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AI can help organizations find, organize and reuse expertise before experienced employees retire or leave—but it cannot preserve judgment on its own. The strongest approach combines searchable, source-linked knowledge with structured interviews, mentoring, documented workflows and human review.
Why organizations lose experience
When a long-serving employee leaves, the loss is not limited to files. Important know-how may live in remembered exceptions, informal workarounds, relationships and the reasoning behind decisions. That knowledge can be hard to replace quickly, especially in specialized roles.
The scale of the challenge is visible in APQC’s 2025 survey of 1,000 respondents: 30% said their organizations capture knowledge from departing retirees most of the time or always, while 41% said they rarely or never do. Lack of time was the most commonly reported barrier (52%), followed by insufficient resources (45%). These figures describe APQC’s respondents, not all employers. APQC’s 2025 State of Knowledge Management report
A 2023 U.S. Government Accountability Office report offers a concrete example of why succession matters. It said, “NIST has a small, specialized workforce with unique institutional knowledge that is difficult to replace in a timely manner.” The report describes mentoring and retirement tracking and recommends succession and workforce planning; it does not report that NIST deployed AI for this purpose. GAO report GAO-23-105521
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Where AI fits in knowledge transfer
AI can assist with capturing information from existing materials, organizing it, helping employees discover relevant guidance and making knowledge easier to reuse. For example, a system might help surface a procedure or past discussion in response to a question. APQC’s 2025 survey of 845 respondents found that 21% use AI to mine conversations. That is one reported practice, not evidence that mining conversations alone captures an expert’s full understanding.
Other approaches remain more common: in the same APQC respondent group, 74% reported using person-to-person transfer and 62% manually documenting knowledge. These methods can complement AI. A tool can retrieve a documented answer, while a colleague can explain why a rule exists, when an exception applies and how to handle a situation the documentation does not cover.
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Adoption is still developing. Among 1,000 APQC survey respondents in 2025, 15% said their organizations were operating or optimizing AI for knowledge management; 55% were considering, evaluating or piloting it. The categories and respondent count matter: the figures should not be read as a universal adoption rate. APQC’s 2025 State of Knowledge Management report
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A searchable archive is not the same as transferred expertise. Experienced employees often use synthesis, heuristics and tacit judgment that have never been written down. Generative AI may help organize explicit information, but it cannot be assumed to recover what was never recorded or to understand every context in which an expert applies it.
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A 2025 article in the ACM’s knowledge-management discussion of generative AI identifies opportunities as well as risks, including over-reliance and the loss of tacit or unarticulated knowledge. That is why AI should support—not replace—structured conversations, mentoring and review by people familiar with the work. ACM article on generative AI and knowledge management
Build a transfer process that uses AI responsibly
- Identify critical knowledge early. Work with managers and subject-matter experts to identify roles, processes and decisions where a departure would create a serious gap. Prioritize expertise that is difficult to replace, rather than trying to record everything.
- Capture different kinds of knowledge. Gather current procedures and records, but also schedule interviews, demonstrations, mentoring and workflow walk-throughs. Ask experts to explain decision points, exceptions, warning signs and the reasons behind common practices.
- Use AI to organize and find material. Apply AI to suitable, approved source material to help categorize content, surface related records or retrieve answers. Treat generated summaries and answers as aids that require validation, not as authoritative replacements for the sources.
- Keep answers traceable. As an implementation recommendation, configure knowledge tools to point employees to the source material behind an answer. Assign owners to important content, review it when processes change and remove or clearly mark superseded guidance.
- Control access and review sensitive material. Set permissions appropriate to the information, and decide who may contribute, see or approve it. Have qualified people validate critical guidance before it is relied on for consequential work.
- Check that knowledge transfers in practice. Do not measure success only by the size of an archive or the number of AI-generated answers. Check whether successors can complete important tasks, find current guidance, recognize exceptions and know when to escalate a question.
These controls are practical recommendations, not survey results. They address the risks APQC respondents identified and the organization’s need to keep knowledge accurate and usable.
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Risks to manage before employees rely on AI answers
In APQC’s 2025 survey, respondents reported concerns about answer accuracy (46%), data privacy and security (38%) and compliance of AI answers (36%). APQC also cautions that systems without structured oversight can return redundant, outdated or inaccurate material. The percentages represent the surveyed respondents; they are not guarantees of the risk level at any particular organization. APQC’s 2025 State of Knowledge Management report APQC on AI and knowledge management
- Accuracy: Require review for high-impact guidance and make it easy to find the underlying source rather than treating a fluent answer as proof.
- Privacy and security: Decide what information may be processed, where it may be stored and which employees have access. Do not feed sensitive conversations or records into a system unless its use is approved.
- Compliance: Have responsible teams assess whether generated guidance meets applicable internal and external requirements before employees act on it.
- Staleness and duplication: Assign content owners, review dates and a process to correct, retire or consolidate obsolete material.
- Over-reliance: Make clear when employees should consult an expert or escalate a question, particularly when an answer is incomplete, conflicting or outside the documented procedure.
How to tell whether the effort is working
Evaluate whether knowledge has moved into use, not merely whether it has been captured. A practical review can ask whether incoming employees can perform priority tasks, whether answers lead to current and approved sources, whether experts are correcting errors and whether teams still depend on a departing person for routine decisions.
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Use those findings to improve interviews, documentation, access rules and content review. AI is most useful as part of a continuing knowledge-management practice: it can make recorded expertise easier to find, while people remain responsible for explaining, validating and applying it.
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