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.

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.

Frontier leaders aren’t simply bolting new technology onto their existing operations. As Microsoft Chief Executive Officer of Commercial Business Judson Althoff has shared in recent articles and keynotes, these leaders are taking a human-centered approach to AI transformation. The people closest to the work understand the real bottlenecks and opportunities. By equipping them with AI, leaders unlock practical solutions that drive measurable performance gains.

3 essentials for building a Frontier organization

Here’s Althoff’s outline for a Frontier approach to using AI and agents that puts capability directly into each employee’s hands.

1. Start with your employees to amplify ambition

At the heart of every Frontier business flow is the notion of democratizing intelligence. Human ambition is at the core, coupled with your AI assistants and agents to get real work done.

—Judson Althoff, Chief Executive Officer of Commercial Business, Microsoft

The idea: The point isn’t to simply deploy more technology, but to deploy it in ways that unlock more potential in people to solve their hardest problems and create more business impact. 

Why it matters: According to IDC,¹ AI adoption is accelerating past the initial experimentation phase, with 68% of companies using AI and 37% using agents. However, providing access to the technology is not the same as providing the guidance and skilling needed to unlock its potential. 

The shift: Frontier leaders focus on applying agents where they matter most—the priority workflows that define performance and growth. AI is at its most powerful when employees have the space and the guidance they need to imagine, experiment, and pursue bolder ideas. 

The big picture: Frontier leaders don’t start with AI capabilities. They start with human ambition, then design the systems, workflows, and guardrails that allow that ambition to scale responsibly. This requires treating AI adoption as a management system—not an IT rollout—with executives and business decision-makers actively redesigning workflows end-to-end.

2. Expand across every business function

There’s a maker in every one of us, and the Frontier Firm has a maker in every room of the house.

—Judson Althoff, Chief Executive Officer of Commercial Business, Microsoft

The idea: The people closest to the challenge are often closest to the opportunity. As AI becomes more accessible, creativity moves from the edges of the organization to the center so that everyone is empowered to innovate.

A striking data point: Frontier Firms aren’t leaving AI adoption to the IT department—they are making it a company-wide leadership priority. According to IDC research, Frontier Firms are using the technology across seven business functions on average.

Real-world innovations: Mercedes-Benz scaled AI innovation across its global production network, diagnosing efficiency declines and reducing energy consumption of buildings and machines—including 20% energy savings in one paint shop. And Althoff highlights how Toyota is pioneering AI intelligence in manufacturing with the O-beya system, a multi-agent AI system that simulates expert discussions virtually. O-beya can auto-select AI agents in fields like fuel efficiency, along with drivability, noise and vibration, energy management, and power management to pinpoint causes and suggest solutions. 

The takeaway: Broadening access to agents can unleash innovation. Frontier leaders don’t need to script how employees should use the technology—they just need to ensure that there are proper guardrails around a wide space for experimentation.

3. Trust, governance, and integration determine ROI

The idea: AI can create more value when people trust it enough to use it. Trust is what allows AI-powered innovation to scale beyond isolated pilots. And that requires human oversight with “observability at every layer of the stack,” according to Althoff. 

The challenge: Not every organization has put the right safeguards in place yet. Microsoft’s 2026 Data Security Index reports that only 47% of companies have fully implemented data security controls for AI.   

The solution: Frontier leaders must ensure security and be explicit about human-in-the-loop observability as a cornerstone of transformation. People adopt AI confidently when they understand how decisions are made, how data flows, and how systems behave—and when to intervene as needed. Finally, Frontier organizations don’t implement new technology and then slow down—or backtrack—to implement responsible practices. They design for trust from the start so they can keep moving quickly. 

Actions you can take to drive measurable impact

The idea: The organizations that will win in the Frontier era are those that view AI not as a one-off tool rollout but as a leadership discipline. They start by clarifying ambition, giving people the space and agency to act, and building trust early so transformation can scale across the business. Importantly, they use AI themselves to guide decisions, surface insights, and stress test ideas about keeping humans at the center of their business transformation. 

Where to start: Microsoft’s new Prompt Guide for Business Leaders was designed to help leaders get a handle on the changing AI landscape and use the technology itself to stress test their ideas and strategies in response to it. The guide offers guidance on how to:  

1. Assess readiness
2. Identify value 
3. Map workflows 
4. Build roadmap 
5. Plan for risk 
6. Define actionable next steps

Example prompt: “Show me the top three workflows where agents could reduce cycle time by at least 20% based on our current operations.”

From vision to value in the Frontier era

The guide demonstrates how AI can be a thinking partner, and helps leaders develop a strategy to help their people harness the technology to achieve goals, innovate, and unlock more value.

Innovation with AI

What every company can learn from Frontier Firms leading the AI revolution

Read the report

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

The post How to start your Frontier Transformation: 3 strategies to start with people appeared first on The Microsoft Cloud Blog.

The question today isn’t whether AI can deliver value, but how quickly organizations can scale it across their business. Challenges such as aligning business and IT, ensuring data quality, navigating governance and regulatory considerations, and avoiding an overemphasis on experimentation can slow progress and widen the gap between leaders and slow adopters.

At Microsoft Ignite, the core message was clear: AI in the flow of work can unlock human ambition, and scaling with trust and observability is essential. Organizations that embed AI into everyday workflows empower people to achieve more, while trust and observability ensure innovation happens responsibly and at scale.

What sets a Frontier Firm apart

According to a recent study from IDC, 68% of organizations are using AI today, but the real difference lies in how they’re using it. Frontier Firms are realizing returns that are three times higher than slow adopters. What sets these leaders apart is the breadth and depth of their AI adoption. On average, Frontier Firms are using AI across seven business functions, with over 70% deploying AI in customer service, marketing, IT, product development, and cybersecurity.

A Frontier Firm is defined not by its size or industry, but by its mindset and execution. These organizations lead with AI-first differentiation, embedding intelligence across every layer of the business—from employee experiences to customer engagements to core processes. “Becoming Frontier” means moving beyond experimentation to enterprise-scale transformation, unlocking creativity, obsolescing the mundane, and driving competitive advantage in the agentic era. Leaders invest in upskilling, culture, and strong foundations—strategy, data, security, and compliance—to align technology with ambitious goals.

Successful Frontier Firms, as highlighted at Microsoft Ignite, share three common traits in their approach to AI. First, they integrate AI seamlessly into the flow of human ambition, amplifying creativity and accelerating decision-making through everyday workflows. Second, they foster ubiquitous innovation by democratizing AI creation—empowering everyone, from frontline employees to executives, to build agents and solutions that address real business challenges. Third, they prioritize observability at every layer, embedding governance, security, and compliance into all AI systems to ensure visibility, control, and trust as they scale. These traits collectively allow Frontier Firms to lead with agility, resilience, and measurable business impact.

Where AI is delivering business value today

The impact of AI is accelerating across industries. For example, Levi Strauss & Co. reduced project timelines from a year to a day with Microsoft Copilot and Copilot+ PCs. ABB is transforming industrial operations with Microsoft Azure and AI-powered insights, and Land O’Lakes is embedding AI into agricultural workflows to optimize supply chains and accelerate decision-making. These stories demonstrate how AI is driving measurable outcomes at scale.

This broad adoption is translating into measurable business impact. Compared to slow adopters, Frontier Firms report better outcomes at a rate that is four times higher across brand differentiation (87%), cost efficiency (86%), top-line growth (88%), and customer experience (85%). They’re not just automating tasks—they’re unlocking industry-specific value, with 67% monetizing AI use cases tailored to their sector and 58% already using custom AI solutions.¹

Partnering for Frontier transformation

As we’ve already alluded to, becoming a Frontier Firm isn’t just about technology—it’s about strategy, culture, and execution. Microsoft partners with organizations to accelerate transformation and measurable impact, combining deep expertise, integrated intelligence, and a global ecosystem. Trust is at the core of every AI experience, with enterprise-grade security, real-time observability, and automated governance built in.

Leading organizations are already putting these principles into practice—accelerating innovation, transforming customer experiences, and creating new sources of growth. These examples prove that becoming a Frontier Firm isn’t theoretical; it’s happening now.

The future of AI-powered business

The divide between AI leaders and others is growing, reshaping the competitive landscape. Frontier Firms are moving rapidly from pilots to widespread AI adoption, raising the bar for business impact. Microsoft Ignite underscored that the future belongs to organizations that embed AI at every level, empower their people, and lead with trust.

Becoming an AI-First Frontier Firm

Learn how bold organizations across industries are combining human expertise with AI agents to achieve faster growth, greater efficiency, and sustained innovation.

Get the e-book

For organizations ready to take the next step, the Becoming an AI-First Frontier Firm e-book offers practical insights and a roadmap for 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 Becoming a Frontier Firm: Unlocking the business value of AI  appeared first on The Microsoft Cloud Blog.

In this edition of FYAI, a series where we spotlight AI trends with Microsoft leaders, we hear from Amanda Silver, Corporate Vice President and Head of Product, Apps, and Agents. Amanda’s leadership has shaped Microsoft’s evolution toward open-source collaboration, and she’s advancing a future where AI transforms how developers build, deploy, and iterate at scale to drive continuous innovation.

In this Q&A, Amanda shares why developer-led AI adoption matters, how agentic DevOps is redefining workflows, and what leaders can do today to maximize impact.  

How is the AI landscape changing how developer teams deliver the apps businesses run on?  

AI is collapsing handoffs across the software lifecycle. DevOps successfully united build, test, deploy, and operate, but the earlier phases—discovery, requirements, shared vision, and initial scaffolding—mostly sat outside that loop. Now copilots can turn natural language ideas into specs and scaffolds, and agents take on tests, upgrades, and runtime operations. The result is a single, faster cycle from idea to impact: lower cost to iterate, quicker transitions, and more freedom to refine until the product fits the business. Think of it like the shift to public cloud: before the public cloud, teams waited weeks to procure hardware and commit capital up front; with the cloud, environments spin up in seconds and you pay only for what you use. AI brings that same elasticity to product definition and delivery—removing friction at the front of the lifecycle and letting teams iterate based on real feedback. Put simply: cloud removed friction from infrastructure; AI removes friction from intent to implementation.

What are some examples of how AI is helping developers re-imagine their daily work?   

AI is turning software delivery into a true idea-to-operate system. For developers, that means less time spent on manual cleanup and more time focused on creative, high-impact work. Copilots and agents now handle the repetitive, often invisible tasks that used to pile up—like debugging, dependency upgrades, and security patches. Instead of waiting for a quarterly “tech debt sprint,” agentic DevOps lets teams pay down debt continuously, in the background.  

A great example is how agentic AI is accelerating migration and modernization. In the past, updating frameworks or moving to new platforms meant months of planning and manual fixes. Now, agents can automate .NET and Java upgrades, resolve breaking changes, and even orchestrate large-scale migrations—compressing timelines from months to hours. This isn’t just about speed; it’s about keeping codebases healthy and modern by default, so developers can focus on building new features and improving user experiences.  

The net effect: developers spend less time firefighting and more time innovating. Technical debt becomes a manageable, ongoing process—not a looming crisis. And as AI agents take on more of the routine work, teams can operate in a steadier flow state, with healthier code and faster delivery.  

What does that mean for apps? Are they getting better? And how does this impact the role a developer plays?

Apps will get better because they become learning systems. With AI in the loop, teams shift from ship and hope to continuous observe → hypothesize → change → validate cycle centered on continuously refining product–market fit. AI can help synthesize telemetry (such as funnels, dropoffs, session replays, and qualitative feedback), surface where users struggle, propose changes (like copy, flow, component layout, and recommendations), and can even wire up feature flags or experiments to prove whether a change works. The effect is a dramatic reduction in time to learning—and faster convergence on what users value.  

PreAI versus PostAI user interaction  

• PreAI: Users navigate dense menus and deep information architectures, scanning screens to find the one control that does what they need. Every step risks a dead end, and context is easy to lose when switching pages or tools.
• PostAI: Users express intent in natural language (like text, speech, or multimodal). The app interprets that intent, keeps context, and routes the user to the right data, action, or workflow—often composing the UI on the fly (for example, drafting a form, filtering to the relevant records, and suggesting the next best action). Think of this as moving from “Where do I click?” to “Here’s what I need—do it with me.”  

What changes for developers  

• From page builders to experience composers. Devs design intent routers and orchestrations that connect models, agents, data, and services—so the app can respond intelligently to varied user goals without forcing rigid click paths.
• From manual analysis to AI-assisted product loops. Instead of hand rolled dashboards and ad hoc investigations, AI highlights opportunity areas, drafts experiment plans, and opens pull requests with proposed code and config changes. Developers review, constrain, and ship—with guardrails.
• From “debt sprints” to continuous modernization. Agents can keep the app current—upgrading frameworks (for example, .NET and Java), repairing dependency drift, patching vulnerabilities, and standardizing pipelines—while feature work continues. That turns tech debt into a managed, always on workload rather than a periodic fire drill.   

Bottom line: AI tightens the loop between what users want and what the app becomes. Developers spend less time on menu wiring and manual forensics, and more time defining intent, composing agentic flows, setting success metrics, and supervising safe, measurable change. Apps improve faster—not just because they’re smarter, but because teams can experiment, learn, and adapt as usage grows.  

Where do you see Microsoft standing out in a sea of AI competition?  

Microsoft’s biggest differentiator is our ability to connect AI agents to the systems, data, and workflows that power real business. We serve organizations with massive, complex codebases and deep operational requirements—and our tools are designed to meet them where they are. With GitHub, Visual Studio, and Azure AI Foundry, millions of developers can access the latest models and agentic capabilities directly in their daily workflow, backed by enterprise-grade security, governance, and responsible AI benchmarks.  

But what truly sets Microsoft apart is the breadth of integration. AI agents built on our platform can tap into a huge ecosystem of business apps, data sources, and operational systems—whether it’s enterprise resource planning (ERP), customer relationship manager (CRM), human resources (HR), finance, or custom line-of-business solutions. Through open standards like Model Connector Protocol (MCP) and Agent-to-Agent (A2A), agents can securely connect, orchestrate, and automate across these environments, making it possible to deliver outcomes that matter: automating workflows, modernizing legacy systems, and driving continuous improvement.  

Yina Arenas’s Agent Factory series shows how Microsoft is building the blueprint for safe, secure, and reliable AI agents—from rapid prototyping to production, observability, and real-world use cases. Our platform isn’t just about building agents; it’s about enabling them to work with the systems and data that organizations already rely on, so teams can move from experiments to enterprise-scale impact.  

At the end of the day, Microsoft’s advantage is not just scale—it’s the ability to make AI agents truly useful by connecting them to the heart of the business, with the tools and standards to do it safely and securely.  

When should developers decide which tasks to delegate to agents versus tackle themselves for maximum impact?  

As my colleague, David Fowler, put it: “Humans are the UI thread; agents are the background thread. Don’t block the UI!” Developers should focus on the creative, judgment-driven work—setting intent, making architectural decisions, and shaping the product experience. Agents excel at handling the repetitive, long-running, or cross-cutting tasks that can quietly run in the background: code health, dependency upgrades, telemetry triage, and even scaffolding out solutions to unblock the blank page.  

The key is to delegate anything that slows down your flow or distracts from high-impact work. If a task is routine, latency-tolerant, or easily reversible, let an agent handle it. If it requires deep context, product judgment, or could fundamentally change the direction of your app, keep it on the human “UI thread.” This way, developers stay responsive and focused, while agents continuously improve the codebase and operations in parallel.  

By striking the right balance, developers can minimize time spent on routine tasks and stay focused on the work that moves products and teams forward. 

Why are AI coding tools attracting so much investment and interest? Why reimagine the developer experience now?  

Because software development already generates the kind of rich, structured signals that AI thrives on. Code and diffs, pull request reviews, test results, build logs, runtime and performance telemetry, issue trackers, and deployment outcomes are all labeled, timestamped, and traceable. That makes the dev environment a natural proving ground for applied machine learning: models can learn from real work, be evaluated against objective checks (like tests, linters, and policies), and improve inside an existing feedback loop (such as Continuous Integration and Continuous Delivery (CI/CD), feature flags, and canaries). In short, we have the data, the instrumentation, and the validation built in.  

There’s also a cultural reason: developers automate away friction—from compilers and build systems to version control, CI/CD, containers, and infrastructure as code. Generative AI is the next step in that lineage. It shifts more work from hand authoring to specifying intent and supervising outcomes: copilots help with exploration and acceleration; agents handle continuous code health, upgrades, and safe, reversible changes. Investment flows here because better developer experience maps directly to throughput, quality, and time to value.  

And yes—the future starts with developers. As dev teams discover where AI delivers real support in their own workflow, those patterns spread to the rest of the business, accelerating how every function experiments, learns, and ships.  

Empowering developers with AI to deliver lasting impact 

We’re entering a new era of software delivery—and it’s agentic, adaptive, and deeply human-centered. With copilots and agents in the loop, developers are building systems that continually adapt to business needs. At Microsoft, we’re empowering developers to move from idea to impact faster by focusing on creativity, product vision, and building with trustworthy AI. 

In fact, Frontier Firms are already showing us what’s possible. They treat software as a dynamic system—refined through telemetry, experimentation, and AI-powered insight. And across all types of organizations, compelling AI use cases are emerging—from customer service to software engineering—setting the pace for what’s possible with the latest AI tooling. 

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

• Learn three skill-building insights that help Frontier Firms drive innovation.
• Get started with GitHub Copilot.
• Build your first production-grade agent in under an hour with Azure AI Foundry.
• Simplify development and meet evolving business needs with Microsoft Cloud solutions. 

The post FYAI: Why developers will lead AI transformation across the enterprise appeared first on The Microsoft Cloud Blog.

Becoming Frontier is the strategic shift to transform your business into a secure, AI-first organization, seamlessly integrating AI throughout your business to drive innovation, empower people, and optimize processes. Organizations who put AI into the center of their operations, we call Frontier Firms.​ Built around on-demand intelligence and powered by human–AI collaboration, they can scale faster, operate with greater agility, and unlock real business value at unprecedented speed. 

We’ll explore how leading companies—Adecco, ABB, Loft Orbital, Air India, and HEINEKEN—are embracing the Frontier Firm mindset and some of them are already experiencing between a 30% to 63% increase in productivity alone. 

We’ll explore how leading companies—Adecco, ABB, Loft Orbital, Air India, and HEINEKEN—are embracing the Frontier Firm mindset. Each is reimagining their industry by putting AI at the core of their strategy, culture, and operations, and their perspectives offer a blueprint and lessons for what’s possible when organizations truly become frontier.  

Let’s look at how some of the world’s most innovative organizations are putting these principles into practice. 

ABB—Uses AI to unlock the future of industry  

ABB is committed to using responsible and sustainable AI innovation that is yielding measurable business impact. By integrating the Azure OpenAI, Genix Copilot unlocks the power of generative and agentic AI to industrial operations—helping customers achieve a 35% cost savings in operations and maintenance, and boost production efficiency by 30%. According to Rajesh Ramachandran, Global Chief Digital Officer, Process Automation, “ABB Genix Copilot transforms the way real-time data insights are delivered to field engineers, functional analysts and industry executives enabling smarter decisions. We are helping our customers outperform by becoming leaner and cleaner—achieving the combined goals of efficiency, reliability, and sustainability.”

Adecco—Revolutionizing recruitment using an AI-powered approach 

Adecco is using the real power of AI and its ability to unlock human potential. Their lesson is clear: upskilling, inclusion, and human-centric innovation are essential. By integrating AI (such as Microsoft 365 Copilot) into their “Recruiter GenAI Suite, we have seen up to 63% of productivity, which is really amazing. And this enables our recruiters to spend more time having valuable conversations with the candidates, and workforce development,” states Carolyn Basyn, Chief Digital and Information Officer at Adecco. Adecco shows that technology can create more meaningful experiences for clients, candidates, and colleagues—ensuring no one is left behind. 

Air India—Powering Air India’s digital transformation with data and AI 

Air India is a testament to the power of democratizing AI. By making AI accessible across their organization, they’re closing skill gaps and empowering teams to make data-driven decisions—whether it’s optimizing flight schedules or enhancing customer service. In fact, AI.g, an AI-powered virtual agent handles an average of 30,000 customer queries daily with 97% of them fully automated. “This means just 3% of the queries get escalated to human agents. That saves us several million dollars a year” explains Dr. Satya Ramaswami, Chief Digital and Technology Officer at Air India. Their journey shows that AI enhances job satisfaction—driving agility and excellence while supporting their on-time performance goals. 

Loft Orbital—Building smarter satellites with AI  

Loft Orbital (Loft) is developing AI-powered satellites to simplify access to space. Their satellites process data with edge computing to more efficiently deliver on-demand intelligence. Loft relies on GitHub Copilot to support their development teams, helping them write code faster, with more accuracy, and now apply it to quality assessment, information management, and even onboard systems. “Our approach highlights the importance of trusted partnerships and secure, scalable infrastructure in scaling AI innovation. Our satellites are autonomously coordinated to help us respond faster to emerging events, reduce reliance on ground operations, and deliver actionable insights right when they’re needed,” states Lucas Bremond, Chief Software Architect at Loft. 

HEINEKEN—Tapping AI to become the best-connected brewer 

Even a 160-year old company like HEINEKEN stands out for their disciplined experimentation with AI and commitment to responsible innovation. By piloting high-impact use cases, rigorously measuring results, and scaling what works, HEINEKEN demonstrates that weaving AI into every part of the business—from brewing and marketing to supply chain and sales—leads to tangible gains and a culture ready for the future. “AI needs to help to grow the business. So it’s around consumer insights, it’s around brand building, it’s around revenue management, it’s around sales execution,” shares Ronald den Elzen, Chief Digital and Technology Officer at HEINEKEN. HEINEKEN’s journey highlights how a culture of experimentation and responsible innovation can prepare a company for whatever the future holds.  

Solving industry specific challenges with AI 

Each organization demonstrates how AI can be harnessed to solve industry-specific challenges: from revolutionizing recruitment and upskilling talent, to optimizing industrial operations, transforming airline performance, enabling smarter satellites, and connecting global supply chains. 

What stands out most is that every Frontier Firm’s journey is unique, but the lessons are universal: put people first, innovate responsibly, experiment boldly, and measure what matters. These companies aren’t just adapting to change—they’re leading it, showing us that when we embrace AI with purpose and vision, we unlock new opportunities for growth, creativity, and impact. 

As you consider your own organization’s AI journey, let these stories serve as inspiration and guidance. The future belongs to those who experiment boldly, measure what matters, and put people at the heart of transformation.  

Are you ready to become a Frontier Firm? 

Explore how Microsoft’s AI solutions can transform your organization.   

Leverage our available resources to innovate with AI and start your journey to becoming a Frontier firm. 

• Advance your AI transformation: AI business resources.
• Explore Microsoft’s AI solutions and get the guide.
• Discover: real-world AI use cases to help you accelerate your business goals.

¹ HAI Stanford University, The 2025 AI Index Report.

The post Frontier Firms in action: Lessons from the AI adoption surge appeared first on The Microsoft Cloud Blog.

In this edition of FYAI, a series where we dive deep on AI trends with Microsoft leaders, we hear from Juan M. Lavista Ferres, Corporate Vice President and Chief Data Scientist for Microsoft’s AI for Good Lab, who is leading Microsoft’s efforts to create a collaborative ecosystem and a team of dedicated data scientists and researchers, domain experts, and organizations worldwide that drives progress toward addressing some of the world’s most pressing challenges. 

In this Q&A, Juan shares his insights on the growing AI for good movement, how Microsoft partners with local organizations to help solve region-specific challenges, and how to measure and scale AI projects to drive impact. 

You’re Microsoft’s Chief Data Scientist and Director of the Microsoft AI for Good Lab. What does that actually look like day-to-day?

My journey at Microsoft started all the way back in 2009 when I was running randomized controlled experiments as part of Microsoft EXP. Looking back, so much of my work then informed what eventually matured into our official AI for Good program. One of our first AI projects focused on better understanding the causes of sudden infant death syndrome. The research we produced with our collaborators helped understand some of the causes—and in so doing, reinforced our conviction that AI can be an indispensable tool in improving people’s lives.

This also illuminated a gap that clearly needed to be filled. The majority of AI expertise in the world is concentrated in the tech and financial sectors, and too many organizations working on solving important societal problems don’t have the resources or technological expertise to apply AI to their work. We help fill this gap by providing AI expertise and compute capacity to bring cutting-edge technology to our partner organizations.

I have a strong bias for action, so each day I am focused on how we can leverage AI to help the most people, as fast as possible. This means I work with our team to conduct applied research, seek out and engage diverse new partners, and guide the build out and deployment of AI solutions. In many ways, this is my dream job. I get to find ways to use data, apply AI, and improve the world, and do so with some of the most gifted researchers and scientists.

Our Lab was one of the first in the AI for Good space, which I’m proud to say has become a growing movement. The United Nations, for instance, hosts an annual AI for Good conference which we participated in this July in Geneva. It’s no surprise that this is catching on. For many of the problems we’re working on, AI isn’t just a solution, it’s the only solution.

Who is Microsoft partnering with to solve region-specific or community-based challenges?

Partnership is at the heart of the work we do. We believe that in order to make the most of AI’s potential to benefit everyone, we must leverage local, on-the-ground expertise to co-create solutions tailored for the communities with whom we’re working. We have teams in Microsoft offices in the United Arab Emirates, South America, and Kenya because it’s vitally important for us to work alongside local researchers.

One example of the importance of partnership in practice is our work to detect historical flooding and improve disaster response globally. Floods are among the deadliest forms of extreme weather, affecting millions of people and causing over USD40 billion in damage each year. To address this, we developed a model using 10 years of Synthetic Aperture Radar (SAR) data, which can detect floods through clouds, at night, and in remote regions. This is the first model of its kind. We worked closely with partners in Kenya, Ethiopia, Spain, and the United Nations to enable real-time response. In Ethiopia, we used the model to detect nearly three times more flood-prone areas than existing datasets. It confirmed known flood zones and revealed new ones, offering a more complete view of flood risk across the country. After devastating floods struck Kenya in May 2024, we used this tool to estimate that 75,000 hectares—about 2% of all cropland—were flooded or at risk.

In many cases, the value proposition we’re bringing to the table is not only increased accuracy but also speed. If we can use AI to, for example, measure deforestation in the Amazon in a matter of days rather than months, governments in the area can respond much faster.

How do you measure the impact of AI for Good projects?

The definition of ‘impact’ is unique to each project, but overarchingly, we assess whether and to what extent people’s lives have been improved through our work. The key to achieving this is being guided by subject matter experts who have a robust understanding of the challenges to be solved and the data available to solve them. Our goal is always to support our partners and advance their missions, whether that means forecasting malnutrition with greater precision or improving how we track giraffe populations to support conservation efforts.

We also want to expand the benefits of this technology to communities that otherwise might not be able to take advantage of it. For example, we’re working on a tool to diagnose retinopathy of prematurity in low-resource settings where pediatric ophthalmologists aren’t available. Retinopathy of prematurity is an increasingly common condition, thanks to improvements in maternal and infant healthcare that saves the lives of prematurely born babies around the globe. But if this condition isn’t diagnosed and treated, it can lead to lifelong blindness. We’ve developed algorithms that demonstrate how AI can help detect this condition using videos captured on a smartphone. This has the potential to empower healthcare professionals to intervene early in an infant’s life and preserve their eyesight.

The more accurate and useful our AI tools are, the greater their downstream impact will be. This is no small feat. AI technology is evolving and changing rapidly, and our researchers work tirelessly to stay at the forefront.

Where do you see the biggest need—and opportunity—to scale this work?

In order to really scale the benefits of AI, we need to ensure that people are connected not just to AI but to digital technology in general, which is still out of reach for 2.6 billion people around the world.

Our team has invested heavily in developing disaster response capabilities using geospatial machine learning and satellite imagery. We plan to continue supporting first responders in the immediate aftermath of natural disasters in the United States and across the globe. We’ve also seen tremendous benefits of using AI for biodiversity conservation, such as in the Amazon and in Sub-Saharan Africa.

As a non-native English speaker from Uruguay, I use AI to ensure my grammar in documents like research papers is pristine and error-free. A vast majority of academic publications and journals are in English. Large language models have made it easier for myself and countless others to contribute their research, which is a tide that lifts all boats.

What’s a project you’re most excited about right now—and why?

At the end of 2024, we announced Project SPARROW, an AI-powered edge computing solution designed to autonomously collect biodiversity data in the most remote corners of the planet. These devices can run for long periods of time on solar power without disrupting the ecosystems in which they’re embedded, which is one of the aspects of the project I’m most excited about. Protecting vital ecosystems benefits people, animals, and our planet. Our goal is to have SPARROW operating on every continent by the end of the year.

In June 2025, I traveled to Nairobi to connect with our AI for Good Lab in Kenya and our partners on the ground there. I was also invited to give a TED talk focused on SPARROW and the opportunities it presents to increase biodiversity. I highlighted earlier the importance of partnerships with local organizations, and I view this as a chance to uplift our collaboration.

Using AI to make new discoveries and improve people’s lives 

When researchers, conservationists, nonprofits, non-governmental organizations (NGOs), and academic institutions are tasked with doing more with less, technology offers a way forward. AI transforms the way we can use data to make new discoveries and improve lives.  

Microsoft is investing not only in solving today’s challenges but also paving the way for a brighter tomorrow. Our work helps showcase the potential benefits of AI, pushing the bounds of what was previously possible. By applying advanced AI to real-world datasets, the AI for Good Lab helps unlock insights that empower communities and accelerate progress on urgent global issues. 

AI for Good Lab

harness the transformative power of AI to tackle global challenges and improve lives.

Explore projects

Want to learn more? Here are a few resources

• Explore the AI for Good Lab’s projects.
• Begin your AI journey with AI resources and solutions from Microsoft.
• Check out our recently announced AI Economy Institute, Microsoft’s corporate think tank dedicated to advancing independent research and developing actionable solutions that can help societies worldwide positively adapt to the economic and social transformations brought by AI.

The post FYAI: Explore the Microsoft AI for Good Lab with Juan M. Lavista Ferres appeared first on The Microsoft Cloud Blog.

But behind these numbers are the real stories—organizations that have moved beyond experimentation to embed AI at the heart of how they operate. These “AI Challengers”—bold, AI-first businesses that are not just adapting to change but actively challenging the status quo and shaping the future.

These AI challengers haven’t just implemented new tools—they’ve reimagined what’s possible when AI becomes a core business capability. Find out how these companies are pulling ahead:

BOQ Group—Human-led transformation for banking reimagined

BOQ Group champions an employee and customer first approach to AI transformation. “At BOQ Group, we are committed to transforming the way we work and serve our customers by building an AI-ready workforce,” explains Chief Information Officer Craig Ryman.

The bank’s strategic rollout of Microsoft 365 Copilot delivered extraordinary results: over 70% of users now save 30 to 60 minutes each day. The impact extends far beyond time savings—completing business risk reviews in one day instead of three weeks, creating training programs in one day instead of three weeks, and improving report quality while speeding up sign-off processes from four weeks to one week.

BOQ’s approach emphasizes the critical insight that having a “human in the loop” is critical to AI adoption. The organization’s experience reveals universal adoption phases: users start with personal productivity gains, then focus on targeted opportunities, before ultimately transforming how work gets done entirely.

AI transformation is not just a technological transformation but a people-led transformation changing how we work. This is why it’s critical that any AI transformation starts by listening to and understanding team needs, spotting opportunities, and tackling challenges head-on to embrace the opportunities.

Enveda—Cracking the chemical code of life with AI

Enveda is innovating with AI by reinventing what is possible in medicine. It is focused on revolutionizing drug discovery by unlocking the potential of the vast majority of undiscovered molecules in nature. Enveda is using generative AI to identify and analyze thousands of compounds simultaneously, which significantly speeds up the drug development process and reduces costs.

“Today, nine out of ten drug candidates don’t make it through clinical trials, despite years of research and massive investment. This 90% failure rate pushes the cost of bringing a single new medicine to market as high as $6 billion. It’s an approach that simply can’t scale,” states CEO and Founder Viswa Colluru. 

At Enveda, AI is changing that. By decoding nature’s chemistry at scale, it is delivering drug candidates to clinical trials four times faster and at nearly one-tenth the cost.

AvePoint—Using AI to transform business inside and out

AvePoint is driving business innovation by embedding AI-powered capabilities into its product suite with Copilot. AvePoint also demonstrates the power of being your own best customer with its internal AI adoption. 95% of pilot participants actively use Copilot, with employees saving one to three hours per week by automating repetitive tasks, allowing them to focus on higher-value work.

As Mario Carvajal, Chief Strategy and Marketing Officer, noted, “Our investors care about efficiency, and AI is helping us deliver that. By freeing up time for higher-value work, we’re making our teams more effective and our business more competitive.” 

To accelerate adoption, AvePoint had two key strategies: upskilling its employees and creating focused, department-specific initiatives. AvePoint also encouraged a dialogue that sparked curiosity, built confidence and helped AI become a natural part of its daily workflows. As AI continues to evolve, AvePoint is positioned to lead the way, proving that the right foundation is key to unlocking its full potential. 

Eaton—Revolutionizing its energy management using AI

Eaton showcases how industrial companies use AI across complex global operations. “AI offers limitless potential for innovation, providing transformative technical opportunities that align with our growth objectives and advance our mission for sustainability and energy resilience by supporting grid flexibility, enhancing energy management, and delivering superior service and outcomes for our customers.,” explains Executive Vice President and Chief Information Officer Katrina Redmond. 

The company’s implementation of Copilot specialized agents brings functional expertise to areas like finance, sales, human resources, and service, handling repetitive, mundane tasks and freeing teams to focus on higher-value, strategic work that enables them to better serve their customers. By embedding AI into its culture and adopting a “think AI first” mindset, Eaton aims to drive further advancements in both technology, organizational strategy and will ensure that it can fully harness AI’s immediate potential while fostering sustained innovation across all aspects of its business.

The agent-enabled workplace

Each organization is driving innovation and demonstrates different aspects of the agent-enabled workplace—environments where AI agents work alongside human teams to accomplish complex business objectives, but all point toward a future where intelligent systems amplify human potential. 

Their success reinforces a critical insight: AI transformation requires more than technology adoption. It demands fundamental rethinking of business processes and competitive strategies. The organizations that recognize this—and act boldly on it—will define the next decade of business innovation. The future is here—and it’s time to claim your place in it. Will you lead or follow? 

Ready to join the AI challengers?

Explore how Microsoft’s AI solutions can transform your organization. Connect with our team to discuss your AI strategy or watch detailed case studies featuring each of these remarkable organizations.

Find the resources to support your AI journey:  

• Explore Microsoft’s AI solutions.
• Advance your AI transformation with AI business resources.
• View more AI Challengers executive insights.
• Listen to the Leading the Shift podcast to hear advice from leaders navigating the AI platform shift.

AI business resources

Help your organization achieve its transformation goals with expert insights and guidance.

Learn more

¹ IDC 2025 Worldwide CEO Survey — Part 1: Overarching Insights, Business Priorities, and Risks, doc #US53155225, April 2025

² IDC, 2025 CEO Signature Report: Transforming Business for an AI World, doc #US53393625, June 2025

The post AI-powered innovation: How leading organizations are shaping the future appeared first on The Microsoft Cloud Blog.

From streamlining enterprise workflows to accelerating scientific discovery, AI agents are transforming how developers build and how businesses operate.

• 15 million developers are using GitHub Copilot, using features like agent mode and code review to handle repetitive tasks, allowing them to focus on the fun, creative parts of software development.
• Hundreds of thousands of customers are using Microsoft 365 Copilot to assist with research, brainstorming, and solution development, allowing increased for efficiency.
• More than 230,000 organizations—including 90% of the Fortune 500—have used Microsoft Copilot Studio to build AI agents and automations to improve productivity and scale business quickly.
• More than 11,000 AI models are now available through Azure AI Foundry, including Microsoft-hosted and partner-hosted models. This extensive library of AI models provides unparalleled resources for organizations to innovate and scale their AI-powered solutions. 

In this edition of FYAI, a series where we dive deep on AI trends with Microsoft leaders, we hear from Yina Arenas, Vice President of Product, Azure AI Foundry, who is leading the work at Microsoft to empower every developer to shape the future with generative AI using breakthrough models and enterprise AI agents.

In this Q&A, Yina shares her insights on the shifting AI landscape, including why businesses are getting stuck in the “proof of concept” phase and how Azure AI Foundry can meet organizations where they are and take their AI projects to the next level.

What shifts in the AI landscape are you seeing that are fundamentally changing how people—and organizations—build and scale AI?

We’re seeing a profound shift from AI as a research experiment to AI as a core business capability. What’s exciting—and challenging—is that organizations are no longer just asking, “Can we build this?” but “How do we build this responsibly, at scale, and with real impact?” That shift requires new tools, new mindsets, and new ways of working across teams. At Microsoft, we’re focused on making AI more accessible and inclusive—so that everyone, from developers to domain experts, can contribute to building solutions that matter. It’s not just about the tech—it’s about empowering people to solve real problems with AI.

Why is it still so hard for businesses to move from experimentation to production with AI—and what needs to change to unlock that next wave of value?

Many organizations get stuck in the “proof of concept” phase because the leap to production is complex. It’s not just about selecting the right model—it’s about integrating it into systems, ensuring it’s secure and responsible, and aligning it with business goals. What’s missing is a cohesive, end-to-end approach that brings together the right tools, governance, and collaboration in a developer-friendly environment. That’s where Azure AI Foundry comes in—it’s designed to help teams not only move faster but do so thoughtfully by providing a cohesive end-to-end platform and offering traceability across prompts, models, and runtime behavior. We’re making it easier and less complex for developers to build apps while also giving business decision makers the ability to see how these apps perform, measure their ROI, and meet compliance requirements. To unlock the next wave of value, we need to make AI development more collaborative, transparent, and outcome-driven.

How does Azure AI Foundry help bridge that gap—and how is it different from other approaches out there?

Azure AI Foundry is built to meet organizations where they are—whether they’re just starting or scaling AI across the enterprise. It brings together the best of Microsoft’s AI capabilities from foundational models to orchestration and monitoring in a unified platform. What sets Azure AI Foundry apart is not only that it’s built on decades of world-class research but that it’s built with humans at the center, so whether you’re a data scientist, product manager, engineer, or business leader, our AI solutions work for you. It also bakes in responsible AI from the start by integrating tools, from testing to monitoring to governance, that support the entire life cycle.

Who is Azure AI Foundry built for, and how does it support cross-functional teams—from data scientists to decision-makers—to build together?

Azure AI Foundry is designed for anyone looking to take their AI projects to the next level—whether you’re part of a big enterprise, a startup, or a software development company. It offers access to the leading frontier models, integrates orchestration frameworks, supports open protocols for multi-agent collaboration, and provides native observability tooling—all within a secure, governed environment. Whether it’s optimizing call centers, analyzing data, improving product searches, or automating workflows, Azure AI Foundry pulls everything—models, tools, and agents—into one user-friendly platform. With tools like GitHub, Visual Studio, and Copilot Studio, Azure AI Foundry makes it easy for developers, data scientists, IT pros, and decision-makers to shorten the journey from idea to production.

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Where are you seeing Azure AI Foundry already making an impact—and what kinds of transformation are customers unlocking?

As the central hub for building, orchestrating, and managing AI solutions, Azure AI Foundry remains the centerpiece of our AI platform strategy. It is now used by developers at more than 70,000 enterprises and software development companies—including Atomicwork, Epic, Fujitsu, Gainsight, H&R Block, and LG Electronics—to design, customize, and manage their AI apps and agents. And just six months in, more than 10,000 organizations have used Azure AI Foundry Agent Service to build, deploy, and scale their agents. Developers are designing agents that act, reason, take initiative, and deliver measurable business outcomes.

Heineken, for example, used Azure AI Foundry to build a multi-agent platform called “Hoppy” that helps employees access data and tools across the company in their native language. Their implementation has already saved thousands of hours, reducing tasks that once took 20 minutes to just 20 seconds.

Fujitsu evaluated Azure AI Foundry Agent Service to automate sales proposal creation. This boosted productivity by 67%, letting their teams to focus on customer engagement. The AI agent integrates with existing Microsoft tools familiar to around 38,000 employees, retrieves dispersed knowledge, and lays the foundation for broader AI-powered innovation.

Draftwise, a digital native offering an AI-powered contract drafting and review platform, is using cutting edge models in Azure AI Foundry (Cohere multimodal and AOAI reasoning) to help streamline the contract drafting process by integrating with a lawyer’s document storage system.

What excites you most about what’s next—for Azure AI Foundry, and for how people can reimagine the way they work and create with AI?

What excites me most about what’s next for Azure AI Foundry is how it’s unlocking a new era of creativity and empowerment—not just for developers, but for everyone. We’re moving beyond the idea of AI as a tool you use to AI as a copilot you build with. Azure AI Foundry is helping people imagine and create agents that understand their goals, adapt to their workflows, and evolve with their needs.

That shift—from writing code to orchestrating intelligence—is profound. It means that a product manager, a marketer, or a frontline worker can shape how AI works for them, without needing to be a machine learning expert. It’s about putting the power of AI into the hands of the many, not the few.

And what’s most inspiring is that we’re just getting started. The agents people are building today are solving real problems—automating complex processes, accelerating insights, and freeing up time for more meaningful work. But the agents of tomorrow? They’ll be collaborators in creativity, partners in problem-solving, and catalysts for innovation we haven’t even dreamed of yet.

That’s the future I see—and it’s being built right now, by people who are reimagining what’s possible with AI.

Design, customize, and manage AI apps and agents at scale

Through leaders like Yina Arenas, Microsoft’s vision for the future of AI is both inspiring and deeply human-centered. With platforms like Azure AI Foundry, we’re entering a new era where AI becomes not just a tool, but a true collaborator—empowering everyone, regardless of technical expertise, to innovate and solve real-world problems. With Azure AI Foundry, the potential of AI is being unlocked by developers everywhere, sparking a wave of transformation and boundless possibilities.

Interested in learning more? Here are a few resources:

• Build your first production-grade AI agent in under an hour: Azure AI Foundry
• Learn how Azure AI Foundry is supporting open Agent2Agent (A2A) protocol
• Read Azure AI Foundry Agent Service documentation
• Empower your team to grow their AI skills
• FYAI: How agents will transform business and daily work

The post FYAI: How to leverage AI to reimagine cross-functional collaboration with Yina Arenas appeared first on The Microsoft Cloud Blog.

And that transformation is already well underway: 

• According to Gartner®, “By 2026, more than 80% of enterprises will have used generative AI APIs or models, and/or deployed generative AI-enabled applications in production environments, up from less than 5% in 2023.”¹
• 97% of executives believe generative AI will transform their company and industry, 93% say their generative AI investments are outperforming other strategic areas, based on a research report from Accenture.²

So, how do organizations accelerate their journey through AI adoption? For many leaders, the success of AI-powered reinvention depends on organizations’ readiness to swiftly experiment and adopt a more risk-tolerant approach to scaling generative AI use cases within their operations. 

What AI leaders do differently 

AI leaders incorporate AI into their culture and operations, treating it as an essential element of their business strategy to enhance business value and foster innovation. Organizations at the forefront of this movement are already experiencing transformative impacts. They are leveraging generative AI to unlock new levels of customization in customer interactions, streamline complex workflows, and innovate at an unprecedented scale. By embedding AI into the fabric of their business operations, these leaders are redefining industry standards and setting new benchmarks for success. 

AI in action: How industry leaders are driving reinvention  

ANZ Bank, Zurich Insurance, Telstra, and Coles exemplify how companies can move beyond experimentation and embed AI into their core operations by modernizing their data foundations to fuel AI-powered insights, reskill their workforce to align with new AI capabilities, and ensure AI strategies are co-owned by business and IT leaders.  

ANZ Bank: Cultivating an AI-enabled workforce 

ANZ Bank is a phenomenal example of integrating AI into operations by equipping 45,000 employees with the skills and tools needed to work effectively with AI. A key part of this effort is the AI Immersion Centre, launched in partnership with Microsoft, where employees gain hands-on experience and develop practical AI skills. This initiative fosters a culture of experimentation and continuous learning, helping teams explore AI’s potential across different functions. 

Leadership engagement is central to ANZ’s approach, with the bank implementing CEO-sponsored executive education programs designed to deepen leaders’ understanding of AI ethics, safety, and business applications. These sessions encourage executives to identify AI-powered opportunities and drive adoption across their teams. Through adding thoughtful initiatives, ANZ is strengthening its AI capabilities while ensuring employees are confident and capable in an AI-powered workplace. 

Zurich Insurance: AI-powered data modernization and decision-making 

For a company built on assessing risk and ensuring stability, Zurich Insurance saw early on that AI could revolutionize underwriting and claims processing. With a global footprint and vast amounts of unstructured data across languages and regions, the insurer needed a scalable AI solution to streamline decision-making and enhance customer experiences. 

The impact has been clear, AI is enhancing the employee experience to improve underwriting precision, which has enabled faster claims resolution, and enhanced customer satisfaction. By making AI a core part of operations, Zurich is demonstrating how data modernization and automation fuel reinvention at scale.

Learn more about Zurich’s AI transformation, or how Zurich is shaping the future of underwriting.

Telstra: Aligning leadership and AI strategy for reinvention at scale 

Telstra, Australia’s largest telecommunications provider, is embedding AI across its entire business to drive both customer engagement and operational efficiency. Recognizing that AI adoption must be a company-wide effort, Telstra has taken a CEO-led approach to AI strategy, ensuring alignment between business and IT leaders. 

Through its AI Academy, employees at all levels gain hands-on AI experience, and with more than 21,000 employees using Microsoft 365 Copilot, this demonstrates how AI is being woven into daily workflows. By embedding AI across its operations—from network management to customer support—Telstra is showing how leadership alignment and strategic implementation can drive AI-powered reinvention at scale.

Coles: AI-powered workforce enablement and operational agility 

As one of Australia’s largest grocery retailers, Coles is using AI to transform both customer experiences and workforce efficiency. With 109 billion daily AI-powered demand predictions across its supply chain, the company is embedding AI into decision-making at every level. 

Coles has deployed machine vision and AI-powered checkout solutions that have saved customers an estimated 400,000 hours annually, reducing friction at checkout while improving efficiency. The company also invested in AI literacy across its workforce, redesigning roles and providing AI-powered decision support for employees in stores and supply chain operations. By integrating AI into its workforce strategy, Coles has gone beyond enhancing operational efficiency, empowering employees to focus on higher-value work while improving customer service. 

Driving competitive advantage in the AI era 

Leaders in AI-powered reinvention aren’t just reacting to change; they are taking bold and decisive action to redefine the future of their organizations. These organizations have moved beyond laying the groundwork for success, they are rapidly experiencing real business value and have turned AI into a competitive advantage.  

AI reinvention is no longer optional. The question is: How will your organization turn AI into a lasting advantage? 

Find the resources to support your AI journey:  

• Explore Microsoft’s AI solutions.
• Advance your AI transformation with AI business resources.
• View more AI Challengers executive insights.
• Listen to the Leading the Shift podcast to hear advice from leaders navigating the AI platform shift.

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¹ Gartner Article, What’s Driving the Hype Cycle for Generative AI, 2024, Arun Chandrasekaran, November 14, 2024.

GARTNER® is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved. 

²Accenture Research Report, Reinventing enterprise models in the age of generative AI, Karalee Close and Kestas Sereiva, March 17, 2025.

The post The AI platform shift is here—Are you ready for reinvention? appeared first on The Microsoft Cloud Blog.