• Introduction
• Chapter 1: Building your agent governance strategy
• Chapter 2: Establishing a solid data foundation for agent governance
• Chapter 3: A matrixed approach to agent governance
• Chapter 4: Tracking, impact, and value
• Conclusion: Governing the frontier

Empowering employees and protecting your organization through agent governance

Welcome to the agentic frontier

Agents are expanding the frontier of enterprise AI. By creating tools that surface knowledge, take actions, and even reinvent workflows, organizations can apply the power of AI to business processes in new and innovative ways.

But this shift raises questions for business and IT leaders: How do you get the benefits of agents without putting your organization and employees at risk? How do you encourage citizen developers to create agents freely while maintaining control, security, privacy, and compliance?

At Microsoft Digital, the company’s IT organization, we’re putting practical governance structures in place to ensure our internal agents are useful, safe, and properly scoped. Through a deliberate strategy of empowerment with established guardrails, we’re unlocking the potential of agentic transformation while maintaining the trust that defines our work.

Read and prepare

Check out our complete guide to deploying and adopting agents across the enterprise, based on our own experience here at Microsoft.

The AI maturity model and frontier transformation

Agentic AI has made a new operational model possible, one that blends machine intelligence with human judgment, creating AI-operated, human-led teams.

We call organizations that enact this model Frontier Firms.

As organizations move toward this new operational state, they progress from foundational AI assistance through escalating levels of agentic maturity and complexity. First, humans operate with help from an AI assistant like Microsoft 365 Copilot. Then, human-agent teams work together. But the future lies with humans leading teams of agent users: AI agents that perform core labor with relative autonomy.

Capturing the benefits of this model relies on many factors, but in our experience as Microsoft Digital, two main tenets are instrumental to a successful transformation:

1. Empowering employees and teams to create and experiment with their own agents
2. Properly governing those agents to protect the enterprise

It’s a balance. If you set agent builders free without the proper guardrails, you risk data overexposure, agent sprawl, and security vulnerabilities. However, being too restrictive about governance stifles individual imagination, workflow reinvention, and innovation that can come from agentic AI.

“At Microsoft, we’ve moved beyond envisioning the agentic future into operating within it every day. Our experience as Customer Zero gives us a unique perspective on what it takes to govern AI agents at scale, turning early lessons into proven practices that help organizations innovate with confidence.”

Brian Fielder, vice president, Microsoft Digital

We’re here to help you find the right balance for your organization.

This guide shares what we’ve learned along the way. As you read, you’ll follow our journey as Customer Zero at Microsoft, and you’ll gain access to tips and resources that we’ve assembled to help you apply our expertise to your own agent governance practice.

Every organization is different, and your experience will differ from ours in terms of risk tolerance, technical capability, resourcing, and more. This guide highlights some principles and best practices you can apply to your own business context, needs, and objectives.

“At Microsoft, we’ve moved beyond envisioning the agentic future into operating within it every day,” says Brian Fielder, vice president of Microsoft Digital. “Our experience as Customer Zero gives us a unique perspective on what it takes to govern AI agents at scale, turning early lessons into proven practices that help organizations innovate with confidence.”

Now is the time to seize this opportunity. Follow along to start your own journey toward frontier transformation and capture the benefits of trusted, connected agentic intelligence.

Learn from our experience governing agents

Within Microsoft Digital, we’ve been acting as Customer Zero for frontier transformation by creating the tools, infrastructure, and processes that power agents at Microsoft.

Our goal is to make it easy for employees to engage with agentic tools freely and adaptably while maintaining safety and responsibility. The path to this objective relies on a three-pronged approach to governance:

• Embedded governance functionality: Agent creation and publishing tools should incorporate good guidance, governance, and guardrails out of the box, making agents people create essentially self-governing.
• IT oversight: This is a new space and a new way of working, so it isn’t feasible for all agents to self-govern at this point. As an IT organization, we fill gaps in governance through reviews and oversight. We establish risk-based policies around types of agents, exposure and sharing, and other pivots.
• User education: It’s almost impossible to predict every governance gap and need, so educating our users helps them avoid accidentally increasing risk. Our Agents at Microsoft team and individual change managers are the guides for these efforts. Employees can also refer to resources like Microsoft Learn courses and the Agent Builders SharePoint hub.

Throughout this journey, we’ve empowered our employees to create all kinds of agents, ranging from simple personal tools built by people working in every function, with every level of technical skill, all the way to AI-powered enterprise tools designed by professional developers for use across lines of business and even the entire company.

As part of the process, we’ve incorporated guardrails to ensure less technical employees are limited to tools that simply retrieve enterprise knowledge, such as SharePoint Agent Builder or Copilot Studio, while software engineers get the full power of any tool they need that can take action or automate workflows, including Microsoft Foundry and Microsoft 365 Agent Toolkit.

Over the course of this journey, we’ve learned valuable lessons about effective agent governance, including:

• How to build an impactful but flexible governance strategy
• Strategies for creating an AI-ready data ecosystem
• Ways to apply appropriate policies and controls for highly diverse agents
• Approaches for tracking the impact and value of agents

Chapter 1: Building your agent governance strategy

Thinking through your organizational needs and building a framework to govern agents

As we’ve incorporated agents into different aspects of our organization, we’ve also deepened their involvement in employees’ daily workflows and core business processes. Because of this, we’re diligent about the governance guardrails and policies that protect our organization.

We’ve accumulated a wealth of knowledge and insights in this area through our efforts governing Microsoft 365 Copilot. Based on this experience, some of the key priorities that we made sure to adhere to included:

• Effectively applying controls to ensure users and apps don’t get access to privileged information
• Preventing employees from creating agents that violate company policies
• Balancing the freedom for employees to share their creations with the need to prevent agent sprawl
• Delineating which agents are authoritative and applicable for enterprise functions and which ones are meant for employees’ own personal use.
• Inventorying agents to provide lifecycle management
• Securing and protecting confidential data while respecting our responsible AI principles: Fairness, reliability and safety, privacy and security, transparency, accountability, and inclusiveness
• Unlocking telemetry that enables us to govern agents effectively

By focusing on each of these dimensions, our governance team has centered its efforts on the value these agents provide to the company while also ensuring organizational safety and trust. To realize this value, we emphasize three key principles that help protect both our employees and the organization:

Security

We’ve established standards for data classification, policies for handling confidential information, and other security measures to protect data from unauthorized access, misuse, and disclosures. Microsoft Purview powers these capabilities through data labeling, rights management, and data loss prevention.

Privacy

Privacy compliance measures keep personal data protected and ensure agents adhere to regulatory frameworks in the regions where we operate. We conduct regular privacy assessments for all applications, including high-impact agents.

Regulation

Regulatory compliance assessments ensure agents meet prevailing legal standards. Our legal and compliance teams carefully monitor AI guidelines, regulations, and laws as they evolve so we can understand and incorporate them into these assessments.

We incorporated elements of our tenant’s minimum bar for governance into how we secure agents. Those include Microsoft Purview Information Protection, a functional inventory, activity logging, lifecycle management, and the ability to properly isolate agents so that they don’t cross data boundaries.

Our overarching tenant governance strategy is to govern items like documents and data at the container level. However, within a SharePoint site, for example, the added functionality of agents demands that we introduce further controls like sharing limits, breadth of knowledge sources, agent metadata, and information about an agent’s behaviors.

Turning priorities into principles

To operationalize governance, we developed six principles that guide our approach to agents. They form the governance foundation for a wide matrix of agent creation and usage opportunities.

1. We ensure a strong data hygiene foundation so we can trust our data estate as employees build and use agents.
2. We empower employees to build personal agents that can access permitted services and data sources to help automate and accelerate their tasks.
3. We empower teams and lines of business to build agents with known lower-risk patterns to accelerate impact.
4. We provide a smooth release path for engineering teams to develop agents designed for enterprise functions so they can access all the services and sources they need. This includes the same software development lifecycle (SDLC) reviews and certifications as other enterprise software, which we outline in Chapter 3.
5. We accelerate innovation through agent and automation templates while maintaining an AI Center of Excellence (CoE) to help teams think through their opportunities.
6. We reimagine employee experiences and task execution to simplify and optimize productivity.

Read and prepare

Powering the technical veracity of AI at Microsoft with a Center of Excellence.

Securing control through agent lifecycles

As we strategized to operationalize good governance, agent lifecycles became one of our most crucial tools. We superimposed the enterprise lifecycle on top of these policies, with both user-based and attestation-based lifecycles.

This means we treat agents owned by individual employees like any other user app and delete them when they leave the organization. Meanwhile, we ensure that agents owned by teams have a lifecycle that’s defined by the tenant and tied to attestation, our internal enterprise SDLC, and accountability confirmations.

This approach helps us combat sprawl by eliminating agents that no longer serve a purpose. It provides a solid foundation for more fine-tuned, matrixed policies and practices.

Governing amid real-time technology acceleration

One recent development illustrates how the rapid advancement of AI technology requires us to stay ahead of policy for new features.

Model Context Protocol (MCP) adds new capabilities, but also new risks and challenges. It’s a simple standard that lets AI systems communicate with the right tools and data without custom integration work. Instead of building a new connection or API every time, teams plug into a common pattern.

That standardization delivers speed and flexibility, but it also changes the security equation. We’ve extended our security and governance practices to account for MCP servers.

Our practices and policies help us govern agents effectively in this new environment. First, we assess security across four layers: Applications and agents, the AI platform, data, and infrastructure. We establish a secure-by-default strategy by positioning every remote MCP server behind our API gateway and establishing practices for vetting, identity management, automation that slows agents at the right moments, context trimming, and server isolation.

As you define policies for governing your own agentic ecosystem, you can take inspiration from our process. Start by asking questions about what you want to accomplish and what you want to protect, then move on to establishing your most important priorities. From there, you can cement those priorities into policies.

Learning from our approach to agent governance strategy

Match policies to progress on your AI journey

The complexity of agent governance depends on the maturity of your organization and where you are in your adoption journey. Start slowly to let that maturity grow over time.

A strong policy framework is the foundation

Lean on existing app governance policies, then layer agent-specific structures on top.

Take your cues from established standards

Global regulations around privacy, security, and responsible AI provide a good baseline for establishing governance policies. Assign teams to work through these regulations and incorporate their insights into your agent governance strategy.

Decide on your comfort level with risk

Bring cross-disciplinary experts together from across your organization to determine what level of risk is acceptable for different agents and their use cases. Put guardrails in place for low-risk scenarios and establish processes for supporting more complex or sensitive use cases. Evaluate what data sources agents can extract information from. Establish whether users have shared sensitive data sources.

Change is constant

Plan to reassess and revise your governance structure regularly. Agents are evolving rapidly, as is the tooling surrounding them, so maintaining good governance policies will be an ongoing practice.

Governance is a value driver for employees

Governance isn’t just about protecting your organization. It also provides the right patterns to make sure your employees are getting value from agents. Establish strong measures of business value and a robust methodology for management and assessment of agents through ongoing tracking. This kind of observation and telemetry is foundational and should be a key part of your governance efforts.

Key takeaways

Use these tips based on what we learned here at Microsoft to build your strategy for agent governance at your company:

• Establish a cross-disciplinary agent Center of Excellence. Bring together stakeholders across the organization to define priorities, goals, and shared practices for agent adoption.
• Right-size oversight based on risk. Determine your organization’s risk tolerance and define which agents require more or less involvement from IT, security, and compliance teams.
• Operationalize agent oversight and management. Establish an oversight model and implement tools that help manage agents at scale.
• Establish change management and adoption. Determine and implement a strategy for driving adoption to educate and empower employees.
• Create a centralized governance and information hub. Provide employees and agent builders with a single place to find guidance, standards, and governance information.

Learn more

How we did it at Microsoft

• Find out how we’re protecting AI conversations at Microsoft with MCP security and governance. This post outlines how we’re streamlining MCP governance through secure-by-default architecture, automation, and inventory.
• Explore our IT playbook for the AI era and learn how we’re becoming a Frontier Firm. This article shares our journey as an IT organization in support of this new operating model.
• Discover how Microsoft is becoming an AI-first Frontier Firm. This story shares how we’re approaching the idea of an agentic future.
• Read our five-step guide for IT leaders who want to drive greater AI maturity. This resource can help you chart a course through AI maturity to reimagine what’s possible for the enterprise.
• Learn from our experience tackling Microsoft 365 Copilot governance. This guide details our governance efforts for Microsoft 365 Copilot and can serve as a starting point for agentic governance.

Further guidance for you

• Secure your agents at scale with Microsoft Agent 365 guidance for unified identity, compliance, and control across platforms.
• Learn about the responsible AI policies and practices that guide our use of AI at Microsoft.

Chapter 2: Establishing a solid data foundation for agent governance

Setting agents up for success using a secure, robust data foundation

Operating according to an escalating maturity model means we’ve done the foundational work to secure and govern our data estate for Microsoft 365 Copilot. Many of the same principles apply to agents, with the added complexity of incorporating additional data sources.

To lead these efforts, we established a cross-functional team of data professionals within our AI CoE. This team is mostly comprised of Microsoft Digital employees who support corporate functions like Corporate, External, and Legal Affairs (CELA) and Global Workplace Services. Together with our AI CoE, this team helped us define what it means to have AI-ready data.

In essence, AI-ready data just means information we’ve certified for AI workloads. We certify those data sources using Microsoft Purview to identify defects in our core data products, and we’ve also built AI-powered assessments to certify which data lakes are AI-ready.

In most ways, governance is tool-agnostic and rooted in basic principles. With robust data labeling, data hygiene, and permissions in place alongside our AI tools, which respect labels by default, we can confidently give every employee the ability to build basic agents and trust in our governance guardrails. For decades, the challenge of data analysts and engineers was maintaining a consistently reliable source of truth despite inconsistent data quality, insufficient governance, and years of collecting data in silos. Microsoft Fabric and Microsoft Purview can help resolve these issues.

We’re embracing a more balanced, federated approach to data management today. We call this approach a data mesh. Rather than allowing unchecked decentralization or forcing all our data into a single centralized system, the data mesh formalizes domain ownership while embedding governance, quality, and interoperability directly into shared platforms.

The data mesh connects and distributes, data products across domains, enabling shared data access and compute while scaling beyond centralized architectures.

Platform services are standardized blueprints that embed security, interoperability, policies, standards, and core capabilities — providing guardrails that enable speed without fragmentation.

Data management zones provide centralized governance capabilities for policy enforcement, lineage, observability, compliance, and enterprise-width trust.

With this approach, our domain teams publish data as well-defined, discoverable products, while common standards for security, metadata, and compliance are enforced through automation rather than manual processes. This model preserves enterprise trust and consistency without sacrificing speed or autonomy. By adopting a data mesh mindset, we can scale analytics and AI more effectively across the organization while still keeping ownership closely connected to the business focus.

Confidentiality labels, the practical framework for data protection

To operate according to Zero Trust principles, we needed a coherent system that lets us see, label, and protect data. Otherwise, the burden of data loss prevention would fall solely on employees, who would have to exercise individual discretion whenever they decided how to house and share potentially sensitive content.

With labeling, it’s important to strike a balance between the depth necessary for supporting an array of data governance controls and the simplicity to ensure labeling isn’t burdensome for users.

We decided on four overarching labels for container and file classification, each with its own sub-labels. The highest-level schema looks like this:

1. Highly confidential: We only share our most critical data with named recipients.
2. Confidential: Any items crucial to achieving our goals feature limited distribution.
3. General: Employees can share daily work–like personal settings and postal codes–internally throughout Microsoft.
4. Public: We share unrestricted data meant for public consumption freely. That includes information like publicly released source code and openly announced financials.

For our risk tolerance and organizational needs, we made the decision to protect data designated confidential or higher. As a result, we contain data flows to their tenants and only trust suitable storage destinations for content. That suitability depends on a storage location’s ability to gate which connectors can work with particular source data and sensitivity labels.

The administrators responsible for workspaces like SharePoint sites set default labels. These labels serve as a foundation for appropriate access and circulation for objects within those containers. It takes the burden of labeling off of employees. The sensitivity labels that administrators apply map to several different categories of policies that can anticipate and help to mitigate data loss and risk.

They communicate four key areas:

1. Breadth of availability: Labels determine whether the workspace is broadly available internally or is a private site.
2. External permissions: We administer guest allowance via the group’s classification, allowing specified partners to access teams when appropriate.
3. Sharing guidelines: We tie important governance policies to the container’s label. For example, can an employee share this workspace outside of Microsoft? Is this group limited to a specific division or team? Is it restricted to specific people? The label establishes these rules.
4. Conditional access: While we haven’t implemented this policy at Microsoft, tying identity and device verification to container labels can introduce additional governance controls.

Within Microsoft Digital, we’ve put a lot of thought into how each of our labels aligns with relevant policies. You can see more of the logic behind our sensitivity labels and their policies in this graphic:

If a container owner needs different policies for a set of files to provide greater external access, they can self-service new groups without accidentally violating our governance practices.

At Microsoft, we use Microsoft Purview, which is our suite of data estate management tools, but you can use your tool of choice to apply labels in your environment. Microsoft tools will respect them. Microsoft Purview helps us accomplish three important tasks: mapping our labeling structure onto the relevant policies, verifying them against our standards, and backstopping self-service data loss prevention practices through automation.

Automation is particularly useful. We’ve configured Microsoft Purview Information Protection to scan automatically for wayward credentials, malicious user behaviors, and other sensitive information in items without the proper protections. When Purview detects a violation, our governance team receives alerts that prompt them to contain the risk by upgrading an item’s sensitivity label or requiring employees to remedy the issue.

The result is a system that allows flexibility for employees to self-manage their digital workspaces while providing guardrails that help our governance experts take appropriate actions without overtaxing their time and resources.

Our approach within Microsoft Digital is just one way to create an AI-ready data estate, but aspects of our story will hold true for almost any organization. Consider establishing a body to take over responsibility for AI-ready data, developing your primary goals for AI-ready data, unifying your data estate, and implementing a system of confidentiality labels.

Learning from our approach to agent governance strategy

Define the responsibility for AI-ready data

Identify and assign enterprise data owners to implement and oversee the processes that guarantee data quality.

Create intuitive labels

Your employees will be the ones applying labels, so make those labels intuitive. For example, “highly confidential” is easy to understand, while “business-critical” could be interpreted in many ways from a sensitivity standpoint.

Don’t overwhelm your users

Make labeling simple and intuitive to ensure it isn’t overwhelming. Employees should have a limited set of choices to keep things comprehensible.

Use existing defaults

Identify the security needs and regulatory compliance that are specific to your organization and use built-in governance controls available through Microsoft tools.

Key takeaways

You can use these tips based on what we learned here at Microsoft to tackle agent governance at your company:

• Establish a cross-functional data council. Form a data council to help promote a culture of AI-ready data with professionals from all relevant disciplines, including human resources, legal, security, IT, and anyone else who can share relevant expertise.
• Certify datasets for AI workloads. Limit agents to datasets that have been certified as “AI-ready” to minimize hallucinations and reasoning errors.
• Define your labeling parameters. Keep the number of labels to five main labels with five sub-labels each. The fewer you use, the better.
• Align your sensitivity labels with policies. Consider how your labels line up with breadth of availability, external permissions, sharing guidelines, and conditional access.

Learn more

How we did it at Microsoft

• Follow our journey as we transform our data culture with AI-ready data. This story highlights how we’re using Microsoft Fabric and Microsoft Purview to revolutionize our approach to data governance and AI readiness here at Microsoft.
• Discover how a data council is powering our unified data strategy at Microsoft. This post tells the story of how the Microsoft Digital Data Council is leading our efforts to adopt an AI-ready data strategy.
• See how we’re conditioning our unstructured data for AI at Microsoft. This article explains our internal strategies for handling unstructured data, which can be surfaced by AI agents.

Further guidance for you

• Learn about governance and security for agents and how to keep your AI initiatives secure across your organization.
• Establish responsible AI policies for AI agents to support ethical, transparent, and accountable AI agent deployment across your organization.

Chapter 3: A matrixed approach to agent governance

Governing different types of agents for different contexts, built with different toolsets

Our customers have expressed a strong desire to start building agents, but they’re concerned about where to begin and how to manage those agents once they’re built. They worry about persistent problems such as hallucinations and agent sprawl. These concerns are especially pronounced on IT teams.

During our Customer Zero journey, we’ve learned that the diversity of agent types and creation methods means there’s no one-size-fits-all approach to governance. Generalized approaches will only get you so far.

We’ve found it helpful to think about different kinds of agents along an escalating spectrum of development complexity:

There’s an entire matrix of different parameters that apply to an agent at any level of this spectrum, and they all require different policies. Those parameters include:

• Level of reach: Personal agents, limited sharing (like development environments or team boundaries), or enterprise-wide distribution
• Agent-building tool: SharePoint agent builder, Agent Builder in Microsoft 365 Copilot, Microsoft Copilot Studio, or tools geared to more professional developers (such as Microsoft Foundry or Microsoft 365 Agent Toolkit)
• Knowledge sources and content accuracy: Public sites, SharePoint and OneDrive, directly uploaded files, enterprise apps and systems, or third-party knowledge bases

Each of these parameters creates a pivot that we need to govern, and we’ve carefully assembled a set of policies and controls to account for them. As our understanding and use of agents advances, we’re continually updating how we match their characteristics and capabilities with relevant policies and any applicable reviews.

Within Microsoft Digital, we’ve adopted a risk-based approach that helps us establish a matrixed model for agent governance. The foundational idea is that we identify potential harms for each kind of agent, then assign policies for the level of review and oversight they require.

For example, simple agents that can only read and present data tend to be low risk. Because their access is tied to their creators’ identities and access, our data governance structures and guardrails can prevent overexposure. But for agents that have capabilities like writing data, taking action, or creating items, more reviews are necessary.

A matrix of agent governance policies, pivoted by parameter

The following matrix enumerates the factors that determine how we govern different kinds of agents created using different tools. This matrix helps our employees understand the agent creation process and helps us maintain safety and control.

In addition to mapping out our policies for governing agents, the matrix illustrates how we see their relative utility across the organization. It demonstrates an escalation from personally useful to organizationally useful agents. Their governance policies and controls escalate accordingly.

Regionality is an additional concern. Regulatory compliance might vary, but it’s important to keep in mind that certain kinds of data access and actions might be perfectly permissible in one region, but not in another.

One example is our Employee Self-Service Agent, a central resource employees can turn to for help with IT support, HR questions, and facilities requests. Because it can access potentially sensitive personal information, this agent required additional review from European works councils to ensure it met all relevant workplace standards.

As you facilitate the experimentation and innovation with agents across your workforce from citizen developers to pro developers, consider adopting a similar matrixed approach to agent governance. It starts with understanding your organization’s needs, your risk tolerance, and the different employee populations you want to equip with agent-building capabilities.

Learning from our matrixed approach to agent governance

Figure out your building environment strategy

Decide which scenarios match up with specific environments and make those environments available to the relevant employees.

Design governance structures that scale from low-code to more advanced agentic tools

With the proliferation of AI agents, platform-level approvals similar to the Power Platform model at Microsoft can ensure rapid innovation while requiring review for individual high-impact scenarios.

Build trust through transparency and structure

A clear, well-documented approval process helps internal regulatory advisors understand new AI technologies and establishes the trust needed for productive, long-term collaboration.

Treat regional partners as strategic allies in the agentic future

Early feedback on digital agents from regional partners like works councils helps improve product design, accelerate approvals, and reduce fear or misconceptions about AI in the workplace.

Don’t forget that Copilot Studio is part of Power Platform

You can use what you’ve learned empowering citizen developers in Power Platform to guide your work with agents.

Key takeaways

Use these tips based on what we learned here at Microsoft to tackle agent governance at your company:

• Establish your tolerance for risk. Determine where the most prevalent risks emerge across different populations and kinds of agents. Remember, you control the guardrails in your environment.
• Determine what agent-building tools you want to roll out and who can use them. Different populations benefit from different agent-building capabilities. Put thought into what individuals and teams can create and the degree of partnership each level will need from IT.
• Define your governance parameters for different kinds of agents. Determine the best ways to hedge against risk at every level. For example, you might choose to trust in tenant governance for simple agents and establish reviews for more complex tools.

Learn more

How we did it at Microsoft

• See how we’re empowering employees with the Power Platform. This article explains how our Power Platform governance model influenced our matrixed approach to agent governance.
• Learn how our employees are building their own AI tools. This article describes how knowledge workers at Microsoft are forging their own agents to help improve efficiency and get more done.
• Check out our lessons learned using SharePoint agents at Microsoft. This post shows how we approached governance for low-risk, knowledge-only agents through our deployment of SharePoint agents.
• Read how we’re unlocking enterprise AI extensibility with Copilot Studio. This story explores how we govern more advanced agents built with Copilot Studio, including connectors, orchestration, and scaled governance.

Further guidance for you

• Explore governance and security for AI agents across the organization with Microsoft Learn content.
• Check out this industry-standard framework that validates matrixed, risk-based governance approaches for AI systems.

Chapter 4: Tracking, impact, and value

Managing agents and assessing their business impact for the organization

It’s clear that agents bring astonishing capabilities to the enterprise. For many organizations, what remains unclear is exactly how to measure their impact. Without that information, businesses are at a loss for ways to articulate value and drive improvement.

Tracking agents is also a crucial component of preventing sprawl: We need to understand what agents we have, how employees are using them, what critical processes they’re supporting, and if they’re contributing value or need to be retired.

We’re at the beginning of our impact-tracking journey, but our work can provide a starting point for your own efforts to measure the value of AI initiatives at your organization.

Managing our agent catalog through comprehensive tracking

Microsoft Digital partners with other internal organizations to ensure we’re prioritizing the right agents and avoiding agent sprawl. Ideally, these engagements take place before teams start building their agents so we can avoid wasted effort or duplicated work.

Still, ongoing management efforts are crucial to keeping our agent ecosystem healthy. Telemetry is the key to assessing usage and ensuring compliance. We’ve developed our own internal tooling to ensure that:

• Metadata is complete and available
• The tooling tells us the right information about our agents
• The tools connect properly with other compliance tooling, like Microsoft Purview

This telemetry also reveals agent behaviors, shows how agents do their work, and tracks events, actions, and policy baselines.

These capabilities help us gain visibility into policy adherence and violations, and then to conduct enforcement actions. We also track the speed of reaction and mitigation. AI-ready data and robust guardrails mean we head off most violations before they occur.

A robust inventory, an agile policy framework, and an automated workflow for enforcement are cornerstones for successfully governing agents at scale.

The release of Microsoft Agent 365, now in early access, represents the next step in agent observability and management, two key aspects of agent governance and sprawl mitigation. This control pane for agents incorporates many of our learnings as we’ve bridged governance gaps through IT intervention.

Some of the key aspects of the control pane:

The registry

Provides a complete view of agents, and the enterprise agent store makes it easy to find the right agents for each role and business process within familiar workflows in Microsoft 365 Copilot and Teams.

Visualization

Delivers the observability layer, including role-specific oversight, compliance and audit features, and performance measurements that can help organizations track their agents’ impact and see where they contribute value.

Interoperability

Ensures Agent 365 is open to any Microsoft-built or partner ecosystem, while delivering work intelligence through access to data and Microsoft 365 apps.

Security features

Provide crucial confidence through visibility into security posture, detection and response capabilities, and intelligent runtime defense.

As Customer Zero for Agent 365, we’re excited to have a platform for observability and telemetry that encompasses everything from agentic creation through usage.

Tracking governance from agent inception

Professionally developed agents add a new dimension of tracking and governance, because we need standards in place for ensuring compliant agent-building and to remediate any issues.

We use our Azure DevOps instance to catalog apps on our tenant, and we’ve applied this practice to agents created professionally for lines of business and enterprise agents. This tool contains our service tree with product and app log registration, which is tied to our KPI dashboard and scoring system that validates agent data against our policies.

Our expectation is that all new apps and agents start from a place of compliance. Any new agent is registered through this platform, and we expect adherence within the first 14 days. In our experience, the introduction of new metrics, policies, or timeframes as our governance policies evolve is where agents tend to drop out of compliance. The priority is restoring compliant status.

We’ve established a series of metrics to help track and manage these expectations:

• Enablement velocity
• Renewal velocity
• Agents in compliance
• Time to remediation of noncompliance

Through a DevOps process built on our preexisting software development lifecycle practices, we’ve applied governance not only to agents themselves, but to the process of building them professionally.

Measuring progress and unlocking value

Properly measuring value depends on concrete definitions of success and metrics that support it. Articulating AI’s impact came with several challenges. First, we had to land on a consistent taxonomy for different measurement areas. Then we needed to make the relevant data accessible, ensure its quality, and confirm it made sense.

The Microsoft Digital AI Value Framework is our flexible, modular tool for measuring the impact of our AI initiatives. With tools for measurement firmly in place, we can effectively demonstrate value and guide further decision-making.

We plan to use the following capabilities to improve the overall ecosystem:

• Filtering our agent inventory on specific criteria like the type of agent or how it was built
• Enhancing governance-specific actions we can take with agents in areas like ownership and quarantining
• Gaining visibility into trends like agent usage
• Ingesting agent blueprints and defining policy templates

We’re still in the midst of our agentic measurement journey at Microsoft, but the blueprint for tracking already exists. Your organization might be in the early stages of agent readiness and deployment. If that’s the case, it could be helpful for you to internalize the lessons we’ve learned as Customer Zero and apply them as early as possible in your own journey toward AI maturity.

Learning from our agent adoption experience

Think proactively, not retroactively

If you put effort into tracking agentic impact early in your AI maturity journey, you’ll be poised to start capturing insights immediately instead of applying your methodology retroactively.

Involve a wide array of stakeholders

This workstream needs oversight from different kinds of stakeholders, including your leadership team, IT, Microsoft 365 administrators, agent developers and builders, and employee champions. That will provide the sponsorship, expertise, and perspective you need for success.

Different measurements will be appropriate for different phases of your initiatives

These measurements include monthly, weekly, or daily active usage; consider which metrics make sense at each phase of an AI initiative.

Establish a continuum of value

Agents need to tie into real business goals, so it’s important to establish metrics that actually speak to those objectives. Cascade business goals to concrete KPIs with well-defined timelines and track those diligently.

Embrace the red

Try to think of underperformance not as failure, but as data. Performance data over time helps you course correct or pivot, making sure you invest where it matters.

Key takeaways

Here are some important steps to keep in mind as you embark on your own tracking and measurement efforts for agents:

• Establish priorities and parameters for tracking agents. Consider measurements that relate to sprawl, usage, and coverage, and build them into your telemetry tooling.
• Pull your stakeholders together to establish measurement parameters. Cascade business priorities into measurable value.
• Conduct ongoing tracking. Establish a cadence for tracking and reviewing progress with your team.

Learn more

How we did it at Microsoft

• Learn how we’re measuring the impact of Microsoft 365 Copilot and AI at Microsoft. Discover our framework for tracking the impact of the investments in AI that we’re making internally.
• Find out how we’re deploying Microsoft Agent 365 internally. This story shares our intentions for using our unified control pane for agents.
• See how we’re building an AI-powered continuous improvement culture at Microsoft. This article outlines our approach to continuous improvement.
• Check out how we’re reshaping Microsoft with continuous improvement and AI. This post discusses the importance of continuous improvement for accelerating AI at Microsoft, including three initiatives already underway.

Further guidance for you

• This Microsoft Learn guidance helps organizations align AI investments with strategic goals and measurable outcomes over time.
• Explore a cloud adoption framework that provides practical guidance for defining success metrics, tracking ROI, and tying AI initiatives to business outcomes.

Governing the frontier to scale innovation

AI agents are rapidly becoming core contributors to how work gets done. As our experience within Microsoft Digital demonstrates, realizing their full potential demands more than powerful tools or enthusiastic builders. It requires thoughtful governance that evolves alongside your AI maturity, protects what matters, and gives employees the confidence to innovate responsibly.

As you consider your own strategy for managing agents, it can be helpful to keep one truth in mind: Governance is a catalyst for progress, not a barrier. By embedding guardrails into tools, grounding agent creation in AI‑ready data, applying risk‑based and matrixed policies, and reinforcing all of it through adoption and education, we’ve been able to expand agentic capability without sacrificing security, privacy, or trust.

From our experience, we’ve learned that governance works best when it’s:

• Proportional, scaling with risk and agent complexity
• Embedded, not bolted on after the fact
• Human‑led, recognizing that accountability and judgment remain essential
• Iterative, adapting as technology, regulations, and business needs evolve

When you design governance this way, it allows experimentation, learning, and impact at scale. Employees feel empowered to build agents that solve real problems, while IT and compliance teams gain visibility and control without becoming bottlenecks. Crucially, leaders can measure value, manage risk, and make informed decisions about where to invest next.

“At Microsoft, we believe the future of agentic AI depends on governance that empowers people first. The structures should be invisible when they’re working, intentional when they’re needed, and trusted by everyone they serve.”

Vijaya Alaparthi, principal group product manager, Microsoft Digital

This is the foundation of the Frontier Firm: Organizations where humans lead and agents operate, guided by clear principles and trusted systems.

As you continue your AI maturity journey, remember that there is no single, correct governance model. Your approach will reflect your risk tolerance, regulatory environment, data maturity, and organizational culture. The practices outlined here provide a proven starting point informed by real-world deployment at enterprise scale.

“At Microsoft, we believe the future of agentic AI depends on governance that empowers people first,” says Vijaya Alaparthi, principal group product manager in Microsoft Digital. “The structures should be invisible when they’re working, intentional when they’re needed, and trusted by everyone they serve.”

Now is the moment to act. Start with strong foundations. Empower your builders. Measure what matters. And treat governance not as a constraint, but as a strategic advantage that allows your organization to move faster, innovate safely, and lead confidently on the agentic frontier.

Key takeaways

Here are the high-level learnings and insights that you need to consider as you embark on your own agent governance journey, based on what we’ve learned here at Microsoft:

• Treat governance as an enabler of innovation, not a brake. Effective agent governance is what makes large‑scale innovation possible. When you embed guardrails into platforms, data, and processes, employees can build and experiment confidently without exposing the organization to unnecessary risk or slowing progress.
• Match governance rigor to agent risk and maturity. Not all agents need the same level of oversight. A risk‑based, matrixed approach lets organizations trust lightweight, personal agents while applying deeper reviews to agents that write data, take actions, or operate across business‑critical systems.
• Start with AI‑ready data and zero‑trust foundations. Strong agent governance rests on secure, well‑labeled, high‑quality data. Clear ownership, intuitive sensitivity labels, default protections, and automation reduce reliance on user judgment and allow agents to operate safely at scale.
• Embed governance where agents are built and used. The most effective governance is built into tools and workflows, not enforced through manual reviews alone. Defaults, limits, identity‑based access, lifecycle controls, and telemetry should apply automatically so agents are governed by design.
• Plan for the full agent lifecycle to prevent sprawl. Agent inventories, ownership models, attestation, and retirement processes are essential. Governance needs to account for how you create, share, evolve, audit, and ultimately decommission agents, whether individuals or enterprise teams are responsible for building them.
• Reinforce governance through adoption and education. Guardrails work best when employees understand them. Targeted adoption programs, clear guidance, prerequisites for advanced tools, and visible leadership sponsorship can help employees build responsibly and recognize their role in protecting the organization.
• Measure what matters to prove value and drive improvement. Visibility drives trust. Telemetry, observability, and clear metrics that span productivity, quality, risk reduction, and experience allow organizations to track impact, course‑correct early, and continuously improve their agent ecosystem.

Learn more

• Discover our IT playbook for becoming an AI-driven Frontier Firm.
• Learn how to secure your agents at scale with Microsoft Agent 365 guidance.
• Read a guide to governance and security for AI agents at your organization.
• Check out this detailed guide to our governance process for the internal rollout of Microsoft 365 Copilot.  
• Explore ways to establish responsible AI policies for agents across the organization.  
• Find out how a data council is powering our unified data strategy at Microsoft.

Try it out

Get started building and managing agents at your company with Microsoft Agent 365.

We’d like to hear from you!

Want more information? Email us and include a link to this story and we’ll get back to you.

The post Governing AI agents at scale: Lessons from our journey at Microsoft appeared first on Inside Track Blog.

Working in Microsoft Digital, the company’s internal IT organization, means being part of a group that operates at massive scale, deploying and managing the technology solutions that enable the company to collaborate, achieve, and fully embrace its shift to a Frontier Firm.

“Being successful in today’s fast-paced environment requires more than technical expertise. Success comes from embracing change, adapting quickly, and continuously learning alongside others. The most impactful teams combine technical capability with curiosity, collaboration, and a mindset of continuous evolution.”

Miguel Uribe, principal PM manager, Microsoft Digital

Our work touches on nearly every aspect of the business, from the network our employees rely on to safely connect to corporate resources, to the productivity apps they rely on every day, to the devices that power our global enterprise. We’re also key to the internal deployment and adoption of agentic AI tools for a global workforce of over 200,000 people.

Microsoft Digital employees have the daily opportunity to tackle complex, real‑world challenges while shaping how Microsoft develops new technologies, serving as Customer Zero for the company’s use of its own products and services.

“Being successful in today’s fast-paced environment requires more than technical expertise,” says Miguel Uribe, a principal PM manager in Microsoft Digital. “Success comes from embracing change, adapting quickly, and continuously learning alongside others. The most impactful teams combine technical capability with curiosity, collaboration, and a mindset of continuous evolution.”

The path to Microsoft Digital

Managing the full breadth of IT responsibilities at an organization the size of Microsoft requires a workforce with a diverse range of perspectives and lived experiences. Accordingly, the people who work here possess a wide variety of backgrounds and skill sets, and hail from around the world.

Networking and relationship-building are often helpful in finding your way into the organization. Mykhailo Sydorchuk, a principal product manager in Microsoft Digital, started his career in Ukraine at a SharePoint solution startup with some prominent global customers. After a successful implementation with one of them, he built a strong relationship with a manager there.

When that person’s company later opened up a SharePoint role, Sydorchuk applied, was hired, and relocated to Los Angeles. He wore many hats in the job, serving as a SharePoint administrator, Microsoft 365 tenant administrator, developer, and project manager for internal IT rollouts and large-scale change management efforts.

Eventually, he was ready for a change.

“My colleague eventually joined Microsoft,” Sydorchuk says. “She felt I would be a good fit and suggested I apply. I went through the interview process and landed the role here about six years ago. So, I got the job very much through networking.”

Some employees at Microsoft Digital have an extensive work history, while others are just getting started in their careers.

“The internship structure is very supportive. Interns are given broad, open‑ended problems rather than tightly scoped tasks, which allows for deeper exploration.”

Jeni Huang, product designer, Microsoft Digital

Internships offer a great opportunity for many candidates who are new to the job market, giving them a way to get a foothold at the company. Microsoft hires thousands of interns each year globally, with year-to-year fluctuations based on hiring conditions and program scope. Within that broader program, design interns are part of a smaller, close-knit cohort, making mentorship and studio connections especially meaningful.

“The internship structure is very supportive,” says Jeni Huang, a product designer in Microsoft Digital who started with the company as an intern in 2022. “Interns are given broad, open‑ended problems rather than tightly scoped tasks, which allows for deeper exploration.”

In Huang’s early work as an intern at the company, she was encouraged to explore more forward-thinking design concepts rather than incremental improvements. That freedom helped her build strong relationships with her manager and others working in the design studio.

“Even though I’m now on a different team, many of the same people remain,” Huang says. “Those connections played a big role in my return as a full‑time Microsoft employee.”

Interesting, impactful work

The people who work at Microsoft Digital routinely tackle ambitious, forward‑thinking projects, with an eye toward reimagining how IT operates at a global scale. Our teams focus on building intelligent, AI‑powered employee experiences, using cloud-native platforms and data-driven insights to simplify work, boost productivity, reduce friction, and help everyone at the company do their best work.

“Microsoft, even after a long and storied history, remains one of the best places for employees to thrive professionally and personally. Experimenting and innovating are at our core—managers are encouraged to provide the time and space for innovation, and to celebrate both successes and learnings.”

Andrew Osten, general manager, business operations and programs, Microsoft Digital

Many of our projects involve large-scale automation, modernizing legacy systems, and embedding responsible AI into everyday workflows, including personalized self‑service technologies, adaptive productivity tools, and predictive insights for decision making. This environment creates a feeling of autonomy for employees and allows them to make significant impact.

“Microsoft, even after a long and storied history, remains one of the best places for employees to thrive professionally and personally,” says Andrew Osten, general manager for business operations and programs in Microsoft Digital. “Experimenting and innovating are at our core—managers are encouraged to provide the time and space for innovation, and to celebrate both successes and learnings.”

Microsoft Digital employees work on front-line technologies that matter. Their efforts serve as living case studies for Microsoft products, testing them in real-world conditions before they reach our customers. The result is a portfolio of work that combines innovation, pragmatism, and long-term thinking.

“We run hackathons sessions like ‘Fix, Hack, Learn,’ where we train ourselves on new technologies and then actively experiment,” Osten says. “That’s one of the most exciting parts of working here: We’re always pushed to explore the latest and greatest technologies and find real value in them.”

The pace can be fast and intense, but it offers the opportunity to work at the cutting edge and be part of transformative software releases. Innovative products result from being given the time and trust to invest and iterate.

“Open-mindedness and flexibility are critical here,” Sydorchuk says. “Technology evolves too quickly to get attached to specific ideas or scopes. Constant change is the norm, and learning to live with uncertainty is essential.”

Customer Zero: Our defining mission

A central component to working in Microsoft Digital is our role as Customer Zero. This concept describes how we use our own products and services internally before releasing them to customers, subjecting them to security, compliance, and productivity demands at an enterprise-level organization.

“Because we deploy these products internally at scale, we learn a tremendous amount, especially since many of these capabilities are early-stage or newly released.”

Andrew Osten, general manager, business operations and programs, Microsoft Digital

This approach surfaces functionality gaps, risks, and usability issues early, turning internal teams into live stress tests for new technologies before they are released to customers. Customer Zero helps ensure our products are resilient, fit for purpose, trustworthy, and grounded in real-world needs, not idealized scenarios. Just as importantly, these practices help create repeatable governance, adoption, and change strategies that customers can reuse, translating internal learning directly into external value.

“Because we deploy these products internally at scale, we learn a tremendous amount, especially since many of these capabilities are early-stage or newly released,” Osten says. “Our role is to generate energy and interest, help teams adopt the tools in ways that deliver real value, and then capture those learnings.”

Customer Zero means that Microsoft Digital functions differently from a typical IT organization, even though we’re still on point for the fundamentals, like keeping the network and its related infrastructure running safely and securely, managing the tenant, providing IT support, driving deployment and adoption, and ensuring our employees have the right tools, devices, and AI-powered services to succeed in a complex global enterprise.

What makes us unique is that we get access to ground-breaking new Microsoft products, features, and capabilities first. We provide early feedback, are the first to try out new experiences, and validate them at enterprise scale.

“We’re often operating at the edge,” Osten says. “For example, I’m currently using early-stage hardware and agentic technologies that haven’t been released yet for general availability, to both provide product feedback and drive value realization as soon as possible. Years ago, through our internal dogfooding program called Elite, I was using a next‑generation Xbox before it launched publicly. Those experiences are part of how we learn about and improve our products.”

Growing AI-based skills

A good example of something truly transformative to emerge from Microsoft Digital recently was our enterprise‑wide deployment and operationalization of Microsoft 365 Copilot—acting as Customer Zero for generative AI technology at scale.

Rather than treating Copilot as a productivity add‑on, we led a full reinvention of how knowledge work happens at the enterprise level. Building everything from governance and data-hygiene standards to role‑based adoption models and change management playbooks, we went all out to change employee habits and safely embed AI into daily workflows across the company.

“AI is behavioral,” Osten says. “To get real value, we work closely with business units to understand the problems they’re trying to solve, map those processes, identify where people can focus on higher-value work, and then build and drive adoption of agents to support that shift.”

In essence, Microsoft Digital is engaged in building an entire business model with AI serving as a governed, trusted, role-aware layer of intelligence. The company refers to this as the Frontier Firm concept, combining human judgment with AI agents—tools that can reason, plan, and execute tasks across systems.

“Building agents just because we can isn’t the goal. The goal is value. Microsoft Digital plays a key role in identifying the right problems, ensuring the right tools are available, and scaling solutions responsibly, so we’re solving problems while not creating new ones.”

Aisha Hasan, principal product manager, Microsoft Digital

The work Microsoft Digital does to conceive, build, and incorporate agents falls under a company-wide initiative known as Microsoft Agent 365. It focuses on three broad questions:

• What problems are we trying to solve?
• How can we build AI agents and workflows to solve them?
• How do we manage and scale this work without creating sprawl or duplicative solutions?

“Building agents just because we can isn’t the goal,” says Aisha Hasan, a principal product manager in Microsoft Digital. “The goal is value. Microsoft Digital plays a key role in identifying the right problems, ensuring the right tools are available, and scaling solutions responsibly, so we’re solving problems while not creating new ones.”

Prospering in Microsoft Digital

In addition to the central role they play as Customer Zero and the opportunity to engage closely with agentic AI, Microsoft Digital employees also benefit from a wide range of opportunities that go beyond technical skills. Rather than limiting our roles within narrow job definitions, we focus on a more holistic career experience that supports pursuing growth opportunities across Microsoft.

“We invest in growth, exposure, innovation, and collaboration in a way that makes the work both challenging and fulfilling,” Osten says.

Employees at Microsoft Digital use traits like curiosity, empathy, and adaptability to thrive within a fast-moving technical landscape. Being curious leads to learning, learning enables adaptation, and empathy pulls it all together, helping people grow as they collectively manage challenges.

“Technology is evolving so fast that keeping up with everything is a challenge in itself,” Hasan says. “Empathy, for yourself and others, matters when everyone is navigating constant change.”

It’s common for employees to leverage a range of responsibilities both within and between different jobs. Open-mindedness and flexibility are critical. Technology evolves too quickly to get attached to specific ideas or job scopes.

“I began in engineering and operations, moved into network engineering, and then gradually ‘peeled back the onion’ by stepping into technical program management,” Hasan says. “That allowed me to see the end-to-end picture: business value, technology, end users, adoption, and long-term maintenance.”

To be successful at Microsoft Digital, technical skills are important, but what really matters is the ability to innovate and work through uncertainty.

“I look for people who thrive in ambiguity, who enjoy taking on new challenges rather than waiting for perfect clarity,” Osten says. “Collaboration is equally important. In an environment this dynamic, you may be accountable for an outcome, but your success depends on the work of many other teams.”

How Microsoft values drive our work

No description of what it’s like to work at Microsoft Digital is complete without a discussion of the principles that fuel us, both at the department level and for the company as a whole.

“It often feels like drinking from a firehose, in terms of the volume of information one needs to process. It’s high-intensity, but being able to work at the cutting edge and be a part of major technological transformation that empowers everyone on the planet to achieve more makes it totally worth it.”

Mykhailo Sydorchuk, principal product manager, Microsoft Digital

Here are four core Microsoft Digital value pillars, as Osten describes them:

1. People development and skilling. This includes technical skills—including around emerging technologies like agentic AI—as well as people skills. We focus on stakeholder management, storytelling, and career development skills that support long‑term employee growth.
2. Leadership and manager development. We continually build leadership capability through a growth mindset, reinforcing principles like creating clarity, generating positive energy, and driving success. We invest heavily in helping both current and future leaders build “model‑coach‑care” skills.

3. Connection and collaboration. We intentionally create opportunities for teams to understand one another’s dependencies, whether through global meetings or structured collaboration initiatives. It’s easy to become siloed in a large enterprise, and these connections are critical, especially as AI continues to blur traditional boundaries.
4. Inclusion. This means being inclusive across communities, geographies, languages, cultures, and work environments. We focus on how we meet, how remote participation works, and how to ensure everyone can contribute effectively, regardless of location or role.

Following our pillars, and being benchmark examples of Microsoft’s value model, contributes to the success of Microsoft Digital and enables our employees to thrive  working at one of the world’s most prominent tech companies.

“Microsoft is a fast-paced environment, primarily due to scale and constant innovation,” Sydorchuk says. “It often feels like drinking from a firehose, in terms of the volume of information one needs to process. It’s high-intensity, but being able to work at the cutting edge and be a part of major technological transformation that empowers everyone on the planet to achieve more makes it totally worth it.”

Key takeaways

Here are five keys to employee success at Microsoft Digital, which can be applied to any IT organization:

• To get a foot in the door, be resourceful. Microsoft Digital employees find their way into the company through a variety of channels, including personal networking, internships, vendor relationships, and Microsoft external and internal career sites.
• Embracing Customer Zero is crucial. The concept of using Microsoft employees as early adopters of new products and services is a strategic cornerstone and an essential aspect of how the company operates.
• Understand what it means to be a Frontier Firm. Orienting your approach to work in a way that corresponds with the benefits of agentic AI can help you align with Microsoft Digital’s journey, as we become a lighthouse example of a Frontier Firm for other IT organizations.
• Develop your curiosity, empathy, and versatility. Technical skills are valuable, but continuous learning and softer skills are foundational to professional and personal growth and success.
• Know your organization’s core values. Collaboration, connection, and inclusion are vital tenets for succeeding at Microsoft Digital, as reflected in the organization’s values.

Try it out

Empower your future with an IT career at Microsoft.

Related links

• Learn more about our company’s mission, values, and products.
• Check out the main Microsoft Careers page.
• Read about Olutunde Makinde’s journey to Microsoft.
• Learn about Anahit Hovhannisyan’s impact on IT at Microsoft.
• See how Microsoft Digital is transforming IT at the company.
• Discover how Microsoft Digital leverages Customer Zero to drive AI innovation.
• Understand more about how agentic AI is core to becoming a Frontier Firm with this deployment readiness guide.

We’d like to hear from you!

• Want more information? Email us and include a link to this story and we’ll get back to you.

The post IT on the cutting edge: Working in Microsoft Digital in the era of 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.

We’d like to hear from you!

• Want more information? Email us and include a link to this story and we’ll get back to you.

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.

We’d like to hear from you!

• Want more information? Email us and include a link to this story and we’ll get back to you.

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

These innovations have come in many different forms across every group and function at the company. It’s impossible to capture them all in a single concept or story, but one of the ways that we’ve activated the power of AI for our workforce is Work IQ.

Work IQ isn’t a product.

It’s a shared intelligence layer that enables Microsoft 365 Copilot and AI agents to reason over and understand your organization’s work data, then use that context to generate more relevant responses and actions. This means that the entire Microsoft Graph—including rich unstructured data from your Teams chats and meetings, Outlook emails, Word documents, PowerPoint presentations, and more—is now part of your AI-powered work experience.

“It’s not really a brand-new capability, but more an evolution of what users already know, which is access to the grounding data in their Microsoft tenant. The difference is that Work IQ adds an additional layer to provide more context, allowing for richer and more relevant results.”

Aisha Hasan, principal product manager, Microsoft Digital

Work IQ enables Copilot to not only tailor answers to your role and responsibilities, but also to understand who your most frequent collaborators are, comprehend details about your latest projects, surface deliverables and deadlines, and intuit next steps. Additionally, Work IQ makes it easy for any AI agent to take advantage of the same rich enterprise data to return and act on more contextual results.

“It’s not really a brand-new capability, but more an evolution of what users already know, which is access to the grounding data in their Microsoft tenant,” says Aisha Hasan, a principal product manager in Microsoft Digital. “The difference is that Work IQ adds an additional layer to provide more context, allowing for richer and more relevant results.”

At Microsoft Digital, the company’s IT organization, we’ve seen firsthand how this intelligence layer is accelerating employee adoption of Copilot and agentic AI as outputs become more perceptive and valuable. Work IQ is a foundational step toward a future where AI has moved beyond isolated assistance and become a trusted professional helper—sometimes described as a digital colleague—that carries out tasks and anticipates needs in every aspect of daily work.

How Work IQ impacts everyday work

One of the most instructive aspects of Work IQ’s impact across our organization is that it happened without a traditional deployment. There was no enablement event for employees or operational playbook distributed to administrators. It didn’t require any changes to the application interfaces. Yet over time, our employee Copilot interactions improved in measurable ways.

“There was a period where we weren’t adding new content to Copilot, and yet I noticed our metrics for quality and user satisfaction kept going up. Why was that? It was because of all these incremental improvements that we refer to as Work IQ.”

Dodd Willingham, principal product manager, Microsoft Digital

This was a direct consequence of introducing a shared intelligence layer into a Microsoft environment that was already rich in work signals. Those work signals are extremely valuable data that was difficult to extract meaning from before the advent of AI. As the technology advanced, we could take full advantage of this data to inform and improve agentic responses.

As Customer Zero for the company, Microsoft Digital was at the forefront of measuring the impact of Work IQ. Our employees saw significant gains in relevance, grounding, and answer coherence in Copilot that were visible in the metrics, even during times when the underlying content remained relatively static. That’s the Work IQ difference.

“There was a period where we weren’t adding new content to Copilot, and yet I noticed our metrics for quality and user satisfaction kept going up,” says Dodd Willingham, a principal product manager in Microsoft Digital. “Why was that? It was because of all these incremental improvements that we refer to as Work IQ.”

At a systems level, Work IQ reasons across a broad cross-section of Microsoft 365 data, including:

• Outlook email content, thread structure, and interaction patterns
• Teams chats, channels, and meeting transcripts
• Calendar events and scheduling metadata
• Documents and files across Word, PowerPoint, Excel, OneDrive, and SharePoint
• Signals that show who collaborates with whom, how often, and in what context

Work IQ can also access structured data in tools like Dynamics 365, Power BI, Power Apps, and other business applications. The ability to extract context and interpret structured and unstructured data in a unified intelligence layer is the reason why Work IQ is making such a difference for our employees.

Making Outlook better

Outlook provides a useful lens on how Work IQ functions because it’s both heavily used by our employees and a highly contextual tool. Although the application hasn’t outwardly changed, the way Copilot interacts with inbox and calendar data has evolved, in part due to richer context provided by Work IQ.

“The intelligence works behind the scenes as you use Outlook. Your inbox just gradually feels more relevant. Outlook adapts to your work patterns, making your inbox feel more like an assistant, instead of a filing cabinet of communications.”

Matthew Marzynski, principal product manager, core experiences, Microsoft Digital

Now when you turn to Copilot in Outlook to summarize email threads, it can surface decision points, action owners, and unresolved issues. Instead of treating email as a collection of messages and providing rote summaries, Copilot perceives it as a record of decisions and commitments over time.

Calendar-related experiences are on a similar trajectory. Meeting preparation and follow‑up suggestions are now drawing on prior interactions with the same participants, relevant documents that were previously shared, and historical patterns around similar meetings.

Work IQ isn’t rule-based automation layered on top of Outlook. Users aren’t configuring new filters or workflows. Instead, the system is adapting based on observed patterns, meaning user behavior can remain the same while output quality improves

“The intelligence works behind the scenes as you use Outlook,” says Matthew Marzynski, a principal product manager for core experiences in Microsoft Digital. “Your inbox just gradually feels more relevant. Outlook adapts to your work patterns, making your inbox feel more like an assistant, instead of a filing cabinet of communications.”

Applying persistent memory

Another important aspect of Work IQ is the ability to retain persistent memory of each employee’s role, responsibilities, and work context. Copilot and other agents no longer need to be continually prompted with details about who the user is and what they’re working on. It learns that information and remembers it going forward.

This feature, also called persistent understanding, builds trust and increases efficiency each time an employee turns to AI for help with their work. AI systems that depend on manual context-setting don’t scale well across large organizations, which we at Microsoft Digital learned as we tested and deployed Copilot across the company.

“The user no longer has to tell the agent, ‘I work in this area, so please tailor your response to that’ every time,” says Anishkumar Ramakrishnan, a principal PM manager in Microsoft Digital. “With Work IQ, Copilot and agents recall it going forward. It remembers things that the user doesn’t even remember themselves about their past work and actions. This is the promise of intelligent context.”

From answers to action: Work IQ and AI agents

As organizations move toward integrating AI agents into all aspects of their day-to-day work, the value of Work IQ increases. Any agent—not just a general-purpose agent like Copilot—that can interpret vast amounts of your unstructured work data is going to produce results that are far more relevant than one that simply draws on general knowledge about a topic or process.

“Before, a builder had to go connector by connector and be very prescriptive—calendar read, email read, meeting access—just to build an agent. Now they can simply point the agent to Work IQ, and it gains contextual access across mail, calendar, meetings, and files through a single connector (API or MCP server).”

Naveen Jangir, principal architect, Microsoft Digital

Early agent implementations relied on narrower task-specific access to data. For each agent, a developer would have to build connections to a particular document library, mailbox, or set of calendar data. Each connection required separate consent and management, which generally resulted in a more limited scope.

But with Work IQ, builders can create agents using Microsoft Copilot Studio or other development platforms (such as Microsoft Foundry) that use APIs or Model Context Protocol (MCP) servers to connect to Microsoft Graph data. This enables them to bring the full power of enterprise data to any agentic creation, not just Microsoft 365 agents.

“Before, a builder had to go connector by connector and be very prescriptive—calendar read, email read, meeting access—just to build an agent,” says Naveen Jangir, a principal architect in Microsoft Digital. “Now they can simply point the agent to Work IQ, and it gains contextual access across mail, calendar, meetings, and files through a single connector (API or MCP server).”

This shift doesn’t just simplify agent development—it fundamentally expands what agents are capable of. Instead of operating within narrow, predefined tasks, agents can now reason across a broader work context to deliver better outcomes. For example, an agent supporting a project manager can surface relevant email threads, identify key stakeholders from meeting activity, reference the latest project documents, and highlight upcoming deadlines—all within a single interaction.

Intelligence without bypassing governance

From a governance perspective, Work IQ doesn’t introduce a new security model. Instead, it operates entirely within the existing Microsoft 365 data protection boundaries that our company and our customers already rely on.

The intelligence layer can access this enterprise data, but it does so while honoring permissions, sensitivity labels, access policies, and compliance controls defined at the source. Work IQ can only surface or act on information that the user—or an agent identity acting on the user’s behalf—is already authorized to access.

This inheritance model is intentional. Governance remains rooted in the data layer, not in the AI layer. Work IQ respects established controls such as identity‑based access and tenant policies, which means agents are generally given less access than human users.

“An agent user only gets access to what is explicitly shared with it,” Jangir says. “Human users typically have broader default access. By design in Work IQ, agents can usually see less than people, not more.”

For IT and security teams, this places the emphasis squarely on data discipline and identity controls, which are complementary security layers. Work IQ amplifies the value of well‑governed data and exposes weaknesses where governance is inconsistent. Admins remain in control of access and can turn off APIs and MCP server connections if they want to limit an agent’s data access.

Work IQ, Fabric IQ, and Foundry IQ

As we’ve scaled up Copilot and agentic AI internally, one lesson has become clear: Intelligence works best when it’s part of a layered infrastructure rather than working on its own.

That’s why Work IQ is just one context layer we’re using at Microsoft. We’ve also developed Fabric IQ and Foundry IQ, which are complementary layers in our overall data strategy. Each of these addresses a different aspect of enterprise intelligence.

The three layers serve distinct but connected purposes:

• Work IQ focuses on unstructured productivity data, helping AI understand how people work across email, meetings, documents, and collaboration signals.
• Fabric IQ applies similar reasoning to analytical and structured data, adding context and explanation to metrics, trends, KPIs, and other business signals.
• Foundry IQ provides the foundation for builders to create agents that draw from both worlds, connecting intelligence across Microsoft 365, analytics platforms, and line‑of‑business systems.

Taken together, these layers also contribute to something deeper: the emergence of a shared business ontology. By extracting and aligning business entities—such as people, projects, and processes—from both structured data in Fabric IQ and the unstructured signals captured by Work IQ, the system perceives meaningful connections that previously were hidden. This unified understanding allows agents to reason across domains with greater precision, linking metrics to the real work and making insights more actionable in context.

This architecture matters because it removes artificial seams. Agents shouldn’t need to shift between separate contexts for work content, enterprise data, or application logic. The IQ layers make it possible to deliver a single agentic experience that reasons consistently, applies governance uniformly, and moves with users across environments. Just as importantly, the same controls—identity, permissions, labeling, and policy—flow through each layer, keeping trust intact as capability expands.

At Microsoft, Work IQ and the other context layers are helping Copilot and agents to accelerate beyond AI experimentation. They are now vital operational tools that make everyone more productive across the global enterprise. Context and intelligence in agentic tools are a key part of the future of work, at Microsoft and for our customers as well.

Key takeaways

Here are some things to keep in mind as you prepare your own organization to take full advantage of Work IQ:

• Treat the technology as infrastructure, not a feature. We didn’t formally roll out Work IQ. Its value emerged gradually as it improved Copilot responses and as our agent builders could more easily tap into unstructured enterprise data.
• Expect improvements in AI quality without changes to your data. We saw measurable gains in relevance and user satisfaction even when underlying content remained the same, driven by better contextual reasoning across existing work signals.
• Focus on how employees work, not just what content exists. Work IQ improves AI outcomes by connecting people, relationships, and activity patterns, resulting in more actionable and grounded responses.
• Use Work IQ to move from assistance to action with agents. By giving agents access to contextual enterprise data through a unified layer, we enabled more automated workflows without requiring developers to manage dozens of connectors manually.
• Invest in data governance early to maximize AI value. Because Work IQ inherits permissions and policies from the data layer, its effectiveness—and safety—relies on clear labeling, intentional access design, and disciplined data management.
• Enable self-service collaboration data so it’s available for Work IQ. WorkIQ can only ground on data that is both available and not purposefully hidden. We make sure that our meetings are AI-enabled (and often recorded) and allow self-service in Teams and SharePoint, so the data is not hidden from Work IQ.
• Build toward a unified intelligence model across work and data. Combining Work IQ with Fabric IQ and Foundry IQ means agents can operate seamlessly across different kinds of data and incorporate more intelligence into their output and actions.

Try it out

Get a closer look at Work IQ.

Related links

• Check out how we’re embracing this agentic moment at Microsoft.
• Explore a Microsoft Learn article for more technical details about how Work IQ works.
• Learn how to get started building agents with Microsoft tools.
• Discover our detailed guide on how we deploy AI agents internally at Microsoft.
• Read an IT playbook for the AI era and learn how we’re becoming a Frontier Firm at Microsoft.
• See how we’re using Model Context Protocol security and governance to protect AI conversations at Microsoft.

We’d like to hear from you!

• Want more information? Email us and include a link to this story and we’ll get back to you.

The post How Work IQ is supercharging our AI usage at Microsoft appeared first on Inside Track Blog.

Engage with our experts!

Customers or Microsoft account team representatives from Fortune 500 companies are welcome to request a virtual engagement on this topic with experts from our Microsoft Digital team.

For more than two decades, SharePoint has been a foundational part of how work happens at Microsoft. This pivotal application supports everything we do, including companywide communications, day‑to‑day collaboration, and empowering our employees to create, share, and manage information.

In 2026, we’re celebrating 25 years of SharePoint at Microsoft. Microsoft Digital, the company’s IT organization, is commemorating this anniversary by reflecting on the journey we’ve taken with the product over the last quarter-century.

In this article, we’ll share our journey as SharePoint’s Customer Zero and step through the lessons we’ve learned building and maintaining an IT stack in the age of agentic AI.

Why SharePoint?

In the early 2000s, we faced a technical challenge familiar to just about any organization: We had important documents and data scattered across siloed file shares, institutional knowledge hidden away in email attachments, and access challenges preventing different teams from collaborating across geographical borders and departmental boundaries.

SharePoint offered the solution to these challenges.

Its flexible, web-based platform gave us the ability to collaborate using shared sites, centralized document libraries, and widely accessible workspaces. The application also fundamentally reshaped our corporate communications and publishing capabilities, providing features that would power key internal portals like Microsoft Web (our longtime internal company homepage, often called MSW), HRWeb, and MS Library.

“At the time, because there were so few customers running SharePoint at scale, the product was in many ways directly built to meet our IT needs.”

Sam Crewdson, principal program manager, Microsoft Digital

The evolution of how we used SharePoint in Microsoft Digital can best be described in three phases:

1. Our on-premises expansion and optimization
2. Our migration to the cloud, self-service growth, and modernization
3. Our incorporation of agentic AI

On-premises expansion and growing pains

When we first adopted on-premises SharePoint at scale, it became indispensable almost immediately. Internal teams used SharePoint to replace their existing file shares, publish information internally, and create many custom workflows and applications tailored to their needs.

Our team at Microsoft Digital was responsible for deploying SharePoint on an enterprise scale. Because we were one of the first enterprise customers to fully use SharePoint’s capabilities, we worked closely with the SharePoint product team from the beginning of its existence as a company. This meant we played a sizable role in influencing what SharePoint ultimately became.

“At the time, because there were so few customers running SharePoint at scale, the product was in many ways directly built to meet our IT needs,” says Sam Crewdson, a principal program manager in Microsoft Digital. “A result of our being their first and best customer at the time was that the SharePoint team often built capabilities for us that no one else was asking for yet, such as specific portals features and supportability needs.”

Our initial adoption of SharePoint exposed some structural limitations and gaps. To meet the goals of our internal customers, we often relied on custom code, which made upgrades more difficult. And data governance and lifecycle management could be challenging, with our internal teams creating thousands of sites with little or no ownership tracking.

Using SharePoint in this way meant rapidly accumulating abandoned sites and outdated content. Trying to conduct even routine maintenance became difficult because there was no reliable way to contact site owners.

“Because of the initial difficulties, SharePoint was frustrating at first, especially for admins. But then I realized how important it was for our users—the product saved them so much time, and they were so happy that it was available. It was a complete 180-degree shift in my mindset towards SharePoint.”

Thomas Snyder, principal service engineer, Microsoft Digital

These challenges meant tensions often ran high for the IT team during the initial adoption phase. Tempers sometimes flared as we navigated this period in SharePoint’s evolution at Microsoft.

However, the time and effort we put into overcoming these growing pains—time and effort our customers didn’t have to invest themselves—made the frustrations well worth it.

“Because of the initial difficulties, SharePoint was frustrating at first, especially for admins,” says Thomas Snyder, a principal service engineer in Microsoft Digital. “But then I realized how important it was for our users—the product saved them so much time, and they were so happy that it was available. It was a complete 180-degree shift in my mindset towards SharePoint.”

Scalable self-service, effective governance, and the cloud

SharePoint’s role at Microsoft quickly expanded from a collaboration platform into a more powerful application where our teams could build workflows, forms, dashboards, and other solutions.

Thanks to a decision to enable SharePoint’s self-service site creation capabilities, our internal customers were able to use it to build the sites they needed without having to wait for us in IT. By removing the friction of having to work with IT, they innovated faster and built new capabilities on their own using SharePoint’s out-of-the-box technology.

However, this self-service power we gave to our users also drove some sprawl that we were not initially ready to manage. By the late 2000s, the information explosion that SharePoint sparked at the company was increasing our operational and governance burden. The rapid growth in sites delayed upgrades and introduced security and compliance issues stemming from a lack of clear ownership when site owners changed jobs or left the company.

As a result of this growth, we made the decision to invest heavily in building up our governance and lifecycle management for SharePoint. We prioritized defining clear ownership for all SharePoint sites, establishing best practices around data cleanup, and building the guardrails necessary to make widespread adoption and use more manageable.

Moving SharePoint to the cloud

Our cloud migration started in late 2010 and quickly became the driving force for us in IT. Rather than see the migration as a simple lift-and-shift activity, we took the opportunity to strategically reconfigure the architecture and customization level of our SharePoint instance.

This was a huge undertaking.

We had to think globally across all our sites in different regions and countries. The tooling suite for migration was immature at the time, meaning some of our portals and sites would require refactoring. We also had to contend with the constraints of varied and sometimes conflicting regional data residency requirements.

“It’s effectively filtering, so you don’t migrate everything. You’re cleaning your house before you move. You don’t move everything in your garage—you clean it out first. The easiest move is the one you don’t have to do.”

David Johnson, principal product manager architect, Microsoft Digital

Our approach to moving SharePoint to the cloud took several phases

First, early adopters who expressed active interest in migrating were provisioned the first sites in the cloud. By harnessing their enthusiasm for cloud services, we allowed them to self-migrate their own site content

Second, we did extensive analysis of all sites to establish actively used sites. Sites where we had no recent usage were backed up, stored offline, and deleted. If nobody screamed, we didn’t move them to the cloud.

Third, we moved the zero- and low-customization sites. These were sites using out-of-box features that had the highest likelihood of a successful migration

Finally, all we had left were the highly customized sites, which often used customization approaches which were not supported in the cloud. These we chose to manually rebuild and often to refactor as part of our migration approach.

While we were making these first-in-the-world migrations, we spent a lot of time with our SharePoint product team partners to learn how best to move sites and to document the approaches for the millions of sites that would follow. Sites which had high levels of customization or features that the cloud couldn’t support were instead rebuilt in the cloud environment from the ground up.

We treated our SharePoint cloud migration as an opportunity to take stock of what we had and decide what we didn’t want to bring with us into the new age of SharePoint at Microsoft. We cleaned our data and retired unused sites based on which content and functions employees told us they regularly used and relied on.

“It’s effectively filtering, so you don’t migrate everything. You’re cleaning your house before you move,” says David Johnson, a principal product manager architect in Microsoft Digital. “You don’t move everything in your garage—you clean it out first. The easiest move is the one you don’t have to do.”

Cloud migration also presented fresh governance challenges for our team. Governance practices had to be established for this new environment that would allow for effective self-service across multiple sites.

Building governance around lifecycle management, attestation, ownership policies, and guarding against oversharing required a significant amount of effort from the team, but it was necessary to ensure a smooth transition from an on-premises tool to the cloud.

Site modernization: Reducing the need for customization

Around 2016, SharePoint rolled out what came to be known as SharePoint Modern. This new version was a game changer for our major portals, as it reduced the need for heavy, developer-driven customization and replaced it with powerful out-of-the-box page creation capabilities, responsive design, and improved accessibility. The product also eventually added seamless built-in integration with solutions like Microsoft Teams and OneDrive.

Less custom code meant we could upgrade faster and dramatically lower our development, support, and maintenance costs. But the best part was the improved user experience and better navigability of the new version. Before this, our IT team fielded numerous questions about SharePoint on a weekly basis. The more intuitive, user-friendly experience of modern SharePoint reduced the volume of inquiries and service requests drastically. Our internal users were happier, and so were we.

SharePoint in the age of agentic AI

We see SharePoint as a key “knowledge platform” for AI. It’s a critical enterprise-scale repository for our documents and data and other information that we use to power our global enterprise.

“Security through obscurity is dead. It’s the double-edged sword of semantic search.”

Thomas Snyder, principal service engineer, Microsoft Digital

As such, it’s one of our key “knowledge platforms,” locations where we store the information that is the lifeblood of our enterprise. And as our enterprise-scale repository for documents, data, and other information used to run our global multinational, it has become the launching point for many of our AI-powered experiences.

AI is only as effective as the quality of the data it can access, which is why we’ve prioritized governance best practices as we make this transition. With these new tools, we’ve had to overcome new challenges.  For example, in the early days of AI, the discovery of previously well-buried personal data is becoming a common occurrence.

“Security through obscurity is dead,” Snyder says. “It’s the double-edged sword of semantic search.”

Prioritizing good governance helps ensure agentic AI only has access to the data it’s permitted to use, avoiding accidental oversharing and related hallucinations.

As an AI-driven Frontier Firm, we’re empowering our non-technical users and engineering and development teams alike to begin building custom AI agents to drive innovation at Microsoft. Our teams can now use agents in SharePoint for tasks like creating applications, knowledge depositories, and sites, saving huge amounts of time and effort.

Many of these agents will eventually be available in Azure DevOps and GitHub, so we’re focused on helping SharePoint site owners put the appropriate data ownership and permissions in place to effectively manage and govern the data for use by agentic AI.

After 25 years, SharePoint remains a core part of IT operations across Microsoft. We look forward to growing alongside it as it continues to evolve and improve.

Key takeaways

These insights can help you mature and transform how you use SharePoint at your company:

• Self-service and good governance go together. Without solid guardrails for your SharePoint instance, your organization could contend with information sprawl and internal friction between departments.
• Cloud migration is a golden opportunity. Before you migrate from on-premises IT to the cloud, take the time to clean your data to avoid carrying technical debt and outdated information into the future.
• Out-of-the-box capabilities are your friend. Customization is useful, but too much of it can be unwieldy and expensive to maintain.
• Make data hygiene a priority. Poorly governed data can undermine users’ trust in AI, expose sensitive information, and delay widespread adoption.

Try it out

Explore how Microsoft SharePoint can help your team collaborate more effectively and be ready for the agentic AI future.

Related links

• Read how we keep our content fresh, findable, and governed with AI-powered SharePoint at Microsoft.
• Learn about how we’re handling Microsoft 365 Copilot governance issues internally in this guide.
• Discover ways to use SharePoint agents to surface knowledge and improve how employees find answers.
• Explore how we’re supercharging SharePoint sites with Microsoft 365 Copilot.

We’d like to hear from you!

• Want more information? Email us and include a link to this story and we’ll get back to you.

The post 25 Years of SharePoint at Microsoft: Our lessons learned as Customer Zero appeared first on Inside Track Blog.

Geoff Belknap, CVP and operating CISO shares the questions he asks when considering when and how to integrate technologies with a merged or acquired company.

Key takeaways

Think about moving slowly with early integration with M&A. Here are some key questions to consider:

• What do we risk by combining tools or technical capabilities too quickly?
• Is the deal still valuable if we do not integrate systems?
• What operational safeguards and governance are needed?

We’d like to hear from you!

• Want more information? Email us and include a link to this story and we’ll get back to you.

The post Microsoft CISO advice: Consider the risks of early integration with mergers and acquisitions appeared first on Inside Track Blog.

• Introduction: Governance in the age of AI
• Chapter 1: Enable self-service
• Chapter 2: Establish container labels and set well-scoped, intuitive defaults
• Chapter 3: Derive file labels from parent containers
• Chapter 4: Train employees
• Chapter 5: Trust employees, but verify their work
• Chapter 6: Implement lifecycle management and attestation
• Chapter 7: Enable company-shareable links
• Chapter 8: Extract inventory to detect and report oversharing
• Conclusion: The way forward

Governance in the age of AI

Unlocking the next generation of productivity tools

Microsoft 365 Copilot combines the power of large language models (LLMs) with your organization’s data to turn employees’ words into some of the most powerful productivity tools on the planet—all within the flow of work. It suffuses the Microsoft 365 apps your people use every day—including Word, Excel, PowerPoint, Outlook, Teams, and more—to provide real-time intelligent assistance.

Findings from workplace surveys that we did internally here at Microsoft show that AI tools are having a significant and measurable impact across some of our major business functions:

Getting governance right

With all the opportunities AI presents, your organization might be in the process of implementing Microsoft 365 Copilot. But it’s important to do that safely.

Copilot combs through your organization’s entire data estate in the blink of an eye, so the old method of security through obscurity doesn’t cut it. You need to assert control over where data flows throughout your tenant, so Copilot knows what it can and can’t access or display.

Learn from our Microsoft 365 Copilot experience

We learned a lot as the first large enterprise to deploy Microsoft 365 Copilot. We used those learnings to create this deployment and adoption guide that you can use at your company—check it out:

• View our full Microsoft 365 Copilot deployment and adoption guide. 
• View a condensed version of the guide, designed for executives. 

To ensure that proper data hygiene extends to AI-powered workflows, Microsoft designed Copilot to respect the sensitivity labels and data loss prevention (DLP) controls that organizations configure in their Microsoft Azure environment. That way, administrators can be confident that the right people and apps have access to the data they need, and that sensitive information doesn’t appear where it shouldn’t.

Our team in Microsoft Digital, the company’s IT organization, created a company-wide governance strategy to address this challenge. In the process, we learned valuable lessons that will be useful to any organization using Copilot.

“We’re entering an age where AI amplifies human capability at unprecedented scale, and the integrity of our data determines the integrity of that transformation. Thoughtful governance ensures that we balance adoption with risk to enable the business.”

Brian Fielder, vice president, Microsoft Digital

This guide outlines our process for developing and implementing a governance strategy that delivers the benefits of Copilot to Microsoft employees while minimizing the risks to our data estate. We share our internal learnings so our customers can get up and running quickly while avoiding pitfalls or surprises.

Follow along to find out how you can safely and effectively deploy Copilot at your organization—backed by rock-solid governance.

“We’re entering an age where AI amplifies human capability at unprecedented scale, and the integrity of our data determines the integrity of that transformation,” says Brian Fielder, vice president of Microsoft Digital. “Thoughtful governance ensures that we balance adoption with risk to enable the business. AI accelerates possibility and does so with clarity, confidence, and unwavering trust.”

Principles for effective AI governance

Use this set of tips to ground yourself as you read through this guide:

Enable self-service. Give employees the ability to create new workspaces across your Microsoft 365 applications. By maintaining all data on a unified Microsoft 365 tenant, you ensure that your governance strategy applies to any new workspaces.

Limit the number of information protection labels. Try to limit your taxonomy to a maximum of five parent labels and five sub-labels. That way, employees won’t feel overwhelmed by the volume of different options.

Use intuitive labels that mean what they say. Make your labels simple and legible. For example, a “business-critical” label might imply confidentiality, but every employee’s work feels critical to them. On the other hand, there’s very little doubt about what “highly confidential” or “public” mean.

Capture container labels for groups and sites. Label your data containers for segmentation to ensure your data isn’t overexposed by default. Consider setting your container label defaults to the “Private: no guests” setting.

Derive file labels from parent containers. Classify files according to their parent containers. That consistency boosts security at multiple levels and ensures that deviations from the default are exceptions, not the norm.

Train employees. Train your employees to handle and label sensitive data to increase accuracy and ensure they recognize labeling cues across your productivity suite.

Trust employees, but verify their work. Trust your employees to apply sensitivity labels, but also verify them. Check against DLP standards and use auto-labeling and quarantining through Microsoft Purview automation.

Implement lifecycle management and attestation. Use strong lifecycle management policies that require employees to attest containers, creating a chain of accountability.

Consider your default link-sharing configuration. Limit oversharing at the source by allowing company-shareable links—at least as secondary options—rather than forcing employees to add large groups for access. For highly confidential items, limit sharing to employees on a need-to-know basis.

Extract inventory to detect and report oversharing. Use Microsoft Graph Data Connect extraction in conjunction with Microsoft Purview to catch and report oversharing after the fact. When you find irregularities, contain the vulnerability or require the responsible party to repair it themselves.

Learn more

How we did it at Microsoft

• See how we’re riding the wave of agents washing over Microsoft with good governance. This story shares how we established our initial policies and matrixed governance model for agents.
• Explore our IT playbook for the AI era and learn how we’re becoming a Frontier Firm. This article shares our journey as an IT organization in support of this new operating model.
• Read our five-step guide for IT leaders who want to drive greater AI maturity. This resource can help you chart a course through AI maturity to reimagine what’s possible for the enterprise.
• Discover how Microsoft is becoming an AI-first Frontier Firm. This story shares how we’re approaching the idea of an “agentic future.”

Further guidance for you

• Secure your agents at scale with Microsoft Agent 365 guidance for unified identity, compliance, and control across platforms.
• Learn about the Responsible AI policies and practices that we use to guide our use of AI at Microsoft.

Chapter 1: Enable self-service

Empowering employees with secure self-service

Applying self-service principles to the way we manage labeling and governance emerged as a crucial step for us. 

Self-service is a core tenet of employee empowerment here at Microsoft. We want to give every employee the independence to create the resources they need without engaging IT. But that level of freedom relies on ensuring our Microsoft Digital governance team identifies and protects valuable data. As a result, our employees can implement and own the containers, workspaces, and content they need to do their work productively. 

A container or workspace is a logical unit of content storage associated with a designated roster of collaborators. Common containers include SharePoint sites, Viva Engage communities, Outlook groups, and Teams channels.

Self-service forms the foundation of our entire governance strategy. Employees can create workspaces and content across many of the Microsoft tools they use for their day-to-day work, including SharePoint, OneDrive, Teams, and Power Platform. That freedom enables a culture of innovation and agility, where people can work together across teams and geographies without encountering “IT gating,” the need for IT to get involved in enabling day-to-day activities.

By encouraging collaboration in place, our tenant structure frees employees from resorting to email attachments or working in overly broad and open workspaces. As an IT team ourselves, we understand the value of eliminating IT gating for minimizing the time and effort our professionals need to invest in keeping employees productive.

This kind of data hygiene isn’t just about Microsoft 365 Copilot. It maintains data security and compliance wherever employees access company content and information. But because Copilot depends on the ability to access an organization’s data estate, good governance is essential for keeping it within bounds—especially in a self-service culture.

Here are the key pillars of our asset governance:

• Support self-service creation
• Use lifecycle management
• Offer user education and awareness/trainings
• Implement monitoring and auditing
• Adopt insider risk management

• Require classification for containers
• Scan with Microsoft Purview Data Loss Prevention and Information Protection services

• Limit reach
• Enforce policy
• Use conditional access or multifactor authentication
• Apply Microsoft Purview Data Loss Prevention and Information Protection services

• Manage group or site ownership
• Review external membership
• Generate reports

Responsible self-service

Self-service container creation has abundant benefits, but it also poses some challenges for content governance and security—things like oversharing, unneeded asset sprawl, and data leakage. To address these challenges, our Microsoft Digital governance team has established self-service principles that balance the needs of employees and the company.

We empower with accountability

Accountability has responsibility. Any full-time employee can create a workspace, but they’re responsible for re-attesting its compliance every six months to ensure it meets our governance requirements. They also need to attest that they still require and maintain the resource. They need to manage their own content and ensure it’s properly classified, labeled, and secured. The content’s accountable owner makes any decisions about the workspace with respect to reach or the desire to maintain it. That removes any guesswork for IT about whether a site is still valued and cared for.

We empower with guardrails

We secure assets by default and expand access based on employee needs.

We trust, but we also verify

Microsoft Information Protection (MIP) sensitivity labels and Purview DLP act as guardrails for employee-led governance efforts.

As we in Microsoft Digital have worked to improve the company’s overall governance posture, we’ve learned several important lessons. When you consider self-service container creation, there are a few questions to ask yourself:

• Who do you trust to create containers? At Microsoft, we reserve complete self-service capabilities for full-time employees. Then, we configure those privileges in Microsoft Entra ID to define who can create Microsoft 365 Groups. These users need to take relevant trainings, and we hold them accountable for the containers they create.
• Where does employee self-service make sense? Different employees will require self-service in different environments. Will yours need to operate within SharePoint? Power Platform? Teams?
• What are your lifecycle rules? Think about your policies and rule sets. Who’s accountable? What does the lifecycle look like?
• What are your naming rules? A clear taxonomy can act as an extra signpost and organizational driver for your users. It can also be useful to think through what names are explicitly helpful or obscure. At Microsoft, we use a blocked word list, but we don’t prefix or suffix all groups or site names to avoid overloading the employee experience.

When you’ve settled on degrees of autonomy and where to apply it, you can begin your AI governance journey. Find out how to configure containers for self-service.

Key takeaways

Use these tips—which are based on what we learned here at Microsoft—to enable self-service in Copilot governance at your organization:

• Front-end load on building your strategy. Put thought into your environment and tenant architecture, key personas, and scenarios before adoption.
• Account for hesitancy. Understand that IT organizations have inherently cautious habits, and self-service might seem like a leap. As you lay out the business value for self-service container creation, illustrate the safety backstops as well. Also consider the risks if you don’t take this step, like employees misusing existing sites or other means not supported by IT.
• Bring leadership on board. Make the business case and offer reassurances that greater flexibility doesn’t equal greater vulnerability.
• Assess your current setup. Consider your existing data hygiene and how it needs to extend to accommodate AI.

Learn more

How we did it at Microsoft

• Read how we’re powering data governance at Microsoft with Purview Unified Catalog. This article explains how we centralized data governance and improve data discoverability at scale with the Purview Unified Catalog.
• Discover how we’re riding the wave of agents washing over Microsoft with good governance practices. This post explores how we established strong governance practices to safely enable the rapid adoption of AI agents across the organization.

Further guidance for you

• Get an overview of pre-deployment preparation for Microsoft 365 Copilot. This guide outlines the key pre‑deployment steps organizations can take to ensure Microsoft 365 Copilot is secure and compliant.
• Learn about our Copilot Success Kit, a one-stop shop for most of our Copilot deployment and adoption resources. This resource walks through a centralized collection of tools, guidance, and best practices for deploying and adopting Copilot.
• Learn more about Microsoft Purview Data Loss Prevention and Microsoft Purview Data Governance, which are key to our organization’s approaches for protecting sensitive information and managing data assets throughout their lifecycle.

Chapter 2: Establish container labels and set well-scoped, intuitive defaults

Balancing freedom with trust through an easy-to-use labeling taxonomy

Self-service container creation forms the foundation of our employee-centric governance strategy. As part of that freedom, our Microsoft Digital governance team has established baseline protections inherent to all containers, and those protections depend on sensitivity labels. Microsoft 365 Copilot respects labels, so establishing effective labeling practices extends data security into our employees’ AI usage.

Baseline labeling habits

Employees need to label every container or workspace they create using Purview Information Protection (PIP) container labels. It’s a matter of policy at Microsoft: If it isn’t labeled, we delete it. We use container labeling for data delineation and to apply consistent protection and governance policies to containers based on their sensitivity and purpose.

Microsoft labels break out into four different categories:

• Highly confidential. This is our most critical data. Employees can only share this with specifically designated recipients.
• Confidential. This is sensitive business data that’s crucial to achieving our goals. Employees should limit distribution to a need-to-know basis.
• General. The “General” label includes data we use and share throughout Microsoft, like personal settings and postal codes. They’re visible internally throughout Microsoft.
• Public. Public data is unrestricted and suitable for open, external consumption. It includes open-source code or financials the company has announced. Employees can share this data freely.

Container labels provide two things:

• First, they drive user awareness over how to handle content. For example, if something is highly confidential, employees shouldn’t talk about it in the café.
• Second, they illustrate what data is appropriate for which container. In other words, they signal to an employee that they shouldn’t store highly confidential documents on a general site.

Our Microsoft Digital governance team predefines and centrally manages labels to align them with broader MIP sensitivity levels used for email, files, meetings, and containers. Those include the same four categories: “highly confidential,” “confidential,” “general,” and “public,” although we don’t use the last one for containers.

Matching labels with policies and protections

Each label we’ve defined has a set of protection settings that include policies around characteristics like guest allowance and membership openness. They also drive inherited file labeling, which we use for encryption.

At its core, container classification communicates four things:

• Privacy level: Labels determine whether the workspace is broadly available internally or it’s a private site.
• External permissions: We administer guest allowance via the group’s classification, allowing specified partners to access teams when appropriate.
• Sharing guidelines: We tie important governance policies to the container’s label. For example, can employees share this workspace outside Microsoft? Is this group limited to a specific division or team? Or is it restricted to specific people? The label establishes these rules.
• Conditional access: While not implemented at Microsoft, tying identity and device verification to container labels introduces additional governance controls.

After extensive experimentation, we arrived at our current schema for how container sensitivity labels align with MIP policies. Your organization might make different choices about your labels’ relationships with information protection policies, but this graphic can give you an idea of what a healthy governance ecosystem looks like:

Building a process around employee ownership

The labeling process works like this: When employees create a new container, they’re responsible for selecting a container label that matches the sensitivity and purpose of the content they intend to store and share. By default, we lock new containers, which means that only the owner and members can access them. Locked containers prevent unauthorized or accidental access to their content.

Container owners can unlock the container if they need to share content with a broader audience within the organization or external partners. Container owners can also change the container label if the sensitivity or purpose of the content changes over time.

At Microsoft, this process provides the right combination of flexibility and protection while empowering employees with effective self-service.

Key takeaways

Here are some of the main insights we’ve gleaned from our own data-labeling practices, which you can apply to your efforts in this area:

• Use intuitive labels. Your employees will be the ones applying labels, so make those labels intuitive. For example, “highly confidential” is easy to understand, while “business-critical” can be interpreted many ways from a sensitivity standpoint.
• Make use of existing defaults. Identify the security needs and regulatory compliance that are specific to your organization and use built-in governance controls available through Microsoft tools.
• Limit the number of labels to 5×5. Keep labels minimal to avoid overtaxing your employees’ understanding. We recommend restricting your labeling schema to no greater than five main labels with five sub-labels each—and the fewer, the better!
• Pilots are powerful. Experiment with sensitivity labeling through a small group of early champions, then roll these features out alongside an adoption and education initiative.

Learn more

How we did it at Microsoft

• Learn how we create self-service sensitivity labels in Microsoft 365. This article covers how we designed and implemented self‑service sensitivity labels to empower employees while reducing administrative overhead.
• Find out how we’re using sensitivity labels to make Microsoft more secure. This post examines how we use sensitivity labels with built‑in guardrails to strengthen security without slowing down employee productivity.

Further guidance for you

• Read about getting started with sensitivity labels. This documentation introduces the core concepts, setup steps, and capabilities of sensitivity labels in Microsoft Purview.
• Explore how to apply sensitivity labels to your files. Read a guide on how to apply sensitivity labels to files and emails across Microsoft 365 apps.

Chapter 3: Derive file labels from parent containers

Using default file-labeling based on container labels

We’re helping our teams stay consistent with how they create and store resources by making sure that default file-labeling happens based on container labels. Here’s how that looks for our employees:

1. SharePoint and other containers support default library labels, which we configure to align with the container label through mapping we define in Purview.
2. For instances where we need to define default library labels for tools that don’t have container labels, like OneDrive for Business, we create custom scripts. For OneDrive, we “secure by default” by using a default label like “Confidential\Internal-only,” which means that any file type that supports protection will remain protected–even if it’s accidentally left behind on a device, emailed externally, or purposefully shared externally. Without lowering the label, the default protection will not be decryptable by external actors.
3. By default, new items that are unlabeled inherit the label of the container that stores them. That helps employees apply the correct label and avoid misclassification. For example, if an employee creates a new document in a SharePoint site labeled “confidential,” the document will automatically receive that label.
4. Employees can change the item label if the sensitivity or purpose of the content differs from the container label. But that only works in one direction; they can’t store files with higher-confidentiality labels in a lower-confidentiality container. For example, they can downgrade a file in a “highly confidential” container to “general” if it doesn’t require heightened protection, but they can’t upgrade a file in a “general” container to anything above that grade. SharePoint will provide warnings to site owners when it detects label mismatches—for example, when a file label is more sensitive than its container’s.

The following graphic shows how default file-labeling is impacted by container labels and other sharing limitations:

By defaulting file labels to their container labels, you can ensure that every item and collaborative space will align with both its context in your organization and your information protection policies. As a result, Copilot will respect those labels and their corresponding information protection policies.

Key takeaways

Here are some key tips for setting up container-file relationships, based on what we’ve learned through our own experience here at Microsoft:

• Communicate the relationship between files and containers. Employees might not understand the relationship between files and their containers intuitively. When you implement your labeling strategy, be sure to include education about container-file derivation.
• Guide through correction. Many employees learn best from practice, not instruction. Include automated messages that correct edge-case behaviors like trying to make a file in a confidential container generally available.
• Ensure you’re comfortable with your label defaults. Employees will more often than not use the default, so ensure your defaults are correct and reflect your organization’s needs.
• Reinforce the importance of file labels. Because a file can be moved or downloaded from its original container, the only way to protect that information is to ensure its label remains durable. Embed that durability in your object label configurations.
• Match container and file label defaults. Whenever possible, make the container and file defaults the same from the outset. If you start with different labels or policy sets at the outset, it will be difficult to reconcile those changes later.

Learn more

How we did it at Microsoft

• Find out how we’re using sensitivity labeling as a new layer of security for Microsoft Teams Premium meetings. This post explains how we apply sensitivity labels to content in Teams Premium to protect sensitive collaboration.

Further guidance for you

• Learn more about configuring default sensitivity labels in SharePoint libraries. This guidance details how default sensitivity labels in SharePoint libraries ensure consistent protection for our stored content.

Chapter 4: Train employees

Empowering our employees: A joint effort between IT and users

Training your employees on how to handle and label sensitive data continues to be a critical step on our governance journey. 

Establishing a robust labeling strategy is only part of good governance. When it comes to getting employees on board, culture is as critical as policy.

At Microsoft, employee learning and development are how we move sensitivity labeling from the administrative sphere into day-to-day practice. It helps us increase the accuracy of how our labels are used and ensures that our employees recognize labeling cues when they appear across our productivity suite.

Every incoming Microsoft employee takes our Standards of Business Conduct and security trainings. As part of that process, we created an internal SharePoint resource dedicated to educating employees about their responsibilities for labeling and adhering to our governance policies. It educates employees about the philosophy behind our policies, shares a simplified overview of our sensitivity label structure, and provides practical, app-specific guidance for self-service labeling.

Effective learning and development assets

As you build out your employee education assets, consider emulating our content with the following elements:

1. Overview

It will be much easier for employees to act according to your governance policies if they understand what the policies do and why they’re so important. Our overview illustrates the relevance of sensitivity labeling for security and compliance and reinforces our employees’ role in maintaining them.

2. A quick-reference guide

A visual guide will help employees understand how labels relate to each other and what they accomplish. At Microsoft, we use a helpful flowchart that provides an outline of our labeling taxonomy without overloading employees with details. Placing it near the beginning of your training content grounds employees in the knowledge early, before they dive deeper into the details.

3. Technical education

Our learning material includes a section on how labeling works within our data estate. Then, it proceeds into an in-depth description of how each label or classification interacts with users’ content. Including this section will make labeling more tangible for your employees.

4. App-specific guidance

At this point, our guidance documentation progresses through the most common app-based use cases for sensitivity labeling: Microsoft 365 files, Teams, Power BI, and PDFs, as well as AIP and other file types separate from Microsoft 365. This app-by-app procedural content will help employees home in on their most common scenarios and educate themselves accordingly.

Aside from laying a solid foundation as an IT team, the most effective way to promote good governance is by bringing your workforce on board. Robust learning and development content is a powerful lever for establishing a culture of data security.

Key takeaways

Here are some of the key insights we’ve drawn from our own employee training in Copilot governance, which can guide you as you set up your own trainings.

• Educate from day one. People will only do what they know, so ensure employees know your policies and how to enact them. Build robust education into your labeling and governance strategy, ideally as part of employee onboarding.
• Don’t neglect in-app education opportunities. Labeling cues are an excellent opportunity for helping employees remember their responsibilities. Make label descriptions brief and tangible during in-app experiences.
• Provide education on-ramps. Nobody’s memory is perfect. Link out to relevant information as part of label descriptions so curious employees have a chance to reinforce their knowledge.
• Engage actively and situationally. If breaches occur or certain teams underperform, coordinate with relevant managers to refresh employee knowledge.

Learn more

How we did it at Microsoft

• Find out how we’re deploying Microsoft 365 Copilot with the help of—you guessed it—Copilot. This article explains how we used Copilot itself to plan, deploy, and scale Microsoft 365 Copilot across the organization.
• Discover how we’re skilling up our employees for the future of work with Agent Launchpad. This post covers our latest training program to help our workers get the most out of agentic AI.

Further guidance for you

• Learn more about sensitivity labels. This Microsoft Learn content provides an overview of sensitivity labels, including how they help classify, protect, and govern sensitive data across Microsoft 365.

Chapter 5: Trust employees, but verify their work

Self-service with guardrails: Backstopping our employee efforts with technology

Trusting your employees while also verifying that their actions are secure via automation is a crucial step. 

Thanks to our education efforts and intuitive labeling interfaces, we trust employees to apply sensitivity labels. But we also verify their work. It’s how we catch the 1% of edge cases where problems might arise.

We accomplish that by checking files against our data-loss prevention (DLP) standards and using auto-labeling and quarantining when we need them. Swiftly tying up any loose ends eliminates wayward items that Microsoft 365 Copilot might scoop up during the course of its work. Another way we verify employee decisions is by asking them to provide a reason when they downgrade a security label.

Data-loss prevention (DLP) is a set of technologies and practices centered around Microsoft Purview that help detect, monitor, and reduce the risk of sensitive data being inappropriately shared or accessed.

At Microsoft Digital, we use Purview DLP policies to define the rules and actions for detecting and protecting sensitive data across Microsoft 365, SharePoint, OneDrive, and Teams.

DLP policies support vulnerable data types and scenarios that require protection. They include any kind of information that might introduce inappropriate access to company data or intellectual property:

• Access credentials like keys or tokens
• Personally identifying information
• Financial data
• Non-public source code
• Sign-in information

Reports and dashboards are available via Purview to help our team monitor and analyze content activity and compliance across the organization. They also provide insights into the volume, location, and usage of sensitive data, as well as any incidents and alerts that indicate potential data breaches or violations.

For example, an employee might label something as “General,” but it contains credentials or other sensitive end-user identification information (EUII). In those instances, Purview will automatically block the file from access beyond its owner or reapply a more appropriate label.

Automation and escalation

We’ve configured Purview to automatically remediate these kinds of issues or escalate them to our Microsoft Digital governance team for resolution when an issue is more complex. DLP remediation and escalation processes can involve several different groups of stakeholders depending on the severity and impact of the incident or alert:

• Content owners
• Content champions
• The MIP team
• Our legal team
• Security

We use Microsoft 365 Purview to run DLP remediation operations at scale.

1. DLP systems acquire telemetry from the Microsoft 365 activity management API. Backend processing cleanses the data to build relevant insights and surface them through Power BI dashboards.
2. We flag information about files and aggregate it at the file level, then assign it to the last modifier for remediation action.
3. If users don’t act on the files quickly, the DLP team scopes risky sites to quarantine any files with vulnerabilities.
4. All activities—including sharing, labeling, and changing labels—get written into the unified audit log and into Sentinel to monitor for possible risks.

Fortunately, all these features and functionalities are available out of the box through Microsoft 365 and Purview. After you’ve established your labeling strategy and policies, it’s just a matter of adding guardrails to your self-service environment. By automating information protection through quarantining content or rightsizing its label, you can keep Copilot from making sensitive information available where it shouldn’t.

Key takeaways

Here’s what we’ve learned from our trust and verification process, which can inform your own process:

• Consider your key escalation partners. When human intervention is necessary, it’s important to have immediate access to the relevant stakeholders. Assemble your list and build it into your process.
• Understand DLP’s limitations. Purview DLP is a powerful set of capabilities, but it still relies on automation, which can miss things humans don’t. For example, DLP might not understand the code name for a product and fail to catch it during automated verification.
• Identify and manage exceptions. There are very few absolutes in IT, so you’ll always need exceptions. For example, finance professionals will often need to include passwords or credit card numbers in working documents, so we exempt them from Purview DLP oversight with that team. At Microsoft, we use exemption groups to exempt certain employees.
• Involve experts. Your legal, HR, and security teams will be key allies in this process. Engage them early to help you flesh out risk factors and vulnerabilities.

Learn more

How we did it at Microsoft

• Learn how we’re empowering our employees with generative AI while keeping the company secure. This story explores how our employees use generative AI tools in their work while maintaining strong security, compliance, and governance controls.
• Discover how we’re using Purview Unified Catalog to power data governance at Microsoft. This post describes how we use Purview Unified Catalog to centralize data governance and improve visibility and trust in data assets.

Further guidance for you

• Find out how Microsoft Purview Data Loss prevention can help you automate remediation. This guidance outlines how Microsoft Purview DLP helps organizations identify, monitor, and automatically remediate risky data activities.
• Explore how Microsoft Sentinel provides cloud-native security and event management. This overview explains how Microsoft Sentinel delivers security and event management to help detect, investigate, and respond to threats at scale.

Chapter 6: Implement lifecycle management and attestation

Pairing trust with accountability: How we’re maintaining our data hygiene with attestation

We focused on strong lifecycle management policies and employee attestation to help us get our lifecycle management right. 

Attestation and self-service go hand-in-hand. In simple terms, it means employees can create what they need, but they’re accountable for its upkeep. In turn, that chain of accountability makes sure Copilot only accesses clean and appropriate data.

To support this, SharePoint now offers both activity‑based and non‑activity‑based attestations through SharePoint Advanced Management, giving organizations flexible ways to validate that their containers are being properly maintained. Microsoft Entra also provides an inactive group expiration policy that requires renewal of any inactive Microsoft 365 Group (like a team, group-connected site, or Outlook group).

At Microsoft, we follow the principle of data minimization. That means only content that’s necessary and relevant for the company’s operations and objectives should exist in storage. Data minimization reduces the risk of oversharing content that isn’t cared for by employees, minimizes asset sprawl, halts data leakage, and improves quality and usability.

To implement this principle, we require that every existing container has attestation. By extension, we delete information that doesn’t have a full-time employee to care for it or that has become stale or irrelevant.

Attestation is the process of verifying and validating the existence, ownership, and purpose of a container and ensuring it complies with content governance and security policies.

At Microsoft, we require attestation from a full-time employee for all shared workspaces every six months to confirm several aspects of their containers:

• It’s correctly labeled.
• Users actually care about its ongoing existence.
• The roster of people with access is accurate and necessary.
• Sharing capabilities are appropriately restrictive or permissive.
• It complies with corporate retention guidelines.

If a container or an item doesn’t have attestation, we consider it orphaned or abandoned, and it’s subject to deletion. Note that we archive deleted items over an extended period, in case our employees decide they need them after the fact.

Managing exceptions

If a container is subject to a retention or hold for our legal team, that supersedes any deletion event. Generally speaking, containers where the legal team is the accountable owner aren’t subject to re-attestation because we handle those lifecycles more granularly based on Purview retention policies.

Ultimately, every organization will have to decide what makes the most sense for them. Applying these principles will help you maintain organization-wide data hygiene, which prevents over-access from Copilot.

Key takeaways

Here are some tips that come from our experience managing the product lifecycle for Microsoft 365 Copilot here at Microsoft.

• Choose a meaningful attestation interval. The attestation interval should be short enough that it doesn’t introduce risk through neglect and long enough that it isn’t unnecessarily burdensome for employees. Think about what makes the most sense for your people by analyzing their behaviors.
• Communication is key. Be sure that the attestation requests you create for employees contain both the objective for motivation and simple instructions. That will increase buy-in and smooth the process.
• Base non-compliance response on severity. The severity of non-compliance will vary based on different files and containers. Some might be more relaxed, and others more strict. Determine a strategy for deciding which is which.
• Include reasonable resolution and recovery options. Consider your resolution and recovery intervals after a lapse in attestation. You’ll need to balance between items’ sensitivity, employees’ bandwidth, and the infrastructure cost of extended archiving for recoverable items.

Learn more

How we did it at Microsoft

• Check out our guide for deploying and driving adoption of Microsoft 365 for executives. This executive-level guide explains how we deployed Microsoft 365 Copilot and drove adoption across the organization.

Further guidance for you

• Find out how to configure the expiration policy for Microsoft 365 groups. Read how expiration policies help to manage the lifecycle of Microsoft 365 groups.
• Learn about managing site lifecycle policies. This documentation outlines how to manage SharePoint site lifecycle policies to govern site creation, retention, and deletion.

Chapter 7: Enable company-shareable links

Enabling fluid, secure collaboration: Extending access with company-shareable links

We’re finding that the best way to reduce oversharing is by addressing it at the source.

At Microsoft Digital, we recognize that content sharing is essential for collaboration and productivity. Employees need to share content with both internal and external audiences. But that also poses a risk of content oversharing when employees expose material to more people or for longer than necessary. It might also mean they’ve shared content without proper protection or classification.

In many cases, employees need to share content outside its container. That might include simply sharing a specific file outside of the container’s roster to enable collaboration in place without resorting to making a copy of the file. On the other hand, someone might need to email the file as an attachment.

Using company-shareable links

We limit oversharing at the source by enabling employees to directly share with users or groups, or by using company-shareable links (CSLs) for all SharePoint sites and items (except ones labeled “highly confidential”).

A CSL is a type of link that allows anyone who receives it within our organization to access the content. CSLs are convenient and easy to use, and they promote a culture of openness and transparency.

Before CSLs, employees were forced to share content with large security groups, because they didn’t know which groups contained everyone who needed access and manually adding every unique user was too cumbersome. That behavior leads to oversharing, because anyone with access can stumble on the content in Microsoft Search or via an answer from Copilot. Any Microsoft 365 discovery scenario will security-trim results, so it’s important that users can’t directly access things they don’t need.

While employees can pass a company-shareable link around within the company, it isn’t discoverable in Microsoft Search or Copilot, because only users who received the link directly via email or chat will have pre-granted access. It might seem counterintuitive that a CSL is more secure, but it eliminates the need for standing access to content and provides greater protection.

Finally, we allow content owners to modify or revoke CSLs if their sensitivity or purpose changes, or if sharing is no longer necessary. The content owner can also set an expiration date or a password for their link to enhance security and control.

Note that company-shareable links are no longer the default option, but they are still available as a sharing option for the reasons outlined here.

Extra protection for highly confidential items

Our governance team at Microsoft Digital determined that we should enable CSLs by default for all containers and items labeled “public,” “general,” or “confidential.” As a result, employees can share content with their colleagues without having to grant individual permissions or manage access requests.

There are some kinds of content that employees absolutely shouldn’t share through a company-shareable link. The risk emerges if someone copies the link into an open location like a broadly accessible document or community. You’ll have to decide where to draw that line for your organization. At Microsoft, we’ve elected to disable CSLs for all containers and items that are labeled “highly confidential.”

At Microsoft, highly confidential items require need-to-know access for specific people. For these files, employees use links they designate for specific people, which allows access to only individuals the content creator or owner explicitly identifies. In those situations, large security groups aren’t appropriate in any case.

We also want to drive broad sharing to SharePoint, so we discourage CSL use on OneDrive by automatically implementing expiration policies on OneDrive-created CSLs.

These policies compel employees to think about who needs access to content and to take deliberate action before sharing. In some ways, the policies act as an extra gate or prompt to keep our people security-conscious during the sharing process.

At Microsoft Digital, we tailored our policies to the company’s specific needs, but it provides a blueprint for other organizations to build a CSL strategy. Deciding what should be sharable and how will help you ensure robust information protection that’s still flexible enough to foster collaboration and productivity.

Key takeaways

Here are some key learnings we took from our CSL strategic work at Microsoft, which you can apply to your own efforts in this area:

• Establish thresholds for company-shareable links or specific-people links. Align your CSL policies with the sensitivity labels that meet your organization’s security needs. Above a certain threshold, it might make sense to require links for specific people.
• Embed education in the process. Employees will need time to get used to this structure. Create education communications early in the process, and configure your labeling interface to display information about the sharing implications of different labels.
• Manage expectations for security teams. CSLs are counterintuitive in terms of safety. They might make security professionals uncomfortable because employees are free to share them internally with anyone. Reinforce that CSLs are safer than giant security groups, which will be the other default behavior for employees. And unlike security groups, they won’t show up in Microsoft Search.
• Build data hygiene on good defaults. Most people will take the simple path, so make the simple path the safe path. Generally speaking, employees leave the defaults intact. If CSLs are your default, that’s the behavior it will drive for your employees.

Learn more

How we did it at Microsoft

• Check out our guide for deploying Microsoft 365 Copilot. This guide details our approach for deploying Microsoft 365 Copilot, from technical readiness to adoption and value realization.

Further guidance for you

• Find out how company-shareable links work in Microsoft 365 environments. This Microsoft Learn content explains how organization‑wide sharing links affect access, security, and collaboration.

Chapter 8: Extract inventory to detect and report oversharing

Remediating oversharing errors when they occur: Reporting on broad-access files and sites with Microsoft Graph Data Connect

When oversharing does slip through, it’s important to have systems in place to catch it. 

In spite of our Microsoft Digital governance team’s best efforts to limit oversharing at the source, it can still occur. In some ways, it’s inevitable.

Organizations are made up of people, and so will always be vulnerable to human error. Left unchecked, content oversharing can have negative consequences for an organization, including data breaches, compliance violations, or reputational damage. It will also give employees access to content through Copilot that isn’t appropriate.

To detect and mitigate content oversharing, we use Microsoft Graph Data Connect to report on every broad-access file or site with more sensitive labels. It helps us access and analyze data from Microsoft 365, SharePoint, OneDrive, and Teams using Azure Data Factory, Azure Synapse Analytics, or Azure Machine Learning. We then connect those datasets in our data estate using Azure Synapse Spark and track how many SharePoint sites and items are currently overshared based on our business rules.

One of the principal benefits of Microsoft Graph Data Connect is accessing the information we need through each of these technologies in a secure and scalable way, with control governed by our tenant admins.

Note that there will always be cases where we create exemptions to sharing limits. The policies around these exemptions are laid out as part of our Enterprise Governance, Risk, and Compliance guidelines.

Reporting for accountability

Our tenant’s data team uses Microsoft Graph Data Connect to generate reports on every file or site on the tenant with a broad access level, like a CSL or link that can be shared with anyone. It also monitors any item with a sensitive label like “confidential” or “highly confidential.”

These reports provide information and insights on the content’s owners, recipients, activity, and content protection and compliance status. They also help identify and prioritize potential cases of content oversharing.

At Microsoft, this output is helpful for several groups of stakeholders:

• We share the reports with the content champions responsible for reviewing and validating any cases of content oversharing.
• We use the reports to contact and educate the content owners on how to resolve oversharing issues and comply with our governance and security policies.
• We share the reports with the legal and security teams responsible for investigating and responding to cases of content oversharing that involve legal or security risks and incidents.
• We track our improvement over time as we enforce policies on our assets.

To help customers benefit from this kind of visibility, we’ve created a freely available reporting template. We encourage you to use this tool to track oversharing.

Beyond weaving your Microsoft Graph data connect and data export into your own data estate, you can now also use SharePoint Advanced Management in SharePoint Premium to get a list of sites that meet a set of criteria that you select. We use this capability to find all of our sites that share Highly Confidential data to more than 5,000 users. We then use the same capabilities to selectively require our site owners to fix any anomalies we discover.

Get more information on this data access functionality in SharePoint from Microsoft Learn.   

With the right controls and policies in place, you can minimize the number of oversharing errors your employees commit. But when errors do occur, a proactive detection strategy quarantines the risk from Copilot, even as your staff stays connected and collaborating.

Key takeaways

Some of the things we learned about setting up an oversharing detection and reporting system included:

• Select the tools that work best for you. Between Microsoft 365 and Azure, it’s likely you already have access to the tools you need to set up your reporting apparatus. Explore out-of-the-box functionality before building your own solution.
• Get reports to the right people. Collaborate with stakeholder teams to nominate point people who will receive oversharing reports and take action or communicate findings.
• Put thought into your communication strategy. Work with internal comms professionals to determine the best communication strategy when you detect oversharing, especially when speaking with content owners.
• Consider the content of your reports. Different stakeholders will require different information. Work with individual teams to determine what their reports should look like.

Learn more

How we did it at Microsoft

• Find out how we’re powering a generational shift in IT at Microsoft with AI. This article explores how we’re using AI to transform IT operations across the company.

Further guidance for you

• See how you can set up your Microsoft Graph Data Connect reporting apparatus. This resource explores how Microsoft Graph Data Connect enables large‑scale reporting and analytics across Microsoft 365 data.

“As AI becomes woven into the fabric of how we work, governance is no longer just an operational requirement—it’s a strategic imperative.”

David Johnson, principal architect, Microsoft Digital

The way forward

Getting governance right in the age of AI

The advent of AI tools like Microsoft 365 Copilot is a once-in-a-generation development. At this point, we’re still learning all the ways that these tools can be used to unlock creativity, productivity, collaboration, and innovation.

But we can be sure of one thing: implementing them securely and effectively should be priority one.

“As AI becomes woven into the fabric of how we work, governance is no longer just an operational requirement—it’s a strategic imperative,” says David Johnson, a principal architect in Microsoft Digital. “When we pair powerful tools like Copilot with thoughtful oversight, we ensure that innovation accelerates our mission without compromising our security or our values.”

If you’re deploying Copilot to your organization, the lessons we’ve learned at Microsoft Digital can act as a roadmap for your own journey.

Ultimately, the most important thing is to consider the data implications of AI assistance and plan accordingly. Diligence and forethought will make sure your employees get all the benefits of next-generation AI technology while your organization stays protected.

Welcome to the age of AI.

Key takeaways

This guide reflects what we learned as we set up and implemented our governance processes during our internal rollout of Microsoft 365 Copilot. Here are some overall insights to keep in mind when establishing governance controls at your own organization.

• Build governance on intentional design, not inherited habits. Thoughtfully define your tenant architecture, sensitivity labels, lifecycle policies, and container defaults to create a governance environment that is both secure and scalable.
• Empower secure self‑service. Give employees the freedom to create the workspaces they need, backed by intuitive labeling and clear accountability for the content they manage.
• Keep labeling simple, consistent, and enforced by defaults. Use a minimal, intuitive sensitivity label taxonomy and rely on container‑based default labeling to ensure that files stay consistently protected wherever they go.
• Trust users—but verify with automation. Use Purview DLP, auto‑labeling, quarantining, and escalation workflows to catch exceptions and prevent sensitive data from being exposed through Copilot.
• Maintain data hygiene with lifecycle management and attestation. Require regular re‑attestation, remove stale or unowned content, and use SharePoint Advanced Management to support both activity‑based and non‑activity‑based attestations.
• Make collaboration safer with thoughtful sharing defaults. Use company‑shareable links (CSLs) and clear link‑sharing policies to reduce oversharing while still enabling fluid, secure collaboration.
• Detect oversharing proactively and remediate quickly. Use Microsoft Graph Data Connect and SharePoint Advanced Management reporting to surface broad‑access content, notify owners, and correct issues before Copilot surfaces inappropriate data.

Learn more

• Read our guide for IT leaders who want to drive greater AI maturity.
• Discover how Microsoft is becoming an AI-first Frontier Firm.
• Find out how we’re powering agentic adoption at Microsoft as Customer Zero.
• Explore the many resources we’ve prepared to help with Microsoft 365 Copilot adoption.
• Check out our full Microsoft 365 Copilot deployment and adoption guide. 
• Learn about the Responsible AI policies and practices that we’ve established at Microsoft.

Try it out

Get your organization and data ready for Microsoft 365 Copilot.

We’d like to hear from you!

Want more information? Email us and include a link to this story and we’ll get back to you.

The post How we’re tackling Microsoft 365 Copilot governance internally at Microsoft appeared first on Inside Track Blog.

At Microsoft, we’ve put that potential into action by building the Employee Self-Service Agent, a centralized “front door” for employee support inquiries on all things Microsoft. Whether the question is related to an IT, human resources (HR), or campus services-related challenge, this agentic solution delivers geographically relevant, role-specific content on demand.

Our agent was rolled out in stages to our global workforce, as we continually added topic categories, features, and geographic availability. It eventually reached our entire workforce—more than 300,000 employees and vendors in 103 countries and regions—before being publicly released last November.

Our team in Microsoft Digital—the company’s IT organization—played a pivotal role in our global rollout, working closely with the product team and providing valuable feedback throughout development. It’s all part of our Customer Zero philosophy here at the company.

The agent proved its value early, piloting in large, primarily English-speaking regions—including Canada, India, the UK, and the US—and reaching more than half of our global workforce. But we wanted to raise the bar, so we turned to the rest of Europe.

The next chapter in the rollout was the Europe North region, which brought in 21 countries that are home to a wide variety of languages, cultures, country-specific HR policies, and nuanced IT support requirements.

“For the Employee Self‑Service Agent to work in Europe North, we had to listen locally to understand each country’s realities and respect those differences, rather than forcing a single global approach.”

Allan Hvass, director, Employee Experience in Europe North, Microsoft Digital

However, early deployments in smaller markets in the region revealed that when local content for a specific geography was missing, the agent sometimes defaulted to policies related to the US or other unrelated countries. Sensitive HR scenarios and strict country-level rules increased the complexity and resulting challenges.

Our team in Microsoft Digital met the challenge by working through front‑end field adoption and back‑end product updates to successfully land the Employee Self-Service Agent in Europe North’s small and midsize countries. This included adapting the product to distinct local realities in each country.

“For the Employee Self‑Service Agent to work in Europe North, we had to listen locally to understand each country’s realities and respect those differences, rather than forcing a single global approach,” says Allan Hvass, director for Employee Experience in the Europe North region of Microsoft Digital.

Mobilizing field representatives

To help with the tricky aspects of driving local adoption of  the Employee Self-Service Agent, our team in Microsoft Digital formed an adoption advisory team. The team included leadership representatives from all major countries and business divisions.

The group established on‑the‑ground field representatives to create better communications channels with the Europe North countries. This helped us learn what was and wasn’t working locally while we extended support for neighboring countries and kept excitement around the agent alive.

“I encouraged my colleagues to use the agent, and then to tell customers about their experience,” Rusen says. “A story grounded in real use is much more powerful and authentic than any slide deck.”

Daniel Rusen, sales enablement and operations leader, Europe North

Because the team had already been communicating about the agent internally, including hosting all-hands meetings to spark early usage, we were able to collect thousands of instances of employee feedback. Key themes surfaced, including policy accuracy by country, quality of language, and IT support variance by market.

Daniel Rusen, a sales enablement and operations leader for Europe North, served as one of the field representatives. He helped the advisory team close the loop between the field and the core project by highlighting the language and local relevancy issues that were reported. He also became an evangelist for the agent, encouraging other sales executives to use the tool and experience it first-hand.

“I encouraged my colleagues to use the agent, and then to tell customers about their experience,” Rusen says. “A story grounded in real use is much more powerful and authentic than any slide deck.”

Driving adoption with contextual experiences

To support the rollout of the Employee Self-Service Agent across Europe North, we designed an adoption approach aligned with regional priorities and local ways of working.

We focused on making the value of the agent immediately tangible. Through Microsoft Viva Engage communications, we connected the agent directly to Europe North business goals and highlighted the most relevant, high-impact scenarios—helping employees quickly recognize when the agent was the right “front door” for their support needs.

“Adoption is not about pushing a tool, it’s about helping people recognize, in their own context, when it truly makes their day easier. By focusing on relevant scenarios, simple communication, and hands-on experiences, we made the Employee Self-Service Agent useful from the start.”

Edith Dubuisson, senior business program manager, Employee Experience in Europe North, Microsoft Digital

To avoid overwhelming users, we prioritized simple, focused communication formats. For example, an Advent calendar campaign combined the agent with Copilot capabilities, enabling employees to discover one practical, actionable use case at a time.

In parallel, we hosted targeted readiness sessions to demonstrate key end-to-end scenarios and share practical tips and best practices. This ensured employees not only understood the value of the agent, but also felt confident using it from day one—creating a strong and positive first experience.

“Adoption is not about pushing a tool, it’s about helping people recognize, in their own context, when it truly makes their day easier,” says Edith Dubuisson, a senior business program manager in Microsoft Digital. “By focusing on relevant scenarios, simple communication, and hands-on experiences, we made the Employee Self-Service Agent useful from the start.”

Fine-tuning the agent

Built in Copilot Studio, the Employee Self-Service Agent works on global, regional, and area levels to make sure that users receive the content that corresponds to their geographical location and preferred language.

The Microsoft Global Support Services group manages the agent capability and improvements, driven by a strong partnership with internal engineering teams. The team triaged feedback and partnered with the product group to tag accurate policies and knowledge by country, and to tune agent behavior and guardrails for localized content. They prioritized quick fixes and high-impact content gaps.

Updating the Employee Self-Service Agent to fix content mismatches in Europe North wasn’t about tweaking the AI in isolation. Instead, we needed to overhaul the content that the agent relies on.

“Instead of treating mismatches as failures alone, we used them as signals to improve the underlying content—revising articles, correcting categorization, and closing gaps in coverage. Over time, this combination of tightly scoped data sources, country-level tagging, and ongoing content curation turned the agent into a far more reliable assistant.”

David Finney, director, IT Service Management, Microsoft Digital

The team “grounded” the agent in a set of trusted, IT-approved sources: About 250,000 vetted knowledge base articles and 15-20 different internal SharePoint sites containing policies, guidelines, how-to articles, and related information.

Then they tackled regional nuances, one of the biggest drivers of content mismatches (when a user gets a reply based on content that doesn’t match their country or region). The team tagged content by geography (such as UK-only or Romania-only), so the agent would be fed the correct information for that geographic area.

The process of fixing mismatches also yielded insights.

David Finney, a director of IT Service Management in Microsoft Digital, frames the process as a clear lesson: AI is only as good as the content behind it, so the real work is often on the back end.

“Instead of treating mismatches as failures alone, we used them as signals to improve the underlying content—revising articles, correcting categorization, and closing gaps in coverage,” Finney says. “Over time, this combination of tightly scoped data sources, country‑level tagging, and ongoing content curation turned the agent into a far more reliable assistant.”

Impact and results

The Global Support team added a continuous feedback loop to keep the agent’s content aligned with reality. Users can flag low-quality and inaccurate answers directly through the agent interface. That data flows to a dedicated knowledge management team, creating an efficient pipeline for feedback to inform back‑end fixes and product improvements.

“We’re measuring success by a reduction in tickets, but that’s based on the user having a better experience using the Employee Self-Service Agent versus calling our global help desk and talking to a person. We can only be truly successful if we are creating a better experience for our users.”

Anders Jepsen, director, Field IT Management, Microsoft Digital

Today, the Employee Self-Service Agent’s metrics are moving in the right direction.

The team is optimistic as the Global Support Services data shows agent activity steadily increasing after it officially went live last October, as shown in the following image. At the same time, usage of Legacy Bot (an existing digital support chatbot) decreased, along with support interactions via phone, email, and web.

This data suggests the agent is meeting its ultimate goal: To provide users with an improved support experience, including better first‑touch answers that build employee confidence and yield faster issue resolution. This reduces escalation to human-run support channels and decreases the volume of tickets our employees have to create.

“We’re measuring success by a reduction in tickets, but that’s based on the user having a better experience using the Employee Self-Service Agent versus calling our global help desk and talking to a person,” says Anders Jepsen, a director of Field IT Management in Microsoft Digital. “We can only be truly successful if we are creating a better experience for our users.”

What’s next for self-service support

Our experience deploying the Employee Self-Service Agent in Europe North has allowed us to create a playbook for other small and midsize countries in similar situations, including dealing with multiple languages and specific regional policies.

“Our long-term ambition is to reduce our human-led support tickets by 40 percent. In some areas, like Europe North, we are already taking a significant step toward that.”

Trent Berghofer, general manager, Microsoft Digital Modern Support

The agent now serves as both a self-service tool and the first contact point for employee questions. It doesn’t completely remove humans from support, because if that first point of contact doesn’t resolve the IT issue, a team of humans is available to help.

In the end, the fewer support tickets that are opened, the more time employees can have back for higher-value tasks.

“Our long-term ambition is to reduce our human-led support tickets by 40 percent,” says Trent Berghofer, a general manager in Microsoft Digital Modern Support. “In some areas, like Europe North, we are already taking a significant step toward that.”

The Employee Self-Service Agent is a great example of using the power of AI to increase employee productivity and efficiency, as they access highly curated support through the tool on demand. It fits in with our company’s overall strategic efforts to evolve into an AI-driven Frontier Firm.

“The agent brings IT, HR, and facilities together in one place,” Dubuisson says. “It’s not just a Q&A bot. It gives you information, guides you, and even holds your hand through troubleshooting. The agent tells you what to do and can even do it for you. It standardizes, simplifies, and still lets you chat with someone or get a call back when you need it.”

Key takeaways

Here are steps organizations can take today to implement an AI-powered employee support hub:

• Evaluate your employee support systems. Assess whether employees have a single, trusted “front door” for support issues, or if your organization’s support is still fragmented across different tools.
• Audit local policy coverage in your AI solutions. Identify where tools may be defaulting to global or geographically incorrect content–especially in regions with multiple countries or languages–to validate accuracy and boost trust.
• Pilot localized AI support efforts in a diversified region. Engage regional HR, IT, and field adoption teams early on to make sure that AI experiences reflect real, country-specific employee needs.

Try it out

Learn all about the Employee Self-Service Agent.

Related links

• Check out our in-depth guide to deploying the Employee Self-Service Agent.
• See how we’re accelerating support services at Microsoft with the Employee Self-Service Agent.
• Read about how the agent improves campus and facilities-related support at Microsoft.
• Explore Microsoft Learn content about the Employee Self-Service Agent.
• Watch a Microsoft Ignite Session about the Employee Self-Service Agent.

We’d like to hear from you!

• Want more information? Email us and include a link to this story and we’ll get back to you.

The post Transforming IT support across Microsoft with the Employee Self-Service Agent appeared first on Inside Track Blog.

On the other: A deep responsibility to protect the enterprise—its data, employees, customers, and regulatory posture—at a time when AI systems are evolving faster than traditional governance models were designed to handle.

“In the era of AI, delaying deployment does not eliminate risk—it often increases it. We need to work even faster to enable our business with AI, while simultaneously protecting our enterprise.”

Brian Fielder, vice president, Microsoft Digital

For CIOs, CDOs, and technology leaders across industries, this is no longer a philosophical debate, it’s an operating reality. How do you accelerate AI‑driven transformation without increasing enterprise risk? And critically, how do you innovate earlier, when learning is most valuable, without sacrificing trust?

At Microsoft, we’re living this tension firsthand, and our experience has led us to clear conclusions.

“In the era of AI, delaying deployment does not eliminate risk—it often increases it,” says Brian Fielder, vice president of Microsoft Digital. “We need to work even faster to enable our business with AI, while simultaneously protecting our enterprise.”

Mastering the delicate balance between risk avoidance and AI-fueled innovation is the new challenge for technology leaders globally. This insight has fundamentally reshaped how we approach release management, AI adoption, and enterprise governance at Microsoft. We call this approach Fast Train, and it has become a core part of how we operate as a Frontier Firm—one that learns early, under control—enabling capabilities that give our employees an edge while carefully balancing enterprise risk.

Rethinking release management for the AI era

Traditional release management was designed for a different world.

“While we’ve never been as risk‑averse as some of our customers, our focus is to always be risk‑aware. When products attest to risk upfront and take ownership at design time, they’re empowered to deploy at full speed—without waiting in a backlog of exceptions.”

B. Ganti, principal architect, Microsoft Digital

Stage‑gated approvals, quarterly releases, and broad “wait until it’s safe” models worked when change was linear, infrequent, and predictable. But AI changes the equation. Models evolve continuously. Capabilities improve weekly. User behavior, as well as risks, emerge dynamically in production.

In this environment, waiting for certainty before deploying often means learning too late.

As Customer Zero for so many of Microsoft’s enterprise products, Microsoft Digital has long been risk aware, with greater tolerance for risk than some of our customers. However, with Fast Train we’re moving at greater speed in low-risk situations.

“While we’ve never been as risk‑averse as some of our customers, our focus is to always be risk‑aware,” says B. Ganti, a principal architect in Microsoft Digital. “When products attest to risk upfront and take ownership at design time, they’re empowered to deploy at full speed—without waiting in a backlog of exceptions.”

Legacy models concentrate exposure until a global rollout, when:

• Dependency has already hardened
• Mitigation options are limited
• The blast radius is at its largest

Frontier organizations take a different approach. They treat release management not as a gate, but as an adaptive operating system—one designed to surface signal early, while controls still matter.

While you won’t have access to Microsoft solutions at design time, these same principles are useful as you consider how to “shift left” when you build or acquire new digital capabilities in your environment. Design time in this context might be early visibility of new features or capabilities in the Microsoft 365 Message Center. Applying a Fast train mentality can help you to quickly identify trusted updates to bring into your environment immediately versus those that might require deeper assessment prior to deployment.

At Microsoft, that shift reframed a core question:

Not “How do we safely deploy change at scale?”, but instead “How do we learn earlier, safely, and continuously?”

Fast Train: Learning early, at enterprise scale

Fast Train is not a shortcut around governance. It is Microsoft’s primary early‑Frontier deployment model for low‑ and medium‑risk innovation.

Under Fast Train, eligible capabilities are deployed earlier—often globally—inside Microsoft’s own enterprise environment, under explicit guardrails. This allows product teams to learn from real usage patterns, real data flows, and real operational behavior before expectations harden and dependencies scale.

Critically, Fast Train operates on a simple principle: speed should align to risk, not to organizational inertia.

Instead of forcing every capability down the slowest possible path, Fast Train uses risk‑adaptive deployment shapes:

• Default‑on Frontier deployment for lower‑risk capabilities
• Admin‑gated Frontier deployment for higher‑impact or tenant‑sensitive scenarios
• Standard or deferred release only where risk truly demands it

In all cases, innovation moves forward. What changes is how it is enabled, not whether it progresses at all.

Why early deployment can reduce risk

From a security and compliance perspective, this may sound counterintuitive. Isn’t early deployment riskier?

In practice, we’ve observed the opposite. The most dangerous moment for an enterprise system is not early exposure, it’s late discovery. Waiting until adoption is widespread before learning how a capability behaves:

• Reduces mitigation options
• Expands blast radius
• Compresses response timelines under regulatory or customer pressure

“The question isn’t how to eliminate risk entirely—it’s where we’re willing to be uncomfortable, so our employees don’t work around IT.”

David Johnson, principal tenant architect, Microsoft Digital

By contrast, Frontier deployment reverses this risk profile. Fast Train allows Microsoft to:

• Surface data flow issues and edge cases earlier
• Tune controls before dependencies harden
• Establish clear accountability for rollback, disablement, and remediation

This is risk‑aware innovation, not risk‑blind speed. Guardrails are built in and not bolted on after the fact.

Governance that adapts instead of blocks

One of the most significant shifts Fast Train enabled was a change in how governance participates in innovation.

“Fast Train is fundamentally a risk-taking exercise—but it’s a deliberate one,” says David Johnson, principal tenant architect in Microsoft Digital. “The question isn’t how to eliminate risk entirely—it’s where we’re willing to be uncomfortable, so our employees don’t work around IT. If the platform honors our non‑negotiables—security, compliance, discovery—then we don’t need to over‑rotate on every new feature built on top of it.”

Traditional models treat governance as a final checkpoint. Governance is an episodic approval that happens after most key decisions are already made. Frontier models embed governance earlier and continuously, focusing attention where it matters most.

“Innovation doesn’t have to be slowed down by governance,” Ganti says. “By shifting risk consideration to design time, we remove friction at the point of deployment—so teams can move straight onto the Fast Train, with no toll booths, no gates, and no delays.”

Under Fast Train:

• Low‑risk change moves quickly under defined boundaries
• Higher‑impact capabilities shift to choice‑based enablement
• Deep governance review is reserved for material risk events like new data flows, boundary changes, or regulatory impact

This keeps governance focused, effective, and credible while avoiding the trap of over‑governing low‑risk change.

Just as importantly, Fast Train makes our Microsoft product teams explicitly accountable. Ownership for quality, rollback, and remediation sits with the teams shipping the capability, not with downstream review bodies. That means product teams have an incentive to build features that meet our Fast Train criteria, increasing the chance that our customers can also deploy new capabilities more quickly and with less risk.

Admin‑gated does not mean anti‑Frontier

A common misconception is that admin‑gated or choice‑based deployment is inherently slower or less innovative. Our experience in Microsoft Digital suggests the opposite.

Admin‑gated Frontier deployments are not a retreat from innovation. They are a different exposure shape for the same learning objective. We use them when impact is higher and explicit tenant choice matters.

In both default‑on and admin‑gated Frontier deployment:

• Capabilities reach real users early
• Deployment is global
• Learning loops start before broad GA expectations harden

The distinction is not speed. It’s enablement mechanics, informed by the risk profile of the deployment.

Becoming a Frontier Firm is a maturity journey

Frontier behavior is a maturity that advances over time.

“Our focus is evolving to put greater focus on speed and enablement. Fast Train lets governance teams focus on truly high‑risk scenarios while giving product teams the guidance and tools they need upfront so they can move faster with confidence.”

Priya Chebiyam, principal product manager, Microsoft Digital

In Microsoft Digital, we measure ourselves against a Frontier Firm capability maturity model, which reflects how organizations evolve from risk averse release models toward risk aware, signal driven operations. Our internal rubric describes 5 stages of enterprise maturity:

Frontier Firm capability maturity model

“Our focus is evolving to put greater focus on speed and enablement,” says Priya Chebiyam, principal product manager in Microsoft Digital. “Fast Train lets governance teams focus on truly high‑risk scenarios while giving product teams the guidance and tools they need upfront so they can move faster with confidence.”

Today, we assess ourselves in the Emerging Frontier stage, operating Fast Train broadly while investing to further institutionalize continuous governance, telemetry, and accountability. A critical step in that journey has been onboarding Microsoft 365 Copilot and first‑party agents into the Fast Train operating model to expand early signal and tighten ownership.

The lesson for customers isn’t to copy Microsoft’s internal processes, but to adopt the pattern:

• Define where early learning is safe through your own criteria—these are effectively your organizational “guardrails”
• Make enablement choices explicit
• Require ownership and rollback readiness
• Let real‑world signal and not assumptions drive your decisions

Trust and innovation advance together

At Microsoft, Fast Train has reinforced a simple truth: speed, trust, and compliance are not tradeoffs. They are outcomes of a risk‑adaptive operating model.

“Fast Train is built on a simple principle: ship fast when it’s safe, and slow down only when it’s necessary,” Chebiyam says. “We empower feature owners to self‑attest low‑risk features using clear criteria, while still protecting security, privacy, compliance, and regulatory requirements.”

By learning earlier—under control—organizations can reduce late‑stage surprises, accelerate transformation, and engage partners and stakeholders from a position of evidence rather than theory.

“We will be deploying earlier under the right guardrails so we can understand real world behavior, build the right controls, and earn customer trust through evidence, not assumptions. Our responsibility is not to slow innovation down, but to enable it safely—at the speed our customers and the market demand.”

Aleš Holeček, chief architect and corporate vice president, Microsoft Security

In the AI era, the greatest enterprise risk isn’t moving too fast—it’s learning too slow.  Fast Train reflects a shift from risk avoidance to risk awareness and near real-time assessment.

“We will be deploying earlier under the right guardrails so we can understand real‑world behavior, build the right controls, and earn customer trust through evidence, not assumptions,” says Aleš Holeček, chief architect and corporate vice president in Microsoft Security. “Our responsibility is not to slow innovation down, but to enable it safely—at the speed our customers and the market demand.”

Frontier firms don’t move fast despite risk. They move fast because risk is understood, bounded, and actively managed.

Key takeaways

For CIOs, CDOs, and technology leaders ready to accelerate AI adoption while minimizing risk, Microsoft Digital’s experience suggests five practical actions you can take today:

• Treat early deployment as a risk‑reduction strategy. Surface issues earlier when mitigation options are still available, instead of discovering them after global dependency sets in.
• Establish a clear Frontier cohort. Identify a workload, geography, or business unit where early learning is safe, intentional, and governed and be intentional in empowering that cohort.
• Separate innovation speed from enablement mechanics. Use default‑on deployment for low‑risk capabilities and admin‑gated choice for higher‑impact scenarios without slowing learning velocity.
• Make governance continuous, not episodic. Shift governance left by embedding it earlier with monitoring, attestation, and clear escalation triggers rather than relying on late‑stage gates.
• Require explicit ownership and rollback readiness. Ensure every deployed capability has a named owner, a defined rollback path, and continuous telemetry to support fast correction.

Try it out

Looking to accelerate your journey to the Frontier? Try Microsoft Agent 365 in your company.

Related links

• Check out our IT playbook for becoming a Frontier Firm.
• Read more about the agentic future that we’re embracing at Microsoft.
• Discover our readiness guide for deploying AI agents, based on our own company’s experience.
• Learn how we’re transforming our enterprise IT operations with AI at scale.
• Explore our five-step guide to enterprise AI maturity for IT leaders.

We’d like to hear from you!

• Want more information? Email us and include a link to this story and we’ll get back to you.

The post Fast Train to the AI Frontier: Balancing risk and innovation in the era of AI at Microsoft appeared first on Inside Track Blog.

As a group program manager in Microsoft Digital, she oversees strategic areas of our license management, key third-party software, and suppliers for our AI models. She is also helping lead our organization into the AI era, all while quietly building a reputation as a sought-after mentor in the IT space. Her approach is rigorous, direct, and deeply human.

Hovhannisyan came to the United States from Armenia as a graduate student in 1997 with no family and no financial safety net. She credits the experience with instilling her with the tenacity, grit, and self-advocacy that define her career.

Building a career from the ground up

Hovhannisyan’s path to Microsoft began at Texas Tech University, where she earned a master’s degree in electrical engineering. She graduated in 1999 just as the software industry began to boom, and she was hired directly into a support engineer role at Microsoft.

“You have to self-advocate, perform at the highest level, and line up mentors to help drive your career forward. That is the recipe.”

Anahit Hovhannisyan, principal group product manager, Microsoft Digital

For her first seven years at Microsoft, Hovhannisyan remained in that same role by necessity. As a foreign national awaiting her green card, switching jobs would have reset the immigration process. She used that time to broaden her technical expertise across multiple disciplines within the developer support space.

“You have to self-advocate, perform at the highest level, and line up mentors to drive your career forward,” Hovhannisyan says. “That is the recipe.”

After she received her green card, Hovhannisyan moved through a series of field-based roles in the Microsoft Dallas office. In 2013, she relocated her family to the Seattle area for an IT-specific role at Microsoft headquarters.

IT was new territory for her, but her appetite for calculated risk is something she now sees as central to her identity as a leader.

From one partnership to an enterprise function

Within Microsoft Digital, Hovhannisyan is best known as the general contractor and product management lead for the company’s long-term strategic relationship with ServiceNow. Over the past few years, her team delivered solutions on the Service Now platform that helped multiple organizations within Microsoft with Service Desk, Help Desk, and operational needs.

“My previous mentor always said, ‘What got you here won’t get you there.’ Things change at a rapid pace. Learn, adapt, and pivot—those are the three things that have moved my career forward, and they matter more than ever in the AI era.”

Anahit Hovhannisyan, principal group product manager, Microsoft Digital

More recently, Hovhannisyan’s responsibilities expanded to include managing a portfolio of 18 third-party software suppliers, in addition to the Service Now product ownership

Today, her team is both building and deploying AI agents; contributing to IntelliLicense, an AI-powered software licensing platform; and rolling out ServiceNow’s NowAssist to drive AI-powered case summarization across the business. Microsoft Digital was an early adopter of these capabilities, and Hovhannisyan’s organization now shares what it learned with external customers seeking to understand how enterprise IT can evolve.

“My previous mentor always said, ‘What got you here won’t get you there,'” Hovhannisyan says. “Things change at a rapid pace. Learn, adapt, and pivot—those are the three things that have moved my career forward, and they matter more than ever in the AI era.”

A mentor who makes the path visible

The importance of building the right network of support is a theme that runs through Hovhannisyan’s career, and she’s precise about who that network should include. She distinguishes between a mentor, a coach, and a sponsor, insisting all three are essential.

“A mentor gives direction and shares experience. A coach asks open-ended questions and helps you find your own answers. A sponsor advocates for you behind closed doors,” Hovhannisyan says. “All three are absolute must-haves.”

“Anahit helped me understand how to build a strategy, gain visibility beyond my core group, and develop relationships with people who will be in rooms I’m not in.”

Katina Reece, principal technical program manager, Infrastructure, Network and Tenant group at Microsoft

Katina Reece, a principal technical program manager in the Infrastructure, Network and Tenant group at Microsoft, has been working with Hovhannisyan as a mentee for nearly eight years. When they met, Reece had been at Microsoft for three years and was watching colleagues advance around her. Hovhannisyan helped her reframe her obstacles, showing her that visibility, relationship-building, and strategic positioning were just as important as performing well at her job.

“Anahit helped me understand how to build a strategy, gain visibility beyond my core group, and develop relationships with people who will be in rooms I’m not in,” Reece says.

“Anahit is very thoughtful about understanding what gives people energy and finding the right places to leverage those strengths. The strongest leaders recognize that different people bring different talents, and Anahit does that well.”

Dawn Lee, principal product manager, Microsoft Digital

For Dawn Lee, a principal product manager who has worked directly with Hovhannisyan for more than two years, the impact has been just as concrete.

“Anahit is very thoughtful about understanding what gives people energy and finding the right places to leverage those strengths,” Lee says. “The strongest leaders recognize that different people bring different talents, and Anahit does that well.”

Why IT is the place to be

Hovhannisyan pushes back on the perception that IT is a less exciting path than working on products.

“IT is an amazing place to learn fast,” Hovhannisyan says. “You have a broader purview across multiple product groups, your knowledge grows dramatically, and you have more opportunity to observe and adapt your career than you would in a narrower role.”

“If I, as a foreign student with nothing, could make that kind of progress, I feel like everybody can do it. The keys are tenacity, grit, and self-advocacy. If you don’t have a mentor, get one. If you don’t have a sponsor, find one. These are not optional.”

Anahit Hovhannisyan, principal group product manager, Microsoft Digital

Looking ahead, Hovhannisyan aspires to eventually lead both program management and software engineering functions within Microsoft Digital.

She continues to mentor a wide network of over 20 employees across the company, understanding that the path she navigated from immigrant student to senior leader is one worth sharing.

“If I, as a foreign student with nothing, could make that kind of progress, I feel like everybody can do it,” Hovhannisyan says. “The keys are tenacity, grit, and self-advocacy. If you don’t have a mentor, get one. If you don’t have a sponsor, find one. These are not optional.”

Meanwhile, she’ll keep showing up the way she always has: Advocating for her team, coaching the next generation, and doing the consequential work that makes the whole enterprise run.

Key takeaways

Here’s what you can learn from Anahit Hovhannisyan’s career:

• Build your support network intentionally. A mentor shares experience and direction; a coach asks the questions that help you find your own path, and a sponsor advocates you when you’re not in the room. Seek out all three.
• Self-advocacy is a career skill. Performing well is the baseline. Actively communicating your aspirations to leadership, courting feedback, and ensuring the right people know your goals is what moves the dial.
• Lead change by helping people understand “What’s in it for me?” When driving organizational change, paint a clear vision, answer what team members gain from it, then back your words with visible action so trust builds over time.
• Good mentors see what mentees can’t yet see in themselves. Spotting someone’s potential before they recognize it—and giving them a specific opportunity to prove it—is one of the most high-impact things a mentor or leader can do.

Try it out

Learn more about working in IT at Microsoft by reading our Inside Track blog.

Related links

• Who else works in Microsoft Digital? Read about Olutunde Makinde, a senior service engineer.
• Considering a career at Microsoft? See current openings in the Microsoft Careers portal.
• Read about a day in the life of a Microsoft employee using Copilot.
• Discover how Microsoft employees use the Total Rewards Portal to understand their value.
• Learn how Microsoft HR is using Viva and Microsoft 365 Copilot to empower our employees.
• Find out how the Microsoft Power Platform at Microsoft empowers employees.
• Connect with regional employees more effectively at Microsoft with Viva Amplify.

We’d like to hear from you!

• Want more information? Email us and include a link to this story and we’ll get back to you.

The post Building from the inside: Anahit Hovhannisyan’s impact on IT 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.

We’d like to hear from you!

• Want more information? Email us and include a link to this story and we’ll get back to you.

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