledge in this vast repository was like finding a particular grain of sand on the beach.

Vlad Stoian, Product Owner for the Legal AI Assistant at UBS

Working with Microsoft Azure, UBS refined its standard process and demonstrated innovation by launching the Legal AI Assistant (LAIA) to help employees pinpoint phrases, clauses, and paragraphs using natural language and semantic similarity, rather than keyword matching. UBS employees can now locate information much more quickly and easily than they could with prior search tools.

Retail: Reinventing the customer experience with AI-powered personalization

Makers of Italian chocolate and gelato since 1878, Venchi shares Italian allegria (joy) worldwide. Venchi built a loyalty program to gather data on customers by working with Dynamics 365. From this foundation, Venchi is introducing AI-powered personalization through Copilot capabilities in the Store Commerce app.

In the future, imagine our sales assistant can see on the app, from Customer Insights, that the customer shopped one year ago for his wife’s birthday, and we know she is allergic to dairy. With Copilot, we can easily access data for all 350 chocolate recipes and figure out which are safe options for the customer in just a few seconds.

Fabio Tormen, Chief Information Officer at Venchi

Venchi saves 1,500 hours annually by automating fulfillment. More accurate accounting and inventory management decreased the cost of goods sold by 2% year over year. And easy sign-up added 800,000 customers to the loyalty program in its first year.

Automotive: Leveraging AI-powered insights during vehicle development

To optimize the performance of its vehicles in development, BMW engineers must access and analyze massive amounts of telemetry data from test vehicles, but only BMW IT specialists have been able to run queries, slowing test cycles and innovation. With Azure and Foundry Agent Service, BMW delivers insights 12 times faster and empowers its engineers to analyze telemetry directly. It also embeds AI-powered workflows into daily R&D, speeding design cycles, and reducing late-stage fixes.

When an engineer asks a question—“How many braking maneuvers were performed by the development vehicles in the last two days?”—the system responds within minutes, complete with charts and written explanations.

“With multi-agent AI, engineers don’t just get data, they get insights they can act on immediately,” says Christof Gebhart, Manager of Advanced Vehicle Measurement Technology at BMW. “Ultimately, the steps of data extraction and pattern recognition can be performed directly in a single step, and in natural language.”

Healthcare: Innovating with AI to help clinicians spend more time on patient care

Clinicians at Cooper University Health Care were experiencing significant burnout due to after-hours documentation. The leadership team sought a solution to reduce administrative burden and restore joy in practice. Cooper implemented Microsoft Dragon Copilot, an AI assistant for clinical workflow and integrated with their Epic EHR, that streamlines documentation, automates tasks, and surfaces information—boosting efficiency, satisfaction, and patient care.

Clinicians at Cooper report saving more than four minutes on documentation time per patient, experiencing less burnout, and engaging more meaningfully with patients. Their notes are more comprehensive, communication is improved, and patient satisfaction is rising. Through ambient capture of the patient visits, clinicians can maintain eye contact and engage more meaningfully with patients.

Patients are instantly noticing their clinicians are looking at them again, making that eye contact. We’ve had several patients actually remark that, basically, hey, wow, you’re not typing today. That’s the power of AI. It gives eye contact back to medicine the way it was supposed to be practiced.

Snehal Gandhi, MD, VP and Chief Medical Information Officer (CMIO) at Cooper

Financial services: Empowering smarter decisions with real-time insights

Aon’s engineering and data teams set out to build a secure, enterprise-grade AI platform that could operate across its solution lines. The result was AonGPT, a generative AI assistant developed entirely on Microsoft Azure.

Over 62,000 users now have access to AonGPT. About 31,000 of those are monthly active users, with more than 6.4 million messages exchanged so far.

Amit Gawali, Head of Engineering at Aon

During the California wildfires, Aon’s catastrophe modeling team partnered with a satellite imagery provider to receive multiple visual updates each day. Using AonGPT, the team wrote code to connect those images to Aon’s proprietary data, producing near real-time insights that helped clients assess damage and plan responses.

Start your Frontier Transformation journey

Frontier Transformation is already taking shape across industries. The question now isn’t whether AI delivers impact—but where to start.

• Clarify your AI priorities with the AI decision brief.
• Explore how AI is driving business value today.
• Go deeper with industry-specific AI use cases.

The post Frontier Transformation is powering growth and innovation across industries appeared first on The Microsoft Cloud Blog.

While adoption of AI technology is now widespread, impact is not. Many organizations are experimenting and running pilot programs, but far fewer have the operating discipline to become what we call Frontier Firms—companies that scale AI in ways that meaningfully reshape work, decisions, and value creation. According to IDC’s Business Opportunity of AI Survey (August 2025), 68% of all respondents use GenAI and only 22% of organizations worldwide are Frontier Firms.¹ These companies are seeing a return on investment in the technology that is several times greater than companies that are slow to adopt.

This gap is why Microsoft developed a newly revised 2026 edition of the AI Decision Brief, a handbook designed to help leaders and business decision-makers embrace the opportunities of Frontier Transformation. It addresses how AI can become a durable source of advantage: where to focus, how to measure value, how agents change workflows, and how trust, governance, and responsibility enable scale. “This is not simply the next stage of technology adoption,” writes Brad Smith, Microsoft Vice Chair and President. “Frontier Transformation is a leadership moment that asks organizations to fundamentally rethink how people, processes, and decisions work together.

We believe that this brief answers the questions many executives are asking about how to stay ahead of the curve. The questions below surface what we’re hearing from business leaders across industries as they plan investments, assess readiness, and look ahead. Each reflects a theme explored in depth in the AI Decision Brief and points to how organizations can begin turning AI execution into lasting impact. 

1. How can my company get the biggest impact from AI? 

The biggest impact comes when AI changes how the business operates—not just how fast someone answers an email. “Frontier Transformation is a holistic reimagining of business, aligning AI with human ambition to achieve an organization’s highest aspirations and growth potential,” writes Judson Althoff, CEO of Microsoft commercial business.

What does this mean in practice? Frontier Firms are leveraging AI to transform customer engagement, core processes, decision-making, and innovation. For them, AI isn’t confined to one team or one tool. Instead, it’s embedded across the enterprise in an average of seven business functions. That’s when the outcomes compound. These organizations are monetizing AI and outperforming slow adopters with roughly 3x higher returns.¹ Agents are accelerating that shift because they don’t just make recommendations; they can take action and complete tasks.

2. How do you graduate beyond early wins with AI adoption?

While AI can boost individual productivity—drafting documents, summarizing meetings, and automating the more tedious aspects of jobs—it can do so much more, according to Jaime Teevan, Chief Scientist and Technical Fellow at Microsoft. “The real opportunity is bigger: not just helping individuals work faster, but enabling teams and organizations to work better, together,” she writes. 

Most AI initiatives stall for the same reason most transformations stall: teams prove their value in specific use cases, but leaders don’t change the system around them. The model isn’t the bottleneck—processes, decision rights, and trust are. Frontier leaders, on the other hand, pick a small number of priority workflows and redesign them end to end. That’s how you move from “we got a nice pilot result” to “AI is embedded in how we run the business.”

3. How do I identify the priority workflows where AI can meaningfully change outcomes? 

“AI integration is often framed as a technical problem: which models to use, how to connect systems, how to mitigate risk,” writes Jared Spataro, Microsoft CMO of AI at Work. “But for most organizations, the real constraint on value is not technology, it’s how work is organized and governed. The bigger challenge is centered on management.”

Frontier organizations don’t ask, “Where can we plug in AI to automate a task?” They ask, “Which workflows most directly affect revenue, cost, risk, customer experience, or speed of decision-making?” Frontier leaders focus on embedding AI, agents, and data directly into those areas of high impact. 

4. As AI agents take more action on behalf of employees and teams, how does my role as a leader need to change?

Leadership has become even more important in the agentic era. “When AI systems can plan and execute over many steps, leadership and engineering rigor become the real bottlenecks,” writes Kevin Scott, CTO of Microsoft. “You need teams that are explicit about goals, careful about feedback and evaluation, and thoughtful about where autonomy is earned versus constrained.” 

The greatest risks are unclear intent, ownership, and accountability. Frontier leaders get ahead of this by redefining roles and decision rights early. Humans set outcomes, constraints, and success measures, while agents operate within clearly governed boundaries. That means treating agents like new employees or privileged service accounts—with named owners, least-privilege access, continuous monitoring, and regular review. 

5. How do you measure the success of AI when it’s embedded across workflows, decisions, and teams—not just individual tasks?

“Early productivity gains from AI are now expected,” writes Alysa Taylor, Microsoft CMO of Commercial Cloud and AI. “But Frontier leaders see beyond those short-term efficiency wins. They understand how AI can also help grow revenue, increase customer acquisitions, reshape processes, and improve operational efficiency.” 

Frontier leaders measure ROI the way they run the business: at the workflow and outcome level, not by counting isolated tasks. Yes, they track early productivity signals, but they don’t stop there—they tie AI to business metrics like faster cycle times, higher quality and consistency, better customer experience, lower risk, and faster decision-making.  

6. We’re under pressure to move fast with AI. Can we tackle security later on?

Great question! The answer is simple: absolutely not. “The AI opportunity is incredible, but speed without security, observability and governance opens the door to significant risk. By embedding these elements from the start, organizations can innovate rapidly while building and fostering trust,” writes Vasu Jakkal, CVP of Microsoft Security Business. 

The moment AI moves beyond pilots and starts touching real data, customers, and decisions, issues with security and accountability can offset gains in efficiency. According to Microsoft’s 2026 Data Security Index, less than half (47%) of companies have fully implemented data security controls for AI. Frontier leaders build observability, Zero Trust security, and clear ownership from day one, so teams can move faster with confidence instead of stopping to clean things up later.  

7. How do you scale AI across an organization without losing control or trust?

“Scaling AI is less about deploying tools and more about preparing people,” writes Nathalie D’Hers, Microsoft CVP of Employee Experience. “A workplace culture grounded in a growth mindset is more important than ever.” Frontier Firms embrace continuous learning and agility. This helps teams fundamentally reimagine processes and think bigger.  

Crucially, Frontier organizations also pair empowerment with guardrails. They give employees access to AI where work actually happens—through copilots, low-code tools, and approved platforms—so innovation isn’t bottlenecked by a small group of specialists. At the same time, they’re very clear about boundaries. That includes shared governance frameworks, approved data sources, identity and access controls, and observability at every layer. That’s what allows creation to scale safely.  

8. How do I balance Frontier Transformation with sustainability? 

“AI and sustainability are often treated as separate agenda items, but they are fundamentally connected,” writes Melanie Nakagawa, Chief Sustainability Officer at Microsoft. “Leaders should understand both sides of that equation: the resource footprint of AI as well as the opportunity it brings to help them operate more efficiently, build smarter, more resilient systems, and lower carbon emissions.”  

As AI grows, it brings real resource and trust questions about environmental impact, supply chains, community impact, and whether the benefits of AI are broadly shared. The Frontier view is that designing for efficiency, responsibility, and equitable diffusion isn’t a nice-to-have; it’s how you unlock durable growth while avoiding backlash, constraints, and extra work later.

At Microsoft, we’re building out AI infrastructure with sustainability in mind while also using AI as a force multiplier for climate progress by optimizing systems, accelerating materials discovery, and improving resource efficiency.     

Next steps to lead in the era of Frontier Transformation

Read the full AI Decision Brief to understand what it takes to lead in the era of Frontier Transformation. The insights, leadership advice, and practical tips found within our brief will help prepare your company to properly utilize and scale a powerful AI strategy. Once you have that knowledge base, you’ll need a trusted, reliable set of AI tools to execute that strategy. 

Explore Microsoft AI tools and solutions for your Frontier Transformation. 

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

The post AI Decision Brief: How leaders can drive Frontier Transformation appeared first on The Microsoft Cloud Blog.

With the help of Microsoft partner SP Peru, María Almenara took the first step towards its digital transformation process. The bakery migrated to Azure, Microsoft’s cloud, leaving acquisition costs or digital infrastructure maintenance behind. “Thanks to the database, we can analyze customer buying frequency and average purchase amounts, optimizing our processes and reducing pressure on the shop and the manager. These steps directly affect the customer experience,” states Carlos Armando de la Flor, the bakery’s General Manager.

Integrating cloud functionality

When María Almenara decided to become a data-driven business, one goal was to reach a 90% fill rate. This indicator shows the ability to serve customers without running out of stock. Today, the bakery has reached a 99% fill rate, assuring an optimal experience for its customers.

Based on the work done with SP Peru, a system was also created with Azure Machine Learning. Its integration with the Enterprise Resource Planning System (ERP) and the development of a specific mathematical technique allows for daily and weekly predictions. This is how the bakery obtained sales estimates for each store and product, allowing for planning and adjustments to cover demand for each of the 8 locations, as well as the ability to ship precise orders.

Furthermore, with a goal of complete business transparency and real-time decision making, the bakery used different Human Resources survey systems like SAP, among others. With the help of Microsoft’s Power BI, management created a control panel to view 18 indicators in 3 areas: finances, expenses and budgets; human resources; and customer satisfaction.

The mathematical technique used for these predictions undoubtedly played an important role in this process. This technique was later replicated and tested by the Massachusetts Institute of Technology, where local partner SP’s technique performed better than that of the team from the prestigious academic institution.

Serving the person, not just the consumer

Thanks to integrating these changes and its technological foresight, the bakery was able to weather a year as challenging as 2020—and managed to reach a milestone for the industry. This also reflects a change in María Almenara’s ethos, which is made of three fundamental pillars: a) a scalable culture and business model with the single goal of making hearts happy; b) data as an essential asset for leading in the digital economy and c) focus on the person, that is, on partners and customers.

With each step of the digital transformation process, María Almenara realized that its partners had to grow with the company. This is reflected in its turnover rate, which is less than 1%. The industry’s turnover rate is usually 25%. Without a doubt, incorporating technology has created business growth and satisfaction for its 223 partners.

Carlos Armando de la Flor concludes: “Here at María Almenara, we see technology as a helpful tool, not just something to solve things. Thanks to the omnichannel retailing platform our business uses, we’ve been able to establish a way to work that allows pregnant women or women with young kids to serve customers from home, prioritizing their health and comfort, and reducing turnover rates.”

COMPANY INFORMATION

• Customer: María Almenara
• Partner: SP Peru
• Products and Services: Microsoft Azure
• Industry: Other
• Organization Size: Medium (50 – 999 employees)
• Country: Peru
• To contact this Partner, click here
• Click here to learn more about this company
• Tags: Customer Stories, María Almenara, Microsoft Azure, Perú

The post María Almenara: The First Data-Driven Peruvian Bakery appeared first on The Microsoft Cloud Blog.

Over the past several years, we have seen customer concerns evolve materially. Early conversations focused primarily on privacy and lawful data handling. Today, those concerns have expanded. Leaders are now asking how they maintain operational continuity during disruption, how they adopt AI responsibly without losing control, and how they protect national, organizational, and customer interests in an increasingly volatile global environment.

These questions are not abstract. They surface in boardrooms, procurement decisions, architecture reviews, and crisis simulations. They reflect a broader shift in how trust is evaluated in digital systems. Today in Brussels we brought together attendees from around the world—policy makers, IT leaders, and enterprises—to approach these questions from the multiplicity of perspectives to move the conversation from headlines to action.

From privacy to resilience and beyond

Privacy remains foundational. But it is no longer the sole lens through which sovereignty is assessed.

Customers are increasingly concerned about business continuity in the face of cyber incidents, geopolitical tension, supply chain disruption, and network instability. They want to understand how critical workloads operate if connectivity is constrained, if dependencies fail, or if policy conditions change with little warning.

At the same time, innovation pressures have intensified. AI is becoming central to public service delivery, national competitiveness, and economic growth. Organizations cannot afford to pause progress while sovereignty questions are debated in isolation. They need approaches that allow them to move forward responsibly, balancing opportunity with control.

What we hear consistently is this: sovereignty concerns will continue to evolve. Any approach that treats them as static is already behind.

For four decades, Microsoft has operated under some of the world’s most demanding data protection, competition, and digital governance frameworks. Working closely with European institutions, regulators, and customers has shaped how we think about sovereignty—not as a regional exception, but as a discipline that must function at scale, under scrutiny, and over time. That experience matters because many of the sovereignty questions now emerging globally were first tested in Europe, long before they became mainstream elsewhere.

A consultative approach to risk management

This is why we believe digital sovereignty must be approached as consultative risk management, not a checkbox or a predefined deployment model.

Every organization faces a unique mix of regulatory obligations, cyber risk, operational exposure, and innovation goals. Even within a single institution, sovereignty requirements differ by workload. Some demand strict isolation and local control. Others require global scale, advanced security capabilities, and rapid innovation.

Our role is to help customers navigate these tradeoffs deliberately. That means working with them to assess risk, align architecture to policy realities, and design environments that reflect both today’s constraints and tomorrow’s unknowns.

This work sits at the intersection of cybersecurity, compliance, resilience, and frontier transformation. It requires ongoing engagement, transparency, and the willingness to adapt as conditions change.

Digital sovereignty posture in practice

A digital sovereignty posture that is flexible recognizes that no single approach can address every requirement. Instead, it focuses on giving organizations options, visibility, and control across a continuum of environments.

Customers operating in public cloud environments expect clear data residency options, strong encryption and access controls, and visible operational discipline. Just as important, they look for transparency into how cloud systems are governed and how exceptional situations are managed, particularly as regulatory scrutiny increases.

Those expectations do not disappear when workloads move closer to the edge. In fact, they intensify. For workloads that require greater isolation, local processing, or operation in constrained environments, hybrid and disconnected solutions become essential. In February, Microsoft announced the expansion of disconnected operations, enabling customers to run critical workloads in air-gapped environments while retaining consistent governance and operational control. This capability extends cloud-based practices into disconnected settings, supporting operational continuity without abandoning security and innovation. 

That commitment shows up in concrete safeguards that customers can independently evaluate and apply. The EU Data Boundary is one example, supporting data storage and processing within the EU and European Free Trade Association (EFTA) regions for cloud services, alongside longstanding investments in encryption, access controls, auditability, and operational transparency. These measures provide practical mechanisms for aligning cloud operations with regulatory and risk requirements, rather than relying on abstract assurances. 

At the same time, we are expanding options across hybrid and private cloud environments to support continuity, resilience, and local control where required. These investments reflect a simple reality: customer needs are not converging toward one model. They are diversifying.

Underpinning all of this are Microsoft’s digital commitments, which frame how we approach privacy, security, transparency, and responsible AI. These commitments are not marketing statements. They guide how systems are built, operated, and governed, and they provide a foundation for long-term accountability.

Practical guidance for leaders navigating sovereignty

As digital sovereignty becomes embedded in policy and procurement decisions, leaders benefit from a practical lens. Based on what we hear from customers and stakeholders, there are a few consistent themes shaping successful approaches:

• Sovereignty requirements will continue to expand beyond privacy to include continuity, resilience, and AI governance.
• Risk management is now inseparable from digital transformation strategy.
• Flexibility and optionality matter more than rigid architectures.
• Transparency and accountability are as important as technical capability.
• Sovereignty posture must consider protections against cyberthreats.

Addressing these realities requires partners who understand the full scope of the challenge and are willing to engage over the long term. It requires platforms and collaboration designed with sovereignty in mind from the start.

So what does this mean for you?

Digital sovereignty is not a destination. It is an ongoing discipline shaped by changing technology, regulation, and global conditions.

At Microsoft, we approach this work with humility and responsibility. We recognize that customer concerns will continue to evolve, and that our own platforms and practices must evolve with them. We remain committed to expanding our sovereign cloud continuum, strengthening our cloud capabilities, and delivering solutions that balance innovation with control.

Most importantly, we remain focused on delivery. Because in moments of uncertainty, what matters most is not what technology promises, but what it allows organizations to do with confidence.

Where does digital sovereignty go from here?

The future of digital sovereignty will be defined by implementation, not rhetoric. Success will depend on collaboration between governments, industry, and civil society, as well as a shared commitment to transparency and continuous improvement.

As we look ahead, our focus remains on helping organizations turn sovereignty principles into durable, scalable outcomes. That means continuing to invest in capabilities that support trust, engaging constructively with policymakers, and listening closely to the evolving needs of our customers.

Digital trust is built over time, through consistent action and openness, and that trust is one of the most important foundations we can help create.

The post Navigating digital sovereignty at the frontier of transformation appeared first on The Microsoft Cloud Blog.

• Wave 3 of Microsoft 365 Copilot
• Expanded model diversity with Claude and next-gen OpenAI models available today
• General availability of Agent 365 on May 1 for $15 per user
• General availability of the new Microsoft 365 E7: The Frontier Suite on May 1 for $99 per user

Frontier Transformation is a holistic reimagining of business, aligning AI with human ambition to achieve an organization’s highest aspirations. It is the next evolution of AI Transformation — not only do we need to deliver efficiency and productivity, but we need to democratize intelligence and do more for humanity. Companies do not want or need more AI experimentation. They need AI that delivers real business outcomes and growth.

In my daily conversations with customers and partners, they typically question what the most important components of an AI solution are. Is it the model? Is it silicon? At Microsoft, we believe the two most essential elements of Frontier Transformation are Intelligence + Trust. Organizations need to harness their own unique work intelligence as they build agents and solutions; and all AI artifacts across their technology stack must be observed, managed and secured to ensure they are delivering value responsibly. 

Intelligence that shows up in real work 

I often say that zero-shot artifact creation is nothing more than a parlor trick. Models can reason over data, produce draft documents, presentations and spreadsheets, but they do not understand work. Real differentiation comes from intelligence — deep work context, embedded in the tools people already use. AI should amplify your intelligence but do so in a manner that protects your differentiation and unique value.

Work IQ amplifies an individual’s IQ by tapping into your organization’s IQ. It is the intelligence layer that enables Microsoft 365 Copilot and agents to know how you work, with whom you work, and the content upon which you collaborate. That is why Copilot is faster, more accurate and more trusted than solutions built on models and connectors alone.

This month, we are unleashing Work IQ with our next generation of agentic experiences in Wave 3 of Microsoft 365 Copilot in Word, Excel, PowerPoint and Outlook. Employees will have an enhanced chat experience in Copilot with the ability to create and augment artifacts, and the power to build their own agents within the canvas they work in every day.

Microsoft 365 Copilot is model diverse by design. Rather than betting on a single model, we built a system that makes every model useful at work. Customers get the choice, performance and flexibility in an open, heterogenous environment.  Copilot leverages leading models from OpenAI and Anthropic, operating openly across clouds and data services without locking customers in. Claude is now available in mainline chat in Copilot via the Frontier program, alongside the latest generation of OpenAI models.

Microsoft 365 Copilot Wave 3 is not just a singular release of new capabilities but rather a commitment to continuous innovation. We will bring frontier capabilities with enterprise promises for our customers in an open and model diverse manner. Another great example of this is Copilot Cowork, which is in research preview. Built in close collaboration with Anthropic, we are bringing the technology that powers Claude Cowork into Microsoft 365 Copilot to enable long-running, multi-step work that unfolds over time.  Click here to learn about our Wave 3 news in more detail.

These announcements come as our customers across industries are already seeing the value of Microsoft 365 Copilot. Microsoft recently delivered its strongest quarter yet with Copilot, with paid seats growing more than 160% year over year and daily active usage up ten times, as customers increasingly make Copilot a core part of everyday work. Expansion is also accelerating as the number of customers deploying Copilot at significant scale — more than 35,000 seats — tripled year over year. Just last week, Mercedes Benz announced a global rollout of Microsoft 365 Copilot, following recent investments from NASA, Fiserv, ING, the University of Kentucky, the University of Manchester, the U.S. Department of the Interior and Westpac. This is in addition to the 90 percent of the Fortune 500 who now use Copilot.

Trust: from agent experimentation and sprawl to enterprise control 

The speed of agent development and proliferation tells us customers see value, but without guardrails the pace of adoption turns into blind spots, diminished ROI and real security risk. As AI agents become more capable and autonomous, trust is nonnegotiable. IDC predicts 1.3B agents in circulation by 2028, and 80% of the Fortune 500 are already using Microsoft agents, led by operationally complex industries like manufacturing, financial services and retail.

That is why I am excited to announce the May 1 general availability of Microsoft Agent 365, the control-plane for AI agents. Priced at $15 per user, Agent 365 gives IT and security leaders a single place to observe, govern, manage and secure agents across the organization — using the same infrastructure, applications and protections they rely on to manage people today.

We are seeing tremendous momentum with our preview customers. In just two months, tens of millions of agents have appeared in the Agent 365 Registry. We have tens of thousands of customers that are already adopting Agent 365 to securely govern and scale AI agents across enterprise workflows.

At Microsoft, we are also using Agent 365 as Customer Zero and the early signals are clear. We now have visibility into more than 500,000 agents across the company with the most widely used focused on research, coding, sales intelligence, customer triage and HR self-service. That adoption is translating into real work. Over the past 28 days alone, agents have been generating more than 65,000 responses every day for employees. This is evidence that we are not simply experimenting, we are embedding agents in the flow of everyday work and empowering human ambition.

Introducing the Frontier Suite

To meet this demand, I am thrilled to announce we are bringing Intelligence + Trust together with Microsoft 365 E7: The Frontier Suite. Microsoft 365 E7 unifies Microsoft 365 E5, Microsoft 365 Copilot and Agent 365 into a single solution powered by Work IQ and integrated with the apps and security stack customers already rely on. It includes Microsoft Entra Suite and advanced Defender, Intune and Purview security capabilities, delivering comprehensive protection across agents and employees.

Customers have told us E5 alone is no longer enough; they do not want multiple tools stitched together, they want one trusted solution. At $99 per user, E7 is priced below purchasing these capabilities à la carte, giving customers a simpler, more cost-effective way to deploy enterprise AI at scale.

With the general availability of Agent 365 and the latest agentic experiences in Microsoft 365 Copilot offered as one Frontier suite, AI moves from experimentation to durable, enterprise-wide value, built on a foundation of Intelligence + Trust. This is how we make Frontier Transformation real. Microsoft is not just imagining the future of AI, we are empowering organizations across industries and around the world to build it.

The post Introducing the First Frontier Suite built on Intelligence + Trust appeared first on The Microsoft Cloud Blog.

In this Q&A, Michelle introduces M12, considers what kinds of AI-powered solutions will drive the next wave of AI innovation, highlights some of M12’s recent investments, and explains how Microsoft Marketplace can help startups reach enterprise customers.

What is M12, and how does it differ from traditional venture capital firms? 

M12 is Microsoft’s venture fund. We’re an early-stage fund focused on finding innovative AI startups and technologies that can leverage the power of Microsoft. And with that, we’re looking at the big picture—not just the companies that are winning, but those that are driving the systems of the future.

In today’s venture environment, there’s a lot of capital fighting to invest in the top AI founders and startups, so investors need something extra to stand out and win deals.

At M12, we’re backed by the power of Microsoft, which means a few things: instant credibility with potential partners, as well as access to its ecosystem,  expertise and research, and global go-to-market (GTM) motion. But most importantly, the team is built for impact. We help ensure that our startups have a custom plan and a dedicated resource to help make that plan a reality.  

Our team is a unique mix of founders, researchers, and folks who have been in the trenches across product innovation, finance, go to market, and business development. While our skill sets may be diverse, we put our collective expertise toward a common goal: to help promising companies win.  

What kinds of AI-powered solutions or business models do you believe will define the next wave of innovation? 

The last few years we’ve seen a lot of experimentation—thousands of AI fueled products have been launched—some finding spectacular product market fit, reaching more than USD100 million annual recurring revenue (ARR) in less than a year of launch, while others stuck in the pilot stage with low adoption or questionable durability.

We believe we are now entering a new phase of AI that’s less about experimentation and more about putting into production and delivering measurable outcomes. Buyers are holding new technology accountable to real return on investment (ROI) on shorter time horizons—is your product saving the enterprise money, driving measurable efficiencies, or generating new revenue opportunities.

We see the next wave of application technology embedded deeper into enterprise workflows, training on proprietary data, more domain-aware agents that move beyond assistance to coordinating multi-step work across teams, and orchestration tools that go beyond copilots. We believe that early use cases of AI like software coding and customer support will continue to scale, but it’s been interesting to see momentum in fields that traditionally have been slower to adopt new technology such as medicine, law, and accounting.

At M12, we’ve been thinking deeply about the next evolution in models beyond text-based large language models (LLMs), particularly world models, and how data from the physical world brings about new innovations, particularly in science.

We are also focused on the foundational layers that make AI possible at scale— infrastructure, tooling, data systems, and how to make AI factories more efficient, sustainable, and performant. You can see that in our recent investments in nEye.AI and Neurophos, and you’ll continue to see us make bets in these areas.

On the business side, we’re seeing rapid change. New pricing models like outcome-based and consumption-based pricing, faster adoption cycles particularly in small and midsize business (SMB) and individual buyers, and a sharper focus on ROI are reshaping how value is created.

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What are the key criteria you look for when evaluating businesses to invest in?

We look at a combination of factors and because we invest early in a company’s lifecycle, we focus deeply on the strength of the team, their conviction, and their long-term vision. The best teams are shipping quickly, adopting the latest models, listening closely to customers, and adjusting based on real-world feedback. We are finding that velocity is “the” moat for many AI companies. We also look at market size and opportunity and increasingly, a founder’s plan for how to differentiate in a crowded market, and how we can support the business.  

Ultimately, we’re looking for incredible teams that are learning quickly, innovating with customers, and building with durability in mind. 

What are some of your recent investments and what made them stand out?

M12 meets hundreds of startups each year and typically invests in 18 to 20 early-stage companies a year. We aim to balance our portfolio with a mix of companies that have nearer term adoption and traction and those with longer time horizons.

• Inception Labs is pioneering a diffusion large language model (LLM) approach, which has some performance advantages over transformers. Its early—a “foundation model” and frontier investment that we’re excited by.
• Outset is an AI-moderated research platform that is fundamentally reshaping how enterprises operate and understand their customers, which is exactly what we look for in teams and technologies—they already have an impressive list of customers, including Microsoft, Uber, and Hubspot. 
• Neurophos is tackling one of the biggest bottlenecks in AI infrastructure: the inability of GPUs to scale efficiently across cost, power, and footprint. The team has developed a manufacturable optical processing unit that compresses GPU-scale compute into a dramatically smaller, cooler, and more sustainable form, delivering up to 100× performance and energy efficiency compared to today’s leading systems.
• Entire is approaching the next general wave of developer platforms from first principles, architecting a new platform from the ground up for agent-to-human collaboration. We’re excited to invest in Entire and support a team that is inventing a new paradigm of how developers and AI work together.

Where do you see the most promising opportunities for AI to disrupt industries globally, and how is M12 positioning itself to lead in those areas?

The biggest opportunities for AI disruption are in industries where complexity, scale, and constraints intersect—areas like enterprise transformation (this could be system of records being unbundled or completely rethought, hyper-personalized employee agents and applications), next gen cybersecurity, tooling and infrastructure that needs to be rebuilt when agents are writing the majority of software code, and innovations brought on by world models deployment are also top of mind.

M12 is uniquely positioned to support founders tackling these challenges because we invest where Microsoft brings deep expertise—enterprise environments, technology, global scale, and mission-critical systems. Our investors are thesis driven and often will spend months getting to know a space, going deep, which builds credibility with founders before an investment.  

How are you seeing founders adapt their strategies to thrive in an AI-first world, and what advice would you give them?

Founders are shipping and executing faster, focusing on deeper integrations with customers, and building community and brand awareness quickly as part of their go-to-market. Many enterprises today are running multiple proofs of concept across similar AI tools and we believe this next year customers will focus and consolidate. There will be a major shift towards deciding which solutions deliver real ROI and are ready for wide-scale production. 

Founders who succeed will be the ones who build deeply integrated, customized workflows that fit into how customers actually operate. We’re seeing startups pairing product innovation with services, like forward-deployed engineers, to accelerate adoption early on, getting feedback loops started quickly as well with access to unique data to make their products sticky and “must haves.”

We’re also seeing companies rethink how they operate internally. One of our portfolio companies, Allstacks, which was founded more than 5 years ago, radically shifted their go-to market strategy last year to become AI native, where AI tools and agents are integral parts of each operating function. It was critical work and a good example of how forward-thinking companies are reinventing their operations to match their product innovation.

Many AI native companies are operating incredibly leanly, even as they scale, due to the use of AI in all elements and functions of their business. 

How does M12 evaluate which AI startups have the potential to scale and make a lasting impact?

We spend a lot of time distinguishing between the hype cycle and the durability cycle. Markets can move quickly, we’re seeing that more than ever, but lasting companies are built deliberately over many years.

The startups with the greatest potential are those that design with constraints in mind, whether that’s power, capital efficiency, inference economics, or customer trust, and still find ways to deliver meaningful value.

Why is collaboration between corporations and startups essential for accelerating AI transformation globally?

AI transformation and adoption especially in the enterprise doesn’t happen in isolation. Startups bring speed, creativity, and new ideas. Corporations bring scale, trust, and real-world deployment environments.

When those strengths come together, we see not an additive but multiplier effect. Corporations like Microsoft can serve as a bridge between early-stage founders and global enterprise customers, helping startups move faster while deploying AI responsibly and at scale.  

For example, Microsoft Marketplace is a great way for startups to transact with enterprise customers—we have a portfolio development team that is solely focused on opportunities like this, helping our portfolio companies join the marketplace and get ready to sell to enterprises, get through procurement and generally navigate the opportunities to partner with Microsoft and our customers.

That collaboration is what can turn breakthrough technologies into lasting impact, and it’s why partnerships are so critical in this next chapter of AI. 

Ready to learn more? Discover resources and tools to accelerate your AI journey

• Learn more about M12, Microsoft’s venture fund
• Explore cloud solutions, AI apps, and agents on Microsoft Marketplace
• Get the Prompt Guide for Business Leaders to turn AI intent into clear, actionable decisions
• Discover how Microsoft AI brings intelligence into the flow of work

The post FYAI: Why startups will help accelerate global AI transformation appeared first on The Microsoft Cloud Blog.

At Microsoft, we see this shift clearly in our conversations with customers globally. Leaders are moving quickly to scale AI, while remaining accountable for sustainability commitments to customers, investors, regulators, and employees. Too often, these goals are positioned as tradeoffs. In practice, they are reinforcing. When AI transformation is approached with intent and discipline, it can drive stronger business performance while advancing sustainability outcomes.

That belief is the foundation of our new Strategic Guide: Aligning AI Transformation with Sustainability Goals.

Why AI transformation and sustainability belong together

The most meaningful impact from AI comes not from isolated pilots, but from transformation—when intelligence is embedded across strategy, operating model, and culture. That’s the premise of Microsoft’s Frontier transformation AI vision, where organizations are enriching employee experiences, reinventing customer engagement, reengineering core business processes, and bending the curve on innovation.

What’s often overlooked is that these same shifts deliver sustainability gains. More efficient processes require less energy and fewer resources, better data reduces waste and overproduction, and modern cloud and AI architectures—when designed intentionally—can shrink digital footprints while increasing speed and resilience.

Five practices for sustainable AI transformation

Our new Strategic Guide: Aligning AI Transformation with Sustainability Goals makes this connection explicit and practical, offering five essential practices leaders can apply today to turn AI ambition into measurable business and sustainability outcomes.

1. Adopt a modern cloud strategy.
   Moving workloads to efficient, hyperscale cloud environments is often the single biggest step organizations can take to reduce energy use while improving performance. Modern cloud platforms enable organizations to scale AI intelligently—optimizing compute, storage, and cooling in ways that are difficult to achieve on‑premises.
2. Assess your cloud provider’s sustainability and trust goals.
   An organization’s environmental footprint increasingly extends beyond its own walls. Transparency, renewable energy commitments, and responsible datacenter operations matter because your partners’ practices become part of your sustainability equation.
3. Manage data responsibly for efficient and accurate AI.
   Efficient data pipelines, strong governance, and thoughtful lifecycle management do more than reduce risk. They also reduce unnecessary compute and storage, helping AI systems become more accurate, scalable, and sustainable.
4. Optimize cloud workloads.
   As AI moves from pilots to production, sustainability outcomes increasingly depend on how workloads are designed and run in the cloud. Right‑sizing compute, reducing idle resources, and streamlining data movement lowers energy use while improving performance and cost control.
5. Fit the model to the mission.
   With efficient cloud foundations in place, leaders can focus on selecting the right AI models for the right jobs. Aligning model choice with business objectives, performance requirements, and sustainability goals enables organizations to scale AI responsibly—maximizing impact without unnecessary complexity or resource use.

Together, these practices help leaders move beyond aspiration to execution—delivering what the guide describes as a dual return: stronger business performance alongside reduced environmental impact.

What the research shows

AI can deliver better results—faster and more sustainably

In a simple experiment highlighted in the Strategic Guide: Aligning AI Transformation with Sustainability Goals, Microsoft set out to understand how efficiently AI could perform a common knowledge work task.

Five professionals were asked to summarize a 3,000-word technical report into 200 words. Completing the task took a median of 41 minutes and consumed an estimated 13.7 watthours of laptop energy.

Using a single prompt, Microsoft Copilot completed the same task in under a minute—using just 0.29 watthours of datacenter energy. That’s roughly 55 times faster and 47 times more energy efficient. Independent reviewers also rated the AI-generated summary higher for clarity, accuracy, completeness, and overall quality.

The takeaway is clear: when AI is applied thoughtfully, it can reduce time, energy consumption, and friction—while delivering stronger outcomes.

What this looks like in practice

Across industries, organizations are already demonstrating how AI transformation and sustainability reinforce one another.

ABB, a global leader in electrification and automation, is using AI to help energy and asset intensive industries operate more efficiently while meeting increasingly ambitious sustainability goals. The Genix Industrial AI Platform helps ABB customers deliver from 25% efficiency gains in data centers to 18% energy savings in cement production.

In the construction sector, Giatec is tackling one of the world’s most carbon intensive materials: concrete. Built on Microsoft Azure, Azure IoT Hub, and Azure OpenAI in Foundry Models, Giatec’s intelligent platform optimizes mix designs, reduced 2.5 million tons of carbon emissions, and increased profit margins for concrete producers by up to 100%.

Space Intelligence uses AI to turn vast amounts of satellite data into trusted, actionable insights for global climate and conservation efforts. The company moved to Microsoft Foundry and the Planetary Computer ecosystem to reduce the time required to map the world’s forests by 75%, completing coverage of more than 50 countries in just one year, something that would’ve taken six years—delaying the ability to drive and verify real world climate impact.

Becoming a Frontier organization—responsibly

These examples point to a broader trend: the organizations leading in AI are also redefining what responsible innovation looks like. Frontier organizations don’t treat sustainability as a separate initiative or reporting exercise. They design it into their transformation from the start.

Solving systemic challenges like climate change requires collaboration—across value chains, ecosystems, and sectors. It also requires leaders who are willing to ask better questions about how technology is deployed, measured, and governed.

This perspective is demonstrated by Microsoft’s recent announcement on community-first AI infrastructure. As we scale AI, we have a responsibility to consider not only what these systems can do, but how and where they are built. That means investing in infrastructure that supports local communities, prioritizes renewable energy, manages water responsibly, and is designed with transparency and long-term partnership in mind. Building AI responsibly isn’t just about reducing risk—it’s about earning trust and ensuring that the benefits of innovation are shared broadly—from the datacenter outward.

Used thoughtfully, AI can help us make smarter decisions, operate more efficiently, and unlock entirely new ways of creating value—while staying within planetary boundaries. Used carelessly, it risks accelerating the very challenges we’re trying to solve.

That’s why clarity matters. Frameworks matter. And practical guidance matters.

What leaders can do next

If you are responsible for shaping your organization’s AI strategy, sustainability agenda, or both, I encourage you to explore the Strategic Guide: Aligning AI Transformation with Sustainability Goals. It is designed to help you cut through complexity, identify where to start, and move forward with clear actionable strategies.

At Microsoft, we’re committed to helping our customers become Frontier organizations that lead with innovation, responsibility, and impact.

The challenges we face are complex. But with the right strategy, the right technology, and a shared commitment to progress, AI can help us build a more sustainable and prosperous future—for everyone.

Strategic Guide: Aligning AI Transformation with Sustainability Goals

Read the guide

The post Beyond Davos 2026: 5 practices to align AI transformation and sustainability appeared first on The Microsoft Cloud Blog.