These questions are especially important in Europe and around the world. Organizations want access to leading AI capabilities while maintaining confidence in how sensitive data, security, infrastructure, and operations are governed. Microsoft’s goal is to enable organizations to adopt AI while helping them maintain the level of control each workload requires to give leaders the confidence to move forward with AI.

Microsoft defines sovereign AI as the design, deployment, and operation of AI workloads under defined controls for data, access, governance, infrastructure, and operations. The right controls depend on the workload, which is why our framework is built around four principles: control, choice, flexibility, and resilience.

This framework is the focus of a new white paper we developed in collaboration with NVIDIA. It helps leaders translate the four principles into practical decisions; from the level of control each workload needs to the operating model that best supports it.

Begin with the workload

We see digital sovereignty as a risk-management principle. Start with the workload and ask: What data will it use or generate, and where can that data be processed? Who needs access to the system, including administrative access? What must keep working if connectivity is interrupted? And how easily must the organization be able to change models or infrastructure as its needs evolve? Consider these questions alongside the workload’s mission, regulatory obligations, and threat profile.

For many workloads, those requirements can be met in the public cloud. Others need greater operational control or infrastructure that remains under the organization’s authority. Some need to operate with limited or no connectivity. Microsoft Sovereign Cloud gives organizations options across the spectrum. Sovereign Public Cloud and Sovereign Private Cloud support different operating models based on the level of control a workload demands.

Sovereignty at every layer

Data location still matters, but an AI workload goes beyond stored data. It can include prompts, models, agents, outputs, and the systems that operate them. Leaders need visibility into which models are being used, the power to switch models if the need arises, where they run, who can administer them, and what happens when connectivity or operating conditions change.

Sovereignty can’t be applied at just one layer. Organizations need control over what an AI system can access and do, which models it can use, and where data and processing can occur. Microsoft provides capabilities across identity, governance, encryption, monitoring, confidential computing, and operational control to help organizations apply and maintain workload-specific controls. Building those requirements into the architecture from the outset, while preserving flexibility in model choice, can make it easier to adapt as workloads, technologies, and regulatory expectations evolve.

Microsoft and NVIDIA bring complementary capabilities across the AI stack. NVIDIA accelerated computing provides the performance required for advanced AI workloads, while NVIDIA Confidential Computing helps protect sensitive data, models, and AI workloads during processing. Together, Microsoft and NVIDIA provide security and control across the AI stack, from the underlying cloud infrastructure to the applications running on top of it.

That shared approach also informed the development of the Microsoft Sovereign AI white paper, created with contributions from NVIDIA to provide organizations with a practical framework for evaluating control, choice, flexibility, and resilience across their AI environments.

Learn more about Sovereign AI

Explore how Microsoft helps governments and regulated industries adopt advanced AI ​with NVIDIA technologies.

Download the Sovereign AI white paper

Accelerating innovation with model and infrastructure choice

Model choice should start with the flexibility to use the best models available and adopt new models as capabilities, quality, availability, and access evolve, without having to redesign the underlying AI platform. AI is moving too quickly to assume that one model will remain the right answer for every workload. Decoupling the platform from the model gives organizations the freedom to evaluate and change models as technology advances and requirements evolve.

Microsoft Foundry and Foundry Local support development and deployment across a broad model ecosystem, so organizations can evaluate models against the requirements of the workload while maintaining consistent approaches to governance and operations. NVIDIA extends that choice across models, AI software, and accelerated infrastructure. NVIDIA AI Enterprise provides software, frameworks, and inference capabilities for production AI. NVIDIA’s model ecosystem, including Nemotron and physical AI models, gives organizations additional options for building and deploying AI. NVIDIA RTX PRO infrastructure extends that choice to the compute layer, supporting demanding inference, agentic, and reasoning workloads.

AI workloads will continue to become more capable and more distributed. Organizations need accelerated computing, software, and model choice that can follow those workloads across cloud, data center, and edge environments. Our work with Microsoft is focused on giving them that foundation with security and control built in.

Dave Salvator, Director, Accelerated Computing Product Marketing, NVIDIA

Model choice is only part of the equation. Organizations also need flexibility in the infrastructure, software, and environments that support those models. An organization may decide to adopt a different model, move processing closer to its data, or change where a workload runs as its requirements evolve.

Scaling from cloud to edge

Where AI runs is becoming as important as the models an organization chooses. For many workloads, the public cloud will remain the right environment, with access to scale and advanced AI services. Others need AI closer to sensitive data or physical operations. An industrial system may need low-latency inference on site. A government or critical infrastructure workload may need to continue operating during a network disruption.

Organizations should not have to reinvent their AI operating model for every environment. Sovereign Private Cloud, built on Azure Local and Foundry Local, extends AI capabilities into environments that remain under the organization’s authority, including connected, intermittently connected, and disconnected deployments. NVIDIA accelerated computing and AI software stack provides a common foundation across these deployment models. With NVIDIA infrastructure and AI stack available across Azure and Azure Local, organizations can bring AI training, inference and increasingly demanding agentic, industrial, and physical AI workloads closer to the data and operations they depend on.

The real test of deployment flexibility is when conditions change. Identify which functions must continue during a connectivity disruption, which can pause, and what data or services each depends on. Then test those assumptions under the conditions the workload may actually face. As AI expands into agentic, industrial, and physical systems, more critical decisions will happen close to data, equipment, and users. The infrastructure needs to follow.

Building for inevitable change

No one can predict exactly what AI requirements will look like five years from now. Models will improve, infrastructure will change, and regulations won’t remain static. New workloads will create requirements that are difficult to anticipate today. IT architecture choices should account for this.

Organizations benefit from a consistent foundation that allows workloads to move across sovereign environments while maintaining common approaches to identity, security, governance, and AI development.

Durable AI strategies will be built for change. Organizations need enough control to use AI confidently, paired with enough choice to keep adopting better technology, and an architecture that can adapt when requirements change.

Learn more about digital sovereignty at Microsoft

• Read the Microsoft Sovereign AI white paper to explore a practical framework for building AI around control, choice, flexibility, and resilience.
• Explore Microsoft Sovereign Cloud resources for your region.
• Read the Microsoft Sovereign Cloud in Europe white paper.
• Watch the Sovereignty Sessions webinar.
• Read why sovereign AI starts with sovereign data, and how Sovereign Private Cloud puts AI to work on sensitive data without giving up control from Douglas Phillips.

Learn more about Sovereign AI

Explore how Microsoft helps governments and regulated industries adopt advanced AI ​with NVIDIA technologies.

Download the Sovereign AI white paper

The post Sovereign AI: Accelerate innovation with control and choice appeared first on The Microsoft Cloud Blog.

AI has moved quickly from possibility to priority, but many leaders are now confronting a more practical question: how ready is the organization to turn isolated wins into repeatable, governed progress? Early pilots and productivity gains matter, but they do not automatically create the leadership alignment, data foundations, governance practices, workforce readiness, or operating discipline required to scale AI across the business.

The AI Strategy Roadmap offers a practical guide for advancing Frontier Transformation. It helps leaders understand where they are today, identify the capabilities they may need next, and strengthen the conditions that can support AI as a durable business capability.

What the AI Strategy Roadmap is

The AI Strategy Roadmap translates insights and patterns observed among organizations advancing Frontier Transformation into practical strategies and actions leaders can consider. Its recommendations are informed by qualitative research conducted by Emerald Research Group on behalf of Microsoft, including in-depth interviews with 70 business and IT decision-makers actively leading AI transformations, as well as Microsoft’s own internal AI transformation experience and the work we’ve done supporting customer transformation efforts across industries.

The Roadmap reflects challenges described by leaders in transformation efforts and provides practical, experience-informed guidance for navigating them.

Why it matters now

This roadmap goes beyond the basic question of where AI can create value. It addresses the organizational conditions that can help teams move from isolated initiatives toward more repeatable, governed progress.

The research surfaced a consistent pattern among the organizations interviewed. Leaders described AI transformation as business transformation that occurs—and improves—over time. Organizations working through the shift often have strong ideas, but can get stuck controlling scope, aligning leaders, managing complexity, and building the workforce capability required to make change happen.

“We didn’t start with a big, fully baked AI strategy. It was much more about identifying a single area where we could realistically make a difference and testing it there first. Once we had something working and could show tangible impact, that’s what helped us expand and bring others along.”

Senior VP of Growth Marketing, Financial Technology

The five drivers of AI value

Every organization is somewhere on the AI readiness journey. Across the research and customer experience reflected in the Roadmap, five core drivers emerged as useful lenses for assessing readiness, prioritizing investment, and planning practical next steps.

1. Business strategy. Align AI investments to measurable business priorities and prioritize use cases that can earn sponsorship, funding, and momentum.
2. Technology and data strategy. Build trusted, governed, AI-ready data foundations and infrastructure designed to support growth, so AI initiatives can become more repeatable over time.
3. AI strategy and experience. Build trust through visible learning, experimentation, and repeatable AI delivery practices that can support user confidence, leadership engagement, and sustained adoption.
4. Organization and culture. Develop AI literacy, change management, leadership alignment, and operating-model practices that can help teams adopt AI more consistently.
5. AI governance and security. Embed responsible AI, governance, security, and compliance from the start, with transparency and human oversight that can help build trust as AI initiatives expand.

Developed in parallel, these drivers can help teams assess readiness, manage risk, and plan the next stage of AI transformation. Each one maps to a maturity model—Exploring, Planning, Implementing, Scaling, and Realizing—so leaders can assess where they are today and identify where to invest next.

Key research highlights

• Culture outweighs code. According to the Microsoft 2026 Work Trend Index, organizational factors drive 67% of the realized value from AI—twice the impact of individual behavior. For many organizations, culture can be a significant barrier to scaling AI initiatives.
• Leadership alignment shows up in the results. In the AI Strategy Roadmap research, more than 70% of organizations with advanced strategy and execution readiness say leadership communicates an AI vision clearly tied to business strategy.
• Direction beats enthusiasm. In the AI Strategy Roadmap research, among organizations that struggled with execution complexity, 15% named a lack of executive buy-in and strategic alignment as a barrier—a reminder that ideas without sponsorship can fragment.

Wherever you are on the journey

Frontier Transformation is a journey, not a destination—and no two organizations start in the same place. By pairing the five readiness drivers with a clear maturity model, the guide helps leaders locate their organization today, identify roadblocks, and focus on practical next steps for moving from experimentation toward more repeatable systems for delivering value at scale.

Start where you are

The next phase of AI transformation will require organizations to understand where they are ready, where they may be exposed, and what capabilities to build next. The AI Strategy Roadmap gives leaders a practical way to assess their current maturity, align teams around the next stage of progress, and strengthen the trusted foundations that can support AI initiatives as they scale.

Download the AI Strategy Roadmap

Discover the key actions leading organizations are taking to scale AI initiatives and advance their journey toward frontier leadership.

Download the roadmap

The AI Strategy Roadmap draws on qualitative research conducted by Emerald Research Group on behalf of Microsoft—70 in-depth interviews with IT leaders and business decision-makers across industries, conducted from February through March 2026—alongside Microsoft’s own Frontier Transformation experience.

The post The AI Strategy Roadmap: Five drivers of successful AI transformation appeared first on The Microsoft Cloud Blog.

The more time I spend working with AI, the more convinced I become that what matters most for her future isn’t how quickly she can answer. It’s whether she has the judgment to know when an answer can be trusted.

I’ve spent decades at Microsoft watching this tension play out: first building tools for other developers, then working across AI as models moved from research curiosities to systems deployed at scale. Now we’re building Microsoft IQ, where we’re exploring how an organization’s collective intelligence can become its greatest advantage. Through every one of those chapters, one thing has remained true: it’s never enough for a system to be powerful; it must also be trustworthy.

Trust is what turns assistance into delegation. When we can trust an agent to do what we intend, within the limits we set, we can hand off the work we never wanted to spend our lives on: the repetitive tasks that drain attention, the mundane work that fills a day without moving anything meaningful forward, the dangerous work humans should not have to do, the work too vast for any individual or team. Agents should take on that toil, extend our reach, and give us back our time for the work that calls for something only humans bring.

My daughter doesn’t know any of this yet. But by the time she’s grown, most of the work that rewards speed and repetition will be work we delegate. What will matter then is exactly what gave her teachers pause: the patience to stay with a hard problem, reason through it, and decide when she’s reached a conclusion she can trust. The very thing they feared might hold her back could be exactly what the next era prizes most.

So no, I’m not worried about the timed test. I hope she grows up in a world where software carries the toil and people are freed for the work that is unmistakably ours—to think, to judge, to create, to care for one another. That is the future I want agents to make real. But my hope is not evidence it will happen. The future I just described depends on a single question: can we trust agents to do the work? Trust is earned one task at a time. So, I went looking for evidence of where it’s been earned, and where it hasn’t.

We partnered with MIT Technology Review Insights on new research that draws directly from the technical leaders building this frontier: not the people talking about it, but the people doing it. We surveyed 300 technical experts across AI, data, and cloud domains, spanning 12 industries and 4 regions of the world, asking them to rank their confidence across 101 of the top tasks. What we got back is the 2026 Agent Confidence Index, an honest map of where agents are delivering real value, so our community can see what’s working and move forward together with conviction.

Learn from where confidence is highest

Across the 101 tasks measured, average confidence already lands at 64 out of 100, and thirty tasks clear 70. The highest scores cluster on work that is both predictable and draining: the late nights, the interruptions, the low-value repetition. Automated report generation leads at 83.5. Boilerplate code generation for new features sits at 82.5—the hours a developer no longer spends rewriting the same patterns, freed for the work that challenges them. Certificate expiration monitoring and renewal, at 81.5, ends the scramble that pulls engineers off high-stakes problems for something entirely routine. Real-time data stream monitoring follows at 80.5, and release note generation from commit history at 79.5—the manual end-of-sprint commit review, gone. This is where frontier teams are already delegating to agents, regularly.

The pattern holds across every discipline. In developer and AI workflows it extends to API client maintenance and code identification; in cloud operations, to ticket routing and cost optimization; in data, to anomaly detection. Wherever it sits in the stack, this is work technical teams now trust agents to own.

What matters most here isn’t what the data says about the tasks; it’s what it says about the people delegating them. When technical experts believe in something deeply enough to hand it real work, that belief ripples outward. It becomes the recommendation they make to their leadership, the solution they build for their customers, and the culture they create for their teams.

Even the toughest agent tasks are gaining traction

Here’s what strikes me most: the tasks ranked lower on the index are still high in absolute terms. Service mesh configuration and troubleshooting sits at 37.5, database schema migration scripting at 46.5, memory leak detection at 48.5. These sit at the very frontier, the interconnected, high-stakes work where investment and innovation are concentrated right now.

Consider what they demand. Service mesh configuration touches many systems at once. Database migration carries real stakes, requiring precision across data, application, and infrastructure layers at the same time. Memory leak detection means diving deep into a system’s behavior under load, accounting for conditions that shift from one deployment to the next. These are the challenges that have separated great engineers from exceptional ones—and even here, experts see agents helping. Not carrying the work alone, but contributing where it used to be unthinkable. That confidence is still climbing, and that’s telling.

We’re shipping new capabilities constantly to support this momentum. Database migration tooling in GitHub Copilot now covers not just scripts but the full application and infrastructure migration story. The Azure Site Reliability Engineering (SRE) Agent brings decades of experience operating Azure at scale and deep profiling capabilities directly into memory analysis and performance diagnosis.

Why human judgment remains paramount

When we asked technical experts how they’re navigating agent adoption, 59% named “keeping humans in the loop” as their top priority—ahead of better observability, ahead of governance documentation, and ahead of everything else. That’s a mark of maturity. Teams moving forward with clarity treat agent oversight as non-negotiable, regardless of how capabilities evolve.

The boundary itself is straightforward. Agents excel at well-specified, high-volume, reversible work: they synthesize data, automate known workflows, and surface anomalies at a speed and scale no human team could match. The moment a decision becomes high-stakes, context-dependent, or hard to undo, a human signs off. That isn’t a limitation of the technology; it’s the architecture of a trustworthy system.

What’s changing, and what remains underappreciated, is the skill it takes to draw that boundary well: the discipline of full-lifecycle evaluations and guardrails. Success means measuring agent output against intent and keeping behavior inside your business strategy. It’s new territory for most engineering teams, and it’s becoming table stakes for modern software faster than most organizations realize. The good news: the same tools generating the work can help you build the harness. Ask GitHub Copilot to write the evals and it will. Frontier teams are already doing this, and it’s why they’re pulling ahead.

Agents are opening career doors for engineering

Across system reliability and site operations, evaluations and quality assurance, and data pipeline management, 80% or more of respondents see meaningful career opportunity ahead. We believe this is one of the most significant moments in the history of building software, not because agents replace what technical people do, but because what’s left when they take on the toil is the work that defines a career: the judgment calls, the architectural vision, the reasoning to navigate complexity under pressure. That fluency will define the next generation of technical leadership.

We’re living this shift at Microsoft, right alongside our customers. Junior developers are using agents to explore codebases on their own and arriving at mentoring conversations with sharper, more sophisticated questions. Senior engineers are covering more ground because the repetitive work that used to fill their days is now delegated, and the work that’s left is harder, interesting, and consequential. Both are growing into more capable versions of themselves. For me, that’s the outcome I’ve always believed technology could deliver.

An integrated approach to intelligence and trust

Designing more sophisticated agent systems has made one thing clear: agents thrive in well-integrated environments, working best when your whole stack draws on a single source of truth. The high-confidence tasks are the ones we’ve already figured out; the meaningful frontier is the harder, interconnected work, and that’s exactly where observability, governance, security, and unified intelligence have to operate as one.

Microsoft IQ brings your enterprise context into a single, continuous intelligence layer. Within it, Work IQ builds semantic understanding of how your business operates across email, calendar, meetings, chats, files, people, and collaboration patterns. Such depth of knowledge is the reason technical teams choose us, and it’s what drives my focus and passion in learning how people actually work so their agents get them. My colleague Kim Manis, CVP of Product for Microsoft Fabric, has written specifically about what this means for data professionals, and the integral role of Fabric IQ.

It’s all part of the Microsoft Agent Platform, which is becoming the operating system for enterprise AI at scale. From building in GitHub and contextualizing with Microsoft IQ, to running in Microsoft Foundry and governing in Microsoft Agent 365, Microsoft is uniquely positioned to help customers bring together data, models, agents, and human judgment into a continuously improving and secure system.

Frontier transformation is being led by builders like you.

Next steps:

• Download The 2026 Agent Confidence Index from our partners at MIT Technology Review Insights. It is a free, ungated deep dive into all 101 tasks, broken out by role and workflow, with the patterns and reasoning behind where confidence is strongest and the frontier is expanding.

• Join us at the AI Engineering World’s Fair (June 29-July 2) where our very own Pablo Castro will keynote, and our teams will offer 16 breakout sessions and 4 labs. Swing by the Microsoft booth as well to explore an interactive 3D visualization of the Index data. We want to hear what’s working for you.

• Learn more about Microsoft IQ and how it connects across Work IQ, Fabric IQ, Foundry IQ, and the newly announced Web IQ. You can catch up on all the developer innovation from Microsoft Build through Satya Nadella’s keynote, Kyle Daigle’s blog post, and the Microsoft Build CLI.

What’s Working in Agentic AI

The 2026 Agent Confidence Index report reveals where agents are trusted, the challenges they face, and what leaders should do next

Download the 2026 Agent Confidence Index

The post The 2026 Agent Confidence Index: Where 300 builders see real momentum appeared first on The Microsoft Cloud Blog.

Our recent research study by Microsoft AI for Good Lab, Microsoft Sustainability, and Azure, published in the peer-reviewed energy journal Joule, answers this question. For organizations evaluating AI adoption, understanding per‑user energy and water impact is essential for scaling responsibly. When a user sends a text request (“a query”) to a large language model (LLM), like the AI models powering Microsoft Copilot, the system reads the input and then generates a response one piece at a time. Each piece is called a “token,” roughly equivalent to three-quarters of a word. This process, known as “inference,” runs on specialized hardware inside datacenters.

The energy used per query depends on how many tokens are read and generated, how fast the hardware processes them, how large and resource-consuming the LLM is, and how efficiently the whole system is managed.

The key finding of this study: AI at scale is significantly more efficient than previously reported in literature and media. The analysis, focused on serving AI at large scale, finds that a typical AI query to some of the largest and most capable LLMs uses between 0.16 and 0.60 watt-hours of electricity, depending on the length of the query, the LLM used, and datacenter specifications. This is equivalent to the amount of electricity used by a PC (~40 W1) for 15 to 60 seconds or running a home microwave oven (1000 W2) for 0.6 to 2 seconds. That is 4 to 20 times less energy than previous measurements, as described in the study, mainly because those past reports didn’t account for how efficient large-scale AI systems are.

Understanding energy per query also allows us to estimate the amount of cooling water consumed by a typical query. For large production models under conservative assumptions, we estimate that a typical query uses in the range of 0.0 to 0.067 mL of water, with a median water use equivalent to about one-hundredth of a teaspoon or less than a single drop. As datacenter designs continue to evolve, including our rollout of zero water datacenter designs, this amount of water is expected to decrease further.

Bigger systems unlock greater efficiency

Our analysis considered the efficiency of AI inference at scale: usually the bigger an LLM serving system is, the more efficient it becomes for each individual query or user. Think of it as a major airline versus a small regional carrier. A small airline running just a few flights can’t do much if a plane is half-empty—that’s just wasting fuel or underutilizing aircraft. But a large airline running thousands of flights every day can constantly adjust, fill up planes, reroute aircraft, and apply fuel-saving techniques across every single flight at once.

AI works the same way. When billions of queries are served by a hyperscaler such as Microsoft Azure, thousands of requests can be processed at the same time, multiple efficiency optimization techniques can be applied at various stages of the AI inference process, and trade-offs can be made to reduce the resource consumption of the whole system or product without compromising user experience or response quality. Usually, the bigger the system, the more efficiency improvement compounds.

At a billion queries a day, efficiency cuts energy use in half

Leading AI products already serve in the order of a few billion queries every single day. The analysis in the study shows that serving one billion queries, assuming those are conversational queries with a few hundred tokens per interaction, takes about 0.7 gigawatt-hours (GWh) of electricity at baseline, roughly comparable to about 0.4% of the energy US households use watching TV each day. But when smart efficiency improvements are applied, that number drops by more than half, to about 0.3 GWh.

Even with 10% of queries consisting of longer, more complex tasks that consume more than ten times the tokens—such as code generation or multi-step reasoning—our study showed that efficiency improvements still cut total energy use by more than half relative to the baseline, effectively mitigating overall consumption.

Microsoft is actively investing in multiple efficiency levers

Efficiency at scale doesn’t happen on its own. It takes deliberate research and development and investment. The study estimates the impact of three main categories of efficiency improvements:

• Optimized models and the right model for a task. Carefully designed and specialized models, such as Microsoft’s Fara-7B and Phi models, can match the performance of much larger ones at a small fraction of energy and cost. In the same way, intelligent model routing, such as Microsoft’s Model Router in Azure AI Foundry, is designed to automatically direct simple questions to lightweight models and reserves large models for complex tasks. Similar model improvements, as described under the modeling assumptions in the study, can lead to 5 to 10x reductions in energy use in the near term.
• Smarter AI serving. Beyond models, queries must be orchestrated in a datacenter to maximize efficiency while providing a great customer experience. Techniques such as disaggregated serving or adapting serving being implemented by Microsoft can reduce energy use substantially. For long queries generating thousands of tokens, these serving optimizations in general are especially impactful, with estimated efficiency gains in the study leading to up to 5x reductions in energy use.
• Better hardware. Next-generation chips deliver substantially more computation per watt. Together with datacenter-level energy use improvements, the study estimates that advances in GPU hardware offer over at least 1.5x to 2.5x energy reduction per query. And custom AI chips built for inference, such as Microsoft’s Maia 200, can provide even larger efficiency gains.

These improvements build on each other. In the study, we estimate that these efficiency gains, many currently being implemented or scaled up, have a combined near-term reduction of energy per query of 8 to 20x. An efficiency gain made in one area becomes the new starting point for everything that runs on the platform going forward.

Scaling AI responsibly

AI is becoming something that billions of people rely on every day—to learn, to work, and to create. As that happens, it is important that we make sure growing access to AI doesn’t mean growing pressure on local energy grids or on water supplies.

This research shows that scaling AI does not require proportional increases in energy or water use. With the right engineering and investment decisions, organizations can grow AI adoption while improving efficiency. Microsoft remains committed to making that possible—combining advancing capability with infrastructure.

Learn about Microsoft’s sustainability efforts

• Microsoft combines commitments, strategic partnerships, and the transformative power of AI to accelerate progress in sustainability.
• Explore the steps Microsoft is taking toward zero waste and building a more sustainable future.

The post Scaling AI with 8 to 20x energy efficiency appeared first on The Microsoft Cloud Blog.

Instead of chasing “AI everywhere,” newsroom teams at USA TODAY focused on a practical goal: using AI with intentionality to remove friction from one of investigative reporting’s most essential workflows — public records requests — without compromising editorial integrity.

The bottleneck hiding in plain sight: records requests

Public records requests (FOIA at the federal level, plus state records requests) are a cornerstone of investigative journalism. But they’re also easy to delay when the day is already full.

As Thomas Elia, a newsroom leader at The Palm Beach Post, put it: these requests “take time to compose,” must be “carefully written,” and routed to the right agency.

Drafting a request could mean “spending an hour drafting out a legal letter”. That’s time that could have gone to reporting, interviews, and the human details that make a story stick.

The shift: a “virtual colleague” inside the tools journalists already use

The team didn’t reinvent the newsroom. They built an assistive experience that fits where journalists already work—Teams and Outlook—so there’s no tool-switch tax.

How it works (in plain terms)

At a high level, the agent supports a workflow journalists already know — while compressing the slow parts:

• A journalist starts with the story question that needs proof
• The agent helps shape it into a usable request
• It helps route it correctly
• The journalist reviews, edits, and sends — accountability stays human

That’s the point: AI handles the mechanics. Journalists keep the judgment.

How they built it: familiar on the surface, deliberate underneath

On the USA TODAY Network side, Stephen Harding, Senior Product Manager, highlighted Microsoft 365 Copilot’s advantage: integration with the Microsoft tools teams already rely on — and the ability to point the agent to internal knowledge sources (including sensitive files in SharePoint and OneDrive).

On the Newsquest side, Calum Banister, AI Agent Orchestrator, described how integrations and flexible knowledge sources helped them “break apart a workflow to its base components,” and — coming from a low-code background — pick it up quickly.

The litmus test: front pages — and what happens after the headline

For Jody Doherty-Cove, Head of AI at Newsquest, usefulness is the only metric that matters: a tool is only real if it works. And in local journalism, front pages are a litmus test — an editor’s decision that this is the story the community needs most.

He’s seen “5–6 front page stories” come from requests enabled by the agent.

But the request is just the start. The reporting that follows: the calls, the interviews, the accountability, the human consequence, that’s what turns information into meaning.

The guardrail that matters most: accountability stays human

If you’re building with AI, you don’t just need capability, you need confidence. USA TODAY’s teams were clear on the boundary: “AI is a tool. It’s not in charge… We’re still in control.”

Stephen described the principle behind responsible use: a human-in-the-loop workflow where AI assistance is reviewed — and accountability stays with the human whose name is attached to the work.

What every business can take from this

If you’re wondering what “AI done right” looks like in your world, start here:

• Begin with a human problem. Find the tasks that drain time and dull momentum.
• Build in the flow of work. Familiar tools reduce friction — and friction decides adoption.
• Keep people in charge. AI can speed the process; people own the judgment.
• Make trust visible. Human review, clear guardrails, and accountability that doesn’t blur.

The best AI stories aren’t about models. They’re about momentum: what people can do when friction disappears. For USA TODAY, that means more time reporting in communities — following leads and telling the stories that matter.

Want to take the next step? Start here:

• Get inspired: See what great “AI in the flow of work” stories look like
• Start practical: A simple on-ramp to identify one high-value workflow to improve
• Go deeper: More real-world examples of Copilot + agents in action

The post USA TODAY brings AI into real newsroom workflows appeared first on The Microsoft Cloud Blog.

The answer isn’t more tools. It’s what people are enabled to do with them.

The friction we see is that many people are unsure how to use AI to their greatest benefit. Companies often struggle to measure the impact of their AI investments because they likely haven’t yet demonstrated return on investment for their employees.

Progress comes when employees actively adopt AI and see meaningful impact on their work—when they’re confident about questioning outputs, applying judgment, and integrating it into their real work.

But there’s another layer to that friction.

Alongside the industry’s excitement and expectations, there’s real hesitation. AI still feels uncertain: Where do I start? Am I already behind? What if I get this wrong?

That hesitation is a signal that access alone isn’t enough; people need to feel confident that AI will elevate their work, not detract from it, or worse, make them irrelevant.

You aren’t behind; you just need to get started. And you do that by learning one new skill at a time. Even skeptics can become strong advocates if they start by learning how to use AI to do the traditional task they dislike most. Once they feel the inevitable benefit, they’re highly likely to try the next task they don’t like doing. From there, we often see a path of continuous learning.

Here’s what too few people realize: technology alone isn’t going to elevate their performance. When everyone knows how to use the tools, the differentiator will be their uniquely human skills that no AI tool can replace.

Human skills aren’t “soft”—they’re foundational

In the New York Times bestselling book Open to Work: How to Get Ahead in the Age of AI, the authors describe five human capabilities that no machine can replace: curiosity, compassion, creativity, courage, and communication.

That same idea extends beyond the individual—organizations aren’t abstract systems; they’re made of people.

What we often call “organizational skills” are simply human skills, practiced consistently and scaled intentionally.

From human potential to organizational capability

A new IDC InfoBrief sponsored by Microsoft, Powering Up: Human Skills for the AI Era,¹ highlights a familiar gap: organizations are investing heavily in AI tools but far less in the capabilities needed to turn them into value.

These capabilities span cognitive, collaborative, leadership, ethical, and business domains.

How do these skills scale? They come together across three levels:

1. Individual. How people think, decide, take risks, and act—especially when working with AI.
2. Teams. How those capabilities show up in collaboration and workflows.
3. Organization. What leaders reinforce through culture, systems, and governance.

This is where personal capability becomes organizational advantage.

How human skills scale in the AI era

The human skills explored in Open to Work don’t disappear at the organizational level; they show up differently at scale.

1. Curiosity: Cognitive and collaborative capability

At the individual level, curiosity starts with a desire to explore and learn what’s possible. At scale, this shows up as:

• Asking better questions to challenge assumptions.
• Exploring different approaches beyond the first answer.
• Sharing learnings across teams.

2. Compassion: Ethical and leadership capability

Compassion is empathy and awareness of impact. At scale, this shows up as:

• Applying ethical judgment and accountability.
• Identifying and addressing bias.
• Practicing responsible data use.

3. Creativity: Cognitive and business capability

Creativity isn’t about aesthetics. It’s about imagining what doesn’t yet exist. At scale, this shows up as:

• Framing problems more effectively.
• Creating new sources of value.
• Driving innovation beyond efficiency.

AI can optimize what exists. Humans decide what’s worth building next.

4. Courage: Cognitive and leadership capability

Courage starts with acting even when outcomes aren’t certain. At scale, this shows up as:

• Applying critical thinking and judgment.
• Making decisions in complex environments.
• Leading change without guaranteed outcomes.

5. Communication: Leadership and business capability

Communication starts with clarity and listening. At scale, this shows up as:

• Setting a clear vision for AI transformation.
• Translating technical capability into business meaning.
• Aligning teams across functions.

What leaders should consider next

Taken together, these examples point to a clear pattern: personal strengths become organizational advantage when they’re built at scale.

If human skills are the differentiator, how do we design for them intentionally? Three mindset adjustments matter most—especially in a moment where excitement about AI is often matched by hesitation about where to begin:

1. Focus on the work, not just the training
   • Human skills develop through real decisions, real collaboration, and real accountability—not one-off courses.
2. Model the behaviors consistently
   • What leaders practice signals what’s safe. Judgment, curiosity, empathy, and learning must be seen, not just stated.
3. Measure what actually changes outcomes
   • Beyond adoption, organizations need to track decision quality, trust and confidence, and cross-functional outcomes.

The real opportunity of AI

AI won’t make organizations less human—but it will raise expectations for how people think, decide, and work.

The organizations that succeed won’t be the most automated. They’ll be the ones that invest in people as intentionally as they invest in technology.

That’s the opportunity—and the work—in front of us.

Continue your learning journey

The future of AI won’t be defined by technology alone—it will be shaped by people who continue to learn, adapt, and grow. Whether you’re just getting started or looking to deepen your expertise, AI Skills Navigator can help you discover learning experiences and credentials that build both AI capabilities and the human skills that help turn knowledge into impact.

¹IDC InfoBrief, sponsored by Microsoft, Powering Up: Human Skills for the AI Era, Doc. US54451326-IB, May 2026.

The post AI needs more than intelligence—it needs humanity appeared first on The Microsoft Cloud Blog.

Despite widespread adoption, many organizations remain early in their AI maturity, constrained by fragmented foundations, unclear governance, and limited organizational alignment. These gaps make it difficult to move from experimentation to repeatable, enterprise‑wide impact.

The difference is not access to technology, but how prepared organizations are to deploy AI at scale—securely, responsibly, and in direct support of business outcomes. New research from Microsoft reveals a clear pattern: AI readiness is the foundation of Frontier Transformation—the next phase of enterprise change, where organizations align AI and human ingenuity to achieve their most ambitious goals.

In this research, AI readiness refers to an organization’s ability to deploy and scale AI in a way that is technically robust and organizationally aligned. It encompasses not only the underlying technology—such as data, cloud platforms, security, and AI models—but also the strategic, cultural, and governance capabilities required to operationalize AI responsibly and at scale. Organizations with high AI readiness can move beyond experimentation, embedding AI into core business processes to drive measurable outcomes.

Frontier Transformation starts with readiness

Frontier Transformation describes how leading organizations are embedding AI across every layer of the business—from employee productivity and customer engagement to core operations and decision-making. These organizations are AI leaders, referred to in the research as Frontier Firms that have moved beyond pilots. AI is not a side initiative; it’s a strategic capability.

The AI Readiness Assessment Whitepaper is based on a global study of 1,000 organizations across 15 countries and eight industries. It connects AI capabilities directly to business performance—and the results are striking.

Organizations with high AI readiness report 47–64% stronger performance across key metrics, including operational efficiency, innovation speed, workforce productivity, customer experience, and revenue growth. Readiness doesn’t just enable progress—it compounds advantage.

The readiness gap is widening

Only 17.7% of organizations qualify as AI leaders, meeting the threshold for both technology and organizational readiness. These Frontier Firms realize 56% higher AI value than organizations earlier in their journey.

This gap matters. While many organizations are investing in AI tools, far fewer are building the foundational capabilities required to scale those tools across the enterprise. As a result, leaders continue to accelerate—while others remain stuck in perpetual experimentation.

Readiness must be balanced, not siloed

One of the clearest insights from the research is that AI readiness must be balanced across both technology and organization. Organizations that overindex on technology often struggle with adoption and trust, while those that focus only on governance lack the platforms needed to scale. Frontier Firms avoid this tradeoff by progressing both dimensions together.

Roughly 30% of organizations reach a strong level of technology readiness. A similar share reaches organizational readiness. But only those that achieve both consistently deliver business impact.

Frontier Firms take a unified approach—aligning strategy, governance, culture, and platforms rather than treating them as separate workstreams.

To make readiness measurable, the Microsoft’s AI Readiness Advisor framework evaluates 10 domains across two dimensions:

Technology readiness

• AI models and generative AI applications
  
• Data and integration
  
• Cloud and hosting
  
• Information security
  

Organizational readiness

• Business and AI strategy
  
• AI experience and skills
  
• Organization and culture
  
• Responsible AI and governance
  

This end‑to‑end view helps organizations understand not just where they’re investing, but where gaps may limit scale.

Four readiness profiles—one clear leader

The research identifies four AI readiness segments:

• Observers are early in their journey, focused on exploration and isolated pilots, with limited operational impact.
  
• Operators excel at execution and governance but lack the modern AI platforms needed to accelerate innovation.
  
• Innovators invest heavily in models and applications but struggle to drive consistent adoption and change at scale.
  
• Frontier Firms lead across both dimensions—enabling secure, scalable AI that is embedded into everyday business operations.
  

Frontier Firms have largely moved from experimentation to optimization. Their focus is on standardization, reuse, and managing AI as a portfolio tied to business KPIs.

Cloud maturity differentiates AI leaders

Cloud strategy is a defining characteristic of Frontier Firms.

Frontier Firms treat the cloud not simply as infrastructure, but as a control plane—where data, models, applications, security, and governance operate together. Approximately 60% of AI leaders run workloads on Azure, reflecting the importance of integrated governance, compliance, and data management for enterprise‑grade AI.

This approach allows AI leaders to standardize security, governance, and data access while enabling teams to innovate faster—without re‑creating foundational capabilities for each new use case.

Leaders also tend to invest platform‑first—building strong cloud, data, and model foundations before scaling applications. That sequencing enables faster innovation and more predictable outcomes over time.

Responsible AI accelerates adoption

Trust is not a barrier for Frontier Firms—it’s a capability.

AI leaders consistently score highest on responsible AI maturity, with formal frameworks, oversight, and monitoring in place. Rather than slowing progress, governance enables scale by building confidence among employees, customers, and regulators.

In Frontier organizations, responsibility and innovation move together—unlocking broader adoption and faster value realization.

AI leadership spans every industry

Frontier Firms appear across every industry studied, from financial services and healthcare to retail, manufacturing, and professional services.

What differs is not ambition—but execution. Leaders report improvements in productivity, accuracy, efficiency, and customer experience tailored to their sector. The takeaway is clear: Frontier Transformation is driven by capability, not industry position.

Turning insight into action

The data is clear: AI value is not unlocked by tools alone, but by readiness across technology, organization, and governance. Frontier Firms don’t wait for transformation—they prepare for it.

Importantly, readiness is not a binary state. Organizations progress through stages as they mature their platforms, operating models, and governance. Understanding where you are today is the first step toward making intentional, high‑impact investments that move the organization forward.

Is your organization ready for AI?

Read the AI Readiness Assessment Whitepaper to understand the research behind AI leadership, then take the AI Readiness Assessment to benchmark your organization and identify the most impactful next steps on your journey to Frontier Transformation.

Download the AI Readiness Whitepaper

Learn how to help your business assess and advance its AI readiness, and unlock Frontier Transformation.

Read the Whitepaper

The post From AI ambition to Frontier Transformation: Readiness defines the leaders appeared first on The Microsoft Cloud Blog.

That tension shows up most clearly when AI systems cross borders—touching sensitive data, operating under different regulations, and supporting teams around the world.

Every four to five days, a new regulation targeting AI, cybersecurity, or data privacy is introduced—with more than 1,000 global policy initiatives across 69 countries, and 100-plus nations enforcing privacy laws.¹

In 2026, digital sovereignty is about managing risk, so you can scale AI using the tools and environments your business depends on as sovereignty requirements evolve. To maintain global velocity while managing risk, your steering committee should answer this fundamental question:

Can we meet localized requirements—controlling where data is processed, who can access systems, and how operations continue during disruptions—without additional complexity as requirements evolve?

To help leaders navigate these challenges, we offer a practical guide: Grow Your Business with AI You Can Trust. This guide provides a grounded approach to navigating sovereignty decisions in real environments, covering governance, operational control, and responsible AI deployment without adding unnecessary complexity.

Navigating the 5 common sovereignty scenarios

Sovereignty rarely shows up as a single requirement. If you’re scaling AI, you’re likely encountering it through a small set of recurring scenarios—often as you expand across regions, partners, and regulatory environments:

1. You operate in markets with evolving regulatory requirements.
   
2. You are scaling AI across regions and need clear governance over data processing.
   
3. You need provable controls over who can access sensitive data—across vendors, operators, and jurisdictions.
   
4. You must meet data residency requirements without fragmenting tools, teams, or operating models.
   
5. You need consistent control across global operations because downtime or loss of control in one region now has immediate impact across your business.
   

One example shows how these scenarios come together in practice.

Sovereignty in practice: Raiffeisen Bank International

Raiffeisen Bank International developed an internal generative AI assistant, using Microsoft Foundry to help employees summarize legal, regulatory, and banking documents and retrieve information more quickly. The platform supports employees across the bank’s operations in multiple European markets, helping staff resolve customer requests faster and focus on higher-value work.

Used by more than 20,000 employees, the solution provides faster access to critical information while supporting the bank’s regulatory and operational requirements across jurisdictions—without compromising safeguards.

Executive checklist: Scaling with resilience

Use the guide to align your AI steering committee on these critical checkpoints:

• Define trust: Establish clear Responsible AI principles for your brand.
  
• Secure by design: Shift to a security-first posture across all AI operations.
  
• Govern the loop: Use the “Map, Measure, Manage” framework to mitigate risks.
  
• Support sustainability: Build systems with socio-economic and environmental impact in mind.
  
• Ensure visibility: Confirm your platform supports the 4 capabilities needed for agent observability.
  
• Address digital sovereignty requirements: Understand common sovereignty scenarios and core principles to help your organization address them.
  

As AI becomes core to how your business operates, sovereignty moves from a technical consideration to a leadership one. Our e-book guide can help you understand sovereignty scenarios and principles to help your steering committee take the next step—clearly, confidently, and at scale.

¹ Footnote includes:

• “The AI regulations that aren’t being talked about,” Deloitte Insights, Deloitte, November 10, 2023
• OECD.AI: The OECD’s Hub for AI Policy, Organization for Economic Co-operation and Development
• “Building a Foundation for AI Success: Governance,” Microsoft Cloud Blog, Microsoft, March 28, 2024
• “2025 AI Index Report,” Stanford Institute for Human-Centered Artificial Intelligence
• “AI Regulations Around the World,” Mind Foundry Blog, Mind Foundry, January 13, 2026
• “Identifying Global Privacy Laws Relevant to DPAs,” IAPP News, International Association of Privacy Professionals, March 19, 2024
• “Data Protection and Privacy Legislation Worldwide,” UNCTAD, United Nations Conference on Trade and Development, February 17, 2026

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

• Author: You’re producing the work, calling on AI to help as needed — a line of code, a sentence, a chart.
• Editor: You set the intent and AI creates the first draft for you to edit and approve.
• Director: You create a spec and hand off entire tasks for AI to execute in the background.
• Orchestrator: You design a system where multiple agents run in parallel across a workflow, flagging exceptions and escalations to you.

Every business leader knows the world is changing, but far fewer have a clear picture of what to do about it. These four patterns are the place to start. The real work ahead for leaders is redesigning their firm’s operating model around the collaboration patterns.

As agent use increases, human involvement doesn’t disappear — it changes shape. What declines is the amount of tactical, step-by-step execution work humans do themselves. And what rises is the need for humans to set direction, define standards and evaluate outcomes.

Ultimately, the goal is not to move every task and business process to the fourth pattern. Instead, it’s up to leaders to help their organizations develop clarity around matching workstreams to the right collaboration pattern. That’s the shape of the Frontier Firm: defined by how deliberately leaders design work across functions, matching the level of human involvement to the outcome.

What the data shows

Our 2026 Work Trend Index research reinforces this shift across roles and industries. We analyzed trillions of anonymized Microsoft 365 productivity signals and surveyed 20,000 workers using AI across 10 countries. We also spoke with leading experts in AI, work and organizational psychology to help us unpack the insights from the data and understand where all this is going. The conclusion is consistent: the constraint is no longer what people can do, it is how work is structured around them.

• AI lifts individual potential. A privacy-preserving analysis of more than 100,000 chats in Microsoft 365 Copilot shows that 49% of all conversations support cognitive work — helping workers analyze information, solve problems, evaluate and think creatively. This shift is already visible in output, with 58% of AI users saying they’re producing work they couldn’t have a year ago, rising to 80% among Frontier Professionals, the most advanced AI users in our research. Additionally, when AI users were asked which human skills are most important as AI takes on more work, they said two topped the list: quality control of AI output (50%) and critical thinking — that is, analyzing information objectively and making a reasoned judgment (46%).
• The Transformation Paradox. We are seeing a pressure point emerge within the organization where the pull to perform collides with the push to transform. 65% of AI users surveyed fear falling behind if they don’t use AI to adapt quickly, yet 45% say it feels safer to focus on current goals than to redesign work with AI. And only 13% of workers say they’re rewarded for reinvention of work with AI even if results aren’t met. The same forces accelerating AI adoption are holding it back.
• Every organization is a learning system. Our results show that organizational factors like culture, manager support and talent practices account for more than 2X the AI impact of individual factors like mindset and behavior (67% vs. 32%). Specifically, the findings underscore the importance of an AI-ready environment: a culture that treats AI as a strategic advantage and encourages experimentation, managers who model and incentivize AI use and talent practices that build skills and create space to apply them. The real question isn’t whether people have the right skills, it’s whether the organization is built to unlock them.

The firms that build a new operating model today won’t just move faster in the short term. They’ll build something more durable, setting themselves up to create value in ways that we can’t yet conceive of: an organization that learns faster than its competitors, compounds its own intelligence and gets harder to catch with every cycle.

For deeper analysis, see the 2026 Work Trend Index Report.

Enabling the Frontier Firm with Copilot Cowork — now mobile, extensible and enterprise-ready

None of an organization’s system scales without infrastructure that brings people and agents into the same flow of work with connected data and the ability to manage and govern it all. Microsoft 365 Copilot is built for exactly that.

Today, we’re expanding Copilot Cowork with new capabilities for Frontier customers to help organizations move from isolated AI tasks to coordinated, multistep work. Cowork enables people to define outcomes and delegate work across apps, business systems and data, with execution that stays directed and controlled throughout.

This update introduces Copilot Cowork Mobile for iOS and Android, along with a growing plugin ecosystem for Cowork, bringing more of an organization’s tools and data into these experiences. This includes native plugins across Microsoft services like Dynamics 365 and Fabric, and partner integrations available in the coming weeks like LSEG (London Stock Exchange Group), Miro, monday.com, S&P Global Energy and more. Organizations can also build custom plugins to turn their own workflows and expertise into reusable, scalable processes. Additionally, a first wave of federated Copilot connectors in Researcher and Microsoft 365 Copilot Chat is generally available today from partners like HubSpot, LSEG (London Stock Exchange Group), Moody’s, Notion and more.

Together, these updates extend Copilot Cowork from a task-based assistant into an extensible platform that helps orchestrate work across Microsoft and third-party systems. With management and governance through Microsoft Agent 365, organizations can deploy and scale agents across core business functions like sales, service and operations.

For more on these product innovations: Microsoft 365 blog.

AI is no longer an experiment. It is an execution challenge. Employees are already working across all four patterns. The open question for every leadership team is whether they can catch up. Access to AI won’t be the advantage for much longer. How the work is designed around it will be.

Jared Spataro, CMO, AI at Work at Microsoft, shapes how every organization applies AI and agents to reduce costs, create new value and define the future of work. He leads research, strategy and product across Copilot, Copilot Studio, Microsoft 365, Dynamics 365 and Power Platform.

The post How Frontier Firms are rebuilding the operating model for the age of AI appeared first on The Microsoft Cloud Blog.

For EY, the question wasn’t whether to invest in AI. It was how to earn the right to scale it.

During the AI Tour New York, Mark Luquire, Managing Director and Global Co‑Innovation Leader at EY, shared how the firm is moving from AI ambition to enterprise execution—by becoming client zero, embedding AI into daily work, and keeping human judgment at the center of transformation.

Becoming client zero at global scale

Operating across more than 180 countries with hundreds of thousands of professionals gives EY a unique vantage point on transformation—and a responsibility to prove what works before advising clients.

Several years ago, EY launched an internal AI assistant built on Azure OpenAI capabilities. What began as an internal deployment moved into full global production in a matter of weeks, reaching hundreds of thousands of employees. That experience reshaped how the firm understood AI’s role.

“The client‑zero story is a way for us to say: we’ve done this for ourselves—now let us help you do the same.”

— Mark Luquire, Managing Director & Global Co‑Innovation Leader, EY

Putting AI directly into employees’ hands helped surface real use cases—how people wanted to work differently, where friction existed, and what actually created value. That lived experience became the foundation for how EY partners with clients navigating similar complexity, often with far less margin for error.

AI as a strategic capability, not a point solution

EY doesn’t treat AI as another productivity layer. It’s embedded into the firm’s global strategy as a platform capability that reshapes services across tax, audit, consulting, and advisory.

AI helps professionals synthesize vast regulatory and financial data sets, accelerate document review, and surface insights that previously took weeks to uncover. The impact isn’t automation for its own sake—it’s consistency, quality, and speed at enterprise scale.

“AI isn’t just another tool—it’s a platform shift in how people work and how we deliver value to clients.”

— Mark Luquire, Managing Director & Global Co‑Innovation Leader, EY

By augmenting expertise rather than replacing it, AI enables more people to operate like experts—delivering higher‑quality guidance with greater confidence.

From experimentation to everyday work

Early experimentation mattered, but scale required something more deliberate. As AI capabilities expanded across EY, two things became clear. First, innovation often emerged from the edges—people closest to the work identifying new possibilities. Second, not every experiment should scale. The firm needed a model that balanced democratized access with enterprise‑grade execution.

A key inflection point came when EY began extending AI beyond individual productivity into repeatable, action‑oriented workflows. With Microsoft 365 Copilot, AI entered the flow of everyday work—helping professionals synthesize information and prepare insights where they already worked. From there, Copilot Studio enabled EY to design AI agents that could take on more structured tasks across processes, helping teams move from experimentation to execution without losing control or trust.

That balance allowed EY to identify which use cases to industrialize, where to invest deeply, and how to embed AI into everyday workflows rather than isolating it as a special initiative. AI stopped being something teams tried and became something they used.

Governance that enables trust

Scaling AI isn’t just a technical challenge—it’s a trust challenge.

For EY, governance isn’t about slowing innovation. It’s about enabling it responsibly. The firm invested early in responsible AI frameworks, transparency, and security to ensure AI outputs could be trusted—by professionals and by clients.

AI is designed to amplify human judgment, not replace it. People remain accountable for decisions, context, and client relationships. That clarity is essential in a profession where trust is foundational.

A human‑centered future of professional services

Looking ahead, Luquire sees AI reshaping professional services by expanding access to institutional knowledge and taking on repetitive tasks—freeing professionals to focus on higher‑value work.

“AI will continue to transform professional services—creating more experts in the organization and allowing people to focus on the work that truly adds value for clients.”

— Mark Luquire, Managing Director & Global Co‑Innovation Leader, EY

As AI agents become part of the workforce, new skills emerge; judgment, oversight, and the ability to work alongside intelligent systems. The future EY is building is human‑led and AI‑powered—where technology accelerates insight, but people remain at the center.

From ambition to execution

EY’s journey offers a clear lesson for leaders: AI transformation isn’t about adopting the latest tools. It’s about earning the right to scale through internal experience, responsible governance, and relentless focus on outcomes.

By becoming client zero, embedding AI into daily work, and designing for trust, EY is turning AI strategy into real‑world execution on a global scale.

Explore real‑world AI leadership stories

• Watch additional conversations with business leaders navigating AI transformation: Organizations leading the future with AI
• Discover how organizations are using Microsoft to turn AI ambition into measurable impact: Explore customer stories

This thought leadership piece is part of Microsoft in Business coverage from AI Tour New York, highlighting how leaders are translating AI strategy into real‑world execution at scale. Explore more conversations:

• Scaling AI with confidence: How leaders are using AI to drive enterprise transformation
• When AI delivers real value, not just potential
• Why AI is an operating model shift—Not a technology upgrade

The post AI as a platform shift, not another experiment appeared first on The Microsoft Cloud Blog.