What can AI agents really do for Knowledge Retention?
At first glance, the answer seems simple: quite a lot.
But I see three distinct families of AI agents, each taking retained knowledge one step further.
š¹ Agents that help elicit knowledge
They guide employees and experts through a structured process of reflection and questioning, helping surface knowledge that is often difficult to articulate.
Knowledge moves from deep in our minds into explicit, documented knowledge.
š¹ Agents that help package knowledge
They transform what has been captured into engaging learning experiences: smart learning modules, training materials, videos and other formats.
Knowledge moves from text into a richer, multisensory experience: voice, image and movement.
These first two types of agents can be developed once, based on a robust methodology, and then used repeatedly across many Knowledge Retention processes.
But the third family is different.
š¹ Agents that turn retained knowledge into organizational capability
When the knowledge being retained involves problem solving, complex decision-making, professional judgment or other thinking processes, documenting it may not be enough.
We can build an AI agent that applies the expert's retained knowledge when a new situation arises: helping people think through a problem, consider relevant factors, evaluate alternatives and make better decisions.
Here, the knowledge does not become just text.
It does not become just a richer learning asset.
It becomes an organizational capability.
And perhaps this is one of the most exciting opportunities AI brings to Knowledge Retention: moving from preserving what experts know to preserving, at least in part, what the organization is capable of doing with that knowledge.
One important caveat, though.
An AI agent is only as good as the professional methodology behind it. The sophistication is not just in the technology, but mainly in knowing how to elicit the valuable knowledge, how to structure it, and how to translate expert thinking into a reusable capability.
And this may be exactly where our expertise as Knowledge Managers becomes even more valuable in the AI era.
We are no longer only preserving knowledge. We are engineering it into capability.