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

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

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

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

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

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

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

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

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

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

Our Frontier Firm journey so far

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

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

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

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

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

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

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

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

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

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

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Expert insights

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

Brian Fielder, vice president, Microsoft Digital

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

Stephan Kerametlian, business program management senior director, Microsoft Digital

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Chapter 1: Advancing good governance to meet the agentic moment

Maintaining privacy, security, and compliance while respecting regulatory frameworks

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

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

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

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

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

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

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

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

A matrixed approach to agent governance

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

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

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

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

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

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

The following examples illustrate two different agent scenarios:

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

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Expert insights

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

David Johnson, tenant and compliance architect, Microsoft Digital

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

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

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Balancing utility and manageability in our agent ecosystem

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

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

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

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

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

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

• The registry provides a complete view of agents. The enterprise agent store makes it easy to find the right agents for each role and business process within familiar workflows in Microsoft 365 Copilot and Teams.
• Visualization provides the observability layer, including role-specific oversight, compliance and audit features, and performance measurement that can help organizations track their agents’ impact and see where they contribute value.
• Interoperability ensures Agent 365 is open to any Microsoft-built or partner ecosystem, while also delivering work intelligence through access to data and Microsoft 365 apps.
• Security features provide crucial confidence through visibility into security posture, detection and response capabilities, and intelligent runtime defense.

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

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

Learning from our experience with agent governance

A strong data foundation is crucial

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

Decide on your comfort level with risk

Bring cross-disciplinary experts together from across your organization to determine what level of risk is acceptable for different agents and their use cases. Put guardrails in place for low-risk scenarios and establish processes for supporting more complex or sensitive use cases. Evaluate what data sources agents can extract information from. Do you have confidence that users haven’t over-shared data access?

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

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

Change is constant

Plan to reassess and revise your governance structure regularly. This technology is evolving rapidly, as is the tooling surrounding it, so maintaining good governance will be an ongoing practice.

Governance is a value driver for employees

Governance isn’t just about protecting your organization. It also provides the right patterns to make sure your employees are getting value from agentic technology. Establish strong measures of value and a robust pane for management and assessment. Observability and telemetry will be foundational, so ensure you build that into your governance efforts.

Continue non-agentic workstreams

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

Key takeaways

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

• Establish a cross-disciplinary agent center of excellence: Bring together stakeholders across the organization to define priorities, goals, and shared practices for agent adoption.
• Put strong data and information protection policies in place: Establish clear governance for your data estate, including labeling and information protection, to support responsible agent use.
• Right-size oversight based on risk: Determine your organization’s risk tolerance and define which agents require more or less involvement from IT, security, and compliance teams.
• Define a clear agent building tool strategy: Decide which tools employees and teams can use to create agents, balancing empowerment with governance.
• Operationalize agent oversight and management: Establish an oversight model and implement tools like Agent 365 that help manage agents at scale.
• Create a centralized governance and information hub: Provide employees and agent builders with a single place to find guidance, standards, and governance information.

Learn more

How we did it at Microsoft

• Explore our IT playbook for the AI era and learn how we’re becoming a Frontier Firm. This article shares our journey as an IT organization in support of this new operating model.
• Read our five-step guide for IT leaders who want to drive greater AI maturity. This resource can help you chart a course through AI maturity to reimagine what’s possible for the enterprise.
• Discover how Microsoft is becoming an AI-first Frontier Firm. This story shares how we’re approaching the idea of an “agentic future.”
• Learn from our experience tackling Microsoft 365 Copilot governance. This article explains the foundation of our governance efforts in the AI space and can serve as a starting point for agentic governance.
• Discover our six dimensions of agent value. They provide our framework for measuring the impact of AI at Microsoft.

Further guidance for you

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

Chapter 2: The Microsoft roadmap for implementing agents

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

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

Putting the Microsoft AI maturity model into practice

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

Stage 1: Awareness and foundation

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

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

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

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

Target outcomes include

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

Stage 2: Active pilot programs and skill building

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

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

Target outcomes include

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

Stage 3: Operationalize and govern

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

Target outcomes include

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

Stage 4: Enterprise-wide adoption

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

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

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

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

Target outcomes include

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

Stage 5: Transforming your business with agentic AI

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

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

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

Target outcomes include

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

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

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

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

Learning from our agent implementation experience

Invest in data infrastructure and AI platforms

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

Foster a culture of innovation and collaboration

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

Align AI initiatives with strategic business goals

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

Implement ethical practices based on our responsible AI principles

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

Position IT to facilitate the transition to a Frontier Firm

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

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

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

Key takeaways

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

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

Learn more

How we did it at Microsoft

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

Further guidance for you

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

Chapter 3: Driving adoption to capture value across the organization

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

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

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

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

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

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

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

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

Cohort-based coordination

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

Adoption communications

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

AI Agent Launchpad

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

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

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

Copilot builder champs

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

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

Integration and incentivization

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

Formalizing change management for professional developers

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

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

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

Learning from our agent adoption experience

Be thoughtful about your audience

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

Don’t just enable agents—empower the enterprise

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

Establish multiple vectors for skilling

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

In many ways, this is a reset

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

Showcase and celebrate success

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

Leadership sponsorship is absolutely crucial

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

Key takeaways

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

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

Learn more

How we did it at Microsoft

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

Further guidance for you

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

Chapter 4: Providing support at the agentic frontier

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

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

But AI is evolving too quickly for that model, and agents are too diverse and individualized for a static approach. As a result, our support apparatus for agents needs to be much more flexible. Within Microsoft Digital, our goal is to make it easy for employees to engage with agentic tools freely and adaptably while maintaining safety and responsibility.

The path to this objective relies on a three-pronged approach to governance:

• Embedded governance functionality: The ideal state is that our agent creation and publishing tools should incorporate good guidance, governance, and guardrails out of the box so the agents people create are essentially self-governing.
• IT oversight: This is a new space and a new way of working, so it isn’t feasible for all agents to self-govern at this point. As an IT organization, Microsoft Digital fills gaps in governance through reviews and oversight. We do this by establishing risk-based policies around types of agents, exposure and sharing, and other pivots we addressed in our governance chapter.
• User education: It’s almost impossible to predict every governance gap and need, so educating our users helps them avoid accidentally stepping out of bounds. Our Agents at Microsoft team and change managers are the linchpins of these efforts, and employees can lean on resources like Microsoft Learn courses and the Agent Builders SharePoint hub.

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

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

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

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

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

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

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

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

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

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

Learning from our experience with providing support around agents

Emphasize proven agents to minimize the need for support

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

Identify opportunities for AI-driven support

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

Meld adoption and support

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

Backstop support as much as possible

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

Key takeaways

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

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

Learn more

How we did it at Microsoft

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

Further guidance for you

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

Chapter 5: Tracking the impact of your agents

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

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

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

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

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

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

Our roadmap to agentic impact tracking

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

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

Our journey has three main goals:

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

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

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

Three feature sets will be especially useful for tracking value:

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

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

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

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

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

Learning from our approach to tracking agentic impact

Think proactively, not retroactively

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

Involve a wide array of stakeholders

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

Establish a continuum of value

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

Embrace the red

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

Key takeaways

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

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

Learn more

How we did it at Microsoft

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

Further guidance for you

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

Applying lessons from our agent deployment at your organization

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

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

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

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

Vijaya Alaparthi, principal group product manager, Microsoft Digital

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

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

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

Key takeaways

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

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

Learn more

How we did it at Microsoft

• Discover how we’re deploying Microsoft Agent 365 internally. This story shares our intentions for using Microsoft’s unified control pane for agents.
• Explore our IT playbook for the AI era and learn how we’re becoming a Frontier Firm. This article shares our journey as an IT organization in support of this new operating model.
• Learn how we’re building an AI-powered continuous improvement culture at Microsoft. This story outlines the work of our Continuous Improvement CoE as it identifies and tracks AI opportunities at Microsoft.
• Access our eight steps for managing your support team content with AI tools. This resource demonstrates one way AI can boost your support team’s efforts.

Further guidance for you

• Secure your agents at scale with Microsoft Agent 365 guidance for unified identity, compliance, and control across platforms.
• Learn about the Responsible AI policies and practices we use to guide our use of AI at Microsoft.
• Follow the Microsoft 365 Copilot Agents Deployment Blueprint to enable agents at scale with security, governance, and measurement strategies.
• Download the Agentic Automation Adoption Guide to learn migration strategies, governance models, and business value for agentic automation.
• Visit the AI Agents Hub on Microsoft Adoption to access webinars, quick-start guides, and business scenarios for Copilot, Foundry, and Copilot Studio.

Try it out

Get started with Microsoft Agent 365 at your company.

We’d like to hear from you

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

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

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.

We’d like to hear from you!

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

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

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

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

Qingsu Wu, principal group product manager, Microsoft Digital

But increasing scale required us to evolve our approach.

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

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

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

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

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

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

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

Evaluating AI for Microsoft

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

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

Ria Khetan, senior program manager, Microsoft Digital

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

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

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

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

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

Building transformation on core pillars

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

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

Don Campbell, principal group technical program manager, Microsoft Digital

The operating model is intentionally simple.

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

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

Our CoE uses these four pillars to guide our work:

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

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

Strategy

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

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

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

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

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

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

Architecture

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

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

Qingsu Wu, principal group product manager, Microsoft Digital

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

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

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

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

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

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

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

Roadmap

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

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

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

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

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

A key part of this approach is disciplined experimentation.

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

Culture

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

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

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

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

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

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

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

Responsible AI is embedded throughout that work.

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

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

Fostering agent innovation

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

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

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

Garima Tiwari, principal product manager, Microsoft Digital

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

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

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

Architecture quickly followed.

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

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

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

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

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

That architectural clarity changed how decisions were made.

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

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

The roadmap lens then brought structure to execution.

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

Don Campbell, principal group technical program manager, Microsoft Digital

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

Culture and enablement ran alongside that work.

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

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

The outcome wasn’t a single standardized solution.

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

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

Key takeaways

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

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

Try it out

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

Related links

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

We’d like to hear from you!

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

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

Humans can usually spot the signs of outdated information and exclude it when answering a question or addressing a work topic. But what happens when there’s no human in the loop?

At Microsoft, we’ve embraced the power and speed of agentic solutions across the enterprise. This means we’re at the forefront of developing and implementing innovative tools like the Employee Self-Service Agent, a chat-based solution that uses AI to address thousands of IT support issues and human resources (HR) queries every month—queries that used to be handled by humans. Early results from the tool show great promise for increased efficiency and time savings.

In developing tools like this agent, we were confronted with a challenge: How do we make sure all the unstructured data the tool was trained on is relevant and reliable?

Many organizations are facing this daunting task in the age of AI. Unlike structured data, which is well organized and more easily ingested by AI tools, the sprawling and unverified nature of unstructured data poses some tricky problems for agentic tool development. Tackling this challenge is often referred to as data conditioning.

Read on to see how we at Microsoft Digital—the company’s IT organization—are handling data conditioning across the company, and how you can follow our lead in your own organization.

Read our guide

Learn how to deploy an Employee Self-Service Agent based on our experience here at Microsoft.

How AI has changed the game

We already fundamentally understand that the power of AI and large language models has changed the game for many work tasks. The way employee support functions is no exception to this sweeping change.

“A tool like the Employee Self-Service Agent doesn’t know if something is true or false—it only sees information it can use and present. That’s why stale or outdated information is such a risk, unless you manage it up front.”

David Finney, director of IT Service Management, Microsoft Digital

Instead of relying on human agents to answer employee questions or resolve issues, we now have AI agents trained on vast corpora of data that can find the answer to a complex question in seconds.

But in our drive to give these tools access to everything they might need, they sometimes end up consuming information that isn’t helpful.

“A tool like the Employee Self-Service Agent doesn’t know if something is true or false—it only sees information it can use and present,” says David Finney, director of IT Service Management. “That’s why stale or outdated information is such a risk, unless you manage it up front.”

Before AI, support teams didn’t need to worry as much about the buried issues with unstructured content because a human could generally spot it or filter it out manually. After we turned these tools loose, they began reading everything, including:

• Older or hidden SharePoint content that humans would never find—but AI can
• Large knowledge base articles with buried incorrect information
• Region-specific content that’s not properly labeled

“For example, humans never saw the old, decommissioned SharePoint sites because they were automatically redirected,” says Kevin Verdeck, a senior IT service operations engineer. “But AI definitely could find them, and it surfaced ancient information that we didn’t even know was still out there.”

Data governance is the key

A major part of the solution to this problem is better governance. We had to get a handle on our data.

“We needed to determine the owners of the sites and then establish processes for reviewing content, updating it, and defining how it should be structured. I would highly encourage that our customers think about governance first when they are launching their own AI tools, because everything flows from it.”

Olivier Cherel, senior business process manager, Microsoft Digital

The first step was a massive cleanup effort, including removing decommissioned SharePoint sites and deleting references to retired programs and policies. The next step was making sure all content had ownership assigned to establish who would be maintaining it. This was followed by setting up schedules for regular content updates (lifecycle management).

Governance was the first priority for IT content, according to Olivier Cherel, a senior business process manager in Microsoft Digital.

“We had no governance in place for all the SharePoint sites, which were managed by the various IT teams,” Cherel says. “We needed to determine the owners of the sites and then establish processes for reviewing content, updating it, and defining how it should be structured. I would highly encourage that our customers think about governance first when they are launching their own AI tools, because everything flows from it.”

Content governance was also a huge challenge for other support areas, such as human resources. A coordinated approach was needed.

“HR content is vast, distributed across multiple SharePoint sites, and not everything has a clear owner,” says Shipra Gupta, an engineering PM lead in Human Resources who worked on the Employee Self-Service Agent project. “So, we collaborated with our content and People Operations teams to create a true content strategy: one source of truth, no duplication, with clear ownership and lifecycle management.”

Cherel observes that this process forces teams to think about their support content in a totally different way.

“People realize they need a new function on their team: content management,” he says. “You can’t simply rely on the knowledge found in the technicians’ heads anymore.”

Adding structure to the unstructured data

The simple truth is that part of what makes unstructured data so difficult for agentic AI tools to deal with is that it’s disorganized.

“Our HR Web content already had tagging for many policy documents, which helped us get started. But it wasn’t consistent across all content, so improved tagging became a big part of our governance effort.”

Shipra Gupta, engineering PM lead, Human Resources

AI works best with content that has as many of the following characteristics as possible:

• Document structure, including:
  • Clear headers and sections
  • Page-level summaries
  • Ordered steps and lists
  • Explicit labels for processes
  • HTML tags (which AI can see, but humans can’t)
• Structured metadata, including:
  • Region codes (e.g., US-only policies)
  • Device-specific tags
  • Secure device classification
  • Country-based hardware procurement policies and HR rules

This kind of formatting and metadata allows the AI tool to more clearly parse and sort the information, meaning its answers are going to have a much higher accuracy level (even if it might be a little slower to return them).

“A good example here is tagging,” Gupta says. “Our HR Web content already had tagging for many policy documents, which helped us get started. But it wasn’t consistent across all content, so improved tagging became a big part of our governance effort.”

Be sure that as part of your content review, you’re setting aside the time and resources to add this kind of structure to your unstructured data. The investment will pay off in the long run.

Using AI to help condition data for use

As AI tools grow more sophisticated, we’re using them to directly work on AI-related challenges. This includes using AI on the challenge of unstructured data itself.

“Right now, these efforts are primarily human-led, but we are applying AI to, for example, help write knowledge base articles,” Cherel says. “Also, we’re starting to use AI to determine where we have content gaps, and to analyze the feedback we’re getting on the tool itself. If we just rely on humans, it’s not going to scale. We need to leverage AI to stay on top of things and keep improving the tools.”

Essentially, the future of such technology is all about using AI to improve itself.

“We’re looking at building an agent to help validate content,” Finney says. “We can use it to check for outdated references, old processes, or abandoned terms that are no longer used. Essentially, we’ll have AI do a readiness check on the content that it is consuming.”

Ultimately, the better the data is conditioned, the more accurate and relevant the agent’s responses will be. And that will make the end user—the truly important human in the loop—much happier with the final outcome.

Key takeaways

We’ve highlighted some insights to keep in mind as you consider how to condition your own organization’s data for ingestion by AI tools:

• Unstructured data becomes a business risk when AI is in the loop. AI agents consume everything they can access, including outdated, hidden, or conflicting content, making data conditioning a critical prerequisite for agentic solutions.
• AI highlights content issues that were previously invisible. Decommissioned SharePoint sites, outdated policies, and region-specific content without proper labels all became visible after AI agents began scanning across systems.
• Governance is a vital part of the conditioning process. Assigning clear content ownership and establishing lifecycle management are essential steps in ensuring the content being fed to AI tools is of high quality and is well managed.
• Adding structure to data dramatically improves AI accuracy. Clear document formatting, consistent tagging, and rich metadata help AI agents return more relevant, reliable answers.
• AI will increasingly be used to condition and validate the data it consumes. Microsoft is already exploring using AI to identify content gaps, analyze feedback, and flag outdated information, creating a continuous improvement loop that can scale faster than human review alone.

Try it out

Learn how your organization can take advantage of the power of the Employee Self-Service Agent.

Related links

• Read how we’re accelerating employee services at Microsoft with the Employee Self-Service Agent.
• Learn how we keep our content fresh and well-governed with AI-powered SharePoint.
• Check out how we’re using good governance to ride the wave of agents washing over Microsoft.
• Discover how we’re becoming an AI-first Frontier Firm at Microsoft.
• See how we’re tackling Microsoft 365 Copilot governance internally at Microsoft.

We’d like to hear from you!

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

The post Conditioning our unstructured data for AI at Microsoft appeared first on Inside Track Blog.

To accomplish these huge tasks, it’s essential to cultivate a range of perspectives, expertise, and lived experiences.

Olutunde Makinde is an example of this.

“A friend once laughed at me back in college when I said I wanted to work at Microsoft, like it was impossible. But I knew I could achieve the impossible if I could just be focused. I never gave up.”

Olutunde Makinde, senior service engineer, Microsoft Digital

Makinde, a senior service engineer in Microsoft Digital, came to the company the long way around—roughly 7,000 miles away from the Redmond, Washington, headquarters, in fact. He’s originally from Lagos, Nigeria.

As a global organization, Microsoft builds teams where people with different experiences and life journeys actively influence how products, services, and internal platforms are designed. Makinde, commonly known around the office as “Tunde” (“rhymes with Sunday,” he notes), embodies that diverse approach, bringing his unique insights and experiences to critical work at the company.

“A friend once laughed at me back in college when I said I wanted to work at Microsoft, like it was impossible,” Makinde says. “But I knew I could achieve the impossible if I could just be focused. I never gave up.”

Launching an IT career in Nigeria

Makinde’s journey to Microsoft began with earning a degree in computer engineering in Lagos, after which he found work as a network engineer. He spent the next several years developing his skills through certifications and other learning opportunities.

“I did a lot of self-paced training, learning how to configure Cisco routers. Eventually I became a Cisco-certified network professional (CCNP),” Makinde says. “Around that time, I had a friend who was preparing for Windows Server 2008 certifications, and through his study materials I started learning more about Microsoft and its products.”

Makinde’s first direct encounter with Microsoft came in 2014, when the company he worked for received a contract to deploy the first Microsoft Azure cloud installation in Nigeria.  

“I spent the last day of 2014 and the first day of 2015 at the customer site, figuring out how to connect their on-premises network to Azure,” Makinde says. “It had never been done before in Nigeria, and taking up that challenge really propelled me into the world of Microsoft-specific technology.”

From there, Makinde set his sights on a career at Microsoft. He parlayed his initial exposure to cloud architecture into a focus on Azure, as well as Amazon Web Services. After spending some time in the United Kingdom, he achieved his goal when he was hired by the Microsoft Digital team in 2022. He moved to the United States in 2025.

He credits support from his family, especially his wife, with helping him achieve his dreams.

“My wife was a pillar of support through every career transition, from Nigeria to the UK to the United States,” Makinde says. “She believed in me when I faced rejections, celebrated with me when I finally got the offer, and now keeps me grounded whenever work gets intense. I couldn’t have made this journey without her.”

Making an impact from day one

Kathren Korsky, a principal technical program manager in Microsoft Digital and Makinde’s hiring manager, remembers the impression he made right away. It was clear that Makinde’s experience and technical background were major assets.

“What caught my attention was how well-prepared he was for the conversation and how well he communicated,” Korsky says. “The stories he shared about his work with Azure deployment in Nigeria really drew my interest. But I was also intrigued by how he was able to bridge technology with the business world, working with different banks across the continent to gather requirements, understand them, and build solutions.”

Upon being hired at Microsoft, he initially worked remotely from the UK on a Redmond-based device and application management team. The team was looking to deploy Cloud PC internally and needed a system in which employees could request access and get approvals to use Cloud PCs.

“He was able to stand up a full Power Automate workflow within a short period, and with a very high degree of quality,” Korsky says. “Rarely did anyone find any defects or bugs in his system.”

Makinde’s designs drove value moving forward as well, as the team made updates to his initial workflows.

“His design was so strong that we were basically able to follow exactly what he had created in Power Platform and build that exact same design in ServiceNow. It really expedited that whole process.”

Kathren Korsky, principal technical program manager, Microsoft Digital

ServiceNow was more commonly used for systems that involved access requests and approvals, but when a platform update from Power Automate was initiated the team found Makinde’s original design was durable enough to weather the shift.

“His design was so strong that we were basically able to follow exactly what he had created in Power Platform and build that exact same design in ServiceNow,” Korsky says. “It really expedited that whole process.”

Driving efficiency and managing change

Since moving to the United States to work at company headquarters, Makinde has continued to push important projects forward—working with different stakeholders to deploy policy changes across Microsoft, managing the Change Advisory Board (CAB) intake process, and driving configuration updates for security and first-party product deployments.

“There’s a lot of diligence required to see the edge cases happening, to pay attention to them, and to watch out for potential problems. Tunde stops rollouts regularly to flag potential defects or risks, which prevents issues from interrupting our work and reducing productivity.”

Jeff Duncan, principal service engineering manager, Microsoft Digital

Makinde learned how to assess change requests and understand risk profiles, as well as enforce best practices for managing change within the security environment. Within about a year, he was able to take the lead in the space and own the deployment process.

A single misconfigured policy can cause major disruption. Makinde’s role puts him in position to be the checkpoint that prevents incidents before they happen.

“There’s a lot of diligence required to see the edge cases happening, to pay attention to them, and to watch out for potential problems,” says Jeff Duncan, principal service engineering manager in Microsoft Digital and Makinde’s manager. “Tunde stops rollouts regularly to flag potential defects or risks, which prevents issues from interrupting our work and reducing productivity.”

Softer skills like transparency, collaboration, and clear communication across levels and teams are key aspects of Makinde’s work as well.

“Tunde is thoughtful and detail-oriented, and he’s very good at explaining the decision-making process when he provides overviews for leadership,” Duncan says. “There’s rational, logical reasoning behind the decisions he makes.”

Makinde has implemented new efficiencies in how he manages the CAB and deployment service using AI. This includes CABBIE—an AI-powered agent that automates CAB communications. For Intune deployments, he uses AI to streamline deployment coordination and package reviews. These innovations reflect our Customer Zero approach to AI adoption here in Microsoft Digital.

“We run weekly CAB meetings to review change requests. That comes with a lot of communication work — status updates, follow-ups, coordination with stakeholders. It was all manual,” Makinde says. “CABBIE pulls the data from Azure DevOps, generates the emails, updates requests, and logs approvals automatically. It saves time and reduces errors.”

Success at Microsoft Digital: Aptitude and curiosity

As the organization at the center of the company’s own digital transformation, we in Microsoft Digital function as a living showcase of what’s possible with Microsoft technology. Our team tests new capabilities at enterprise scale as Customer Zero for Microsoft, identifying gaps and providing insights to ensure our customers benefit from what we’ve learned.

Because the impact of Microsoft Digital extends far beyond internal systems, team members have to set the standard for digital excellence. They must demonstrate what enterprise transformation looks like in practice and empower customers with the confidence to pursue their own modernization journeys.

 Hiring talented people like Makinde is essential to this mission.

“There are three core traits I look for when hiring—aptitude, attitude, and curiosity,” Korsky says. “Aptitude is not only what you currently know, but your propensity and desire to learn and grow those skills. Attitude goes hand in hand with that—are you willing to demonstrate grit and perseverance? And then curiosity, because so much of what we do from an innovation perspective requires a willingness to challenge assumptions and think of completely new ways of doing things.”

Makinde’s journey here at Microsoft Digital embodies and illustrates the company’s larger story: how technical expertise, innovative thinking, and a commitment to continuous learning combine to deliver world-class results.

“I’m now up to 25 certifications, and I continue to learn how to do more at Microsoft to positively impact the organization and protect our employees’ experience across applications and devices.”

Olutunde Makinde, senior service engineer, Microsoft Digital

That attitude of persistent curiosity and the willingness to keep learning continue to fuel Makinde’s experience at Microsoft. 

“Self-improvement is a way of life for me that has driven my career forward,” Makinde says. “At an early stage in my career, I did a lot of self-training—from learning how to configure Cisco routers and switches, to migrating on-premises workloads to Azure and managing cloud resources. I’m now up to 25 certifications, and I continue to learn how to do more at Microsoft to positively impact the organization and protect our employees’ experience across applications and devices.”

Key takeaways

Olutunde Makinde’s career experience here in Microsoft Digital offers some important insights that you can apply to your own organizational development:

• AI adoption starts with practical problems. Makinde’s use of AI to streamline CAB communications and deployment coordination shows how Customer Zero teams find real-world applications for emerging technology.
• Different experiences and perspectives contribute to business success. Achieving ambitious goals as an organization is dependent upon attracting talented people like Makinde from a range of backgrounds, disciplines, and lived experiences.
• Strong technical skills paired with innovative thinking drives value. Makinde’s contributions to flexible cloud deployment workflows are an example of how this combination pays dividends.
• Proactive risk management and attention to detail can prevent large-scale disruptions. By being willing to stop rollouts and flag risks before they become problems, Makinde’s approach to his work exemplifies how thoughtful decision-making safeguards productivity and security.
• Persistence, curiosity, and continuous learning are critical career accelerators. Having a long and successful career at a company like Microsoft goes beyond just technical aptitude; it also requires perseverance and a passion for learning. Makinde’s self-driven training efforts and his refusal to give up have enabled him to achieve what once seemed impossible.

Try it out

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

Related links

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

We’d like to hear from you!

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

The post Olutunde Makinde: From Lagos to Redmond, a Microsoft IT engineer’s journey appeared first on Inside Track Blog.

• The case for AI in employee assistance
• Chapter 1: Governance means getting your data right
• Chapter 2: Implementation with intention
• Chapter 3: Driving adoption by breaking old habits
• Begin your journey with the Employee Self-Service Agent

The case for AI in employee assistance

The advent of generative AI tools and agents has been a game changer for the modern workplace at Microsoft. And one of the foremost examples of how we’re reaping the benefits of this agentic revolution is our deployment of our new Employee Self-Service Agent across the company.

Thanks to the power of AI, agents, and Microsoft 365 Copilot, our employees—and workers everywhere—are discovering new ways to be more productive at their jobs every day. Recent research shows that knowledge workers are increasingly seeing big gains from using AI tools for work tasks. According to our Microsoft Work Trend Index:

As an AI-first Frontier Firm, Microsoft is at the leading edge of a transformation that’s bringing this technology into all aspects of our workplace operations. With tools like Microsoft 365 Copilot providing “intelligence on tap,” we’re forging a human-led, AI-operated work culture that enables our employees to accomplish more than ever before.

Bringing AI to employee assistance

As part of this move to embed AI across our enterprise, it was a natural step for us to apply this burgeoning technology to a common pain point for us and many workplaces today—employee assistance.

Workers in organizations large and small face many common issues in their day-to-day jobs. Whether it’s a problem with their device, a question about their benefits, or a facilities request, our typical employee was often forced to navigate a bewildering array of tools, apps, and systems in order to get help with each specific task.

This confusion is reflected in research showing that most workers are dissatisfied with existing employee-service solutions.

Our studies show that most employees have trouble finding the appropriate tools and resources they need to address their workplace-related questions.

Realizing that this was an ideal opportunity for AI, we set out to develop a state-of-the-art agentic solution. At Microsoft Digital, the company’s IT organization, we partnered with our product groups to develop and deploy the Employee Self-Service Agent, a “single pane of glass” that employees can turn to any time they need help. The product is now broadly available in general release.

“With this employee self-service solution, we’re shaping a new era in worker support. With AI, every interaction is intuitive, every resource is within reach, and help feels seamless—creating an experience that empowers our people and accelerates business outcomes.”

Nathalie D’Hers, corporate vice president, Microsoft Employee Experience

Because Copilot is our “UI for AI,” the Employee Self-Service Agent is delivered as an agent in Microsoft 365 Copilot. If your employees have access to Copilot, you can deploy the agent at your company at no extra cost. If your employees don’t have a Copilot license, they can access it via Copilot Chat if it’s enabled by your IT administrator.

For the initial development and launch of our Employee Self-Service Agent, we decided to provide agentic help in three categories: Human resources, IT support, and campus services (real estate and facilities). Every organization will have to make its own determination for which functions to include in their implementation. Note that the agent is inherently flexible and expandable; we plan to add additional capabilities, such as finance and legal, in the future.

We learned many lessons in the almost year-long process of developing and implementing the Employee Self-Service Agent across our organization worldwide. The goal of this guide is to pass on what we learned—including how we used it to provide value to our employees and vendors—to help you prepare for, implement, and drive adoption of your own version of the agent.  

“With this employee self-service solution, we’re shaping a new era in worker support,” says Nathalie D’Hers, corporate vice president of Microsoft Employee Experience. “With AI, every interaction is intuitive, every resource is within reach, and help feels seamless—creating an experience that empowers our people and accelerates business outcomes.”

Before you start: Developing your plan

As you embark on your Employee Self-Service Agent journey, make sure to establish a clear and structured plan. This was a critical step for us in our deployment, and we can say with confidence that it will help you avoid surprises and increase your chances of a successful outcome.

Based on our experience here at Microsoft, the below is a high-level outline of the steps you should consider as you prepare for deploying your agent.

1. Define prerequisites
Start by making sure that all foundational elements for the agent are in place.

• Assign licenses to your employees who will interact with the agent. They will need Microsoft 365 Copilot or Copilot Chat.
• Verify readiness by configuring your Power Platform environments, applying Data Loss Prevention (DLP) policies, and setting up isolation (limited and controlled deployment with guardrails in place) where needed.
• Ensure connectivity with critical systems by confirming that you have appropriate APIs and connectors available and functioning for the essential workplace systems that your organization uses (e.g., Workday, SAP SuccessFactors, and ServiceNow).

2. Identify your core team and responsibilities
Successful implementation of the Employee Self-Service Agent requires collaboration across multiple roles and departments in your organization.

• Business owners from the areas your agent will cover—such as human resources and IT support—can help you define requirements, priorities, success criteria, and telemetry needs.
• Platform administrators, particularly for Power Platform and tenant/identity teams, can manage your technical configuration.
• Content owners and editors are needed to identify the knowledge sources to surface in the agent, curate new knowledge sources, and maintain the data underpinning these sources on an ongoing basis.
• Subject matter experts can provide important “golden” prompt and user scenarios that the agent should prioritize and answer accurately.
• Compliance, privacy, and security leaders and their teams are needed to address risk considerations.
• Support professionals can help build a structure for live agent escalation and ticketing operations (in situations where the agent is unable to provide a solution).
• Focus groups of end users assist with validating requirements and scenarios, as well as help with testing the agent.

3. Establish a clear timeline
We found that creating a schedule for the creation, implementation, and adoption of the agent is crucial. This phased approach will help you maintain momentum and accountability over the duration of the project.

For example, here’s a rough implementation timeline that you might use to gauge your progress:

4. Articulate your vision

Communicate your rollout plan to your team, including timelines and phases, and adjust it based on feedback. Establish clear goals and meaningful success metrics to guide you and make sure your efforts are in alignment with your company objectives. (Note: You may want to consider key upcoming projects or events in your organization and link the agent roadmap to them. This will help you meet your project’s success criteria faster and encourage quicker agent adoption.)

5. Define your governance

This phase will allow you to define policies and standards and conduct a thorough content audit to ensure accuracy, relevance, security, and sustainability.

6. Implement your agent

This phase involves configuration and integration, followed by testing.

7. Roll out the agent while driving adoption and measurement

We advise deploying the Employee Self-Service Agent using a phased, or ringed, approach. We started with a small group of employees, then gradually rolled it out to larger and larger groups  before finally releasing it to our entire organization.

We encouraged adoption with internal targeted communications and promotional efforts. Careful measurement enabled us to track impact and optimize agent performance. This type of concerted change management allowed us to share the latest product developments with our employees and to keep them excited and engaged with the tool.

By investing sufficient time and effort in the planning phase of your deployment, you’ll create a strong foundation for a secure, scalable, and successful self-service agent experience.

Chapter 1: Governance means getting your data right

When a Microsoft employee enters a query into an AI chat tool like Microsoft 365 Copilot, they know that they may not receive an individualized response that is directly specific to their situation. They are aware that they might need to verify the answer they receive with further research and additional sources.

But when it comes to our company-endorsed self-service agent, the stakes are different. Our employees expect to receive accurate and personally relevant responses when they ask for help. This is particularly true for queries related to important personal details, like HR-related questions about leave policies or benefits.

“People expect personally tailored and highly accurate answers, especially for HR moments that really matter. We designed the Employee Self‑Service Agent with that expectation in mind, pairing trusted data and deep personalization with strong governance controls so that privacy, security, and trust are built into every interaction.”

Prerna Ajmera, general manager, HR digital strategy and innovation

Although the Employee Self-Service Agent comes pretrained with basic HR and IT support data, we found that the quality of the responses that our employees receive is directly connected to the accuracy, currency, and depth of the information we provide to the tool. You’ll want to spend the necessary time and effort to make sure that your data governance process is well thought-out and thorough, so that your employees experience the best possible results.

“Employee self‑service has a higher bar than generic AI tools,” says Prerna Ajmera, general manager of HR strategy and innovation. “People expect personally tailored and highly accurate answers, especially for HR moments that really matter. We designed the Employee Self‑Service Agent with that expectation in mind, pairing trusted data and deep personalization with strong governance controls so that privacy, security, and trust are built into every interaction.”

Major considerations for governance

We learned that before you configure your agent, you need to establish guardrails that protect your data’s integrity and that build your employees’ trust. These considerations will form the backbone of your governance framework:

• Managing requirements: Define what the agent must deliver and align your stakeholders on clear, prioritized goals and objectives.
• Determining and managing resources: Ensure you have the right people, systems, and funding in place to support your full product lifecycle.
• Data security: Protect your sensitive employee information with strong controls, compliant storage, and least‑privilege access.
• User access: Establish who can use, administer, and update your agent, with appropriate permissions and guardrails.
• Change tracking: Monitor your updates to content, configurations, and workflows so your agent always reflects your current policies.
• Reviewing: Regularly evaluate your content’s accuracy, the agent’s performance, and your organizational fitness to help you keep your employees’ experience with the agent trustworthy.
• Auditing: Maintain traceability for compliance, incident investigation, and quality assurance across all of your data flows.
• Deployment control: Manage where, when, and how you roll out new versions of the agent to reduce disruption and ensure consistency.
• Rollback: Prepare a fast, safe path to reverting your changes if something breaks.

We found that addressing these considerations early in the process creates a governance structure that is proactive rather than reactive, increasing the quality of responses and setting your organization up for success.

Architecture essentials

Understanding the architecture of our agent helped our governance teams make informed decisions about our configuration and integration. To do that, they needed to review and understand its key architectural components. You’ll need to do the same.

Here’s a list of the different architecture components that our team assessed, to help you get started on your own process:   

• Topics: Structured intents (e.g., “view paystub”) that align to employee questions and drive consistent answers.
• Domain packages: Pre-curated bundles for different agent segments (like HR and IT support) that provide reusable patterns, prompts, and integrations.
• Knowledge sources: Documents, intranet pages, FAQs, and databases that ground responses in authoritative content.
• Connectors: Secure integrations to systems of record (like Workday or SAP SuccessFactors) can help enable read/write operations. (Because the Employee Self-Service Agent was built with Copilot Studio, it has access to more than 1,400 different connectors.)
• Instructions: Governance-approved rules and prompts that shape tone, guardrails, and escalation behavior.

Assessing and preparing your content

A key early governance step is to audit all relevant content in your knowledge bases. This process should include assessing, updating, and, if necessary, restructuring this information before it is ingested by the agent.

An important caveat here is that the agent’s ability to understand which policies and procedures apply to which employee relies on your content having consistent metadata, permissions, and content structure. We found that before feeding your data into the agent, you need to:

• Inventory existing content: Your content will incorporate many different types, such as SharePoint pages, Microsoft Teams posts, PDFs, intranet articles, and knowledge-base documents. The goal of the inventory process is to identify content that is complete rather than outdated, duplicative, or siloed; if there are issues with the content, they should be addressed before loading into the agent.
• Assign knowledge owners: The owners should be SMEs who can help validate, tag, and maintain the content going forward. Part of this process is training up knowledge owners to be able to prepare and maintain content in ways that make it easily consumable by both agents and people.
• Structure content for discoverability: All your content needs to have accurate metadata, well-defined topic pages, and consistent naming so that the agent can surface the right information at the right time.

We found that completing a thorough content audit helps us ensure that the Employee Self-Service Agent isn’t just chatting—it’s delivering trusted, up-to-date answers that save your workers time and effort as they go about their day.

Be aware of tone and conversational flow

Providing vetted and well-structured data to the agent is important, but it’s not the entire battle. You’ll also need to make sure your agent is given clear guidance on conversational tone and instructions on what to do in specific scenarios.

Make sure you incorporate:

• Global instructions: Define the agent’s voice, behavior, and escalation rules to ensure consistency and trust. 
• Topic-level triggers: Map natural language phrases to specific workflows (such as “reset password” or “check PTO”) so the agent routes these common queries correctly.
• Advanced knowledge rules: Prioritize which data sources to use in ambiguous scenarios, and define when the agent should ask clarifying questions.

Taking these steps gave our agent a better chance of being accurate, helpful, and aligned with our organization’s specific preferences.

Addressing common scenarios with “golden” content

Another vital aspect of your content audit is identifying the most frequently accessed information in each topic area.

A good example comes from the preparation of our IT support content for ingestion by the Employee Self-Service Agent. One of the focuses of this effort was on so-called “golden prompts:” the 20 or so topics that generate up to 80 percent of our employee queries (a version of the famous “80/20 rule”).

Our golden prompts are a curated set of scenarios that:

• Represent our critical user workflows and edge cases
• Possess clear, expected responses (golden responses)
• Cover core functionality that must never break

We made sure that the agent was providing high-quality responses for these common scenarios—we recommend you do the same.

Including “zero prompt” content

Another important aspect of your content process should be to develop “zero prompts.” These are preconfigured prompts in the agent that the user can simply click on to get an answer for a common issue or request.

For example, if one of your employees wants to understand how to set up a VPN, they simply click on the zero prompt provided for that topic. The tool then gives them complete instructions on how to set one up.

During our deployment of the agent, one case where we prepopulated the tool with content for a specific, high-demand scenario came when Microsoft made a major announcement regarding employees returning to the office. We knew this policy change would generate a lot of questions from our employees.

In preparation for this, we asked Microsoft 365 Copilot to create a single document that pulled in all the “return to office” material found in its verified HR content database. We then made this document available to the agent. Just by taking that simple step, we saw our user satisfaction ratings in the tool jump from 85 percent to 98 percent for that issue!

In your own deployment, think about what issues and topics generate the most questions from your employees. You can then prepare specific content to address these scenarios, which will increase your chances of success with the agent.

Data security and compliance

Data security was a high priority when we developed our agent, especially because it must necessarily access sensitive HR information on a regular basis. During product development, we made sure that the agent adhered to enterprise-grade security standards, including identity federation, least-privilege access, and encrypted storage.

Because the agent is built on Copilot Studio, it supports robust data-loss prevention features. The agent also complies with regulatory frameworks like General Data Protection Regulation through built-in auditing and data-retention policies.

One of the big advantages that an AI agent has over a static website or similar data source is the ability to personalize responses for each user. At the same time, we had to make sure that the agent had guardrails in place to avoid overexposing sensitive information. This included detailed disclaimers to help call out these kinds of responses and flag them for more careful handling.

Our agent complies fully with our accessibility standards as well. Like all Microsoft products and services, the tool underwent a rigorous review to ensure it was fully accessible for all users.

Responsible AI

Whenever a new AI application is launched, there may be concerns raised about potential challenges regarding bias, safety, and transparency. That’s why the Employee Self-Service Agent follows the Microsoft Responsible AI principles by default.

When you enable the sensitivity topic in your agent, it screens all responses for harassment, abuse, discrimination, unethical behavior, and other sensitive areas. We tested the agent thoroughly for objectionable responses before it was launched to a broad internal audience at Microsoft.

In addition, the agent includes an emotional intelligence (EQ) option. This feature is designed to make responses more empathetic, context-aware, and relevant for diverse user audiences. It analyzes the conversation’s context and tailors the agent’s replies to ensure that users feel understood and valued throughout their session (which could be particularly relevant for any conversations related to sensitive HR topics, such as family leave). The EQ option is customizable and can be turned off by your product admins.

Key takeaways

The following are important considerations for data governance when you deploy your Employee Self-Service Agent:

• Employee expectations regarding accuracy and relevance are high for employee self-service tools, which makes data governance a key aspect of your deployment.
• Consider which data repositories are best to incorporate into your agent, and make sure they are up-to-date and well-structured. This process requires a thorough content audit.
• Pay special attention to the so-called “golden prompts” that make up a large percentage of expected queries. The agent’s answers to these questions should be top-notch.
• Restructuring content can improve response quality. When we anticipated huge interest in a particular topic, such as workplace policy changes, we restructured our content on that subject and saw a significant jump in user satisfaction.
• Build your agent to meet or exceed high standards for data security, privacy, and Responsible AI. These are vital concerns for any product that has access to sensitive personal information.

Learn more

How we did it at Microsoft

• Learn how we’re getting the most out of agents internally here at Microsoft with good governance. This story shares what our Microsoft Digital governance team learned while deploying AI-driven tools here at Microsoft.   
• Find out how we’re unlocking enterprise AI extensibility internally with Microsoft Copilot Studio. This post explains how we’re empowering our employees to create and extend Copilot-based agents using Copilot Studio.
• Read about how we’re infusing responsible AI principles into all our AI tools and projects at Microsoft. An article that explores the Microsoft Responsible AI Standard and how it informs our work with AI technologies at Microsoft Digital.

Further guidance for you

• Explore Microsoft Learn documentation for Microsoft Copilot Studio to read about key concepts in governance and security.
• Examine Microsoft Learn content related to security and data protection in Power Platform.
• Conduct the Microsoft 365 Copilot Optimization Assessment to understand your level of AI adoption and chart your course.
• Access Microsoft Learn documentation for Microsoft 365 Copilot to learn about data, privacy, security, and Zero Trust principles.

Chapter 2: Implementation with intention

Deploying a powerful and versatile tool like the Employee Self-Service Agent is no simple task. It requires guidance and buy-in from top leaders at the company, as well as detailed planning and execution across disparate parts of your organization. Here, we identify some of the key steps that we took here at Microsoft that can help guide you when launching your own self-service agent.

Determine category parameters

One of the first major decisions around implementing the agent is deciding which business function—we call them agent starters—to choose for your initial implementation.

We recommend starting with HR support or IT help (we started with HR). Both agent starters can be deployed into a single Employee Self-Service Agent experience, but they must be deployed one at a time. 

So you know, we’ve built the Employee Self-Service Agent to be connectable with other first- or third-party Copilot agents, enabling a seamless handoff to these agents without having to navigate to other tools or interfaces.

Understanding your deployment steps

There were four essential stages involved in the deployment of our agent, each with multiple steps. Here’s a quick rundown that you can use at your company:

1. Preparation for deployment
   • Establish roles: Define who will manage, configure, and support the tool, assigning responsibilities to ensure accountability during deployment.
   • Set up your environment: Prepare the necessary hardware, operating system, and network configurations so the agent can run smoothly.
   • Set up third-party system integration: Ensure your infrastructure can securely connect and exchange data with external systems that the agent will need to integrate with.
2. Installation
   • Install the agent: Deploy the core Employee Self-Service Agent software on the designated servers or endpoints.
   • Install accelerator packages: Add any desired connectors that enable the agent to communicate with commonly used systems for HR, payroll, IT support, etc.
3. Customization
   • Configure the core agent: Adjust default settings to align with your organization’s policies and workflows.
   • Identify knowledge sources: Specify where the agent will pull information from, such as internal knowledge bases or FAQs.
   • Provide common questions and responses: Add employee FAQs to improve the agent’s ability to respond quickly and accurately.
   • Identify sensitive queries: Flag questions and responses that involve confidential or regulated information to ensure they’ll be handled securely.
4. Publication
   • Approve the agent: Complete internal reviews and compliance checks to confirm the agent meets your organizational standards before full rollout.
   • Publish the agent: Make the configured agent available to your employees in your production environment.

Customization

The Employee Self-Service Agent operates as a custom agent within Copilot Studio, using our AI infrastructure via the Power Platform. The agent is constructed on a modular architecture that allows you to integrate it with your own enterprise data sources using APIs, prebuilt and custom connectors, and secure authentication mechanisms.

To streamline this integration process, we provide a library of prebuilt and custom connectors through both Copilot Studio and Power Platform. Preconfigured scenarios include connecting to major enterprise service providers such as Workday, SAP SuccessFactors, and ServiceNow. (View the full list of connectors offered by Copilot Studio.)

These connectors facilitate data exchange with the following systems and other agents in this ecosystem:

• HR information systems
• IT systems management
• Identity management
• Knowledge base platforms

We found that third-party integrations require setup effort and technical expertise across stakeholders in your tenant. Be sure to get buy-in and involve all relevant departments that will be impacted.

Rollout: A phased approach

As previously noted, we started our agent with HR content and then added IT support (we later expanded to include campus services help as well). We rolled the agent out to different groups of employees and geographic regions around the world over the course of months, adding new knowledge sources to the different categories at each step along the way. This gave us an opportunity to gather user data and refine performance of the tool as we went.

Adding campus support services required us to handle queries and tasks related to dining, transportation, facilities, and similar subjects. This was a challenging addition, because the facilities and real estate space—unlike the HR and IT support areas—doesn’t have many large service providers, which are easier to provide prebuilt connectors for.

One area that did lend itself to prebuilt connectors, however, was facilities ticketing.

Because many of our campus facilities vendors use Microsoft Dynamics 365, we were able to create an out-of-the-box connector in the agent for their ticketing process. You can take advantage of these kinds of preconfigured tools in your deployment.  

Key takeaways

Here are some things to remember when implementing the Employee Self-Service Agent at your organization:

• Decide which starter agent you will deploy first. We recommend starting with a single agent covering one area (vertical), such as HR or IT support, and then expanding from there.
• Consider a phased rollout to allow time to refine responses and ramp up the number of topic areas and knowledge sources installed in your agent.
• Use the prebuilt connectors to make it easier to integrate the agent with your existing systems.We developed customized connectors for major HR and IT service providers and a Microsoft 365 Dynamics connector to integrate with our many facilities vendors around the world.

Learn more

How we did it at Microsoft

• Learn how we’re embracing this “agentic” moment at Microsoft. This post describes how we’re integrating agents into many of our workflows and processes at the company.
• Get our step-by-step guide to how we’re unleashing API-powered agents at Microsoft. This article describes what we’ve learned from deploying agents internally at the company.

Further guidance for you

• Learn how to prepare for a successful Microsoft Employee Self-Service Agent implementation. This Microsoft 365 Copilot Blog story includes a webinar video on the same topic.
• Discover a step-by-step guide to getting your organization prepared for the Employee Self-Service Agent.
• Check out our Microsoft Learn content about deploying the Employee Self-Service Agent.
• Learn more about the customizable accelerator packs that come with the Employee Self-Service Agent.
• Read our Microsoft Learn content about installing the agent.
• Explore Microsoft Learn content about customizing your agent.
• Dive into detailed information about integrating the agent with Workday, ServiceNow, and SAP SuccessFactors.
• Work through this learning path to discover how you can create agents with Copilot Studio.

Chapter 3: Driving adoption by breaking old habits

Once upon a time, when our employees needed help with a technical issue or an HR question, they literally picked up the phone and called the relevant internal phone number. That quickly evolved into an email-centered system, where employee questions were sent to a centralized inbox that would then generate a service request. Still later, chat-based help was introduced.

Using AI to handle employee questions and service requests is a natural step in this evolution, as large-language models were built to parse vast data repositories and return the right information (often with the help of multi-turn queries and responses). And by encouraging self-service, an AI agent can help meet employee needs faster while saving the organization’s staffing resources for other needs.

But getting employees to change their habits and use a tool like the Employee Self-Service Agent wasn’t going to be as easy as just flipping a switch. Here’s how we handled this important change management task at Microsoft.

Adoption across verticals

A key principle that we learned during the adoption process was that 80% of our change management activities for the agent are applicable to all our verticals (whether it be HR, IT support, campus facilities, or another category). We didn’t need to reinvent the wheel each time we added to the topics that the agent covered.

This allowed us to create a change management “playbook” that we could use each time we expanded to a new category. So, while roughly 20% of the strategies we used were specific to that vertical, the vast majority were the same, which saved time as we moved through onboarding the different categories.

Leadership is key

To get our employees to change the way they ask for help, we found it essential to get the support of our key leaders, something we refer to as “sponsorship.”

We found that good sponsorship doesn’t just come from your central product, communications, or marketing groups. It is equally vital to invest in relationships with local leadership in different regions as you roll out the agent (especially in multinational companies like ours).

Local leaders understand the various regional intricacies—including language, functionality, and the rhythm of the business—that can help inspire their segments of the workforce to adopt a new tool, and then evangelize it to others in turn. Working closely with these kinds of sponsors will help you pull off a successful adoption campaign.

If you have works councils, be sure to seek out your representatives and solicit their feedback on your agent experience early on. You can help them understand how the agent was developed and trained, then address any concerns they raise.

We’ve found that once our works councils are made aware of the careful processes we go through to protect user privacy, and to ensure compliance with our Responsible AI standards, they become enthusiastic supporters and can help promote agent adoption. (Read more about our experience with our works councils and the Microsoft 365 Copilot rollout.)

Defining your messaging

Work with your internal communications team to come up with a well-planned messaging framework for your agent rollout. Based on our experience, it’s likely you’ll need to communicate across a wide variety of teams and organizations like HR, IT, facilities, finance, and so on.

It’s important to be clear about how you’re positioning the product for your employees. This will allow you to develop both overall messaging for general use, but also content tailored to specific teams or employee roles. The more sophisticated your messaging, the more likely it is to be effective in encouraging user adoption of the agent in their regular workflow.

Listening to feedback

As Customer Zero for the company, our employees are our best testers and sources of feedback during our product development process. The Employee Self-Service Agent was no different, and we continue to gather crucial feedback and user data throughout the internal adoption process.

Because the agent is a tool centered on helping your workers resolve challenges and get quick answers to questions, you’ll want to set up your own systems for capturing their feedback and make sure the agent is meeting a high-quality bar.

We found that setting yourself up for success when it comes to listening to your employees involves two major aspects: Developing and deploying a system for gathering employee sentiment about the product, and then creating a system for analyzing that feedback and funneling the findings back to your IT team.

Some of the types of feedback and methods we used to gather it during the development process included:

• User-testing data
• User satisfaction ratings
• User surveys, interviews and other research
• Voice of the customer (in-product feedback)
• Pilot projects and focus groups (smaller segments of users)
• IT support incidents
• Usage data and telemetry
• Community-based early adopter feedback (similar to our Copilot Champs community)
• Social media feedback and comments

You can choose from among these options to set up your own feedback mechanisms, or come up with something customized to your implementation.

Calibrating your usage goals

Remember that the Employee Self-Service Agent is not an all-purpose AI tool like Microsoft 365 Copilot, which your employees might use a dozen times a day. Instead, they may only need assistance from HR or IT support, tools, and information sources a few times a week (or even less). Your usage targets should be calibrated accordingly.

At the same time, the more categories of assistance you add to the agent, the more your usage levels can grow—along with user expectations.

When we decided to add campus support (dining, transportation, and facilities-related needs and queries), one of the motivators was to provide information that users might need on a more regular basis. This addition helped us increase adoption and build daily usage habits for the agent among our employees.

Making the agent your front door for employee assistance

Your employees may have longstanding habits around the ways that they seek assistance, such as moving quickly to email a service request, or immediately engaging a live support technician. There might even be someone helpful in the office next to them that they lean on for IT support. We’re aware that breaking such habits can be a challenge.

That’s why we decided to change our own employee-assistance workflows. In the case of HR, we are planning to remove the option to email a centralized alias for help, which was the default in the past. This forcing function will instead prompt our employees to turn to the agent first for assistance, creating a “front door” for all our HR service requests.

For our IT support function, we are switching from a Virtual Agent chatbot to the Employee Self-Service Agent, which should provide users with a richer experience and a higher rate of resolution.

Of course, our main goal is for the agent to handle an employee’s issue without having to seek further assistance. But what happens when the agent cannot resolve their problem or handle their request? That’s why we’ve also implemented a “smooth handoff”—either to create a service request or connect the user to a live agent for specialized assistance.

There are three key steps in this process:

1. The Employee Self-Service Agent can identify when the user has reached a point where they need to move to a higher level of assistance via a live agent or a service request. (Note that we also allow the employee to make that determination for themselves.)
2. We then give them different options for how they want to connect to live support.
3. When the employee is transferred to a live technician, the Employee Self-Service Agent is able to pass on the chat history from its session with the user. That way, the technician or staff support can quickly get up to speed on the situation, see what the employee has already asked about and tried, and start helping them immediately.

Enabling the employee to quickly and smoothly transition to a higher level of support without leaving the chat increases user satisfaction and makes them more likely to return to the agent the next time they need assistance.

Strategic outreach to employees

Of course your workers, like ours, are busy with their day-to-day job functions. They may be resistant to trying a new tool or going through special training on how to access employee assistance. Or they may just not know about it.

Because of our regionally phased rollout of the agent, email was one of the most effective tools we used to connect with specific audiences and make them aware of the tool. With specific email lists, we could make sure that only employees in that phase of the rollout were seeing the message.

A key aspect of getting our employees to adopt any new tool is reinforcement—the process of sustaining behavior change by providing ongoing incentives, recognition, and support. Some of the reinforcement strategies we used for the agent included:

• Targeted communications: Emails and organizational messages invited employees to try the agent as they received access
• Multi-channel campaigns: Promotion of the agent via portals, newsletters, digital signage, and more to keep it at the forefront of employee minds
• Training: Workshops and micro-learning sessions about the agent
• Social campaigns: Posts highlighting the tool to increase awareness and gather employee feedback (see details below)
• Leadership support: Managers modeled usage of the agent and promoted it regularly
• Processes: The tool was part of regular employee workflows

One very important communications channel that we used in our adoption efforts was Microsoft Viva Engage. We set up a private Engage community for the Employee Self-Service Agent, then populated it with each new wave of users as they were given access to the tool (eventually all were given access when the tool went companywide).

We used this channel for various kinds of messaging:

• General product awareness
• Updates on new or changing functionality
• Answering questions or addressing frustrations (two-way dialogue between users and the product team)
• Fun and helpful “tips and tricks” that users could try (these could come from the product team, leadership, or individual product “champions”)

We also inserted messages about the new agent into our regular communications with different audiences, including HR professionals, IT support personnel, and internal comms staff at the company. And we regularly messaged company leaders about it, so they could encourage their teams and direct reports to support the effort and evangelize for the tool.

One thing we did was make clear to our employees that even though the agent was not able to handle an issue today, it might be able to in a month or two. That’s why ongoing communications to users was important.”

Prerna Ajmera, general manager, HR digital strategy and innovation

Of course, as a natural language chat tool, the Employee Self-Service Agent doesn’t require formalized training. The product itself is designed to guide users and allow them to experiment, simply by stating their needs in plain language. Most employees will already be familiar with AI tools like Microsoft 365 Copilot, so effectively using an AI-powered employee-assistance agent should be a low bar to clear.

Managing expectations

Your Employee Self-Service Agent rollout will be an ongoing journey as you add topic areas, functionalities, and other product features. Your product roadmap will evolve as you learn more about what your employees need with this kind of AI solution.

One factor to consider is how to set realistic user expectations about what the agent can do while the product matures and improves. As we gradually rolled out the tool, we messaged that the agent was in “early preview,” which helped avoid employee disappointment when it couldn’t handle a specific request.

“One thing we did was make clear to our employees that even though the agent was not able to handle an issue today, it might be able to in a month or two,” Ajmera says. “That’s why ongoing communications to users was important, as new capabilities were added and speed and accuracy improved.”

We also created messaging for early users indicating that their testing was an integral part of making the tool more effective. This created a positive feedback loop while also keeping employee expectations reasonable.

How we measured success

Carefully tracking and analyzing your success metrics throughout your development and release of the product is a high priority. Without this step, you are working in the dark.

At Microsoft, we identify the key performance indicators (KPIs) for a particular product and then use them as our North Star for any internal release. But the specifics of those KPIs can vary from product to product.

For example, measuring the monthly average user (MAU) statistics might be extremely important for an all-purpose productivity tool like Microsoft 365 Copilot. But for an employee-assistance tool, the goal is not necessarily regular use, because employees aren’t constantly facing challenges that require help (we hope). Usage statistics may also be affected by certain events or cyclical needs, such as annual employee reviews or a major technology change (like a significant Windows update).

With this in mind, we identified certain key metrics for the Employee Self-Service Agent. In this case, the top KPIs included:

• Percentage of support tickets deflected
• Net satisfaction score
• Latency period
• Reliability
• Total time savings
• Total cost savings
• Identified and prioritized issues (reported back to product group)

Overall, we focused on the rate at which employees were able to resolve issues without opening a support ticket, as this would likely generate the greatest return on time and cost savings. We came up with an overall target across the different verticals of 40% ticket deflection, and we’re making solid progress toward this goal as we continue to refine and improve the agent.

Part of our measurement process is a monthly progress meeting of key project stakeholders, where all KPIs are evaluated to see if our targets are being met. If the results do not meet expectations, we identify the potential causes and discuss what adjustments need to be made to address these shortfalls.

Key takeaways

Here are some key things to remember when it comes to adoption efforts for your Employee Self-Service Agent:

• Don’t reinvent the wheel. Most of your change management and adoption strategies for the agent will be the same across different regions and help categories.
• Line up product sponsors. Finding leaders and others across the organization to help you promote the Employee Self-Service Agent within their own groups, functions, and regions can make a big difference in gaining employee trust and encouraging adoption.
• Set up proper listening channels. You’ll want to gather as much feedback as possible from your employees as you roll out the agent so you can understand what is working well and what needs improvement. This kind of feedback loop can also make your employees feel heard and help them shape the tool.
• Make the shift to agent-first help. Employee habits for seeking assistance can be resistant to change. We decided that turning off the “email to create a service ticket” workflow was a great way to nudge our workers to recognize the agent as the first option for their assistance needs.
• Be strategic in your communications. Use tools like email, Viva Engage, and other appropriate communications channels to target your communications and encourage a two-way conversation with employees about the agent. Sharing fun tips and encouraging peer support are other ways to increase awareness and engagement with product.
• Identify your key metrics. We determined our benchmarks for success for this particular type of agent, then tracked them and made the results available to key stakeholders. This allowed us to measure the impact and effectiveness of the product.

Learn more

How we did it at Microsoft

Although some of the blog posts below are about adoption efforts related to Microsoft 365 Copilot, they can give you ideas on how we promote internal adoption of agentic AI products at Microsoft.

• Find out how we’re reimagining campus support with the Employee Self-Service Agent. This post describes how we targeted popular employee queries—such as “What’s for lunch?”—to drive adoption of our new agentic tool in the facilities and real estate space.
• Read about how we drove Copilot adoption using Microsoft Viva. This article explains how Viva provides a powerful suite of tools that can assist with your adoption efforts.
• Learn how we created an internal Microsoft 365 Copilot Champs community to help drive adoption of the tool. This article covers how we created a special community of Copilot enthusiasts at Microsoft, who provided peer training and support for their coworkers’ Copilot journeys.  
• Discover how we built an AI-powered continuous improvement culture at Microsoft. This article provides a detailed examination of how we use data to track adoption and continuously improve our products and processes within Microsoft.
• See how we’re supercharging our internal communications with Microsoft Viva. This post explains how we use tools like Viva engage to build trust and encourage two-way dialogue with our employees about important topics, company initiatives, and key product rollouts.

Further guidance for you

• Explore the formal Adoption Guide for the Employee Self-Service Agent.
• Read the product group’s guide for deploying an Employee Self-Service Agent.
• Check out our Microsoft Learn content about collecting usage data and feedback on the Employee Self-Service Agent.

Begin your journey with the Employee Self-Service Agent

Agentic AI offers incredible promise to transform employee productivity, giving individuals access to powerful tools that enable them to accomplish more. We believe the Employee Self-Service Agent is another step along that path, allowing workers to get instant help with tasks that used to be cumbersome and time-consuming.

“We’re excited to get the Employee Self-Service Agent out and into the hands of our customers, so that they can reap the same benefits that we’re already seeing from it. As we continue to refine the product and expand the number of verticals it can cover, we expect to realize exponential efficiency gains and capture even more cost savings across our entire organization.”

Brian Fielder, vice president, Microsoft Digital

Now that you’ve read about our experience deploying the tool, it’s time to start your own journey. Successful implementation means your people will spend less time on the phone with support staff or hunting through web pages and other resources for help with routine employment tasks and more time devoted to their productive work, reducing job-related pain points and frustrations.

You can benefit from the lessons we’ve learned and the many helpful features and capabilities that we’ve built into this product, all of which are designed to make your implementation as fast, easy, and effective as possible.

“We’re excited to get the Employee Self-Service Agent out and into the hands of our customers, so that they can reap the same benefits that we’re already seeing from it,” says Brian Fielder, vice president of Microsoft Digital. “As we continue to refine the product and expand the number of verticals it can cover, we expect to realize exponential efficiency gains and capture even more cost savings across our entire organization.”

Key takeaways

Here are some of the essential top-level learnings we gleaned from our deployment of the Employee Self-Service Agent, which you should keep in mind as you start out on your own deployment path:

• Identify and engage the right people. You’ll need buy-in and advocacy from leaders across the organization; the involvement of key stakeholders from HR, IT, legal, and compliance; and technical guidance from admins, license administrators, environment makers, and knowledge-base subject matter experts.
• Develop your plan. Understand the major phases of governance, implementation, and adoption of the tool, and make sure that you have adequate resources and support for each phase.
• Verify the quality of your content. Your chances of success will be better if you undertake a thorough content assessment to address the currency, accuracy, and structure of all relevant knowledge bases. Pay particular attention to the topics and tasks that are in greatest demand by employees when they access help services.
• Consider a phased rollout. Releasing your Employee Self-Service Agent to progressively larger groups of workers across your organization allows you to gather data and feedback and improve the performance and relevance of the agent over time. You can also expand the number of categories that your agent covers as you go, increasing the impact and appeal of the tool.
• Communicate strategically to promote adoption. Convincing employees to break longstanding habits when seeking help is a challenge. Email is helpful for targeting specific groups of employees, but be sure to use tools like Viva Engage to create community, answer questions, provide fun tips and tricks, and announce new capabilities and options.
• Set clear goals and measure against them. Come up with a targeted set of KPIs that reflect your organization’s needs and aspirations, then develop a plan to capture data for each of these indicators and a regular reporting cadence to keep stakeholders informed of progress toward your goals.

Learn more

How we did it at Microsoft

• Read the official Employee Self-Service Agent product release announcement.
• Learn how we’re accelerating employee services at Microsoft with the Employee Self-Service Agent.
• View a series of Microsoft Learning Community videos about deploying the Employee Self-Service Agent.
• Explore the Microsoft Learn content related to the Employee Self-Service Agent.
• Discover how we’re tackling Microsoft 365 Copilot governance internally at Microsoft.
• Explore our leaders’ guide to enterprise AI maturity in five steps.
• Watch a video from the Understanding Microsoft Agents series devoted to the Employee Self-Service Agent.

Try it out

• Learn more about how you can begin your own journey with the Employee Self-Service Agent.

We’d like to hear from you!

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

The post Deploying the Employee Self‑Service Agent: Our blueprint for enterprise‑scale success appeared first on Inside Track Blog.

That’s where the promise of AI transformation comes in.

We at Microsoft Digital, the company’s IT organization, have developed and implemented a diverse portfolio of agentic, AI-driven capabilities that are now embedded directly in our day-to-day IT operations. These agentic systems—AI solutions that can reason across data, recommend actions, and, in some cases, execute workflows with human oversight—turn telemetry and insights into action, making our IT infrastructure and processes more resilient, auditable, and proactive.

“We’ve crossed an important threshold in the evolution of AI for IT. We’re now using the capabilities these technologies provide to transform all our core IT services, making everything we do on that side more efficient and secure.”

Brian Fielder, vice president, Microsoft Digital

While your organization’s IT infrastructure may not match our size or complexity, we believe any company can benefit from the AI-driven innovations that we’ve implemented in recent years.

We focus our AI investments across three core areas:

• Network management and infrastructure
• Tenant and device management
• Employee and engineering productivity

We’re also using AI across our IT systems to increase security, both as a standalone initiative and an integrated priority. This principle is baked into all our compliance, vulnerability response, and governance scenarios.

“We’ve crossed an important threshold in the evolution of AI for IT,” says Brian Fielder, vice president of Microsoft Digital. “We’re now using the capabilities these technologies provide to transform all our core IT services, making everything we do on that side more efficient and secure.”

Pillar One: AI in network management and infrastructure

We have applied AI throughout our global network and IT infrastructure, enabling us to keep up with the ever-increasing demands for capacity and services while reducing disruptions and incidents.

The different innovations we’ve made that fall under this pillar demonstrate the breadth of the opportunity to reimagine IT services with AI.

Supporting enterprise IT at Microsoft: Our three pillars

Network management with AI

• AIOps
• Network infrastructure Copilot (NiC)
• Vuln.AI
• MyWorkspace AI Assistant

Tenant and device management with AI

• Digital Asset Management Copilot
• Works councils and tenant trust review
• Enterprise vulnerability management
• IntelLicense
• myDevice AI Agent

Employee and engineering productivity with AI

• ADO Copilot
• ADO Work Item Assistant
• Automation Hub/Automation Catalog

The impact of AI technologies on enterprise IT operations at Microsoft can be divided into three main areas: network management, tenant and device management, and employee and engineering productivity.

AIOps: Transforming network management with operational excellence

AIOps, or Artificial Intelligence for IT Operations, involves the application of machine learning, big data analytics, and automation to streamline and improve IT operations processes. In Microsoft Digital, we use AIOps to help us to manage our complex global IT infrastructure.

Our AIOps solution leverages sophisticated data insights to detect and remediate network issues before they become impactful. We use our internally developed AIOps tools to turn raw signals and institutional know-how into guided actions that have led to major time and cost savings.

AIOps benefits include:

• Enhanced productivity: AIOps reduces cognitive load by automating routine tasks, allowing teams to focus on more strategic activities.
• Proactive issue resolution: AIOps executes automatic troubleshooting and remediation, minimizing downtime and reducing incident impact.
• Improved decision-making: AIOps leverages advanced analytics and machine learning to provide actionable insights, which enhances our decision-making capabilities.

The impact of our AIOps work is huge: thousands of hours of engineering time saved and a significant reduction in total disruption time for employees across the company’s global workforce.

Related products:

Microsoft 365 Copilot and Azure AI Services

NiC: A network engineer’s companion

Our Network Infrastructure Copilot (NiC) serves as an everyday companion for our network engineers and field IT professionals. With NiC, our IT pros can use natural-language queries to gain quick, accurate insights into network health, configuration states, documentation, troubleshooting resources, and live device data—all in one place.

Some of the typical use cases for NiC include:

• Summarizing syslogs for specific devices
• Recommending circuit upgrades
• Checking deployment status
• Listing devices missing required controls (such as AuditD)

In aggregate, NiC streamlines network device lifecycle management and operation, delivering significant time savings while improving the consistency of operational decisions.

Related products:

Microsoft 365 Copilot, Microsoft Foundry, Azure OpenAI, Azure Data Explorer

Vuln.AI: Proactively keeping our systems safe

Leaving just a single connected device unpatched could put our entire enterprise at risk. That’s why we developed Vuln.AI (Vulnerability Management Copilot), our intelligent agentic system that has transformed the way we identify, prioritize, and resolve these vulnerabilities across our enterprise network.

Vuln.AI coordinates two agents that enable our network engineers to gather, analyze, and respond to vulnerabilities proactively using AI insights. The research agent maps the vulnerability to the Microsoft infrastructure, significantly increasing accuracy and reducing manual effort and time involved. It then feeds this information to an interactive AI agent, which becomes a gateway for a security engineer or device owner to interface with the data, ask detailed questions, and gather the required information.

Thanks to Vuln.AI, we’ve been able to accelerate infrastructure compliance, reduce exposure windows, streamline security operations, improve endpoint hygiene, and lower operational risk. Our data show thousands of hours of engineering time saved and meaningful improvement in the accuracy of impacted-device identification.

Related products:

Microsoft 365 Copilot, Microsoft Foundry, Azure OpenAI, Azure Data Explorer

MyWorkspace AI Assistant: Scaling support to meet demand

Engineering disciplines across Microsoft rely on production-like Azure lab environments for testing Windows updates, investigating incidents, and building customer demos. We created the MyWorkspace AI Assistant to enable the rapid creation and management of these lab environments in the face of increasing user demands across our operations. This tool uses AI to help speed tasks such as the development and testing of Windows updates, investigating security incidents, and creating prototypes for customer demos.

Time is a critical component for all lab scenarios, whether it be resolving a customer support issue or testing a Windows Update ahead of a patch release. Our goal is to reduce “Customer Pain Time” (CPT), which measures the amount of time it takes to solve a customer’s problem. Every hour saved in the support process represents a multi-hour reduction in customer pain.

Our most recent data shows that My Workspace AI Assistant reduced tickets submitted to our Tier 1 teams by 50% and saved 500 hours by leveraging support chats, configuration guides, and other artifacts In addition, new user onboarding training tickets were reduced by 90%, and individual support interaction time was reduced from an average of 20 minutes to 30 seconds.

Related products:

Azure OpenAI, Azure Cognitive Search, Azure Bot Framework, Azure Adaptive Cards

Pillar Two: Tenant and device management

One of the most complicated dimensions of managing IT services at Microsoft is our tenant. This refers to the internal instance of all our cloud services, including Teams channels, SharePoint sites, Power BI workspaces, apps, and email accounts, as well as the millions of devices used by our global workforce.

In Microsoft Digital, we’ve developed a number of AI-powered tools and solutions to help us manage this gigantic management challenge.

Digital asset management with AI: Governing the tenant

Microsoft empowers our employees to create assets—apps, groups, sites, Power Platform environments, Power BI workspaces—at self-service speed, and our governance must match that pace. Our Digital Asset Management Copilot is a multi-agent solution that surfaces risk and policy violations, recommends fixes, and enables self-service remediation.

Our employees can access a Copilot-like experience to self-manage their assets and ensure app compliance accountability. The agent surfaces insights and recommendations related to asset compliance like oversharing of sensitive documents, highlights tenant assets that pose a security risk, offers remediation mechanisms, and can execute compliance tasks with end-user or admin validation.

The benefits include a more secure enterprise tenant and an embedded culture of compliance: Simplify compliance responsibilities, making them intuitive and seamless for our employees. Success is gauged through end user NSAT scores from our compliance solutions.

The scope of this tool spans more than 1.5 million digital assets in the tenant. The benefits include a more secure enterprise tenant and an embedded culture of compliance. With the help of the Digital Asset Management Copilot, we aim to reach our overall goal of 90% compliance with policies covering ownership, labeling, oversharing, and periodic attestation across the tenant.

Related products:

Microsoft 365 Copilot, Dynamics 365 Copilot, Azure AI Service, Power BI Copilot

Works councils and tenant trust reviews: Optimizing tenant onboarding

In the past, fragmented and manual processes around works councils and tenant trust reviews consultations in the European Economic Area  could result in delays to our product launches by as much as four to six months. Our AI-driven optimization program streamlines the end-to-end process, improving submission quality and routing and providing other efficiency recommendations.

The result of these efforts is significant: We’ve managed to reduce the average works councils and tenant trust review cycle times from 133 days to 40—about a 70% improvement—while strengthening trust and transparency across roughly 17 European Economic Area countries.

Related products:

Microsoft 365 Copilot, Azure AI Service, Power BI

Enterprise Vulnerability Management: Reducing risk to our device fleet

Our extensive companywide Windows device fleet is exposed to vulnerabilities for extended periods after remediations (patches) are applied, increasing the risk of security breaches and operational inefficiencies. Relying on manual processes can lead to slow response times.

Enterprise Vulnerability Management (EVM) is a multi-phase strategy that uses AI technology in combination with Microsoft first-party vulnerability management solutions to proactively secure and maintain the fleet. While Vuln.AI helps us keep our enterprise infrastructure safe and secure, EVM does the same for our fleet of Windows devices.

EVM minimizes risk and reduces manual effort by integrating advanced detection, automated remediation, and compliance acceleration, minimizing risk and manual effort. This holistic approach ensures our devices stay secure and compliant with minimal IT intervention, delivering resilient, self-healing endpoints across the enterprise.

AI-driven EVM delivers measurable impact across our security, compliance, and IT efficiency. Our goal is to reach 95% compliance within a week of a major patching event while reducing operational overhead and enhancing enterprise resilience.

Related products:

Windows Autopatch, Intune, Windows Update

IntelLicense: Our AI-driven license optimization and audit readiness

Managing a software estate the size of ours—including 28 disconnected systems, 400,000 software assets, and more than 800 suppliers—requires license intelligence. IntelLicense is a set of advanced, AI-driven solutions we’ve developed to help us revolutionize our software discovery and acquisition processes.

These solutions optimize our software asset management throughout the enterprise software lifecycle, reducing fragmented data, lowering audit risk, and accelerating decision-making. These changes have delivered substantial cost savings and efficiency improvements. One standout example: Our external vendor audits that previously took an average of 154 days are targeted to drop to about 15 minutes, thanks to IntelLicense changes.

Related products:

Microsoft 365 Copilot, Microsoft Fabric, Power BI Copilot, Microsoft Foundry, Azure AI Service

myDevice AI: Transforming our IT asset management

Ensuring the security of our physical assets requires a unified and accurate inventory. Fragmented IT asset data leads to inconsistent policies and exposes vulnerabilities, making it difficult for security teams to quickly isolate threats and limit potential impact.

The myDevice AI Agent advances an AI-native approach to IT asset management across our IT tenant. The agent automates our high-volume employee requests, clarifies inventory, and streamlines our procurement. While this is occurring, the agent’s recommendation engine matches devices to our users’ needs to improve satisfaction and security.

Early results from myDevice AI include an approximately 50% reduction in time and costs in asset management (eliminating thousands of hours in manual processes annually), as well as improved security and a more personalized device-procurement experience for employees. In time, we will broaden this impact as agentic workflows expand to include labs, printers, conference rooms, and Internet of Things devices.

Related products:

Microsoft 365 Copilot, Azure AI Service

Pillar Three: Our employee and engineering productivity

Building the software and systems needed to power Information Technology at Microsoft is a time-intensive job. Our engineers have been hard at work building AI-powered solutions that make building and maintaining those systems more efficient and streamlined, answering the question, “How can we apply AI to make this more efficient?”

Here are a few of the solutions we’ve found to help cut down the time and effort involved in some of the routine, day-to-day IT procedures that help keep our systems running smoothly.

ADO Copilot: AI with Azure DevOps

ADO Copilot empowers all our developers and product managers by providing instant, AI-driven insights and automation within Azure DevOps (ADO). This AI-driven assistant seamlessly integrates into ADO and acts as a “trusted copilot” with natural-language capabilities that automate workflows; enhance productivity, compliance, and velocity; and amplify decision-making across the planning, building, and deployment phases.

This agentic solution reduces the time we spend searching for information, managing permissions, planning sprints, summarizing KPIs, and resolving engineering friction points. It enables our engineering teams to move from planning to execution faster and with greater quality and consistency.

The early results from our use of this tool show extensive time savings, which projected over a full year would mean 73,000 fewer hours of engineering time required for the same output.  We’ve also seen greater developer satisfaction and faster movement from planning to execution.

Related products:

Azure DevOps, Azure AI Service

ADO Work Item Assistant: Automating our ADO processes

Building consistent, high-quality ADO work items manually can be time-consuming and prone to errors. Our ADO Work Item Assistant is a generative AI-powered tool that streamlines the creation and understanding of Azure DevOps work items, including features, user stories, tasks, bugs, and custom item types.

The benefits of our assistant include:

• Greater efficiency: The potential to cut the amount of time it takes to craft an ADO feature or user story in half (50%).
• Project delivery enhancement: A streamlined approach mitigates errors and inconsistencies.

By leveraging the power of AI within Azure DevOps, we can significantly simplify and accelerate the work-item authoring process for our product management and engineering teams, improving quality and reducing workload.

Related products:

Azure DevOps, Copilot Studio, ES Chat

Automation hub and catalog: Solving task fragmentation

Large enterprises face major productivity challenges stemming from scattered information, fragmented systems, and reliance on numerous disconnected apps. This fragmentation leads to increased meetings, duplicative effort, and significant time spent on lower-level tasks.

Automation Hub/Automation Catalog is our customizable Teams app—built on Power Platform and Power Catalog—that addresses this challenge by applying AI-powered automation solutions that integrate seamlessly with your existing systems. Common automations include a daily consolidated task list, cancelled-meeting alerts, flags for important emails, and nudges on unanswered messages. The app streamlines workflows and jump-starts productivity gains, enabling you to enhance operational efficiency while maximizing your ROI.

Related products:

Microsoft 365 Copilot, Microsoft Teams, Power Platform

The future of AI in IT

As enthusiastic as we are about our progress so far, we’re even more excited about the great potential that AI agents show in terms of lowered costs, time saved, and boosted productivity across our IT operations.

“The advent of AI agents is the next big step in AI-powered innovation. We are actively working towards our vision of deploying, governing, and managing a fleet of agents across our IT organization, pushing Microsoft to the boundaries of the AI Frontier.”

Monika Gupta, partner group engineering manager, Microsoft Digital

We’re anticipating that these solutions will continue to scale up as we further optimize and standardize large language models and agent patterns in our engineering organizations. Multi-agent orchestration will make an impact on governance and vulnerability response, and autonomous actions will become more common in everyday IT workflows. Measurement rigor will continue to sharpen, ensuring that value is tracked and amplified as AI tools and technologies proliferate across the enterprise.

“As exciting as it’s been to see the many practical applications of AI across our IT portfolio the last two years, 2026 is shaping up to be even more exciting,” says Monika Gupta, partner group engineering manager in Microsoft Digital. “The advent of AI agents is the next big step in AI-powered innovation. We are actively working towards our vision of deploying, governing, and managing a fleet of agents across our IT organization, pushing Microsoft to the boundaries of the AI Frontier.”

Key takeaways

Here are some important factors to consider as you contemplate adding AI tools and innovations to your IT operations and workflows:

• Think holistically: Evaluate the major categories of your IT organization where AI can drive transformation—network management, tenant and device governance, and employee productivity.
• Leverage AIOps for resilience: Use AI-driven operational tools to automate troubleshooting, reduce downtime, and improve decision-making across your network infrastructure.
• Embed compliance into workflows: Implement AI-fueled governance solutions that make compliance intuitive and self-service, reducing risk while fostering a culture of accountability.
• Accelerate vulnerability response: Adopt multi-agent AI systems to proactively identify, prioritize, and remediate security vulnerabilities, minimizing exposure windows and operational risk.
• Boost productivity with AI assistants: Deploy AI Copilots and automation hubs to streamline engineering tasks, reduce cognitive load, and eliminate inefficiencies caused by fragmented systems.
• Plan for scale and autonomy: Prepare for the next wave of AI in IT—multi-agent orchestration, autonomous workflows, and rigorous measurement frameworks to amplify value across the enterprise.

Try it out

Learn more and sign up for a free trial of Microsoft Azure AI apps.

Related links

• Learn how we’re enhancing our network reliability with AIOps and Network Infrastructure Copilot.
• Find out more about our AI-powered leap into vulnerability management at Microsoft.
• Check out our playbook for becoming a Frontier Firm in the AI era.
• Explore our 5-step guide to enterprise AI maturity.
• Discover how we’re transforming the employee experience at Microsoft with AI.
• Read about how we’re using AI to reinvent our network security practices.

We’d like to hear from you!

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

The post AI at scale: How we’re transforming our enterprise IT operations at Microsoft appeared first on Inside Track Blog.

To help us and all enterprises respond to this new opportunity, the company just announced Microsoft Agent 365 at Microsoft Ignite. This product serves as the control plane for AI agents—a new evolution of the existing systems that organizations like ours use to manage people and apps.

“We’re empowering our employees and teams to build agents with guardrails. We have governance structures in place to ensure our internal agents are useful, safe, and properly scoped.”

David Johnson, principal program manager architect, Microsoft Digital

Our team—Microsoft Digital, the company’s IT organization—is now using Agent 365 to track agents that employees and teams from across the company are building and deploying. We’re also using it to access the dashboard that allow us to manage and govern agents companywide. We plan to utilize the new platform to comprehensively manage our agent workload.

Agent 365 will enable Microsoft Digital to help our employees, teams, and organizations to build and deploy agents safely and effectively, according to David Johnson, principal program manager architect for governance for the organization.

“We’re empowering our employees and teams to build agents with guardrails,” says Johnson, who notes that we have more than 100,000 agents on the Microsoft tenant today. “We have governance structures in place to ensure our internal agents are useful, safe, and properly scoped.”

Agent 365 is the control plane for AI agents and will play a key role in accelerating our journey toward becoming an AI-powered Frontier Firm. Whether your agents are created with Microsoft platforms, open-source frameworks, or third-party tools, Agent 365 helps you deploy, organize, and govern them securely.

“Agent 365 delivers unified observability across your entire agent fleet through telemetry, dashboards, and alerts,” says Charles Lamanna, president of Business Apps & Agents for Microsoft. “IT leaders can track every agent being used, built, or brought into the organization, eliminating blind spots and reducing risk.”

Here in Microsoft Digital, we’re planning to use Agent 365 for multiple purposes, including:

• Filtering our agent inventory on specific criteria, such as 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

If you are unfamiliar with an agent blueprint, it’s a portable specification for an AI agent’s identity, capabilities, constraints, data access, and lifecycle.

Agent 365 is part of our Frontier Firm organizational blueprint, which we’re using to blend machine intelligence with human judgment to create agents that are AI-operated but human-led.

Boosting governance with Agent 365

Agent 365 maximizes the value of agents while minimizing tenant risk. These are capabilities that play well with the data governance foundation that we’ve already laid here in Microsoft Digital, in which we use data sensitivity labels and data loss prevention controls to govern the data that agents use in our environment.

We incorporated elements of our tenant’s minimum bar for governance into how we secure agents. Those include Microsoft Purview Information Protection, a functional inventory, activity logging, lifecycle management, and the ability to properly isolate agents against crossing data boundaries.

Our intention is always to act as proactively as possible while putting reactive structures in place to catch any issues that arise. After all, this is a new technology, and there are bound to be some surprises. By combining all of these elements, we’ve landed on six core principles for governing agents:

1. We built a data hygiene foundation: This enables you to trust your data estates with which employees build and use agents.
2. We empower employees to create and share simple, low-risk agents: We provide a safe space and personal flexibility that allows individual employees to experiment, without implicating company data or content that users don’t own.
3. We capture and vet sensitive data flows at the enterprise level: More complex or far-reaching agents owned by teams or lines of business need enterprise documentation to account for external audits or security and privacy validation.
4. We protect data designated confidential or higher: We contain data flows to tenant mandates and only trust suitable storage destinations for content. This depends on the ability to gate which connectors can work with which particular source data and sensitivity labels.
5. We enable internal teams and organizations with a smooth path to develop agents: This provides them with all of the services and sources they need along a path to release to the company.
6. We honor the enterprise lifecycle: Both user-based and attestation-based lifecycles come into play. We treat agents that individual users own like any other user app, and delete them when the employee leaves the organization. Agents owned by teams have a lifecycle defined by the tenant and tied to attestation, the software development lifecycle, and accountability confirmations.

“We want and need feedback from our own IT team. It will help ensure all our customers are able to move quickly to deploy the platform with speed and safety.”

Charles Lamanna, president, Business Apps & Agents

Customer Zero for Agent 365

In our role as Customer Zero for Microsoft, our team in Microsoft Digital shares our insights on Agent 365 and our suite of agentic AI products with Lamanna and the product team. This makes the products more effective for our customers.

“We want and need feedback from our own IT team,” Lamanna says. “It will help ensure all our customers are able to move quickly to deploy the platform with speed and safety.”

While it’s still early days for Agent 365, the potential for transformative impact is significant.

“I meet with many of our top enterprise customers, and some of their primary questions are around how Microsoft manages agents to prevent sprawl, allows agent enablement against company data, and governs those agents,” Johnson says. “Agent 365 gives us a powerful new tool to manage our agentic estate, ensuring that our agents are delivering the transformative impact we expect while also enabling us to manage and secure our environment more effectively. Enabling self-service agent creation at scale necessitates enterprise observability and governance.” 

We’re excited to share more about our Customer Zero journey with Agent 365 on Inside Track soon.

Key takeaways

Here are five ways you can use Agent 365 to unlock agent observability and management at your company:

• Registry: Get the complete view of all agents in your organization, including agents with agent ID, agents you register yourself, and shadow agents.
• Access control: Bring agents under management and limit their access to only the resources they need. Prevent agents from being compromised with risk-based conditional access policies.
• Visualization: Explore connections between agents, people, and data, and monitor agent behavior and performance in real time to assess their impact on your organization.
• Interoperability: Equip any agent with apps and data to simplify human-agent workflows. Connect them to Work IQ to provide context for the work to onboard them into business processes.
• Security: Protect agents from threats and vulnerabilities, and detect, investigate, and remediate attacks that target agents. Protect data that agents create and use from oversharing, leaks, and risky agent behavior.  

Try it out

Get started with Microsoft Agent 365 at your company.

Related links

• Read the official blog post announcing Microsoft Agent 365.
• Learn how we’re powering agentic AI adoption at Microsoft through our ‘Customer Zero’ story.
• Explore how we’re becoming an AI-first frontier firm in the agentic future.
• See AI-powered agents in action and discover how we’re embracing this new ‘agentic’ moment at Microsoft.
• Check out how we’re riding the wave of agents washing over Microsoft with strong governance.
• Discover how we unlocked enterprise AI extensibility at Microsoft with Microsoft Copilot Studio.
• Find out how we powered AI value with Microsoft 365 Copilot extensibility at Microsoft.
• See how our employees are extending enterprise AI with custom retrieval agents.
• Learn about our strategy and vision for deploying the Employee Self-Service Agent internally at Microsoft.
• Dive into how we’re reimagining campus support at Microsoft with the Employee Self-Service Agent.

We’d like to hear from you!

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

The post Deploying Microsoft Agent 365: How we’re extending our infrastructure to manage agents at Microsoft appeared first on Inside Track Blog.

In short, we’re all-in on agentic AI, and we want to help you get there, too.

“We’ve made a lot of progress deploying and driving adoption of Microsoft 365 Copilot since it was released, and we’re now doing the same when it comes to enabling our employees and our teams to build agents that make us more productive,” says Brian Fielder, vice president of Microsoft Digital, the company’s IT organization. “We’re Customer Zero at Microsoft, which means we’re the first to deploy and use the technology and services that we sell to our customers. Those learnings give us a unique perspective and story to share with you about the journey we’ve been on with AI and agents.”

We have two collections of agentic AI content that we think will be useful to you.

“When it comes to agents, we’re still at the start. We expect to learn much more as we continue, lessons we’ll share here—stay connected and we’ll continue to share our story with you.” 

Brian Fielder, vice president, Microsoft Digital

The first set of stories documents our vision and strategy for agents. They walk you through our experience deploying agentic AI, our work to create tools that enable our employees to dive in, and, through smart governance, empower everyone at Microsoft to be confident and creative with how they use agents while keeping the company safe and secure.

Our second set of stories highlights some of the most interesting and effective agents that our employees, teams, and organizations have built. These stories will not only give you examples of agents that we’ve built, they show how you can go about building  similar agents for your organization based on the collective experience of our employees and teams at Microsoft.

“We hope you find reviewing the journey we’ve been on practical and useful,” Fielder says. “When it comes to agents, we’re still at the start. We expect to learn much more as we continue, lessons we’ll share here—stay connected and we’ll continue to share our story with you.”  

November 13, 2025

Alex Fleck

The agentic future: How we’re becoming an AI-first Frontier Firm at Microsoft

January 30, 2025

Keith Boyd

AI-powered agents in action: How we’re embracing this new ‘agentic’ moment at Microsoft

Deploying agentic AI at Microsoft

May 15, 2025

Alex Fleck

Riding the wave of agents washing over Microsoft with good governance

Agentic extensibility is expanding the frontier of enterprise AI. By creating agents that surface knowledge, take actions, and even reinvent workflows, people can personalize AI’s power like never before. But how do you move into the agentic future without putting your organization and employees at risk? How do you encourage citizen developers to create agents Read More

Agents we’ve deployed internally at Microsoft

November 13, 2025

David Hirning

Accelerating employee services at Microsoft with the Employee Self-Service Agent

Microsoft is a huge and complex organization, with more than 200,000 full-time employees working in hundreds of locations around the world. Engage with our experts! Customers or Microsoft account team representatives from Fortune 500 companies are welcome to request a virtual engagement on this topic with experts from our Microsoft Digital team. Previously, when our Read More

Key takeaways

We hope that you find our agentic AI stories useful. We wanted to share a mixture of our strategy and vision around enabling our employees to deploy agents, and to share stories that feature some of the most promising agents that our employees and teams have built and deployed.

We also understand that it can feel challenging to know where to start—it was for us. Here are some things we learned along the way that should help you:

• Governing agents is complex, and dependent on the overall AI maturity of your organization. Start slowly to build that maturity before unleashing too many new agents in your environment.
• A strong policy framework is the foundation. Lean on existing app governance policies, then layer agent-specific structures on top.
• Invest in data infrastructure and AI platforms. Building robust data infrastructure ensures your organization is prepared to leverage AI, and supports scalable, innovative, and secure AI-driven solutions.
• Develop a building environment strategy. Decide what scenarios match up with specific environments and make the right environments available to the relevant employees.
• Global regulations around categories like privacy, security, and responsibility provide a good baseline for establishing governance policies. Set relevant teams to work thinking through these regulations and incorporate their insights into your agent governance.
• Foster a culture of creativity and teamwork. Champion an AI-forward culture where innovation and collaboration drive the adoption of agentic AI.
• Develop AI expertise through training and development. As agentic AI transforms workflows and business outcomes across every industry, upskilling will empower your teams to navigate the rapid advances of AI, drive innovation, and ensure your organization stays competitive.
• Align AI initiatives with strategy. Ensuring AI initiatives align with business goals maximizes their impact and positions your organization to succeed in the rapidly evolving world of agentic AI.
• Implement ethical AI practices. You can use Microsoft’s Responsible AI principles as a guide. 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.

Try it out

• Get started with AI agents with Microsoft 365 Copilot, Azure AI Foundry, and Copilot Studio.

Related links

• Learn how to create and publish agents with Microsoft Copilot Studio.
• Find out how to create and deploy agents with Microsoft 365 Agents SDK.
• Check out these 12 lessons to get started building AI agents.

The post Powering agentic AI adoption at Microsoft: Our ‘Customer Zero’ story appeared first on Inside Track Blog.

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.

We’d like to hear from you!

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

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

In the past, Julie might have interacted with different apps and websites to get help with each of those tasks. Today, thanks to the power of agentic AI and Microsoft Copilot Studio, Julie can turn to a single portal to handle all of it: the Employee Self-Service Agent.

This agentic tool, which will soon be released publicly as a free add-on for the Microsoft 365 Copilot license, has already made a big impact on the lives of our employees, saving them time, effort, and frustration. We call it the “one-stop shop” experience of employee self-service.

“Before we had the Employee Self-Service Agent, the employee-assistance experience was fragmented across mobile, websites, and physical kiosks,” says Becky West, a principal group product manager in Microsoft Digital, the company’s IT organization. “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 help ticket, etc.

“Our employees rely on AI tools like Copilot to help get their work done. And the same is now true for resolving an issue related to facilities.”

Becky West, principal group product manager, Microsoft Digital

Of course, employees like Julie also need assistance with other common job-related tasks, like getting their human resources (HR) questions answered or fixing a technical issue with their device.

Those are also important categories included in the Employee Self-Service Agent, something the flexibility and extensibility of Copilot Studio makes possible.

“Our employees rely on AI tools like Copilot to help get their work done,” West says. “And the same is now true for resolving an issue related to facilities, HR, or IT support. We live in an AI-powered world, and this agent meets the moment for our people.”

In this story we share how we’re using the Employee Self-Service Agent in the real estate and facilities space, but it does much more than that. Our employees also use it to get help with IT problems and answers to their HR queries, and we expect to add other key areas soon, such as finance and legal. Available to all Microsoft employees worldwide, the full agent is already delivering a significant boost in productivity, cost savings, and user satisfaction across the company.

Everyday use cases for agentic assistance

Julie might not need IT support or help with an HR issue every day. But she’s always on the hunt for her favorite foods for lunch.

In our existing dining app, employees could look up that day’s menu for a specific building cafeteria, but they couldn’t just ask, “Hey, where can I get some good teriyaki on campus today?”

With the Employee Self-Service Agent, now they can.

“Searching on type of cuisine or dish is one of the top requests we were getting,” says Balaji Radhakrishnan, principal software engineering manager for the dining team. “It was an important feature missing from our existing apps, and we solved that with the employee-assistance agent.”

Employee Self-Service Agent screenshot

Not only can the agent help Julie locate the perfect lunch, it also connects her to the tool where she can order and pay for it. This streamlines the process for her—she doesn’t have to remember which website or app to call up to procure her teriyaki treat. (In the future, we plan to extend the functionality so the agent remembers your previous food choices, and you can order right from the agent.)

Dining is just one of the facilities-related experiences we targeted when developing the Employee Self-Service Agent. Other tasks include:  

• Lobby and visitor services – registering a campus guest
• Parking – registering a car to park on campus
• Maps – navigating around a building or a campus
• Facilities tickets – getting help with office furniture, lighting, HVAC, or other building issue
• Transportation – calling a shuttle for a ride between buildings or finding commuting help
• Finding a space – locating a place to relax, work, or connect with colleagues

“We started out by looking at the services we already offered,” West says. “We thought about what tasks would be in highest demand, where that information or transaction lived now, and how best to surface it. The more we explored the power of the agent, the wider the variety of experiences we were able to incorporate.”

Saving time and reducing frustration

Resolving employee pain points and saving time are two of the key advantages inherent to this area of agentic employee assistance. Consider the common employee task of registering a business-related campus guest (such as an interview candidate or a prospective customer).

“If we can handle 50%—600,000—of these business-related visitor registrations through the Employee Self-Service Agent, that adds up to 50,000 hours of employee time each year.”

Bhavani Paruchuri, senior product manager, Microsoft Digital

According to Bhavani Paruchuri, a senior product manager in Microsoft Digital, in 2024 Microsoft saw more than 2 million registered visitors at our buildings worldwide. Roughly 1.2 million of these were business-related guests.

Previously, employees had to email or talk to lobby hosts (front-desk staff) when they wanted to register a guest; the host would then enter visitor 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, building number, and date. Once the form is submitted, the system confirms it and sends a QR code directly to the guest via email.

“We calculated that this new process could save at least five minutes for each guest registration,” Bhavani says. “If we can handle 50%—600,000—of these business-related visitor registrations through the Employee Self-Service Agent, that adds up to 50,000 hours of employee time each year. So, just in this one area alone, the agent can have a big impact on overall productivity.”

Those savings add up, and quickly.

“Once you start using the agent for dining, you use it daily. As we added in cuisine and price filtering and other functionality that wasn’t available before, you could see it was a big differentiator from what the previous tools could do.”

Erik Downing, principal product manager, Microsoft Digital

One of the reasons we decided to include facilities-related help early on in the development of the Employee Self-Service Agent is that these common tasks would help increase usage of the new portal—building a habit with our workers that would have long-term benefits.

We have already seen employees used to finding a meal with the agent also using it to solve other challenges, including in the HR and Support spaces.

“Once you start using the agent for dining, you use it daily,” says Erik Downing, a principal product manager with Microsoft Digital. “As we added in cuisine and price filtering and other functionality that wasn’t available before, you could see it was a big differentiator from what the previous tools could do.”

West explains how this can have an outsized effect on promoting product adoption.

“If people get in the daily habit of using the agent for these routine tasks, they’ll be more comfortable going to it for other things,” West says. “Then you can really start to scale the agent up and see the larger impact across more areas.”

Filing a service request with the help of AI

Julie gets to work one morning and is dismayed to discover that her adjustable desk will no longer rise to a standing position. She needs to open a facilities ticket for help.

“The AI automatically picks out the problem class and the problem type; presents a form with the details; asks for confirmation; then kicks off the ticket right from there. It’s all in one place, AI-driven, and truly agentic in terms of task completion—and it will only get better.”

Sonaly Choudary, senior product manager, Microsoft Digital

In the past, this would have required Julie to send Facilities an email with a description of the problem, or she would have had to track down the right app or web form for the same purpose.

Now, she can simply snap a photo of the broken desk and upload it to the Employee Self-Service Agent.

The agent will open a form and use information from the photo to create the help ticket right there. This image-based technology, like natural-language chat, is something that our previous apps couldn’t do, which reflects the power of AI. 

“Whether you upload a photo or just describe your issue using natural language, we’ve really pushed this tool to be as agentic as possible,” says Sonaly Choudary, a senior product manager who works on facilities technology products for Microsoft Digital. “The AI automatically picks out the problem class and the problem type; presents a form with the details; asks for confirmation; then kicks off the ticket right from there. And then you can query the agent to get status updates on it. It’s all in one place, AI-driven, and truly agentic in terms of task completion—and it will only get better.”

How Customer Zero makes our products better

Because Microsoft employees are the first ones to use our newest products and features, we have the opportunity to roll them out gradually and test them under actual enterprise-work conditions, which enables us to gather valuable feedback and telemetry. This data is then fed back into the product development process to make key improvements. We call this our Customer Zero philosophy.

“We were pioneers as Customer Zero in showing the need for these services in an employee-assistance portal, and the product group saw that need.”

Michelle Schaefer, principal product manager in Microsoft Digital

In the case of the Employee Self-Service Agent, we began product development by tackling HR and IT support, which were key areas to capture cost savings.

But how could we get even wider usage of the product? We turned to our real estate and facilities functions.

“The facilities and real estate aspect of Microsoft Digital is unique, in that it focuses on the employee experience at the company, literally in the buildings,” says Michelle Schaefer, a principal product manager in Microsoft Digital. “All those tasks—getting lunch, parking, filing a facilities ticket, moving around the campus, inviting a guest—are universal for all our employees. We were pioneers as Customer Zero in showing the need for these services in an employee-assistance portal, and the product group saw that need. And we’re constantly gathering telemetry to learn how our workers can more easily discover the agent and have a better experience with it each time.”

Adding the facilities and real estate category to the Employee Self-Service Agent also helped our engineers learn more about building an agent that presents a “single pane of glass” to the user on the front end but incorporates so many different functions on the back end.

“Our strategy with this new natural-language agent is to augment our existing tools, which brings AI to the experience and gets the user to the right place.”

Thomas Po, senior product manager, Microsoft Digital

Each team has its own tools that compete for our employees’ attention.

“The challenge was to turn all those into a common experience for the user,” says Erik Orum Hansen, a principal engineering manager for Microsoft Digital. “That’s been a learning journey for us, as the organization pivoted to developing a single agent incorporating all these different functions.”

This single-portal approach makes it so much easier for users to explore their options and figure out the best way to accomplish the task, even as the underlying tools are still available.

We still have as many as 15 different tools that employees use today for campus related tasks, but we’re managing them more effectively—now our employees only need to use them when their use case is more challenging or detailed in nature.

“Our strategy with this new natural-language agent is to augment our existing tools, which brings AI to the experience and gets the user to the right place,” says Thomas Po, a senior product manager for Microsoft Digital. “The user may not have the specific facilities app they need on their phone, but everyone has Copilot, right? It’s about giving our employees access to information in more places and connecting them to the right tool or function.”

Employee Self-Service Agent screenshot

The Employee Self-Service Agent can also see when an employee took prior action, recognize that they might want to take the same action again, and suggest that action—for example, suggesting that they may want to reserve a shuttle ride to the same location they’ve visited previously.

“This allows users to have a more contextual, conversational experience,” says Ram Kuppaswamy, a principal software engineering manager in Microsoft Digital. “For example, for transportation needs they can just type, ‘Help me book a campus shuttle,’ and the agent can suggest options based on their previous ride history. Then it can call up a form to help complete the booking. Users really love it.”

Built on the power of Copilot Studio

We built the Employee Self-Service Agent with Microsoft Copilot Studio, a powerful platform that allows you to create and extend AI agents. The agent is designed so that our customers can customize it to fit their own business needs and integrate it with their existing technologies.

“We didn’t want a custom connector; we wanted to go with an out-of-the-box connector that worked with Dynamics,” he says. “There were some product iterations to deal with while we made sure it met Microsoft’s data-compliance standards, but ultimately it made it easier to show customers how simple it is to implement the agent—it’s a very low-code/no-code solution.”

Erik Orum Hansen, principal engineering manager, Microsoft Digital

When we built the part of the Employee Self-Service Agent that handled HR and IT Support needs, we were able to create connectors for major third-party service providers in those areas, such as Workday, SAP, and ServiceNow. (These connectors are now “out-of-the-box capabilities” that are included in the product.)

In the facilities and real estate space, we have numerous vendors that we work with to provide various campus services. Since we already used various existing internal applications to connect employee requests with these vendors, we were able to create connectors for the agent easily using Copilot Studio. More importantly, we were also able to use the out-of-the-box Dataverse connector that worked with our Dynamics 365 data, which cut down on development time.

“The agent functions as a single entry point, which then connects with the Microsoft Dynamics data,” Schaefer says. “We have numerous different facilities vendors in different parts of the world, but we didn’t have to build multiple connectors to those vendors because of the common Dynamics back end.”

Orum Hansen says this caused a small delay in the internal deployment of the product, but that it was worth it in the end.

“We didn’t want a custom connector; we wanted to go with an out-of-the-box connector that worked with Dynamics,” he says. “There were some product iterations to deal with while we made sure it met Microsoft’s data-compliance standards, but ultimately it made it easier to show customers how simple it is to implement the agent—it’s a very low-code/no-code solution.”

“We’re also previewing more multi-agent capabilities that are coming from Copilot Studio, which our customers will be able to incorporate into their own solutions. The product is just going to get richer and richer over time, as it extends into other lines of business.”

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

The future of workplace AI

In many ways, we’re still in the early stages of the revolution that AI agents are going to bring to the workplace.

But the Employee Self-Service Agent is a significant early marker on that path.

“The first step is to develop this agent that’s optimized for the HR, IT, and facilities verticals,” says Kirk Gregersen, corporate vice president of product for Microsoft Viva and Microsoft 365 Copilot Experiences. “We’re also previewing more multi-agent capabilities that are coming from Copilot Studio, which our customers will be able to incorporate into their own solutions. The product is just going to get richer and richer over time as it extends into other lines of business.”

As employees like Julie are already finding out, this new era of agentic AI is going to be a major improvement over what came before.

“Most companies already have some kind of employee-assistance portal solution,” Orum Hansen says. “With this new agent, there’s an opportunity to really reimagine the entire experience—to shed some of the old baggage and figure out how to do things differently. It’s going to lead to a more efficient workplace, along with more satisfied employees.”

Key takeaways

Here are a few factors to remember when implementing an AI-powered employee-assistance solution at your company:

• Pick high-value targets. Consider employee needs and the most commonly used assistance functions (using data where available), then develop a solution that addresses those areas. This will drive adoption and daily use of the agent.
• Customize the solution. Take advantage of the extensibility of Copilot Studio to develop an agent that fits your organization’s specific needs.
• Augment existing tools. Your employee-assistance agent can be the front door through which users find the tool they need. Over time, you can retire legacy tools and portals as the agent is able to complete the same functions on its own.
• Go beyond information retrieval. Employees want to be able to carry out tasks right from the agent, so incorporate forms and other technologies that allow them to accomplish their goal as quickly and easily as possible.
• Think outside the box. The image-driven feature we developed for filing a facilities ticket is a great example of applying the revolutionary abilities of AI to solve problems in new and innovative ways.    

Try it out

Learn more about the Employee Self-Service Agent.

Related links

• Read about the full scope of the impact the Employee Self-Service Agent is having on our organization.
• Learn about our strategic approach to agentic AI internally at Microsoft. 
• Explore 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.

We’d like to hear from you!

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

The post Reimagining campus support at Microsoft with the Employee Self-Service Agent appeared first on Inside Track Blog.

At Microsoft, we’re deploying a spectrum of agents to fulfill different needs, from acting as knowledge sources for our individual employees, to helpers that handle specific tasks for our teams, organizations, and for the full company.

Of the different kinds of agents, the easiest to implement are retrieval agents, which employees can build using Microsoft Copilot Studio agent builder or SharePoint. After a few quick steps, the agents they create retrieve information for them from data grounded in our Microsoft 365 tenant, like a SharePoint library or collection of libraries, reason over it, summarize it, and answer their questions.

As one of the first enterprise IT organizations to deploy this capability to our employees, we’re starting to see their impact first-hand, and along the way, we’re learning lessons that our customers can use to unlock their own agentic abilities.

Copilot + retrieval agents: A new way to drive enterprise AI value

So, what are retrieval agents?

First, it’s important to understand where these Microsoft 365 Copilot extensions fit within the emerging agentic environment.

Copilot agents expand Copilot’s knowledge and skills, and they can even operate autonomously to complete tasks or automate processes. Retrieval agents operate at the simplest end of the agentic spectrum and are the easiest for employees to create.

Types of agents

“Retrieval agents wrap around knowledge sources and data sets, and they include system prompts so they behave the way their creators want,” says Aisha Hasan, Power Platform and Copilot Studio product manager for Microsoft Digital. “They’re AI helpers that our employees can create to find what they want without having to search around manually.”

“If we think of Copilot as the UI for AI, retrieval agents are a further layer on that UI, that can access and reason over their organization’s data.”

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

A retrieval agent is essentially Copilot, plus its creator’s instructions, plus grounding in a particular data set. These extensions can accomplish a wide variety of jobs, from acting as an event planning assistant to sourcing insights into business optimizations to surfacing internal guidance around leadership best practices.

“If we think of Copilot as the UI for AI, retrieval agents are a further layer on that UI, that can access and reason over their organization’s data,” says Mykhailo Sydorchuk, Customer Zero lead for Microsoft 365 integrated apps at Microsoft Digital. “They can also address other data sets and systems using Copilot, without the need to build custom connectors or orchestration.”

At Microsoft, retrieval agents are accelerating our AI journey by enabling employees to tailor Copilot’s capabilities to their own work and specific knowledge sources. Their value comes from creating micro-experiences that meet specialized needs to enhance productivity and information discoverability.

“With Copilot Studio agent builder and retrieval agents, we’re empowering our employee citizen developers to experiment freely and create agents easily, then share them out, all surrounded by the right governance and management process.”

Amy Rosenkranz, principal product manager for Customer Zero Extensibility, Microsoft Digital

Creating retrieval agents couldn’t be easier. One option is through Microsoft Copilot Studio agent builder, accessible through Copilot Chat within Microsoft Teams. Employees can use natural language prompts and a simplified configuration process to provide custom instructions, tell their agents how to behave, and provide specific data and knowledge sources.

SharePoint agents are another opportunity to add AI assistance into everyday work. These enable users to turn SharePoint sites and documents into scoped agents that are subject matter experts for your business needs. Site owners or admins simply customize their SharePoint agent’s branding and purpose, specify the sites, pages, and files it should get information from, and define customized prompts tailored to its purpose and scope.​​​​​​​

“We’re targeting our core enterprise professional developer scenarios with more advanced tooling,” says Amy Rosenkranz, principal product manager for Customer Zero Extensibility in Microsoft Digital. “But with Copilot Studio agent builder and retrieval agents, we’re empowering our employee citizen developers to experiment freely and create agents easily, then share them out, all surrounded by the right governance and management process.”

Enabling retrieval agents while ensuring our organization’s integrity

While agents represent a leap forward in AI-powered productivity, capturing that value means balancing the freedom to explore with the need to protect our company.

Microsoft is one of the first and largest organizations to extend Microsoft 365 Copilot by enabling agents. As a result, our team here in Microsoft Digital, the company’s IT organization, has been hard at work ensuring those agents don’t put the company at risk.

“The beauty of retrieval agents is that, for the most part, they’re grounded in Microsoft 365 data, so they provide a single-pane view within Teams, instead of forcing users to go from one source to another to seek out information.”

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

The level of risk an agent presents largely depends on its access to data sources and the actions it can take. More advanced task and autonomous agents need to cross Microsoft 365 tenant boundaries to enable actions. But retrieval agents are much simpler.

Retrieval agents typically only access data within tenant boundaries through graph connectors. Although they occasionally need to connect with information outside the tenant, they only retrieve data and don’t transmit it externally. As a result, administrating and governing these agents is much simpler.

“The beauty of retrieval agents is that, for the most part, they’re grounded in Microsoft 365 data, so they provide a single-pane view within Teams, instead of forcing users to go from one source to another to seek out information,” Hasan says. “Whatever your window of productivity might be, you can interact with the information you need without constantly switching context.”

We started small, experimenting with retrieval agents with trusted stakeholders and reviewing each one to ensure they didn’t present unacceptable risks to the company. Through what we learned during that process and the data safety controls we maintain across our tenant, we’ve minimized the scenarios where agents require reviews, which only come into play for more complex agents that build on bespoke graph connectors, API plugins, or custom orchestration to access external knowledge sources and take actions.

Our confidence in retrieval agents’ safety comes from a few key factors.

Administration and configuration

Retrieval agents’ simplicity also helps us keep the risk of data overexposure low. Unlike more complex agents that require security assessments, threat modeling, privacy assessments, and Responsible AI reviews, we’re able to define our policies for retrieval agents at the agent builder environment level.

We empower tenant administrators and our partners on the Microsoft Security team to apply data loss prevention policies that configure what individual employees can enable for their retrieval agents. At this level, everyone in the company has the same configuration and tools available, and automation largely handles agent reviews and assessments. We based these pre-configured settings on the same security, privacy, and regulatory compliance standards we apply to any internally built application.

Approved graph connectors

Graph connectors increase the discoverability of external data by integrating it into an agent’s grounding. At Microsoft, we’ve onboarded a series of approved connectors that creators can use to incorporate additional data for their agents to reason over. They include connectors for external websites as well as tools like Azure DevOps and ServiceNow.

Our criteria and review process for connectors ensure that agents don’t put our tenant at risk. As long as a connector is approved, employees are free to use it to create their agents.

Ensuring Responsible AI standards at the platform layer

Microsoft has been at the forefront of establishing Responsible AI principles: fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability. To ensure we enabled retrieval agents that would respect Responsible AI standards, we needed to translate those concepts into concrete policies we could apply at the platform level.

Microsoft’s Office of Responsible AI has been an indispensable resource during this process. They maintain a comprehensive and evolving list of policy statements around restricted uses for AI capabilities. Those include things like using AI to infer emotions or personal characteristics, assess employee performance, or social scoring.

As our implementation of retrieval agents matured, we instituted controls at the platform layer to block these restricted uses for AI, identifying what kinds of information an agent can retrieve. Now, Copilot Studio agent builder knows how to evaluate responsibility against a wide array of parameters and make determinations based on the parameters we’ve set out.

For example, if a manager attempted to create a retrieval agent that would assess employee performance based on meeting attendance, guardrails at the platform layer would curtail that ability. Naturally, as we develop our policies around responsible AI further, the parameters of Responsible AI will shift and grow, and we’ll continue to nuance our configurations.

Thanks to these foundations, we’re now at the point where we feel comfortable giving every Microsoft employee access to Microsoft Copilot Studio agent builder and the freedom to create retrieval agents. It’s all part of our principle of employee self-service with guardrails.

“It’s a constant evaluation,” says Hasan. “Our goal is to allow as much freedom as we can with retrieval agents so employees can increase productivity without going down the path of greater customization that requires more intensive review.”

Different organizations are at different stages of their AI maturity journey. As you experiment with Copilot extensibility, it will be important to define your organization’s level of experience implementing AI tools, your employees’ state of readiness and training, key risk areas, and sensitive scenarios.

“Users who want to build agents with no code can select from premade templates using natural language, or they can fill out a few fields.”

Brian Moran, senior product manager, Employee Experiences team, Microsoft Digital

From there, you’ll be able to use out-of-the-box configuration capabilities in Copilot Studio agent builder to establish guardrails that work for you. It will take careful collaboration across security, privacy, legal, and IT teams, but we’re already learning that the benefits are worth the effort.

Ease and access drive creativity and new ways to work

Now that we’ve empowered our employees to build retrieval agents organization-wide, examples of creativity and innovation are popping up all over the company. Ease of use and freedom have a lot to do with this proliferation.

Using Copilot Studio agent builder

“Users who want to build agents with no code can select from premade templates using natural language, or they can fill out a few fields,” says Brian Moran, senior product manager on the Employee Experiences team at Microsoft Digital. “They can get their agents up and running in minutes.”

Creative examples of the ways that employees and teams are using retrieval agents include:

• IDEAS Copilot democratizes access to our Insights, Data, Engineering, Analytics, AI, and Systems (IDEAS) knowledge base to help users act on crucial usage information without the need for technical expertise. The agent fully integrates with Microsoft Teams, so employees can dig into data across sales, marketing, finance, operations, and more using natural language queries in their familiar working environment.
• Security Comms Agent helps our communications team create blog posts by providing a prompt that includes the content’s purpose and context. It accesses internal documents about business objectives, positioning frameworks, voice guidelines, and our Microsoft Digital communications and marketing plan, as well as the internet and specific Microsoft-owned learning sites for added context. From there, the agent creates a first draft that aligns with our Microsoft Digital positioning, objectives, and voice.

“Empowering our people to create retrieval agents in a responsible environment is the ideal combination of human creativity and AI capabilities, and we’re confident it will unlock a new era of innovation.”

Nathalie D’Hers, corporate vice president, Employee Experience

• Know Your Customer leverages AI to provide a comprehensive view of customer profiles. It accesses an overview of a customer’s tenant, usage metrics for Copilot, service incident reports, and more to provide usage statistics and health data for Microsoft 365 apps, email, meetings, Microsoft Viva, and other products to enhance customer engagement and support. The agent can even generate a tenant-specific Microsoft PowerPoint dossier for ease of use.
• Prompt Buddy Agent helps employees discover ready-to-use prompts that eliminate the need for experimentation and prompt engineering. Employees use natural language queries to discover AI prompts their colleagues have shared across industries, roles, personas, and topics, all without leaving Copilot Chat. As a result, they can save valuable time by streamlining AI-assisted workflows.
• Communications Plan Assistant accesses a library of prompts our Microsoft Viva communications team has developed to quickly draft content. The team communicates with the agent conversationally, providing feedback and selecting from the options it provides, then populates pre-defined sections in their communications plan template. At the end of the interaction, they can request a summary with all the final content that will go into the plan.

“By trusting our employees to imagine and create their own extensions for Microsoft 365 Copilot, we’re making it possible to personalize enterprise AI like never before,” says Nathalie D’Hers, corporate vice president of Employee Experience. “Empowering our people to create retrieval agents in a responsible environment is the ideal combination of human creativity and AI capabilities, and we’re confident it will unlock a new era of innovation.”

Key takeaways

Here are some tips for getting started with retrieval agents at your company:

• Establish early communication and collaboration with members of your security, legal, compliance, IT, and any other teams who can help you define ways to configure Copilot Studio agent builder safely.
• Agents rely on data, so ensure your enterprise data is clean, well-governed, and accessible through scalable pipelines.
• Start slowly. Enable retrieval agents for smaller, select groups to work through any configuration issues or concerns before widening access. Plan to review everything you do at each step, and use those learnings as a basis for configuration and automation as time progresses.
• Balance employee empowerment with organizational safety. That balance will evolve as your organization’s AI maturity progresses.
• Use simple retrieval agents as a springboard to more complex extensions that require a structured review process.

Try it out

Want to explore the possibilities for creating agents with Microsoft Copilot Studio? Try it free here.

Related links

• Witness AI-powered agents in action to see how we’re embracing this new ‘agentic’ moment at Microsoft.
• Learn how we’re finding deeper AI value at Microsoft with Microsoft 365 Copilot extensibility.
• Discover ways we’re unlocking enterprise AI extensibility at Microsoft with Microsoft Copilot Studio.
• Check out how we’re riding the wave of AI agents washing over Microsoft using good governance.

We’d like to hear from you!

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

The post How our employees are extending enterprise AI with custom retrieval agents appeared first on Inside Track Blog.