Q & A

AI does not change the basic distinction for me, but it does sharpen the conversation.

When we write something down, record a meeting, or build a document, we are not capturing knowledge. We are creating information. That information can be useful, sometimes very useful, but it is not the knowing itself.

AI is trained on vast bodies of that information. It can recombine, summarise, and generate responses that appear knowledgeable. In many cases, it can outperform humans in recall and synthesis. That is impressive, but it does not mean the knowledge has been transferred in any real sense.

What remains missing is lived context. Experience. Judgement in the moment. The subtle cues that shape how we act in a particular situation. That is where tacit knowing sits, not hidden in the head as a substance, but emerging in action.

What AI does offer is a new kind of partner in that process. It can help us reflect, question, and see patterns we might otherwise miss. But the knowing still comes into being in the interaction, in the doing.

So no, AI does not collapse the distinction. If anything, it makes it more important to understand.

SearchUnify Lens:

Many businesses mistake AI-generated responses for knowledge transfer. In reality, value comes from contextual intelligence layered on top of information.

SearchUnify enables AI to operate within context — combining user intent, historical interactions, and enterprise knowledge to deliver responses that are not just accurate, but actionable.

This is where AI customer support agents move beyond summarization to true assistance.

→ Read: What Makes AI Customer Support Agents Truly Effective

If I had to reduce it to one thing, it would be this. We start in the wrong place.

Most KM programmes begin with tools, systems, or content strategies. They assume that if we can store knowledge, organise it, and make it accessible, people will use it and behaviour will change. That assumption is deeply embedded and rarely questioned.

In practice, it does not work like that.

What gets in the way is not a lack of information. It is how people relate to each other. Trust, curiosity, willingness to ask for help, willingness to admit not knowing. These are relational issues, not technical ones.

Before thinking about platforms or AI, organisations need to pay attention to how people talk together. Do they feel able to question? Do they listen? Are conversations open or performative? Is it safe to explore uncertainty?

If those conditions are not there, no tool will fix the problem.

So the starting point is not technology. It is conversation. Create the conditions where people can think together, and the rest becomes much easier. Ignore that, and KM becomes another system that people quietly work around.

SearchUnify Lens:

This aligns with a pattern we see across KM transformations —organizations often invest in platforms before addressing adoption and behavior.

SearchUnify addresses this by embedding knowledge directly into the tools agents already use (CRM, ticketing systems, chat), ensuring that adoption happens naturally, not forcefully.

This reduces friction, increases trust, and drives consistent usage — which is where real KM success begins.

→ See how global teams drive knowledge adoption at scale

The phrase “capturing expert knowledge” is part of the problem. It suggests that what matters can be extracted, written down, and stored. In reality, much of what experts know only becomes visible in context, in interaction, in the way they respond to situations.

What I have seen work are approaches that bring people together around real work. Apprenticeship, shadowing, peer conversations, storytelling in context. Not polished stories, but accounts of what actually happened, what was noticed, what mattered.

In those interactions, less experienced people begin to develop their own judgement. They do not receive knowledge as a package, they participate in its formation.

AI can help at the margins. It can summarise discussions, surface patterns across cases, and make it easier to access relevant information. It can support reflection.

But it cannot replace the relational process. It cannot recreate the subtle, situated nature of expertise as it is enacted.

So the focus should be less on capturing and more on creating opportunities for people to work and think together. That is where the real transfer happens.

SearchUnify Lens:

Instead of trying to “capture” expertise, SearchUnify focuses on learning from behavior at scale.

Every case resolved, every query searched, every interaction – becomes part of a continuous learning loop. AI identifies patterns, surfaces recurring issues, and improves knowledge delivery over time.

This ensures that expertise is not just documented, it is operationalized and reused in real-time.

The shift is from seeing knowledge as something we possess to seeing knowing as something we do.

Traditional knowledge management tends to treat knowledge as a resource. Something that can be captured, stored, and reused. That has value, but it rests on an assumption that what matters is already known and can be made available when needed.

Conversational leadership starts elsewhere. It recognises that in many situations, especially the more complex ones, we do not yet know what we need to know. The issue is not access to knowledge, it is how we come to understand what is going on and what to do about it.

That is a conversational process. It happens in interaction, in how we question, challenge, and build on each other’s thinking. Meaning is not transferred, it is formed between us.

When we take this seriously, the focus of the organisation shifts. Less attention on repositories and more on the quality of conversations. Less emphasis on sharing what we know and more on exploring what we do not yet understand.

This matters because action follows from how we make sense of things together. If our conversations are narrow, rushed, or constrained, our thinking will be too. If they are open, reflective, and attentive, we create the conditions for better judgement and wiser action.

SearchUnify Lens:

This shift aligns with how modern KM platforms are evolving — from static repositories to dynamic, learning systems.

SearchUnify enables organizations to build closed-loop knowledge ecosystems, where:

• Knowledge is continuously refined through usage
• Feedback is automatically captured
• AI improves relevance over time

This creates a system where knowledge is not just stored – it is constantly evolving through interaction.

In stable environments, where problems are well understood, knowledge can often be applied. Best practice works, and access to information is enough.

But much of the world we now operate in is not like that. It is uncertain, fast moving, and often ambiguous. In these situations, there is no clear answer waiting to be found. We have to work things out as we go.

That is why shared thinking becomes critical. No single individual holds the full picture. Different people see different aspects. It is only through bringing those perspectives into a relationship that something more complete can emerge.

Conversational leadership is about creating the conditions for that to happen. Not debate in the sense of defending positions, but dialogue in the sense of thinking together. Attending to how understanding develops in the interaction itself.

This exposes a limitation in conventional KM. If we assume that knowledge can be captured and reused, we overlook the fact that much of what matters is context dependent and emergent. What worked before may not apply now, or may need to be reinterpreted.

So the task is not simply to access what is known, but to engage in a process of ongoing sensemaking. Conversation becomes central, not as a way of exchanging views, but as the medium through which new understanding comes into being.

SearchUnify Lens:

In complex support environments, the challenge is not access to knowledge — it’s making the right decision in the moment.

SearchUnify delivers contextual intelligence at scale, using AI to surface the most relevant insights based on:

• User behavior
• Case context
• Historical resolution patterns

This enables teams to align faster, respond smarter, and operate with shared clarity, even in uncertain scenarios.