The handover is complete. The files are saved. A replacement is on the way.

Then something goes wrong, and someone asks: “Who knows how we dealt with this last time?”

Everyone names the person who has just left.

It is a familiar workplace moment. It also reveals a weakness that a tidy handover document can easily hide. Organisations depend on knowledge that people rarely write down: which warning signs matter, when to escalate a problem, and who can bring the right people together when the usual process stalls.

A paper by Aartee Geshwaree Huzooree, our CBISS theme lead, published in Organizational Dynamics, explores what happens when organisations keep losing this experience while dealing with one disruption after another. It also considers how generative AI could help preserve some of it.

The starting point is simple. A crisis puts people under pressure. Some leave through exhaustion, retirement or restructuring. Their departure leaves the remaining team with less experience to draw on. If another crisis arrives before that experience has been rebuilt, the organisation starts from a weaker position.

The paper calls this a “knowledge depletion spiral”.

Replacing the person does not immediately repair the gap. A capable new colleague can learn the procedures quickly and still need years to develop the judgement their predecessor carried.

Think of an experienced project manager who notices that a supplier’s reassuring updates have become unusually vague. The report still looks fine. The deadlines have not yet slipped. But something feels familiar, and they know which questions to ask.

A handover might list the supplier’s contact details. It may never explain what made that manager pick up the phone.

That is the kind of experience the paper argues organisations should make a greater effort to preserve.

Its proposed approach starts by identifying where important knowledge rests with very few people. These may be senior specialists, but they may also be the colleague everyone calls when a problem crosses departmental boundaries.

The next step is a conversation about real events. Instead of asking someone to “write down everything you know”, ask them to revisit a difficult decision. What did they notice first? What would a newcomer have missed? Who did they contact, and why? When would the same approach be a mistake?

Generative AI could help a facilitator develop follow-up questions and organise the answers into short, usable records. The employee would check and approve those records, including the circumstances in which their advice applies.

The final step matters just as much: getting that experience back into use. A carefully maintained archive offers little protection if nobody consults it. The proposal is to bring relevant past experience to the team before its first major decision during a new crisis.

There are limits. An AI summary cannot capture everything a person knows. Some skills need to be learnt through observation and practice. Previous experience can also mislead when circumstances change, so the team still needs to question the advice.

Trust matters too. People need to know what is being collected and how it will be used. Participation in the knowledge-sharing conversations should be voluntary. A system intended to preserve experience could quickly lose its value if staff experience it as monitoring.

The paper presents a framework for organisations to test, rather than evidence that the full approach already works in practice. Its immediate value is the question it puts to leaders.

Before the next departure, ask whose experience the team would struggle most to lose. Then make time to understand how that person thinks through a difficult situation.

That conversation may reveal a gap no staffing spreadsheet can show.