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 

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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

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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.

The 2026 Formula 1 regulation changes usher in a new era of racing for the sport, with increased electrification, efficiency and sustainability, representing one of the most significant technical evolutions in modern Formula 1 history. As the Mercedes-AMG PETRONAS F1 Team prepares for this transformation, it is partnering with Microsoft to harness the power of its trusted cloud and enterprise AI technologies across the business, from the factory to the racetrack.

“Our sport is driven by those who lead through innovation,” said Toto Wolff, CEO and Team Principal, Mercedes-AMG PETRONAS F1 Team. “We are delighted to partner with Microsoft, one of the world’s foremost technology leaders, whose name is synonymous with groundbreaking innovation. This partnership also reflects our commitment to staying at the forefront of performance and progress. By putting Microsoft’s technology at the center of how we operate as a team, we will create faster insights, smarter collaboration and new ways of working as we look ahead to the next generation in F1.”

In a sport where races are decided by tenths of a second and every decision is data driven, Formula 1 represents the ultimate stress test for modern enterprise systems: extreme data volumes, real-time decision making, global operations and zero margin for error. United by the belief that technology is a competitive advantage, Microsoft and the Mercedes-AMG PETRONAS F1 Team will look to set a new standard for how enterprise technology drives performance at the highest levels of competition.

“This partnership puts Microsoft’s cloud and enterprise AI technologies at the heart of racing performance, where milliseconds matter and data determines outcomes,” said Judson Althoff, CEO, Microsoft commercial business. “Together with the Mercedes-AMG PETRONAS F1 Team, we are harnessing data and turning it into real-time intelligence that powers faster decisions, smarter strategies and sustained competitive advantage — both on and off the track.”

Racing at the speed of data

Modern Formula 1 cars are defined by precision and pace. Each Mercedes-AMG PETRONAS F1 Team car carries more than 400 sensors, generating over 1.1 million data points per second. From tire degradation and aerodynamic behavior to Energy Recovery System deployment and evolving track conditions, every variable must be interpreted in real time.

Microsoft Azure and its AI capabilities will expand the Team’s existing high-performance computing and data capabilities, both the factory and trackside, with scalable cloud and AI resources supporting simulation workloads, performance analysis, race strategy modeling and cross-team analytics. The flexibility and agility of this platform will help ensure engineers and strategists have real-time insights available at the moments that matter most.

“It is a privilege to welcome Microsoft into the Mercedes-AMG PETRONAS F1 Team partner ecosystem,” said Richard Sanders, Chief Commercial Officer, Mercedes-AMG PETRONAS F1 Team. “Microsoft’s technology already plays a central role in how we operate as a business, and this partnership opens new opportunities to innovate as we look toward the next era of technological development. I look forward to seeing how our teams collaborate to unlock new ways of working across the organization.” 

Fueling human ambition

Microsoft 365 and GitHub already underpin many of the Mercedes-AMG PETRONAS F1 Team’s engineering and operational workflows across its headquarters in Brackley and Brixworth, as well as trackside in the paddock. Building on this foundation, the team will expand its use of Microsoft 365 to unlock new levels of agility, accelerate innovation and enhance operational efficiency across the business.

Microsoft’s GitHub development tools and platforms help engineers innovate faster, optimize performance and push the boundaries of design, modeling and simulation. Going forward, the team’s engineering, simulation and software development groups will deepen their integration of GitHub to modernize and accelerate development workflows enabling greater consistency, speed and efficiency.

Scaling for performance

Working with Microsoft Azure to accelerate AI technology experimentation and scale, the Mercedes-AMG PETRONAS F1 Team used real-time sensor data and Azure cloud tools to pilot intelligent virtual sensors, enabling rapid testing without waiting for new on-premises infrastructure.

With Azure Kubernetes Service (AKS), they were able to easily adjust computing power, scaling up when demand is high and down when it’s not, delivering meaningful technological advancements while meeting strict financial and regulatory requirements.

From road to track

For more than 30 years, Microsoft and Mercedes-Benz have collaborated across the automotive value chain, from AI-powered smart factories and electric vehicle telemetry to onboard vehicle intelligence and cloud-enabled engineering systems. From the factory floor to Formula 1, this new partnership builds on that foundation, bringing the same innovative mindset and digital capabilities into the world’s premier motorsport.                                                  

About the Mercedes-AMG PETRONAS F1 Team 

Mercedes was born to race — and we’ve been doing it since 1901. Today, the Mercedes-AMG PETRONAS F1 Team competes at the pinnacle of motorsport: the FIA Formula One World Championship. 

The pioneering spirit of our company founders lives on in our commitment to innovation and performance. As the world’s original automobile manufacturer, Mercedes-Benz has defined the cutting edge of technology for over a century. Today, our F1 team exists to demonstrate the best of the brand’s performance on the global stage. 

Based in Brackley and Brixworth, UK, over 2,000 committed team members work with a singular mission: to win the world championship. From 2014 to 2021, we secured a record eight consecutive Constructors’ Championships, and we are hungry for more. 

Our journey is not just about performance on the track; we also strive to make a positive impact on the world and inspire future generations. We are proud signatories of the Climate Pledge, and we are leading the way in building a more sustainable and inclusive sport. 

For more information, please visit www.mercedesamgf1.com. 

The post Microsoft and Mercedes-AMG PETRONAS F1 Team unite to drive innovation from factory to circuit appeared first on The Microsoft Cloud Blog.

Today’s business environment demands more with less. Organizations must deliver better personalization, higher volume, and increasingly complex insights while operating with greater efficiency. The gap between stakeholder expectations and what teams can realistically deliver continues to widen. 

Microsoft’s recent insights on Frontier Transformation address these challenges by embedding AI into the core of operations. Frontier Firms are organizations that treat AI as a foundational capability and are already transforming how they work. 

Frontier Firms don’t simply automate; they adapt. By adding adaptive intelligence to existing systems, they unlock three advantages: 

• Awareness: Systems perceive conditions in real time. 
• Reasoning: They prioritize tasks based on business needs. 
• Interaction: They communicate in natural, intuitive ways. 

Early adopters see small improvements compound quickly. These include faster service, more accurate recommendations, fewer equipment surprises, and clearer insights into peak times and bottlenecks. As agentic AI matures, companies can offer guidance and assistance that feels intuitive. Employees gain more time for high-value work, and leaders gain deeper visibility into operations. 

Frontier Transformation is more than a technology upgrade. It represents a shift in operating model. Organizations that treat AI as a foundation will lead the next wave of business innovation. 

Agentic AI is reshaping customer experience 

This shift is already visible in retail, where agentic robots are transforming customer experience and improving operational performance. Customers expect fast, personalized service, yet retailers often face staffing constraints, training gaps, and unpredictable demand. 

Industry studies show: 

• 75% of consumers are more likely to purchase when recommendations feel relevant. 
• Nearly 40% of in-store complaints relate to wait times. 
• Inventory inaccuracies account for 4–8% of lost sales. 

These challenges reflect a broader pattern across frontline-heavy industries. Customer expectations continue to rise, and employee workloads grow more complex. 

Microsoft’s Work Trend Index reinforces this dynamic. Frontline employees say AI tools that reduce repetitive tasks, surface real-time information, and streamline customer interactions have the biggest impact on satisfaction and performance. As organizations integrate adaptive intelligence into daily workflows, these benefits build on each other and help accelerate Frontier Transformation. 

Recent industry research shows that retail and consumer packaged goods organizations are generating significant business value from generative and agentic AI, with early deployments consistently delivering multi-times ROI and accelerating impact across frontline operations.

Agentic AI creates new possibilities for stores. Instead of relying on rigid automation, it blends environmental awareness, adaptive reasoning, and conversational interaction to help teams respond in real time. 

ADAM: From beverage service to customer care 

Richtech Robotics’ ADAM beverage robot illustrates how quickly agentic systems can enhance the customer experience. Richtech, based in Las Vegas, designs and commercializes autonomous robotic solutions for hospitality, retail, logistics, and manufacturing. Through a close, hands-on collaboration between Richtech’s engineering team and the Microsoft AI Co-Innovation Labs, the two companies jointly developed new adaptive intelligence for ADAM—transforming it into a conversational, context-aware assistant powered by Microsoft Azure AI. These enhancements enabled ADAM to move beyond routine beverage preparation and support richer customer interactions.

Today, ADAM: 

• Adjusts recommendations based on weather, time of day, and promotions. 
• Responds naturally to customer requests like “less sweet,” “extra ice,” or “what’s seasonal?” 
• Notifies staff about ingredient or equipment issues before problems occur. 
• Uses vision models to maintain speed and quality during busy periods. 

Retailers report smoother operations and better customer feedback. ADAM is context aware, conversational, and reliable—qualities customers consistently reward and areas where AI has historically struggled. 

While ADAM is a retail example, the pattern extends far beyond beverage automation. Across logistics, healthcare, hospitality, and manufacturing, Frontier Firms are adding ambient intelligence and agentic workflows to physical operations and seeing meaningful gains as a result. 

Unlocking retail transformation at scale 

Once retailers see how intelligence enhances a single customer interaction, the next question naturally follows: where else can this help? Building on the advancements made with ADAM, Richtech Robotics is extending these capabilities through its Agentic Store initiative. By applying vision, voice, and agentic reasoning to common in-store tasks, the initiative helps retailers address friction points that slow down the shopping experience. 

Examples under development include: 

• Robots that guide customers to products.
• Systems that detect empty shelves or misplaced items.
• Voice-enabled, in-aisle assistance.
• Real-time adjustments based on foot traffic or local events.

This approach does not require heavy hardware investments. These workflows are software-driven and build on existing store infrastructure. It reflects how Frontier Firms drive transformation by spreading intelligence across the ecosystem rather than upgrading a single process at a time. 

Retailers gain clearer visibility into peak demand, customer behavior, product movement, and service quality without increasing manual tracking. As one store manager described it, “it feels like having a second set of eyes that never gets tired.” 

Convenient, high-quality service becomes a blueprint for store-wide intelligence. In the coming years, a clear difference will emerge between retailers that treat AI as a tool and those that treat it as a foundation. The latter will set the pace for the industry. 

Steps toward Frontier Transformation 

Agentic AI gives retailers a practical and achievable path forward. It elevates customer experience, reduces operational strain, and creates the foundation for smarter and more adaptive stores. Organizations that embrace Frontier Transformation position themselves as Frontier Firms, ready to scale faster, work more intelligently, and unlock new value through the combination of human judgment and AI-driven insight. 

The journey begins with small, strategic steps and a bold vision for what is possible. To explore the broader business impact of AI across frontline and customer-facing roles, review Microsoft’s Work Trend Index: The year the Frontier Firm is born.

Explore how organizations are transforming with AI, and learn how you can build your own generative AI proof of concept with the Microsoft AI Co-Innovation Labs.

The post Frontier Transformation in retail: How agentic AI robots are redefining store experiences appeared first on The Microsoft Cloud Blog.

Akers lives with ADHD, dyslexia and dysgraphia, meaning tasks like reading, writing and organizing information require extra effort and creativity. She recalls having to turn down an invitation to read a passage at her brother’s wedding, and the confusion in one of the first teams she led at work when she referred to everyone by their first names, although several shared the same one, because she couldn’t easily read more complicated last names.

But as technology has evolved, so has Akers’ toolkit. AI-powered aids such as Copilot are helping her manage the cognitive load, shifting the focus from hurdles to strengths so she can communicate and lead in ways that once felt out of reach. She’s part of a growing wave of business professionals with neurodivergent traits — differences in brain function, including autism and attention-deficit/hyperactivity disorder (ADHD) — who are finding greater confidence and efficiency through AI.

There’s so many positive things that come out of having a brain that thinks differently.

Kim Akers
“When I saw the ability to take an input in, like here’s what I’m trying to communicate in an email, and then get it back in seconds and have it be 90% of the way there, that was a game changer,” says Akers, who uses Copilot at work and at home. “When the tech got good enough that you could use prompts, it really effectively cut down a lot of your prep work.”

Now that she can set her own meetings with Copilot’s help in Outlook, she has more control over her calendar and her days. She uses Microsoft 365 Copilot across the apps to do things like summarize documents, write emails and streamline meeting preparation by building lists of questions to ask her team about projects underway.

The tool helps her analyze sales data and draft outlines for presentations. It even helps her support her kids with their homework by generating practice problems or breaking down big assignments into manageable steps.

“Dr.
Dr. Cornelia C. Walther, a researcher and author who focuses on “prosocial AI” — systems designed to amplify human potential and foster equity (photo provided by Walther)
“Neurodivergent leaders who harness the full range of their natural and artificial assets are a beautiful illustration of the potential that the hybrid future offers for all of us,” says researcher and author Dr. Cornelia C. Walther, who focuses on “prosocial AI” — systems designed to amplify human potential and foster equity.

AI can be a bridge to greater inclusion and a connector that helps people participate more fully in society, says Walther, a senior fellow at the Wharton Neuroscience Initiative and Harvard’s Learning and Innovation Lab. The tools can help people with neurodivergence curate a new inner dialogue, moving beyond the self-judgment that can come with feeling different, she says.

“AI can serve as a sort of translator, not of language, but of ability,” Walther says. “It can make sure there is a path that connects your ability and makes it useful in the way in which society is currently normed.”

Recent research from professional services network EY underscores this, finding that generative AI can reduce barriers and support more inclusive ways of working. That’s significant for a workforce where an estimated 15-20% of people — and an even higher share of Gen Z — identify as neurodivergent.

In the EY survey of 300 employees with disabilities or neurodivergence across 17 organizations worldwide, respondents described how tools like Copilot helped with initiating tasks, organizing thoughts, spotting mistakes and improving accuracy. They said Copilot helped them stay on top of emails, focus in meetings instead of taking notes, and draft documents, spreadsheets and presentations — especially useful for those with dyslexia.

The study found Copilot’s impact goes beyond productivity. Participants said the tool’s support in making it easier to communicate, manage information and stay organized in turn boosted their confidence, motivation and impact. Many noted that Copilot helped them play to their strengths and overcome common hurdles, with 68% saying it reduced work anxieties and 71% saying it gave them hope.

“Hiren
Hiren Shukla, who founded the Neuro-Diverse Centers of Excellence at EY Global (photo provided by EY)
Neurodivergent professionals don’t just benefit from AI tools; they’re often the ones who find the most creative and effective ways to use them, says Hiren Shukla, who founded EY’s global neurodiversity program and lives with ADHD and dyslexia.

When EY ran a six-week innovation sprint with neurodivergent team members using Copilot earlier this year, Shukla says, ideas poured in: 60 to 80 process improvement suggestions, many sparked by the inventive approaches employees took to tackle problems.

“It’s not just AI helping neurodivergence,” Shukla says. “It’s the power of neurodivergence maximizing the use of Copilot. When you harness that divergence and partner with AI, you’ll see greater innovation, higher use cases, more ideation and application of AI.”

As organizations increasingly recognize the value of neurodivergent talent, and as AI tools become more inclusive, the ripple effects go beyond individual careers and corporate innovation to benefit everyone, he says.

This dynamic is especially pronounced at the leadership level, he says, where disclosure is often rare and role models are few.

“We hear a lot about frontline workers using AI, but not enough about neurodivergent leaders,” Shukla says. “Having executives like Kim Akers share their stories is crucial. It activates other leaders out there so they see themselves, lean in more and celebrate how they use AI, whether they disclose their neurodivergence or not.”

AI tools are creating opportunities for people who have been historically left out of mainstream companies and institutions, says Maitreya Shah, the American Association of People with Disabilities’ technology policy director.

“Maitreya
Maitreya Shah, the American Association of People with Disabilities’ technology policy director (photo provided by Shah)
“AI also gives you a level of independence and privacy for things you might not want to ask for help with from others,” he says, such as being able to communicate more effectively or understanding complicated yet sensitive health or financial documents. “That feeling of agency, of being able to do things independently, with AI helping you without involving family members or caregivers — all of that feels very transformative.”

As technology removes barriers, it also helps make room for the unique qualities neurodivergent professionals bring to their teams. For example, people with neurodivergence sometimes have a little extra empathy for and curiosity about others, Akers says, recognizing that they don’t necessarily know “what everybody’s bringing to the table.”

That curiosity draws Akers to set aside time every night to experiment with new tools and prompts, whether it’s exploring a competitor’s product, trying out a new Copilot feature or reading up on the latest advances in AI.

“I like to get my hands dirty, to actually physically try it and see what happens,” she says. “That’s how I stay up on top of it, just because it’s changing so fast.”

But it’s not only about keeping pace with technology; it’s about staying open to new ways of working and connecting. Akers credits her neurodivergence with making her more willing to lean into trial and error and with helping her appreciate the different perspectives her colleagues bring.

AI can serve as a sort of translator, not of language, but of ability.

Dr. Cornelia C. Walther
“When you’re neurodivergent, you have to always be figuring out little hacks,” she says. “You spend a lot of time learning from other people, like, ‘That worked for you, let me try it out.’ Collaborating, problem-solving, being creative, not being stuck on one way to do something, but being pretty open to trying things, and if they don’t work, just trying again with the next thing.”

It’s a blend of empathy, curiosity and adaptability that Akers sees as a leadership advantage — one that’s increasingly vital as AI tools reshape the workplace. By embracing experimentation and valuing difference, she’s not just finding ways to make her own work easier; she’s helping build a culture where everyone’s strengths have room to shine. It’s a commitment she carries into her role as co-executive sponsor of Microsoft’s Disability and Neurodiversity Inclusion Networks, groups dedicated to supporting and empowering employees across the company.

“There are so many positive things,” she says, “that come out of having a brain that thinks differently.”

Lead photo: Kim Akers, chief operations officer for Microsoft’s commercial business and co-executive sponsor of Microsoft’s Disability and Neurodiversity Inclusion Networks (photo by Scott Eklund)

The post How AI helps neurodivergent professionals showcase their strengths 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.

1. Preparing for the integration of AI and agents.
2. Strengthening training so that everyone has the necessary skills. 
3. Fostering a culture that prioritizes cybersecurity. 

Preparing for the integration of AI and intelligent agents

Preparing for AI and agent integration calls for careful strategy, thoughtful business planning, and organization-wide adoption under solid governance, security, and management. Microsoft’s AI adoption model offers a step-by-step guide for businesses embarking on this journey and the guide offers actionable insights and solutions to manage AI risks.

Strengthening training so that everyone has the necessary skills

Technology alone isn’t enough. People are your strongest defense—and the foundation of trust. That’s why skilling emerged as a central theme throughout these past months and will continue beyond. Frontier Firms—those structured around on-demand intelligence and powered by “hybrid” teams of humans plus agents—lead by fostering a culture of continuous learning. Our blog “Building human-centric security skills for AI” offers insights and guidance you can apply in your organization.  

• Lean into your unique human strengths: Your team’s judgment, creativity, and experience are irreplaceable. Take time to invest in upskilling and reskilling them, so they can confidently guide and manage AI tools responsibly and securely. Explore Microsoft Learn for Organizations for resources to support your learning journey.
• Stay curious and agile through continuous learning: Building security resilience is an ongoing process. Regularly refresh your AI and security training, offer time and resources for employees to explore new skills, and create a supportive, engaging environment that motivates continuous growth. Find in AI Skills Navigator, our agentic learning space, AI and security training tailored to different roles.  

Investing in skilling doesn’t just reduce risk—it accelerates innovation by giving teams the confidence to explore new AI capabilities securely. 

Skilling is an ongoing practice that needs to constantly evolve alongside the business and technology landscape. Staying ahead requires an enterprise-wide strategy that aligns ever-changing business priorities with always-on skill-building. 

—Jeana Jorgensen, Corporate Vice President, Microsoft Learning

Fostering a culture that prioritizes security

As AI impacts everyone’s role, make security awareness and responsible AI practices shared priorities. Encourage your team to weave security thinking into their daily routines—creating a safer environment for all. As Vasu Jakkal, Corporate Vice President of Microsoft Security highlighted in her blog “Cybersecurity Awareness Month: Security starts with you,” it is critical that security become part of your organization’s culture and norms. 

Check out our new e-book, Skilling for Secure AI: How Frontier Firms Lead the Way for practical steps for leaders to upskill their workforce in identity management, data governance, and responsible AI practices.

From awareness to action

In the agentic AI era, people continue to be our most valuable resource. It’s essential to empower them with AI and equip them with the skills they need to use AI responsibly and securely. Cybersecurity awareness should go beyond designated months or campaigns; true awareness means taking meaningful action.   

Here are three actions you can take today to maximize your AI investments: 

1. Share the Be Cybersmart Kit with your employees. It includes tips for protecting yourself from fraud and deepfakes, guidance on safe AI usage, and key security best practices.
2. Invest in people: Focus on upskilling initiatives that support your AI transformation, cloud modernization, and security-first strategies.
3. Champion a security-first culture: Ensure cybersecurity is integral to every business discussion and woven into your overall strategy. 

Microsoft guide for securing the AI-powered enterprise

Download the guide

The post From awareness to action: Building a security-first culture for the agentic AI era appeared first on The Microsoft Cloud Blog.

After several years of experimentation, 2026 is shaping up to be the year AI evolves from instrument to partner, transforming how we work, create and solve problems. Across industries, AI is moving beyond answering questions to collaborating with people and amplifying their expertise.

This transformation is visible everywhere. In medicine, AI is helping close gaps in care. In software development, it’s learning not just code but the context behind it. In scientific research, it’s becoming a true lab assistant. In quantum computing, new hybrid approaches are heralding breakthroughs once thought impossible.

As AI agents become digital colleagues and take on specific tasks at human direction, organizations are strengthening security to keep pace with new risks. The infrastructure powering these advances is also maturing, with smarter, more efficient systems.

These seven trends to watch in 2026 show what’s possible when people join forces with AI.

AI will amplify what people can achieve together

Aparna Chennapragada, Microsoft’s chief product officer for AI experiences, sees 2026 as a new era for alliances between technology and people. If recent years were about AI answering questions and reasoning through problems, the next wave will be about true collaboration, Chennapragada says.

“The future isn’t about replacing humans,” she says. “It’s about amplifying them.”

AI agents are set to become digital coworkers, she says, helping individuals and small teams punch above their weight. Chennapragada envisions a workplace where a three-person team can launch a global campaign in days, with AI handling data crunching, content generation and personalization while humans steer strategy and creativity. She predicts organizations that design for people to learn and work with AI “will get the best of both worlds,” helping teams tackle bigger creative challenges and deliver results faster.

Her advice for professionals: Don’t compete with AI, but focus on learning how to work alongside it. The coming year, she says, “belongs to those who elevate the human role, not eliminate it.”

AI agents will get new safeguards as they join the workforce

AI agents will proliferate in 2026 and play a bigger role in daily work, acting more like teammates than tools, says Vasu Jakkal, corporate vice president of Microsoft Security. As organizations rely on these agents to help with tasks and decision-making, building trust in them will be essential, Jakkal says — starting with security.

“Every agent should have similar security protections as humans,” she says, “to ensure agents don’t turn into ‘double agents’ carrying unchecked risk.”

That means giving each agent a clear identity, limiting what information and systems it can access, managing the data it creates and protecting it from attackers and threats, Jakkal says. Security will become ambient, autonomous and built-in, she says, not something added on later. In addition, as attackers use AI in new ways, defenders will use security agents to spot those threats and respond faster, she says.

“Trust is the currency of innovation,” Jakkal says, making these shifts vital to helping organizations keep up with new risks as AI continues to become more central to how work gets done.

AI is poised to shrink the world’s health gap

AI in healthcare is marking a turning point, says Dr. Dominic King, vice president of health at Microsoft AI.

“We’ll see evidence of AI moving beyond expertise in diagnostics and extending into areas like symptom triage and treatment planning,” King says. “Importantly, progress will start to move from research settings into the real world, with new generative AI products and services available to millions of consumers and patients.”

That shift matters because access to care is a global crisis. The World Health Organization projects a shortage of 11 million health workers by 2030 — a gap that leaves 4.5 billion people without essential health services.

King points to achievements demonstrated in 2025 by Microsoft AI’s Diagnostic Orchestrator (MAI-DxO), which solved complex medical cases with 85.5% accuracy, far above the 20% average for experienced physicians. With Copilot and Bing already answering more than 50 million health questions daily, he sees advances in AI as a way to give people more influence and control over their own health and wellbeing.

AI will become central to the research process

 AI is already speeding up breakthroughs in fields like climate modeling, molecular dynamics and materials design, says Peter Lee, president of Microsoft Research. But the next leap is coming. In 2026, AI won’t just summarize papers, answer questions and write reports — it will actively join the process of discovery in physics, chemistry and biology.

“AI will generate hypotheses, use tools and apps that control scientific experiments, and collaborate with both human and AI research colleagues,” Lee says.

This shift is creating a world where every research scientist soon could have an AI lab assistant that can suggest new experiments and even run parts of them. That’s the logical next step, Lee says, building on how AI works alongside developers with “pair programming,” for example, and uses apps to automate everyday tasks like shopping and scheduling in other domains.

It’s a transformation that promises to accelerate research and change how scientific discoveries are made, he says.

AI infrastructure will get smarter and more efficient

 AI’s growth isn’t just about building more and bigger datacenters anymore, says Mark Russinovich, chief technology officer, deputy chief information security officer and technical fellow for Microsoft Azure. The next wave is about making every ounce of computing power count.

“The most effective AI infrastructure will pack computing power more densely across distributed networks,” Russinovich says. Next year will see the rise of flexible, global AI systems — a new generation of linked AI “superfactories” — that will drive down costs and improve efficiency, he says.

AI will be “measured by the quality of intelligence it produces, not just its sheer size,” he says.

Think of it like air traffic control for AI workloads: Computing power will be packed more densely and routed dynamically so nothing sits idle. If one job slows, another moves in instantly — ensuring every cycle and watt is put to work. This shift will translate into smarter, more sustainable and more adaptable infrastructure to power AI innovations on a global scale, Russinovich says.

AI is learning the language of code — and the context behind it

Software development is exploding, with activity on GitHub reaching new levels in 2025. Each month, developers merged 43 million pull requests — a 23% increase from the prior year in one of the main ways teams propose and review changes to their code. The annual number of commits pushed, which track those changes, jumped 25% year-over-year to 1 billion. The unprecedented pace signals a major shift in the industry as AI becomes increasingly central to how software is built and improved.

Mario Rodriguez, GitHub’s chief product officer, says that sheer volume is why 2026 will bring a new edge: “repository intelligence.” In plain terms, it means AI that understands not just lines of code but the relationships and history behind them.

By analyzing patterns in code repositories — the central hubs where teams store and organize everything they build — AI can figure out what changed, why and how pieces fit together. That context helps it make smarter suggestions, catch errors earlier and even automate routine fixes. The result will be higher quality software that helps developers move faster, Rodriguez says.

“It’s clear we’re at an inflection point,” he says. Repository intelligence “will become a competitive advantage by providing the structure and context for smarter, more reliable AI.”

The next leap in computing is closer than most people think

Quantum computing has long felt like science fiction. But researchers are entering a “years, not decades” era where quantum machines will start tackling problems classical computers can’t, says Jason Zander, executive vice president of Microsoft Discovery and Quantum. That looming breakthrough, called quantum advantage, could help solve society’s toughest challenges, Zander says.

What’s different now is the rise of hybrid computing, where quantum works alongside AI and supercomputers. AI finds patterns in data. Supercomputers run massive simulations. And quantum adds a new layer that will drive far greater accuracy for modeling molecules and materials, he says. This progress coincides with advances in logical qubits, which are physical quantum bits grouped together so they can detect and correct errors and compute — a critical step toward reliability.

Microsoft’s Majorana 1 marks a major development toward more robust quantum systems, Zander says. It’s the first quantum chip built using topological qubits, a design that inherently makes fragile qubits more stable and reliable. It’s also the only quantum solution engineered to catch and correct errors. That architecture paves the way for machines with millions of qubits on a single chip, providing the processing power needed for complex scientific and industrial problems.

“Quantum advantage will drive breakthroughs in materials, medicine and more,” Zander says. “The future of AI and science won’t just be faster, it will be fundamentally redefined.”

The post What’s next in AI: 7 trends to watch in 2026 appeared first on The Microsoft Cloud Blog.

Across industries, organizations are racing to harness the power of AI. The potential in intelligent automation alone motivated a $252.3 billion corporate AI investment in 2024,¹ and those investments have been evolving almost as quickly as this rapidly changing technology itself.

While longer-standing AI technologies like machine learning and chatbots continue to perform well, agentic AI has moved to the front of the pack, offering the kind of autonomous decision-making that companies crave. Early wins with generative AI—drafting emails, summarizing documents, automating routine tasks—have shown what’s possible when a single intelligent agent is put to work.

Microsoft defines agentic AI as the pairing of traditional software strengths—such as workflows, state, and tool use—with the adaptive reasoning capabilities of large language models (LLMs). This allows agents to understand intent, take action, and interact with other systems dynamically, moving beyond the limits of rule-based automation.

As organizations look to scale AI across more of their operations, many are finding that a single agent can’t always manage complex, multi-step tasks. This is where multi-agentic systems become valuable. 

Multi-agentic systems use a series of agents, with a single coordinating agent, to work as a sort of AI team. The coordinating agent works to understand complex queries and delegate workflows to other agents, making multi-step, multi-system queries possible. In collaboration with people who are essential for escalation, understanding significant ambiguity, and creative thinking, multi-agentic systems are becoming integral to digital-first workforces.

Because these AI teams often operate across different tools and systems, organizations need solutions that are secure and enterprise-ready. With Microsoft technologies, agentic systems are built with the security, compliance, and reliability businesses expect. For a deeper dive on multi-agentic AI, read “Designing Multi-Agent Intelligence” on Microsoft Dev Blogs.

Today, Microsoft customers are already seeing the impact of multi-agentic AI. Here are three stories out of the Microsoft AI Co-Innovation Lab in San Francisco that show how multi-agentic AI is transforming security, science, and retail.

Three real-world examples of multi-agentic AI transformation

1. Contraforce: Turning the tide in cybersecurity

In cybersecurity, every second matters. For managed service providers (MSPs), responding to threats quickly can mean the difference between business as usual and a major incident. Contraforce, a Microsoft partner, set out to change the game with a multi-agentic security delivery platform built on Microsoft Foundry.

The multi-agentic solution automates 90% of incident investigations and response tasks, working as an always-on security operations team to analyze security data, identify suspicious activities, and autonomously managing incidents. These autonomous AI agents don’t just automate tasks—they help create a new cyber defense workforce.

The results are striking:

• Incident response times plummeted from 30 minutes to just 30 seconds.
• The cost per incident dropped from $15 to less than $1.
• MSPs can now scale their services without scaling their teams.

Contraforce’s story is a testament to how agentic AI can transform security operations from reactive to proactive, delivering speed, scale, and cost-efficiency.

3. Stemtology: Accelerating discovery in health sciences

Medical innovations can move slowly, and sometimes for good reason. But in regenerative medicine, lengthy research cycles and complex data analysis can be optimized with AI intervention.

Regenerative medicine innovator Stemtology worked with the Microsoft AI Co-Innovation Lab to accelerate biomedical discovery using a multi-agentic platform.

By combining Azure Cognitive Search, GPT-based agents, and domain-specific knowledge graphs, Stemtology’s system allows agents to:

• Parse scientific literature
• Generate therapeutic hypotheses
• Design and evaluate experiments

The impact? Research timelines have been cut by up to 50% at Stemtology. Minimum viable products are delivered in weeks instead of months. And the path from idea to patient-ready therapy is shorter than ever. This has freed up researchers to focus on highly complex evaluation and design strategies for treatments, rather than spending hours on gathering and synthesizing research.

Stemtology’s journey shows how agentic AI can support critical human discovery and bring life-saving treatments closer to reality.

3. SolidCommerce: Personalizing customer engagement at scale

For retailers, delivering personalized experiences while managing vast product catalogs and backend operations is a constant challenge. SolidCommerce specializes in providing AI solutions that address these challenges in the retail industry.

Hoping to address time-consuming support processes, inconsistent customer communications, and operational inefficiencies handling customer support, they approached the Microsoft AI Co-Innovation Lab in San Francisco to create an AI agent that could automate accurate and brand-aligned responses to meet customer needs.

Their solution brings together multiple agents for customer triage, FAQ handling, account management, product recommendations, and compliance checks. Built on Microsoft’s Agentic AI framework and integrated with Microsoft Copilot Studio and Foundry Agent Service, the system is easy to deploy and scale.

The payoff:

• Richer, multimodal customer experiences
• Scalable automation across channels
• Real-time personalization with memory and context

SolidCommerce’s story demonstrates how multi-agentic AI can turn retail complexity into seamless, intelligent engagement, ensuring customer satisfaction to keep pace with technological change.

Learn more about the agentic advantage

Microsoft customers are realizing benefits every day across industries. As we’ve seen in the customer examples above, multi-agentic AI delivers speed and scale in operations, accelerates innovation in research and development, and enables personalized engagement at scale.

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Multi-agentic AI isn’t just a technical upgrade—it’s a strategic shift. And companies that harness these systems to transform legacy processes can benefit not only from automation, but from truly intelligent optimization.

Learn how other customers are transforming with AI and explore creating your own generative AI proof of concept at Microsoft AI Co-Innovation Labs.

¹ The 2025 AI Index Report, Stanford HAI.

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