AI adoption is accelerating—but adoption alone isn’t transformation. Across industries, leaders are moving beyond experimentation and confronting a deeper challenge: How to reshape the way work gets done, decisions get made, and value gets created in an AI-driven world.

This executive series brings together perspectives from Microsoft leaders who are navigating that shift firsthand. Rather than focusing on tools or technology milestones, these conversations explore the leadership choices that determine whether AI delivers incremental efficiency or lasting impact—how leaders set direction, build culture, redesign work, and guide their organizations through change.

As Corporate Vice President, Business Applications and Agents at Microsoft, Bryan Goode spends his time at the intersection of technology, business process, and leadership, working to turn innovation into outcomes. In conversations with customers and partners across industries, he frequently hears the same underlying concern: Are we already too late to implement AI?

Leaders see headlines about rapid adoption and accelerating innovation, and assume that meaningful advantage now belongs only to early movers. From Goode’s perspective, that assumption misunderstands where real advantage is actually created and what kind of leadership this moment truly requires.

From my perspective, you’re not behind the curve if you haven’t started yet—but the time is now to really act.

Bryan Goode, Corporate Vice President, Business Applications and Agents, Microsoft

AI adoption is not the same as AI transformation

AI usage is undoubtedly increasing. More executives are experimenting with copilots, more employees are testing generative tools, and more organizations are exploring automation. But Goode consistently draws a distinction between adoption and transformation. Adoption reflects individual behavior. Transformation reshapes how workflows and value are created. Leaders who blur this distinction often feel progress without impact.

That distinction is critical. Many organizations feel progress because AI appears in daily routines, yet core business processes remain unchanged. Decisions are still delayed. Work still moves across disconnected systems. Potential value remains unrealized. In Goode’s view, this gap explains why so many leaders feel both excited and unsatisfied at the same time—progress is visible, but impact remains elusive.

Why functions—not tools—are the real starting point

From Goode’s perspective, the most effective starting point isn’t a tool, platform, nor architecture—it’s the function. Sales, marketing, finance, HR: each function contains friction that compounds quietly until performance stalls. When AI is applied directly to those processes, transformation can become tangible. Outcomes may improve, not because AI exists, but because work is redesigned.

Leadership sponsorship turns experimentation into execution

Functional ownership matters as much as technical capability. When senior leaders actively sponsor AI initiatives, teams gain clarity on priorities and permission to change how work gets done. That leadership signal is often what separates experimentation from execution. Without that sponsorship, AI remains an experiment rather than a catalyst.

Assistants and agents: Complementary forces

Goode also points to the role of assistants and agents as complementary, not competing, forces. Assistants improve individual productivity in the flow of work. Agents reduce friction across end‑to‑end processes. Together, they create space for human judgment where it matters most.

That’s really how you transform and how you get business value from AI.

Bryan Goode, Corporate Vice President, Business Applications and Agents, Microsoft

Culture is the hidden multiplier

Technology, however, is only part of the equation. Goode consistently highlights culture as the deciding factor. Organizations that treat AI as a shared learning journey where employees are encouraged to experiment, share insights, and iterate, are more likely to scale what works than those that pursue perfection upfront. In organizations that scale AI successfully, culture doesn’t follow transformation—it enables it.

It actually ends up being about culture more than anything else.

Bryan Goode, Corporate Vice President, Business Applications and Agents, Microsoft

Why starting small is a leadership advantage

Importantly, AI transformation does not require a massive rollout. In Goode’s experience, the organizations that make durable progress start small, focus on one function, learn quickly, and then scale intentionally. Transformation can compound as confidence grows.

For leaders who feel left behind, the reality is reassuring: in most organizations, the work itself has not yet changed. That means the opportunity remains.

The number one priority for every business leader is asking: how is AI changing my industry, how is it changing my company, and how am I going to use it to drive competitive advantage?

Bryan Goode, Corporate Vice President, Business Applications and Agents, Microsoft

The question is not how quickly AI can be adopted—it’s how deliberately leaders are willing to redesign the work that matters most and how ready they are to lead that change.

This is the first post in an executive series exploring how leaders navigate AI transformation—from culture and creativity to functions and outcomes.

The post You’re not late to AI—you’re early to Frontier Transformation 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 ebook 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.

As Australia looked to extend its winning streak to four straight Ashes on home soil, it was clear that left-handed batter Travis Head was leading the way for Australia as the runs piled up. But just how good was his performance? Fans using the Cricket Australia Live app had an instant answer. 

Thanks to the app’s new AI Insights feature, which provides live insights on player milestones, records and key moments using OpenAI’s GPT-5 within Microsoft Foundry, cricket aficionados and newcomers can now access much-needed context to better engage with the game. They can also dig deeper by asking follow-up questions about the insights provided. It’s an exciting development for Cricket Australia, the governing body of the sport in the country. 

“The recent series where England were here in Australia had a couple of key moments where I saw the insights come to life in real-time,” says Cricket Australia CEO Todd Greenberg. “And you can see the engagement through the analytics and the tracking that when something is delivered in the right time frame, in the right format, into the right hands, it has a huge effect.” 

Indeed, AI Insights showed that Head’s 172 runs for the match were his fifth-highest aggregate total in a test. His only higher efforts were 220 runs against Sri Lanka, 213 against West Indies, 181 against India and 180 against England. Head’s big day earned him Player of the Match honors and helped Australia claim a five-wicket victory in the match and a 4-1 Ashes series victory against its archrivals. 

Going beyond the box score 

“Scores and highlights tell you what happened. But the context tells you why you should care about it,” says Balamurugan P M, chief technology and digital officer at Cricket Australia. 

“It comes down to the storytelling. From my perspective, I thought it was essential for fans to learn more about the story rather than just following the scores or watching highlights. So, we wanted to give a different experience.” 

Cricket Australia had a corker in its arsenal as AI Insights came into focus – an extensive archive of official scorecards that dates to 1886, providing a wealth of historical data that could bridge the gap between the past and present. Those scorecards were carefully integrated over a period of three months to ensure the information would pass muster among the serious cricket experts. 

“We had hundreds of years of data, and when it comes to fans, trust is non-negotiable,” Balamurugan says. “When you’re dealing with records and milestones, you can’t make mistakes. There are some hardcore fans who know these stats like the back of their hand. History is core to cricket’s identity. And instant context turns a scoreboard into a story. 

“Getting that volume of data, integrating it and surfacing greater context for live games required huge data alignment and validation. With our systems and with the skilled team that we’ve got, that was made possible.” 

Creating a solution fans can use in real time 

Cricket Australia joined forces with Microsoft, alongside technical partners Insight Enterprises, HCL Tech and Skewer, to create the new iteration of the app. With the important Ashes and T20 international tournaments on the horizon, time was of the essence to launch the app before the bats were raised on those key fixtures. 

The app is anchored by Microsoft Azure, the cloud foundation that Cricket Australia uses to run and scale its digital platforms and the app experience. AI Insights takes advantage of Azure OpenAI Service in Microsoft Foundry, which generates the real-time, match-aware insights that serve as a companion to what fans are seeing on the field. 

“What we’re talking about is a really good example of solving a fan-facing problem with deep technical capability and a shared vision on delivery,” Greenberg says. “Microsoft brought world-class cloud and AI foundations. Without them, we would not have been able to get as far as we have. And our partners have helped accelerate the build, the integration and, importantly, operational readiness.” 

One of the biggest challenges with AI Insights is ensuring that fans watching a match and using the app can get updates and context within the flow of the game, making it an additional resource for fans at the grounds or watching alongside with commentary. 

Azure Cosmos DB supports Cricket Australia’s ecosystem of apps – including Cricket Australia Live with AI Insights and PlayCricket, which hosts scores for up to 7,000 community matches a weekend. The technology provides a fast, scalable data layer that can update quickly during live play, always keeping fans aware of the latest scores. 

“All live sport has one thing in common. There are no pauses,” Greenberg says. “It’s not like reality television. So, the experience has to be fast, reliable and consistent, especially when it’s under peak demand and when you have millions of people enjoying it at the same time.” 

An experience for every type of fan 

While cricket has its ardent supporters, especially in Australia, it can also be difficult for newcomers to pick up. As Cricket Australia looks to cultivate the next generation of fans, Greenberg realizes that the app can prevent sticky wickets for the sport’s novices. 

“I mean, we play a crazy sport that goes over five days and sometimes at the end of the five days, you still don’t get a result,” Greenberg says. “We can’t expect people to be tuned in at every moment, but what we can do is we can hyper-personalize the way they would like to engage with the sport during the contest.” 

The Seddon Cricket Club in Melbourne has been in existence since the 1920s and is now home to several senior, junior and all abilities sides that compete in associations across Australia. It is also home to a loyal supporters group, featuring fans who love the game in all forms. For them, the AI Insights on the Cricket Live App has been a value add as they go deeper into the game. 

“It’s definitely made it more interesting to follow along and learn more about the players,” says Cassie Gray, a Seddon Club supporter and cricket fan. “You could follow a player, you could see what they’re known for, as well as figure out what’s their next step or what do they need to get an amazing moment next. 

“Cricket is a game of history. It’s been around for a really long time, and the players influence other players, and countries influence other countries. With the insights, it gives me an understanding of not just what’s happening today, but what’s led up to that in the game itself.” 

The next step for AI insights is to create greater personalization within its levels of information for different types of fans. A user can select “newcomer,” “history buff” or “stats guru” and receive insights tailored to their persona. 

“We want to understand every fan and cater to how they want to be served by the app,” Balamurugan says. “We have moved from scores to storytelling, but we want to move from storytelling to fans setting up the narrative themselves. Fans should hear the story how they want to hear it. That is one of our lodestars.” 

With the initial success of the AI Insights feature, Greenberg said other sports organizations have reached out to learn more about how it was developed and the impact on the fanbase. Most people working at Cricket Australia have a deep love of the sport, often having played for many years. Greenberg hopes the app’s success and further innovation can continue the sport’s momentum. 

“The thing we’ll never know until much later on is the impact that we’re having on young kids falling in love and choosing cricket as their preferred sport,” he says. “And if we help them love it, what we can create for a fan on their journey between the ages of 8 and 80 is astronomical for a sport like cricket. And so, we’re very mindful of ensuring kids get the opportunity to engage in cricket so we can form lifelong partnerships.” 

Top Image caption: Supporters at the Seddon Cricket Club in Melbourne love the game in all forms, and the Cricket Live App featuring AI Insights has allowed them to gain further insights into the sport, whether they are a novice fan or stats guru. Photo by Graham Denholm for Microsoft.  

Elliott Smith writes about AI and innovation at Microsoft, from how the Premier League is transforming its online presence to why AI may play a major role in saving the Amazon rainforest. Previously, Smith worked as a sports reporter in Washington, D.C., Washington state and Texas, covering high schools to the pros. You can contact him on LinkedIn. 

The post Cricket Australia uses AI Insights to bring fans closer to the action appeared first on The Microsoft Cloud Blog.

To maintain velocity and control in 2026, steering committees must answer a fundamental question:

Do we have line-of-sight and control over the AI agents being deployed knowing exactly what they are, what data they touch, and what they are doing?

To help leaders navigate this complexity, we have refreshed our core framework: Grow Your Business with AI You Can Trust. This guide is a practical roadmap for structured decision-making across security and governance, now introducing a critical new pillar for 2026: Observability.

AI committee checkpoint: You cannot govern what you cannot see

As AI spreads across teams and tools, observability becomes the prerequisite for scaling. Without a centralized view, “shadow AI” and unmanaged agents may create significant risks, from security vulnerabilities to sensitive data leakage.

To achieve enterprise readiness, your AI steering committee should be able to answer four foundational questions:

• Inventory: What agents currently exist across our environment?
• Identity: Who is using these agents and for what purpose?
• Access: What systems and specific data sets do they have permission to touch?
• Outcomes: What workloads are they driving and what results are they producing?

Four capabilities for AI platform visibility

In our updated guide, we frame observability through four technical capabilities every enterprise platform should support:

1. Registry: A single source of truth to track every AI asset in the organization.
2. Agent analytics: Real-time data on performance, usage patterns, and costs.
3. Agent map: A visualization of the connections between agents, users, and data.
4. Role-specific oversight: Tailored dashboards that give IT, security, and business leaders the specific metrics they need.

The strategic impact: Accenture

Accenture saw innovation stall at the pilot stage as fragmented tools slowed their path to production. By implementing a centralized platform with built-in observability, they unified monitoring across development and deployment.

Accenture has already deployed more than 75 use cases across industries, with 16 in production, reducing AI app build time by 50%.

Executive checklist: Scaling with control

Your AI steering committee can use the refreshed guide as a checklist to support a secure foundation for AI scaling:

• 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.
• Achieve sustainability: Build systems with socio-economic and environmental impact in mind.
• Address digital sovereignty requirements: Understand common sovereignty scenarios and core principles to help your organization address them.
• Ensure visibility: Confirm your platform supports the 4 capabilities for agent observability.

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

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

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

AI use cases across industries

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

Learn more

How Frontier Transformation unfolds across industries

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

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

Frontier customers in action

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

Financial services: Transforming legal research with AI-powered insights

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

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

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

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

Retail: Reinventing the customer experience with AI-powered personalization

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

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

Fabio Tormen, Chief Information Officer at Venchi

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

Automotive: Leveraging AI-powered insights during vehicle development

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

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

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

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

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

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

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

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

Financial services: Empowering smarter decisions with real-time insights

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

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

Amit Gawali, Head of Engineering at Aon

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

Start your Frontier Transformation journey

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

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

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

It is a task that is not taken lightly by the Jets’ coaches, front office and scouting department, who will be featured in the team’s draft room as they make their final decisions.

But that gathering is just the tip of the iceberg when it comes to player evaluation, and the Jets are careful to explore every avenue available when assessing the thousands of players who are draft-eligible each season. Utilizing the latest technology to help them make more informed decisions is now a critical part of the team’s overall strategy.

“The draft is one of the primary ways in which we’re able to acquire talent, and it’s an extremely important event for us,” says Dan Zbojovsky, senior director, football operations for the Jets. “It’s a year-long process and oftentimes multi-year process for us to evaluate these players coming out of college and then get the opportunity to select them.”

A ‘Titan’ off the field

In the traditional draft scenario, teams receive dispatches from scouts around the country who file reports on players that range from height and weight measurements to 40-yard dash times to vertical leaps. Colleges will conduct Pro Days, in which scouts are invited to see a team’s top performers run drills and catch passes. And independent scouting services provide roundups of prospects major and minor, with their own evaluation systems.

In short, there’s a lot of information on a lot of potential draftees, and that doesn’t even include each NFL team’s own preferences based on organizational philosophy, coaching schemes and roster needs. While some teams prefer to keep things more analog, the Jets have been at the forefront of embracing technology to help them prepare not only for the draft but also the fast-paced nature of an NFL season.

The team’s proprietary Titan app (winkingly named after the team’s original moniker, The Titans of New York) is the team’s “mothership” for football operations – a custom-built web application that contains essential tools for draft preparation, scouting and personnel strategy.

“Titan is really the hub behind everything we do on the football side,” says Paul Marsh, senior director of application development. “It’s a legacy application of 15 years now through many, many different iterations, but it’s always remained Titan. It is where all of our scouting and football data is housed. It is the view into that data and it enables the powers that be to help make their decisions and come up with their plans to help make the team win.”

Titan is built on Microsoft technology, including Microsoft Azure, GitHub Copilot and GitHub Actions. Marsh’s team relies on GitHub Copilot to speed up coding, prototyping and iteration, helping them gain greater efficiency when time is tight leading up to the draft. GitHub Actions are used to automate, build and deploy pipelines, enabling frequent updates and continuous integration across Titan’s modules.

Another key element of Titan is the team’s draft/trade calculator, a points-based tool the Jets use to evaluate draft-day trade scenarios. In real time, New York’s football brain trust can plug in picks, compare values and determine whether a proposed trade would result in a net gain or loss for the team.

“This is a UI that was designed really by our [general manager] and his close advisors to work the way that they want to work,” says Marsh, who has been with the Jets for 24 seasons. “And it simply allows them to kind of dig into the information and game plan on what they’re going to do going forward into the draft.”

The Jets’ process has paid off with several important contributors being acquired via the draft, including wide receiver and 2022 Offensive Rookie of the Year Garrett Wilson, 2025 No. 7 selection Armand Membou, defensive end Will McDonald IV, running backs Breece Hall and Braelon Allen, and tight end Mason Taylor.

Old school, meet new school

For Zbojovsky, who is entering his 19th season with the franchise, the draft successes reflect the balance the team uses when combining the old-school scouting mentality and the technology and analytics of the new school of player evaluation.

“[Titan] is an extremely important internal website for us, and we’ve made a lot of really cool advancements over the years on it,” he says.

“I think everything has its piece of the puzzle. On certain players, some parts might be a bigger piece of that puzzle. We like to, of course, rely on our film work as the foundation of our reports and our scouting evaluations, and then we can utilize all these other cool tools or data points to help inform those evaluations and really help us, whether it be our stacking of our players or comparing players to each other. And really it adds a little bit of an objective piece into what can be a largely subjective evaluation off film.”

Zbojovsky and Marsh work closely with each other to ensure that any late-bloomers, fast-risers or strategic adjustments are reflected quickly in Titan so that everyone is on the same page.

“We always joke that if the GM wanted to come down to our office and say, ‘I need a button here, here and here, and I need it to do these things,’ we’re working on that right out of the gate as soon as he leaves that office,” Marsh says.

“We’re able to turn around very, very quickly because we’re able to push those changes right into our Microsoft stack and get them in front of him before he hits the end of the hall. It’s the trust that we can get things done very quickly because these guys have deadlines that don’t move. We can’t push back the draft. We can’t push back free agency.”

The fastest agent at the Combine

The Jets recently wrapped their time at the NFL Combine in Indianapolis, where all 32 teams convene to scout draft prospects as they go through a whirlwind of testing and drills. Numbers and measurements are flying fast and furious, so the Jets, along with the league’s other squads, use the NFL Combine App to help surface the official Combine data to coaches and scouts.

A custom Copilot AI agent is built into the NFL Combine App to allow coaches and scouts to surface fast insights and prospect comparisons with natural language questions that allow teams to get information on, for example, the average, highest and lowest linebacker results for each drill since 2015.

“The Copilot feature not only allows us to ask questions and filter through the information that’s present at the time, but also compare that back to previous years,” Zbojovsky says.

“So you start to really be able to stack how this player not only performed against this cohort here, but also against players that are currently in the NFL. And that helps you start to really understand where that player’s performance metrics on the field might fit within the players that he’s going to be joining in the league.”

Let the countdown begin

In a league where every decision matters and every potential advantage could swing the final score, both Marsh and Zbojovsky are thankful that the Jets continue to see technology as an integral part of scouting and preparation.

“We really are in a great spot for what we need to do. It keeps us nimble,” Marsh says. “Talking to other organizations and other technology companies, they are impressed with how quickly we’re able to iterate and move to get those solutions. We’re not bogged down. We’re given a lot of flexibility and the trust to do what we need to do.”

When the Jets turn in their pick, it will be so much more than writing a name on a card to hand to the commissioner. It will be the final product of research, data, scouting and technology all coming together to welcome the next potential superstar to the NFL.

“A lot of work goes in from a lot of people throughout the organization,” Zbojovsky says.

“We incorporate a lot of different data points and different types of evaluations, whether it be analytics or our scouts. We put a lot of work behind that to make sure we get our board right in advance and then we see how things fall on draft day. We look forward to success in April.”

Top photo courtesy of the Jets.

Learn how the NFL is using AI on and off the field to enhance operations and read how technology could help the Minnesota Vikings build next year’s winning edge. 

Elliott Smith writes about AI and innovation at Microsoft, from how the Premier League is transforming its online presence to why AI may play a major role in saving the Amazon rainforest. Previously, Smith worked as a sports reporter in Washington, D.C., Washington state and Texas, covering high schools to the pros. You can contact him on LinkedIn.

The post The New York Jets are happy to have a ‘Titan’ in their corner at the NFL Draft appeared first on The Microsoft Cloud Blog.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

8. How do I balance Frontier Transformation with sustainability? 

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

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

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

Next steps to lead in the era of Frontier Transformation

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

Explore Microsoft AI tools and solutions for your Frontier Transformation. 

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

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

This is where the frontier is forming. While copilots help individuals be more effective, agents act on behalf of people. They carry out tasks, orchestrate multi-step workflows, and operate across systems continuously. And they’re moving quickly from experimentation to mainstream use. An IDC InfoBrief, sponsored by Microsoft, shows that 37% of organizations surveyed use agentic AI, another 25% are experimenting with it, and 24% are planning to use it the next 24 months.¹ Organizations that have already invested in people, skills, and responsible practices may be better prepared to operationalize agents at scale—and convert AI’s promise into real business performance.

Five strategic moves for introducing agents responsibly

The new Agents in the Workforce Handbook builds on those earlier foundations. Where the first blog in this series focused on empowering your people, and the second explored how to pair human judgment with AI systems, this third chapter looks ahead: How do you introduce agents into your workforce responsibly and intentionally? Below are five strategic moves leaders should consider. These are high-level guideposts; the Handbook goes much deeper with templates, examples, and decision frameworks to support implementation.

1. Start with your most persistent pain points

When organizations begin exploring agentic AI, a common challenge is prioritization. Imagining use cases is easy. Choosing where to start is harder. Successful organizations don’t begin with futuristic ideas—they begin with the familiar, recurring friction points that quietly drain time and introduce risk.

These are often the workflows teams have learned to “live with”: manual triage, routine follow-up, coordination across systems, repeated reporting steps, or tasks with high error potential. Leaders should observe how work truly happens—shadowing teams, reviewing process maps, and asking simple but revealing questions:

• Where do we lose time?
• What gets done manually that shouldn’t be?
• What feels broken—but no one owns?

These pain points typically offer the clearest path to early value. Addressing them not only frees capacity but also demonstrates to teams how agents can meaningfully improve the day-to-day. The Agents in the Workforce Handbook includes a readiness assessment and real-world patterns to help leaders identify and sequence the right opportunities.

2. Define your AI goal—and lead the change yourself

Introducing agents isn’t only a technical shift—it’s a leadership shift. Frontier Firms choose to align their early agent initiatives around bold, measurable goals: reducing manual work, accelerating cycle times, improving customer responsiveness, or expanding sales capacity. These goals create alignment and momentum, helping teams understand why agents matter and what success looks like.

But goals alone don’t change culture—leaders do. The organizations that move fastest are those whose executives personally model new ways of working. They use agents in their own workflows, talk openly about learnings, and recognize early adopters who demonstrate impact. They also acknowledge that change requires habit‑building. Experimenting with agents for even 20 to 30 minutes a day can materially improve adoption and confidence.

Skilling plays a central role. As Jeana Jorgensen, Corporate Vice President of Global Skilling, notes:

We’re hearing from many of our customers and partners that they expect employees across different roles to spend about 15 to 20% of their week learning and integrating AI into their daily work.

The Handbook offers guidance for identifying the roles, skills, and operating rhythms needed to support agent adoption.

3. Measure what works—and double down where it does

As with any transformative technology, early wins with agents need to be measurable and repeatable. Leaders should ensure visibility into how agents behave, how frequently they’re used, and the outcomes they produce. This isn’t about policing technology—it’s about giving teams the insights needed to improve and scale what’s working.

Effective organizations treat agent adoption like an operational discipline:

• They log and monitor agent activity.
• They measure time saved and business impact generated.
• They expand agents that demonstrate clear value.
• They refine or retire agents that don’t.

These data-driven insights help organizations move from experimentation to a consistent, enterprise-wide model for agent development—one where new ideas become shared services rather than isolated automations. The Handbook goes deeper into measurement strategies, including examples of what high-performing organizations track.

4. As agents become teammates, optimize continuously

Once an organization begins deploying agents across teams, a new challenge emerges: coordination. Agents that start out as individual productivity tools often become shared digital teammates—relied upon by multiple people, processes, and business functions. With that shift comes the need for thoughtful ownership, governance, and communication.

Successful organizations establish clear roles and responsibilities:

• Who owns each agent?
• Who can modify or update it?
• How are changes communicated to the people who rely on it?
• What happens when an agent’s behavior needs tuning?

Agents also require continuous improvement. As they’re used, they encounter edge cases, nuanced team preferences, and shifting processes. Over time, agents become more capable, and employees naturally evolve into “AI managers”—guiding digital apprentices the way they onboard and develop human teammates.

The Handbook provides deeper recommendations for governance models, centers of excellence, and cross-team alignment mechanisms that help organizations scale responsibly.

5. Reinvest the time saved—and push into innovation

While early value often shows up as efficiency, the long-term impact of agentic AI is much bigger: it creates renewed capacity for innovation. Frontier Firms understand that the goal isn’t to simply do the same work faster—it’s to free teams to pursue higher-value ideas, explore new business models, and elevate customer experiences.

Across industries, leading organizations are already demonstrating what this reinvestment looks like:

• L’Oréal is using AI agents to deliver personalized, at-scale beauty guidance.
• AT&T accelerated customer care resolution by 33%, saving millions while improving experience.

These examples highlight a crucial point: agents are not just workflow optimizers. They’re catalysts for reimagining how organizations deliver value. And the companies that begin investing now are positioning themselves for meaningful advantage.

Treat agents like teammates, not tools

The organizations achieving the strongest results view agents not as automations but as digital collaborators—systems that require feedback, tuning, and iteration. They integrate agents into team rhythms, treat them like growing contributors, and help their people evolve into confident AI managers.

This marks the natural third step in the Frontier journey: after empowering employees and strengthening the partnership between human expertise and AI (as explored in the first two blogs), organizations are now ready to bring digital teammates into the workflow in a structured, scalable way.

If your organization is ready to move from experimentation to scaled impact, the Agents in the Workforce Handbook offers the detailed guidance, examples, and templates to support your next phase of Frontier Transformation.

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

The post How to introduce agents into your workforce: 5 actions leaders can take appeared first on The Microsoft Cloud Blog.