{"id":24648,"date":"2026-07-16T09:00:00","date_gmt":"2026-07-16T16:00:00","guid":{"rendered":"https:\/\/find.codeghost.online\/insidetrack\/blog\/?p=24648"},"modified":"2026-07-15T15:11:16","modified_gmt":"2026-07-15T22:11:16","slug":"ai-for-knowledge-management-keeping-support-content-up-to-date-at-microsoft","status":"publish","type":"post","link":"https:\/\/find.codeghost.online\/insidetrack\/blog\/ai-for-knowledge-management-keeping-support-content-up-to-date-at-microsoft\/","title":{"rendered":"AI for Knowledge Management: Keeping support content up-to-date at Microsoft"},"content":{"rendered":"\n

Our AI agents and self-help channels are often our employees\u2019 first stop for support, and like anyone, they expect the answers they get to be correct.<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

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\u201cKnowledge 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.\u201d<\/p>\nSilvina Olkies, senior director, Service Management, Microsoft Digital<\/cite><\/blockquote>\n\n\n\n

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.<\/p>\n\n\n\n

Internally here at Microsoft, that\u2019s where our team got involved.<\/p>\n\n\n\n

We\u2019re Microsoft Digital, the company\u2019s IT organization, and our team saw an opportunity to use AI to dynamically and proactively update our knowledge management systems.<\/p>\n\n\n\n

Our first step was\u2014in partnership with our Global Help Desk\u2014to strengthen our self-serve help and reduce the number of steps users need to take to find the right answers. It\u2019s a process that many of our own customers can apply to their knowledge management transformation.<\/p>\n\n\n\n

\u201cKnowledge management today is about making sure people can find the right answers the moment they need them,\u201d says Silvina Olkies, a senior director of Service Management in Microsoft Digital. \u201cWhen knowledge stays current, employees get unblocked faster and productivity improves, making the overall support experience far more efficient.\u201d<\/p>\n\n\n\n

The challenge: Fragmented knowledge, manual reviews<\/h2>\n\n\n\n

For our Global Help Desk, the challenge wasn\u2019t 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.<\/p>\n\n\n\n

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\u201cIf the knowledge isn\u2019t accurate and current, the experience breaks down immediately. Bad content leads to bad answers.\u201d<\/p>\nKevin Verdeck, senior IT service manager, Microsoft Digital<\/cite><\/blockquote>\n\n\n\n

In today\u2019s fast-changing AI-powered world, it doesn\u2019t take long for knowledge to become incomplete, out of date, or redundant. This shows up in the inaccurate answers employees might receive.<\/p>\n\n\n\n

In an environment increasingly powered by search and AI, weak knowledge equals weak results.<\/p>\n\n\n\n

\u201cIf the knowledge isn\u2019t accurate and current, the experience breaks down immediately,\u201d says Kevin Verdeck, a senior IT service manager in Microsoft Digital. \u201cBad content leads to bad answers.\u201d<\/p>\n\n\n\n

At our Global Help Desk, keeping that content current required a manual review process.<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

The result was a system that was reactive and hard to scale.<\/p>\n\n\n\n

When employees couldn\u2019t 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.<\/p>\n\n\n\n

\u201cOne five-member team was reviewing 1,900 self-service KB articles and 1,700 agent-facing KB articles every six months, and that didn\u2019t even include the many SharePoint sites,\u201d Verdeck says. \u201cIt was basically their full-time job doing regular reviews.\u201d<\/p>\n\n\n\n

Turning raw data into knowledge<\/h2>\n\n\n\n

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.<\/p>\n\n\n\n

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\u201cWhen 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.\u201d<\/p>\nAnkit Guddewala, software engineer II, Microsoft Digital<\/cite><\/blockquote>\n\n\n\n

An AI pipeline solution made sense because we wanted to fix the issue at scale.<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

\u201cWhen you have a large volume of data, it’s a silent gold mine,\u201d says Ankit Guddewala, a software engineer in Microsoft Digital. \u201cThe sheer brilliance lies in taking that data, making it sing, and letting it tell you exactly where the treasure is.\u201d <\/p>\n\n\n\n

The stages to complete the work happen as follows:<\/p>\n\n\n\n

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  1. Ingest the raw support data:<\/strong> The team pulls in large volumes of incident data from our ticketing systems.<\/li>\n\n\n\n
  2. Clean and structure noisy data:<\/strong> 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.<\/li>\n\n\n\n
  3. Find patterns across incidents:<\/strong> We cluster tickets to help identify recurring issues and avoid cluttering the knowledge base with one-off scenarios.<\/li>\n\n\n\n
  4. Compare patterns against existing knowledge: <\/strong>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:\n