How AI Can Preserve Employee Knowledge Before It Retires
- 1 day ago
- 4 min read
A Practical Approach Companies Are Starting to Use
One of the most underestimated AI use cases I currently see across Europe is preserving employee knowledge before it disappears.
In many organizations I work with, the same concern comes up again and again. Experienced employees are approaching retirement.And with them often 30 or even 40 years of operational knowledge.
The challenge is not that companies lack documentation.
The challenge is something else: tacit knowledge.
Tacit knowledge is the experience that lives in people's heads:
the exceptions in processes
the supplier situations that require special handling
the lessons learned from past mistakes
the reasoning behind certain decisions
the small shortcuts that make systems work in practice
Traditional knowledge management tries to solve this problem by asking employees to write documentation.
In reality, this rarely works.
People are busy, and decades of experience cannot easily be written down in manuals.
This is where AI can support knowledge preservation in a very practical way.
At one client, we approached the problem differently.
Instead of focusing on writing documents, we focused on capturing experience.
Step 1: Knowledge Harvesting Through Expert Interviews
The first step was to conduct structured expert interviews with experienced employees. These were not long workshops or theoretical discussions. Instead, they were short and focused conversations.
We asked questions such as:
Where do new colleagues usually struggle most?
Which situations require the most experience?
What typical exceptions occur in this process?
What mistakes happen again and again?
What would you warn your successor about?
Some companies even turn these conversations into internal podcasts. Experts simply talk about their experience while the conversation is recorded.
The key idea is simple:
It is often easier for experts to talk than to write.
Step 2: Turning Conversations Into Structured Knowledge
Once the conversations are recorded, AI can take over a large part of the work.
The audio is:
automatically transcribed
structured into topics
tagged with relevant processes or systems
This transforms conversations into searchable organizational knowledge.
We then combine this material with existing sources such as:
process documentation
ERP manuals
guidelines and policies
project documentation
All of this becomes part of a central knowledge base.
Step 3: Building an AI Knowledge Assistant
On top of this knowledge base, we implemented a simple AI assistant.
Employees can now ask questions such as:
“What usually goes wrong in this process?”
“Why do we handle supplier X differently?”
“What should I watch out for when approving this case?”
“What changed in the latest system release?”
The assistant answers using both:
formal documentation
insights from expert interviews
This makes knowledge accessible exactly when employees need it.
A Practical Trick That Works Surprisingly Well
During expert interviews we always ask one additional question:
“Tell me about a situation where the standard process did not work.”
These stories are incredibly valuable.
We structure them as:
situation
decision taken
reasoning behind the decision
outcome
This creates something very powerful for AI systems: Decision memory.
Instead of explaining only how a process should work, the AI can explain how experienced people actually make decisions in difficult situations.
Three Practical Techniques That Make These Projects Work
Over time, we have learned that successful knowledge preservation projects depend less on technology and more on how knowledge is captured.
Three small techniques make a big difference.
1. The “Golden Questions” Framework
Generic interview questions produce generic answers.
Instead of asking about the process, we ask about experience.
Examples:
Where do new employees make mistakes?
Which cases always take longer than expected?
Which situations require the most experience?
When does the standard process not work?
These questions reveal tacit knowledge, which is often far more valuable than written documentation.
2. AI Knowledge Gap Detection
Once the knowledge base is built, AI can analyze it to identify missing knowledge.
For example, we ask the AI:
Which topics are frequently mentioned but not explained?
Which processes contain many exceptions but little documentation?
Which recurring questions have no clear answer?
This helps organizations identify knowledge gaps and decide which experts to interview next.
3. Expertise Mapping Before Retirement
Before starting the interviews, we create a simple expertise map.
Managers are asked a simple question:
“Who knows what in this organization?”
The answers are often informal:
“If something goes wrong in logistics, ask Peter.”
“Maria understands all supplier contracts.”
“Thomas knows the ERP configuration.”
We map this knowledge by topic, expert, and process importance.
This helps prioritize which expertise must be captured before it disappears.
What This AI Use Case Brings
Many AI discussions focus on automation or productivity.
But in the coming years, many European companies will face a different challenge:
A generation switch, where a large wave of experienced employees leaves the workforce.
If their knowledge disappears with them, organizations lose:
operational stability
historical context
problem-solving experience
AI cannot replace expertise.
But it can help ensure that expertise remains accessible to the organization.
And that may become one of the most important AI applications in the workplace.
