Building trust in AI-powered support at Microsoft: Our Employee Self-Service agent story  

When we launched our Employee Self-Service (ESS) Agent to our more than 300,000 employees and contingent staff in the fall of 2025, we expected that most adoption challenges would be solved through product improvements. Like many organizations introducing AI-powered support experiences, our focus was on accuracy, response quality, and technical performance.

And those were definitely important—but we were leaving out a huge factor: trust.

This finding emerged from a series of user-research studies our team in Microsoft Digital—the company’s IT organization—conducted internally after early adoption rates of the agent were not as strong as we’d hoped. The studies revealed that our employees’ reactions to agentic support were categorically different than other software rollouts.

“We needed to change the agentic support scenarios to create trust and empathy—a more humanized approach. That was a big paradigm shift for us.”

Olivier Cherel, senior business process manager, Microsoft Digital

People were making decisions about using the Employee Self-Service Agent based on more human factors, including emotion, expectations, habits, and previous experiences with support. Those quintessentially human factors played a significant role in whether the employee opted for agentic support or chose a different service modality, even when the technology itself was clearly capable of meeting their needs.

Our research highlighted this crucial distinction: technical quality mattered, but it wasn’t enough. It was about the employee’s total support journey.

“We realized after doing the research that ESS was not completely ready for prime time yet,” says Olivier Cherel, a senior business process manager in Microsoft Digital’s employee support organization. “We needed to change the agentic support scenarios to create trust and empathy—a more humanized approach. That was a big paradigm shift for us. And I think it’s going to be the same for any organization that wants to implement an IT help-desk agent.”

This process ultimately gave us a practical framework for designing agentic support experiences that employees choose to use first, and return to repeatedly. These are lessons that our customers can apply as they introduce AI support agents of their own.

Agentic employee support: A social experience

Our research began with a broad question: What was limiting employee adoption and acceptance of the Employee Self-Service Agent?

We needed to explore how our employees interacted with different modes of support, what shaped their expectations, and where friction could be found in the process.

“We had the chance to work with researchers and designers at Designit, a vendor that conducted qualitative research, UX design work, and adoption analysis,” Cherel says. “The findings helped us better understand how employees experienced support, and also what would make an agent-driven experience more useful.”

Experts at Designit—a global experience innovation company that is the strategic design arm of Wipro—conducted interviews and focus groups, analyzed Microsoft employee feedback, examined support workflows, and studied interactions between employees and the agent across multiple geographic regions. The findings pointed toward factors that extended well beyond product functionality.

“We started realizing there was a lot more under the hood than we expected,” says Chaitrasri Rao, executive design director at Designit. “Trust was a major issue. People approached an AI agent very differently than the way they approached traditional software.”

“With traditional support chatbots, it was like a search engine—you plugged in your problem and it spat out a solution. With ESS, it’s really more about how to engage in conversation with these end users so that they get as much as they can out of it.”

Kevin Verdeck, senior IT service manager, Microsoft Digital

When a user interacts with a traditional application—such as a spreadsheet, a dashboard, or a ticketing system—they have a clear mental model: They are operating a machine. They expect predictable, deterministic outputs.

When that same user interacts with an AI agent, something shifts. They don’t operate it; they talk to the agent. They ask questions. They negotiate. They even say “please” and “thank you.” Users treat AI agents less like tools and more like interlocutors—somewhere between a colleague and a customer service representative.

This was a big leap for the team developing the Employee Self-Service Agent. They realized that user trust was not based on technical reliability. It has more to do with social alignment.

“With traditional support chatbots, it was like a search engine—you plugged in your problem and it spat out a solution,” says Kevin Verdeck, senior IT service manager, Microsoft Digital. “With ESS, it’s really more about how to engage in conversation with these end users so that they get as much as they can out of it. The user needed to be aware that if ESS didn’t understand them the first time, they can reword the issue and provide more context in the conversational interface.”

What’s more, our employees were seeking indications that the system understood—and even empathized with—their situation.

“We realized that ESS was too much of a machine,” Cherel says. “It was not human enough. So that’s something that we really prioritized and addressed.”

This insight reframed the work. The team shifted from asking how to improve a support tool to asking how to improve the overall support experience.

Understanding context in the support experience

The Designit research uncovered a second finding that proved equally important: the overall support context matters.

“We had to build trust, that ESS is not just a dead end for the user. It can help them self-solve their issue, but it also needs to tell them, ‘Hey, if I can’t fix it, I’ll hand you over to a live agent.’”

David Finney, director of IT Service Management, Microsoft Digital

Most employees didn’t make the Employee Self-Service Agent their first stop when they needed help. Instead, they typically tried a number of other resources before opening the agent. They’d consult coworkers, search through documentation, explore existing tools, and/or try their own troubleshooting steps.

As a result, people were sometimes frustrated when the agent made suggestions for possible fixes that they had already tried. We needed to be sensitive to what the employee was experiencing before using the Employee Self-Service Agent and demonstrate its tangible value to connect user context to known outages or problems, provide relevant self-help, and enable seamless escalation to a live agent when users need human support.

“We had to build trust, that ESS is not just a dead end for the user,” says David Finney, director of IT Service Management in Microsoft Digital. “It can help them self-solve their issue, but it also needs to tell them, ‘Hey, if I can’t fix it, I’ll hand you over to a live agent.’ This helps create trust, so the employee will turn to ESS as the first point of entry—because they know that it won’t leave them hanging.”

This broader perspective influenced the design recommendations that followed. Researchers concluded that employees evaluated support experiences across the entire process rather than judging each interaction in isolation.

When context was lost, employees noticed. When systems forced them to retrace their steps, frustration increased. When support channels felt connected, confidence improved.

“Maybe the biggest learning was that we needed to earn users’ trust before expecting them to consistently turn to self-service tools,” says Cherel. “It’s fine if they initially used ESS mainly as a trusted path to other support options. But if we could offer relevant self-help consistently, and help them escalate quickly when it’s not useful, users will increasingly choose self-service because it works—not because we force them into it.”

Turning research into design decisions

The next challenge involved translating these research insights into practical improvements in the Employee Self-Service Agent.

In addition to the above insights, the team identified specific situations that repeatedly caused employees to lose confidence in the agent. Some employees felt trapped in troubleshooting loops. Others encountered responses that seemed disconnected from what they had already tried. Some were uncertain when escalation was available or what would happen next.

The resulting work focused on helping the Employee Self-Service Agent acknowledge previous troubleshooting efforts, recognize employee context, communicate limitations clearly, and explain next steps. Researchers also documented a series of trust-breakdown scenarios, and then paired each scenario with specific behaviors the agent should demonstrate.

“The first thing we asked ourselves was, ‘How can we make the Employee Self-Service Agent more trustworthy?’” says Monserrat Oliver Sanchez, a senior design researcher with Designit who contributed to the research. “So we defined a personality for ESS that incorporated the qualities that should characterize the agent, and we created guidelines for how it should behave in different situations.”

The emphasis remained on practical employee needs. We examined how the agent should respond when it lacked confidence in an answer, when a user had already completed troubleshooting steps, and when escalation would provide a better outcome than continued self-service.

Ultimately, the research findings were turned into design guidance that could be tested, refined, and incorporated into future improvements.

The results we’ve gotten so far from the Employee Self-Service Agent are significant: a 30% overall reduction in IT support tickets being created and a 50% rate of employees starting their support journey with the agent (up from 27% before we began this trust-building initiative). We continue to pursue our goal of having every employee support need begin with a digital self-service experience.

Putting the user first

As our journey with the Employee Self-Service Agent progressed, the team arrived at a broader view of adoption.

Employees expected agentic support tools to work. They assumed that accurate information and reliable performance were baseline requirements. But embracing an AI-driven support agent depended on additional considerations that went beyond technical competence.

“It’s not about speed or performance; it’s more about continuity. It’s about ensuring that the user can do their work, that they can overcome the problems and challenges they have during their day seamlessly. That’s when we start building AI support agents that people will choose to rely on.”

Alex Calero, lead service designer, Designit

Employees wanted relevance. They asked for systems that recognized where they were in the support process and responded accordingly. And they looked for interactions that respected their previous efforts and connected naturally to the next step.

In other words, they wanted AI to treat them like a human being that needed understanding and help. It’s all part of designing software tools that put the user first.

“At the end of the day, the user should always be at the center of the equation,” says Alex Calero, a lead service designer with Designit. “The tool is there to support them. It’s not about speed or performance; it’s more about continuity and relevance. When a user encounters an IT, HR, or facilities issue, they shouldn’t spend time deciding where to go. They should know that they can simply ask ESS and it will take it from there. That is the ultimate measure of a human-centered AI adoption strategy.”