Empowering people to thrive at work

When thoughtfully integrated into everyday work, AI has the potential to expand what we can do. It can help us explore and solve new kinds of problems, synthesize information to support decisions, and reduce time spent on some repetitive tasks. This can ultimately create more space for us to apply our expertise, develop new skills, contribute ideas, and focus on more meaningful, higher-value work.

The leaders we spoke with at several Microsoft AI Tours around the world described how these changes can give employees greater opportunities to learn, create, and grow. They also shared how a stronger employee experience can contribute to better outcomes for their organizations.

This is one part of what Frontier Transformation looks like in practice.

Providing broader access to AI at work

Two factors can help accelerate AI adoption: broader access and deeper integration into everyday workflows. When AI is embedded in the tools people already use, it becomes part of how work happens rather than a separate initiative.

Leaders at EY, a global professional services organization, describe a shift from experimentation to everyday use. By making AI broadly available across the workforce, they have helped create a culture where employees at every level are finding new ways to work and create value.

The biggest shift in our organization with AI has been moving from curiosity to everyday use.

Jenelle McMaster, EY Regional Deputy CEO and People & Culture Leader, Oceania

There are a couple of things that help you get beyond the experiment. One is democratizing AI for everyone […] As a result of that culture, we don’t have a constraint around AI adoption.

Katherine Boiciuc, EY Regional Chief Technology and Innovation Officer, Oceania

For Scope, a UK disability equality organization, accessibility is central to its approach. Leaders of this organization see AI as a way to put powerful capabilities into the hands of more people regardless of technical expertise, helping more people contribute ideas and participate in problem-solving across the organization.

You do not have to be a coder. You do not need to have studied AI for four years at a college or university. All you need is a desire, a problem you are trying to solve, and the passion to see it through and resolve that problem.

Kwesi Afful, Executive Director for Digital, Data, and Technology, Scope

The Salvation Army UK and Ireland has taken a people-first approach to adoption. Rather than starting with technology, the organization focused on helping employees, officers, and volunteers build confidence using AI so they could better serve communities and further their mission.

When it came to AI, it started off with putting people at the center. Our first major focus was rolling out Microsoft Copilot across the organization and supporting the rollout with structured training to give every officer, employee, and volunteer the confidence to serve our people with compassion and innovation.

Lev Malinin, Head of Enterprise Systems, Data and AI, The Salvation Army UK and Ireland

Measuring returns beyond efficiency

Organizations often begin their AI journey looking for efficiency gains. But the leaders we spoke with described returns that go well beyond time savings. They shared that AI can help reduce repetitive work and administrative burden, giving employees the opportunity to focus more on activities where their expertise creates the greatest value.

At EY, one of the biggest benefits has been enabling people to apply their knowledge and skills in new ways. One of their leaders describes seeing employees become more confident in their ability to create value with AI and contribute in ways that were previously out of reach.

The most meaningful outcome for employees at EY because of AI is the ability to do something new. We sell expertise, we sell knowledge, content and information. We still do that, but AI enables us to do that in a new way.

Mary Elizabeth Porray, EY Global Vice Chair, Client Technology

At The Salvation Army UK and Ireland, value is measured by helping people focus on the work that matters most. As the organization expands its use of AI, leaders have emphasized the importance of implementing it thoughtfully and responsibly so it can deliver lasting impact.

It’s really about maximizing value over everything and doing things right. Starts with caution and thinking about what you need to get right.

Lev Malinin, Head of Enterprise Systems, Data and AI, The Salvation Army UK and Ireland

The same theme emerged at Advania UK, an IT services provider. Leaders there see AI creating value not simply through efficiency, but by helping employees spend less time on routine work so they can focus on applying their expertise to higher value activities.

It’s actually about the experience that we’re creating for our employees in the workplace […] It’s going to help us all to become better versions of ourselves as we work across the businesses, and allow our staff to really enjoy the work that they do because they’re not doing the mundane […] they’re able to apply their skills to higher value activities, whether they’re in creative roles or analytical roles.

Nick Isherwood, Chief Operating Officer, Advania UK

AI that works alongside people

Based on these and other customer conversations, we can see that the most effective leaders use AI to support their people while keeping them in charge of making decisions. Organizations pursuing lasting value view AI not as a replacement for human expertise, but as a tool that helps people achieve more.

Baringa, a global management consultancy that helps organizations navigate AI transformation, encourages clients to focus first on building trust and proving value before scaling their efforts.

Have the bigger picture. Have a vision. Have a strategy. But start from a use case, which is going to be small enough for you to be able to build trust and to build confidence that that use case is going to deliver value.

Stela Bokun, Partner, Baringa

Educators can use AI tools to support learning, while remaining responsible for evaluating outputs and directing the learning experience. 

Teach For America, a nonprofit dedicated to expanding educational opportunity, is using AI for exactly that. Through AI incubation programs and hands-on experimentation, employees and educators are exploring new ways to support learning while keeping teachers at the center of the experience.

We don’t want AI to replace great teachers. We want it to augment that. We want it to help them in the classroom. I need to transform this organization to really reimagine how learning happens through AI. That’s virtual coaches and tutors and things like that. Things that we couldn’t do before that.

Alan Murray, Chief Data & Technology Officer, Teach for America

The organization’s Reinvention Lab illustrates what happens when employees are given both the tools and the freedom to experiment. Educators have been trained through hackathons and hands-on learning experiences, resulting in hundreds of AI-powered tools designed to address real classroom challenges.

We have our Reinvention Lab. They have educated over 1,200 educators through a whole bunch of hackathons to educate these folks on how to use AI. That has then resulted in over 720 tools that these educators have built. And these are all kinds of things built in AI for the classroom.

Alan Murray, Chief Data & Technology Officer, Teach for America

Across these stories, we can see a common pattern: organizations can create value when AI works alongside people, supporting human judgment, creativity, and expertise rather than replacing them.

Make three practical moves now

1. Start with the friction. Find the repetitive or draining tasks that slow people down, and identify appropriate ways AI can help address those moments.
2. Widen who can contribute. Use AI to help more people build skills and take on higher-value work, expanding what the team can achieve together.
3. Build confidence. Keep people in the lead, make the role of AI clear, and show where it adds value and where human expertise should guide decisions.

This is where Frontier Transformation to enrich employee experience begins. 

As Kwesi Afful of Scope put it:

AI is the train that won’t wait for us. If we avoid it, we will probably be doing a disservice to your organization or the community or customers […] so cautiously embrace it, start small and look for the practical value.

Kwesi Afful, Executive Director for Digital, Data, and Technology, Scope

Learn more

• Visit the Frontier Transformation site to explore how you can put AI to work, build trust, and scale transformation with Microsoft across everyday workflows
• Download the e-book Four Paths to Business Value with AI to learn how you can move from AI experimentation to Frontier Transformation with intelligence and trust.

Next in the Accelerating Frontier Transformation series: Reinvent customer engagement.

The post Accelerating Frontier Transformation: Enrich employee experience appeared first on The Microsoft Cloud Blog.

I drink a lot of tea. English breakfast, usually, in a real cup, every day. So, when my team came to me with an idea for a new series where they wanted to provide a platform for AI leaders to share how they are learning the name felt relatable. What’s the tea? captures something I find genuinely energizing right now: the pace at which leaders are transforming their organizations with AI, and how generously and honestly they’re sharing what they’re learning with one another.

That generosity is the whole reason my podcast, What’s the Tea?, exists. As I told Rachel Thornton, Enterprise CMO at Adobe, in our very first conversation, what excites me most about this moment isn’t only the speed of change, it’s “the eagerness for leaders to learn from one another.” This is the most significant technology shift of our careers, and it’s playing out not in years, but in days. I thought there was no better way to make sense of it than to get leaders together over a cup of tea and ask them, plainly, what they’re seeing inside their businesses. We bring in people who are driving real transformation, pour the tea, and drink in what they spill. That’s the tea.

Across five conversations—with CMOs at Adobe, Lumen, and Sitecore, a behavioral scientist who studies how teams perform, and one of the most provocative voices in modern marketing—a clear pattern emerged. Transformation is a question of mindset that supersedes any technology question. This first post is about the shift in how leaders and their people relate to AI, and why that shift is the precondition for everything else.

The fear has flipped and adoption is now an enterprise expectation

A year ago, the dominant emotion around AI in most organizations was apprehension. Rachel named it precisely. She described “the shift from maybe some trepidation around AI and what would it mean,” to people realizing, “Hey, this is really a game changer.” I’ve watched that exact arc play out, not just in marketing but across functions: the move from quietly wondering whether these tools would replace people, to discovering they can compress work that used to take weeks into minutes.

Ryan Asdourian, EVP and Chief Marketing Officer at Lumen Technologies, took the point to its logical conclusion, and his framing has stuck with me. Reflecting on the instinct to apologize for using AI, he said he’d flipped it entirely: “You should almost apologize if you don’t use AI and you don’t use tools.” For a leader running a function inside a large enterprise, that’s a profound reset of the default. Adoption is no longer the exception to be justified; it’s the expectation. The leadership task is to normalize it—to make it, as Ryan put it, “just common practice to say that, of course I use AI.”

How enterprise AI creates collective intelligence across teams

The trepidation faded fastest wherever leaders stopped framing AI as a personal-productivity gadget and started treating it as a way to raise the intelligence of the whole organization. Jon Levy, the behavioral scientist and author of Team Intelligence, made the sharpest case. The earliest phase of AI adoption, he observed, fixated on cost savings and reductions—which is what happens at the start of every technological revolution. But the real prize is bigger. “The ultimate goal of any organization,” he told me, “is to be as smart as possible. How quickly can you and your team solve the problem?”

That reframing matters enormously for transformation, because it moves the unit of value from the individual to the team and ultimately, the enterprise. Ryan gave me the line I now repeat most often: an individual versus an individual with AI, a team versus a team with AI—”the team with AI always wins.” Rachel described the healthy version of the same idea, that AI “amplifies what I can do, supports what I can do. Not necessarily, oh, this is totally replacing me.” Enterprise impact doesn’t come from a thousand people each saving a few minutes. It comes from teams solving harder problems faster, together—across customers, operations, and growth.

Scaling AI successfully requires leadership, culture, and change management

If the fear has flipped and AI genuinely raises collective intelligence, the decisive variable becomes leadership. Specifically, the ability to move an organization from scattered experimentation to scaled adoption. Every one of my guests came back to this.

Ryan made the case for leading visibly and vulnerably. “Vulnerability as a leader is so important,” he said, “to show how we are using these tools to enhance what we can do with our team.” He practices it: in a company town hall with roughly five thousand people on the call, he used Copilot live, in the moment, to verify an answer before giving it. That’s a leader modeling the behavior he wants to scale.

Rachel described the structural side of the same work—building what she calls “a culture of experimentation,” where training, reskilling, and upskilling become “critically important” and happen continuously, in bite-size pieces, every week rather than once a quarter. That cadence is how experimentation becomes an operating model rather than a pilot.

Jon offered the deeper psychology of why scale so often stalls. People, he reminded me, don’t relate to the present—they relate to the future they believe they’re heading toward. “It is the responsibility of leadership,” he said, “to make sure that their people feel the future will be brighter.” I learned that the hard way. I once unveiled an impressive AI-native platform to my team without first painting the picture of where it would take them, and I watched the energy leave the room. All they could see was a tool that might replace what they did every day. I hadn’t given them a future to walk toward—and without that, adoption never scales.

That’s the through-line of this series. The technology is extraordinary, but transformation is dependent on leaders bringing people with them.

In the episodes ahead, I’ll explore how AI is reshaping the way work gets done and the outcomes it produces, and what the agentic enterprise looks like. For now, the tea is simple: the fear is fading, the value compounds at the level of teams and the enterprise, and the leaders who move deliberately from experimentation to scale are the ones whose organizations will transform.

Watch What’s the Tea? episodes

• What’s the Tea? with Rachel Thornton | Why Marketers Aren’t Afraid of AI Anymore
• What’s the Tea? with Ryan Asdourian | The New Expectations of AI
• What’s the Tea? with Jon Levy | The Human Side of AI
• What’s the Tea? with Michelle Boockoff-Bajdek | AI’s Impact on Customer Experience
• What’s the Tea? with Gary Vaynerchuk | What Leaders Are Missing About AI

The post What’s the Tea? Lessons from leaders navigating AI transformation appeared first on The Microsoft Cloud Blog.

Everything we are doing at Microsoft is empowering Frontier Transformation: Copilot enables AI in the flow of human ambition, Microsoft IQ amplifies and protects an organization’s IQ and Agent 365 is the trust platform that enables observability at every layer of the stack.

Businesses are not static, and neither are the AI systems that support them. As organizations evolve, AI systems must continuously learn and improve. Agentic workflows need to be built, observed and tuned against the outcomes organizations seek and the ROI they demand. Microsoft’s open, model-diverse and heterogenous platform powers that improvement loop. We also recently announced Microsoft Frontier Company, bringing our AI engineering approach to customers around the world to help them build these AI systems to accelerate measurable business outcomes.

Throughout the past year, we saw customers put these capabilities to work in powerful ways — embedding AI into core business processes, building agentic systems, strengthening security, accelerating innovation and creating new sources of value. The stories below highlight organizations leading Frontier Transformation, demonstrating how intelligence, trust and human ambition come together across industries.

To advance its journey to become a global AI-powered company, Atos Group deployed Microsoft 365 Copilot to 56,000 employees across 54 countries — from consultants to engineers to frontline workers — and was one of the first organizations globally to adopt Microsoft 365 E7: The Frontier Suite. Using Microsoft Foundry, Microsoft Copilot Studio and Agent 365, Atos is building, operating and governing a growing ecosystem of 19,000 AI agents through a unified operating model that brings together productivity, security, compliance and agent governance. As Atos embeds secure agentic AI across its workforce, the company is creating a repeatable model to continuously improve thousands of agents at scale while applying the same playbook to help customers accelerate adoption across highly regulated industries.

Facing state-sponsored threats and complex global operations, ASM is strengthening cyber resilience with Microsoft Security Copilot, helping protect the intellectual property behind advanced semiconductor manufacturing. By bringing threat investigations into a unified AI-powered experience, ASM enables analysts to investigate incidents faster, apply consistent decision-making across global operations and accelerate the development of cybersecurity talent. The company reduced incident triage time by 68%, cut laptop compromise investigations from 25 minutes to eight and now saves 337 hours each week on investigations while redeploying 20% of its security operations staff to governance, risk and compliance initiatives.

Banco Popular Dominicano, the largest private-sector bank in the Dominican Republic, transformed operational risk management from periodic, sample-based reviews into continuous, AI-powered supervision. Using AURA — an ecosystem of specialized agents built on Microsoft Copilot Studio and Microsoft Power Platform — the bank monitors 100% of its operational risk universe in real time, up from roughly 40% coverage, and can automatically analyze changes, validate controls and surface issues as they occur. The shift has delivered seven times greater analytical capacity, reduced manual operating effort by 70%, achieved 98% methodological accuracy and enabled continuous processing of approximately 80,000 documents per week and more than 300 cases per day. Just as importantly, risk teams have moved from reacting to problems after the fact to anticipating and preventing deviations before they occur. These results demonstrate the power of AI democratization. By enabling business teams to build intelligent solutions themselves through low-code tools, Banco Popular transformed operational risk management while fostering a culture of innovation led by domain experts.

To help reduce the manual burden for employees while meeting the pharmaceutical industry’s strict data security requirements, Cactus Life Sciences modernized scientific workflows with Microsoft 365 Copilot and agents. The company has deployed more than 30 custom automation agents to streamline document review and structure data extraction and information retrieval across scientific writing and project management teams. Supported by a centralized knowledge repository and the Copilot Champions community, the company reports efficiency improvements of approximately 35% to 50% in structured data extraction. By automating labor-intensive tasks and maintaining human review and quality controls, the company is enabling scientific writers to focus on deeper analysis, synthesis and delivering exceptional science to clients.

Chow Tai Fook is redefining luxury retail with Microsoft 365 E5, Microsoft Purview, Microsoft Azure OpenAI Service, Microsoft Fabric and Microsoft Foundry. The company has deployed over 400 customized AI agents supporting more than 24,000 employees, with millions of AI interactions each month and core business-process efficiency gains exceeding 70%. Through its AI Fook super-agent ecosystem, frontline associates can instantly access product expertise, inventory insights and personalized recommendations, helping drive sales conversion improvements of up to 57% while delivering hyper-personalized omnichannel experiences at scale. With hundreds of AI agents operating across the business, Chow Tai Fook is creating a foundation where customer, product and operational intelligence can be applied across every interaction, helping personalize experiences and improve decision-making across its global retail network.

To accelerate AI adoption, EY moved AI from experimentation into enterprise-wide transformation. After deploying Microsoft 365 Copilot to 150,000 employees and realizing a 15% productivity gain, the firm is expanding the Microsoft 365 Frontier Suite across its global workforce of more than 400,000 people, embedding agentic AI capabilities across the enterprise. As Client Zero, EY is applying Microsoft technologies across its own operations, including Microsoft Power Platform, Microsoft Copilot Studio, Microsoft Azure, Microsoft Foundry and Microsoft Fabric. The results include 95% faster lead times, a more than 37% reduction in finance operating costs and up to a 90% reduction in manual workloads across key business processes.

To reimagine the grocery shopping experience, Grandiose Supermarkets created an AI-powered shopping companion — GrandChef — built on Microsoft Foundry and Azure OpenAI Service. By connecting meal inspiration, recipe discovery and product purchasing into a single experience grounded in live product and inventory data, GrandChef helps shoppers move from intent to purchase faster and with greater confidence. This has led to a 31% increase in conversion, a 20% lift in average basket value and shopping journeys that are 40% faster. Together, these gains are helping Grandiose Supermarkets create more personalized customer experiences while driving measurable business growth. By connecting customer intent, product data and purchasing decisions in a single experience, Grandiose is creating a feedback loop that helps continuously improve recommendations and shopping experiences.

To support audits across its global organization, Grupo Bimbo built two agents with Microsoft Copilot Studio and deployed them through Microsoft 365 Copilot and Microsoft Teams: the result was Audit Assist, an idea generated from a Microsoft-supported internal hackathon and Comatrix. By connecting auditors directly to approved guidance, procedures and templates in SharePoint, the company is helping teams work more efficiently across 39 countries while improving consistency and audit quality. The solution/AI agents reduced planning-phase audit time by 20% and accelerated risk and control matrix creation from days to seconds, enabling auditors to spend less time searching for information and more time on analysis and decision-making. By making approved audit knowledge instantly accessible across its global audit organization, Grupo Bimbo is creating a foundation where expertise can scale with the business — improving consistency, reducing rework and enabling auditors to focus on higher-value analysis and decision-making.

To create a unified foundation for data, automation and AI across its operations in 47 countries, Navien built a connected, intelligent operating model with Microsoft Fabric, Microsoft 365 Copilot, Microsoft Foundry and Microsoft Copilot Studio. By connecting fragmented data and processes across procurement, manufacturing, quality and customer service, the company is enabling more consistent, data-driven decision-making across its operations. Navien saved 28,000 hours annually through AI agents and automation, with 32% of employees using self-service analytics to make decisions without IT support. Through its migration to Azure, it also expects more than 1.4 million in total cost-of-ownership savings over five years. By connecting data, insights and workflows across the business, Navien is enabling faster, more consistent decision-making at scale across its global operations.

NHS England is accelerating AI adoption across the healthcare system with the largest implementation of its kind in the healthcare sector. Following a trial involving 30,000 workers across 90 NHS organizations — where users saved an average of 43 minutes of administrative time per day — NHS England is rolling out Microsoft 365 Copilot to over 500,000 clinicians and support staff. Organizations can use Microsoft Copilot Studio to build and deploy AI agents that streamline clinical, operational and administrative workflows. Through Agent 365, NHS England can govern and scale those agents across the healthcare system while enabling individual trusts to build solutions for local needs — creating a secure framework where agentic workflows can be deployed, managed and expanded consistently to improve service delivery, reduce costs and create more time for patient care.

Novo Nordisk is using AI to help researchers make faster, more quantitative decisions in pharmaceutical R&D. Working with Microsoft’s AI Acceleration Studio within the Forward Deployed Engineering team, the company built a governed reasoning agent on Microsoft Azure with its proprietary dataset, including more than 200,000 patient-years of harmonized clinical trial data, while maintaining rigor, oversight and compliance. The system has expanded the team’s capacity to evaluate potential opportunities from 5 to 10 strong ideas per quarter to more than 50, and the company expects it to reduce time to insight for exploratory analyses from weeks to minutes. By grounding AI in its datasets and governed workflows, Novo Nordisk is helping scientists turn decades of institutional expertise into a reusable intelligence layer that can help accelerate discovery across the organization.

As AI becomes part of investment decision-making, SimCorp is helping financial institutions bring AI into investment workflows without compromising governance, auditability or control. By unifying SimCorp One on Microsoft Azure and leveraging Microsoft Foundry, the company is helping portfolio managers, risk analysts and operations teams access insights faster, automate manual processes and spend more time on higher-value decisions. By standardizing how AI is deployed and governed across global investment operations, SimCorp is creating a foundation for trusted AI at scale, helping organizations embed AI into investment workflows while maintaining the controls required in highly regulated markets. One study found SimCorp One customers realized 134% ROI over three years, improved operational efficiency by 45 percent, saved 10 hours per person per week and accelerated time to market by 50 to 60 days.

Stellantis is accelerating AI-led strategy and digital transformation across its global business, co-developing more than 100 AI initiatives across sales, customer care, product development and operations. By applying AI-powered insights across the business, the automaker is streamlining product development and validation, advancing predictive maintenance and bringing new digital features and services to market faster for customers. Stellantis is also deploying an AI-driven global cyberdefense center to help protect vehicles, customers and operations worldwide while modernizing its infrastructure on Microsoft Azure with a targeted 60% reduction in its datacenter footprint by 2029, powering a more scalable and interconnected digital ecosystem for future digital and connected services and resilient operations.

Facing a growing volume of cyberthreats across a complex healthcare environment, St. Luke’s University Health Network is using Microsoft Security Copilot to help protect the systems clinicians and patients depend on every day. Across 15 campuses, 300 outpatient sites and more than 2.5 petabytes of data and patient records, the organization needed a unified view of threats across a complex environment. Security Copilot connects Microsoft Defender, Microsoft Sentinel, Microsoft Entra, Microsoft Purview and other security tools, helping analysts correlate threats faster, eliminate silos and respond with greater precision. St. Luke’s is saving nearly 200 hours each month in phishing alert triage and creating incident reports in minutes instead of hours, helping security teams focus more time on protecting patient care.

University of Kentucky unified more than 150 AI initiatives across classrooms, research labs, healthcare settings and administrative offices through its CATS AI governance framework. Standardizing on Microsoft’s AI portfolio — including Microsoft 365 Copilot, Microsoft Dragon Copilot, GitHub Copilot and Microsoft Azure — the university achieved campus-wide deployment, providing more than 70,000 students and employees with access to AI capabilities. Clinicians are using Dragon Copilot to reduce documentation burdens and spend more time with patients, and students are using GitHub Copilot to become active builders of digital solutions, such as the Socratic Tutor: an AI-powered learning platform aimed at helping medical students master complex curriculum.

Looking back on FY26, I am inspired not only by the continued pace of AI innovation, but by what our customers are achieving. Across every industry and segment, organizations are turning their unique IQ into strategic advantage with continuously improving agentic workflows. The companies leading this next phase are building, observing and tuning agentic workflows against business outcomes. Intelligence compounds, trust scales and AI can work in the flow of human ambition. As Frontier Firms redefine what is possible with AI, we remain focused on helping our customers amplify and protect their intelligence so they can transform how they operate, compete and grow.

The post Looking back on Microsoft’s FY26: From AI experimentation to Frontier Transformation appeared first on The Microsoft Cloud Blog.

These stories don’t have much in common on the surface, but together, they point to a broader shift: AI is helping nonprofit organizations across the globe tackle some of society’s most pressing challenges.  

Animal Protection Denmark, Answer ALS, and Everything Suarve are three nonprofits that turned to Microsoft to solve different problems. Today, AI is helping them expand opportunity, strengthen communities, and create new possibilities for the people, animals, and causes they serve—extending their impact far beyond what their size or resources might otherwise allow.  

Animal Protection Denmark: From guesswork to insight in kitten care

Every year, Animal Protection Denmark prepares for “kitten season,” when shelters see a surge in vulnerable animals needing care. As the organization grew, so did the volume of data flowing across shelters, foster networks, volunteers, and supporters. Teams often spent long hours every month reconciling information from different systems, making it harder to forecast needs and coordinate resources.

Using Microsoft data and AI capabilities, Animal Protection Denmark unified that data into a single source of truth. Teams can identify trends earlier, forecast demand for foster placements, and monitor shelter capacity in real time. Staff can also access complete records for each animal, helping ensure continuity of care and supporting faster adoption decisions.

By combining a unified data foundation with AI-powered insights, Animal Protection Denmark spends less time managing information and more time acting on it. The result is earlier planning, faster decisions, and better outcomes for the animals that depend on them.

Answer ALS: Turning data into discovery

Answer ALS was created to accelerate progress toward treatments and a cure for amyotrophic lateral sclerosis (ALS), a progressive neurodegenerative disease that affects over 450,000 people worldwide. Together with Microsoft, the organization built Neuromine, one of the world’s largest ALS research hubs, bringing together trillions of data points contributed by more than 2,500 people living with the disease. Azure AI Search powers Neuromine’s query function so researchers can quickly learn details about an individual’s disease trajectory or even their DNA, which helps pinpoint cell lines to further study.

Additionally, Answer ALS is developing a chatbot in Microsoft Foundry that uses generative AI to answer user questions and direct researchers to relevant data. Before Neuromine, researchers often spent months, and sometimes more than a year, assembling data and biological samples before meaningful analysis could begin. Today, researchers around the world can access hundreds of patient lines and related clinical data in hours rather than months, helping them move more quickly from questions to insights.

By making high-quality research data easier to explore, share, and build upon, Answer ALS is helping accelerate research by as much as 65% over the next few years. The platform is enabling researchers around the world to collaborate more effectively, uncover new patterns in the data, and advance the search for better treatments and, ultimately, a cure.

Everything Suarve: Scaling second chances with AI 

Everything Suarve helps young people in Australia rebuild their lives through job training, mentorship, mental health support, and practical life skills. Many participants arrive after facing challenges such as housing insecurity, unstable home environments, or trauma. For a small team, the work depends on trust, consistency, and showing up for young people when they need support most.

As demand for its programs grew, so did the administrative burden. Referrals arrived through emails, paper forms, and phone calls, while staff tracked participant progress across disconnected systems. To streamline operations, Everything Suarve used Microsoft data and AI capabilities to build a solution that centralizes enrollment, case management, reporting, and communications. The organization also uses Microsoft 365 Copilot to help with grant writing, document summarization, and other administrative tasks, reducing the time spent on manual work.

Today, Everything Suarve’s staff can manage referrals, case notes, reporting, and participant communications in a single workflow. The new platform saves up to eight hours per participant during enrollment, while Copilot reduces grant application work by as much as two weeks. By automating processes and putting AI to work, Everything Suarve is reducing administrative overhead and creating more time to help young people build confidence, skills, and a path forward toward employment, education, and long-term stability.

What other nonprofits can learn from these stories

These three organizations serve different communities and pursue different goals. Yet together they show that organizations create the most value from AI when they use it to strengthen human expertise and start with a problem worth solving or a mission worth advancing.

At Animal Protection Denmark, staff can spot needs before they peak. At Answer ALS, researchers are compressing years of discovery into months. At Everything Suarve, staff spend less time on forms and more time with young people rebuilding their lives.

Together, these stories reflect a broader shift already underway across organizations of every size. AI is becoming part of how decisions are made, how services get delivered, and how people work. Success is no longer defined by the technology alone. It comes from combining technology with human judgment, experience, and purpose.

We call this Frontier Transformation: to move beyond isolated tools and individual tasks to rethink how they achieve their mission. Sometimes the result is greater efficiency. Often, it creates something more meaningful: the ability to reach more people, respond more quickly, and extend impact in ways that were previously out of reach.

As AI becomes more accessible, these nonprofits are showing that meaningful transformation is not reserved for the largest enterprises. It can begin anywhere people are willing to pair innovation with a clear sense of purpose.

The post Inside nonprofits using AI to extend their impact 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.

Summary In the days following the United Nations Micro-, Small, and Medium Enterprises (UNMSME) Day, we take a closer look at what the data shows, and why it matters for the 400 million businesses that power the global economy.

This year, I want to address something we’re watching happen in real time: small and medium businesses (SMBs), also referred to globally as Micro, Small, and Medium Enterprises (MSMEs), are stepping into AI leadership, moving quickly, and applying it directly into the work that drives their business forward.

We mark UNMSME Day to raise public awareness of their contribution to sustainable development and the global economy. The numbers deserve repeating every year: SMBs represent 90% of all businesses worldwide, 50% of global gross domestic product (GDP), and 70% of the world’s workforce.¹

SMBs are not a segment of the economy; they are a foundational part of it. They operate under real pressure. The median small business carries just 27 days of cash reserves.² There is no room to bet on the wrong transformation, they must pick the right direction and get it right the first time. That pressure is exactly why what is happening right now matters so much.

AI has moved from conversation to competitive advantage

The latest Microsoft Work Trend Index 2026 makes something clear: AI is no longer a productivity add-on. It is shifting what work is possible. 58% of AI users say they are already producing work they could not have done a year ago and 66% report spending more time on higher-value work as AI takes on execution.

In a small team, this shift affects capacity and growth. For a financial planning firm streamlining client reviews, a law firm cutting document preparation time, a title company accelerating closings, or a property management team eliminating administrative overhead, that is not a marginal improvement. It represents a structural advantage.

A different pattern is emerging among SMBs. Organizations that move beyond individual use and embed AI into how work happens across teams, workflows, and decisions are seeing step-change results. Not just better work, fundamentally different work. And because SMBs have leaner structures and shorter decision cycles, they can get there faster than large enterprise organizations ever could.

What Frontier Transformation looks like in practice

Frontier Transformation becomes clear when the SMB journey moves from simply experimenting with AI to achieving transformation at growth and scale. Frontier Firms are the ones making that shift real by embedding AI into productivity tools they already are using across their teams workflows and decision processes. The result is not just better output, but a different operating model and one that unlocks more creativity, innovation, and growth.

Here are three SMB’s doing exactly that

Turning bottlenecks into real-time workflows

At Dunaway, a multi-discipline design, planning, and engineering firm in Texas, regulatory research and compliance checks were once manual, time-consuming steps that slowed project delivery. By bringing AI agents into the workflow, engineers can access regulatory insights in real time, answer questions faster, and apply knowledge consistently across teams. The result: a 90% reduction in research time and roughly 10,000 hours saved annually. What once lived with a few experts now works for the whole team. That is what a Frontier Firm looks like in practice.

When I first saw that number, 10,000 hours, I sat with it for a moment. That is not an efficiency gain. That is an entire team’s year given back.

Scaling craft and personalization with trusted intelligence

Businesses built on craftsmanship, trust, and deeply personal service, where consistency is hard to standardize, must tackle a different kind of scale challenge. Chow Tai Fook, a 97-year-old global luxury jewelry brand based in Hong Kong with thousands of stores across Asia, integrated AI across its operations. The company moved from isolated digital efforts to a connected, real-time intelligence model—giving frontline associates the insights they need in the moment to better understand each customer and deliver more personal, relevant experiences at scale. The result is more than 70% efficiency gains across millions of monthly interactions, and a 97-year-old brand that feels personal at every counter.

For me, it always comes back to the customer. When sales associates are empowered with the right insight in the moment, we’re not just making the business more efficient, we’re creating richer, more personal experiences. That’s the power of AI when it’s done right, it doesn’t replace human expertise, it amplifies it.

Making security part of how the business runs

DT Swiss AG, a Swiss manufacturer of high-performance cycling components with teams across Europe, North America, and Asia, faced complexity from fragmented systems, manual compliance processes, and administrative overhead. By moving toward a unified security model, it made identity, access, and governance part of daily operations rather than separate layers of work. The result was a 60% reduction in administrative overhead and a stronger compliance posture. Security did not slow the business down. It made scale more practical.

This is the story I find myself telling most often right now. Security is not a tax on transformation. Done right, it is what makes transformation sustainable.

Across these examples, the pattern is consistent. The SMBs pulling ahead are not simply adopting AI earlier. They are applying it with more intention, moving from isolated use cases to integrated workflows, from individual productivity to team-wide execution, and from security as a separate control to security as the foundation for growth.

Trust is the precondition, not an afterthought

One thing these businesses share: AI adoption and security are unequivocally connected. A 2024 Microsoft Security study found one in three SMBs hit by a cyberattack in the past year, at an average cost of USD254,445. 94% consider cybersecurity critical. And 81% say AI increases the need for stronger controls.³

The businesses moving fastest are solving productivity, data protection, identity, governance, and compliance together. SMBs do not have the time or resources to make five separate technology decisions for one business outcome. Security by design is not a feature, but a foundation for lasting AI adoption.

The partner ecosystem is the multiplier

No SMB transforms alone. Across these customer stories, partners play a consistent role: they help leaders decide where to start, where to incorporate technology into real workflows, and how to support adoption after deployment.

The Microsoft Partner ecosystem brings AI, productivity, and security into one practical conversation. For many SMBs, that begins in Microsoft 365 Copilot supporting how teams create, communicate, and make decisions. Next, extend those workflows using Microsoft Copilot Studio, connect data, and add security tools like Microsoft Defender for Business and Microsoft Purview all working together on a foundation with built-in, secure AI. With more than 1,400 connectors to third-party business applications, these solutions integrate into how businesses already operate across a broader secure cloud foundation.

The Microsoft Partner blog post, “Partner-led momentum, broader availability for SMB: Microsoft 365 Business with Copilot,” has more information on our Microsoft Defender for Business bundles.

• If you are an SMB owner or leader: Start where the time cost is most visible. You do not need a grand transformation plan. You need a first process, a secure foundation, and the decision to act.
• If you are a partner: Almost every SMB conversation is now an AI conversation. Customers are ready. Many still need help knowing where to begin. The partners and Managed Service Providers (MSPs) who lead with outcomes, secure adoption, and real workflow change will be the ones SMBs trust to reach the frontier.

Recognizing UNMSME

I am grateful for the resilience and ambition of small business owners everywhere. I know firsthand, the challenges of operating a business are real. In addition to my role at Microsoft, my husband and I run a small design-build construction company. This experience shapes how I see Frontier Transformation. Together, we are proving that the AI era will not be defined by company size but by leadership. SMBs are leading this moment.

What is the one workflow your team has transformed with AI this year? Connect with me and look forward to the conversation.

¹ United Nations, Micro-, Small and Medium-sized Enterprises Day, June 27, 2026.

² JPMorgan Chase Institute, Cash is King: Flows, Balances, and Buffer Days.

³ Microsoft Security, New research: Small and medium business (SMB) cyberattacks are frequent and costly, 2024.

The post Small and medium businesses aren’t waiting for an AI invitation—they’re already leading 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.

Yet a gap remains. While leaders have embraced AI’s potential, much of that energy is still concentrated in isolated use cases. The result is progress that feels real but rarely scales. Efficiency improves in pockets. Insights surface in moments. But enterprise impact remains limited. Frontier Transformation begins where this pattern breaks.

It signals a shift in how organizations think about AI—from something applied to specific tasks to something embedded across the business. AI moves into the flow of work, shaping decisions, powering processes, and enabling entirely new ways of operating. This shift is not about doing the same work faster. It is about redefining what work can be.

The organizations leading this transition are distinguished not by how much AI they deploy, but by how deliberately they align it to outcomes that matter. They focus on where AI can unlock new forms of value—across employees, customers, operations, and innovation. And they build the conditions for that value to scale, grounded in both intelligence and trust.

For business decision makers, this moment requires a different lens. The question is no longer where AI can save time. It is where AI can change the trajectory of the business.

The four paths to business value

While every organization’s journey looks different, leading organizations are converging on four core areas where AI drives meaningful impact.

These four paths define how AI moves from experimentation to enterprise value.

1. Enrich employee experiences

AI is transforming how work happens.

When intelligence is embedded into the tools employees already use, it reduces friction and elevates contribution. People spend less time searching for information or coordinating tasks and more time applying judgment, creativity, and expertise.

For example, organizations are using AI-powered knowledge hubs to surface institutional knowledge from documents, meetings, and media—making it easier for employees to access and apply critical information in real time.

This shift improves not only productivity but also decision quality, enabling employees to act faster and with greater confidence.

2. Reinvent customer engagement

Customer expectations continue to rise, but traditional engagement models struggle to keep pace.

AI enables organizations to deliver faster, more relevant, and more personalized interactions across channels. It can respond instantly to routine inquiries, connect customers to specialized expertise, and generate tailored recommendations in context.

In practice, this shift is already reshaping front-line experiences. AI-powered systems can eliminate wait times for common requests while routing more complex issues to the right experts with full context, improving both customer satisfaction and employee efficiency.

For example, Alaska Airlines created a natural language destination discovery experience that helps travelers find and book trips more intuitively. The result was 90% user satisfaction and 75% less planning time, showing how AI can make customer engagement both more personal and more efficient.

3. Reshape business processes

AI’s greatest potential lies in rethinking how work gets done.

Instead of optimizing individual steps, organizations can redesign entire workflows, accelerating execution and improving outcomes. Companies applying AI in this way are already seeing measurable gains in speed, efficiency, and scalability.

In some cases, organizations have reported dramatic results, such as significant reductions in time spent searching for business-critical information and the ability to scale complex analysis without increasing headcount.

These kinds of gains illustrate how AI enables new operating models rather than incremental improvements.

4. Bend the curve on innovation

AI expands what organizations can create and achieve.

Consider how organizations are using AI to integrate vast, distributed datasets or analyze unstructured content—such as interviews and videos—to unlock new insights. This capability is accelerating how quickly teams can experiment, learn, and bring new ideas to market.

For example, Space Intelligence used Microsoft AI capabilities to accelerate large-scale forest mapping—reducing the time required to map global forests by 75% while scaling coverage to billions of hectares.

When innovation becomes faster, more accessible, and more repeatable, it begins to compound across the organization.

Together, these four paths show how AI evolves from isolated initiatives into a driver of sustained business growth.

Why intelligence and trust matter

As organizations scale AI, a familiar challenge emerges: complexity increases, data becomes fragmented, and systems grow increasingly disconnected. As a result, early gains become harder to sustain.

The difference between organizations that stall and those that scale comes down to how they build their foundation.

At Microsoft, we see two elements as essential.

Intelligence ensures AI is grounded in real work—connecting data, workflows, and business context so outputs are relevant and actionable.

Trust ensures AI can scale safely—embedding security, governance, and responsible AI practices from the start so organizations can innovate with confidence.

These elements reinforce each other: intelligence drives value, and trust enables that value to scale. Together, they transform AI from a set of tools into a durable enterprise capability.

What BDMs should do next

For business decision makers, the priority is not adopting more AI. It is realizing more value from it.

Leaders seeing the greatest impact focus on a few consistent moves:

• Start with clear business outcomes where AI can deliver measurable impact.
• Demonstrate value early through focused deployments that build confidence.
• Scale through repeatable systems that extend success across the organization.

This approach helps organizations move from pilots to platforms—and from isolated results to enterprise impact.

Moving forward

Frontier Transformation is already underway. The opportunity now is to move beyond isolated gains and use AI to reshape how the business creates value.

To learn more, read the e-book Four Paths to Business Value with AI and explore how these paths can accelerate your organization’s journey.

The post 4 paths to Frontier Transformation: From AI experimentation to real business value appeared first on The Microsoft Cloud Blog.

1. Will AI amplify the intelligence of my organization and the attributes that make my company unique within its industry to grow my business; or will it use my intelligence for its own benefit, learning from my most important business flows and leveraging my intellectual property?
2. Can I trust that the outcomes are providing durable return on investment and that these solutions are running within the confines of my governance and security standards?
3. How do I get the visibility, control, flexibility and business model innovation needed to manage the costs associated with AI and maximize value?

I consistently advise customers that they need to build their own IQ on a platform of intelligence that is model-diverse, open and heterogeneous at every layer of the stack. Models are commoditizing. No company should be dependent upon any one model or any one model’s harness. Over the weekend, Satya warned of a world where every company across every sector is ceding value to a few models that eat everything they see. AI that is intended for growth should amplify the intelligence of an organization so that it compounds from within.

Companies also need an observability platform that provides governance, management, security and Financial Operations (FinOps) to ensure the ROI with AI. This enables AI to be trusted within the environment over which it reasons and puts the business in control of the outcomes.

Intelligence + Trust is embedded across Microsoft 365 Copilot, GitHub Copilot and Copilot Studio, where model diversity aligns cost and performance to each task. Microsoft IQ optimizes workflows, so context is routed efficiently and reduces unnecessary compute. Agent 365 is the control plane to observe, govern, manage and secure agents. We have built a system to manage AI spend as a core enterprise capability, not an afterthought. It is delivered across clouds and model providers without locking customers into a single approach.

Managing costs at scale

As agent usage scales, organizations need a clear set of levers to manage cost:

Model diversity. Any given inferencing model, model harness or agentic loop on its own does not help build out an organization’s IQ in ways that compound its intelligence. Both Microsoft 365 Copilot and GitHub Copilot are model-diverse by design without locking customers into a single provider. Different models — like GPT-5.5 or Claude Opus 4.8 — serve distinct roles with different economics. Matching the right intelligence to each task optimizes performance and cost.

Your IQ. Agents struggle with raw data. Significant compute is spent interpreting structure and context before useful work begins. The Microsoft IQ platform empowers your IQ by turning raw data into usable intelligence, continuously building a semantic understanding of how your organization operates across Microsoft 365 and line-of-business systems. It provides agents with the context they need upfront rather than requiring them to reconstruct it. The result is measurable: faster execution, higher accuracy and lower token usage. This is how intelligence compounds within your organization.

Financial operations. FinOps became critical when companies moved to the cloud and requires even greater attention as AI shifts from fixed pricing to usage-driven models. With Foundry and Agent 365, we are providing tools to help our customers optimize their AI costs today.

Frontier business models

Business models are evolving as we use AI to drive business outcomes. The User Subscription License (USL) has become the foundation, providing a package of capabilities for a predictable per-user-per-month fee. Usage-based licensing has emerged for long-running, multi-tasking agents, where cost aligns directly to the work performed.

Microsoft gives customers a unique combination of business model flexibility and integrated product experiences that is unmatched in the market. Microsoft 365 Copilot and GitHub Copilot use both models — a USL offering with not only value and capabilities, but flexible consumption on top. Today we’re announcing the general availability of Copilot Cowork worldwide, which requires the Microsoft 365 Copilot USL and is then usage-based.

Our model-diverse strategy allows customers to purchase capacity with the flexibility to use the right model for the job based on model strengths, economics and the latest innovations. Microsoft Agent Factory provides a single consumption model spanning Microsoft 365 Copilot (including Cowork), GitHub Copilot and agents built in Fabric, Foundry and Copilot Studio.

Our integrated product experiences put AI in the flow of work for both knowledge workers and software developers and manage capacity fluidly across the two. Historically these personas have been distinct, but increasingly the line between them is blurring. Coding is becoming a mainstream knowledge worker skill and chat and Cowork are becoming modalities important for software development. With Microsoft 365 and GitHub, we offer market-leading tools for both roles and make it easy to seamlessly manage capacity based on availability and need.

Agent 365: The control plane

As organizations adopt agents from Microsoft, another provider or build their own, a control plane is essential. Agent 365 gives IT and security leaders a single place to observe, govern, manage and secure agents across the organization. It builds on the Microsoft stack that enterprises trust: Entra for identity, Defender for threat protection, Purview for data governance and Intune for endpoint management. We are extending Agent 365 to include cost management, so organizations can monitor and manage agent spend alongside security and compliance. As the Frontier Firm operating model takes hold, leaders will manage human and agentic work as a single system, with visibility into both performance and cost.

The two most essential elements in any AI solution are Intelligence + Trust. At Microsoft, this conviction shapes how we design every layer of our AI platform. Microsoft IQ enables organizations to harness their own unique IQ, bringing context to data and embedding AI directly into the flow of work to deliver faster, more accurate and more trusted outcomes while safeguarding assets and protecting intellectual property. Agent 365 provides that trust layer, ensuring every agent and AI artifact is observed across the environment so organizations can move decisively from experimentation to enterprise impact with confidence. As Jay Parikh put it at Build, AI alone will not change your business. The system running it will.

We have built this system for our customers and partners, where intelligence compounds from within and every agent operates with control, visibility and trust. Together, we can scale human ambition and define how AI delivers measurable business impact across every role, organization and industry.

Judson Althoff is the chief executive officer of the commercial business at Microsoft. He is responsible for the product strategy, sales, services, support, marketing, operations and revenue growth of the company’s commercial business, which operates in more than 120 regional and national subsidiaries globally.

The post Achieving success with AI 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.

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