Here at Microsoft, we’re no different.

Complaints about the time and effort required to approve expense reports have been consistent from managers across our organization.

“We continuously heard feedback from our leaders that they were spending too much time approving expense reports,” says Michael He, a senior business program manager in the Greater China Region. “They didn’t know where the potential risk actually was, so they had to review everything in detail.”

At Microsoft Digital, the company’s IT organization, we’ve taken this challenge on by introducing an AI-powered Intelligent Risk Engine.

“The Intelligent Risk Engine cuts through complexity, pointing approvers straight to the expenses that warrant attention. This clears up the noise that has been driving review fatigue, especially at quarter’s end.”

Sangay Wangmo, Microsoft Digital experience director, Middle East and Africa

With this new tool, we shifted approvals from uniform, manual scrutiny to automated, risk-based decision making. This enables faster reviews, reduced cognitive load, and improved compliance outcomes.

“The Intelligent Risk Engine cuts through complexity, pointing approvers straight to the expenses that warrant attention,” says Sangay Wangmo, a Microsoft Digital experience director for our Middle East and Africa region. “This clears up the noise that has been driving review fatigue, especially at quarter’s end.”

Looking ahead, we plan to expand the tool to include automated approvals for low-risk cases, creating a more scalable, intelligent, and efficient process. This will ease the pain for our managers and allow them to focus on their strategic work.

Manual approvals in a complex compliance environment

Across our global enterprise, we handle nearly a million expense reports annually. In regions such as Central and Eastern Europe, the Middle East, and Africa (CEMA), our expense approval processes are shaped by diverse regulatory requirements in many different countries.

“As organizations scale, managers naturally have more direct reports, which means more approvals to process. At the same time, accountability for all compliance still sits with the manager, which adds pressure.”

Kethan Parbhoo, general manager, Central and Eastern Europe, Middle East, and Africa

To take one example, the Middle East and Africa—featuring multiple subregions, languages, and local policy nuances—presents a complex challenge for expense management. Applying a consistent risk lens to every case is difficult.

To ensure they stay compliant, leaders often review expense reports in detail, including verifying receipt accuracy, matching invoice data, and checking supporting information (like attendee lists). This level of review requires substantial time and attention, particularly for managers with large teams who receive a high volume of submissions. This becomes even more time-consuming as groups grow.

“As their organizations scale, managers naturally have more direct reports, which means more approvals to process,” says Kethan Parbhoo, a general manager in the Central and Eastern Europe, Middle East, and Africa region. “At the same time, accountability for all compliance still sits with the manager, which adds pressure.”  

The current tool, MS Expense, which was useful in a pre-AI environment, doesn’t provide an optimal user experience. The process was repetitive and depended heavily on manual validation. Existing tools provide limited support for prioritizing risk or simplifying these tasks, resulting in a similar effort being applied to both low- and high-risk expenses. 

As a result, leaders experience increased workload, slower approval timelines, and continued exposure to potential compliance gaps, despite careful review. 

The four top-level internal pain points of the old approval process can be summarized as: 

• Not knowing where the risk is 
• Approvals take too much time, especially at quarter’s end 
• Too much effort is spent on low-risk, routine reviews 
• Issues are found too late, triggering audits and resubmissions after the fact

The Intelligent Risk Engine is helping us address all of these in a unified, cohesive way.

“The system leverages a combination of AI-based risk checks and policy-driven risk checks. This produces a quantifiable baseline score that allows for easier comparison and risk assessment.”

Eric Carnrite, principal product manager, Travel and Expense

AI-assisted risk scoring embedded in MS Approvals 

The Intelligent Risk Engine that our team developed integrates with the existing MS Approvals system, shifting from volume-based checks to risk-based decisioning. We embed this analysis directly into the workflow.

The risk engine evaluates each expense report against multiple criteria, including receipt matching (which leverages AI and optical character recognition), spending patterns, and policy alignment. It assigns a risk score (1-100) and a risk level—1 at the low end and 5 at the high end—and then highlights specific areas that might require attention. 

“The system leverages a combination of AI-based risk checks and policy-driven risk checks,” says Eric Carnrite, a principal product manager for the Travel and Expense team. “This produces a quantifiable baseline score that allows for easier comparison and risk assessment.”

Expense risk score table

The tool also explains why something has been flagged. This allows approvers to quickly understand where to focus their review and to catch issues early, rather than after the fact. 

“AI is effectively doing the initial assessment that a human would otherwise have to do,” Parbhoo says. “It gives you a strong signal, so you can decide quickly where deeper review is needed.” 

With our new model, low-risk expenses can be reviewed and approved with minimal effort, while higher-risk items receive closer examination. It just makes sense to prioritize our work this way.

“Previously, a $10 coffee receipt required the same level of scrutiny as a $300 invoice, which doesn’t make sense at scale,” Wangmo says. “Leaders are forced to treat everything the same, even when the risk level is clearly different.”

Early returns indicate significant improvements. These include:

• Immediate productivity gains, as approvers stop reviewing all expenses manually
• Reduced rework and late‑stage audit findings 
• Stronger governance at scale, without adding headcount or introducing new manual processes

“It pulls the three parts together: Employees, approvers, and auditing and compliance. It will ultimately make it more proactive for all parties involved, rather than reactive—making sure the whole flow of the expense process is more meaningful.”

Michael He, senior business program manager, Greater China Region

Future direction: Expanded automation and standardization 

This kind of AI-powered technology will eventually allow us to pull everything together in one unified system, meeting the needs of all the major players in the expense management process.

“It pulls the three parts together: employees, approvers, and auditing and compliance,” He says. “This will ultimately make it more proactive for all parties involved, rather than reactive—making sure the whole flow of the expense process is more meaningful.”

And we’re not done innovating. The current risk engine implementation establishes a foundation for further automation, and that’s where we’re headed.

One planned enhancement is the automatic approval of low-risk expenses, subject to compliance approval. This could produce significant savings and greater efficiency across our organization.

“With auto-approvals, we’re not talking about a nominal amount,” Carnrite says. “At this point, we’re targeting up to 75 percent of expense reports being automatically reviewed and approved. This could save us around 150,000 to 200,000 person-hours a year—and that’s at the manager and director level.”

We’ve also added advanced optical character recognition (OCR) technology into our expense tools. This now allows for automatic categorization of expenses, so employees don’t have to enter the category manually for each item.

“The end goal is an AI agent that can proactively create an expense report for you and ask you to review it. You would just validate it and move it forward, instead of building it from scratch.”

Salvador Segura, director of business programs, Field Capability Services

Additional future improvements could include expanded use of AI for data validation, receipt processing, and identification of inconsistencies across submissions. Over time, the goal is to support a standardized approval framework that adapts to regional differences while maintaining consistent risk evaluation and reducing manual workload.

At the next level, we’re hoping to use AI to eventually fully automate the creation of expense reports as well. This would be essentially the Holy Grail for this function.

“The end goal is an AI agent that can proactively create an expense report for you and ask you to review it,” says Salvador Segura, a director of business programs in Field Capability Services. “You would just validate it and move it forward, instead of building it from scratch.” 

It’s this kind of AI-powered work environment that we’re pushing for at Microsoft Digital as we play a leading role in our company’s ongoing Frontier Firm journey.

The post Simplifying expense approvals at Microsoft with AI-powered risk assessment appeared first on Inside Track Blog.

At Microsoft Digital, the company’s IT organization, we’re using a set of employee councils and connected capability groups to guide and accelerate how we deploy and adopt AI across our enterprise. Our goal is to focus our energy on the AI-enabled scenarios that matter most, reducing duplication, strengthening accountability, and making sure our investments create measurable value.

“Our AI decisions and direction must be grounded in business strategy. AI councils provide guidance and enablement for our organization, ensuring that our investments in AI generate tangible benefits to our business. It’s not just developing technology and then looking for a problem to solve with it—we start with the opportunity.”

Don Campbell, principal group technical program manager, Microsoft Digital

That focus matters, because AI success doesn’t come from usage alone. It comes from connecting strategy, enablement, data readiness, responsible AI, continuous improvement, change management, and measurement into one driving force.

That’s how we’re moving from experimentation to repeatable outcomes and from AI enthusiasm to AI accountability.

“Our AI decisions and direction must be grounded in business strategy,” says Don Campbell, principal group technical program manager in Microsoft Digital. “AI councils provide guidance and enablement for our organization, ensuring our investments in AI generate tangible benefits to our business. It’s not just developing technology and then looking for a problem to solve with it—we start with the opportunity.”

Our council-based approach is helping us accelerate our Frontier Firm transformation. The councils work together to set direction for AI adoption at Microsoft Digital, ensuring that our business needs drive solution development that can keep up with the pace of AI change. This work includes building visibility into all our AI solutions, including agents and Model Context Protocol (MCP) servers, while establishing governance and proven practices; developing training and learning pathways; and connecting teams together that are working on similar solutions across the enterprise.

We’re excited for a future where our employees use intelligent agents and human judgment together to work smarter, move faster, and unlock new value for Microsoft and our customers.

Why we use councils to guide internal AI efforts

Effective AI needs both enterprise guidance and business-owned direction. That’s why we’re using councils and connected capability groups as the operating model for our AI deployment.

Each group has a distinct role, and none of them work alone. Together, they help us connect the strategy for AI to the work currently happening across Microsoft Digital.

• Our strategy council sets priorities by aligning AI work to business goals, identifying top scenarios, prioritizing investments, and keeping KPIs and value in focus.
• Our enablement council uses our AI Center of Excellence to turn strategy into action through technical guidance, proven practices, ideation, learning, knowledge sharing, culture, and governance.
• Our data council strengthens the AI foundation via data strategy, governance, access, quality, literacy, and prioritization.
• Our process council drives continuous improvement through operational excellence, problem solving, prioritization, value realization, coaching, and learning.
• Our compliance council applies Responsible AI principles to ensure compliance, inclusiveness, fairness, transparency, and reliability.
• Measurement ties it all together by tracking both business outcomes and engineering artifacts, ensuring we can clearly demonstrate real-time value realization.  

These councils help us see across the business landscape through the lens of AI. They enable us to reduce duplication, scale what works, and make better decisions about where AI can create value. That allows our teams to keep moving fast without letting activity get ahead of accountability.

Aligning AI strategy to business value

Our strategy council helps us decide which AI-enabled scenarios deserve the most attention, which investments align to our business priorities, and how we’ll know whether the work is creating value. It gives leaders a practical way to look across the portfolio and keep our AI work tied to the outcomes we’re accountable for.

“Business strategy defines the what and the why. AI defines the how, enabling execution of the strategy and delivering real value. We should use AI to advance our business strategy, not the other way around.”

Qingsu Wu, principal group product manager, Microsoft Digital

This is important, because broad experimentation is useful early on in your AI journey. It helps teams learn and build momentum. But experimentation has to mature into focus. Without that shift, organizations can end up with too many different agents, agent skills, MCP servers, and other artifacts, without a clear view of what’s actually impacting the business.

We’re using the strategy council to keep that from happening.

“Business strategy needs to lead the AI strategy,” says Qingsu Wu, a principal group product manager in Microsoft Digital and an influential member of the strategy council. “Business strategy defines the what and the why. AI defines the how, enabling execution of the strategy and delivering real value. We need to use AI to advance our business strategy, not the other way around.”

That principle shapes how we work. We use the strategy council to identify our top AI-enabled scenarios, clarify the value we expect to create with each one, and connect that work to a monthly operating rhythm. Product owners still manage delivery and the council keeps the portfolio focused, visible, and aligned.

Tuning strategy into repeatable execution

Our AI Center of Excellence (CoE) is at the heart of our approach to enablement. It helps us translate enterprise AI priorities into practical guidance and execution support for teams building AI-enabled solutions.

“We can see patterns that a single team can’t. We’re translating AI CoE strategy and enterprise priorities into clear execution plans that work in each organization’s context. That allows us to align priorities and make sure our biggest bets are actually landing.”

Ria Khetan, senior program manager, Microsoft Digital

The AI CoE extends the reach of the strategy council. It gives teams what they need to build, govern, reuse, and scale what matters, while the strategy council assists us in deciding where to focus.

That connective role is central to the broader council model. The strategy council identifies the top AI-enabled scenarios. The AI Center of Excellence connects strategy to execution across the organization, operating as a cross-functional coordination layer that sets direction and creates shared accountability.

“We can see patterns that a single team can’t,” says Ria Khetan, a senior program manager in Microsoft Digital, who is a member of the council. “We’re translating AI CoE strategy and enterprise priorities into clear execution plans that work in each organization’s context. That allows us to align priorities and make sure our biggest bets are actually landing.”

The COE helps teams move those scenarios forward with answers to important questions:

• What initiatives are in flight?
• What initiatives bring the most return on investment?
• Where is there potential duplication?
• Where do we need clearer guidance?
• Where do we need stronger governance?

It also helps reduce fragmentation. When teams build in isolation, they can solve the same problem in different ways. They can choose different patterns, interpret standards differently, or create solutions that don’t scale beyond a single context. Enablement gives us a shared way to look across that activity and ask better questions.

“We use the CoE to bring consistency to how AI work gets done,” Campbell says. “It gives us a way to step back and ask whether we’re solving the right problems and whether we’re set up to scale.”

“High-quality, well-governed data is essential to accelerate AI implementation and adoption, and to ultimately unlock its full value. Data quality, accessibility, and governance are imperatives for AI systems to be reliable, scalable, and business-critical. Recognizing this principle is propelling our data strategy.”

Miguel Uribe, principal PM manager, Microsoft Digital

Building AI on trusted data

Our AI scale depends on trusted and reliable data. That makes our data council central to our council-based approach. This council makes sure our teams work with data that’s governed, discoverable, accessible, and ready for AI.

“High-quality, well-governed data is essential to accelerate AI implementation and adoption, and to ultimately unlock its full value,” says Miguel Uribe, a principal PM manager in Microsoft Digital and member of the data council. “Data quality, accessibility, and governance are imperatives for AI systems to be reliable, scalable, and business-critical. Recognizing this principle is propelling our data strategy.”

We’re applying a data mesh mindset to balance domain ownership with enterprise consistency. Teams stay close to the data that they know best. Shared standards for governance, quality, metadata, and compliance provide a framework to make that data useful across Microsoft Digital.

Microsoft Fabric and Microsoft Purview are key to that approach. Microsoft Fabric unifies our siloed data in a shared data mesh. Microsoft Purview enables governance and best practices to ensure that we manage our data responsibly through discovery, classification, protection, and monitoring.

Our goal is AI-ready data that’s available, complete, accurate, and high quality. Our data council also works with the AI Center of Excellence to strengthen data and AI fluency through learning pathways, operational practices, and community programs.

“Our capacity to drive process improvements has been crucial to our AI transformation as a company. We’ve adopted a ‘CI before AI’ approach to ensure that we don’t end up automating inefficient processes.”

David Laves, director of business programs, Microsoft Digital

Improving the process before applying AI

AI works best when it’s applied to the right problem. That’s why continuous improvement is part of our council-based approach. Before teams automate a workflow or build an agent, we want them to understand the process, identify waste, and decide where AI can create measurable value.

“Our capacity to drive process improvements has been crucial to our AI transformation as a company,” says David Laves, director of business programs in Microsoft Digital and a member of the Continuous Improvement Center of Excellence. “We’ve adopted a ‘CI before AI’ approach to ensure that we don’t end up automating inefficient processes.”

Continuous improvement helps teams make sure the underlying work is worth scaling. That’s when a continuous improvement approach can help. It encourages practices like Gemba walks, Kaizen events, bowler cards, and monthly business reviews that allow our teams to understand where work gets stuck and where AI can help.

Continuous improvement keeps the council model grounded in real work. We’re applying it where the process is understood, the value is clear, and the outcome can be measured.

Scaling AI responsibly

Our compliance council encourages the application of Responsible AI, so our teams can move faster with confidence. As our AI work scales across Microsoft Digital, responsible AI has to connect directly to the same council ecosystem that guides strategy, enablement, data, process, and measurement. That connection helps teams understand what they’re accountable for before they build too far, too fast.

Our responsible AI work focuses on compliance, inclusiveness, fairness, transparency, reliability, privacy, security, and accountability. It’s grounded in the Microsoft Responsible AI Standard and supported by responsible AI champions who help teams apply those expectations in real development workflows.

This approach gives teams structure. It allows them to assess impact, identify risks, document decisions, and bring in the right reviewers. It also creates consistency, as more AI agents and solutions move from experimentation into enterprise use.

The goal is to enable AI project teams to move in the right direction with the right safeguards. Responsible AI gives the strategy council, the AI Center of Excellence, the data council, and product teams a shared standard for trust—to turn ambition into accountable execution. It also makes sure the AI systems we scale are worthy of the trust that employees, customers, and the company place in them.

Measuring our AI outcomes

Our councils choose the right AI work, support teams as they build, strengthen the data foundation, apply responsible AI, and improve processes before we scale. But we still need to answer the most important question: What changed because of the AI investment?

That’s why we have built a common value measurement framework across Microsoft Digital. Our teams use the framework to define expected value before they build. With it, they can establish a baseline, track results, and review what they learn with the right business and AI owners.

We organize AI value across six areas: Revenue impact, productivity and efficiency, security and risk management, employee and customer experience, quality improvement, and cost savings. Not every initiative needs to deliver value in every category. The point is to create a shared language that leaders and teams can use to compare investments, make tradeoffs, and understand progress.

Measurement also pushes us past simple savings claims.

If AI saves time, reduces cost, improves quality, or increases coverage, we want to know what happens next. Did teams reinvest that capacity? Did service improve? Did risk go down? Did quality increase?

AI accountability depends on that full loop. We define value, measure results, review progress, and adjust. Then we use what we learn to guide the next round of decisions.

Operating as one connected AI system

Our AI councils make a difference because each group has a different focus.

“What got us here won’t get us to where we need to go next. We started with broad experimentation—getting teams excited and building—but now we’re evolving as an organization to think about scale, alignment to business goals, and making sure our investments are driving the right outcomes.”

Myron Wan, principal group product manager, Microsoft Digital

Strategy assists us in choosing the right priorities. Enablement helps our teams to build with shared patterns. Data readiness gives AI systems a trusted foundation. Responsible AI allows us to move faster with confidence. Continuous improvement makes sure we’re improving the work before we automate it. Measurement tells us whether the investment changed anything meaningful.

Together, this system means we can operate AI as a business-driven enablement system.

“What got us here won’t get us to where we need to go next,” says Myron Wan, a principal group product manager in Microsoft Digital. “We started with broad experimentation—getting teams excited and building—but now we’re evolving as an organization to think about scale, alignment to business goals, and making sure our investments are driving the right outcomes.”

There’s more work ahead. We need to keep scaling enablement, improving data readiness, increasing high-value use cases, showcasing measurable impact, and tightening alignment across teams.

We also need to keep asking the hard questions: Where should we invest? Where are we reducing risk? Are we reinvesting the value that AI creates?

Our council-based model allows us to answer those questions with discipline. It helps us connect AI ambition to business outcomes and move from experimentation to repeatable enterprise value. And it provides a practical model that other IT organizations can adapt as they guide their own AI deployment.

The post Guiding our AI deployment with a set of employee councils appeared first on Inside Track Blog.

Today, the company has grown into a global organization with more than 200,000 employees. They all rely on Microsoft Digital—the company’s IT organization—to provide the tools, technologies, and solutions that empower them to accomplish more every day.

The need for digital transformation

The history of information technology is one of constant evolution, and the pace of change has never felt greater than it does right now. The AI capabilities and other groundbreaking innovations unveiled in the last few years show the potential to radically transform our world and change the way we think about and operate all IT services.

When the world pivoted to remote online work and collaboration because of the COVID-19 pandemic, it was just one example of how digital transformation doesn’t always happen in a straight line or on a predictable schedule. Our company’s history of shaping and adapting its IT organization to the latest challenges faced by employees and partners is no different; marked by big bets and strategic shifts that reflect our ever-changing world.

Mapping our IT journey

Today, Microsoft Digital is the team that powers, protects, and transforms the digital employee experience across all devices, applications, and hybrid infrastructure at the company. Using our deep knowledge and experience in enterprise IT, we’re pivoting to help lead the company’s AI transformation while also sharing our journey with customers so they can take advantage of this generational opportunity to reshape their businesses and IT operations.

To understand where we’re going, it helps to take a look at where we’ve been. This article explores the details of the major eras of our IT history and then shifts to examine the trendlines and technological innovations that are shaping Microsoft now.

On-Premises IT (founding to 2009)

It’s useful to break the history of our IT operations into different eras. For the first three decades or so from its founding in 1975, Microsoft operated with on-premises IT systems. This era was characterized by the setup, operation, and maintenance of onsite physical technology—servers, datacenters, and other hardware infrastructure.

During this time, IT roles were narrowly defined. IT team members functioned primarily as “order-takers,” with limited influence over strategic decisions.

Because funding was inconsistent, our IT organization had limited growth opportunities and relied on vendors for development work. Gaps were filled in with “shadow IT,” where internal teams would sometimes procure their own hardware or software without formal IT approval or standards.

We established security as an early priority for the company. Cofounder Bill Gates launched the Trustworthy Computing initiative more than two decades ago, an effort emphasizing the importance of security, privacy, and reliability across Microsoft products and services both internally and externally.

Our On-Premises IT era established the foundation that would become crucial to the company’s future digital transformation.

All in on the cloud: The Cloud and Culture era (2010-2018)

Cloud computing marked the next significant shift in the history of IT at Microsoft. It began in 2010 under the leadership of CEO Steve Ballmer, signaling a major break with the previous era of physical IT infrastructure and an important step toward today’s distributed-computing world.

The launch of the cloud computing platform then known as Windows Azure heralded this new era, as we transitioned away from an IT philosophy focused on the Windows desktop client toward a more platform-agnostic view. Cloud computing offered extensive advantages for customers and for our own IT environment, in terms of cost, performance, security, and scalability.

We started our journey by moving productivity workloads (Exchange and SharePoint) to the cloud. Then, we shifted new development to Azure and optimized modern applications to run in the cloud. We also moved existing applications targeted for migration to virtual machines. Today, more than 98% of our IT workloads run on Azure.

Cultural transformation

Another important shift during this era was the profound cultural transformation at Microsoft sparked by new CEO Satya Nadella, who rose to the top job at the company in 2014. Nadella had previously run the Microsoft cloud computing and enterprise group, so he was already steeped in the idea of transformational change at the company.

“Achieving our mission requires us to evolve our culture. It all starts with a growth mindset—a passion to learn and bring our best every day to make a bigger difference in the world.”

Satya Nadella, CEO, Microsoft

Before Nadella’s ascension, Microsoft had long been known for its extremely competitive, “know-it-all” culture. Employees succeeded by showcasing their own individual achievements and how their accomplishments exceeded their peers.

Nadella changed this ethos by championing a growth mindset, encouraging employees to be “learn-it-alls” rather than “know-it-alls.” The shift included placing new importance on how employees contributed to the success of others, a value that was incorporated into individual performance reviews. Nadella made this transformation his personal mission and directed leadership to propagate the new philosophy at all levels across the organization.

“Achieving our mission requires us to evolve our culture,” Nadella says. “It all starts with a growth mindset—a passion to learn and bring our best every day to make a bigger difference in the world.”

The combination of the shift to cloud computing infrastructure and overhauling the company culture helped set the stage for the major technological innovations to come.

A new vision: The Modern Engineering era (2018-2023)

For years, IT at Microsoft had been order takers, doing what the business requested with limited ability to impact strategic priorities. That changed as we shifted to become a modern engineering organization. With support from our executive leadership, IT was elevated to become a peer engineering function at Microsoft.

Rather than simply taking orders, the team was empowered to lead with a strong vision for the future. In fact, leading with vision is the primary hallmark of our Modern Engineering era. As we moved into this era, we needed a clearly articulated view of our goals as an IT organization aligned to the needs of our business partners, as well as the resources needed to achieve them.

Role transformation

Transitioning to become a modern engineering organization required Microsoft Digital to adapt our legacy approach to IT.

Operating an engineering organization in a cloud environment meant new roles, new skills, and a new mindset. With no need to manage physical hardware, our modern IT professionals were freed to work more closely with business partners, requiring greater strategic acumen. The team was now focused on DevOps, Agile program management, and user-centric design principles.

User-centric, coherent design

Our design philosophy puts the user—an employee or guest—at the heart of every decision we make at Microsoft Digital.

The goal of this approach is to make tasks that might have previously caused friction to become simpler. Instead of dealing with disconnected systems, user-centric design introduces consistent and logical flow between services. This makes it easier for people to access services, learn how to use them, and then put them to good use.

Microsoft also embraces coherent design across all our products. A familiar look and feel, along with consistent usage patterns, accelerates employee usage and adoption. 

Embracing work-from-anywhere capability

During the pandemic, when our workforce was still fully remote, our organization was already starting to think about what the new hybrid workplace would look like when people started returning to the office. We identified three key dimensions of the employee experience:

• Physical spaces: We partner with Global Workplace Services to plan and deploy meeting spaces with amazing digital capabilities that support an inclusive approach to hybrid productivity.
• Digital capabilities: We keep employees productive and their digital environment safe and secure, no matter where they’re located or how they connect.
• Culture: A strong partnership with HR ensures that digital experiences support our company culture.

Managing shadow IT with a culture of trust

Shadow IT is the unknown and unmanaged set of applications, services, and infrastructure that are developed and managed outside standard IT policies. Shadow IT typically crops up when engineering teams are unable to support the needs of non-engineering partners, a situation that could arise from a lack of available capacity or the need for specialized domain solutions. 

While earlier eras of our IT history focused on trying to prevent shadow IT, we are now concentrating on managing it. We use Azure best practices to optimize shadow IT and Microsoft 365 governance policies to ensure that our corporate security, privacy, and accessibility standards are met. We empower our employees to create whatever they need within our tenant, including PowerApps, SharePoint sites, Teams channels, or agents, mitigating the need for “shadow” solutions while also providing visibility into how our employees are using our own technology.

Learn how optimizing our Microsoft Azure usage is helping us manage our Shadow IT.

The Era of AI (2023 to present)

The latest chapter in the history of our organization’s digital transformation is defined by the integration of AI and agents into IT operations. AI is revolutionizing how Microsoft does IT at enterprise scale, driving efficiency and innovation across the board. From the apps, workflows, and services that power our employee experience to the network, infrastructure, and devices that enable employee productivity, our AI-focused investments provide a solid foundation for the innovations that we are constantly implementing. As we look at the future of Microsoft Digital, we’re focused on four key priorities: security, service fundamentals, acting as Customer Zero, and AI-powered innovation. We’re working to excel in all four domains with the help of our industry-leading AI capabilities.  

“Our mission is to power and protect Microsoft, and that starts with an unwavering commitment to the Secure Future Initiative.”

Brian Fielder, vice president, Microsoft Digital

Securing our future

Security is our highest priority. The Microsoft Secure Future Initiative aligns every team with a shared approach, common priorities, and consistent milestones to harden our security posture across all products and services.  

“Prioritizing security above all else is critical to our company’s future,” Nadella says. “Every task we take on—from a line of code to a customer or partner process—is an opportunity to help bolster our own security and that of our entire ecosystem. If you’re faced with a tradeoff between security and another priority, your answer is clear: Do security.”

The Secure Future Initiative is built on three core principles: Secure by design, secure by default, and secure operations. As the company’s IT organization, we work relentlessly to fulfill the key pillars of the Secure Future initiative across all our systems, including:

• Safeguarding identities and secrets
• Protecting tenants and isolating production systems
• Securing networks and engineering systems
• Enhancing threat detection
• Expediting response and remediation

“Our mission is to power and protect Microsoft, and that starts with an unwavering commitment to the Secure Future Initiative,” says Brian Fielder, vice president of Microsoft Digital.

Secure Future Initiative | Microsoft

Foundations: Service fundamentals

The second pillar is to maintain the highest standards of service fundamentals. These are the essential capabilities and practices that enable us to deliver reliable, secure, and compliant services companywide. Adhering to the highest standards of service fundamentals ensures that our organization continues to play a critical role in running the company’s business and enabling innovation, agility, and resilience in a fast-changing and competitive environment.

Customer Zero

The third pillar is acting as Customer Zero for Microsoft’s most important products and services, like Copilot Studio, Microsoft Teams, and Agent 365. In Microsoft Digital, we take pride in being the first customer for a wide variety of Microsoft products and services, relentlessly focusing on our own employee experience to create products that enable every person on the planet to achieve more.

Being Customer Zero means forging a deep partnership between our IT organization and product engineering groups to envision the right experiences, co-develop innovative solutions, and then listen to and act on insights gathered from our employees. We work together to stay grounded in the way our employees use our products every day, so your employees can benefit from our insights prior to external product launches.

Read about how we’re improving our employee experience through our Customer Zero focus.

AI-powered innovation

The final pillar of this era is innovating with AI to transform the digital experience at Microsoft. By doing all the fundamental work detailed above—security, foundations, and Customer Zero—extremely well, we gain the confidence and earn the trust necessary to embed AI across our full portfolio of services. We do this over three key dimensions: core IT services, employee experiences, and corporate functions.

Core IT services: Transforming and securing our network and infrastructure

We’re focused on using AI to infuse data-driven intelligence into every part of our infrastructure and network operations. This allows us to optimize operations and increase security while simultaneously improving outcomes.

Examples include:

• Network observability and governance: Ensuring data accuracy, eliminating non-compliant hardware and software, and real-time updates
• Securing endpoints: Device management, asset management, and patching
• Zero Trust networking: Isolating device classes and limiting attacker’s movements across the network
• Network access: Azure VPN, identity management, and Secure Access Workstation (SAW) infrastructure security

Learn how we’re transforming our enterprise IT operations at Microsoft.

Core IT services: Tenant management

We manage one of the most complex tenants anywhere. Governance today is a somewhat fragmented experience, with no clear mechanism for IT to safely enable self-service asset creation for sites, Teams, groups, Power Apps, and so on. These unmanaged assets increase the risk of over-sharing sensitive data and compromise the health and security of our IT environment.

In the world of AI, security through obscurity is no longer a viable option. This means data hygiene, permission management, and data protection are essential to providing trustworthy AI tools that don’t overexpose sensitive content, while still providing quality responses.

Read about one way we’re improving security by protecting elevated-privilege accounts at Microsoft.

Core IT services: Support

We’re using generative AI to transform the way our employees interact with our support services. IT issues will be either auto-remediated or resolved remotely and instantly through conversational, personalized, and contextualized solutions, often without a human agent’s intervention.

We’ll accomplish this with a focus on the following:

• User experience: Our employees are using the AI-powered Employee Self-Service Agent to access personalized, accurate, and cost-effective issue resolution. Future goals include implementing a seamless transition to a human agent while the user stays within the agentic Copilot experience.
• Human agent experience: Operational efficiency and automation are being integrated into the Service Operations Workspace. The service includes chat and incident summarization that recommends best next actions and drafts contextual answers to queries.

Find out how we’re transforming IT support at Microsoft with AI and the Employee Self-Service Agent.

Defragmenting the employee experience

The second dimension where we’re implementing our AI vision to make a difference is our employee experience. Our vision is to deliver a unified, connected, and personalized experience where users can access employee data, tools, and insights from one place.

“We see AI as the key to unlocking the full potential of our employees, delivering personalized experiences that empower us to work smarter, faster, and happier—unleashing the innovation and collaboration necessary for our success.”

Vijaya Alaparthi, principal group product manager, Microsoft Digital

One of the key ways we’re doing this is with Microsoft 365 Copilot, which functions as a “UI for AI” across our employee tools and services. An example is our Employee Self-Service Agent, an AI-driven tool based on Copilot that helps employees more efficiently find context-specific answers to their questions using natural language queries.

“We see AI as the key to unlocking the full potential of our employees, delivering personalized experiences that empower us to work smarter, faster, and happier—unleashing the innovation and collaboration necessary for our success,” says Vijaya Alaparthi, a principal group product manager in Microsoft Digital.

To achieve our vision, we’re building a workplace where AI defragments the employee experience by:

• Providing contextual support in the flow of work
• Reducing the number of sites and apps an employee must remember
• Using Microsoft 365 Copilot as the “UI for AI,” making it simple for employees to find information, take action, and even fully automate certain repeatable tasks

Corporate functions growth

Our third major priority in Microsoft Digital is to improve how we support the company’s corporate functions organizations, including legal and real estate and facilities.

“With AI, we have so many new ways to innovate. From optimizing building occupancy, to streamlining commute services, to automating contract and document management, we have incredible potential to make our corporate functions more efficient and impactful.”

Becky West, principal group product manager, Microsoft Digital

This is a particular challenge, as these teams are being asked to do more with less today; Microsoft can no longer afford to grow operational costs at the same rate as in the past.

AI is playing a fundamental role in transforming the business workflows of our corporate functions partners while improving operational efficiency, user productivity, regulatory and corporate compliance, and data-driven decision making. It’s revolutionizing the way they operate by automating repetitive and time-consuming operational tasks.

“With AI, we have so many new ways to innovate,” says Becky West, a principal group product manager in Microsoft Digital. “From optimizing building occupancy, to streamlining commute services, to automating contract and document management, we have incredible potential to make our corporate functions more efficient and impactful.”

Some of the corporate functions taking advantage of AI capabilities and related increased efficiencies include:

• Real estate and facilities: In supporting the technology needs for more than 500 company buildings worldwide, we are poised to use AI and related innovations to implement cost savings in the areas of workspace systems, facilities management, and space management.

Find out how we’re transforming facility operations at Microsoft with AI maps.

• Travel and expense: Our plan is to work for near-elimination of the traditional expense reporting process through AI-based and touchless experiences, driving simplification and productivity gains.

Check out how OneExpense transformed our employee expense reporting.

• Legal: Our vision for integrating AI into Corporate, External, and Legal Affairs (CELA) includes more discoverable legal findings, better corporate document management with the Docufy platform, enhanced engagement with Microsoft Philanthropies, and accelerated support for business-critical functions such as immigration, contracting, and insider trading compliance.

Read how AI is revolutionizing the way we support corporate functions at Microsoft.

Agentic AI: Becoming a Frontier Firm

This era of AI in IT has quickly morphed into a world in which agents are having major impacts across the enterprise. Microsoft Digital plays a central role in helping the company embrace this change and transform into a Frontier Firm: an organization that has deeply embedded AI and agents into its operations, products, and culture

As a Frontier Firm, we go beyond simply adopting AI as a discrete tool or additional technology. We’re actively integrating intelligent systems, rich data platforms, and human knowledge into a unified operating model, where automation, decision making, and innovation combine to spark acceleration at scale. Agentic AI is a core enterprise capability for us, powering everything from employee productivity to customer experiences and strategic decisions.

As Microsoft progresses into this agentic AI future—where autonomous or semi-autonomous AI agents understand context, take actions, and collaborate alongside humans—Microsoft Digital has played a lead role in deploying these capabilities internally. We’ve led the early adoption of tools like Microsoft 365 Copilot, Azure AI services, and custom-built agents that help us automate repetitive tasks, surface insights, and orchestrate workflows across systems while enforcing strict governance policies. Examples include AI-powered agents that assist in IT service management, network monitoring, and enterprise knowledge retrieval, which allow employees to focus on higher-value work and maximize their individual impact.

As AI agents continue to grow in power and functionality and become more deeply integrated into the daily workflows of knowledge professionals, Microsoft IT will maintain our leadership role and operate at the bleeding edge of this technological revolution. 

A catalyst for change and growth

Microsoft’s digital transformation is a story of evolutionary change, resilience, and adaptation across multiple eras of information technology. From our origins as a traditional IT organization to becoming a modern engineering organization focused on driving AI-powered innovation, we in Microsoft Digital remain a catalyst for change within the company and our industry.

With our insights born from customer and employee obsession, we’re committed to streamlining IT operations while prioritizing security, revolutionizing user services, and facilitating corporate functions growth and development. All with the overarching goal of making Microsoft employees everywhere more productive while showing our customers and partners what’s possible as we move forward together into the future of IT.

The post Digitally transforming Microsoft: Our IT journey appeared first on Inside Track Blog.

Our Global Collection team in the Microsoft Treasury division makes sure payments are seamlessly executed in our fast-moving global enterprise environment. However, our case managers were often losing valuable time figuring out things like who the right contact was for a given customer, which issues were likely to be challenged by a customer, and where an exception should be routed next. This information was spread across systems or buried in handoffs.

To solve these challenges, our team built a human-led, AI agent-assisted support system to reduce preparation time and streamline their processes.

“Building the AI assistance wasn’t the hard part,” says Kathy Brustad, a director in the Global Treasury and Financial Services division at Microsoft. “The hard part was reimagining the collection experience with AI front and center, and bringing the underlying infrastructure up to speed to get it there.”

In this post, we explain how we did it so you can learn from our experience.

“We have over 1,000 collectors around the world who perform collections for Microsoft. They had multiple systems they had to go to in order to find out things like the totality of the customer’s invoice and what conversations a different team had with the customer. The information was fragmented.”

Kathy Brustad, director, Global Treasury and Financial Services

Stitching together information across systems

Our AI agent is focused on helping our case managers prioritize high-value work by:

• Predicting late payments and possible customer disputes
• Summarizing customer case interactions for use by case managers
• Routing customer emails to the right collections manager faster and with greater precision Automatically matching payments to invoices
• Automatically responding to customer inquiries

“We have over 1,000 collectors around the world who perform collections for Microsoft,” Brustad says. “They had multiple systems they had to go to in order to find out things like the totality of the customer’s invoice and what conversations a different team had with the customer. All of this information was fragmented. We didn’t have a single view of how much a customer owed us.”

We started by consolidating these dispersed tools and systems into an SAP and Microsoft Dynamics 365 environment, creating a single source of truth for all relevant customer, invoice, and payment data.

On that foundation, we layered on Microsoft’s IQ intelligence platform to infuse semantic understanding and business context. That standardized our workflows by simplifying templates and worklists to reduce complexity and put consistent global practices into place. Routine communications became fully automated.

We then applied AI to improve payment matching accuracy from 40% to 90%, generate customer response drafts, and intelligently route cases to reduce time-consuming back‑and‑forth.

Copilot assistance was embedded directly into the daily workflow of our case managers to reduce administrative load by providing inline knowledge suggestions, summarizing calls, and automatically drafting replies. With these standardized automated workflows, we could apply 98% of payments within 48 hours.

“In a nutshell, this is the collection story: We have various agents and models deployed to assist our human agents with all the activities they have to do, saving hundreds of thousands of hours that we spent on manually tracking things before.”

Kathy Brustad, director, Global Treasury and Financial Services

Moving faster on ‘act ready’ work

Deploying the agent was only the starting point. The harder work was helping our collection team change established ways of working. Brustad described the shift as learning to “run it in a different way,” moving from manual, fragmented preparation toward workflows where prioritization, context gathering, and routing were increasingly supported within the system.

To make that shift possible, the team introduced a change management work stream program and role-based training focused on real, day-to-day scenarios alongside the rollout. By anchoring the work in clear business pain points and showing tangible improvements, our team saw how the new approach made their work easier. Each morning, the agent prioritized each case manager’s workload according to urgency and past client behavior so case managers could immediately focus on the accounts that were the most pressing.

We reduced repetitive communications using automatically drafted responses and automated statements.

“In a nutshell, this is the collection story: We have various agents and models deployed to assist our human agents with all the activities they have to do, saving hundreds of thousands of hours that we spent on manually tracking things before,” Brustad says.

After deploying this system to our case managers, we saw measurable improvements in both productivity and speed, including:

• Hundreds of thousands of hours unlocked annually in order to do more human-led high-value work rather than routine administrative tasks
• 40% reduction in call preparation time
• 2X growth in automatic cash applications
• 2.5X acceleration of customer inquiry resolution time

Operationally, the team also saw up to 60% reduction in inquiry handling time through inline suggestions, summarized calls, and automatically drafted replies. To ensure these improvements were real and repeatable, we emphasized observability in our evaluation approach. Our team tracked dollars collected through collections and hours worked to create productivity metrics.

Data, trust, and good governance

When introducing AI systems or agents into finance workflows, leaders often ask two questions:

1. Can we trust the outputs?
2. Can we govern the process?

“The biggest takeaway is to know your own process very, very well. You need to understand where all the bottlenecks and pain points are. Start from there to design the new agent-enabled process instead of saying, ‘I’m going to just inject the agent into my existing process.’”

Kathy Brustad, director, Global Treasury and Financial Services

For us, trust came from getting the basics right in the form of right-sizing our enterprise data, standardizing our workflows, and establishing clear ownership for each part of the work. When we tested early and included frontline users throughout the process, outcomes improved.

“The biggest takeaway is to know your own process very, very well,” Brustad says. “You need to understand where all the bottlenecks and pain points are. Start from there to design the new agent-enabled process instead of saying, ‘I’m going to just inject the agent into my existing process.’”

Embed custom agent assistance directly into the moments where time disappears, such as prioritization, preparation, routing, and drafting so adoption feels natural and can be measured. You can prove impact with a small set of metrics like cycle time, throughput, dollars collected, and hours saved, and iterate from there.

Key takeaways

Modernizing collections is about fixing the fundamentals first, before you add AI into the mix. As you begin to streamline your own finance workflows, keep these lessons in mind:

• Fix fragmented workflows before adding intelligence: AI delivers the most value when it’s layered on top of standardized processes and a unified data foundation rather than disconnected systems and ad hoc handoffs.
• Embed assistance where time is actually lost: Copilot-style support works best when it shows up directly in prioritization, preparation, routing, and drafting to reduce friction without changing how people work.
• Focus AI on high‑ROI decisions, not just automation: Predicting late payments, flagging likely invoice disputes, and surfacing context can help teams spend time where it matters.
• Design around the practitioner’s day: When work arrives prioritized and prepped, case managers spend less time chasing context and more time resolving exceptions.
• Measure what matters to prove impact: Cycle time, dollars collected, throughput, and hours saved provide a clear, repeatable way to track productivity gains and cashflow velocity.
• Pair generative AI with strong governance: Trust comes from clear ownership, standardized workflows, quality data, and ongoing human oversight.

Editor’s notes:

• SAP is an enterprise finance system that many organizations use to manage invoices, payments, and financial records in a single, centralized platform.
• All metrics cited are based on Microsoft internal data gathered during the writing of this article. They’re best read as directional signals from that period, and they may change as systems, processes, and behaviors evolve. Microsoft makes no warranties, express, implied, or statutory.

Try it out

• Build and test an AI‑assisted workflow with Microsoft Copilot Studio to see how agents can help prioritize work, summarize context, and route cases more efficiently.
• Start a Dynamics 365 free trial.

Related links

• Discover the five lessons we learned on how to make AI stick for sellers during our Copilot rollout.
• See how we revamped and modernized our Microsoft Treasury infrastructure using Azure.
• Explore how we’re supercharging our support experience with Microsoft Dynamics 365 and AI.
• Learn how we’re transforming the marketing function at Microsoft with AI.
• Read about our role as Customer Zero in an AI-powered world.
• Check out how we’re measuring the impact of Microsoft 365 Copilot and AI internally.

The post Streamlining finance cash collection at Microsoft with AI appeared first on Inside Track Blog.

But this kind of fragmentation slows down reasoning. Engineers spend more time navigating tools than diagnosing issues.

To address this, the Microsoft Infrastructure, Networking, and Tenant organization in Microsoft Digital, the company’s IT organization, is building Infrastructure Graph (IGraph), a unified platform that brings topology, real-time telemetry, and operational context into a single view.

On top of this foundation, agentic capabilities enable AI agents to reason across these signals, surfacing insights, explaining issues, and recommending next steps. This shifts the experience from exploring data to making decisions faster and with greater confidence.

“Engineers increasingly face fragmented visibility. We wanted to unify live telemetry, topology, and context into one single intelligent visualization experience and show engineers what’s really important, so they don’t have to dive into oceans of data.”

Astha Sinha, product manager, Infrastructure, Networking, and Tenant team, Microsoft Digital

This visualization layer and intelligence platform provides a view of our entire Microsoft enterprise network—including more than 20,000 on-premises devices across 900 sites worldwide—to instantly surface the most critical issues and offer proactive recommendations to our engineers.

“Engineers increasingly face fragmented visibility,” says Astha Sinha, a product manager in the Infrastructure, Networking, and Tenant team in Microsoft Digital. “We wanted to unify live telemetry, topology, and context into one single intelligent visualization experience and show engineers what’s really important, so they don’t have to dive into oceans of data.”

Network insight at speed

IGraph displays the following in a single pane-of-glass view for a given site:

• Topology and dependency context: Visualizes routers, switches, access points, client devices, and their relationships, enriched with path and dependency awareness to localize impact areas
• Real-time health and telemetry insights: Surfaces live performance signals (utilization, errors, abnormal behavior) correlates directly onto the topology to highlight where the network is degraded or “running hot”
• Operational and incident context: Integrates incidents, tickets, and change signals into the graph, enabling engineers to understand what is happening and where and what systems are affected in a single view

“Fragmentation across operational data sources was only part of the problem. The harder challenge was externalizing and structuring the implicit domain knowledge engineers rely on, then integrating it with real-time telemetry and topology to enable low-latency, context-aware reasoning in the agentic layer.”

Vinod Kumar Singh, principal software engineer, Infrastructure, Networking, and Tenant team, Microsoft Digital

On top of this visualization layer, the team is building an agentic layer using Azure Foundry that allows AI agents to discover and use external tools and data sources.

Without IGraph agent, accessing data involves pulling from multiple existing sources, including servers and logs, with mixed latency (from minutes to hours). This fragmentation makes near-real-time reasoning almost impossible, as agents lack a unified, low-latency view of topology and telemetry.

“Fragmentation across operational data sources was only part of the problem,” says Vinod Kumar Singh, a principal software engineer in the Infrastructure, Networking, and Tenant team in Microsoft Digital. “The harder challenge was externalizing and structuring the implicit domain knowledge engineers rely on, the integrating it with real-time telemetry and topology to enable low latency, context-aware reasoning in the agentic layer.”

How IGraph works

The user starts in context. Say they’re on the IGraph UI for Building 32. They can already see the building topology, recent incidents, support tickets, and live health and performance metrics.

The engineer can ask a natural language question such as, “The internet is not working in Building 32—what’s going on?”

The AI agent begins reasoning across UI context (location, devices, open incidents), topology (involved devices and neighbors), historical metrics, and real-time device calls. It works with specialized MCP servers and agents to identify impacted devices, test live responsiveness, measure neighboring impact, verify data flow, and flag abnormal utilization or error trends.

“Engineers spend a lot of time firefighting. The visualization layer gives them the view they need to quickly solve the incidents. It helps free up their time to engage in more systemic improvements on their applications.”

Abhijit Vijay, principal software engineer manager, Infrastructure, Networking, and Tenant team, Microsoft Digital

Using this context, IGraph pulls in the relevant logs, real-time telemetry, and incident history to complete the analysis.

Instead of raw metrics and hundreds of rows of data, the agent returns a clean summary that provides a view of the failing device, the health of neighboring devices, and the blast radius. It shows what’s broken, what’s still healthy, the likely causes, and next actions.

The engineer stays in one UI for all this, and isn’t forced to use different tools or manually correlate data.

“Engineers spend a lot of time firefighting,” says Abhijit Vijay, a principal software engineer manager on the team in Microsoft Digital. “The visualization layer gives them the view they need to quickly solve the incidents. It helps free up their time to engage in more systemic improvements on their applications.”

The impact of incident visibility

IGraph offers a new real-time telemetry layer that:

• Uses a UI that surfaces telemetry and topology by correlating data from upstream systems
• Decreases effective latency for users, enabling near-real-time insights (often within seconds)
• Provides near-real-time signals in the UI on health, performance, routing state, and neighboring device relationships

“Our goal is to accelerate how network engineers understand what’s happening, enabling them to shift from reactive troubleshooting to proactive prevention—identifying and mitigating issues before they occur.”

Nevedita Mallick, principal product manager, Infrastructure, Networking, and Tenant team, Microsoft Digital

Combined, these capabilities give network engineers an up-to-the moment view of what’s happening across the network, before small issues can cascade into larger incidents.

By making live telemetry easier to access and interpret, IGraph helps teams move from reactive troubleshooting to proactive prevention.

“Our goal is to accelerate how network engineers understand what’s happening, enabling them to shift from reactive troubleshooting to proactive prevention—identifying and mitigating issues before they occur,” says Nevedita Mallick, a principal product manager for the Infrastructure, Networking, and Tenant team in Microsoft Digital.

That speed and clarity are especially important for new engineers.

“The tool delivers value right away, especially for newer engineers. Instead of having to piece things together, they get an instant view of the network that shows how devices are connected and displays the already-surfaced incidents directly on the graph.”

Manjiri Keskar, principal cloud network engineer, Infrastructure, Networking, and Tenant team, Microsoft Digital

Complex networks rely on unwritten knowledge and experience built up over time, which can slow onboarding and make troubleshooting harder than it needs to be. IGraph shortens that learning curve by making the network’s relationships and current state immediately visible.

“The tool delivers value right away, especially for newer engineers,” says Manjiri Keskar, a principal cloud network engineer in the Infrastructure, Networking, and Tenant team in Microsoft Digital. “Instead of having to piece things together, they get an instant view of the network that shows how devices are connected and displays the already-surfaced incidents directly on the graph.”

What’s next for IGraph Agent

Without IGraph Agent, network analysis is largely reactive.

Teams often address failures after customers have already felt the impact, instead of preventing issues by acting when early warning signs appear.

“Agentic AI is transforming networking DevOps from manual, reactive operations into intelligent intent-driven systems that can provision, validate, and troubleshoot networks autonomously. Looking ahead, it will power self-healing networks and dramatically accelerate buildouts, allowing engineers to focus on architecture, strategy, and innovation.”

Sonika Munde, senior network engineer, Infrastructure, Networking, and Tenant team, Microsoft Digital

Teams often address failures after customers have already felt the impact, instead of preventing issues by acting when early warning signs appear.

“Agentic AI is transforming networking DevOps from manual, reactive operations into intelligent, intent-driven systems that can provision, validate, and troubleshoot networks autonomously,” says Sonika Munde, a senior network engineer in the Infrastructure, Networking, and Tenant team in Microsoft Digital. “Looking ahead, it will power self-healing networks and dramatically accelerate buildouts, allowing engineers to focus on architecture, strategy, and innovation.”

That unified network intelligence will let IGraph Agent communicate with multiple lightweight agents that continuously analyze network conditions, dramatically compressing response times.

“What used to happen in hours will happen in minutes,” Munde says.

Now, the team is pushing further. One example is layering in weather intelligence to help engineers anticipate issues before they materialize, as big storms can trigger power fluctuations that ripple through the network. By visualizing this data, engineers can proactively communicate with customers and take mitigation steps that protect operational workloads.

Overall, IGraph lets teams focus on prevention. Engineers spend less time navigating dashboards and cross-checking data and more time detecting patterns and surfacing emerging risks. Manual analysis is reduced as the agent highlights insights in real time.

“By bringing telemetry, topology, and AI together in one intelligent layer, we’re turning fragmented signals into real-time intelligence so teams can move faster, act earlier, and protect the critical workloads that power Microsoft.”

Jason Thompson, principal group product manager, Infrastructure, Networking, and Tenant team, Microsoft Digital

The technology is poised to go even further. IGraph will eventually help power self-healing networks and speed up network build-outs, freeing engineers to focus on architecture and innovation. The future vision for the tool includes fully automated predictive network intelligence across all Microsoft campuses, with agents that monitor, reason, recommend responses, and safely take action.

“By bringing telemetry, topology, and AI together in one intelligent layer, we’re turning fragmented signals into real-time intelligence so teams can move faster, act earlier, and protect the critical workloads that power Microsoft,” says Jason Thompson, a principal group product manager for the Infrastructure, Networking, and Tenant team in Microsoft Digital.

Key takeaways

To move from reactive operations to proactive AI-supported network management, we recommend starting with these steps:

• Start consolidating real-time telemetry into a single view. Even a lightweight dashboard is enough to prepare for AI-driven insights later.
• Identify high-frequency incident types to target for AI triage. Pick the most common or disruptive scenarios and map out what data engineers currently review for them.
• Document the decision logic your engineers use today. Before implementing AI, capture the human reasoning steps to help guide your approach.
• Pilot an agentic solution with one network segment or site. Start with one building, one lab, or a small testbed.

Try it out

Learn more about what Microsoft Azure can do for you.

Related links

• See how we’re boosting network performance at Microsoft with real-time telemetry and performance visualization.
• Discover how we’re reclaiming engineering time with AI tools in Azure DevOps at Microsoft.
• Learn how Microsoft is reducing operational overload with AI-guided insights.

The post Supercharging network operations at Microsoft with AI-based unified network intelligence appeared first on Inside Track Blog.

But in practice, the technical side of transformation is only part of the story. The missing piece is us humans.

When we encounter these kinds of challenges internally at Microsoft, we think of ourselves as “Customer Zero.” We roll out our technology across our own organization first, learning what works and what doesn’t in real time and at scale so we can pass our lessons on to you.

“After an early wave of enthusiasm for Copilot, adoption declined. People questioned whether AI was relevant to their role, worried about what it might mean for their work, and disengaged when the change they experienced didn’t match the change they imagined.”

Daniel Bertrand, senior director, AI Transformation Office

We learned valuable lessons about AI adoption and sustainable change when we deployed Microsoft 365 Copilot across our Microsoft Commercial organization, one of the company’s largest sales and service organizations. What we observed led us to reset our strategy and build a more human-centered process for deploying and driving adoption of our AI technology.

Driving AI adoption with role relevance and daily habits

Here on the Customer Zero team in Microsoft Customer and Partner Solutions (MCAPS), our 60,000-employee strong sales organization, we saw that getting access to Copilot didn’t automatically result in widespread AI adoption.

“After an early wave of enthusiasm for Copilot, adoption declined,” says Daniel Bertrand, a senior director on the AI Transformation Office team in MCAPS. “People questioned whether AI was relevant to their role, worried about what it might mean for their work, and disengaged when the change they experienced didn’t match the change they imagined.”

Initially, people used Copilot like a search engine and expected it to make work go away. When that didn’t happen automatically, they didn’t know how to approach prompting the AI, or how to create value with it. The gap between access and know‑how is where adoption slowed.

“I knew from experience that people prefer to hear from—and learn alongside—those closest to their day-to-day work, to build trust and confidence.”

Susan Neece Robien, senior director of adoption and change, AI Transformation Office

We reframed the problem from “How do we scale the technology?” to, “What does this change feel like for people doing the work every day?”

By talking to people in our larger organization about why they were reluctant to work with Copilot, we discovered the adoption barrier was less about the technology being available and more about whether people trusted it, understood how it fit their role, and felt confident enough to build new habits around it.

The ‘Adoption-in-a-Box’ approach

After these conversations, we changed our strategy across the board.

“I knew from experience that people prefer to hear from—and learn alongside—those closest to their day‑to‑day work, to build trust and confidence,” says Susan Neece Robien, a senior director of adoption and change on the AI Transformation Office team. “That led me to conceptualize Adoption‑in‑a‑Box—a repeatable approach that combines behavior‑change guidance, peer influence, habit‑forming activities, and light gamification so people can experiment with AI in a non‑threatening way and build confidence over time.”

We rolled out the Adoption-in-a-Box concept across the team in the following ways:

• Emphasized visible leadership support: We circulated videos and “day in the life” PowerPoint 1-pagers of how our leaders were using Copilot.
• Formed a community of early adopters: They becamepeer champions for adoption, evangelizing best practices and leading workshops.
• Created a Role Hub: The hub contained practical, role-specific learning about how to use Copilot rather than doing high-level general trainings.
• Ran prompt campaigns: To get our team started with habitually using AI in their daily roles, we ran prompt campaigns to make prompt learning accessible and actionable.
• Created the Copilot Cup: We encouraged friendly competitions with leadership support. We also ran hackathons and prompt-based scavenger hunts to gamify learning about and using the AI for our team.
• Created ongoing measurement mechanisms: We stood up dashboards with monthly, weekly, and daily average usage reports. We also ran quarterly surveys to track sentiment around AI adoption on the team.

After our initial success with Adoption-in-a-Box, we scaled it to adoption leads, who brought the model to life within their teams.

When people feel safe in experimenting with AI and incorporating it into their day-to-day work, that’s when it provides real value for the organization and the individual. We’ve learned that sustainable, scalable AI transformation succeeds when we put people first.

Key takeaways

If you’re wondering how to encourage your own team to adopt new AI technology into their workflows, you can learn from our experience:

• Prioritize visible leadership participation. Leaders set the tone of any transformation, and AI adoption is no exception.
• Roll out for role relevance. Specificity is the key here: How does AI relate to each person’s individual role? If the tool provides value and saves time, people will incorporate it into their workflow.
• Establishing habits is crucial. Sustainable adoption means people use the tool on a daily basis in the natural flow of their work. Give them low-friction opportunities to learn the ropes.
• Encourage peer-to-peer experimentation. Early adopters can be a valuable resource for showing others the way. Lowering the stakes by having a peer guide employees in a workshop or one-on-one can take the pressure off as they experiment with the tech.

Try it out

Test drive an AI-assisted workflow of your own with Microsoft 365 Copilot to see how agentic AI can transform your team.

Related links

• Discover the five lessons we learned about how to make AI stick for sellers during our Copilot rollout.
• Check out our full guide describing how we deployed and drove adoption of Microsoft 365 Copilot across our global organization.
• Read about our internal change management strategy for Copilot and how it helps us drive the future of work.
• Learn how the creation of a Copilot Expo propelled AI usage at Microsoft.
• See our seven tips for driving Copilot adoption with a virtual skilling event.
• Discover how one Microsoft Digital employee powers her typical workday using Copilot.

The post Staying human: How we’re using AI to transform the sales experience at Microsoft appeared first on Inside Track Blog.

But curiosity alone doesn’t change how work gets done.

Very quickly, we saw the difference between interest and impact. Turning early excitement into meaningful, sustained behavior change required more than access to new technology—it required trust, relevance, and new habits embedded into daily work.

As our employees moved beyond experimentation, a consistent set of questions emerged:

• Is this relevant to my role?
• Can I trust the output?
• How does this fit into the way I already work?

That shift reframed how we approached adoption. Instead of treating Copilot as a deployment milestone, we began treating it as a change experience, one grounded as much in people and behavior as in technology.

Five lessons from our journey stood out.

1. Leadership makes change visible

Adoption accelerated when leaders didn’t just endorse Copilot—they used it.

Early on, we saw hesitation in teams where leadership signals were unclear. Employees were cautious about changing how they worked without explicit, visible support.

What made the difference was modeling.

When our leaders shared how they were using Copilot in their own workflows—and what they were learning along the way—it reduced uncertainty and made the change tangible.

“In the era of AI, ‘do as I say, not as I do’ won’t cut it. Leaders need to be visible and accountable for modeling the way forward in their organizations.”

Pam Maynard, chief AI transformation officer, Microsoft Customer and Partner Solutions

2. Peer networks scale trust faster than top-down messaging

Enterprise-wide communications created awareness but didn’t create confidence.

Employees needed to see how Copilot applied to the reality of their own work—ideally from someone who understood it firsthand.

That’s where our champion network became essential. Early adopters ran workshops, shared practical examples, and offered real-time support grounded in everyday scenarios. Their proximity to the work made their guidance credible. Adoption became more social, and trust built faster.

3. Relevance matters more than generic training

We quickly learned that generic training wasn’t enough.

While easy to scale, broad guidance often failed to connect with employees who couldn’t immediately see how AI applied to their responsibilities.

What worked instead was role-based immersion:

• Prompts grounded in real workflows
• Examples aligned to specific responsibilities
• Scenarios that reflected day-to-day tasks

Whether drafting customer account plans, summarizing meetings, or synthesizing research, the most effective experiences mirrored the work employees already owned.

As relevance increased, so did confidence. Copilot shifted from an abstract capability to a practical tool.

4. Habits—not enthusiasm—drive lasting change

Initial experimentation was widespread. Sustained usage was not.

Like any new tool, Copilot didn’t become part of daily work by default. The real challenge was helping employees return to it often enough to form new habits.

What moved the needle were small, repeatable actions:

• Simple prompts embedded into existing workflows
• Shared examples that lowered the barrier to entry
• Low-friction ways to experiment without risk

Over time, these patterns changed behavior. Copilot became less of a novelty and more of a natural extension of how work gets done.

Some examples of practical prompts that helped to change habits include:

• “Summarize recent news, earnings highlights, and strategic priorities for (company name) and suggest three conversation starters relevant to their digital transformation goals.”
• “Based on my meeting notes, draft a follow-up email summarizing what we discussed, the next steps we agreed on, and any open questions—keep the tone warm and professional.”
• “Review my sent emails and meeting notes from the past week and list any customer commitments or action items I may still need to follow up on.”

5. Measurement only works when paired with listening

Usage data provided valuable signals—but it didn’t tell the whole story.

To understand what was really happening, we paired quantitative data with qualitative feedback such as:

• Employee surveys
• Live discussions
• Direct, in-the-moment input

This combination gave us a clearer picture of what was resonating, where friction remained, and how to adjust. Measurement shifted from just reporting outcomes to also enabling continuous learning.

Adoption without employee feedback can easily turn into guesswork. Leaders don’t have time for that when the stakes of frontier transformation are so dramatic. Organizations that win in the era of AI are ones that can measure and see the impact on their day-to-day operations.

The bottom line

Scaling AI isn’t just about access—it’s about absorption.

Our experience reinforced a simple truth: Value is created when people integrate AI into the way they already work. That requires more than tools. It requires trust, relevance, habits, and continuous feedback.

“Even with intuitive technology like Microsoft 365 Copilot, you can’t underestimate the criticality of getting human-centered change right,” says Pam Maynard, chief AI transformation officer for Microsoft Customer and Partner Solutions. “Our experience makes it clear that modeling the right behaviors, engaging with champions, helping employees to build the habit, focusing on role-immersive training, and measuring what matters while listening to our employee signals are the keys to driving successful AI-transformation at scale.”

When these elements come together, adoption becomes durable, and based on our experience at Microsoft, transformation becomes sustainable.

Key takeaways

How can you replicate our success in your own organization? Focus on these key lessons:

• Leadership visibility is critical. Leaders need to model expectations to set the right tone from the top.
• Peer networks scale credibility faster than top-down messaging. Peer influence can scale further and faster than policy alone because examples are closer to real work.
• Role based immersion beats generic training. Generic training doesn’t always connect. Role specific prompts and resources tied to real seller workflows made the value concrete and raised confidence.
• Habit formation is the real adoption engine. Repeatable micro actions like practical prompts, shared examples, and low friction experiments are what move the needle, turning AI from a novelty to a productivity partner.
• Measurement without listening creates blind spots and risk. Usage data is just part of the story; pairing telemetry with employee signals prevents “guesswork” and turns measurement into learning, which is important for catching where people get stuck.

Try it out

Ready to embrace Microsoft 365 Copilot in your organization? Discover all the ways that Copilot can accelerate your AI-powered journey, especially with deliberate change management to drive adoption.  

Related links

• Read our guide to how Microsoft Digital deploys, governs, supports, and drives usage of Microsoft 365 Copilot at global scale.
• Discover how a Copilot Expo supercharged AI usage at Microsoft by promoting role-based immersion.
• Learn about our internal change management strategy for Microsoft 365 Copilot and how it helps us drive the future of work.
• Meet Opeoluwa Burnett and see how she uses Copilot to power a typical workday.
• Find out how you can pick the right Copilot for the job, based on our own experience at Microsoft.
• Check out our seven tips for driving Copilot adoption with a virtual skilling event.

The post Making AI stick for sellers: Five adoption lessons from our Copilot rollout appeared first on Inside Track Blog.

At Microsoft, we’re leading the way in exploring how workers can use AI agents to help them save time, automate workflows, and amplify human impact. It’s all part of our journey to becoming an AI-first Frontier Firm.

As part of this AI transformation, the Microsoft Azure AI marketing team is modernizing its work through intelligence on tap. Together with a group of Microsoft Foundry developers, the team has been using Foundry to create agent-based tools that are changing the way the marketers work and accelerating their impact.

Microsoft Foundry is our unified, enterprise‑grade Azure platform for building, deploying, and governing AI models and agents—bringing development, operations, security, and governance together in one place.

Marketing: Human challenges, AI opportunities

Marketers today face a challenging work landscape. They’re responsible for reaching diverse and dynamic audiences, adjusting to rapidly shifting market conditions, and promoting ever-expanding product portfolios with tight branding and messaging control—all under intense time pressure at an escalating scale.

At Microsoft, our marketing organization is no exception. It has experienced a 40% year-over-year increase in product launches. This job function is also highly multi-disciplinary, with many marketing professionals wearing different hats and adapting to new capabilities, often involving an array of disparate tools.

It can be an overwhelming space, which makes it easy to overlook outdated content and terminology, produce incomplete materials, or misalign messaging. All of that pressure doesn’t just lead to poor performance, but also employee burnout. It’s not surprising that plenty of marketers feel overtaxed.

“Frontier marketing is about helping our team navigate the AI transition to thrive in their roles. Contrary to people’s fears about AI, this technology is tremendously helpful for amplifying marketers’ ability to connect products and services to their audiences.”

Don Scott, general manager, Azure AI Marketing

Leaders on our Azure AI marketing team recognized these challenges, so they started exploring ways that AI could make their workers’ jobs easier. Two new AI-driven projects have come out of this effort:

• MarThrive: A marketing platform featuring a suite of complementary agents and grounded data designed to improve blog quality, assist with product launches, and deliver competitive intelligence on demand.
• AI Messaging Assistant: A generative AI application grounded in 100,000-plus proprietary customer voices that embeds this intelligence directly into marketing workflows, influencing business decisions in real time.

These tools benefit from the power of AI agents while keeping human creativity firmly at the center of our marketers’ work. Both represent function-aligned agentic design aimed specifically to meet the needs of our marketing team.

These aren’t generic AI platforms. They’re tools built by marketers, for marketers. And they’re a big part of equipping our marketing team to embrace the world of the Frontier Firm.

“Frontier marketing is about helping our team navigate the AI transition to thrive in their roles,” says Don Scott, general manager for Azure AI Marketing. “Contrary to people’s fears about AI, this technology is tremendously helpful for amplifying marketers’ ability to connect products and services to their audiences.”

But these capabilities don’t happen by accident. Before either tool could come to fruition, we first needed to ensure we had a tightly unified, AI-ready marketing data ecosystem.

“If you feed your agents the right data, they’ll be so much more useful,” says Brett Mills-Meiner, a director of AI intake and platform strategy for Microsoft Foundry. “Agent development isn’t the hard part—it’s getting the data in the right place.”

MarThrive: An agentic toolkit built for marketers

After a months-long effort to build secure, scalable integrations across core systems, the marketing team had an agentic toolkit they could use to accelerate product launches. They dubbed it MarThrive.

The creation process relied on a strong strategic vision and close alignment between marketers and AI agent developers.

“AI allows the people who do the work to be a lot closer to the technology they’re using.”

Brett Mills-Meiner, director of AI intake and platform strategy, Microsoft Foundry

The process for developing MarThrive started with getting a handle on the tasks and human needs that AI can fulfill. In many ways, the platform acted as an internal proving ground for agentic patterns by making use of Microsoft Foundry’s platform capabilities.

It was also a way to establish closer collaboration between employees who have specific business needs and Microsoft Foundry developers who can build more complex agents.

“We knew we wanted to use Microsoft Foundry to empower our own organization,” Mills-Meiner says. “AI allows the people who do the work to be a lot closer to the technology they’re using.”

The Azure AI marketing team began by establishing what it wanted to accomplish, the ideal capabilities for the necessary tools, and what their functional requirements would be. One major step was defining the specifications and workflows the tool needed to support. Another was getting the live data connections set up, which helped them properly contextualize and ground the agents (with FoundryIQ playing a big role in getting the most from the organizational data).

The main goal was to improve the consistency of the many blogs and messaging surfaces the team oversees, while also minimizing the need for review. From there, it was a matter of experimenting with how individual agents could accomplish those goals.

The results were astounding, as the tool enabled a small team to generate a host of agents on a very tight timeline. In just three weeks, the agent-builder team created 12 agents and released them over 12 days: Azure AI marketing’s so-called “12 Days of Shipmas.” The agents covered a wide variety of functions, as shown here:

MarThrive users in action

Sharmila Chockalingam and Jenn Cockrell are both senior product marketing managers on the Microsoft Foundry team. The agents they access through MarThrive have become instrumental to their work and productivity.

“We typically don’t get all the information about a model until a few days before its launch on Foundry; the MarThrive tool has made rapid iteration and review possible.”

Sharmila Chockalingam, product marketing director, Microsoft Foundry Models

One of Chockalingam’s greatest challenges has been working with partner contributors to launch third-party models as they get added to Foundry. Model releases vary in scope, so they require a spectrum of marketing assets like blog posts, social copy, pitch decks, sizzle videos, product demos, and FAQs.

For Chockalingam, MarThrive provides the greatest value through the Social Copy Generator and Edit Suggester. These agents help her get incoming copy from model partners into consistent shape quickly. Meanwhile, the BOM Generator agent helps her team rapidly spool up full complements of assets to support launches properly.

“On one of our major, late-breaking model launches, MarThrive really proved how crucial it could be,” Chockalingam says. “We typically don’t get all the information about a model until a few days before its launch on Foundry; the MarThrive tool has made rapid iteration and review possible.”

One of Cockrell’s areas of responsibility is managing one of our Tech Community blogs. This blog relies heavily on multiple internal and community contributors, so it can be a challenge to review output and ensure quality at scale.

“The main benefit is the single pane of glass that gives marketers access to the agents they need.”

Jenn Cockrell, senior product marketing manager, Microsoft Foundry

The Blog Grader agent provides an initial scrub of a contributor’s work, giving immediate feedback and a grade for aspects like technical depth and visuals. From there, Cockrell can provide contributors with specific, actionable feedback so they can improve their submissions.

At a more strategic level, the Blog Tree Explorer helps her position different blog posts within our overall approach to content. It also gives her team the comprehensive visibility it needs to establish baseline standards around branding, quality, and best practices.

“MarThrive really only rolled out in December of last year, and we’ve already seen immediate value and better output, as well as improvements to the AI tool,” Cockrell says. “The main benefit is the single pane of glass that gives marketers access to the agents they need.”

To keep our blog quality standards fresh and evolving, the team uses an agent that connects to the rest of the MarThrive ecosystem: Calibration Studio.

When a blog post performs particularly well, the team works with this agent to apply its learnings to other tools like the Edit Suggester and Blog Grader. This produces a multi-agent workflow that relies on human judgment to make adjustments that align with our priorities as a business.

Thanks to these tools, the team has seen the conventional product marketing cycle shrink from 18 months to as low as 18 hours. We’ve also boosted our blog post engagement metrics by 10–12 points.

On the popular Microsoft Tech Community site, publishing a blog post used to involve at least a week of reviews and communication back-and-forth between the author and our marketers. With an average of 250 posts a year by our marketing team, that was no small commitment.

Today, writers submit their work, and a product marketing manager can run the draft through the Blog Grader agent. If their post gets a high enough score, the marketer will proceed with publication. That translates to at least four hours of time saved per post for our product marketing managers.

The overall result is a substantial reduction in human effort while quality improves, velocity increases, and our marketers can spend more time on strategy and big-picture guidance.

The AI Messaging Assistant: An audience marketing ally

As the discipline of marketing has modernized, the possibilities for reaching highly tailored and targeted segments have only increased. But to be truly effective, this requires greater granularity and deeper insights, all in the context of accelerating market changes. That analysis takes time—time that marketers don’t usually have.

With that pressure in mind, the Azure AI market research team set out to augment its ability to flow audience insights directly into their work. The result was the AI Messaging Assistant.

At the outset of this project, there were questions about whether to use Microsoft Copilot Studio or Microsoft Foundry to create the AI Messaging Assistant tool. The team eventually decided that Foundry offered the end-to-end capabilities it needed—from building, deploying, and governing the agent to iterating and updating it as time went on.

Research is a very specific discipline, so creating this tool relied on close collaboration between the Microsoft Foundry team, data scientists, and researchers. The core goal was to help the research team scale their skills by extending their work through AI agents.

In defining the solution, the teams mapped the process from research to marketing output, identifying processes that often get left by the wayside in day-to-day workflows because of time pressure and resourcing.

The AI Messaging Assistant was built to bridge those gaps. It accesses our rich store of customer intelligence and builds models on top of it, then applies that data to produce outputs grounded in what real audiences actually think, feel, and prioritize.

Marketers select their audience and parameters and the tool generates or refines content accordingly, including messaging, naming, and feature prioritization. Because every output is rooted in real customer intelligence, the result is marketing content that is more personalized, engaging, and relevant to the audiences that matter most.

“As the speed of marketing increases, the AI Messaging Assistant makes sure we can still represent the voice of the customer. We’re closing the gap between marketer intent and marketing output.”

Robert Graves, senior director, Data Management and Science

A simple user interface was crucial to keeping the process streamlined. Users access the AI Messaging Assistant through an easy-to-manage web portal, then select from 12 different audiences. Examples include gamers and Microsoft 365 users on the consumer side, or IT decision-makers and developers in the commercial space.

Then the user chooses a pre-made output type to guide their messaging. While marketers mostly use the tool for last-mile naming and messaging support, researchers have more flexibility to pore over data through a blank workbook.

The AI Messaging Assistant is not designed to replace humans. Instead, it expands what our human researchers and marketers can do, extending customer intelligence into decisions and moments that would otherwise be out of reach. The process remains human-led. Marketers set the parameters, assess the output, and make the final decisions before deploying.

“A lot of use cases are things we normally wouldn’t have time to research,” says Robert Graves, senior director with Data Management and Science. “As the speed of marketing increases, the AI Messaging Assistant makes sure we can still represent the voice of the customer. We’re closing the gap between marketer intent and marketing output.”

AI Messaging Assistant user in action

Ben Loeb is a product marketing manager on the Microsoft Edge team. His work focuses on ways we’re bringing AI into the browsing experience.

Perceptions of AI, habits around using it, and even the nature of engaging with the internet all mean that the browser marketplace is in a constant state of change. Agile intelligence is key.

“This is a highly competitive space, so we need to adapt quickly,” Loeb says. “We’re always thinking with an audience lens to create messaging that resonates.”

In the course of Loeb’s day-to-day tasks, he tends to use the AI Messaging Assistant to work with pre-built prompts for research projects he’s conducting and populate them with elements specific to a particular initiative. Typically, he’ll specify the product he’s working on, identify the perceptions or attributes he wants to work with, and give the agent the context it needs to craft messaging or naming. He’ll then test the outputs against different audiences, like IT decision makers versus employee users.

“Now we don’t feel like we have to make a trade-off between research and velocity.”

Ben Loeb, product marketing manager, Microsoft Edge

For example, he might suggest that a feature name needs to combine the concept of innovation with objective descriptions of its functionality. The AI Messaging Assistant will deliver options based on the parameters he provides, and he can then take those suggestions through the final, human mile of refining and decision making.

Of course, any product or feature name will still need oversight from our product and branding teams. But the tool provides a starting point grounded in audience insights.

The Microsoft research team is a strategic asset. And like any high-value resource, its impact is greatest when focused on the decisions that most benefit from deep human expertise.

The AI Messaging Assistant expands what’s possible by providing initial intelligence that marketers can act on with confidence, backed by data rather than instinct alone. Teams no longer have to be selective about where customer voice enters the conversation—the tool ensures it’s present across a much broader range of decisions.

The immediate outcome for Loeb and his peers is that they save time and increase output, all while operating with greater confidence.

“Now we don’t feel like we have to make a trade-off between research and velocity,” Loeb says.

The impact has been quite dramatic. Thanks to the AI Messaging Assistant, message testing cycles have accelerated by up to 90%. We estimate the tool has generated at least $10 million in value to date; in one Windows 11 campaign, AI Messaging Assistant marketing enhancements contributed to sales that were 25% above target.

From a confidence standpoint, it’s clear that the Azure AI marketing team trusts and values this tool. So far, the AI Messaging Assistant has informed more than 250 significant business decisions.

Exploring opportunities for AI across the enterprise

The benefits of AI-driven tools like MarThrive and the AI Messaging Assistant aren’t unique to Microsoft. Our experience is just one part of a new approach to work, one where anyone can build the agents they need to make their jobs and lives easier.

This is true whether it’s simple agents that employees create through Copilot Studio Agent Builder or more advanced tools tailored to lines of business, created in partnership with professional developers using Copilot Studio or Microsoft Foundry. It’s clear there are opportunities everywhere for highly personalized, human-centered workflow reinvention.

With the right data foundations, a responsible outlook, a focus on human problems, and a process of experimentation and iteration, you can follow in our footsteps to seek out frontier transformation.

It’s important to note that in the case of both MarThrive and the AI Messaging Assistant, the end product isn’t static. Keeping these tools relevant and effective relies on regular evaluation, feedback loops, and continual calibration to ensure consistent quality.

“What we’ve discovered as we’ve enabled different disciplines to create agents is that there’s tremendous innovation waiting in all of these pockets,” Scott says.

Ultimately, these tools are about reducing cognitive load, not adding process. They’re about helping marketers thrive, not replacing them. And by accomplishing those goals, we’re driving greater impact in marketing: improved quality signals, more consistent application of standards, the ability for small teams to have an outsized impact, and faster experimentation without sacrificing trust.

Key takeaways

If you’re ready to start creating agents that support work in any discipline, consider taking these steps:

• You can use agents for every function. You may not be part of a technical team, but that doesn’t mean agents don’t have a place in your discipline. With simplified tools for agent creation, it’s important for all different parts of your organization to experiment with these initiatives.
• Assess challenges before building solutions. Identify problems where AI solutions could apply, then triage those use cases according to the greatest potential impact.
• These tools need iteration by users to ensure effectiveness. AI tools won’t get things right the first time. You need a good feedback loop to ensure they grow and evolve to fully meet your needs.
• Agentic tools represent a fundamental change in what humans focus on. Human oversight is the key component of Frontier Firm transformation. Think of the human’s role as creating the notion of what a good outcome will be, identifying the data sources needed to get there, and experimenting with AI solutions.
• Managing agents will require resources. Consider explicitly creating a role to manage the strategic planning of agent processes: identifying goals, setting targets, and managing feedback and iteration.

Try it out

Explore Microsoft Foundry on Microsoft Learn.

Related links

• See how we’re becoming an AI-first frontier firm at Microsoft.
• Learn how we’re extending our infrastructure to manage agents at Microsoft by deploying Microsoft Agent 365.
• Find out how we’re powering agentic AI adoption at Microsoft: Our ‘Customer Zero’ story.
• Read our IT playbook for the AI era and explore ways Microsoft is becoming a Frontier Firm.
• Find out how we deployed the Employee Self‑Service Agent and explore our blueprint for enterprise‑scale success.
• See how knowledge workers are forging their own AI tools at Microsoft.
• Learn how we’re shaping AI management at Microsoft with Agent 365 and Copilot controls.

The post Transforming the marketing function at Microsoft with AI appeared first on Inside Track Blog.

Our teams across Microsoft Digital—the company’s internal IT organization—leaned in. We built tools, workflows, and AI enabled solutions at speed. Momentum followed, along with real enthusiasm and growth.

“We did a lot of good work building community and excitement. But at some point, we needed to evolve and put more structure around what we’d built.”

Qingsu Wu, principal group product manager, Microsoft Digital

But increasing scale required us to evolve our approach.

As adoption accelerated, we began to see duplication, uneven governance, and growing gaps between strategy and delivery. What helped us move fast early on wasn’t enough to sustain impact over time.

“We did a lot of good work building community and excitement,” says Qingsu Wu, a principal group product manager who leads the AI CoE at Microsoft Digital. “But at some point, we needed to evolve and put more structure around what we’d built.”

AI agents and solutions began appearing across Microsoft Digital. Different teams solved similar problems. Standards were interpreted differently. Reporting was inconsistent, and in many cases manual.

The question was no longer, “How do we help teams try AI?” It became, “How do we turn AI into consistent, measurable outcomes at scale?”

Answering that question required a change in how our CoE operated.

Rather than acting primarily as an advisory group, the AI CoE evolved into an execution‑focused function. Its role expanded from guidance to coordination, helping set priorities, define guardrails, and connect AI work directly to business outcomes.

The goal wasn’t to slow AI innovation down, but to help it move in the correct direction with more agility and better scalability.

Evaluating AI for Microsoft

The AI CoE connects AI strategy to execution across Microsoft Digital. It operates as a cross‑functional coordination layer that sets direction and creates shared accountability for how AI work gets done.

“We can see patterns that a single team can’t. We’re translating AI CoE strategy and enterprise priorities into clear execution plans that work in each organization’s context. That helps us align priorities and make sure the biggest bets are actually landing.”

Ria Khetan, senior program manager, Microsoft Digital

The CoE brings our leaders and practitioners together from AI, data, responsible AI, and operations to answer questions collectively. We use that cross‑disciplinary view to operate above individual projects without losing touch with day‑to‑day reality.

The CoE looks across the organization and answers questions individual teams can’t answer on their own.

• What AI initiatives are already in flight?
• Which ones matter most to the business?
• Where are teams duplicating effort?
• Where do we need clearer standards or stronger governance?

“We can see patterns that a single team can’t,” says Ria Khetan, a senior program manager in Microsoft Digital who helps lead program management for the AI CoE. “We’re translating AI CoE strategy and enterprise priorities into clear execution plans that work in each organization’s context. That helps us align priorities and make sure the biggest bets are actually landing.”

We’ve designed the AI CoE to act as the connective tissue between leadership intent and execution on the ground. It helps ensure that AI work across Microsoft Digital moves forward with purpose, consistency, and measurable impact.

Building transformation on core pillars

The AI CoE establishes a common structure that helps our teams work toward the same outcomes, even when they are building different solutions.

“We use the CoE to bring consistency to how AI work gets done. It gives us a way to step back and ask whether we’re solving the right problems and whether we’re set up to scale.”

Don Campbell, principal group technical program manager, Microsoft Digital

The operating model is intentionally simple.

AI initiatives are reviewed against shared pillars that help teams think beyond individual projects. These lenses ensure the work aligns to business priorities, can scale safely, has a clear delivery path, and supports responsible adoption.

“We use the CoE to bring consistency to how AI work gets done,” says Don Campbell, a principal group technical program manager who leads AI strategy here in Microsoft Digital. “It gives us a way to step back and ask whether we’re solving the right problems and whether we’re set up to scale.”

Our CoE uses these four pillars to guide our work:

• Strategy. We work with product and feature teams to determine what we want to achieve with AI. They define business goals and prioritize the most important implementations and investments.
• Architecture. We enable infrastructure, data, services, security, privacy, scalability, accessibility, and interoperability for all our AI use cases.
• Roadmap. We build and manage implementation plans for all our AI projects, including tools, technologies, responsibilities, targets, and performance measurement.
• Culture. We foster collaboration, innovation, education, and responsible AI among our stakeholders.

These pillars are the common language that helps us connect strategy to execution and make decisions across all teams and scenarios at Microsoft Digital.

Strategy

Our CoE strategy team’s role is to step back and create clarity.

Our strategy is driven from the organization’s top level, and executive sponsorship is crucial to executing our implementation well. When our transformation mandate comes from the organization’s leader, it resonates in every corner of the organization, every piece of work, and every task. We also encourage and welcome ideas from every level of the organization, empowering individuals to contribute their AI insights.

We maintain a centralized view of AI initiatives across Microsoft Digital, including agents, workflows, and AI‑enabled solutions. That visibility allows our CoE team to identify duplication, surface opportunities to scale successful ideas, and align investments to enterprise priorities. This creates a shared intake and prioritization model.

One of our CoE strategy team’s most significant responsibilities is prioritizing the idea pipeline for AI solutions. All employees can feed ideas into the pipeline through a form that records important details. The strategy team then evaluates each idea, analyzing two primary metrics:

• Business value. How important is the solution to our business? Potential cost reduction, market opportunity, and user impact all factor into business value. As our business value increases, so does the idea’s position in the pipeline priority queue.
• Implementation effort. We focus on clearly defining the problem statement—what the problem is, why it matters, who the customer is, the baseline metrics, and the plan to attribute value pre‑production. This ensures we prioritize AI for the most critical business problems and can measure impact before and after deployment.

By anchoring AI work in business outcomes from the start, the strategy pillar helps ensure the organization’s energy is spent on the work that matters most.

Architecture

Our architecture pillar defines how we help teams scale AI solutions without creating security gaps, compliance issues, or technical debt they’ll have to unwind later.

“The CoE introduces a framework to enable design reviews in the early development phase. We help make sure teams are choosing the right platforms and thinking about security and compliance from the beginning.”

Qingsu Wu, principal group product manager, Microsoft Digital

Before solutions move into broader use, our architecture team helps think through data readiness, platform alignment, and governance requirements. The goal isn’t to prescribe a single architecture, but to make sure foundational decisions won’t limit scale or create risk down the line. Many times, this means doing things before development, while other times it means making improvements after the initial development is done and the product or scenario is launched and being used. We also track our efforts with measurable metrics like usage.

One common pitfall is that teams may gravitate toward the most flexible platforms with full control, without fully understanding the associated security and compliance implications. To address this, we publish clear guidance to help teams choose the right platform—one that strikes the appropriate balance between flexibility and the security and compliance effort required.

Our architecture pillar helps prevent that by reinforcing a set of common expectations. Teams still build locally and move fast, but they do so within a framework that supports reuse, interoperability, and responsible operation built on enabling teams and employees to experiment with guardrails that keep our production systems safe.

“The CoE introduces a framework to enable design reviews in the early development phase,” Wu says. “We help make sure teams are choosing the right platforms and thinking about security and compliance from the beginning.”

Teams are encouraged to build on recommended platforms and services that support enterprise‑grade security, observability, and lifecycle management. This helps ensure solutions can be monitored, governed, and supported over time.

Security and compliance are never treated as downstream checkpoints. Architectural guidance reinforces the need to design with identity, access controls, auditability, and responsible AI principles from the start.

When solutions prove valuable, we look for opportunities to reuse architectural patterns, components, or services rather than rebuilding them in isolation. This reduces duplication and accelerates future work.

Roadmap

Our CoE roadmap team examines our employee experience in the context of our AI solutions and governs how we achieve the optimal experience in and throughout AI projects. It focuses on how our employees will interact with AI. Getting the roadmap right ensures user experiences are cohesive and align with our broader employee experience goals.

We’ve recognized AI’s potential to impact how our employees get their work done.

Their experiences and satisfaction levels with AI services and tools are critical. Our roadmap pillar is designed to encourage experiences across all these services and tools that are complementary and cohesive.

We’re focusing on the open nature of AI interaction.

“We’re surfacing AI capabilities and information when the user needs them, according to their context,” Campbell says. “It makes the user experience and user interface for an AI service less important than how the service allows other applications or user interfaces to interact with it and harness its power.”

A key part of this approach is disciplined experimentation.

Rather than treating every idea as a long‑term commitment, the roadmap pillar helps teams validate value early. Our teams know when they’re in an experimental phase and when they’re expected to operationalize. This gives our leaders a more consistent view of progress and risk. The net result is that dependencies between teams surface earlier, when they’re easier to resolve.

Culture

Our culture pillar ensures that AI adoption across Microsoft Digital is intentional, responsible, and sustainable.

Culture underpins everything we do in the AI space. Ensuring our employees can increase their AI skillsets and access guidance for using AI responsibly are critical to AI at Microsoft.

“We’re driving a shift from ad‑hoc AI usage to intentional, outcome‑driven adoption,” Khetan says. “That requires clarity, education, and shared expectations.”

In practice, that means the culture pillar defines how our teams are expected to adopt AI and integrate it into their work, not just what tools they can use.

Our culture team works with AI champions across the organization to translate enterprise AI priorities into local execution. Those champions act as two‑way conduits, bringing real‑world feedback and blockers back to the CoE and carrying guidance, standards, and learnings back to their teams.

Without this structure, AI adoption tends to fragment as teams experiment in isolation.

Our culture team has published training, recommended practices, and our shared learnings on next-generation AI capabilities. We work with individual business groups at Microsoft to determine the needs of all the disciplines across the organization. That work extends to groups as diverse as engineering, facilities and real estate, human resources, legal, sales, and marketing, among others. 

Responsible AI is embedded throughout that work.

The CoE reinforces responsible AI practices as part of everyday decision‑making—during design, experimentation, and scale. Teams are expected to understand not just what they’re building, but the implications of how they build it.

In the AI CoE, culture isn’t abstract. It shows up in how teams propose ideas, how they design solutions and how they measure success.

Fostering agent innovation

The true value of the AI CoE is evident when strategy, architecture, roadmap, and culture come together around real work.

A clear example of that is how we addressed the rapid growth of AI agents across the organization.

“That’s the core problem we’re trying to solve. In the past, admins had to go to multiple portals just to understand how many agents exist, and they all give different answers.”

Garima Tiwari, principal product manager, Microsoft Digital

Our teams were building agents in different platforms, for different scenarios, and at very different levels of maturity. That flexibility accelerated innovation, but it also made it difficult to answer basic questions.

• How many agents exist today?
• Which ones are in production?
• Which ones touch sensitive data?

The strategy lens helped clarify what mattered most. Our goal wasn’t to inventory every experiment. It was to gain visibility into agents that were active, scaling, or depended on by others, and to ensure those agents aligned to business priorities and Responsible AI expectations.

Architecture quickly followed.

As the CoE looked at how agents were built, we quickly discovered that information about agents was fragmented across tools. Different platforms showed different numbers. Ownership wasn’t always clear. And governance signals were hard to reconcile.

“That’s the core problem we’re trying to solve,” says Garima Tiwari, a principal product manager in Microsoft Digital leading our internal strategy and adoption of Agent 365. “In the past, admins had to go to multiple portals just to understand how many agents exist, and they all give different answers.”

This is where Agent 365—which we use to govern agents here at Microsoft—became a critical enabler.

Agent 365 brings together signals from multiple agent‑building platforms into a single, consolidated view. That visibility allows the CoE and administrators to understand agent inventory, ownership, lifecycle state, and governance posture in one place.

“Agent 365 is really about accurate inventory and observability,” Garima says. “It provides one number we can trust and a way to see how agents are behaving, who they’re interacting with, and whether they’re compliant.”

That architectural clarity changed how decisions were made.

Instead of guessing what was safe to scale, the CoE could see which agents were production‑ready, which needed remediation, and which should remain in experimentation. Security, privacy, and compliance considerations moved to earlier in the lifecycle.

“We can’t scale what we don’t understand,” Wu says. “Agent 365 helps us see what’s actually running so we’re not scaling something blindly.”

The roadmap lens then brought structure to execution.

“What changed was the mindset. Teams started thinking about manageability, security, and scale much earlier, not after an agent was already deployed.”

Don Campbell, principal group technical program manager, Microsoft Digital

Rather than standardizing everything at once, the CoE helped teams sequence work. Some agents stayed in pilot. Others moved toward broader rollout, informed by architectural and governance signals surfaced through Agent 365.

Culture and enablement ran alongside that work.

Teams began factoring operational readiness into design decisions instead of treating governance as a final checkpoint. Agent 365 isn’t positioned as a control tool at the end of the process, but as part of building agents the right way from the start.

“What changed was the mindset,” Campbell says. “Teams started thinking about manageability, security, and scale much earlier, not after an agent was already deployed.”

The outcome wasn’t a single standardized solution.

It was a repeatable approach within a shared CoE framework, supported by platforms like Agent 365, that made scaling AI more visible, more manageable, and more intentional.

That’s what the AI CoE enables at Microsoft Digital.

Key takeaways

If you’re just starting to consider AI usage at your organization, or if you’re already creating a standardized approach to AI, consider the following:

• Start with outcomes, not tools. AI work scales faster when teams align on the business problem first and select technology second.
• Design for scale from day one. Early architectural decisions around data, security, and platforms determine whether solutions can grow—or need to be rebuilt.
• Make experimentation disciplined. Clear paths from prototype to production help teams move fast without committing to ideas that haven’t proven value.
• Treat governance as an enabler, not a gate. Visibility and manageability, supported by platforms like Agent 365, make it easier to scale AI responsibly.
• Create shared accountability. Standard metrics and automated reporting turn AI activity into measurable progress.

Try it out

Learn more about how Microsoft Agent 365 enables agent governance and manageability.

Related links

• Discover, create, and onboard an agent in Agent 365.
• Use and collaborate with agents in Agent 365.
• Learn about responsible AI at Microsoft.

The post Powering the technical veracity of AI at Microsoft with a Center of Excellence appeared first on Inside Track Blog.

As AI reshapes how we work and live, it brings with it both transformative potential and complex challenges. Across the industry, concerns about bias, safety, and transparency are growing.

At Microsoft, we believe that realizing AI’s benefits requires a shared commitment to responsibility—one we take seriously. As a result, we aren’t just creating AI solutions. We’re taking the lead on infusing responsible AI principles into our technology and organizational practices.

Prioritizing responsible AI across Microsoft

The most impressive AI-powered capabilities in the world mean nothing if people don’t trust the technology. Microsoft and many of our customers across all industries are working to strike the right balance between innovation and responsibility.

“We’re on a multi-year journey born out of the need to support innovation—and do it in a way that builds trust. Along the way, we’ve continued to iterate and evolve the program through a series of building blocks.”

Mike Jackson, head of AI Governance, Enablement, and Legal, Microsoft Office of Responsible AI

IT leaders and CXOs aren’t just deploying AI tools. They’re also thinking of the right guardrails to implement around those tools as their organizations mature. Meanwhile, developers and deployers want to be sure they’re building and implementing AI solutions within the bounds of responsibility.

As an organization that’s mapping the frontier of AI while creating business-ready tools for our customers, Microsoft is shaping the global conversation on responsible AI. We don’t only accomplish that through policy and governance, but also by embedding responsibility into the ways we build, deploy, and scale AI.

Laying the foundation for this work is the duty of our Office of Responsible AI (ORA). This team brings policy and governance expertise to the responsible AI ecosystem at Microsoft.

“We’re on a multi-year journey born out of the need to support innovation—and do it in a way that builds trust,” says Mike Jackson, head of AI Governance, Enablement, and Legal for the Office of Responsible AI. “Along the way, we’ve continued to iterate and evolve the program through a series of building blocks.”

ORA advances AI development, deployment, and secure and trustworthy innovation through governance, legal expertise, internal practice, public policy, and guidance on sensitive uses and emerging technology. The team focuses on empowering innovation while ensuring it falls within Microsoft’s governance, compliance, and policy guardrails.

ORA also partners closely with product and engineering teams as well as other trust domains like privacy, digital safety, security, and accessibility. The team created our Microsoft Responsible AI Standard, the cornerstone of our governance framework, and ensures internal AI initiatives align with it.

The Responsible AI Standard translates our six principles into actionable requirements for every AI project across Microsoft:

ORA reports into the Microsoft Board of Directors and collaborates with stakeholders and teams across the company to operationalize these principles, implementing policies and practices that apply to AI applications. They determined that every AI initiative should undergo an impact assessment to ensure it aligns with the standard.

If ORA is our compass for responsible AI, our companywide Responsible AI Council has its hands on the steering wheel.

The council, led by Chief Technology Officer Kevin Scott and Vice Chair and President Brad Smith, was formed at the senior leadership level as a forum and source of representation across research, policy, and engineering. It provides leadership, strategic guidance, and executive support and sponsorship to advance strategic objectives around innovation and responsible AI.

“ORA has established clear principles and a step-by-step assessment framework and tool. Our responsibility is to rigorously follow this process and ensure compliance across our products and initiatives.”

Naval Tripathi, principal engineering manager and co-lead, Microsoft Digital Responsible AI team

Under the council’s guidance, responsible AI CVPs, division leaders, and a network of responsible AI champions across the company operationalize the implementation of our Responsible AI Standard and compliance with our policies.

The structure of these teams is straightforward.

Every division has a designated CVP and division lead to steer the work and connect their team to the overarching Responsible AI Council. Within those divisions, each organization has a lead responsible AI champion or a set of co-leads to steer their team of champions. Those champions act as subject matter experts, reviewers for the impact assessment process, and points of contact for the teams developing AI initiatives.

Implementing AI governance within Microsoft IT

As members of the company’s IT organization, Microsoft Digital’s responsible AI division lead and champion team have a special role to play. They helped develop a critical internal workflow tool, which has now become a mandatory part of our responsible AI assessment process.

“The key is to ensure full alignment of responsible AI practices with ORA,” says Naval Tripathi, principal engineering manager and co-lead for Microsoft Digital’s Responsible AI Team. “ORA has established clear principles and a step-by-step assessment framework and tool. Our responsibility is to rigorously follow this process and ensure compliance across our products and initiatives.”

This tool logs every project, guides AI developers through initial impact assessments all the way to final reviews, and facilitates those workflows for champions.

“As organizations develop a diverse ecosystem of AI agents, often created by multiple engineering teams, it becomes essential to establish a standardized evaluation process. This ensures every agent adheres to enterprise-level standards before we deploy and distribute it to end users.”

Thomas Po, senior product manager, Microsoft Digital

By streamlining the process through a unified portal, the tool increases efficiency and minimizes errors that can arise from manual processes. It also encourages teams to make responsible AI part of the software development lifecycle (SDL) itself, not a hurdle or an afterthought.

“As organizations develop a diverse ecosystem of AI agents, often created by multiple engineering teams, it becomes essential to establish a standardized evaluation process,” says Thomas Po, a senior product manager working on Campus Services agents. “This ensures every agent adheres to enterprise-level standards before we deploy and distribute it to end users. That makes it more manageable in the long term, and having it all in one tool gives us more transparency.”

Our unified internal workflow looks like this:

• Project initiation and system registration: During the design phase for an AI initiative, the engineering team accesses the portal and registers a new AI system. From there, they fill out fields with crucial information, including a title, description, the developer team’s division, whether the project will include internal or external resources, the relevant champion who should review their initiative, and other details. Within this initial form, different scenarios will trigger different review parameters and requirements, for example, when a team intends to publish a tool externally or engage with sensitive use cases.
• Release assessment: After the system registration is complete, the team initiates the release assessment, a much more thorough review designed to ensure the AI-powered solution is ready to go live. At this point, the engineering team needs to provide detailed documentation. That includes the volume and kinds of data the system will use, potential harms and mitigations, and more. A release assessment includes experts in our Office of Responsible AI, Security, Privacy, and other teams, who review sensitive use cases or initiatives that include generative AI.

If the project clears all the requirements and reviews, it’s ready to go live. Crucially, we don’t think of these stages as a set of hurdles teams need to clear to complete their projects. Instead, the process guides engineering teams through the design elements they need to consider and provides opportunities for feedback from subject matter experts.

“The tool captures all the requirements from ORA and incorporates them into a developer-friendly workflow,” says Padmanabha Reddy Madhu, principal software engineer and responsible AI champion for Employee Productivity Engineering in Microsoft Digital. “It’s also a great way to pull AI champions into the design phase so we can support our colleagues’ work.”

With more than 80 AI projects currently underway across Microsoft Digital, logging and streamlining are essential. Teams are working on all kinds of ways to boost enterprise processes and employee experiences, like the following examples from Campus Services that users can access through our Employee Self-Service Agent:

• A facilities agent helps employees take action when they discover an issue at one of our buildings, like a burnt-out light, a spill, or physical damage. The agent creates a ticket to alert a Facilities team so they can resolve it and allows the submitter to follow up on progress.
• A campus event agent makes onsite gatherings like talks and Microsoft Garage build-a-thons more discoverable through simple queries. Using this agent, employees can more easily discover and plan around events that interest them, adding value to the in-person experience and incentivizing community.
• A dining agent addresses the challenges of multiple on-campus restaurants featuring menu options that shift daily. Employees can use natural language queries like “Where can I get teriyaki today?” The agent does the rest. This kind of agent can be especially helpful for employees with allergies or dietary restrictions, providing a boost to accessibility for the on-campus dining experience.

“AI is rapidly becoming a standard part of how we build and operate. As adoption accelerates, Responsible AI becomes imperative and enables teams to innovate at speed while maintaining safety and accountability at scale.”

Qingsu Wu, principal group product manager, Microsoft Digital

Our policies and practices have embedded a culture of responsibility and trust into our internal AI development processes. With that trust comes the confidence to experiment.

“AI is rapidly becoming a standard part of how we build and operate,” says Qingsu Wu, principal group product manager in Microsoft Digital. “As adoption accelerates, Responsible AI becomes imperative and enables teams to innovate at speed while maintaining safety and accountability at scale. By embedding Responsible AI into our engineering practices, teams have the clarity and confidence they need to manage risk proactively and deliver value without compromising safety or trust.”

Far from thinking of responsible AI assessments as an administrative or policy burden that creates additional work, teams now recognize their benefits. They look at the process as an extra set of eyes from a trusted partner. By minimizing legal and compliance risks through our Responsible AI Council’s expertise, our teams save time and stress, and we avoid problems like delayed releases or rollbacks.

“What we’re doing is entirely novel in the tech world. Microsoft is really the lead learner here, and we have a passion for corporate citizenship that we’re embedding in our tools.”

Jamian Smith, principal product manager and co-lead, Microsoft Digital Responsible AI team, Microsoft Digital

Lessons learned: Embedding responsible AI into our development efforts

Throughout this process, we’ve learned lessons that will be helpful for other organizations just beginning their AI journeys:

• We empowered early adopters and enthusiasts as responsible AI champions. They act as anchors and resources for developers who use AI, so we made sure they had the knowledge and training they needed to unlock downstream value.
• Culture has been crucial to our success, especially our growth mindset and our focus on trust. Emphasizing these aspects of our company culture helped us embed responsible AI into core SDL processes and naturalize it on our engineering teams.
• Processes are one thing, and tooling is another. If your responsible AI assessment workflow isn’t attuned to your needs, simply building a review portal tool won’t get you the rest of the way. First, we thought about the process we needed to put in place to solidify responsible AI practices and support our teams’ work. Then we built a tool that supports those workflows as easily and seamlessly as possible.
• Accuracy is reliant on data, and data has a tendency to reflect the biases of the humans who organize it. It’s necessary to correct bias actively through introspection and testing.

“What we’re doing is entirely novel in the tech world,” says Jamian Smith, principal product manager and co-lead for Microsoft Digital’s Responsible AI team. “Microsoft is really the lead learner here, and we have a passion for corporate citizenship that we’re embedding in our tools.”

As your organization begins to experiment with its own AI projects, take these concrete steps to infuse responsibility into the solutions you create:

1. Establish a strong foundation based on core principles and standards that align with your organizational culture. The Microsoft Responsible AI Standard is a great place to start because it reflects our experience and the expertise we’ve built as AI technology leaders and providers.
2. Seek out the activators across your organization: people with a passion for AI, security, transparency, and other challenge areas, along with a willingness to learn and the ability to lead. Think about how to place them in both centralized and distributed positions.
3. With the rapidly evolving regulatory climate around AI, it’s crucial to have a broad understanding of compliance and continue to follow its developments. Involve dedicated regulatory, compliance, and legal professionals in researching and monitoring global standards while communicating that information to your organization, particularly through training and updates that help teams adapt new regulations into their core processes.
4. Create a process for responsible AI assessment. Consider ways to break it into stages that propel projects forward rather than hindering them. Enlist the right people to assess projects, and consider tooling that streamlines actions for both creators and assessors. Our AI Impact Assessment Guide can help you get started.
5. Benefit from pioneers in the space, including our experts at Microsoft. Our journey has produced ready-to-use resources that can accelerate your progress. Examples include our Responsible AI Toolbox for GitHub, hands-on tools for building effective human-AI experiences, and our AI Impact Assessment Template.

“It’s not about how fast you can move, but how prepared you are. Responsible AI processes might seem like speed bumps, but ultimately they’re accelerators.”

Naval Tripathi, principal engineering manager and co-lead, Microsoft Digital Responsible AI Team

Building your capacity to create AI tools responsibly won’t happen without careful planning and strategy. As part of that process, embed responsible AI into your development workflows by emulating the practices we’ve pioneered at Microsoft.

“It’s not about how fast you can move, but how prepared you are,” Tripathi says. “Responsible AI processes might seem like speed bumps, but ultimately they’re accelerators.”

By prioritizing responsible AI, businesses of all kinds, all over the world, can ensure that the AI revolution is a truly human movement.

Key takeaways

These insights can help you as you begin your own journey through responsible AI:

• Realize that this isn’t just a technical transition. It’s also a gradual evolution and an ongoing journey.
• Work with people across your organization to establish goals and standards, because different disciplines bring different expertise and insights to the table. This will also align your responsible AI standards with your organizational values.
• Start with the basics and build from there. Establish principles, create processes, and construct tooling around those structures.
• A wide array of tooling is readily available in the world of AI. Seek out providers that model responsible values.
• Lean on your existing experts across privacy, security, accountability, and compliance. Their skills will be crucial in this new technological landscape.
• Conducting your own responsible AI groundwork is crucial, but you can also partner with Microsoft. We run on trust, and we’ve thought about these issues to pave the way for your success. Follow our lead, consider the best ways to adapt our lessons to your organization, and come to us with questions.

Try it out

Learn more about our responsible AI principles here at Microsoft.

Related links

• See how we’re responding to the AI revolution with an AI Center of Excellence.
• Learn how a data council is powering our unified data strategy at Microsoft.
• Read our guide to becoming a Frontier Firm in the age of AI.
• Explore ways that we’re accelerating employee support with our new Employee Self-Service Agent.

The post Responsible AI: Why it matters and how we’re infusing it into our internal AI projects at Microsoft appeared first on Inside Track Blog.

At an organization the size of ours—with a global workforce, massive cloud footprint, and millions of identities to protect—relying on passwords wasn’t a sustainable security posture. We needed something stronger, simpler, and more secure.

This led to the introduction of Windows Hello for Business, which was first built into Windows 10 and then Windows 11. Windows Hello for Business replaces traditional passwords with hardware‑backed keys tied to a user’s device.

So, instead of typing a “secret phrase” that can be phished or leaked, our employees authenticate with biometrics or a PIN that never leaves the device. It’s fast, intuitive, and—most importantly—resistant to the kinds of attacks that plague password‑based systems.

“This wasn’t just a technology shift—it was a structural change in how we establish trust across the organization. The lessons we learned offer a practical blueprint for any organization looking to strengthen their security while also reducing friction for their workforce.”

Abu Kabir, director of IT service management, Microsoft Digital

Rolling out passwordless authentication at a large company like ours took more than just introducing new technology. It also required that we come up with a new way to onboard our employees securely, no matter where they work.  

The first step we took toward passwordless credentials was to create Identity Pass, which included an emphasis on Day 1 authentication (on a new employee’s first day at Microsoft). By combining strong identity proofing, a Temporary Access Pass (TAP), and automated onboarding workflows, we forged an identification system where employees could unbox their device, sign in securely, and register their credentials without ever needing a password.

The result wasn’t just a smoother user experience.

“This wasn’t just a technology shift—it was a structural change in how we establish trust across the organization,” says Abu Kabir, a director of IT service management in Microsoft Digital, the company’s IT organization. “The lessons we learned offer a practical blueprint for any organization looking to strengthen their security while also reducing friction for their workforce.”

How we launched passwordless authentication

To understand how we worked through the details of passwordless authentication, it’s helpful to explain how it was implemented in the first place.

Our passwordless security system includes several components, including face or fingerprint, a PIN tied to their device, and a physical security key (like a YubiKey), but this story focuses these on two:

• Identity Pass: the internal system for secure, passwordless onboarding and recovery
• Windows Hello for Business: the phishing‑resistant credential that Identity Pass helps users register

Identity Pass

Identity Pass, which is only used internally here at Microsoft, uses several tools to “bootstrap” the user, which is the first step in establishing trust among a user, a device, and an identity system. It’s the moment when you go from “nothing trusted” to “something trusted.” Everything that happens afterward depends on getting that moment right.

Identity Pass relies on three core elements:

• Verified ID is what we use internally to establish proof of identity. It’s an initial step and is valid for 30 days.
• Temporary Access Pass (TAP) establishes authentication.
• Conditional access enforces policy.

Identity Pass is where risk signals matter most, because onboarding and recovery are the moments when identity assurance is weakest. Those risk signals include:

• Authentication behavior detection: If a user tries to redeem a TAP or Verified ID from an unusual location, device, or pattern, Authentication Behavior Detection can flag a sign in as risky. Identity Pass can then require stronger identity proofing or block the flow.
• Global high‑risk detection: If our threat intelligence determines the user is likely compromised, Identity Pass will not allow TAP issuance or passwordless registration until the risk is remediated.
• Strong fraud indicators: If the user’s session or token shows signs of fraud (token replay, hijacking, malicious infrastructure), Identity Pass will force remediation and block bootstrap flows.
• Risk‑based identity assurance: This is the decision engine that takes security signals and determines what level of assurance is required. For example:
  • Low risk = allow TAP issuance
  • Medium risk = require Verified ID reproofing
  • High risk = block and escalate

Identity Pass is essentially the front door where these signals decide whether a user can even begin the passwordless journey.

Windows Hello for Business

Windows Hello for Business is the strong, phishing‑resistant credential that Identity Pass helps users register. Once this is in place, the risk signals listed above continue to influence authentication.

• Authentication behavior detection: Windows Hello for Business sign‑ins are evaluated like any other. If the user suddenly authenticates from an impossible location or unusual device, this system flags it as a sign‑in risk.
• Global high‑risk detection: If our detects a high‑confidence compromise, Windows Hello for Business sessions can be revoked via Continuous Access Evaluation. The user then reregisters through Identity Pass.
• Strong fraud indicators: If a Windows Hello for Business token is replayed or misused, this system triggers immediate revocation and forces secure recovery.
• Risk‑based identity assurance: This determines whether Windows Hello for Business alone is sufficient, or whether the user must step up to a stronger method based on risk.

Windows Hello for Business is the credential, but the risk signals determine whether that credential is trusted at any given moment.

What we learned: Rollout and implementation

While our toolsets and protocols offer a clear path for any organization moving toward passwordless authentication, transferring users from a typical user/password security setup can have a variety of challenges—especially at the outset.

Devices, environments, and remote work all matter

When an organization adopts identity‑based, passwordless authentication, one of the first realities it confronts is that the onboarding experience isn’t uniform. Employees don’t all show up with the same hardware, the same operating system version, or the same security capabilities. That diversity has a direct impact on how smoothly a user can complete the initial Day 1 setup and register a strong, phishing‑resistant credential.

“It’s not one-size-fits-all. The onboarding experience can be different by platform, version, and device. The further away you get from a homogenized environment, the more complexity you introduce.”

Matt Scott, senior IT service manager, Microsoft Digital

Device and platform diversity is one of the defining factors in designing a successful passwordless onboarding experience. Any organization adopting identity‑based authentication needs an onboarding system that can adapt to a wide range of hardware, OS versions, and security capabilities while still enforcing a consistent, high‑assurance security model.

Identity proofing and credential registration don’t look the same across platforms. A laptop might support credential setup directly at the login screen, while a mobile device might require an app‑based flow, and a non‑traditional platform might rely entirely on browser‑based enrollment. The underlying model stays consistent, but the user experience varies depending on where the user begins.

“It’s not one-size-fits-all,” says Matt Scott, a senior IT service manager in Microsoft Digital. “The onboarding experience can be different by platform, version, and device. The further away you get from a homogenized environment, the more complexity you introduce.”

Support volume

With Identity Pass in place, we have seen dramatic reductions in password reset volume (80%), onboarding delays, and help desk tickets related to account access. At the initial rollout stage, however, most organizations should anticipate a temporary spike in support needs.

“We expected an increase in volume, because we had recently gotten to 99% in terms of users being identified through Phish-Resistant Multi-Factor Authentication,” Scott says. “In reality, what’s happening is you have a lot of users who are unhappy with the experience as part of the move to a passwordless environment.”

No matter how solid the argument is for a passwordless approach or how cleanly an organization implements it, our experience shows that organizations should expect initial confusion from employees and increased pressure on support teams.

“Moving into a passwordless environment is obviously good for everyone, but we needed to make it easier for users to get the information they needed,” Scott says. “It’s not just one fell swoop of moving from password to passwordless. It’s truly a journey. And it’s very important that change management is part of that journey.”

Helping employees help themselves

Another key learning during our implementation of passwordless authentication was the importance of accessible documentation. This gives users who have yet to establish their identity credentials a way to get unblocked without having to immediately call IT support.

That documentation must stay accurate over time, so it’s crucial to build a governance strategy that ensures updates are made quickly as new devices, platforms, and scenarios emerge.

“During onboarding, if there’s a problem and a user is locked out, they may not have access to the corporate network,” Kabir says. “Having a site that they could access, with actual instruction based on which device they’re using and that shows them how to get past key blockers, was very helpful.”

Maintaining a direct line to leadership in order to help unblock lingering change requests also proved to be essential. In one case, bugs lingered in the engineering queue for days, even weeks, because the escalation path was limited (by design).

“Approval requests were blocked, and so approvals needed to be accelerated to the skip-level approver,” Kabir says. “We were able to move fast to fix that, because we had a clear understanding of the pain that folks were feeling on our side and could effectively communicate that to leadership.”

Short-term pain, long-term gain

The impact has been significant. Instead of spending long cycles troubleshooting forgotten passwords or manually verifying user identities, IT teams can focus on higher‑value work: strengthening identity protection, refining automation, and improving the user experience. This shift not only reduces operational overhead, it also aligns with our Zero Trust principles by removing weak authentication steps from the identity lifecycle.

For employees, the experience is equally transformative. New hires can unbox a device, authenticate using a TAP delivered through a secure Verified ID workflow, and immediately register passwordless methods like Windows Hello for Business. Although the onboarding journey may vary across platforms and devices, the process is fast and intuitive.

For existing users who lose access—whether due to a forgotten PIN, a lost device, or a credential reset—Identity Pass provides a self‑service recovery path that avoids the delays and security risks of traditional reset processes.

Our experience demonstrates that when these processes are redesigned around strong, hardware‑backed, phishing‑resistant credentials, organizations gain both security and efficiency. The result is a more resilient identity foundation that supports the realities of modern work.

Key takeaways

Here are some suggestions for getting started with Windows Hello for Business and Day 1 onboarding:

• Passwordless authentication start with strong identity proofing. Establishing user identity up front is essential to creating a secure foundation for all future authentication.
• Day 1 onboarding is the riskiest moment. The initial bootstrap step is where trust is first established, and risk signals matter most.
• Temporary Access Pass replaces temporary passwords. TAP provides a secure, time‑bound way for users to authenticate and register passwordless credentials without exposing the network to attack.
• Device and platform diversity shapes the user experience. Different hardware, operating systems, and compute environments require flexible onboarding paths that still enforce consistent security.
• Support demand spikes before it drops. Organizations should expect short‑term confusion and increased help‑desk volume before passwordless security benefits fully materialize.
• Long‑term gains are significant. Once deployed, passwordless authentication reduces operational overhead, strengthens security, and improves the user experience across the identity lifecycle.

Try it out

• Explore Windows Hello for Business.
• Check out Microsoft identity and access management services.

Related links

• Learn more about implementing strong user authentication with Windows Hello for Business.
• Read about phishing-resistant authentication with Microsoft Entra ID.
• See how Microsoft is using AI to reinvent network security.
• Discover how we developed a Zero Trust security model at Microsoft.
• Find out how Windows Hotpatch transformed security and compliance at Microsoft.
• Learn how Microsoft hardened our defenses with Microsoft Baseline Security Mode.

The post Getting started with Windows Hello for Business and Day 1 authentication at Microsoft appeared first on Inside Track Blog.

Maybe you’re a product manager with a backlog that you can’t ever get to. Perhaps you’re a designer who can never seem to get engineering resources assigned to you. Or maybe you’re a program manager who routinely gets stuck copying data between systems by hand.

These are common challenges knowledge workers face everywhere, including here at Microsoft. A year ago, AI enthusiasts knew agents with tools could fix these problems—they just didn’t know where to start.

Some of our employees in Microsoft Digital, the company’s IT organization and Customer Zero for the company, took a grassroots approach to solving this problem. They built something called the Frontier Forge, our pro‑code “harness” that enables our less-technical employees to get work done with agents. They use it to quickly build agentic instructions and instantly share their solutions with peers, which accelerates our productivity across the company.

The Frontier Forge represents a cultural shift in how our product managers, designers, program managers and other “I’m not an engineer but I want to build stuff” employees now apply AI tools directly to their work.

What first began as a hackathon experiment has evolved into a thriving Microsoft-internal community with nearly 100 engaged contributors, an active Teams channel, and a GitHub repository filled with templates, learning modules, and ready-to-use AI agents. The impact is measurable: Forecasting, backlog grooming and communication tasks that collectively took weeks now take hours or minutes.

“I saw myself and others spending too much of our time on data wrangling and admin tasks when we wanted to be strategizing. Nobody was building what felt truly agentic. So, we did it ourselves.”

Brett Reifers, senior product manager, Microsoft Digital

Employees who never saw themselves as technical are now building sophisticated data visualizations, automating workflows, creating prototypes, and generating learning modules. These were capabilities previously reserved for specialized engineering teams.

The “Forge” is where it’s all happening now.

From a hackathon to a movement

In early 2025, Brett Reifers, a senior product manager in Microsoft Digital, spotted a problem he couldn’t ignore. His peers, smart and driven product managers, kept asking the same question: “How do I use agents for my actual work?”

Beginner tutorials about prompt engineering felt trivial. Advanced agents with tools assumed engineering expertise. The middle ground, where AI meets real jobs, didn’t exist.

“I saw myself and others spending too much of our time on data wrangling and admin tasks when we wanted to be strategizing,” Reifers says. “Nobody was building what felt truly agentic. So, we did it ourselves.”

So, Reifers partnered with colleague Humberto Arias, a senior product manager in Microsoft Digital whose work explores the intersection of AI and productivity. Arias had been independently researching agentic solutions that could click through interfaces, open applications, and complete tasks autonomously.

The insight that unlocked everything came from a deceptively simple observation:

“Everything on the internet is a form—every site, mobile app, every click,” Reifers says. “If agents could fill out my forms in Azure DevOps, they could handle any web-based task.”

They pitched the concept of Copilot fulfilling form-based processes as an entry for Microsoft’s annual hackathon to Sean MacDonald, partner director of product management in Microsoft Employee Experience. MacDonald immediately recognized its potential.

“My reaction was simply, ‘This sounds amazing,’” MacDonald says. “This solution was exactly what we needed.”

The event proved agents could automate PM workflows: managing Azure DevOps items, generating summaries, and querying data systems. After the hackathon validated the concept, Arias suggested pushing the project to GitHub for wider exposure. Reifers then used GitHub Copilot itself, recursively using the very tools they were building, to open source the first Frontier Forge repository in 15 minutes.

A pro-code environment with natural language accessibility

The Forge combines GitHub Copilot, Visual Studio Code (VS Code), and MCPs into a framework that makes professional development tools easily accessible to non-engineers.

“The Frontier Forge is a place where you can learn regardless of your skill level. You can adopt what’s out there, even if you don’t know where to start.”

Sean MacDonald, partner director of product management, Microsoft Employee Experience

The core idea: Give employees a workspace seeded with community-created templates, learning modules, and custom agents tailored to Microsoft Digital contexts. Then let them build from there.

For MacDonald, the Forge has proven to be an accessible entry point for almost anyone, regardless of experience.

“The Frontier Forge is a place where you can learn regardless of your skill level,” MacDonald says. “You can adopt what’s out there, even if you don’t know where to start.”

An architecture for context-first AI

The technical architecture of The Frontier Forge leverages three layers simultaneously:

• VS Code provides the enterprise managed workspace where everything happens.
• GitHub Copilot offers chat functionality and AI assistance, with access to multiple models including Claude, GPT, and Gemini.
• Tools like Model Context Protocols (MCPs) act as standardized connectors that let agents access tools, data, and services locally. This unlocked what Copilot could decide and do with user approval.

“With GitHub Copilot and MCPs, there are literally no boundaries. It’s hard to explain just how transformational this can be for a product manager. Whatever you ask is transformed into code with a purpose, allowing you to do something you couldn’t before.”

Humberto Arias, senior product manager, Microsoft Digital

The MCPs connect to services like Azure DevOps (for roadmap planning and backlog management), Microsoft Documentation, Figma (for design work), and dozens of other platforms that are essential to product manager workflows. New MCPs appear daily, expanding capabilities organically as the community builds them.

Employees can even ask GitHub Copilot to build custom MCPs for services lacking official integrations. When Arias needed a PowerPoint creator that didn’t exist, he asked GitHub Copilot to create one.

“With GitHub Copilot and MCPs, there are literally no boundaries,” Arias says. “It’s hard to explain just how transformational this can be for a product manager. Whatever you ask is transformed into code with a purpose, allowing you to do something you couldn’t before.”

The shift from prompt engineering towards context engineering is another reason why the Forge works. Its workspace settings, agent instructions, skills and hooks provide a harness with guardrails that help colleagues adopt and use this.

The Forge provides a curated starting point: Microsoft Digital-specific templates, governance frameworks, security guidelines grounded in Microsoft’s Responsible AI framework, and working examples employees can immediately use and modify.

Transformational impact

The productivity gains generated by The Frontier Forge are very real. Our employees report saving weeks or even months on certain projects, especially those that previously required extensive manual work or specialized technical skills.

Case in point: Laura Oxford, a senior content program manager in Microsoft Digital, had four years’ worth of Excel files and communication metrics reports. She had always intended to use the data to create marketing forecasts, but she could never find the necessary time or resources to perform the analysis.

“The key to creating the agent was going deep into the context. It was an iterative conversation, going back and forth to fine-tune the agent until I was consistently getting the output I wanted. But it truly was just a conversation—no tech skills needed.”

Laura Oxford, senior content program manager, Microsoft Digital

Through iterative, conversation-based prompting, Oxford’s agent analyzed patterns, created projections, and produced visualizations. Oxford now has a robust historical analysis that enables prediction of future campaign performance.

“The key to creating the agent was going deep into the context,” Oxford says. “It was an iterative conversation, going back and forth to fine-tune the agent until I was consistently getting the output I wanted. But it truly was just a conversation—no tech skills needed.”

Drafting clear, executive-ready communications for complex initiatives was what brought Mark Stratford, a senior product manager with the email and calendaring service team in Microsoft Digital, to the Forge.

Before the Forge, communicating status updates to leadership meant he had to manually synthesize data from CSVs, track several approval chains at once—often in messy emails—and iterate on visualizations for what seemed like days and days.

Put more succinctly, these tasks are time-consuming chores that are perfect for AI.

“The Forge’s architecture changes how you think about the problem,” Stratford says. “Instead of iterating on prompts, you declare intent and desired outcome. The Forge’s architecture handles the rest.”

Using this pattern, Stratford created:

• Over a dozen interactive dashboards for portfolio roadmaps, migration tracking, and service health monitoring.
• Approval matrix visualizations mapping multi-stakeholder sign-off dependencies.
• Data analysis pipelines transforming raw telemetry into executive-ready narratives.

“I didn’t need to fight ambiguity or handhold the model. The architecture gave the agent a stable, skills-driven foundation from the start, which dramatically accelerated development time and improved clarity.”

Mark Stratford, senior product manager, Microsoft Digital

The Forge’s clean separation between intent, constraints, tools, and data inputs eliminated the prompt-tuning loop. Stratford mapped his objectives into the agent framework once, relying on built-in structure and guardrails.

His analysis and drafting time dropped from days to minutes. Outputs like roadmaps and data visualizations went directly into decision workflows with no manual cleanup required.

“I didn’t need to fight ambiguity or handhold the model,” Stratford says. “The architecture gave the agent a stable, skills-driven foundation from the start, which dramatically accelerated development time and improved clarity.”

Building community and sharing knowledge

A simple continuously improving repository has grown into something larger: a community of nearly 100 enthusiasts. Contributors are building templates, learning modules, and specialized MCPs tailored to their job functions. Teams are sharing wins and unlocked achievements.

“At its core, The Frontier Forge is an open-source, community‑driven experience. It’s a safer environment that will help people learn and apply Microsoft’s AI at work.”

Brett Reifers, senior product manager, Microsoft Digital

The Forge succeeds because of its emphasis on community and knowledge sharing. Its GitHub repository serves as collaborative workspace where employees contribute agents, templates, and learning resources.

This sharing culture creates a compounding cycle. One employee’s outcome becomes another’s starting point. Contributors share useful agents immediately, without lengthy approvals. This grassroots approach lets innovation spread at the pace of curiosity.

“At its core, The Frontier Forge is an open-source, community‑driven experience,” Reifers says. “The Forge is a safer environment that will help people learn and apply Microsoft’s AI at work.”

Building a safe-to-fail path

For IT leaders looking to replicate something like the Forge, MacDonald’s guidance starts with reframing the challenge.

“Find the people who are super curious and who want to learn. They will be the ones who drive innovation with AI agents and other newly developed tools.”

Sean MacDonald, partner director of product management, Microsoft Employee Experience

The barrier to agent adoption for non-engineering roles isn’t access to tools. It’s all about giving them the confidence needed to build them and then put them to work. Providing a safe, hands-on environment where people can learn at their own pace, regardless of skill level, has been an essential key to success.

Another key has been to empower the people in your organization who are eager to innovate and try new things. The Forge began with two curious product managers who decided to experiment and then shared their idea with peers.

“Find the people who are super curious and who want to learn,” MacDonald says. “They will be the ones who drive innovation with AI agents and other newly developed tools.”

For IT leaders currently trying to prepare their organizations for an AI-driven future, the story shows that the answer isn’t to wait around for perfect tools or comprehensive employee training.

“The leaders that create safe spaces for non-engineers to build with AI now will compound that advantage for years,” Reifers says. “The ones that wait will spend 2027 trying to catch-up.”

Our knowledge workers don’t need to wait for help any longer, now they can forge their own path with an agent or other AI tool they build themselves.

Key takeaways

Here are some insights your leaders can use to build grassroots-led, AI-forward communities in your organization:

• Start with volunteers, not mandates. The Forge grew to 100 contributors with zero top-down requirements. Organic growth from curious employees creates sustainable adoption.
• Highlight your quick wins. Reifers’ and Arias’ live demos of MCPs, Oxford’s 90-minute forecast and Stratford’s 20-minute drafts became the recruiting pitch for the next wave of adopters. Show your people results like these, then hand them the tools.
• Lower barriers without lowering standards. Accessibility and quality aren’t mutually exclusive. Governance and security are non-negotiable. Configure it all into the harness.
• Prioritize knowledge sharing and attribution. When one person solves a problem and shares it, dozens benefit immediately. Reward provenance.
• Ship fast, improve later. The Forge repo was built in 15 minutes. Four months later, it contained 50+ templates and agents. As much of 80% what is produced in the Forge is rewritten every other week as tools evolve. Ship MVPs and evolve based on real usage.
• Reframe outcomes > tools. Shifting from “developer tool” to “Copilot workspace” helps knowledge workers see they belong.

Try it out

Start building and sharing AI agents today with GitHub Copilot.

Related links

• See how we enable Microsoft employees to build AI agents that help them complete important tasks.
• Learn more about GitHub Copilot for Azure.
• Discover how we’re transforming our employee experience with AI. 
• Take a course on building AI agents for beginners in GitHub Copilot.
• Follow our step-by-step guide to building API-powered agents. 
• Find out how continuous improvement helps us identify agentic opportunities, and how it can help your company, too. 

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