Our AI agents and self-help channels are often our employees’ first stop for support, and like anyone, they expect the answers they get to be correct.
This makes accurate content essential. If our content is stale, even the best agent or search engines will return wrong answers, which is frustrating to everyone.
“Knowledge management today is about making sure people can find the right answers the moment they need them. When knowledge stays current, employees get unblocked faster and productivity improves, making the overall support experience far more efficient.”
Silvina Olkies, senior director, Service Management, Microsoft Digital
That means the knowledge bases that get tapped for answers need be accurate, and for that to happen, they need to be updated frequently and without delay.
Internally here at Microsoft, that’s where our team got involved.
We’re Microsoft Digital, the company’s IT organization, and our team saw an opportunity to use AI to dynamically and proactively update our knowledge management systems.
Our first step was—in partnership with our Global Help Desk—to strengthen our self-serve help and reduce the number of steps users need to take to find the right answers. It’s a process that many of our own customers can apply to their knowledge management transformation.
“Knowledge management today is about making sure people can find the right answers the moment they need them,” says Silvina Olkies, a senior director of Service Management in Microsoft Digital. “When knowledge stays current, employees get unblocked faster and productivity improves, making the overall support experience far more efficient.”
The challenge: Fragmented knowledge, manual reviews
For our Global Help Desk, the challenge wasn’t just the volume of content, but also its condition. Support knowledge is spread across thousands of self-service articles, agent-facing systems, and SharePoint sites, all constantly evolving.
“If the knowledge isn’t accurate and current, the experience breaks down immediately. Bad content leads to bad answers.”
Kevin Verdeck, senior IT service manager, Microsoft Digital
In today’s fast-changing AI-powered world, it doesn’t take long for knowledge to become incomplete, out of date, or redundant. This shows up in the inaccurate answers employees might receive.
In an environment increasingly powered by search and AI, weak knowledge equals weak results.
“If the knowledge isn’t accurate and current, the experience breaks down immediately,” says Kevin Verdeck, a senior IT service manager in Microsoft Digital. “Bad content leads to bad answers.”
At our Global Help Desk, keeping that content current required a manual review process.
Our teams analyzed usage data, depended on support agents to report missing or outdated content, and worked through recurring review cycles that relied on subject-matter experts to help confirm whether articles were still accurate. This took significant time and coordination, and even then, some issues were identified only after employees had already hit a dead end.
The result was a system that was reactive and hard to scale.
When employees couldn’t find answers, issues were pushed to advanced support. Poor knowledge quality created poor outcomes, while those responsible for fixing it were struggling with maintaining it.
“One five-member team was reviewing 1,900 self-service KB articles and 1,700 agent-facing KB articles every six months, and that didn’t even include the many SharePoint sites,” Verdeck says. “It was basically their full-time job doing regular reviews.”
Turning raw data into knowledge
Our team in Microsoft Digital set out to build AI for Knowledge Management, a centralized system that could scale across multiple repositories, cut the manual work of keeping content current, reduce reliance on busy content owners, and prepare knowledge for people and AI to use.
“When you have a large volume of data, it’s a silent gold mine. The sheer brilliance lies in taking that data, making it sing, and letting it tell you exactly where the treasure is.”
Ankit Guddewala, software engineer II, Microsoft Digital
An AI pipeline solution made sense because we wanted to fix the issue at scale.
The real opportunity was to turn every resolved support ticket into a signal that looked at what the employee was asking for (nature of the issue or request), whether the answer was already documented, and how the AI and human agents handled it. So, we began with our large amounts of support ticketing data and systems as the starting point.
“When you have a large volume of data, it’s a silent gold mine,” says Ankit Guddewala, a software engineer in Microsoft Digital. “The sheer brilliance lies in taking that data, making it sing, and letting it tell you exactly where the treasure is.”
The stages to complete the work happen as follows:
- Ingest the raw support data: The team pulls in large volumes of incident data from our ticketing systems.
- Clean and structure noisy data: Tickets can include conversation notes, incomplete details, inconsistent writing styles, and abandoned issues. We use the AI enrichment layer to turn those details into structured fields, such as reported versus actual problems and remediation steps.
- Find patterns across incidents: We cluster tickets to help identify recurring issues and avoid cluttering the knowledge base with one-off scenarios.
- Compare patterns against existing knowledge: We use the system to search current articles and rank how closely the resolution aligns to current content to determine what steps to take next. For example:
- Below 40 percent: Create knowledge
- 40 to 80 percent: Update existing knowledge with missing details
- Above 80 percent: No update needed
- Notify the appropriate knowledge managers: Subject-matter experts are notified by email so they can validate the change(s) and add more detail if needed.
“Our goal is to free people from the manual work of maintaining content so they can focus on improving its quality. With the right human-in-the-loop balance, AI can do the heavy lifting while people make sure the final knowledge is accurate and useful.”
Namrata Ladda, product manager II, Microsoft Digital
The result is far less time spent reviewing thousands of articles during every review cycle. We keep humans in the loop to validate the output. Their feedback helps tune the AI model, so it improves over time.
“Our goal is to free people from the manual work of maintaining content so they can focus on improving its quality,” says Namrata Ladda, a product manager in Microsoft Digital. “With the right human-in-the-loop balance, AI can do the heavy lifting while people make sure the final knowledge is accurate and useful.”
Impacts and what’s next on the journey
Using our new AI for Knowledge Management platform, our Global Help Desk teams can identify knowledge gaps without waiting for someone to report them. They’re using AI to generate structured article drafts so humans can focus on quality, not search through data.
The Global Help Desk projects the solution will save them an estimated 16,000 hours annually; result in a 10 percent reduction in support tickets; and reduce the number of advanced support escalations. This leaves everyone on the team more time to directly help employees more quickly, when needed.
“Turning our knowledge base from a static thing into a living knowledge base is a big step forward,” Ladda says.
Other Microsoft teams, including HR, have expressed interest in leveraging the content management platform. Once the product has completed internal testing, AI for Knowledge Management will be released to all company employees and customers.
“This solution moves knowledge management from manual maintenance to an intelligent capability that helps organizations scale and apply what they know more effectively,” Olkies says.
Key takeaways
Here are some actions your organization can take right away to strengthen your own knowledge foundations:
- Start with knowledge. Treat your knowledge base as the source of truth that determines whether AI and self-help succeed.
- Audit how knowledge is maintained. Look beyond publishing workflows to understand how gaps and outdated content show up in real usage.
- Spot and remove manual bottlenecks. Identify where people are spending the most time searching and reporting knowledge and target those steps for automation.
- Use AI to maintain, not just serve, content. Apply AI to identify gaps and refresh out-of-date information.
