• Introduction
• Chapter 1: Understanding Work IQ
• Chapter 2: Establishing trust: How we govern Work IQ
• Chapter 3: How our employees experience Work IQ day-to-day
• Chapter 4: Work IQ beyond Microsoft 365
• Chapter 5: Working with Work IQ: The Customer Zero impact
• Conclusion: Where we’re heading: Work IQ, Fabric IQ, and Foundry IQ

Improving agentic workplace results with Work IQ

Adding deeper contextual intelligence to Microsoft 365 Copilot responses

Enterprise knowledge is perhaps a company’s most valuable asset, but for AI and agents, it can be difficult to take advantage of. Years of emails, documents, chats, meeting recordings, and workflows have created enormous volumes of rich data, scattered across systems and teams in a fragmented way. This data captures how work actually happens, but harnessing it broadly—especially in ways that support better decision making—has traditionally been almost impossible.

Enter the power of agentic AI tools.

In the modern agentic workplace, employees and teams here at Microsoft and elsewhere are finally able to take advantage of all that rich, unstructured knowledge. Microsoft 365 Copilot and AI agents can now access all this data and not simply retrieve information but also reason over it—learning how work gets done and then providing rich contextual responses and guidance.

“By giving AI the ability to reason across the vast repositories of unstructured data that our enterprise possesses, Work IQ fundamentally changes what’s possible for Copilot, agents, and employees alike.”

Brian Fielder, vice president, Microsoft Digital

We’ve given this new, dynamic way of leveraging your enterprise data to boost productivity a special name: Work IQ.

Work IQ represents a big step forward.

For us, it’s enabling the concept of “intelligence on tap” across our enterprise, making our organizational knowledge and work context accessible in real time, grounded in the signals employees generate every day. This transforms unstructured data from a challenge into a strategic resource—one that can support workflows at scale.

“Work IQ represents the next phase of the agentic workplace of the future—and it’s here,” says Brian Fielder, vice president of Microsoft Digital. “By giving AI the ability to reason across the vast repositories of unstructured data that our enterprise possesses, Work IQ fundamentally changes what’s possible for Copilot, agents, and employees alike.”

“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

Internally here at Microsoft, Work IQ is having a tangible effect on how we work every day. A few simple scenarios that illustrate the power of Work IQ—described in greater detail in Chapter 3—include:

• Helping our employees understand which emails require their immediate attention, so they can focus on what matters
• Connecting meeting transcripts to the people involved in a meeting, accelerating actions through a deeper understanding of the participants and their work patterns
• Enabling our employees to create, organize, and publish Microsoft 365 content more quickly and with higher quality

This is just the beginning. As AI continues to permeate our business workflows, nearly every day-to-day task at Microsoft will be simplified, expedited, and improved by the intelligence of Work IQ. This includes the agents that are managing routine business and operational processes, giving them critical business context that helps their reasoning abilities.

This guide explores the ways that Work IQ is impacting how work gets done at Microsoft, and how Microsoft Digital—the company’s IT organization—has played a key role as Customer Zero, validating how Work IQ behaves under real enterprise conditions. It also examines the challenges and considerations that IT organizations will face as we enter an era where AI agents have access to unstructured data to complete workflows.

Chapter 1: Understanding Work IQ

Providing deeper insights through the power of context

Before we can fully explore the implications of Work IQ, it’s important to start with a clear understanding of what it is.

Work IQ is not a new application or service that users interact with directly. Rather, it’s a shared intelligence layer that continuously interprets work happening across the tenant. Understanding this distinction is critical, because it explains why Work IQ shows up everywhere Microsoft 365 Copilot works—and why it must be treated as foundational infrastructure, not as optional, add‑on functionality.

“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.”

Work IQ is built on three layers:

• Data: It unifies signals from files, emails, meetings, chats, and business systems.
• Memory: It builds persistent understanding of how people and teams work.
• Inference: It combines models, skills, and tools to reason and act.

At a high level, Work IQ consists of the systems that collect and interpret signals from everyday work. These signals come from many familiar Microsoft 365 applications—Word, Outlook, PowerPoint, Teams, SharePoint, and more—as well as structured data sources (such as those contained in Power Apps and Dynamics 365 resources).

The fact that Work IQ unifies unstructured and structured data into a shared ontology is a key differentiator from traditional search tools. This combination, referred to as semantic unification, means that it can combine the authoritative data contained in structured sources with the intent, nuance, and narrative found in unstructured data.

Work IQ draws from a broad range of work data from your Microsoft tenant. The unstructured data includes:

• SharePoint sites, files, and other content
• OneDrive activity that reflects individual work and collaboration patterns
• Teams content, including chats, channels, and meeting data
• Outlook emails and attachments

In addition, calendar signals—such as meeting participation, recency, and frequency—add time-based context that helps Work IQ understand priority and relevance of different data. This is what it means to go beyond simple information retrieval.

When all these are combined, it provides rich context that allows Work IQ to reason across all our employees’ work in a way that would be impossible if each signal were evaluated independently.

In practice, this means that when an employee asks a question about a current work project in Copilot, the tool’s response is not simply informed by the model’s capabilities or general source material. Responses are shaped by Work IQ’s understanding of the employee’s role, recent work, collaboration patterns (who they work with), and the larger enterprise context and conversations surrounding the question.

How our employees interact with and understand Work IQ depends on their role in the organization.

Our personas and their relationship with Work IQ

End user

Gains richer, context-aware agent conversations

IT admin

Enables and governs Work IQ access

Agent builder

Manages data connectors quickly and easily

Security professional

Knows that sensitive data stays protected with Work IQ

AI agents using Work IQ behave similarly. They use the intelligence to ground their reasoning in real organizational data, ensuring that their actions and recommendations are aligned with how work is happening inside the tenant. Although there are differences in how they are configured, all agents in a Microsoft tenant can be set up to take advantage of the power of Work IQ.

The impact of Work IQ on our company has been dramatic—we’re seeing agentic responses and actions that go deeper than surface-level answers. Our ability to reason over both our structured and unstructured data is producing richer, more nuanced contextual results that are boosting our productivity.

As your organization assesses your level of AI readiness, think of Work IQ not as an abstract concept but as critical infrastructure. It’s the key to connecting enterprise knowledge, trust, and productivity in a single, shared foundation.

Work IQ versus Microsoft Graph

Work IQ does not replace what we call the Microsoft Graph, the general term for unified, API-enabling, secure, permission-aware access to Microsoft 365 data, insights, and services. While the Microsoft Graph provides our employees with access to all their work data, Work IQ turns those signals into meaningful context that AI can reason over. In other words, Graph answers the general question “what info exists,” while Work IQ interprets what that information means and weaves it into responses to make them better.

Key takeaways

As you prepare for Work IQ, these points can help frame how to think about its role in your organization:

• Work IQ is foundational infrastructure, not a user-facing feature. It operates as a shared intelligence layer across the tenant, continuously interpreting signals from everyday work.
• Work IQ draws its power from context, not isolated data. By combining signals from email, meetings, documents, calendars, and collaboration patterns, it enables Copilot and agents to reason about work in a way that goes beyond simple search or retrieval.
• Better agentic outcomes depend on Work IQ being in place. When agents and Copilot are grounded in Work IQ, their responses and actions align more closely with real enterprise work, delivering better relevance and measurable productivity gains.

Learn more

How we did it at Microsoft

• Discover how Work IQ is supercharging our AI usage at Microsoft. This post delves into the ways that Work IQ is boosting agentic response and changing the way we work at the company.  
• See how we’re embracing this agentic moment at Microsoft. This story covers how we use the power of Copilot and AI agents to boost employee productivity.
• 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 toward the AI-powered future.

Further guidance for you

• Learn more about Work IQ with this post from the Microsoft 365 Copilot Blog.
• Check out this Microsoft Learn overview of the different Work IQ agent tools.
• Read all about the Microsoft Graph on the Microsoft Learn site.

Chapter 2: Establishing trust: How we govern Work IQ

Building on an existing foundation of solid governance and security

Like all Microsoft products, Work IQ was built with security foremost in mind. As the IT team at Microsoft, it is our responsibility to work in tandem with the product groups to ensure that all data that Work IQ has access to is well governed and secure.

The fact that Work IQ does not introduce new data into Microsoft 365 makes meeting this commitment easier. Embedded directly into the Microsoft 365 intelligence stack, Work IQ inherits the same compliance, security, and access controls that already govern the tenant.

“With great power comes great responsibility, and it’s up to your IT team to think about what it means to give your users full access to all this Work IQ data. It can greatly accelerate what people can build and what they can do.”

David Johnson, principal PM architect, Microsoft Digital

For Microsoft 365 Copilot–native agents, Work IQ is enabled to provide governed, context‑aware access to Microsoft 365 work data without requiring developers to build or manage individual data connectors.

As our governance experts note, this represents an inherent trade-off. Giving an agent access only to certain isolated data types reduces risk but also limits its value. Granting access through Work IQ means an agent can reason across everything the employee can access. This simplifies enablement but also requires stronger confidence in governance foundations.

Microsoft 365 intelligence stack

As our governance experts note, this represents an inherent trade-off. Giving an agent only access to certain isolated data types limits risk, but it also limits its value. Granting access through Work IQ means an agent can reason across everything the employee can access. This simplifies enablement but also requires stronger confidence in governance foundations.

“With great power comes great responsibility, and it’s up to your IT team to think about what it means to give your users full access to all this Work IQ data,” says David Johnson, a principal PM architect in Microsoft Digital. “It can greatly accelerate what people can build and what they can do. At the same time, organizations will want to think about the downstream implications of access.”

Exposing underlying governance issues

Our overall solution was to anchor Work IQ to our governance and security policies that already existed for our data. Sensitivity labels, data protection rules, and data-loss prevention policies remain the primary guardrails, as they do for all data across our enterprise. All these controls live at the data layer.

A critical aspect of this governance model is how sensitivity labels propagate through Work IQ experiences. In Microsoft 365, the label that is applied to a source document determines the label of any derived outputs, including summaries, insights, or AI-generated responses. This ensures that users have immediate context about the information’s sensitivity and how it should be handled. The label effectively travels with the data, reinforcing both user awareness and policy enforcement.

Labels also play a key role in controlling access beyond simple permissions. Even if a user has baseline access to a location, sensitivity labels can further restrict whether content can be extracted, shared, or surfaced through AI experiences. In some cases, organizations can configure policies so that content with specific labels is not returned at all in Work IQ or Copilot responses. This gives IT teams an additional layer of control to prevent exposure of particularly sensitive information.

These labeling principles extend across collaboration scenarios as well. For example, meeting labels determine the classification of all downstream artifacts—including recordings, transcripts, and notes. Sensitive discussions remain governed consistently, even as Work IQ helps make them more discoverable and actionable.

For example, even with Work IQ enabled, a document labeled Highly Confidential cannot be exposed through Copilot to someone without access, even if it is referenced in a Teams meeting transcript or included in an AI-generated summary. Copilot may understand that the document played a role in a particular decision, but it cannot extract or reveal its contents beyond what permissions allow.

This distinction—discoverable versus extractable—proved critical in our deployment of Work IQ. The intelligence layer makes data relationships visible, but it does not override protection. In one internal scenario, a sensitive document was found to be accessible through a Copilot query. The root cause was not Work IQ, but a missing sensitivity label—the AI tool simply honored what governance allowed. We treated the incident as a governance signal and corrected labeling at the source.

Remember that Work IQ can only access data that:

• Exists inside your Microsoft 365 tenant or is explicitly connected via approved connectors
• The current user already has permission to access
• Is allowed by tenant‑level admin policy, compliance, and sensitivity controls

The security and governance considerations also extend to how new agents are released across our enterprise. For example, an agent created for use within one internal team has lighter governance controls than one that is published to our internal Microsoft agent portal, which offers companywide access. The latter requires additional review, approval, and monitoring as part of our due diligence for governance and security.

Ultimately, Work IQ adheres to all of the security and governance policies and procedures in our tenant, preserving the trust that our security-first approach creates and maintains.

Key takeaways

The following are important considerations for data governance and security when you consider adopting Work IQ for your organization:

• With Work IQ, governance and security are top-line priorities. We made sure that Work IQ would always inherit the same compliance, access controls, and data protection policies that already govern Microsoft 365 data.
• Work IQ doesn’t introduce new data access—it changes how existing access functions. By packaging tenant data into a single intelligence layer, it facilitates easier agent builder access to the data you already have in your Microsoft 365 tenant.
• The distinction between discoverable and extractable data is central to safe AI deployment. Copilot and other agents can understand how work information is connected and referenced without exposing protected content beyond existing permissions.
• IT admins and leaders should consider the ramifications to their tenant. Work IQ makes agents more powerful and context-aware by opening up access to vast quantities of Microsoft 365 data, but IT professionals should always think through downstream effects on data security and governance.
• Work IQ surfaces governance gaps instead of masking them. When issues arise—such as misapplied sensitivity labels—the solution is not to restrict intelligence, but to strengthen data governance at the source.

Learn more

How we did it at Microsoft

• See how we’re governing AI agents at scale internally. This readiness guide explains how we approach security and compliance for AI agents at Microsoft.
• Read about our experience tackling Microsoft 365 Copilot governance. This guide details our internal governance efforts for Microsoft 365 Copilot as we rolled the tool out across our organization.
• Explore how we’re powering data governance at Microsoft with Purview Unified Catalog. This story explains how Purview is helping us redefine our internal approach to data governance.

Further guidance for you

• Learn more about governance and security for AI agents and find out how to keep your AI initiatives secure.

Chapter 3: How our employees experience Work IQ day to day

Transforming the way work happens at Microsoft

To understand how Work IQ shows up and impacts the workflows of people across our organization, we spoke to several Microsoft employees. They explained how Work IQ makes a difference in the results they’re getting from Copilot and other agentic AI tools and how the intelligence is supercharging their work.

Work IQ in Outlook    

Outlook email and calendars are the space where many of our employees feel the heaviest cognitive load of their day‑to‑day work. It’s also where Work IQ is surfacing some of the most innovative ways to help employees accomplish more.

“You open your Outlook in the morning and Copilot—by drawing on Work IQ context and through features like priority scoring and summarization—can help you see which messages need your attention first.”

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

Rather than treating messages and meetings as isolated items, Work IQ allows Copilot in Outlook to reason across email signals, conversation history, meeting patterns, and calendar behavior to deliver responses that reflect how work actually unfolds.

This means Copilot goes beyond keywords or unread status indicators to determine importance. Through Work IQ, it understands the context of each conversation—which threads are more urgent and relevant to your work and which are less vital.

“You open your Outlook in the morning and Copilot—by drawing on Work IQ context and through features like priority scoring and summarization—can help you see which messages need your attention first,” says Matthew Marzynski, a principal product manager for core experiences in Microsoft Digital. “Copilot is now beginning to offer proactive nudges to help you stay on top of what matters, surfacing what’s changed and what you need to focus on.”

The deeper context also aids Outlook in generating rich summaries of lengthy threads, which can highlight owners, decisions made, and next steps. This allows employees who are added to the thread or who have been away to quickly catch up on complex conversations without manually digging through seemingly endless past messages or related documents.

Marzynski frames Work IQ as an invisible intelligence layer that quietly reshapes how Outlook behaves over time. His core thesis is simple: Users never have to think about Work IQ; they just observe that Outlook is more helpful than before, and that their work gets easier.

“There are no complex commands to learn or rules to create. The intelligence works behind the scenes as you use Outlook,” he says. “Your inbox just gradually feels more relevant. Outlook adapts to how you work, rather than the reverse, and becomes more like an assistant instead of a filing cabinet of communications.”

Work IQ in Teams + Researcher Agent

Another immediate and tangible way our employees experience Work IQ is in Microsoft Teams meetings. The value begins the moment a meeting is recorded. Transcripts, speaker contributions, shared content, and AI‑generated summaries are automatically captured and folded into the attendees’ ongoing work context—without requiring manual note‑taking or follow‑up documentation.

Ray Peer is a senior product manager in Microsoft Digital who observed the power of Work IQ in a recent project he completed with our internal legal team. According to Peer, the team was struggling to find specific content in their data lake, which contains tens of thousands of documents, articles, and other content items.

“Based just on what people shared in that meeting, and what it knows about their work and about SIPOC diagrams, Researcher was able to generate a fully formed, detailed solution for me. That’s the intelligence layer at work.”

Ray Peer, senior product manager, Microsoft Digital

So, he facilitated a Teams meeting for a free-form process‑mapping discussion with a few members of Microsoft Legal. Days later, he put the meeting transcript into the Copilot Researcher agent and asked it to generate a structured SIPOC (Suppliers, Inputs, Process, Outputs, Customers) diagram and accompanying documentation.

He was amazed by the results.

“Based just on what people shared in that meeting, and what it knows about their work and about SIPOC diagrams, Researcher was able to generate a fully formed, detailed solution for me,” Peer says. “That’s the intelligence layer at work. It reasoned over what we said—there were no visuals shared or anything—and it came up with something that I could cut and paste into the final format. I used to have to do that manually, and it took hours.”

Work IQ connected the meeting transcript to the people involved, the SharePoint sites they used, and similar work done elsewhere in the organization. Copilot was able reason across different tools and unstructured data, rather than just treating the meeting transcript as a static artifact.

Note that this works differently from third‑party meeting tools, because the data never leaves the tenant. Work IQ treats Teams meetings as part of a continuous Microsoft 365 workstream—honoring permissions and sensitivity labels throughout—so conversations can become durable inputs for future work without adding risk or effort for employees.

Work IQ in SharePoint

In SharePoint, Work IQ is helping employees create, organize, and publish content by drawing on the rich context of their Microsoft 365 data. Rather than starting from a blank page or text block, content development is sped up as Copilot draws on their relationships, collaboration history, and metadata to help produce sites and documents.

“Copilot will recommend text changes, but also layout suggestions, image and graphic options, and other helpful assistance. It makes it easy to create more compelling content, more rapidly.”

Sam Crewdson, principal product manager, Microsoft Digital

For example, when you ask Copilot to create a new section in a SharePoint site—such as a project overview, status update, or other material—Work IQ enables the tool to look deeper than the prompt itself. When generating the content, it can draw on documents you’ve recently edited, your emails and Teams conversations, and related work happening across the organization. The output you get from Copilot is highly relevant and grounded in real work.

Sam Crewdson is a principal product manager at Microsoft Digital who has been a part of the SharePoint team for more than two decades. He’s excited about what Work IQ is enabling users to accomplish in the product using Copilot, as well as other agentic tools like Knowledge Agent (a domain-specific agent that can drill down on SharePoint sites and libraries).

“Copilot in SharePoint is now able to not only help you produce better written content, it’ll also offer more contextual and visual help,” Crewdson says. “Copilot will recommend text changes, but also layout suggestions, image and graphic options, and other helpful assistance. It makes it easy to create more compelling content, more rapidly.”

Another emerging scenario Crewdson described is conversational agentic authoring in SharePoint. In these workflows, employees refine their SharePoint pages by interacting directly with an agent—asking it to add sections, adjust tone, or suggest visuals. Over time, these agents will reduce repetitive setup steps and help teams move from draft to publish faster.

Across these experiences, Work IQ is helping shift SharePoint from a manual content creation tool to an application where agents automate everyday content tasks based on your overall work context and related Microsoft 365 data.

Key takeaways

Here are some things to remember when thinking about how Work IQ can impact your employee workflows:

• Work IQ reduces cognitive load in Outlook by understanding work context. By recognizing decision‑driven threads, collaboration patterns, and urgency over time, Copilot helps employees focus on what truly needs their attention without relying on manual rules or keyword searching.
• Email and calendar intelligence improves prioritization, summaries, and follow‑through. Work IQ allows Copilot to highlight owners, decisions, and next steps in long threads and nudge users toward timely action, based on how they typically work with colleagues.
• Teams meetings become durable inputs for future work when powered by Work IQ. Copilot and the Researcher agent can reason across meeting content, people, and related SharePoint work—creating structured outputs while honoring tenant security and permissions.
• Work IQ helps Copilot speed up and enrich content creation in SharePoint. By drawing on Microsoft 365 data, Copilot can generate more relevant content for your SharePoint sites and offer helpful layout and graphics suggestions that accelerate the site development process.

Learn more

How we did it at Microsoft

• See how to pick the right Copilot for the job. This post explores different Copilot agents and modules and discusses how to choose the correct one for your task.
• Discover how we deployed Copilot internally at Microsoft. This step-by-step guide breaks down the deployment and adoption process we followed to roll out this groundbreaking technology across our entire organization.
• Find out how we’re using AI to redefine work in Outlook. This blog post explores the latest AI features that our employees are using to work smarter in Microsoft Outlook.
• Learn five ways we’re getting more out of Teams in the era of Copilot and AI. This article details different Teams features that take advantage of the latest in AI technology.
• Read about the lessons learned using SharePoint agents at Microsoft. This post covers some of the insights gleaned through our deployment of different SharePoint agents.

Further guidance for you

• Check out the Microsoft Copilot Success Kit and learn how to accelerate your Copilot implementation with templates, onboarding guides, and technical readiness resources.
• Explore the company’s essential guide to Copilot adoption for practical steps to onboard, engage, and measure impact in your organization.

Chapter 4: Work IQ beyond Microsoft 365

Integrating Work IQ across the enterprise

As organizations adopt Copilot and other AI agents at scale, the question arises: How does Work IQ show up in different contexts? Is it something that only impacts your work in Microsoft 365 applications, or does it also play a role in external applications and other areas of your IT enterprise?

Based on our experience here at Microsoft, the answer is that Work IQ shows up differently depending on where it’s consumed, and those differences matter for admins, agent developers, and other IT professionals.

For most of our employees, Work IQ operates entirely behind the scenes inside Microsoft 365. It is not something users configure, enable, or interact with directly. By reasoning over your entire Microsoft 365 data graph, Work IQ improves the results that Copilot generates in apps like Outlook, Teams, Word, SharePoint, Copilot Chat, and GitHub Copilot.

In this mode, Work IQ is:

Built directly into the Microsoft 365 intelligence stack

Governed by existing permissions, sensitivity labels, and compliance policies

Invisible to users, with no new controls or UI to manage

Continuously learning from signals across emails, meetings, and files

You don’t “implement” Work IQ—it’s already present in first-party Microsoft products by default. If you have enabled Copilot, you are getting the benefits of Work IQ across all of these applications. 

Similarly, any agents you build for Microsoft 365 apps (such as using Agent Builder in Microsoft 365 Copilot) are scoped for use specifically in these apps, rather than outside of them. These agents do not require separate connectors, such as APIs or Model Context Protocol (MCP) servers, to access Work IQ. In fact, Work IQ MCP is a great tool to make your context ubiquitous to whichever agentic experience can be imagined.

Extending Work IQ beyond Microsoft 365: explicit by design

Implementation works somewhat differently outside of native Microsoft 365 experiences. When it comes to custom agents, line‑of‑business applications, or Azure‑hosted solutions, Work IQ does not show up automatically. In these contexts, it is intentionally enabled by our builders and governed by our administrators.

In these scenarios:

• Developers access Work IQ through APIs or MCP servers
• Admins explicitly control which capabilities are enabled or disabled
• Work IQ provides rich enterprise context without duplicating data
• Permissions and governance remain enforced at the tenant level

For us, this design is deliberate and has advantages. Rather than asking our developers to configure dozens of individual connectors for mail, calendars, files, and meetings, Work IQ offers them a single-entry point for enterprise context. Builder tools like Microsoft Foundry and Copilot Studio allow our teams to take the same Work IQ intelligence that Copilot uses and apply it to workflows that live outside Microsoft 365. Examples include automating newsletters, generating insights for account teams, or powering custom agents to handle specific scenarios.

The key distinction is accountability. Inside Microsoft 365, Work IQ is ambient. Outside it, Work IQ is a conscious architectural choice, one that requires actions upfront to enable.

Work IQ does not “open up new data” when used externally. It ports intelligence, not raw access, applying the same rules no matter where it’s consumed. At the same time, it gives organizations flexibility to decide when and how far that intelligence should travel.

This continuum—from implicit use inside Microsoft 365 to explicit use beyond it—also clarifies our roles:

• Our end users benefit without needing to learn anything new
• Our IT teams retain centralized control at the tenant level
• Our builders gain a faster path to context‑aware solutions

Work IQ works best when treated as a shared intelligence foundation, not a feature toggle. It is present by default where trust is already established, and it can be incorporated deliberately where your organizational requirements or innovation needs demand more reach.

Model Context Protocol servers and Work IQ

For organizations that move beyond native Microsoft 365 experiences and begin building custom agents, Model Context Protocol (MCP) servers are the primary mechanism for connecting those agents to Work IQ. While Work IQ is always available inside Copilot, MCP servers are what make much of that same intelligence accessible to agent builders.

At a high level, MCP servers are an open-standard technology (not proprietary to Microsoft) that act as governed tool interfaces to enterprise context. Each Work IQ MCP server represents a scoped slice of Microsoft 365 signals—such as email, calendar, Teams activity, or SharePoint content—and exposes them in a form that agents can reason over. Rather than wiring individual connectors or APIs for each workload, builders can rely on MCP servers to assemble relevant context automatically, while still honoring permissions, sensitivity labels, and tenant policies.

When we’re building agents, Work IQ becomes explicit, and MCP servers are how our builders declare their intent. This includes determining which types of enterprise context the agent needs, how broadly it should reason across work signals, and where governance boundaries apply.

From an IT perspective, MCP servers also provide a critical control point. Our administrators decide which Work IQ MCP servers are enabled in the tenant and which of our builders are allowed to use them. This ensures that extending intelligence beyond Microsoft 365 remains a deliberate choice rather than an accidental one.

Using these servers to connect with your enterprise data also represents real—but manageable—risk. They make existing permissions more actionable, which can amplify the impact of overshared content or weak data hygiene. The best practice is to treat these servers as governed infrastructure: enable them selectively at the tenant level, start with the minimum set required for defined agent scenarios, restrict usage to approved builders, and pair expansion with regular permission reviews and labeling discipline.

Your readiness plan should be to ensure that governance is in place, then selectively enable MCP servers where agents require deeper context. The servers are the bridge that lets agent builders tap into Work IQ safely, allowing you to bring enterprise intelligence into custom solutions without breaking the trust model that makes Copilot effective at scale.

Key takeaways

Here are some things to remember when thinking about how Work IQ shows up across your organization—especially if you plan to extend this intelligence into custom agents and applications:

• Work IQ is foundational inside Microsoft 365 and intentional outside it. Within Copilot experiences, Work IQ operates implicitly, while custom agents introduce a conscious decision to consume that intelligence through MCP servers.
• Governance principles don’t change when extending Work IQ, but they become more visible. MCP servers enforce existing permissions, labels, and tenant policies, making it critical that governance foundations are solid before agents rely on deeper context.
• Agent builders declare intent through MCP server selection. Choosing which Work IQ MCP servers to use defines what enterprise signals an agent can reason over and how broadly it reflects real work patterns.
• Preparing to extend Work IQ beyond Microsoft 365 is about readiness. Organizations that are already ready for Copilot can selectively enable MCP servers to unlock richer agent scenarios without introducing new security or compliance risk.

Learn more

How we did it at Microsoft

• Learn how we’re shaping AI management at Microsoft with Agent 365. This post discusses how we use the Agent 365 control plane to govern Microsoft 365 Copilot and agents internally.
• Read our complete guide to deploying AI agents internally at Microsoft. This Inside Track readiness guide covers all aspects of how we’ve implemented agentic AI across our organization.
• Discover 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.
• Check out how we’re protecting AI conversations at Microsoft with Model Context Protocol security and governance. This article delves into MCP governance through secure-by-default architecture, automation, and inventory for faster, safer agent development.

Further guidance for you

• Read this Work IQ MCP overview on the Microsoft Learn site to find out more about how agents connect using Model Context Protocol servers.
• See the Work IQ Copilot reference on the Agent 365 Microsoft Learn site for further technical detail.

Chapter 5: Working with Work IQ: The Customer Zero impact

Change management lessons from our experience with an ambient intelligence layer

Work IQ wasn’t rolled out across our organization as an abstract platform decision or deployment milestone. Its development has been one aspect of our overall transformation into an AI-first Frontier Firm.

Along the way, Work IQ has been shaped by our long‑standing Customer Zero mission at Microsoft Digital: Using our own products at enterprise scale first, learning directly from how employees experienced it, and allowing those lessons to shape how the technology is refined and extended to customers.

In our tenant, Work IQ benefits emerged gradually through incremental improvements to relevance, context, and intelligence across Microsoft 365. These gains were driven by advances in AI that made it possible to interpret everyday work signals more effectively.

There was no formal product implementation or adoption campaign when we launched Work IQ at Microsoft. As ambient infrastructure, Work IQ is an unseen part of all employee workstreams—nearly every experience benefits from it. At the same time, the power of Work IQ depends on everyone in our organization being effective stewards of their own unstructured data, preserving security, governance, and relevance.

Enablement and adoption

To fully realize the value of Work IQ, we have found that organizations must invest in the foundational behaviors that make their organizational knowledge accessible. One of the key steps in this effort is enabling and encouraging the use of meeting transcripts. Work IQ depends on the artifacts of daily work to build context, and without transcripts, a significant portion of meeting insights and decisions remain inaccessible to the intelligence layer.

Making transcription a standard part of our employees’ everyday collaboration proved essential. Transcripts create a durable, searchable record that Work IQ can connect to documents and actions, helping employees quickly understand what happened, even if they weren’t present. When paired with existing governance controls like sensitivity and meeting labels, organizations can capture this data securely while unlocking great value from this collective knowledge.

This is actually a cultural shift.

We gave our teams clear guidance and encouraged meeting transcription as part of their normal workflow. When paired with the enhancements to meeting recaps in Microsoft Teams, this becomes a powerful tool for preserving and leveraging organizational knowledge.

Of course, Copilot adoption and training efforts were also a vital part of our getting the most from Work IQ. Our employees needed demonstrations of all the things that Copilot could help them accomplish, along with encouragement to jump in and try it out for themselves. Our data shows that internal AI usage has grown significantly over time—from a few thousand users to hundreds of thousands across the company—in large part due to:

• Employee-driven champions programs
• Scenario‑based learning efforts
• Timely and consistent internal communications

Usage also grew internally as our product teams continually refined our AI tools, aided by our collection of user feedback on agentic answers to identify low-quality output and irrelevant detail.

Another major insight we captured was the importance of persistent memory to the Copilot and Work IQ experience. Through our work as Customer Zero, we collected a large volume of feedback from employees indicating that this was a priority—users should not have to repeatedly explain who they are or what they are working on.

The experience was subsequently improved, and Work IQ now helps enable Copilot to remember user history and tailor responses accordingly—delivering summaries for communicators and deeper technical detail for engineers, for example.

Our Customer Zero efforts also validated a critical governance principle for us. As intelligence improved, some teams were surprised by how much context Copilot could surface. In every case, investigation showed that the underlying data access already existed. Work IQ did not change permissions or expose new data—it made existing relationships more visible. This reinforced the importance of strong data hygiene, sensitivity labeling, and permission management as prerequisites for trusted intelligence.

Ultimately, our work as the company’s Customer Zero validated that Work IQ is best understood as shared infrastructure. Its value compounds when organizations focus on readiness—governance, learning, and trust—and allow intelligence to scale naturally across work, rather than treating it as a feature to deploy.

When these conditions are in place, Work IQ quietly raises the quality of Copilot and agent experiences without adding complexity for users or additional burden for IT.

Key takeaways

As you consider how Work IQ might take shape in your own organization, consider these observations from Microsoft Digital’s Customer Zero experiences with this new intelligence layer:

• Meeting transcription is the key. Making sure all meetings are transcribed is essential for Work IQ, so it can build context on how work happens in your organization. This is a technical and cultural change that you need to facilitate and encourage.
• Awareness and learning are keys to usage and feedback. Our internal Copilot adoption grew when employees were shown practical scenarios and encouraged to experiment, supported by champions programs and ongoing internal communication.
• Change management drives results. Use employee champions, role-based immersive learning, and timely internal communications to help your employees understand what Work IQ is and how it can help your enterprise maximize the value of AI.
• Treating Work IQ as shared infrastructure unlocks compound value. When governance, learning, and trust were in place, intelligence could reason across all our rich unstructured data —improving Copilot and agent experiences without adding additional work for users or IT.

Learn more

How we did it at Microsoft

• Read about how we serve as Customer Zero in an AI-powered world. This story describes how Microsoft Digital’s role as Customer Zero has grown and evolved as we move into the Frontier Firm era.
• How to pick the right Copilot for the job. This post explores different Copilot agents and modules and discusses how to choose the correct one for your task.
• Recapping our meetings with Microsoft Teams Premium. This Inside Track story explains how Intelligent Recaps have changed the game when it comes to getting the most out of meetings at Microsoft.

Further guidance for you

• Check out the Microsoft Copilot Success Kit and learn how to accelerate your Copilot implementation with templates, onboarding guides, and technical readiness resources.

Where we’re heading: Work IQ, Fabric IQ, and Foundry IQ

Combining different layers of intelligence to transform the workplace

While impactful on its own, Work IQ is just part of larger story of how we’re using the power of rich data and agentic AI to transform how we work at Microsoft.

“While Work IQ can access your Microsoft 365 data, Fabric IQ will connect to your organizational data, such as analytics. Foundry IQ can leverage both, plus other domain data, to help developers build powerful agentic solutions.”

Naveen Jangir, principal architect, Microsoft Digital

Work IQ is one layer. It allows our AI tools to reason over unstructured data so this powerful resource can be a part of our larger enterprise intelligence system. But it also includes two other aspects of this three-layer system—Fabric IQ and Foundry IQ. Combined, these three capabilities enable organizations to take full advantage of your knowledge estate to forge the AI-powered workplace of the future.

“While Work IQ can access your Microsoft 365 data, Fabric IQ will connect to your organizational data, such as analytics,” says Naveen Jangir, a principal architect in Microsoft Digital. “Foundry IQ can leverage both, plus other domain data, to help developers build powerful agentic solutions.”

Here’s how these capabilities work together in complementary roles to impact how work gets done at Microsoft:

• Work IQ handles unstructured data—like documents, emails, PDFs, and web content—by extracting meaning and context from human language.
• Fabric IQ operates over structured data—like tables, databases, metrics, events, and transactions—to bring consistency and analytic rigor to our work.
• Foundry IQ provides the knowledge-grounding layer, where entities, relationships, and ontologies allow reasoning to stay aligned with enterprise truth.

While each component is powerful on its own, the deeper value is what becomes possible when they are used together.

The intent is to enable agents that can reason across all enterprise knowledge, regardless of where it originated or how it was stored. An agent should be able to read a policy, connect it to operational data, understand who and what is involved, explain its conclusions, and take an action (if desired) through a shared ontology based on organizational context.

That kind of capability can’t emerge just from information retrieval. It requires shared meaning across systems, content, and data types.

This is where the role of Work IQ becomes especially important. We have found that unstructured data contains some of the most critical institutional knowledge an organization has, but it rarely arrives in a form that is ready to be reasoned over. Documents reference people, systems, processes, and timelines in ways that make sense to humans, but not to machines. They can also fall out of date or represent a draft state that was never meant to be presented as verified information.

Work IQ bridges this gap by transforming the raw text into structured understanding, without stripping away nuance.

“Work IQ is already helping us change the way that work gets done. Instead of hunting for information or stitching context together manually, our employees can focus on decisions, creativity, and outcomes—because the intelligence is already there, working with them every day. It’s an integral part of preparing our organization for our agentic AI future.”

Vijaya Alaparthi, principal group product manager, Microsoft Digital

The crucial mechanism for that transformation is entity extraction, paired with a shared ontology. When a document mentions an employee, a system, a regulation, or a product, Work IQ identifies that reference as something concrete and reusable. Over time, those entities become the connective tissue between unstructured content, structured records in Fabric IQ, and the semantic backbone that Foundry IQ relies on to ground reasoning in the agents we create.

We can already see signs of this promised future at Microsoft today. Take a tool like our Employee Self-Service Agent, which we launched late last year. What before was a collection of static HR documents becomes a living knowledge system: policies are parsed, roles and eligibility criteria are extracted, and guidance is grounded in an understanding of employee role and location. The agent can answer a question and explain why the answer applies, because it understands both the document and the organizational context behind it.

This is why Work IQ is such a strategic capability. Improving document quality, normalizing metadata, resolving entities, and establishing governance are not one-off hygiene tasks. They expand what future agents will be able to do safely and reliably. The more coherent your unstructured data becomes, the less guesswork agents must do and the more context they can absorb.

“Work IQ is already helping us change the way that work gets done,” says Vijaya Alaparthi, a principal group product manager in Microsoft Digital. “Instead of hunting for information or stitching context together manually, our employees can focus on decisions, creativity, and outcomes—because the intelligence is already there, working with them every day. It’s an integral part of preparing our organization for our agentic AI future.”

For us, the direction forward is clear. The better your data foundation, the more capable—and trustworthy—your agents become. As unstructured and structured knowledge converges, intelligence stops being a set of isolated features and becomes a system.

Organizations that invest in technology like Work IQ to harness their unstructured data as enterprise knowledge are the ones that will deploy the most capable agents going forward and will be best positioned to take advantage of the agentic future.

Key takeaways

If you want your organization to be able to use Work IQ to propel your own agentic transformation, consider what we’ve learned on our journey:

• Work IQ transforms unstructured enterprise data into actionable intelligence. By reasoning over emails, documents, meetings, and chats, it unlocks institutional knowledge that was previously fragmented and underused.
• The intelligence operates as foundational infrastructure, not a user-facing feature. Work IQ runs continuously behind the scenes across Microsoft 365, improving Copilot and agent responses wherever they appear without configuration.
• Context is what makes Copilot feel truly intelligent. By combining signals from collaboration patterns, conversations, documents, and more, Work IQ enables agents to respond based on how work actually happens, not just what information can be retrieved.
• Security and governance remain intact because Work IQ inherits existing controls. It doesn’t create new access to data; it reveals relationships while fully honoring permissions, sensitivity labels, and compliance policies.
• Employees experience Work IQ as reduced cognitive load, not added complexity. Inbox relevance, richer summaries, and clearer follow-through improve naturally over time.
• Using Work IQ beyond Microsoft 365 is a deliberate, governed choice. MCP servers allow builders to bring enterprise context into custom agents while giving IT teams clear control over scope, access, and risk.
• Work IQ is the foundation for the next generation of agentic intelligence, especially when combined with Fabric IQ and Foundry IQ. The more coherent and well-governed your unstructured data is today, the more capable, explainable, and trustworthy your future agents will become.

Learn more

• Explore how Work IQ is helping supercharge AI usage at Microsoft.
• Discover our chapter-by-chapter internal deployment guide for Microsoft 365 Copilot.
• Check out how we’re embracing this agentic moment at Microsoft.
• Dive into a Microsoft Learn overview of different Work IQ agent tools.
• 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.
• Learn about our experience governing AI agents at scale in this readiness guide.
• Find out how we’re unlocking enterprise AI extensibility with Copilot Studio.

Try it out

Get a closer look at Work IQ.

The post Intelligence on tap: How Work IQ enables AI and agents at Microsoft appeared first on Inside Track.

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.

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

• 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.

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

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

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

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

Marketing: Human challenges, AI opportunities

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

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

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

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

Don Scott, general manager, Azure AI Marketing

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

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

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

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

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

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

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

MarThrive: An agentic toolkit built for marketers

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

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

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

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

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

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

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

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

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

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

MarThrive users in action

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

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

Sharmila Chockalingam, product marketing director, Microsoft Foundry Models

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

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

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

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

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

Jenn Cockrell, senior product marketing manager, Microsoft Foundry

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

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

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

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

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

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

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

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

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

The AI Messaging Assistant: An audience marketing ally

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

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

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

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

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

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

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

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

Robert Graves, senior director, Data Management and Science

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

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

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

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

AI Messaging Assistant user in action

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

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

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

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

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

Ben Loeb, product marketing manager, Microsoft Edge

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

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

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

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

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

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

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

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

Exploring opportunities for AI across the enterprise

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

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

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

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

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

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

Key takeaways

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

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

Try it out

Explore Microsoft Foundry on Microsoft Learn.

Related links

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

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

Previously, when our employees had a question or a problem—whether it be a technical issue, an HR query, or just wanting to know what’s for lunch—they had to navigate through a variety of different apps, tools, and SharePoint sites to find the answer or get help with their task.

It was a time-consuming and frustrating experience. But the advent of generative AI has given us a new opportunity.

Microsoft 365 Copilot and the power of agentic AI have created a world where people simply type in questions or requests to get prompt and helpful assistance. Now we’re applying the capabilities of Copilot and agentic technology to the ongoing challenge of employee assistance.

“At Microsoft, our mission is to transform the employee experience with AI solutions that provide personalized and seamless interactions for our employees throughout the workday. What we’ve created with the Employee Self-Service Agent is a powerful example of a solution doing just that.”

Nathalie D’Hers, corporate vice president, Employee Experience

The result is the new Employee Self-Service Agent, a “one-stop shop” providing vetted and personalized solutions to our workers across a range of high-demand topics and tasks, including human resources (HR), IT support, and facilities and real estate.

The agent combines the help functions for human resources, IT support, and facilities and real estate into one tool, allowing our employees to handle a range of tasks, such as requesting parental leave, resolving a problem with their device, or getting something fixed in their office. The Employee Self-Service Agent is available to all Microsoft employees worldwide and is also now available to customers.

“At Microsoft, our mission is to transform the employee experience with AI solutions that provide personalized and seamless interactions for our employees throughout the workday,” says Nathalie D’Hers, corporate vice president of Employee Experience. “What we’ve created with the Employee Self-Service Agent is a powerful example of a solution doing just that.”

The power of a ‘single pane of glass’

The essential premise of the Employee Self-Service Agent is that it serves as the one place for Microsoft employees to go when they need assistance. This means that they don’t have to remember what tool or website offers the best way to handle their question or task—it’s all available in one seamless, AI-powered interface.

“With this agent, we wanted a ‘single pane of glass’ for our employees and managers,” says Rajamma Krishnamurthy, principal PM architect manager for Employee Experience in Microsoft HR. “The idea is that they can come in and get all their questions answered, rather than have to go to multiple tools or URLs in different areas.”

Employee-Self Service screenshot

The workflow is simple—launch Microsoft 365 Copilot, select “Employee Self-Service,” and type in your query. The agent then orchestrates an authoritative response and/or offers a form that can be used to carry out the desired action (auto-populating the form with details from the chat where possible).

“Many support tools that could benefit employees go unused because of limited awareness and the friction involved in completing tasks. This tool gives employees a new way to access that helpful information.”

Prerna Ajmera, general manager, HR digital strategy and innovation

If the question or task can’t be resolved by the agent, it hands the employee off to the appropriate tool, subagent, or support person.

The Employee Self-Service Agent is driving usage of support tools that our employees often overlook.

“Many support tools that could benefit employees go unused because of limited awareness and the friction involved in completing tasks,” says Prerna Ajmera, general manager for HR digital strategy and innovation. “This tool gives employees a new way to access that helpful information.”

An early focus on HR and IT Support

In developing the Employee Self-Service Agent, we initially identified two main categories of employee assistance to focus on: HR and technical support. These are areas that generate millions of internal queries and support cases (help tickets) from our employees every year, which means the potential for a significant return on investment (ROI). (We subsequently added real estate and facilities later in the process.)

In the case of human resources, this meant looking at all the HR experiences that employees need help with and figuring out what could be handled with AI. Whether it was a question or task related to personal time off (PTO), performance, compensation, learning, internal job listings, well-being, or something else, we needed to make sure that the information the agent returned was relevant and helpful to that employee.

This is what distinguishes the Employee Self-Service Agent from Microsoft 365 Copilot Chat, which provides a more general answer that may not apply to that particular worker’s situation, and can’t access all relevant information about that employee.

“When it comes to HR, you need to make sure the answers are coming from authoritative sources, because HR is a very sensitive and vital part of how a company runs.”

Rajamma Krishnamurthy, principal PM architect manager, Employee Experience, Microsoft HR

With Copilot, you might ask for an overview of everything to do with a given project. But when it comes to employee-assistance topics, casting a wide net is not the desired outcome. An employee doesn’t want to hear about HR policies in India when they work in the U.S., or to get Mac-focused tech help when they use a PC. The needs of each of our employees are different, and so we built the agent to reflect that.

A major task in developing the agent was making sure that all the content that it draws from is accurate and up to date. This was especially important for HR-related responses, which sometimes deal with sensitive topics. We’ve carefully thought through privacy and security issues, are following our company Responsible AI principles, and making sure the agent adheres to regulations for each country or region.

“When it comes to HR, you need to make sure the answers are coming from authoritative sources, because HR is a very sensitive and vital part of how a company runs,” Krishnamurthy says. “Our new agent was built so that only vetted sources are responding to these questions.”

One advantage of the Employee Self-Service Agent is its ability to provide real-time assistance. Rather than having to file a ticket and then wait 24 to 48 hours for a response, the employee can get on-demand help and hopefully resolve their problem without waiting. 

“Previously, resolving an HR help request could take a couple of days,” Ajmera says. “These delays often came from the back-and-forth of traditional support channels—‘OK, you told me this; now, what’s the policy for that? What’s next?’ With the agent, employees can get answers in minutes. That’s the beauty of it.”

“The agent’s content is specifically grounded in our authoritative IT service sources, and it also knows relevant details about you as a user. All of this context makes it better at guiding employees to solve their own support issues.”

Trent Berghofer, general manager, Microsoft Digital Modern Support

Agentic assistance to accomplish more

Another differentiator from previous employee assistance tools is that the Employee Self-Service Agent enables task completion, not just information retrieval.

For example, consider technical support (such as dealing with an audio issue on an employee’s device). Our workers are now able to get detailed, contextual, and specific help with their technical issues, helping them solve the issue without having to engage with assisted support and get a ticket created.

An agentic solution for employee assistance

Retrieve

The agent retrieves information from authoritative sources and delivers responses based on contextual data about the employee.

Take action

The agent helps the employee take action right from the chat and avoid having to navigate multiple tools and workspaces.

Extend

The agent is designed to be flexible and customizable, based on the organization’s existing software workflows and third-party solutions.

The Employee Self-Service Agent retrieves authoritative information with natural-language queries and enables users to take action from within the chat.

“The agent’s content is specifically grounded in our authoritative IT service sources, and it also knows about you as a user—that you have this particular device, and the compliance state of that device, and what country you’re located in,” says Trent Berghofer, general manager of the Microsoft Digital Modern Support team. “All of this context makes it better at guiding the employee to solve their own problem, versus doing a generic search on the issue.”

If the employee does have to connect to live support via phone or chat, the technician will have access to their conversation with the agent. This way, the support professional can view details the user has already provided and the solutions that have already been tried. This saves time and decreases frustration.

Task completion is a primary gauge of return on investment (ROI) for the Employee Self-Service Agent. The overall goal across all help categories is for the agent to result in at least 40% fewer support tickets.

Each ticket represents a significant cost to any organization, and those costs add up, especially at large companies. With more than 2 million IT support interactions (via Virtual Agent, chat, and phone) across Microsoft annually, we project that the Self-Service Agent will produce substantial savings in tech support alone.

HR is another area where we hope to generate impact, as employees meet their needs with the Employee Self-Service Agent. Our specific goals include:

• Reduce monthly HR tickets by 44% by mid-2026 through expanded self-service capabilities
• Save employee time with rapid, frictionless fulfillment of requests 
• Boost overall discovery and use of HR programs to deliver increased ROI
• Increase business agility and reduce end-to-end process time

“Once it’s fully adopted, we’re expecting the agent to manage somewhere between 400,000 and 600,000 employee interactions a year that used to result in an HR support ticket,” Ajmera says. “That’s a significant shift and learning curve for our organization, in terms of how employees get help. Scaling the agent up to have this major business impact has been one of the biggest challenges for us.”

Saving time with AI support

Employee time savings is another significant driver of ROI. This is a key part of the third vertical we’ve targeted with the Employee Self-Service Agent—real estate and facilities.

“Before we had the Employee Self-Service Agent, the employee-assistance experience was kind of fragmented across mobile, websites, and physical kiosks. The new agent unifies all of these experiences and puts them in the same place.”

Becky West, principal group product manager, Microsoft Digital

With hundreds of office buildings around the world, including dozens of cafés and other specialized sites, Microsoft must handle a constant stream of employee inquiries and activities related to real estate and facilities. These include things like:

• Transportation – calling a shuttle for a ride between buildings
• Dining – learning where your favorite dish is being served (and ordering it to go)
• Booking a room – locating a space to relax or connect with colleagues
• Lobby and visitor services – registering a campus guest
• Facilities tickets – getting help with a repair or other building issue
• Parking registration – recording where your car is parked
• Maps – finding your way around a building or a campus

“Before we had the Employee Self-Service Agent, the employee-assistance experience was kind of fragmented across mobile, websites, and physical kiosks,” says Becky West, principal group product manager in Microsoft Digital. “The new agent unifies all of these experiences and puts them in the same place. Now our employees can ask questions in natural language, and it guides them through whatever campus experience they need to do—invite a guest, find dining options, create a ticket, etc.”

The number of working hours currently spent by our employees trying to find the answer to their facilities-related question or filling out a form to complete a task is difficult to quantify precisely across such a large organization. But consider just one common exercise: registering a visitor at a Microsoft building.

According to Digital Workplace Services data, in 2024 there were 2 million registered visitors at Microsoft buildings worldwide, with roughly 1.2 million of these considered business-related.

Previously, employees had to email or talk to lobby hosts (front-desk staff) to invite guests to Microsoft; the host would then enter the guest details into the Guest Management System.

Now, the Employee Self-Service Agent provides a simple form within the chat, asking for details like guest name, email, purpose (business or personal), building number, and date. Once the form is submitted, the system generates a confirmation and sends a QR code directly to the guest via email. That alone has the potential to save us 50,000 hours of employee time per year.

“One benefit of this is that anything you can do with Copilot Studio in terms of a custom engine agent, you can do in the Employee Self-Service Agent. Our product documentation goes into detail on how to configure it based on your particular needs.”

Kyle von Haden, principal group product manager, Microsoft 365 Copilot product group

Another great example is a common facilities request, like replacing a light bulb, reporting broken furniture, or workspaces that require cleaning. Instead of having to figure out which tool to use to report the issue and then filling out a request, the individual can go straight to the Employee Self-Service Agent and upload a photo.

“The agent detects the problem based on the image, fills in details, and enables the user to file their service request right from the chat,” West says.

Customizable and extensible

The Employee Self-Service Agent was built with Microsoft Copilot Studio, a tool that enables users to create and extend AI agents. The product is intentionally designed so that our customers can customize it to fit their own business needs using preconfigured workflows and accelerator packs that come with the agent.

“One benefit of this is that anything you can do with Copilot Studio in terms of a custom engine agent, you can do in the Employee Self-Service Agent,” says Kyle von Haden, a principal group product manager for the Microsoft 365 Copilot product group. “Our product documentation goes into detail on how to configure it based on your particular needs. We’re even including code samples that show you how to extend the agent further than what you get right out of the box.”

For instance, many of our customers rely on third-party solution providers such as Workday, SAP, or ServiceNow. So, our development process included producing connectors for some of these third-party offerings, making it easier for customers to integrate the Employee Self-Service Agent into their existing workflows.

This extensibility is an advantage of adopting the Employee Self-Service Agent, according to von Haden.

“The beauty of this product is that it comes with all these accelerators that help customers jumpstart their ability to deliver AI-driven employee assistance, because there’s no inherent limitations,” he says. “They have all the same flexibility they’d get by building a solution from scratch, but they get to build on this Copilot Studio foundation that offers powerful capabilities and will continue to grow as we invest more in it.”

The role of Customer Zero

With a new product like the Employee Self-Service Agent, having Microsoft employees use it as part of their everyday work and then provide detailed feedback was a valuable aspect of the development process. This is the essence of the company’s commitment as Customer Zero.

“For the Employee Self-Service Agent, the role of our internal users as Customer Zero has been incredibly important—in this case, doubly so,” says Kirk Gregersen, corporate vice president of product for Microsoft Viva and Microsoft 365 Copilot Experiences. “Because not only are we learning how to deploy the product in a real, complex environment, but we’re doing it in a world that’s completely new, given all of the changing variables around AI.”

To that end, we began rolling the agent out to employees more than a year ago in a geographically phased approach—first to the United Kingdom and Canada, then India, then to the United States and the rest of the world. Regular communications to employees—via email, Microsoft Viva, and other channels—raised awareness and encouraged use of the agent. And a sophisticated plan for listening and gathering product telemetry was implemented, so that all feedback could be captured and routed back to the product team.

This process was particularly important for building stakeholder trust in the tool. For example, our HR professionals worked closely with the product group to make sure the answers produced by the Employee Self-Service Agent met their high bar for accuracy and reliability.

“Engaging our stakeholders early was key,” Ajmera says. “We iterated with them as they went through the various prompts and responses manually and rated them for accuracy. We learned a lot. It’s still a work in progress, but we’ve gotten to the point where the agent is able to automatically generate responses that meet stakeholder expectations.”

“This product is very significant for us, both from the user perspective and the cost-savings angle. We can get the right answers to and solve issues for our employees faster, which increases their satisfaction and helps them be more effective.”

Kirk Gregersen, corporate vice president, Microsoft Viva and Microsoft 365 Copilot Experiences

This “virtuous flywheel” development process played a role in making the Employee Self-Service Agent better and preparing it for general release, as a feature available to all Microsoft 365 enterprise customers with a Copilot license. That release is expected soon.

Because the agent is built on Microsoft Copilot Studio, it gives us flexibility to adapt and grow as needed. We plan to eventually expand the Employee Self-Service Agent to other key areas across the company, like finance, legal, and more—to become a true single-pane-of-glass portal for all our employees’ needs.

In the end, the agent offers the potential to deliver the kind of impact that only truly breakthrough business software can: delighted users and major ROI.

“This product is very significant for us, both from the user perspective and the cost-savings angle,” Gregersen says. “We can get the right answers to and solve issues for our employees faster, which increases their satisfaction and helps them be more effective. And the solution scales up to real cost savings for the organization.”

Key takeaways

Here are some things to consider when tackling employee assistance at your organization:

• Approach it from the user perspective. Offering a “single pane of glass” portal from which an employee can access help on a wide variety of topics may present some technical challenges, but it meets users where they are and resolves their pain points.
• Start with high-demand categories. We launched our Employee Self-Service Agent journey with two core verticals that offer potential for ROI: HR and IT support. We then added facilities and real estate, in part because the high usage rates (such as for dining and transportation) would drive greater employee awareness and boost user-session numbers.
• Think about task completion. Employees need to not only access authoritative information, they also want the ability to accomplish their goal right from the agent interface. If their issue can’t be handled by the agent, it should be able to make a smooth handoff to the tool that can.
• Spend time up front on data governance. An employee-assistance agent must supply clear, current, and accurate information that is highly relevant to that user. Vague, inaccurate, or irrelevant answers can damage product credibility with your employees.
• Customizable rather than a turnkey solution. It’s important to note that the Employee Self-Service Agent is a flexible template built on top of Copilot Studio; it requires customization by your organization in terms of implementation, categorization, data selection, third-party integration, privacy, legal considerations, and other factors.
• Make sure to collect feedback and iterate. Generative AI tools are still new, and your help solutions can be improved by listening to your employees and acting on what they tell you about their experience.

Try it out

Learn more about the Employee Self-Service Agent.

Related links

• Learn more about our strategic approach to agentic AI internally at Microsoft.
• Find out how we’re transforming the way our employees get help on campus with the Employee Self-Service Agent.
• Read how the Employee Self-Service Agent is helping Puget Sound-based Microsoft employees with their transportation needs.
• Explore how Microsoft HR is using Viva and Copilot to empower our employees.
• Learn how we’re enabling modern support at Microsoft with AI.
• Discover our five-step guide that helps IT leaders get to enterprise AI maturity.

The post Accelerating employee services at Microsoft with the Employee Self-Service Agent appeared first on Inside Track.

In Microsoft Digital, the company’s IT organization, we’ve implemented a Power Platform governance strategy and vision that is empowering our employees to build solutions that are improving how they get their work done, including crafting their own AI agents with Microsoft 365 Copilot and Copilot Studio.

How are we doing this?

With proper governance mechanisms that keep them safe while letting their creativity flow—and our employees are running with it, creating innovative and dynamic solutions that are streamlining our processes and enhancing our productivity.

And that’s exactly the point—the Power Platform democratizes development, allowing the citizen developer in all of us to come out and play. For us, this is an essential ingredient for fostering the kind of innovation that can and will continue to drive our company forward.  

Our strategic approach to governance on the Power Platform

Locking in on the right approach to governance is pivotal.

“Governance provides essential guardrails, by ensuring that we have visibility into what is being built and enforcing policies to maintain security and compliance with the Power Platform,” says Aisha Hasan, a senior product manager in Microsoft Digital. “Governance allows us to balance the freedom to innovate with the need to protect our tenant from risks.”

Governance is a key strategic enabler to our approach.

With the wide variety of users at Microsoft developing solutions in the Power Platform, it’s vital they understand how to develop within governance parameters and deploy their solutions effectively. This ensures that while innovation flourishes, it does so within a framework that maintains security and compliance.

Starting at the top with environments

Power Platform environments are the foundation for providing structure and organization with our Power Platform tenant.

“Our governance approach is rooted in a holistic environment strategy,” Hasan says. “We’re applying policies at the environment (or container) level to assess and manage risk uniformly and at scale.”

We use an intentional environment structure to enable good governance practices, and we apply our policies and rules to that structure to ensure that every single environment is governed to our standards. This approach avoids broadly shared environments that are difficult to govern effectively.

“We’re routing developers out of the default environment. It’s about getting away from that ‘big bucket’ approach. We want every flow, bot, and app to be in an environment that is purpose-built and intentionally configured.”

Aisha Hasan, senior product manager, Microsoft Digital

Every environment in our tenant has a specific purpose and we ensure we have the proper information about each environment to maintain proper governance. Every environment in our tenant is tied to an owner who is accountable for everything within that environment. This ownership model ensures clear responsibility and accountability.

To enable this specific approach to environments, we make clear distinctions.

First, the default environment—a built-in environment that comes with every single Power Platform tenant—is not the default.

“We’re routing developers out of the default environment,” Hasan says. “It’s about getting away from that ‘big bucket’ approach. We want every flow, bot, and app to be in an environment that is purpose-built and intentionally configured.”

To streamline the process and ensure proper governance, we’ve turned on automated routing. This means that if anyone tries to use the default environment, they’re automatically routed to a specific environment type instead, according to their use case and our environment groupings. This helps in managing the lifecycles of our environments and ensures that every Power Platform solution is in the right place with the right governance.

Shifting to environment routing

We group environments based on their usage and apply specific rules to each group. There are groups for personal productivity, team collaboration, and enterprise development; each with its own set of rules and compliance requirements and tied to a specific environment type in the Power Platform.

Personal productivity environments are designed for individual use, where users can create and experiment with applications and automations without the need for extensive governance. These environments are typically developer environments, which are highly restricted in terms of sharing capabilities. Users can build apps, flows, and other solutions for their personal productivity, but they can’t share these solutions with others. This ensures that any experimentation or personal projects remain isolated and don’t impact the broader organization.

Team collaboration environments are intended for team-based projects and workflows. These environments are usually built within Dataverse for Teams, which integrates with Microsoft Teams. Dataverse for Teams environments are tied to a Microsoft 365 group, which helps manage governance and lifecycle through the group’s settings. These environments are perfect for team productivity solutions that are used within a specific team but aren’t meant to be shared companywide. While Dataverse for Teams environments have some quirks and limitations, they provide a balance between flexibility and governance, making them suitable for moderate governance and security controls.

Enterprise development environments are used for large-scale, enterprise-level projects that require stringent governance and compliance. These environments are typically sandbox or production environments and are subject to a rigorous request and approval process. Users must provide detailed information about their project, including data sensitivity, regulatory requirements, and business justification. These environments are governed by stricter usage policies, custom ownership policies, and regular security reviews. The goal is to ensure that any enterprise-level solutions are secure, compliant, and properly managed.

Using groups, roles, and policies

Microsoft runs on trust, and our success depends on earning and maintaining it. Our Secure Future Initiative ensures that security is first in everything that we do, and that extends to how we govern the Power Platform.

Our governance strategy revolves around applying controls at the environment level. We categorize environments, using the three main groups already identified. There are specific security controls and approaches in place in each group:

“Our security controls and policies are really about enablement as much as possible,” says Jake Visser, a principal architect manager at Microsoft. “If you create a developer environment, we’ll assign an appropriate DLP policy for you to work with your solution and send you a Teams message indicating what policy we’ve assigned and what you can do there. It’s about making sure that people can build and innovate while staying within the guardrails of our governance policies.”

DLP policies are crucial for preventing data breaches and ensuring that data remains within the organization’s boundaries. We apply DLP policies at various levels to control which connectors and actions are allowed within different environments to prevent the unintentional sharing of sensitive information and ensure that data stays within the organization. These policies control which actions and endpoints connectors are allowed to interact with. For instance, if a connector is used to write data, the policy ensures that the data is protected and only interacts with approved endpoints.

For enterprise development environments, teams can request custom DLP policies if they need to use specific connectors or actions that aren’t covered by the standard policy. This involves providing a threat model and other relevant information to justify the need for the custom policy.

Harnessing proactive and reactive governance

Within each environment group, we apply a set of practices that apply our governance strategy. These practices maintain a balanced approach that incorporates both reactive and proactive measures.

We use a proactive governance approach to anticipate issues before they arise. Gaining visibility into what’s being built within our environment is a critical first step. Our inventory data collection processes collect data on apps, connectors, flows, and shared resources. By having a clear picture of our digital landscape, we can enforce policies that ensure security and compliance from the outset. We collect and integrate this data with the following methods:

• Automated data collection tools. We use automated tools to gather data on all assets within the Power Platform. These tools scan our environment to identify and catalog apps, connectors, flows, and shared resources. By automating this process, we ensure that our inventory is always current and accurate.
• Policy enforcement. With visibility and usage data in hand, we can enforce governance policies. This includes defining and applying DLP policies, custom ownership policies, and regular security reviews. These policies help ensure that solutions are secure and compliant with organizational standards. Even if users follow certain policies during the development phase, we need to keep them in check post-deployment to ensure ongoing compliance.
• Regular audits and updates. To maintain the accuracy of our inventory, we conduct regular audits. These audits involve cross-checking the data in our repository with actual usage and configurations in the Power Platform. Any discrepancies are investigated and resolved promptly.
• Integration with governance policies. Our inventory data collection is tightly integrated with our governance policies. For example, we use DLP policies to enforce data flow and access. The inventory data helps us enforce these policies by providing visibility into how data is being used and shared across the platform.
• Custom reporting and dashboards. We’ve used Power BI to develop custom reporting and dashboards to visualize our inventory data. These tools provide insights into asset usage, compliance status, and potential risks. They help us make informed decisions about governance and resource allocation.
• Collaboration with stakeholders. Collecting inventory data is a collaborative effort. We work closely with various stakeholders, including IT, security, and business units, to ensure that our data collection processes align with their needs and requirements. This collaboration helps us address any gaps and continuously improve our inventory management.

Reactive governance, on the other hand, deals with issues that arise after the fact. Even with stringent policies in place, there’s always a need to monitor and manage ongoing activities. The general application of our reactive measures is similar to the proactive measures—they even share some categories. However, our reactive governance measures are built around quickly identifying—as quickly as possible—events in the tenant that might compromise the integrity of our governance.

• Visibility and inventory. Without a good inventory, it’s impossible to govern effectively. To overcome this, we worked closely with the product group to develop an inventory solution. This tool collects data on all the apps, connectors, flows, and connections being used. By having a comprehensive inventory, we can see what’s being built and shared now, which is the first step in reactively enforcing governance.
• Usage data and metadata. After visibility is established, the next step is collecting usage data and metadata. This information tells us who is doing what within the Power Platform. By understanding usage patterns, we can enforce governance policies more effectively. For example, we can identify high-risk activities and take appropriate actions to mitigate potential issues.
• Continuous monitoring. Reactive governance also involves continuous monitoring of the Power Platform environment. This means regularly reviewing the inventory and usage data to identify any anomalies or potential risks. By staying vigilant, we can quickly address any issues that arise and ensure that our governance measures remain effective.
• Ownership accountability. One of our key reactive measures is the periodic attestation process. Every six months, we require asset owners to confirm their ownership and compliance with our policies. This includes verifying that they aren’t using unauthorized data, not sharing data outside the tenant, and adhering to all security protocols. This process helps us catch any deviations and address them promptly.
• Collaboration with product teams. Our reactive governance efforts are supported by close collaboration with the product teams. By working together, we can develop and refine tools and policies that enhance our governance capabilities. This ongoing partnership ensures that we stay ahead of potential risks and continue to improve our governance practices.

By combining proactive measures to prevent issues and reactive measures to address them, we can provide environments that allow our developers to innovate freely while safeguarding our digital assets. It’s a win for everyone involved and it’s truly enabling innovation at Microsoft.

“With Sentinel, we can perform real-time monitoring of all activities within the Power Platform. We must have visibility into what’s being built and how the platform is being used.”

Jake Visser, principal architect manager, Microsoft

Integrating with Microsoft Sentinel detection and response

Microsoft Sentinel plays a crucial role in our governance strategy. It’s an essential tool that helps us monitor, detect, and respond to various activities within the platform, ensuring that our governance policies are enforced effectively.

Sentinel integrates with Microsoft Purview audit feeds to monitor all activities within the Power Platform. This integration allows us to capture events such as bot creation, environment creation, flow runs, and edits. Essentially, any action performed by a user or admin within the Power Platform generates an event that is captured by Sentinel.

“With Sentinel, we can perform real-time monitoring of all activities within the Power Platform,” Visser says. “We must have visibility into what’s being built and how the platform is being used. For instance, if a user creates a new environment or modifies an existing one, Sentinel captures this event and allows us to cross-reference it with our governance policies.”

Sentinel enables us to automate governance actions based on the events it captures. For example, when a user creates a personal developer environment, we use the Sentinel events and an Azure Logic App to automatically assign a DLP policy to that environment and send a Teams message to the user, informing them of the assigned policy and what they can do within that environment.

Sentinel’s integration with the Power Platform’s inventory service allows us to maintain an up-to-date inventory of all environments, apps, and flows within the platform. This inventory is crucial for proactive governance, as it provides us with the necessary metadata to enforce policies and ensure compliance. If an environment’s configuration is altered against policy, Sentinel can trigger an alert and send an email to the environment owners, asking them to rectify the issue.

Collaborating and innovating in the framework of governance

Appropriate governance and environment ownership has opened up a whole new wave of collaboration between departments and teams at Microsoft. The controls and assurances provided by an effective governance strategy have enabled our teams to work side-by-side in the Power Platform with confidence.

“Internally, our employees and our team, especially in Microsoft Digital, are building solutions to look at different aspects of how we can continue to improve productivity,” Hasan says. “The Power Platform offers so much freedom to create quickly and with the introduction of AI and Copilot, we can add more intelligence and use all Power Platform tools to create more robust solutions across the organization.”

Streamlining financial reporting with Power Apps

The finance team at Microsoft has been using the Power Platform to streamline their processes. By collaborating with us in Microsoft Digital, they’ve developed a series of Power Apps to automate financial reporting and budget tracking. This collaboration allowed the finance team to reduce manual data entry and improve accuracy in their reports.

We provided the necessary technical support to ensure the apps were secure and compliant with company policies. For instance, they created an app that pulls data from multiple sources, consolidates it, and generates real-time financial reports. This has significantly reduced the time spent on manual data consolidation and reporting.

Simplifying legal reviews with Power Automate

The marketing and legal teams have also found common ground on the Power Platform. They worked together to create a Power Automate flow that simplifies the approval process for marketing materials. The flow ensures that all marketing content is reviewed and approved by the legal team before publication.

This collaboration has significantly reduced the time it takes to get marketing materials approved, allowing the marketing team to be more agile and responsive. For example, the flow includes automated notifications and reminders, ensuring that the legal team reviews and approves content promptly.

Enhancing onboarding with integration and collaboration

The HR and support teams have used the Power Platform to enhance employee onboarding and support processes. By building a Power App, they created a centralized onboarding portal where new hires can access all the necessary resources and complete required tasks.

The support team integrated this app with their existing systems to provide seamless support for new employees. This collaboration has improved the onboarding experience and ensured that new hires have all the support they need from day one. The app includes features like task checklists, document uploads, and direct links to support resources.

Creating cross-functional innovation with Copilot Studio

One of the most exciting examples of collaboration is the use of Copilot Studio across various departments. Teams from finance, marketing, CELA, HR, and IT have all contributed to developing AI-infused solutions using Copilot Studio.

For instance, the marketing team created an AI-powered agent to handle customer inquiries, while the HR team developed an AI assistant to help employees with common HR-related questions. Using Copilot Studio, we’ve been able to increase discoverability and productivity by using Copilot as our “UI for AI”, bringing the power of these specialized agents to answer frequently asked questions, provide product information, and even assist with troubleshooting.

Looking forward

Governance on the Power Platform at Microsoft is poised to become even more robust and comprehensive. As the platform continues to evolve, so will our strategies for ensuring its secure and effective use.

We’re working to develop and implement more granular governance controls. Currently, our governance strategy revolves around applying rules at the environment level, using environment groups and rules to manage security and compliance. However, the future holds the promise of even more detailed control mechanisms and as customer zero, we’re working with the Power Platform PG to ensure that our learnings related to governance are reflected in the product. This includes the ability to nest environment groups, allowing for more specific governance based on criteria such as geography, data sensitivity, and regulatory requirements.

The Power Platform has proven to be a transformative tool for fostering collaboration and innovation across various departments at Microsoft. From streamlining the approval process for marketing materials to enhancing employee onboarding and creating AI-driven solutions, the platform has enabled teams to work more efficiently and effectively. Our focus on robust governance helps ensure that the Power Platform remains a secure and innovative environment for all users—and Microsoft customers.

Key takeaways

Do you want to implement effective and enabling governance in your Power Platform? Apply these best practices:

• Establish clear environment groups. Categorize environments into distinct groups such as personal productivity, team collaboration, and enterprise development. This helps apply appropriate governance controls based on the usage and risk level of each environment.
• Implement DLP policies. Use DLP policies to control which connectors and actions are allowed within different environments. This prevents unintentional sharing of sensitive information and ensures data remains within organizational boundaries.
• Use proactive and reactive governance. Employ both proactive measures, like predefined rules and policies, and reactive measures, such as custom tools and scanning mechanisms, to enforce governance. This ensures that potential risks are mitigated before they become issues.
• Use Sentinel for real-time monitoring. Integrate Sentinel with Purview audit feeds to monitor all activities within the Power Platform. This allows for real-time detection of any actions that might violate governance policies.
• Maintain an up-to-date inventory. Keep an accurate inventory of all environments, apps, and flows within the Power Platform. This is crucial for proactive governance and ensures that all necessary metadata is available for enforcing policies.
• Conduct regular security reviews. Register all enterprise development environments in a service tree and conduct biannual security, privacy, and accessibility reviews. This ensures that all solutions comply with stringent security and privacy standards.
• Enable lifecycle management. Implement lifecycle management tied to inactivity or attestation. This ensures that unused environments are deleted after a certain period, minimizing security risks.

Try it out

Discover security and governance considerations in Power Platform.

Related links

• Learn more about how we’re unlocking enterprise AI extensibility at Microsoft with Microsoft Copilot Studio.
• Discover how we’re embracing AI agents at Microsoft.
• Read about how we helped our HR team get started with the Power Platform.
• Explore admin and governance best practices for Microsoft Power Platform.
• Check out Microsoft Power Platform adoption best practices.

The post Empowering employees with the Microsoft Power Platform at Microsoft appeared first on Inside Track.

That’s exactly the promise of citizen development through low-code/no-code solutions augmented by AI. Throughout our organization, we’re empowering all kinds of employees—not just developers—to create their own business solutions and services using our citizen development toolkit, the Microsoft Power Platform.

Low-code/no-code puts development tools in the hands of people who aren’t technical developers or don’t have well-resourced software engineering teams.

—Lianne Zelsman, product manager, Power Platform governance

At Microsoft Digital, the company’s IT organization, we’re enabling citizen development internally by encouraging and enabling our employees to become citizen developers while also making sure we put guardrails in place to protect the company.

[Unpack how a revamped Microsoft business intelligence platform boosts data handling and builds trust. Discover powering decision making at Microsoft by analyzing data with Microsoft Power BI. Explore building a content management system at Microsoft with Microsoft Power Platform.]

The promise of citizen development

There are plenty of circumstances when someone needs a process, tool, or service to support their work but can’t access the formal engineering resources to create it. In those cases, it makes sense for our employees to build something for themselves. Historically, software developers or engineers would code their own solutions, while people without those skills were out of luck.

“Low-code/no-code puts development tools in the hands of people who aren’t technical developers or don’t have well-resourced software engineering teams,” says Lianne Zelsman, product manager in charge of Power Platform governance within Microsoft Digital. “For us, this means enabling our people in HR, Finance, and other teams to build solutions with the Power Platform. They can use it to do a whole range of things—from implementing automations to building their own apps—with very little ramp-up time or expertise.”

If you work in a large organization, you know that much of any employee’s day gets eaten up by mundane or menial tasks. Those are just the kinds of things that simple hand-made automations or apps can handle.

“We’ve seen a lot of Power Platform usage for project management teams that need to streamline their workflows or email communications,” says Bert Byerly, solution manager for Power Platform and Microsoft Fabric. “Even our developers who write code as their regular job are now using our low-code/no-code platform to spin things up quickly, like automations and alerts.”

More about the Microsoft Power Platform

At Microsoft, we believe in empowering our teams with tools that make their lives easier and their work more innovative. The Microsoft Power Platform is our low-code/no-code development solution that helps everyday employees turn great ideas into impactful tools. And because the technical professionals within Microsoft Digital have put the platform through its paces internally as Customer Zero, we’ve been able to add all kinds of features and functionality that better serve our customers.

“Power Platform comes with around 1,100 out-of-the-box connectors,” says Zohar Raz, group product manager for Power Platform governance. “This gives you the ability to build custom connectors that you can use to link up with any data source on the planet.”

We’re infusing Microsoft 365 Copilot into the platform so you can use AI to convert your natural language queries into solutions.

—Zohar Raz, group product manager, Power Platform governance

It also gives you access to a business layer called Dataverse that makes it easy for you to create and run thousands of solutions on top of your data layer.

And we’re also adding AI into the mix.

“We’re infusing Microsoft 365 Copilot into the platform so you can use AI to convert your natural language queries into solutions,” Raz says.

Giving your employees all this new richness and power is great, but we also recognize that you’ll want to govern and guide this usage. In response, we’ve made a lot of investments to give customers that kind of flexibility.

“The product gives organizations a lot of visibility and control and empowers them to determine which connectors and functionality to enable where,” Raz says.

As part of our overall technology stack, Power Platform plays very well with other Microsoft tools and platforms. That means organizations that use the Microsoft ecosystem benefit even more.

“For me, the greatest value comes from integration because Microsoft has such a wide-ranging product suite,” Zelsman says. “Building Power BI reports off of Power Platform assets, pulling SharePoint information into your Outlook, or automating reminders in Teams is really simple.”

Understanding the risks of low-code/no-code development

Along with the benefits, opening the development process to non-technical professionals presents certain risks.

“Enablement can be a double-edged sword,” says David Johnson, tenant and compliance architect with Microsoft Digital. “You’re empowering employees to be successful, but at the same time, you’re effectively creating applications that get used for business purposes without IT oversight, without security oversight, without privacy oversight—just an employee putting something together on their own.”

In deeply connected environments that have the power to extend company data outwards, that can be a dangerous situation if we don’t properly control it.

“The risk is mostly around data leaks,” Raz says. “This kind of technology works really well for good actors, but it also opens up opportunities for bad actors to find nuggets they can use to hurt the company.”

Addressing the risks through technology and governance policy

Two factors help us limit the risks associated with citizen development at Microsoft: the technology of Power Platform itself and the governance efforts of our Microsoft Digital team.

As a well-integrated piece of Microsoft technology, Power Platform gives IT a lot of control at the platform level to govern what people do in their individual apps.

—David Johnson, tenant and compliance architect, Microsoft Digital

“What Power Platform has done well for us is give us the control to lock things down tight,” Byerly says. “And then, as we look at different features or connectors and their interactions, we can start to loosen things up and create policies so they’re safe to use.”

That control is part of the core functionality of Power Platform.

”As a well-integrated piece of Microsoft technology, Power Platform gives IT a lot of control at the platform level to govern what people do in their individual apps,” Johnson says.

As a result, Power Platform enables a robust compliance strategy. Building and deploying that strategy has been a collaborative effort between Microsoft Digital’s governance professionals, the Power Platform product team, and the Microsoft Data-Loss Prevention team.

Our overall governance strategy breaks down into three sets of activities: Protect, measure, and enforce. Within this strategy, we divide our efforts between the macro-level, which sets policies for the overall tenant, and the micro-level, where individual groups within Microsoft can apply governance policies that complement our all-up guardrails.

“We’re forever finding the right balance between empowerment and safety,” Zelsman says. “So a lot of what we do is risk-based, essentially giving everything an internal risk rating, and that’s going to generate the scope of the compliance requirements any employee-developed solution will have to go through.”

That means we have to break our governance efforts down into tiers where we apply policies to employee-created solutions based on their risk profile and then channel them through permission reviews. For example, simple connectors associated with Microsoft Teams or SharePoint that operate in the Microsoft Personal Productivity environment need no permissions before pushing to production. On the other hand, a Dataverse connector built in the Microsoft Pro Dev environment requires an employee to request permission to access that environment or to change their environment before going live.

And of course, you can’t govern what you can’t see, so our teams have set up a thorough oversight apparatus to support these efforts. There’s a comprehensive tenant inventory, a reporting suite, cost and utilization monitoring, and compliance telemetry.

All of these governance policies aren’t meant to hinder citizen development, but help it move forward safely and quickly.

“Ultimately, good governance is employee empowerment with guardrails,” Johnson says.

Power Platform success stories

Thanks to our enablement and governance activities, low-code/no-code development is spreading rapidly across Microsoft. Recently, we crossed the threshold of 1 million Power Platform citizen development assets within the internal ecosystem at Microsoft—and that number continues to rise. All told, our employees have built more than 18,000 environments, 170,000 Power Apps, 50,000 Power Automate flows, and 1,200 chatbots.

But the numbers aren’t the whole story. The variety and creativity our employees have developed continues to increase.

We’re definitely looking to push this technology further. There are so many different data sources for us to analyze and so many workflows we can support.

—Bert Byerly, solution manager, Power Platform and Microsoft Fabric

Here are a couple of examples of important experiences our citizen developers have built internally at Microsoft:

• Cosmic: A revenue processing tool featuring data capture via optical character recognition (OCR), data validation through a business rules engine, and data entry using robotic process automation (RPA) that has yielded around $14.2 million annually in savings.
• AV design standards: A Real Estate and Facilities team app that helps configure AV equipment across 16,000 Microsoft conference rooms worldwide by simplifying the equipment ordering process.

And we’re only just beginning this journey. With so many connectors and the portfolio expanding every day, the possibilities are endless.

”We’re definitely looking to push this technology further,” Byerly says. “There are so many different data sources for us to analyze and so many workflows we can support.”

As this journey unfolds, we’ll continue to see Microsoft employees flexing their creativity and innovation through accessible citizen development with Power Platform.

Here are some tips for getting started with citizen development and the Power Platform at your company:

• Start with simple wins like automating approval flows to build up your users’ confidence.
• Establish some very secure baseline defaults to act as controls, then expand from there.
• Understand that your lines of business will adopt this technology on their own, so it’s best to guide their adoption through guided enablement.
• Take advantage of training material from Microsoft, especially the Microsoft Power Platform Center of Excellence Starter Kit.
• Put thought into your environment and tenant architecture, key personas, and scenarios before adoption.
• Identify the security needs and regulatory compliance that are specific to your organization and use built-in governance controls available for Dataverse for Teams and Personal Developer environments.
• Don’t reinvent the wheel: Use the open APIs and connectors that Microsoft already offers.

Try the Microsoft Power Platform at your company.

• Unpack how a revamped Microsoft business intelligence platform boosts data handling and builds trust.
• Discover powering decision making at Microsoft by analyzing data with Microsoft Power BI.
• Explore building a content management system at Microsoft with Microsoft Power Platform.

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