• 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.

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.

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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.

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.

Or, as we sometimes like to call them: Our fantastic four!

• Analyzers
• Collaborators
• Innovators
• Planners

Everyone has a work style. Find the one that matches yours, and we’ll show you the Microsoft 365 apps and Copilot features that best complement it—helping you get more done every day. 

The challenge of modern work: Focus

Let’s be real: Work today is a lot.

We’re switching between apps, juggling priorities, sitting through “quick syncs” that aren’t, and trying to protect a shred of focus time before the next <bloop> from Teams throws you off again.

Focus feels like a fantasy.

The numbers don’t lie, and they do explain a lot. Data from the most recent Microsoft Work Trend Index report shows that:

• Most of us get interrupted an average of 275 times a day.
• Nearly 2 out of 3 people say we don’t have the time or energy for meaningful work.
• We get an average of 58 off-hours messages a week.
• 60% of meetings are totally ad-hoc. 

And here’s the kicker: The same study showed a whopping 68% of us say we’d gladly hand off as much work as possible to AI. And that’s where Microsoft 365 Copilot comes in!

Finding and embracing your work style

Here’s the good news: You’ve got a distinct work style: instincts, strengths, and quirks that guide how you get things done.

What if you’re not sure what work style best describes you exactly? Well, that’s where Copilot comes in handy! Here’s a sample prompt that you can copy into Copilot and find out where you land on the work style continuum:

“What’s my work style? Let’s find out. Analyze my full work patterns across all channels—emails, Teams chats, documents, and meetings—and match me to one of our fantastic four work styles that follow. Choose the dominant work style that shows up most clearly in my patterns, tone, and behavior. The four work styles are:

• Analyzer: Data-driven, focused on insights and logic, makes decisions based on facts.

• Planner: Structured, detail-oriented, thrives on process and minimizing chaos.

• Collaborator: Empathetic, people-focused, team-oriented, facilitates communication.

• Innovator: Visionary, idea-oriented, brings bold energy to innovation and content creation.”

When you know your work style, you can use Copilot in the way that makes the most sense for you. That’s true no matter whether your current task involves planning ahead, jumping into team brainstorming, making sense of the data, or coming up with bold ideas.

(Note: It may be helpful to think of these categories as describing your primary work style. We all have many talents and different characteristics, and you might find yourself occasionally adopting aspects of other work styles, depending on the context and the type of work you are doing.)

How Copilot can help

Check out this list of the four major types of work styles, along with the ways in which you can use Copilot to help excel in your role and become a work superhero.

1. The Analyzer

Detail lover. Data decoder. Insight seeker.
Motto: “In data we trust.”

What you and Copilot can do:

Microsoft 365 app highlights

• Excel: Ask Copilot to analyze trends, flag outliers, or write formulas. Prompt: “Analyze this spreadsheet and highlight trends.”
• Word: Summarize research docs, compare documents, or outline decisions. Prompt: “Summarize this 10-page document into 5 key takeaways.”
• PowerPoint: Turn analysis into visual storytelling. Prompt: “Turn this data into a visual-heavy presentation for leadership.”
• Outlook: Let Copilot summarize long threads or extract action items. Prompt: “Pull all emails from (client/project) and summarize findings.”
• Teams: Enable Facilitator in meetings to get notes, action items, and a chat summary without asking. Skip to the key insights in a recap. Prompt: “Summarize key takeaways from the data discussion in this thread.”
• Viva Engage: Ask Copilot to tell you what’s trending across the company. 

Copilot features you’ll love

• General search: Get additional context when searching for people, files, and resources.
• Search your Outlook folders: Ask Copilot to check specific email folders to surface the data hiding there.
• Voice chat: Ask follow-ups out loud, because faster answers = faster decisions; just select the microphone in the composer. Remember that you can also have Copilot read the answer aloud by selecting the speaker icon at the end of the response.
• Notebooks: Select the Notebook tab and add your resources there to create your own searchable insight vault. 
• Memory: Context = clarity. Copilot can track what matters, so your questions hit harder, faster, and with less repetition. Select the memory feature in your Copilot settings. 

2. The Collaborator

Connector. Team player. Chaos coordinator.
Motto: “Teamwork makes the dream work.”

What you and Copilot can do:

Microsoft 365 app highlights

• Teams: Here are some features you can take advantage of:
  • Enable Facilitator in meetings to have it compile all the details automatically. Take it further by selecting the chat in Teams and tapping the Copilot icon at top right. Then prompt Copilot: “Summarize this conversation and suggest a next step.” 
  • No time to listen to a meeting recording? Check out the Audio Recap feature to get an engaging audio summary in the style of your choice.
• Outlook: Prioritize the topics and people you want. Select the Copilot icon from the ribbon and choose Prioritize in the dropdown list.
• PowerPoint: Copilot helps turn everyone’s updates into one polished deck. Prompt: “Turn these brainstorm notes into a presentation for team review.”
• Word: Reference a meeting transcript from Teams and hand it off to Copilot to start the draft. Prompt: “Summarize these meeting notes into a proposal draft.”
• Excel: Do you use shared workbooks? Copilot can help interpret them and suggest next steps. Prompt: “Highlight changes from the latest version and comment on flagged data.”
• Whiteboard: After your team brainstorming session, use Copilot to summarize the info. Export this to Loop so the team is all on the same page—literally.
• Viva Engage: Ask Copilot to give you the latest updates from communities that you follow. 

Copilot features you’ll love

• Referencing people in Copilot Chat: Get the skinny on teammates without asking them IRL. Just type “/[name]” for all the latest updates.
• Schedule with Copilot in Outlook: Use the Schedule with Copilot button in the ribbon to find a meeting time and schedule your meeting with ease. Or, you can schedule a meeting right in Copilot Chat!
• Voice chat: Talk it out instead of typing it out. Just select the microphone or the vertical lines in the composer.
• Search: Search for people or track down that shared doc or Teams thread from last week’s brainstorm.
• Notebooks: Document work and decisions and add a Loop to keep the collaboration alive. Select the Notebook tab in Copilot Chat to get started. 
• Pages: Create shareable pages for generating, organizing, and refining content in real time. Select the Edit in Pages icon at the bottom of your prompt responses. 
• Memory and personalization: Names, roles, team quirks—it’s all here. Copilot can remember who prefers slides over spreadsheets. Find the Memory feature in your Copilot settings. 

3. The Innovator

Idea generator. Vision-caster. Creative problem-solver.
Motto: “The blank page doesn’t scare me.”

What you and Copilot can do:

Microsoft 365 app highlights

• PowerPoint: Start from scratch or import a Word doc and let Copilot build out slides with visual flair. In PowerPoint, select New, then choose Create with Copilot to get going.
• Word: Draft an idea, rewrite it in a new tone, or punch up your intro. Prompt: “Write a first draft of a creative brief for this new idea.”
• Teams: Share a concept from the meeting notes and ask Copilot to summarize or expand on it. Prompt: “Summarize this brainstorm session and turn into a list of next-step ideas.”
• Excel: Create visual dashboards or ask Copilot to simplify data for storytelling. Prompt: “What’s the story behind this data?”
• Outlook: Reframe your emails in a different tone. Go from “per my last email” to “let’s sync!” When composing a message, select the Copilot icon on the left. Choose from different tones, get coaching on your writing style, and more.
• SharePoint: Build beautiful pages with layout suggestions and smart content generation. In SharePoint, create a new page and choose Create with Copilot to start designing. 
• Viva Engage: Use Copilot in the Viva Engage composer to turn your ideas into a polished Engage post. (Note: Access may depend on which Copilot license you have.)

Copilot features you’ll love

• Create: Turn blank screens into bold starts. With Copilot, custom images, videos, and documents are just a few clicks away.
• Voice chat: Your best ideas don’t wait for a keyboard. Just select the microphone or vertical lines in the composer to get started dictating your thoughts.
• Search: Instantly find the details about that idea you had last week
• Scheduled prompts: Nudge your muse with ideas exactly when you need them. When you create a prompt, just hover over it and choose the clock icon to schedule a time for it to pop up for you in the future.
• Referencing people in chat: Loop others into your creative drafts and builds.
• Memory: That random spark of genius from three meetings ago? Copilot can recall it for you, because it remembers everything. Find the memory feature in your Copilot settings. 

4. The Planner

Structure-first. Deadline-driven. The master of the plan behind the plan.
Motto: “I’ve never met a checklist I didn’t love.”

What you and Copilot can do:

Microsoft 365 app highlights

• Outlook: Summarize long threads, pull out key actions, and draft follow-up plans. Just select the Summarize button at the top of an email. Use Prepare with Copilot in the new Outlook to set yourself up for success.
• Planner: Turn a goal into a structured task list with due dates and owners.
• Word: Draft templates for recurring processes, reports, or meeting agendas.
• Excel: Analyze project timelines or compare team capacity over time.
• Teams: Keep track of action items in chats and meetings, and ask Copilot turns them into tasks. Enable Facilitator in Teams to draft agendas for you. 
• SharePoint: Build team hubs with agents that surface what matters most. In SharePoint, create a new page and select Create with Copilot to start designing. 

Copilot features you’ll love

• Pages: We love it when a plan comes together. Create shareable pages for your projects. Select the Edit in Pages icon at the bottom of your prompt responses. 
• Voice chat: Rattle off your to-do list and watch Copilot keep up.
• Scheduled prompts: Trigger reminders and next steps right when you need them. When you send a prompt, hover over it to select the clock icon to schedule.
• Search: Find files, plans, or past decisions without opening 10 tabs. 
• Referencing people in chat: Assign tasks or gather input from others by typing the forward slash (/) and entering the name. 
• Notebooks: Store recurring process docs, SOPs, or onboarding checklists. Select the Notebook tab in Copilot Chat and go. 
• Create: Create forms, documents, and more to put your plans into action.
• Memory: Copilot can keep track of your project goals, recurring tasks, and who owns what.

Your work style, transformed

Whether you’re a Planner trying to make a schedule out of spaghetti, a Collaborator trying to decode feedback in a 17-reply email thread, an Analyzer wading through data, or an Innovator with five big ideas and zero time to pitch them, you and Copilot have got this. It helps you turn the mess into momentum.

And be sure to share what you’ve learned with your coworkers. The more your team can harness the power of Copilot in their work, the more you can all achieve together.

Key takeaways

Here are some important insights as you consider how understanding your work style can help you and your team get the most out of tools like Microsoft 365 Copilot:

• Everyone has a work style—and knowing yours is a force multiplier. Whether you’re an Analyzer, Collaborator, Innovator, or Planner, understanding how you naturally work helps you get more value from Copilot.
• Copilot adapts to you, not the other way around. Whether you’re digging into data, aligning a team, generating ideas, or building structured plans, Copilot meets you where you are and amplifies your strengths.
• Less busywork, more impact. Copilot helps offload repetitive tasks like summarizing emails, drafting documents, recapping meetings, and organizing notes—freeing time for higher‑value work.
• Better insights, faster decisions. From turning spreadsheets into stories to surfacing action items and trends, Copilot converts information overload into clear next steps.
• Workflows improve across the entire Microsoft 365 ecosystem. Deep integration with apps like Teams, Outlook, Word, Excel, PowerPoint, Loop, and Viva means Copilot supports end-to-end work—not just isolated tasks.
• Teams win when individuals work smarter. When everyone uses Copilot in a way that fits their work style, collaboration improves, alignment gets easier, and momentum builds.

Try it out

Learn more about how you can embrace your work style and supercharge your productivity with Microsoft 365 Copilot.

Related links

• Learn how to pick the right Copilot for the job.
• Read about a day in the life of a Microsoft employee using Copilot.
• Jump into action with the Copilot prompt gallery (sign-in required).
• Explore how Copilot and agents are combining to fuel the Frontier Firm.
• Discover how we’re powering AI value at Microsoft with Copilot extensibility.

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 Our fantastic four: Embracing your work style with Microsoft 365 Copilot appeared first on Inside Track Blog.

Imagine a facilities service technician responding to a high‑priority heating issue. The service ticket has the right building, floor and space, but no clear indication of where in the space the problem exactly is—this can be a particularly challenging problem when dealing with large spaces like we do here at Microsoft. Making matters more complicated, the technician might also need specialized schematics that are behind walls and ceilings.

In the past, it might have taken that technician a long time to get that necessary context.

Not anymore.

Thanks to a solution we created that is internal to Microsoft, our indoor maps are now always current. (And while this solution isn’t presently available to customers, we’re sharing our story around it in hopes that you can learn from our approach.)

With these maps available, the service ticket mentioned above now includes a visualization of the floor plan, which immediately indicates the correct room and highlights the faulty equipment’s exact location (if already on the floor plan).

The technician can now diagnose the problem in minutes. This can be done on their equipment, without the need to understand how to use specialized software or having knowledge of the building’s layout.

This shows the value of indoor maps when they work correctly. But our maps here at Microsoft didn’t always work this way.

A long-standing map gap

For years, facility service technicians at Microsoft could access floor plans that were stored in a central repository but needed specialized software to view them. Our floor plan files came from a wide variety of vendors, with different naming conventions and drawing standards.

Initially, we created indoor maps using this data for some buildings requiring a lot of manual work. As a result, updates to the maps were slow and expensive. As soon as a map slipped out of sync with reality, teams stopped relying on it.

“Enterprises have struggled for years to maintain accurate indoor maps. The heart of this struggle is ultimately standards that are applied inconsistently to the source material.”

Vishu Admal, program and product lead, AI indoor maps initiative, Microsoft Digital

These issues had a real impact on our day‑to‑day operations:

• Our facilities teams didn’t have the spatial context in their work order to understand exactly where problems were happening, because they didn’t have convenient access to detailed map layers that indicated the precise location of building elements (such as plumbing, or heating and cooling systems).
• Our security teams couldn’t easily overlay incident data on floor plans.
• Our IT teams couldn’t map device locations to the real world with confidence and relied on PDF version of maps.

“Enterprises have struggled for years to maintain accurate indoor maps,” says Vishu Admal, our program and product lead for our AI indoor maps initiative here in Microsoft Digital, the company’s IT organization. “The heart of the struggle is ultimately standards that are applied inconsistently to the source material.”

Recently, we’ve developed an intriguing solution: An AI‑driven mapping data pipeline—with out-of-the-box large language models (LLMs)—that recognizes patterns, identifies inconsistencies, and produces updated indoor maps every day, as the floor plans evolve and change.

Today, that data pipeline is keeping our indoor maps up to date for more than 500 buildings around the world. It supports the systems and teams that keep our campuses operating every day—facilities, space management, security, and IT.

And more importantly, this solution makes sure that when someone has a high-priority need for an indoor map, it’s completely accurate and current.

“The problem has always been tripping up on the varying quality and consistency of the AutoCAD files. This is especially true at enterprise scale, where drawings come from different firms and have different standards.”

I.M. Ndimubanzi, engineering manager, Microsoft Digital

From CAD to operational maps: Finding a solution

Some indoor mapping projects start with a simple assumption: The floor plan is already structured data.

Our project didn’t have that. What we had was computer-aided design (CAD) geometry, plus text, plus years of vendor variation.

Different architecture and construction partners drew buildings in different ways. Labels, layers, symbols, and even basic conventions (like how rooms were “closed” in a drawing) weren’t consistently followed. That inconsistency is what broke any attempts at automation.

“The problem has always been tripping up on the varying quality and consistency in the AutoCAD files,” says I.M. Ndimubanzi, an engineering manager in Microsoft Digital who is the technical lead for our indoor maps initiative. “This is especially true at enterprise scale, where drawings come from different firms and have different standards.”

So, we built a data pipeline that assumes the input will be messy, but it can still produce a reliable output, time and again.

Converting CAD geometry into render-ready maps

We split this work into three stages. In brief, these can be labeled as: parse, interpret, and serialize.

1. Parse CAD input into machine-usable signals

We start by extracting raw geometry and text from CAD using open-source parsing libraries. That gives us the basic data we can feed into downstream steps without forcing every file to look identical first.

2. Use AI for interpretation and hygiene

The hardest part of the work isn’t reading the CAD file. It’s interpreting what the drawing means when dealing with variations in room names, abbreviations, and other conventions (which may differ by vendor, region, or even building).

This is where we use AI-driven large language models to transform the extracted CAD signals into structured data.

Instead of manually cleaning and translating each file, we use AI models to ingest CAD drawings directly and interpret what the data represents. Walls become walls, rooms become rooms. Doors, elevators, and fixtures are identified as distinct, usable elements rather than raw line work.

That same approach helps solve a long‑standing data hygiene issue: inconsistent naming. For example: across the portfolio, the same type of space can appear as “Conference Room,” “Conf. Rm.,” “MPR,” or “Multi‑Purpose Room.”

The AI helps normalize those variations into standardized space categories, turning messy labels into consistent, structured data that can be reused across systems.

3. Serialize to GeoJSON with proven tooling

Once AI produces a structured representation, we convert the data into GeoJSON—a popular spatial data exchange and rendering format—using open-source tooling.

GeoJSON gives us a clean, reliable data source for our mapping tools. This keeps the final output consistent and predictable, which is critical for rendering at scale and integrating into other applications.

Note that this design is intentional: AI does the interpretation, while deterministic tooling does the formatting. This separation is what makes the pipeline stable.

“As long as they can add this SDK in their application or interface, they can connect to our databases. It gives them access to our map library.”

Amr Dawood, senior software engineer, Microsoft Digital

Creating an SDK that makes maps usable everywhere

A mapping pipeline is only valuable if other teams can use the results without becoming mapping experts. That’s why we paired the mapping pipeline with a software development kit (SDK) that makes indoor maps embeddable inside operational tools.

“As long as they can add this SDK in their application or interface, they can connect to our databases,” says Amr Dawood, a senior software engineer in Microsoft Digital. “It gives them access to our map library. They can use predefined functions to choose the buildings, the layers they want to render, and how they want to display and order those layers.”

We built this SDK so product teams can treat the new indoor maps like any other UI component:

• Drop it into a web app and connect to our map storage without building custom integration
• Choose buildings and floors using built-in selectors and navigation patterns
• Toggle layers to show only what matters for the scenario, including specialized operational layers
• Overlay operational data on top of the floor plan, so teams can visualize work in spatial context, not just in tables
• Make the map interactive by adding pins, polygons, and other spatial annotations directly in the app experience

Under the hood, the SDK is built on MapLibre, and open-source toolset for interactive maps and geospatial visualization. It gives the team a mature rendering foundation without locking them into a bespoke mapping stack.

We also built the SDK for “plug and play” adoption. That means providing examples, tutorials, and guidance so teams can embed maps quickly and consistently, instead of reinventing the same integration patterns across multiple apps.

This is the part of the solution that turns the pipeline into a platform. It’s how we move from “we have maps” to “any team can build with maps.”

Turning floor plans into user interfaces

We’re currently integrating indoor maps directly into several of our facilities processes and applications. As we do, we’ve noticed an important change: Employees stop treating the floor plan as reference material and start treating it as the user interface for detailed building information.

“Managing a physical space through tables and charts only gets you so far. It’s much more powerful when that information is visualized through a floor plan.”

Harris Thamby, integration lead, Microsoft Digital

Take our LiveCampus app, for example.

Live Campus is an internal app used by the Microsoft Facilities team to aggregate all operational information about Microsoft buildings into a single, comprehensive view. It simplifies facilities management by integrating various data points and presenting them on a visual floor plan.

Historically, building data lived in tables, tickets, and dashboards scattered across multiple systems. None of it was spatially oriented by default. If something broke, you read a text description, then tried to figure out the location.

With our new indoor map solution, location comes first, and it’s bringing life to Live Campus.

Instead of navigating through multiple systems to gather information, Facilities employees can access everything they need in one place. The floor plan serves as the primary interface, allowing users to overlay different types of information, such as facility tickets, service issues, and role-based employee data.

This visual approach helps facility managers quickly identify and address problems, improving operational efficiency.

“Everything is aggregated and presented as a building view,” says Harris Thamby, who leads integration work for Live Campus in Microsoft Digital. “But managing a physical space through tables and charts only gets you so far. It’s much more powerful when that information is visualized through a floor plan.”

Live Campus uses the latest AI‑generated maps through our SDK. That means facilities teams always see the current layout, not a snapshot from months ago.

And because the map is up-to-date, teams can trust what they’re seeing.

We’re also making big changes to FacilityLink, our internal implementation of Dynamics 365 Field Service. The new indoor maps solution is becoming the center of the technician experience.

“Our facilities teams will use the same maps in Dynamics 365 Field Service. They can turn layers on and off to see exactly what they need. Where are the tickets? Where are people sitting? What areas are impacted?”

Sonaly Choudary, program manager, Microsoft Digital

As the integration progresses, technicians will be able to open a work order and see the associated indoor map alongside the request, either in FacilityLink on the web or through Microsoft Dynamics 365 Field Service Mobile on a phone or tablet.

From the same screen where they read the details of the issue, they can also visualize the exact floor, room, and surrounding spatial context of the problem. This will reduce the need to switch between systems or return to a desk to look up drawings, helping technicians diagnose issues faster and with more confidence while they are already on site.

That spatial context—available on a mobile device instantly—changes how work gets prioritized.

“Our facilities teams will use the same maps in Dynamics 365 Field Service,” says Sonaly Choudary, a program manager with Microsoft Digital. “They can turn layers on and off to see exactly what they need. Where are the tickets? Where are people sitting? What areas are impacted?”

“When a technician responds to a work order, every minute matters. Historically, they had to jump between systems to find drawings, interpret layouts, and understand what was behind walls or ceilings. We’re eliminating that friction and giving technicians the spatial context they need to diagnose and fix issues faster.”

Michelle Schaefer, principal program manager, Microsoft Digital

Instead of scanning lists of open issues, facilities managers and technicians can see clusters of problems on a single floor.

They can spot patterns and determine when a single issue is affecting multiple teams or when a problem is isolated.

“When a technician responds to a work order, every minute matters,” says Michelle Schaefer, a principal program manager in Microsoft Digital. “Historically, they had to jump between systems to find drawings, interpret layouts, and understand what was behind walls or ceilings. By embedding AI‑generated indoor maps directly into FacilityLink, we’re eliminating that friction and giving technicians the spatial context they need to diagnose and fix issues faster.”

The result?

It’s faster and easier for facilities teams to understand where an issue is, what assets are involved, and how to act—without leaving the system they already depend on. Indoor maps become a practical extension of FacilityLink, embedding spatial awareness directly into day‑to‑day facility operations. If a service ticket is opened, it’s no longer just text; it’s a pin on the map.

Outcomes and what’s next

As our maps become reliable and embedded into daily workflows, the solution stops being a mapping project. It becomes a platform with the potential for impact beyond facilities operations.

The most immediate outcome has been scale. We’re moving from selectively supporting a limited set of buildings to supporting the entire real estate portfolio. Maps will be onboarded, updated, and maintained automatically, without the manual effort that had slowed previous approaches.

That automation changed the economics.

Instead of paying for one‑off conversions or ongoing vendor updates, the mapping pipeline runs continuously. As layouts change, the maps are automatically updated. That consistency is what allows downstream systems to depend on the data.

We’re continuing to refine the mapping pipeline as models improve and standards evolve. The SDK can be expanded to support more scenarios and platforms. And additional layers and integrations will unlock new operational use cases across facilities, IT, and security.

As teams across Microsoft have learned more about AI indoor maps, the excitement and adoption potential keeps growing. When spatial data is accurate, current, and reusable, teams stop asking whether they can visualize a problem and start asking what else they can do with it.

That’s the real outcome of this work: Not just better maps, but better decisions, built on a shared, trusted source of spatial truth.

Key takeaways

Use these lessons to help you with your own efforts to produce indoor maps you can trust and embed in day-to-day operations:

• Start by standardizing your source drawings and naming conventions. Inconsistent CAD layers, labels, and symbols are the main blockers to automation, so define what “good input” looks like before you scale.
• Design your pipeline to expect messy input, not perfect files. Separate the work into clear stages (for example: parse, interpret, serialize) so you can improve each step without rebuilding everything.
• Use AI for interpretation and deterministic tooling for formatting. Let models infer meaning from CAD files, then convert to a stable format, such as GeoJSON, with proven conversion tools for predictable rendering.
• Build (or adopt) an SDK, so other teams can add maps to tools without becoming mapping experts. Provide common user interface patterns (building/floor lection, layer toggles, overlays, annotations) to standardize implementations across apps.
• Make maps useful by embedding them inside the tools people already use. Adoption accelerates when maps show up in tickets, dashboards, and mobile field workflows.
• Plan for automatic and continuous updates, governance, and trust. Daily automatic refresh, clear ownership, and validation checks keep maps aligned to reality and avoid drift.

Try it out

Learn more about the Dynamics 365 Field Service mobile app.

Related links

• See how we’re reimagining campus support at Microsoft with the Employee Self-Service Agent.
• Explore documentation for Azure Digital Twins, a Microsoft solution for modeling real-world spaces.
• Discover our strategies for making Microsoft transportation systems seamless with the power of data and AI.
• Dive into full details on Azure Machine Learning pipelines.
• Read about how to create an interactive search map with Azure Maps.

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

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At Microsoft Digital, the company’s IT organization, we shape and propel many of our groundbreaking products through our role as the company’s Customer Zero—and we want to tell that story. At this year’s Microsoft 365 Community Conference, we hosted a variety of sessions focused on change management, AI adoption, and how we manage governance in the era of the Frontier Firm.

As Customer Zero for Microsoft 365 Copilot, we embedded the technology into our employees’ daily workflows and carefully monitored the results. That journey from early experimentation to broad adoption of the tool across our organization continues to guide the company as we explore what comes next.

Today, that’s agents.

“Copilot changes how our employees work. Agents are changing how the work gets done. Our focus is to make the technology practical and valuable, so people want to use it daily.”

Stephan Kerametlian, senior director, business program management, Microsoft Digital

We’ve reached a level of maturity with Copilot that allows us to move from individual productivity to systems that can reason and collaborate on our behalf. Our focus now is on driving the adoption of agents across the company, grounding them in our workflows to solve problems.

“Copilot changes how our employees work,” says Stephan Kerametlian, a senior director in Microsoft Digital. “Agents are changing how the work gets done. Our focus is to make the technology practical and valuable, so people want to use it daily.”

Adoption doesn’t happen without trust

As we’ve empowered employees with more capable AI tools that can help automate tasks and make decisions, we’ve been equally focused on making sure the right safeguards are in place.

Innovation and safety are extremely important—the challenge is to enable both at the same time. And this is where governance comes in.

“We’ve spent a lot of time getting governance right. This means giving people confidence, not slowing them down. When employees know the guardrails are there, they feel empowered to experiment and innovate safely.”

David Johnson, principal PM architect, Microsoft Digital

At Microsoft, good governance is what makes innovation sustainable. It’s how we protect the company, our data, and our customers, while still giving employees the freedom to build and push boundaries with AI.

“We’ve spent a lot of time getting governance right,” says David Johnson, a principal PM architect in Microsoft Digital. “This means giving people confidence, not slowing them down. When employees know the guardrails are there, they feel empowered to experiment and innovate safely.”

How Microsoft does IT: Managing and governing agents—empower with risk-aligned oversight

Session description: See how Microsoft Digital empowers employees with tools to build and manage agents. From agent management with Microsoft Agent 365, to securing our environment with Microsoft Defender, to managing our productivity estate with Microsoft Purview, this session offers broad insights into how we use our own technology to accelerate agentic innovation while mitigating risk.

Speakers: David Johnson, Naveen Jangir, and Mike Powers

David Johnson leads our internal Microsoft 365 and productivity services with responsibility for tenant strategy, architecture, and governance. He manages how we empower employees with guardrails and manages our capability onboarding and tenant configuration.

Naveen Jangir is a principal architect in Microsoft Digital. He drives Microsoft 365 security and compliance strategy and leads tenant architecture and capability onboarding, while overseeing secure adoption of services across the enterprise.

Mike Powers is a senior service engineer and AI administrator in Microsoft Digital who manages Copilot features, Agent 365, and enterprise AI operations. He partners with internal product groups and security stakeholders to make sure AI tools and agents are deployed responsibly and governed effectively.

More on AI agents and governance at Microsoft

• Read the guide to how we’re tackling Microsoft 365 Copilot governance internally at Microsoft.
• See how we’re embracing the agentic future to become a Frontier Firm.
• Learn how we deployed the Employee Self-Service Agent at Microsoft. 

Inside Microsoft: Reclaiming engineering time with AI in Azure DevOps

Session description: AI tools embedded directly into Azure DevOps (ADO) are changing how engineering teams work, eliminating manual tasks without creating separate tools or increasing cognitive load. This session explores how ADO AI Chat and the AI Work Item Assistant accelerate coding workflows at Microsoft. You’ll learn how to improve your backlog quality, sprint hygiene, and downstream effectiveness of GitHub Enterprise and Copilot, helping your teams reclaim capacity and focus on the work that moves products forward.

Speakers: Gopal Panigrahy and Sumit Dutta

Gopal Panigrahy is a product leader and member of our product management team in Microsoft Digital. He’s an advocate for our customer-first approach to product development and is passionate about helping people overcome challenges in the era of AI.

Sumit Dutta is a product-minded technology leader working at the intersection of AI, enterprise platforms, and scalable product design. Offering a strong blend of engineering knowledge and product strategy, he focuses on building systems that are not just functional but also extensible and reliable.

More on AI and IT engineering at Microsoft

• Learn how we’re reclaiming engineering time with AI in Azure DevOps at Microsoft.
• Read about powering the new age of AI-led engineering in IT at Microsoft.
• Discover how we’re streamlining engineering at Microsoft with Azure DevOps.

How Microsoft does IT: Microsoft 365 governance in the age of Copilot and agents

Session Description: Microsoft 365 Copilot and Copilot agents are powerful tools, but without proper governance, you could be putting your company at risk. In this lightning talk, you’ll learn how Microsoft Digital protects our enterprise while enabling employee innovation with Copilot and agents.

Speaker: David Johnson

Johnson brings hands-on experience operating Copilot and AI-powered agents inside Microsoft, with a focus on identity, permissions, data boundaries, and real-world misuse prevention. He takes real-world lessons and makes them practical for others.

More on governance at Microsoft

• Learn about how we’re tackling Microsoft 365 Copilot governance internally at Microsoft.
• Check out our guide to deploying Microsoft 365 Copilot in five chapters.
• Read how we’re powering data governance with Purview Unified Catalog.

Accelerating AI adoption with Copilot controls: Lessons from Microsoft Digital

Session description: Microsoft 365 Copilot and AI agents unlock productivity gains, but without careful oversight they can also introduce security and compliance risks. The session covers how the Copilot Control System helps scale AI safely, including adoption insights and satisfaction signals. You’ll also see demos of popular agents, including the Employee Self-Service Agent and the Admin agent.

Speakers: Amy Ceurvorst and Reshma Kapoor

Amy Ceurvorst is a director of business programs In Microsoft Digital. She’s worked extensively with Copilot controls and evangelizes a unified way to view Copilot health reports that help administrators understand Copilot health.  

Reshma Kapoor is a senior product manager in Microsoft Digital with 20 years of experience leading and shipping products at scale. She is customer‑obsessed, grounding product decisions in real customer signals to deliver intuitive, high‑impact experiences.

More on AI and Copilot adoption and deployment

• Read how we’re shaping AI management at Microsoft with Agent 365 and Copilot controls.
• Explore our five-chapter guide to deploying Microsoft 365 Copilot internally.
• Learn how we deployed Microsoft 365 Copilot in Viva Engage at Microsoft.

How Microsoft does IT: Driving adoption of Microsoft 365 Copilot and agents across Microsoft

Speakers: Cadie Kneip and Stephan Kerametlian

Session description: Our team at Microsoft Digital led the first enterprise-scale deployment of Microsoft 365 Copilot, launching to more than 300,000 employees and vendors worldwide. Learn how the team drove adoption using change management strategies to encourage employees to thread Copilot into their daily work. Now we’re doing the same for agents across the enterprise. Learn best practices for accelerating adoption and maximizing value while guiding your own journey with Copilot and AI agents.

Cadie Kneip is a senior business program director and the Copilot Champs community lead in Microsoft Digital. She specializes in turning complex AI initiatives into confidence-building pathways that help employees thrive in an AI-powered workplace. 

Stephan Kerametlian is a senior director in Microsoft Digital, where he leads our global change management efforts for Copilot and agents. He thrives on learning how people use AI and on finding ways to get more people to embrace the technology.

More on adoption and deployment of Copilot and agents

• Explore our guide on how we deployed Microsoft 365 Copilot internally.
• Find out how we’re skilling up for the future of work at Microsoft with Agent Launchpad.

Real-world adoption stories: A fireside chat with a key customer

Session description: Pull back the curtain on the customer experience with Copilot adoption. Join this fireside chat with a Microsoft customer to hear about lessons learned and the real impact that Copilot is delivering across their organization. You’ll glean practical insights you can apply immediately at your own company. 

Speakers: Karuana Gatimu and Sam Crewdson

Karuana Gatimu is a director of Customer Advocacy – AI & Collaboration in Microsoft Digital and a solution architect driven by a passion for people, storytelling, and leadership. With 30 years of experience at the intersection of technology and human impact, she turns complex innovation into compelling narratives that help organizations adopt change and deliver business value.

Sam Crewdson, a principal product manager in Microsoft Digital, is passionate about turning user insights into product improvements. His work focuses on driving adoption of the latest SharePoint features and helping users take advantage of the power of both SharePoint and OneDrive. Working at the intersection of IT, users, feedback, and strategy, he translates real‑world business needs into collaborative experiences that scale.  

More insights on Copilot adoption

• Learn about how we’re tackling Microsoft 365 Copilot governance internally at Microsoft
• Check out our guide for deploying Microsoft 365 Copilot in five chapters.
• Read our executive-level guide covering our Copilot deployment.

Try it out

• Learn more and sign up for the 2027 Microsoft 365 Community Conference.
• Start using Microsoft 365 Copilot.
• Learn how to build your own AI-powered agents and workflows.

Related links

• Find out about the keynote presentations that happened at the 2026 Microsoft 365 Community Conference.
• See how we’re serving as Customer Zero at Microsoft in the era of AI.
• Read about Copilot Control System security and governance.
• Learn about deploying Microsoft Agent 365: How we’re extending our infrastructure to manage agents at Microsoft.
• Discover how the Employee Self-Service Agent helps employees quickly complete IT and HR tasks.

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 Unfolding our AI-in-IT story: What to expect at the 2026 Microsoft 365 Community Conference appeared first on Inside Track Blog.

• Our journey to becoming a Frontier Firm at Microsoft
• Chapter 1: Advancing good governance to meet the agentic moment
• Chapter 2: The Microsoft roadmap for implementing agents
• Chapter 3: Driving adoption to capture value across the organization
• Chapter 4: Providing support at the agentic frontier
• Chapter 5: Tracking the impact of your agents
• Applying lessons from our agent deployment at your organization

A how-to guide for governing, implementing, adopting, supporting, and measuring the impact of AI agents from Microsoft Digital, the company’s IT organization.

The agentic future: Our journey to becoming a Frontier Firm at Microsoft

A new way of working, a modern way to achieve more

The rate of change for AI tools and technology continues to accelerate, and new opportunities to reimagine business processes and employees’ day-to-day workflows are emerging. Agents are the driving force behind this next leap forward.

As a result of this technological shift, a new organizational blueprint is emerging. It blends machine intelligence with human judgment to create systems that are AI-operated but human-led.

We have a name for an organization that enacts this model: The Frontier Firm.

As organizations progress toward this goal, they move 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 in humans leading teams of agent users: AI agents that perform core labor with relative autonomy.

This has been a three-year process for us at Microsoft, and throughout our journey, we’ve had to allow adequate time for deliberate planning and careful execution. Just as importantly, we invested early in clear, consistent internal communications to help employees understand what agents are, why they matter, and how they could safely participate in building them. That shared understanding created the confidence and momentum required to scale agent creation across a global workforce.

“It’s a truly transformative time,” Brian Fielder, vice president of Microsoft Digital. “What we’ve learned from embracing the agentic future at Microsoft is only making us more eager to see organizations empower their employees to take the lead in a world where human judgment and machine intelligence work in harmony.”

Our Frontier Firm journey so far

Within Microsoft Digital, the company’s IT organization, we’re taking a leadership role in reimagining core processes and workflows. These efforts rest on four pillars of practice:

• We envision and implement the AI-first workplace of the future.
• We empower our employees to build their own agents that help supercharge their productivity by providing the training, resources, and inspiration they need.
• We define guardrails and safeguard our environment so our employees can maximize the power of AI while keeping our enterprise safe and secure.
• We’re the voice of company’s internal AI transformation, and we provide the blueprint for our customers to accelerate their own AI journeys.

To guide our steps, we’ve established a cross-disciplinary initiative we call Agents at Microsoft. We’re looking at agentic transformation from an end-to-end perspective that reaches into every aspect of building, publishing, governing, managing, and getting the most value out of agents.

As we’ve incorporated agents into more and more aspects of our organization, key questions have surfaced:

• How do we balance freedom for employees to create agents against the need to manage sprawl?
• How do we put guardrails around agentic capabilities so they can be useful, without introducing undue risks?
• How do we differentiate between agents of different complexity and capability, and how do we adjust our strategies around them accordingly?
• Where can we use agents to fill enterprise functions, and who should be responsible for creating those crucial tools?
• How can we adapt existing software development standards to AI tools?
• How can we minimize the risk of data over-exposure through AI?

It’s possible you’re also considering where agents fit into your organization. If so, it’s likely that you’re wrestling with many of the same questions. We’re here to help.

This guide shares our experience as Customer Zero for agents at Microsoft. As you read, you’ll be able to follow our journey to defining what it means to govern agents safely, implement them effectively, guide their adoption by employees, build a foundation for support, and track their impact through effective measurement.

We’ll share some of the most important lessons we’ve learned so far, along with readiness checklists and resources that can help you advance agentic maturity at your organization. With this guide in your toolkit, you’ll have a framework for building a strategy that incorporates agents into your business goals safely, responsibly, empathetically, and impactfully.

“As we harness the transformative power of AI agents, it’s our responsibility in IT to ensure that technology not only enhances decision making but also fosters a culture of innovation and collaboration across the organization,” says Stephan Kerametlian, a business program management senior director in Microsoft Digital.

The agentic future is here. We’ve explored the path forward, and we’ve seen the exciting places it leads. This guide can help you take your first steps and start realizing those possibilities today.

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Expert insights

“It’s a truly transformative time. What we’ve learned from embracing the agentic future at Microsoft is only making us more eager to see organizations empower their employees to take the lead in a world where human judgment and machine intelligence work in harmony.”

Brian Fielder, vice president, Microsoft Digital

“As we harness the transformative power of AI agents, it’s our responsibility in IT to ensure that technology not only enhances decision-making but also fosters a culture of innovation and collaboration across the organization.”

Stephan Kerametlian, business program management senior director, Microsoft Digital

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Chapter 1: Advancing good governance to meet the agentic moment

Maintaining privacy, security, and compliance while respecting regulatory frameworks

Agents offer powerful opportunities to enhance employee productivity, but they also introduce concerns. For example, how do we keep privileged information where it belongs? And how do we keep employees from building agents that violate company policies?

In answering these questions, Microsoft Digital’s governance team focused on the value the company is trying to derive from agents.

We wanted to give employees and teams the freedom to build without risk to the business or introducing agent duplication and sprawl. We wanted to weave robust, reliable agentic experiences into enterprise workflows. We also needed to secure and protect confidential data while respecting responsible AI principles.

“Our principles haven’t changed, but they’ve evolved,” says David Johnson, a tenant and compliance architect at Microsoft Digital. “With AI, the need for proactive governance is far greater than ever before, so we’re putting structures in place that take some of the labor around managing agents off of IT.”

There are some cornerstone constructs that underpin our agent governance strategy. There’s a tenant that holds employees accountable, a reasonably clean data estate, a lifecycle for the agents users-they disappear when the employee leaves. 

We’ve developed six core principles to guide our approach to governing agents:

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 services and data sources those users can already access 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 of the services and sources they need.
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.

As a result of our experience establishing strong governance for Microsoft 365 Copilot, we’d already laid a firm foundation for an agent-ready data estate. In some ways, governance is tool-agnostic, rooted in basic principles. With appropriate data labeling, data hygiene, and well-managed permissions in place alongside tools that respect labels by default, we can confidently give every employee the ability to build basic agents and trust in our governance guardrails.

A matrixed approach to agent governance

The sheer diversity of agents and their use cases means we need a multifaceted approach to governance. A matrix of different parameters applies to any agent, and each of those elements requires its own approach to policy.

In practice, agent governance structures echo our overall maturity approach. Simple, personal, lower-risk agents with built-in guardrails act as a starting point for employee experimentation and require very little oversight. As a result of our robust data hygiene foundation, if an employee has access to the grounding content, these agents are low-risk accelerators for things they can already do on their own. Meanwhile, higher-impact agents demand greater attention that echoes our security development lifecycle (SDLC) for internal apps, which include more extensive, cross-disciplinary reviews.

To accommodate agent-creation experiences across this spectrum, we’ve enabled several different building platforms and processes employees and teams can use to create the AI tools they need.

1. We opened up Agent Builder in Microsoft 365 Copilot for all employees to create read-only declarative agents.
2. We created an environment strategy and governance in Power Platform to manage personal environments featuring data connectors with lower risk but high value.
3. We enabled a process to flow the data that teams need into production Power Platform environments featuring data connectors. These agents initially come with sharing limits until the agent receives risk approval.

This structure provides the ability to safely create agents of increasing complexity while ensuring they remain secure and contained until they get the necessary reviews for wider sharing and data exposure.

Our governance guardrails, review policies, and publishing scope varies based on the tool used to create an agent, the level of technical proficiency it requires, its grounding in knowledge sources, its capabilities, the actions it can take, the plug-ins it requires, and whether it includes a custom engine or a bring-your-own model.

The following examples illustrate two different agent scenarios:

As you consider your own governance structures and policies, think about where agents and the ability to create them fit your needs and risk tolerance. Then learn from the different parameters of our governance matrix to access a working model for your own agentic transformation.

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Expert insights

“Our principles haven’t changed, but they’ve evolved. With AI, the need for proactive governance is far greater than ever before, so we’re putting structures in place that take some of the labor around managing agents off of IT.”

David Johnson, tenant and compliance architect, Microsoft Digital

As you consider your own governance structures and policies, think about where agents and the ability to create them fit your needs and risk tolerance. Then learn from the different parameters of our governance matrix to access a working model for your own agentic transformation.

Aisha Hasan, Power Platform and Copilot Studio product manager, Microsoft Digital

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Balancing utility and manageability in our agent ecosystem

Empowering employees and teams to simply and securely create agents has been a top priority as we move toward AI maturity at Microsoft, but we also want to eliminate agent sprawl.

Aside from complicating agent management, sprawl has several user-side disadvantages. For example, if more than one team were to create an agent that points to HR information, the employee experience would suffer, because our users wouldn’t be sure which agent serves as the authoritative source of truth.

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

If a pre-existing agent fits the target scenario, we encourage a team to use that agent instead of creating a redundant solution. For employees who want to create their own agents, we recommend that they first search for an existing tool in our agent catalog to avoid duplication.

User-based lifecycles and periodic attestation are also key pieces of the puzzle. Requiring attestation helps ensure that agents cease to exist once they’re no longer useful or their owner leaves the company.

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

• The registry provides a complete view of agents. 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 provides the observability layer, including role-specific oversight, compliance and audit features, and performance measurement 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 also 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.

“The next step in our governance journey will be using AI to help us govern AI,” says Aisha Hasan, Power Platform and Copilot Studio product manager at Microsoft Digital. “We’re looking at ways AI can help us manage this new space, and we believe Agent 365 will be the foundation for our deterministic approach to governance.”

As you strategize to deepen AI maturity at your organization, our experience will help you operationalize many of the aspects of governance we’ve pioneered as Customer Zero for agentic AI, especially with the wide release of Agent 365. By adopting the principles we’ve illustrated in this chapter, you can accelerate your transformation and advance your maturity rapidly and securely.

Learning from our experience with agent governance

A strong data foundation is crucial

We’ve built respect for labeling and data governance policies into the tooling for AI assistants and agents, but it’s dependent on a well-governed data estate. Invest time and effort in establishing that foundation.

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. Do you have confidence that users haven’t over-shared data access?

Agents aren’t always like applications—adjust your processes accordingly

We quickly learned that reasonable processes, approvals, and workflows for internal application development didn’t scale well with agents. Consider a risk-based assessment model.

Change is constant

Plan to reassess and revise your governance structure regularly. This technology is evolving rapidly, as is the tooling surrounding it, so maintaining good governance 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 agentic technology. Establish strong measures of value and a robust pane for management and assessment. Observability and telemetry will be foundational, so ensure you build that into your governance efforts.

Continue non-agentic workstreams

Enterprise technology environments are additive and incremental. Don’t cease your efforts to create and govern other internal technologies. Instead, maintain a holistic ecosystem.

Key takeaways

Use these tips based on what we learned here at Microsoft to tackle 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.
• Put strong data and information protection policies in place: Establish clear governance for your data estate, including labeling and information protection, to support responsible agent use.
• 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.
• Define a clear agent building tool strategy: Decide which tools employees and teams can use to create agents, balancing empowerment with governance.
• Operationalize agent oversight and management: Establish an oversight model and implement tools like Agent 365 that help manage agents at scale.
• 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

• 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.”
• Learn from our experience tackling Microsoft 365 Copilot governance. This article explains the foundation of our governance efforts in the AI space and can serve as a starting point for agentic governance.
• Discover our six dimensions of agent value. They provide our framework for measuring the impact of AI at Microsoft.

Further guidance for you

• Secure your agents at scale with Microsoft Agent 365 guidance for unified identity, compliance, and control across platforms.
• Find out more about security and governance in Microsoft Copilot Studio with our product‑level guidance on DLP, environment controls, audit logs, and security features specific to Copilot Studio agents.

Chapter 2: The Microsoft roadmap for implementing agents

Developing a plan to advance AI maturity while unlocking agentic value at every level of our organization

Implementing agents across your organization is intertwined with your larger AI transformation efforts. At Microsoft, we’ve adopted an escalating maturity model that unfolds across five stages.

Putting the Microsoft AI maturity model into practice

Whatever stage you’re at in your AI journey, you’ll likely experience many of the same challenges and opportunities we do at Microsoft.

Stage 1: Awareness and foundation

Building a foundation means setting a bold vision for your AI journey, anchored in clear business outcomes. At this stage, it’s important to engage your executive sponsors early to foster cross-functional collaboration and empower experimentation.

At Microsoft, we established our AI Center of Excellence (CoE) to help guide and drive adoption of Microsoft 365 Copilot, as well as a Data Council that powers our AI-ready data strategy. As we’ve moved into the agentic future, these teams have been instrumental in maintaining forward momentum.

The company also established the Office of Responsible AI (ORA) to advance AI development, deployment, and secure and trustworthy innovation through governance, legal expertise, internal practice, public policy, and guidance on sensitive uses and emerging technology. ORA partners closely with product and engineering teams alongside other trust domains like privacy, digital safety, security, and accessibility to align our work with Microsoft’s six responsible AI principles:

• Fairness
• Reliability and safety
• Privacy and security
• Transparency
• Accountability
• Inclusiveness

Target outcomes include

A foundational strategy, governance principles, and leadership buy-in to kickstart AI projects.

Stage 2: Active pilot programs and skill building

We started by launching targeted pilot projects across different areas of the company. This process encouraged experimentation and used hackathons to surface a broad range of ideas. From there, we selected the most promising initiatives by evaluating business value against implementation effort and focused resources on a select group of high-impact projects.

To establish early-stage governance, we required all pilots to undergo responsible AI and architectural reviews.

Target outcomes include

The first tangible benefits of AI, including efficiency gains, time and cost savings, quality improvements, and an emerging internal talent pool that paves the way to scale successful solutions.

Stage 3: Operationalize and govern

At this point, we worked to scale and integrate AI solutions across the company. We strengthened our data and AI infrastructure to support this transition by formalizing enterprise governance with clearly defined steering teams. Our AI CoE, Data Council, and Office of Responsible AI helped accelerate implementation, ensure the ongoing quality of structured data, and oversee ethical AI use and compliance. Collaboration among these groups was crucial for ensuring our AI initiatives remained within acceptable bounds while delivering tangible business impacts.

Target outcomes include

Multiple AI use cases running at enterprise scale under robust oversight, with cross-functional alignment on AI objectives and the business value they’re delivering.

Stage 4: Enterprise-wide adoption

To consolidate our gains and achieve AI adoption across the enterprise, we prioritized making AI a core consideration in every new project and process by asking where AI-driven intelligence could deliver real impact. That could be by boosting efficiency, enhancing user experiences, or unlocking new business value. From there, we aligned our AI initiatives with our organization’s strategic goals by empowering business leads to synchronize efforts and continuously update our AI roadmap.

We also cultivated a data-driven culture through ongoing, large-scale training while making AI tools a natural part of everyday work. To accomplish that, we established rigorous impact tracking with clear measurement of the amount of value delivered. Key metrics include time savings, cost reduction, and quality improvements. We reviewed these outcomes regularly at the leadership level to maintain accountability.

Our Continuous Improvement CoE has been instrumental in the process of aligning AI initiatives with our organizational goals and providing a framework for progress. It operates according to four principles:

1. A clear definition of winning, based on expectations
2. Disciplined execution
3. Constrained problem-solving with urgency
4. Sustained replication and acceleration

Target outcomes include

Measurable, data-driven monitoring of AI for your business that’s powered by a continuous improvement mindset.

Stage 5: Transforming your business with agentic AI

At stage five, we’ve been working to embed AI into every aspect of our operations and culture. We started by leveraging the expertise of our AI CoE to foster innovation, drive continuous improvement, and keep our AI initiatives evolving using structured mechanisms like a Kaizen funnel to crowdsource, prioritize, and advance ideas that extend the impact of AI across the enterprise.

We also further strengthened governance to address the advanced challenges of agentic applications, including responsible scaling of generative AI and effective mitigation of AI hallucinations. Finally, we focused on refining human-AI collaboration so our teams can offload routine tasks to AI agents and concentrate on higher-value work.

One tactic that’s been highly successful here at Microsoft Digital is conducting “Fix, Hack, Learn” weeks, where we encourage employees to identify opportunities for improving our services. So far, these initiatives have yielded multiple AI-powered breakthroughs that are already in production.

Target outcomes include

Significant efficiency gains and innovations from AI, including recognition as a leader in enterprise AI adoption.

As you advance along the AI maturity curve at your organization, keep these essential ingredients in mind:

1. Executive sponsorship and governance
2. Responsible AI by design
3. Data foundations, architecture reviews, and technical readiness
4. Talent, skills, and culture
5. Impact tracking and accountability
6. Change management and communication
7. Continuous improvement, innovation, and partnerships

It’s important to remember that these elements aren’t static, but iterative. You’ll need to continue to evolve them over time as your enterprise AI transformation continues. But the five stages of enterprise AI maturity we’ve outlined in this chapter form an overarching framework to keep you moving forward.

Learning from our agent implementation experience

Invest in data infrastructure and AI platforms

Building robust data infrastructure ensures your organization is prepared to leverage AI, supporting scalable, innovative, and secure AI-driven solutions.

Foster a culture of innovation and collaboration

Champion an AI-forward culture where innovation and collaboration drive the adoption of agentic AI.

Align AI initiatives with strategic business goals

Ensuring AI initiatives align with business goals maximizes impact and positions your organization to succeed in the rapidly evolving world of agentic AI.

Implement ethical practices based on our responsible AI principles

Adopting ethical AI practices builds trust, ensures responsible innovation, and prepares your organization to navigate the evolving landscape as AI becomes central to business operations and decision-making.

Position IT to facilitate the transition to a Frontier Firm

At a minimum, your IT leaders and practitioners need to prepare your data estate for agentic workloads, partner to identify and enable prioritized business scenarios, and then actively participate in enterprise transformation through skilling, change management, and measurement activities.

Evolve your enterprise IT infrastructure to embrace dynamic and adaptive agent-based systems

Moving from traditional deterministic systems to agentic systems that introduce probabilistic behaviors, autonomous decision-making, and continuous learning requires new architectural thinking, audit capabilities, and governance models.

Key takeaways

Here are some key tips for implementing agents at your organization, based on what we’ve learned through our own experience here at Microsoft:

• Align agent efforts with business priorities: Partner with leadership to establish clear business priorities that guide agent adoption and investment.
• Define success and how you’ll measure it: Determine business goals and metrics of success that allow you to track impact and value over time.
• Put the right governance structures in place: Establish steering committees across implementation, data, responsible AI, and continuous improvement to guide decision-making.
• Start with early adopters and focused pilots: Identify enthusiastic users and promising pilot programs to validate value and refine your approach.
• Scale what works across the enterprise: Determine which initiatives deliver the greatest value and are ready for broader, enterprise-wide adoption.
• Support change through targeted skilling and enablement: Develop skilling and change management strategies that address the needs of both technical and nontechnical employees.

Learn more

How we did it at Microsoft

• Take a look inside the councils steering AI projects at Microsoft. This article describes the overall impact of our AI CoE, data council, and Office of Responsible AI at Microsoft.
  • Learn more about our AI CoE.
  • Learn more about our Microsoft Digital Data Council.
  • Learn more about our Office of Responsible AI.
• See how we’re building an AI-powered continuous improvement culture at Microsoft. This story outlines the work of our Continuous Improvement CoE as it identifies and tracks AI opportunities at Microsoft.
• Learn how we’re unleashing API-powered agents at Microsoft. This article shares our internal learnings and a step-by-step guide.

Further guidance for you

• Follow the Microsoft 365 Copilot Agents Deployment Blueprint to enable agents at scale with security, governance, and measurement strategies.
• Microsoft Copilot Studio implementation guidance on how to plan, build, manage, and improve Copilot Studio agents.
• Get our AI Agent adoption guidance for planning, governing, building, and managing AI agents across your organization.
• Define your solution architecture with architecture-level guidance for designing scalable, enterprise-ready agents.

Chapter 3: Driving adoption to capture value across the organization

Readying our workforce for the agentic future through targeted enablement, skilling, and cross-company collaboration

Change management is an important part of our AI maturity journey. All the technical readiness in the world means nothing if we don’t build a transformative culture. The spectrum of agents, use cases, and creation methods is wide, but enabling them all requires one thing: an AI-first mindset.

Driving adoption for agents represents a fundamental shift from an AI assistant like Microsoft 365 Copilot, which delivers a comparable experience for every employee. With the agentic mindset, the point is for individuals to be selective about the agents they choose to use—and more significantly, the agents they choose to create.

We also structure our enablement efforts to channel employees into different behaviors based on what’s available and what they might need to build:

• First, we enable employees to discover and use agents that are already published and available.
• If an agent that serves their use case doesn’t exist, employees can build their own, starting with simple no-code agents.
• For complex agents, we channel employees, teams, and lines of business into using Copilot Studio and other, more full-featured pro-code tools.

Regardless of the behavior we’re trying to enable, we follow a four-phase strategy that takes inspiration from Prosci’s ADKAR model, which progresses through awareness, desire, knowledge, ability, and reinforcement. Our adoption efforts align with the Microsoft Engagement Framework, which we’ve developed specially for driving adoption of our products. You can learn more about our overarching approach in our Microsoft 365 Copilot readiness guide.

“An important part of agentic adoption is telling stories to help people understand where AI’s value comes alive or why they should build agents,” says Amy Rosenkranz, a principal product manager on the Copilot Extensibility team within Microsoft Digital. “Examples from peers and real-world use cases are two of our most effective methods for getting people into the AI-first mindset.”

We’re applying several tried-and-tested change management techniques to our organization-wide adoption efforts. These are relevant to both non-developer employees who want to create simple agents and professional developers working on tools for their teams, lines of business, and the entire enterprise.

Cohort-based coordination

We divide our adoption campaigns along two pivots: Internal organizations like legal or sales and marketing, and regions like North America or Europe. Different cohorts have different focuses, but the strategy is similar. Our company-wide adoption leads spearhead our efforts, and we identify members of target cohorts who can support the adoption, including change managers, leadership sponsors, and employee champions.

Adoption communications

We treat internal communications as a primary driver of agent adoption and creation, not just a distribution channel for training. Our initial communications focused on building confidence, reducing fear, and reinforcing clear norms for responsible agent building. We used consistent messaging across leadership communications, learning content, and employee channels to normalize experimentation and help employees understand when to create an agent, when to reuse one, and where to go for guidance.

AI Agent Launchpad

During our deployment of Microsoft 365 Copilot, we experimented with event-driven skilling in the form of Camp Copilot and Copilot Expo. Now, we’ve adapted these kinds of skilling events to agents as well. AI Agent Launchpad takes employees on a learning path through five modules to help them discover, use, and build agents confidently:

1. AI mindset in motion: Employees learn about the concept of the Frontier Firm.
2. Introduction to agents: This module covers the basic principles and definitions of AI agents to establish a foundation of understanding for agent creation and usage.
3. Explore existing agents: Participants build the new habit of discovering available agents to see if any existing tools meet their needs.
4. Build agents with ease: Employees polish their agent building skills in Copilot Chat and SharePoint with an expert in a hands-on lab environment.
5. Build with Copilot Studio: This module goes deeper into designing, connecting, testing, and publishing more powerful agents.

Each module features self-learning readiness, live sessions, gamification, and Credly badges. Instead of a global, centralized event, we’ve modularized the experience so local or organization-level leaders can adapt it to their particular cohort’s needs, while still providing support from centralized adoption leads. We’ve also created a freely available resource organizations can use to plan and run their own virtual skilling events around AI adoption.

Copilot builder champs

Our initial AI rollout showed us first-hand the power of peer leadership in driving adoption, so we adapted the strategy behind our highly successful Copilot Champs Community into our Copilot builder champs program. This initiative makes use of peer connections, success stories, and a Viva Engage community, and we refocused it on enabling employees to create the agentic solutions they need.

These champions represent some of our strongest adoption evangelists on their respective teams. We also created a Microsoft SharePoint hub with resources, best practices, agent publishing information, and more.

Integration and incentivization

We collaborate with managers to integrate AI into their teams’ routines. Often, we’ll use mini-challenges or gamification strategies to encourage agent usage. We recognize top contributors with shout-outs or small awards. We’ve also found that it makes these efforts more engaging to blend work tasks with personal interests.

Formalizing change management for professional developers

We apply more focused adoption initiatives for the professional developers who create team, line-of-business, and enterprise agents. Because their efforts are reimagining how work gets done across the organization, we need to ensure these agents are aligned with business goals, built securely and responsibly, and drive the impact the company needs. The process unfolds across five steps.

Whatever your goals and whichever segment of your workforce you target, it’s important to understand that adoption doesn’t happen by accident. True workforce transformation won’t take place without appropriate adoption activities.

As you launch your own adoption initiatives, consider who your audience is, what they need to build confidence and competence, and how you can unlock agentic value for them across your organization.

Learning from our agent adoption experience

Be thoughtful about your audience

Vary your efforts between non-developer and developer audiences, different geographies and internal organizations, and specific goals. Put together a methodology for thinking about what agents you want and what benefits they’ll provide, then determine who the best builder is.

Don’t just enable agents—empower the enterprise

Your goal isn’t just to activate agents for agents’ sake. Think carefully about what workflows and value you’re trying to unlock, and how agents can get you there. Break down aspects of roles and workflows, and see how agents fit in.

Establish multiple vectors for skilling

Different modalities work for different employees. Use every tool at your disposal, from live events to peer leadership to self-guided learning, and communicate them across all available channels.

In many ways, this is a reset

Your employees may have just become comfortable with Copilot, and agents might feel like a whole new horizon. That’s true. Have patience and understand that this is an entirely separate adoption path.

Showcase and celebrate success

People need to see value and possibilities for agents in their own work. When pilots or personal agents create results, socialize them widely and encourage employees to try them out. Nothing encourages experimentation with agents like successful usage.

Leadership sponsorship is absolutely crucial

Leaders both set expectations and bear the standard of your organization’s culture. They can be the figureheads of transformation by setting priorities, participating in communications, and leading by example.

Key takeaways

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

• Establish strong adoption leadership early: Assign a dedicated adoption lead, form a cross-functional adoption team, and align change managers, executive sponsors, and employee champions around clear ownership and cadence.
• Design adoption around real work and real people: Identify priority cohorts, personas, and usage scenarios, then tailor messaging, enablement, and communications to how each group works and learns.
• Define success before you deploy: Set clear KPIs and success criteria likefeature usage, scenario adoption, and employee sentiment, and put a measurement and feedback plan in place from day one.
• Enable employees through structured onboarding and learning: Combine readiness communications, live learning, self-service resources, and a centralized enablement asset library to help employees build confidence and momentum.
• Activate champions and leadership to amplify adoption: Launch champion communities, empower leaders to model usage, and use internal channels to reinforce behaviors and share progress.
• Continuously listen, learn, and iterate: Gather feedback through surveys and listening sessions, surface success stories, and apply insights to refine adoption, reinforcement, and resistance management plans.
• Extend and optimize for professional developer teams: Support advanced agent ideation, development, discovery, and advocacy while using ongoing feedback to drive workforce transformation at scale.

Learn more

How we did it at Microsoft

• Revisit our adoption practices for Copilot. This chapter of our Copilot readiness guide will help lay a base for your understanding of AI adoption practices.
• See how we made AI engagement fun with Camp Copilot. This article tells the story of our skilling event series to show you how this kind of initiative could look at your organization.
• Try our 7 tips for driving Microsoft 365 Copilot adoption with a virtual skilling event.  This post distills our learnings from Camp Copilot into a handy list for change leaders.
• See how we enabled meaningful AI adoption with a Microsoft 365 Copilot Expo. This story outlines our follow-up and evolution of the Camp Copilot experience into Copilot Expo, a more flexible and modular skilling event.
• Learn how we drove Microsoft 365 Copilot adoption with our Copilot Champs Community. This article showcases the peer leadership efforts that supported Copilot adoption at Microsoft.

Further guidance for you

• Download the Agentic Automation Adoption Guide to learn migration strategies, governance models, and business value for agentic automation.
• Explore the company’s official AI agent adoption guidance to plan, govern, and operate agents using proven frameworks and best practices.
• Visit the AI Agents Hub on Microsoft Adoption to access webinars, quick-start guides, and business scenarios for Copilot, Foundry, and Copilot Studio.
• Check out our Copilot user engagement tools and templates, which we used to encourage adoption across our organization.

Chapter 4: Providing support at the agentic frontier

Bolstering agentic transformation through solid groundwork, human oversight, and AI-driven support

With many forms of technology, support is fairly simple. You identify pain points and common issues with a relatively static technology, create self-service tools to help users with those challenges, and make subject matter experts available in the form of a dedicated support team.

But AI is evolving too quickly for that model, and agents are too diverse and individualized for a static approach. As a result, our support apparatus for agents needs to be much more flexible. Within Microsoft Digital, 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: The ideal state is that our agent creation and publishing tools should incorporate good guidance, governance, and guardrails out of the box so the agents people create are 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, Microsoft Digital fills gaps in governance through reviews and oversight. We do this by establishing risk-based policies around types of agents, exposure and sharing, and other pivots we addressed in our governance chapter.
• User education: It’s almost impossible to predict every governance gap and need, so educating our users helps them avoid accidentally stepping out of bounds. Our Agents at Microsoft team and change managers are the linchpins of these efforts, and employees can lean on resources like Microsoft Learn courses and the Agent Builders SharePoint hub.

Of course, we do have a support team of AI subject matter experts available to employees for any questions they can’t answer themselves. Our HelpDesk support team operates independently from other enablement vehicles, but human support representatives can only accomplish so much. It’s important not to create bottlenecks by relying on conventional support. After all, the promise of AI is to reduce the burden on humans, and that’s no different for our support teams.

“On our journey to Frontier Firm, we’re working really hard to accelerate processes and remove roadblocks so people can get to value much faster. This is crucial for agentic scenarios because we’re using these iterations to polish and improve the tools we create.”

Mykhailo Sydorchuk, Customer Zero lead for Microsoft 365 integrated experiences, Microsoft Digital

AI itself is becoming a cornerstone solution for this challenge. An AI-driven approach aligns with the idea of the Frontier Firm, where humans lead and agents operate, in this case by supporting other humans as they explore AI more deeply.

This is a relatively new approach, but we’re already using agents to provide support in several ways:

• We operate an agent called Ask MICA (Microsoft Intelligent Compliance Agent). This tool provides information and support for compliance issues.
• Agents help us evaluate the risk profiles of other agents. Automating risk assessment accelerates publishing by minimizing human reviews or questions to support specialists.
• We use an agent to perform checks against standards for responsible AI, security, privacy, and access to sensitive information.
• We’re also partnering with our product groups to develop automated agent-building enablers and accelerators that can support ideation and evaluation for new ideas instead of relying on groups like the AI CoE to step in for that kind of support.

In reimagining the support experience this way, we’re focused on maximizing efficiency so that humans remain in the loop, but only for edge cases where AI can’t help. That’s the best use of their time and unique human talent. Meanwhile, we’re continuing to develop and implement agents to support employees for increasing numbers of non-edge cases.

Continuous improvement practices help propel this work forward. Much of that work comes from targeted conversations around pain points. For example, an agent builder might share that it’s taking too long to get security reviews for their projects. To us, that signifies that a security review agent may be useful.

“On our journey to Frontier Firm, we’re working really hard to accelerate processes and remove roadblocks so people can get to value much faster,” says Mykhailo Sydorchuk, a Customer Zero lead for Microsoft 365 integrated experiences at Microsoft Digital. “This is crucial for agentic scenarios because we’re using these iterations to polish and improve the tools we create.”

It’s important to remember that humans will always need to be involved in supporting other humans. But the more assistance agents can provide your support specialists, the more they can focus on tasks that absolutely require human attention. As you consider where AI might fit into your support efforts, our journey can shed some light on the possibilities agents represent.

Learning from our experience with providing support around agents

Emphasize proven agents to minimize the need for support

If you’ve built dedicated first-party agents within your organization, encourage employees to favor those through internal communications. They’re less likely to require support in the first place.

Identify opportunities for AI-driven support

Listen to employees’ pain points and concerns. Recurring themes and issues probably mean there’s an opportunity for agentic support.

Meld adoption and support

Education and skilling initiatives build employee competency to minimize their need for support. If people understand standard use cases thoroughly or know where they can find the right information, they’re more likely to reach out to support specialists only on real edge cases.

Backstop support as much as possible

Microsoft is working to make our tools as self-service as possible. Where gaps appear for your organization’s specific use cases, fill those with IT backstops and employee enablement resources. Hopefully, your support team can be your final resort.

Key takeaways

Here are some key things to remember as you develop your support plan for agents at your company:

• Build agent expertise within support teams early: Provide targeted training, skilling, and early access so support teams can become trusted agent subject matter experts.
• Reduce support demand through proactive enablement: Identify IT backstops and employee enablement opportunities that prevent common issues before they require support intervention.
• Operationalize agentic support at scale: Identify recurring issues across non-developers and professional developers, select high-value opportunities for agentic support, build and test support agents, and actively promote them to drive adoption.

Learn more

How we did it at Microsoft

• Revisit our support practices for Copilot. This chapter will help you lay a base of understanding for AI adoption practices.
• Access our eight steps for managing your support team content with AI tools. This resource demonstrates one way AI can boost your support team’s efforts.
• See how AI is revolutionizing the way we support corporate functions at Microsoft. This story outlines ways that AI tools can contour to different functions across your organization.

Further guidance for you

• Get our Microsoft Agent 365 overview documentation for our control plane for managing, governing, and securing agents across the enterprise.
• Learn how to manage AI agents across your organization. This article provides guidance on integrating AI agents into business workflows and managing their lifecycle from deployment to retirement.

Chapter 5: Tracking the impact of your agents

Building the apparatus for effective measurement to ensure our agentic ecosystem drives business value

Effective governance, implementation, adoption, and support don’t mean anything if your agents aren’t driving the impact your organization wants. But how do you understand that impact if you can’t track and measure it? And what should your measurement criteria be?

Within Microsoft Digital and the company’s leadership team, we’re currently thinking through these ideas to ensure we’re capturing all the value agents have to offer. We’re still developing our approach, but the questions we’ve asked and our measurement parameters will be helpful to consider as you track your own agents’ impact.

First, there’s a difference between tracking agent volume, agent usage, and agent value. Employees creating massive numbers of agents that never get used don’t drive impact. Agent usage is closer to the mark, and it can be a good indicator of which tools are meaningful to employees or might deserve potential promotion for use throughout your organization. Still, usage doesn’t necessarily correlate to business value.

To really articulate value, you need to dive into the specifics of what you intend your agents to do. There are several dimensions to consider:

• Types of agents: First-party enterprise agents, third-party agents, line-of-business or team-based tools and individually created agents all have different purposes and capabilities. They need different measurement strategies.
• Personas: Who is creating the agent, and what are their maturity and needs? What value does a user get compared with a developer or administrator? There’s also team versus individual value. For teams, we tend to measure impact in terms of workflows automated or pain points relieved. For individual users, it’s all about satisfaction, productivity, quality, and efficiency gains.
• Data: Different agents access varying degrees of data. How do you assess the ways they provide access and deliver insights?
• Creation versus discovery and usage: We want to encourage both agent creation when it meets a unique need and agent discovery when a useful agent already exists. Each requires its own measurement parameters.

Our roadmap to agentic impact tracking

We aren’t starting from scratch when it comes to tracking agentic impact. Our Continuous Improvement CoE has already done extensive work aligning targeted and sanctioned AI initiatives with greater business value and tracking them over time. The concept is based on defining top-level value, cascading that value into operational drivers that deliver results, creating action plans and delivering AI solutions to achieve those goals, and then tracking them over time.

We’re currently progressing along a roadmap to a more holistic impact tracking methodology we can use to identify, consolidate, and build agent analytics for all makers, developers, administrators, and Microsoft Digital teams. As time goes on, this approach will accelerate product improvements, improve the builder experience, and cater to reporting and analysis requirements.

Our journey has three main goals:

1. Authoritative, clean, deduplicated data
2. A baseline for creation and usage, and well-defined key performance indicator (KPI) targets
3. Advanced insights to accelerate the agentic ecosystem at Microsoft

In service of these goals, we’re progressing through a five-phase process:

As this methodological structure for tracking agentic impact has come together, we’ve used various tools to help us gain visibility. These include Viva Insights, Microsoft 365 admin center, and an internally built declarative agent tracker, with visibility typically provided by Microsoft Power BI. With the release of Microsoft Agent 365, now available through the Frontier program, we’ve gained a more streamlined vehicle for observability and telemetry.

Three feature sets will be especially useful for tracking value:

• Registry provides a complete view of agents to give us maximum visibility and trackability across our entire agentic ecosystem.
• Visualization includes measurement features to track agent performance, speed, and quality so we can assess ROI and make informed deployment decisions.
• Interoperability ensures we can connect to an open ecosystem of both Microsoft and partner tools.

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.

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 may be in the early stages of agent readiness and deployment. If that’s the case, it will 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 to AI maturity.

Learning from our approach to tracking agentic impact

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 after the fact.

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 builds, and employee champions. That will provide the sponsorship, expertise, and perspective you need for success.

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 tips as you develop a strategy for measuring the impact of agents at your organization:

• Assemble a cross-functional analytics and adoption team: Bring leadership, IT, Microsoft 365 administrators, agent builders, and employee champions together to ensure shared ownership and accountability.
• Clarify analytics and insight requirements up front: Identify, source, and clearly articulate the data and insights needed to measure agent adoption and impact.
• Build an analytics foundation and iterate over time: Consolidate data sources, establish baselines, and develop initial analytics that can evolve as usage grows.
• Define and standardize agent KPIs: Finalize a clear, consistent set of metrics aligned to business outcomes and adoption goals.
• Turn insights into action through reporting: Apply analytics and reporting to inform decisions, optimize adoption efforts, and drive continuous improvement.

Learn more

How we did it at Microsoft

• 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.
• Learn 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

• Read this guide for measuring adoption and business value with Copilot Analytics.
• Get our guidance on using Microsoft 365 admin center activity reports. This resource shows you how to get insights into how people in your business are using Microsoft 365 services.

Applying lessons from our agent deployment at your organization

You’ve learned from our AI maturity journey. It’s time to get started on yours.

Becoming a Frontier Firm might seem daunting. But the agent-building and agent-adoption practices we’ve articulated in this guide can help you gradually and thoughtfully progress toward a new organizational blueprint, one that blends machine intelligence with human judgment. It can help you build systems that are AI-operated but human-led.

By capitalizing on the lessons we’ve learned during our internal deployment, you can both speed up the process of building and deploying agents at your company while avoiding frustrating pitfalls. If you anchor your work in careful planning and use the steps and resources we’ve provided here, you’ll be on the path toward true business transformation through agentic workflows.

“Embracing AI transformation is an opportunity for IT leaders to take part in defining the future of their organizations. Our role as technical professionals has never been more revolutionary, and our team can support yours as you reimagine workflows to make AI part of your everyday reality.”

Vijaya Alaparthi, principal group product manager, Microsoft Digital

You’re not in this alone. If you’re looking for support or knowledge on any aspect of your deployment, reach out to our customer success team.

“Embracing AI transformation is an opportunity for IT leaders to take part in defining the future of their organizations,” says Vijaya Alaparthi, a principal group product manager at Microsoft Digital. “Our role as technical professionals has never been more revolutionary, and our team can support yours as you reimagine workflows to make AI part of your everyday reality.”

Frontier opportunities are present across every aspect of your organization today. Partner with us and take your first steps toward this exciting agentic future.

Key takeaways

This guide captures what we’ve learned as we’ve deployed agents across our entire global organization. Here are the key things to remember as your company moves from early AI adoption to a large and thriving agentic ecosystem:

• Advance governance early: Establish a strong and trusted data foundation that includes labeling, protections, and a risk-based governance model before enabling broad agent creation. Establishing your governance foundations for Microsoft 365 provides the confidence to open up Copilot without hiding data. Clear guardrails, differentiated oversight, and lifecycle management help ensure safe innovation without sprawl.
• Follow a maturity roadmap: Use an escalating AI maturity model that progresses from awareness to enterprise-wide adoption and agentic transformation to sequence your rollout. This staged approach aligns AI investments with business goals while building the culture, skills, and infrastructure you need to scale.
• Drive targeted adoption: Treat agent adoption as its own transformation journey, distinct from assistant-based tools like Microsoft 365 Copilot. Cohort-driven skilling, champion communities, localized learning, and leader-led communications accelerate confidence and empower both makers and users.
• Empower builders at all levels: Support no-code creators and professional developers with tailored enablement, clear publishing workflows, and accessible resources. This ensures individuals can create personal agents while teams can safely build enterprise-grade tools that unlock high-value scenarios.
• Reimagine support with AI: Blend embedded governance, flexible IT backstops, and AI-driven support agents to reduce friction and scale help resources. As employees experiment with agents, automated checks, accelerators, and intelligent support tools keep humans focused on true edge cases.
• Track impact holistically: Distinguish between agent creation, usage, and value by establishing KPIs that map directly to real business outcomes. A unified telemetry and observability layer powered by tools like Microsoft Agent 365 enables clear measurement, optimization, and proof of return on investment.
• Continuously evolve toward becoming a Frontier Firm: Advance your culture, architecture, governance, and workforce practices iteratively as agentic capabilities grow. By combining human judgment with autonomous agentic operations, your organization can unlock transformational efficiency, innovation, and scale.

Learn more

How we did it at Microsoft

• Discover how we’re deploying Microsoft Agent 365 internally. This story shares our intentions for using Microsoft’s unified control pane 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.
• Learn how we’re building an AI-powered continuous improvement culture at Microsoft. This story outlines the work of our Continuous Improvement CoE as it identifies and tracks AI opportunities at Microsoft.
• Access our eight steps for managing your support team content with AI tools. This resource demonstrates one way AI can boost your support team’s efforts.

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 we use to guide our use of AI at Microsoft.
• Follow the Microsoft 365 Copilot Agents Deployment Blueprint to enable agents at scale with security, governance, and measurement strategies.
• Download the Agentic Automation Adoption Guide to learn migration strategies, governance models, and business value for agentic automation.
• Visit the AI Agents Hub on Microsoft Adoption to access webinars, quick-start guides, and business scenarios for Copilot, Foundry, and Copilot Studio.

Try it out

Get started with Microsoft Agent 365 at your company.

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 Becoming a Frontier Firm: A guide for deploying AI agents based on our experience at Microsoft appeared first on Inside Track Blog.

Microsoft Azure DevOps (ADO) is our company’s end-to-end software development lifecycle (SDLC) solution for planning, coding, testing, and delivery. It combines tools for work tracking, source control, pipelines, and artifacts so teams can manage the entire SDLC in one environment.

Although ADO excels at streamlining the development process, we found that users were still spending significant time performing repetitive administrative tasks, like creating and breaking down work items, writing and managing queries for reporting, and reclaiming lost permissions.

Our Engineering Systems Platform team successfully embedded AI into ADO, resulting in ADO experiences that replace manual workflows and free up our IT professionals to concentrate on work that makes a real impact.

Identifying the opportunity

The Engineering Systems Platform team supports 15,000 active users across one of the largest ADO platforms at Microsoft.

“We saw the toll these processes took on users, whether they were compiling information or performing manual tasks. Even with automation, there was still an opportunity to give time back to engineers.”

Gopal Panigrahy, principal product manager, Microsoft Digital

Three years ago, the team began exploring opportunities to automate repetitive ADO tasks like creating and updating work items, navigating project data, gathering statuses, and breaking large initiatives into sprint-ready work.

While they found ways to automate some of these tasks, they discovered decision-making and information synthesis still consumed valuable time and occasionally introduced some human errors.

“We saw the toll these processes took on users, whether they were compiling information or performing manual tasks,” says Gopal Panigrahy, a principal product manager in Microsoft Digital. “Even with automation, there was still an opportunity to give time back to engineers.”

Adding AI to ADO workflows

ADO spans a vast area at Microsoft, serving a wide range of enterprise use cases and personas. What these workers have in common is heavy workloads. With this in mind, different categories of ADO users expressed the desire for AI-powered experiences that could help streamline workflows and speed up day-to-day development tasks.

As generative AI matured, our team explored whether they could embed AI technology inside ADO to act as a real-time assistant, handling administrative work and answering contextual questions using natural language.

“We saw it as a win-win experiment. If we could give engineers back in ADO, they could spend it building, not managing artifacts.”

Debashis Sahoo, principal group engineering manager, Microsoft Digital

The guiding principles of the experiment were simple: Stay in context and preserve user control while aligning with existing ADO permissions and processes.

That vision led to the creation of two complementary Microsoft Copilot agents: The DevOps Assistant and the AI Work Item Assistant.

“We saw it as a win-win experiment,” says Debashis Sahoo, a principal group engineering manager in Microsoft Digital. “If we could give engineers time back in ADO, they could spend it building, not managing artifacts.”

What makes this initiative distinctive is it brings AI closer to the core ADO product and its users. It allows for secure, confidential, and context-rich ADO data to be used safely for meaningful AI-powered experiences.

DevOps Assistant offers conversational, in-context support

DevOps Assistant is a chat‑based experience present in the ADO user interface (UI). It’s activated in a side panel where users can ask natural language questions to retrieve information, check project statuses, and run common DevOps actions without navigating away from their main ADO display.

The DevOps Assistant enables cross-source discovery, which reduces context switching and discovery time and helps lower the cognitive load for engineers and product managers. By reducing the time it takes to switch contexts and search for information, the DevOps Assistant helps ADO users move faster and stay focused on product delivery.

Under the hood, the DevOps Assistant is a constellation of specialized agents, each of which is focused on a different segment of the DevOps lifecycle:

• Work Item Agent creates, refines, and scopes work into sprint-ready backlogs
• Knowledge Board Agent surfaces the right DevOps knowledge at the right moment
• Permission Agent handles access and permission requests
• Bulk Complete Agent runs repetitive, large-scale updates
• Sprint Board Agent summarizes sprint status and provides instant, prompt‑driven insights

“We didn’t just build a chatbot. We built a distributed system of agents that understands the intent of the DevOps user and acts on it securely and in context.”

Apoorv Gupta, principal software engineer, Microsoft Digital

Agents are built in Copilot Studio and coordinated by Orchestrator Agent, Copilot Studio’s front door.

For example, if a user asks to create or refine work items, the Orchestrator Agent routes the request to the Work Item Agent to handle. If the question is about permissions, then it delegates the work to the Permission Agent. It does this for each task.

“We didn’t just build a chatbot,” says Apoorv Gupta, a principal software engineer in Microsoft Digital. “We built a distributed system of agents that understands the intent of DevOps user and acts on it securely and in context.”

At present, the DevOps Assistant is available across all our internal ADO environments at Microsoft. The plan is to make it available to external customers soon.

AI Work Item Assistant provides inline assistance

The AI Work Item Assistant is a real-time embedded experience within ADO work items. Powered by Microsoft Foundry, it helps users create and refine work items using context and business requirements.

The assistant works immersively, keeping users focused and within ADO as they structure work items or generate child items from the parent.

For product and program managers who start with high‑level ideas, the assistant understands intent. It can automatically suggest logical, sprint‑ready breakdowns, helping to dramatically reduce the time spent on planning, sorting, and prioritizing work items.

Turning newfound time into innovation

The key to reclaiming time for your workforce isn’t just the introduction of new AI-driven features. It’s using the technology to enforce structure and quality at the beginning, so that everything downstream moves faster.

Panigrahy describes the practice as three reinforcing feedback loops.

The first loop is upstream quality amplification. AI agents help consistently structure work items with clear acceptance criteria and templates. The structure then feeds other tools (such as GitHub Copilot), allowing them to generate higher-quality code and more predictable outcomes—shortening the overall software development lifecycle.

The second feedback loop is acceleration of execution. In a typical sprint planning session, a team of eight engineers might:

• Take an hour (or more) to manually break user stories into more than 100 tasks
• Create different tasks in their own style, introducing inconsistency and ambiguity
• Generate uneven details, then spend time clarifying data later

With DevOps Assistant and AI Work Item Assistant, that same task breakdown turns into a prompt-driven action that no longer requires hours of work.

“It burns a lot of time for everyone to manually create each item in their own way, making sure they’re using the correct inputs from the product manager and confirming they aren’t missing anything,” Panigrahy says. “Now, with AI magic, it takes less than three minutes.”

The third feedback loop is capacity reinvestment. Instead of spending hours on tactical DevOps mechanics, teams can now spend more time on engineering judgment, resulting in better estimation, technical decisions, and design. They can use these reclaimed hours to learn new tools, experiment with new agents, and innovate on the SDLC.

“Capacity saving keeps giving back, in a loop,” Gupta says. “You get more capacity back. You innovate. You learn. You do better.”

What’s next on the AI-in-ADO journey

The DevOps Assistant and the AI Work Item Assistant can help change user behavior, shifting from time spent doing tactical DevOps tasks to performing higher‑value, judgment-based work. These tools can help teams increase work quality and reduce wasted time.

“Our next chapter is about making AI smarter, more action-oriented, and truly agentic,” Sahoo says. “The goal is to reduce cognitive load and allow the experience to live wherever users are—from Azure DevOps to Microsoft Teams and Microsoft 365—so the agent works seamlessly across their workflow.”

AI-driven productivity gains are arguably the biggest opportunity in the industry. It’s fundamentally redefining the engineering experience at an unprecedented pace.

“While we’ve made huge strides embedding AI into the everyday Azure DevOps experience, it still feels like we’re just getting started,” Sahoo says. “Staying relevant means continuously evolving to deliver ever-greater value and efficiency to engineers.”

Key takeaways

Keep these tips in mind as you get started on your own journey with AI and Microsoft ADO:

• Treat AI as a strategic accelerator, not as an add-on. Identify where your engineering process can use AI to move from simple assistance to transforming your workflows.
• Target high-effort, high-volume tasks first. Analyze where your teams are spending significant manual time, even if AI tools are already in place in those workflows.
• Validate productivity with measurable data, not intuition. Track time reclaimed, workflow efficiency, reduction in manual steps, and user satisfaction. Tangible data can help your initiative earn trust and justify the expansion of AI tool use on your team.

Try it out

Get started using the AI Work Item Assistant in Azure DevOps.

Related links

• Discover how we’re powering the new age of AI-powered engineering in Microsoft IT.
• Learn how to build and manage agents in Copilot Studio.
• Read about ways we’re transforming our enterprise IT operations at Microsoft using AI.
• Check out our IT playbook for becoming a Frontier Firm in the era of AI.

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 Reclaiming engineering time with AI in Azure DevOps at Microsoft appeared first on Inside Track Blog.

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

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

Qingsu Wu, principal group product manager, Microsoft Digital

But increasing scale required us to evolve our approach.

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

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

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

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

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

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

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

Evaluating AI for Microsoft

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

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

Ria Khetan, senior program manager, Microsoft Digital

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

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

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

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

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

Building transformation on core pillars

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

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

Don Campbell, principal group technical program manager, Microsoft Digital

The operating model is intentionally simple.

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

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

Our CoE uses these four pillars to guide our work:

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

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

Strategy

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

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

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

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

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

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

Architecture

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

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

Qingsu Wu, principal group product manager, Microsoft Digital

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

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

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

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

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

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

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

Roadmap

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

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

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

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

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

A key part of this approach is disciplined experimentation.

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

Culture

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

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

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

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

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

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

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

Responsible AI is embedded throughout that work.

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

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

Fostering agent innovation

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

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

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

Garima Tiwari, principal product manager, Microsoft Digital

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

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

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

Architecture quickly followed.

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

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

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

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

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

That architectural clarity changed how decisions were made.

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

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

The roadmap lens then brought structure to execution.

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

Don Campbell, principal group technical program manager, Microsoft Digital

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

Culture and enablement ran alongside that work.

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

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

The outcome wasn’t a single standardized solution.

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

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

Key takeaways

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

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

Try it out

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

Related links

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

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