How many times have you searched your organisation’s knowledge base, found three different versions of the same policy, and had no idea which one was current? Or asked a question that clearly comes up every single week — and found nothing?

As a Knowledge Manager, this isn’t a rare moment. It’s the everyday reality of running KM in an organisation that moves faster than any team can document.

And every time it happens, the same thought creeps in: We have so much content. Why does it still feel like we can’t find the right content or right people to point us to the right content?

The honest answer is that most KM programs are built to store knowledge — not to actively manage its health. Gaps go undetected. Outdated content sits published for years. Nobody knows if an article is still worth the effort of keeping it around.

This is exactly where AI stops being a buzzword and starts being useful, everyday infrastructure.

1. Finding the Gaps We Can’t See

Normally, gap analysis depends on someone noticing a gap. A customer complains, an agent flags it, and only then does someone go check if an article exists. AI doesn’t wait for that. It listens all the time.

By scanning search logs, chatbot questions, and support tickets, AI can find out what people are actually asking — even when the same question is worded fifty different ways.

A simple AI-led gap analysis can:

group similar questions together, even if the wording is different, to reveal a gap hiding behind messy phrasing; spot articles that almost answer the question but stop just short; compare what exists against a list of all the topics that should be covered, to expose entire missing areas; rank gaps by how often they come up and how much they matter to the business, instead of guesswork

This is the difference between fixing a gap after someone complains, and knowing it’s there before anyone has to ask.

Case in point: At XYZCorp, agents kept getting asked about VPN errors — but each ticket used different wording (“can’t connect to VPN,” “VPN keeps failing,” “remote access not working”). No single article was written to catch all of these. AI grouped the tickets and showed there were over 200 such questions a month, with no clear article answering any of them well. That gap had existed for over a year, completely unnoticed.

2. Knowing What’s Actually True Anymore

Publishing an article isn’t the finish line. Content goes out of date. Policies change. Products change. And most KM teams have no easy way to know which articles have quietly become outdated or wrong.

AI can act as a constant accuracy check by:

pulling out facts, numbers, and steps from articles and checking them against the real source of truth (like product documentation or policy systems); flagging two articles that say different things about the same topic using an AI reviewer to catch old terms or steps that no longer make sense; sending anything flagged to a human expert to confirm — AI should never publish the fix on its own

The point isn’t to let AI decide what’s true and do all the work on its own. It’s to stop asking the knowledge team to re-read everything manually, all the time, just to catch what’s gone wrong.

3. Catching Content That’s Technically There, But Practically Dead

This is the quiet failure mode of KM — content that still exists, still shows up in search, still gets used, but refers to a policy or standard that’s no longer in force.

This matters most in places like presales, where proposal and RFP content constantly pulls from policy documents, compliance standards, and certification references. If the source policy has moved on and the content hasn’t, that outdated reference ends up in a client-facing document — and nobody notices until it’s already out the door.

AI can catch this by:

linking each article or proposal template to the exact policy or standard version it was written against watching for updates to policies, certifications, and standards, and flagging every linked article or template the moment a newer version is published setting simple rules so old, untouched reference content gets reviewed automatically on a schedule, not by chance building a simple dashboard that shows, at a glance, which policy-linked content is going stale, so nothing outdated makes it into a client-facing document

Case in point: XYZCorp’s presales team kept a standard security-compliance annexure that got copy-pasted into almost every proposal. When the underlying compliance standard was revised, nobody updated the annexure — it had been reused so often that no one remembered where it originally came from. AI flagged it the same week the standard changed, because the annexure was linked to that specific policy version. Without that link, an outdated compliance claim could have gone out in the next client proposal.
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The stakes get even higher in industries like pharma. A drug’s prescribing information — dosage, interactions, side effects, usage guidelines — can change after a regulatory update, a new clinical finding, or a safety alert. If a sales rep, call centre agent, or patient-facing article is still working off the old version, that’s not a minor inconsistency — it’s outdated medical guidance reaching a doctor or a patient. A pharma company linking every piece of content to its exact regulatory version, and getting flagged the moment that version changes, isn’t a nice-to-have. It’s the difference between staying compliant and putting someone’s health at risk.

4. Knowing What Should Exist — Before Someone Has to Ask

Filling gaps is reactive. The real shift is planning content ahead of time — AI suggesting what needs to be written next, based on patterns that would take a human months to spot.

This looks like:

pulling common themes from tickets, calls, and search behaviour into content suggestions recommending the right format, not just the topic — a simple decision-tree for troubleshooting, not another wall of text drafting a rough first version from scattered sources like emails or chat threads, for a human to finish and check comparing the product roadmap against current content, so articles are ready when a feature launches, not three weeks later

5. Measuring What Actually Matters

Page views were never a real measure of value. They only show attention, not impact. AI lets KM finally measure what content actually achieves.

This means tracking things like:

whether an article actually solved the query, or the customer still had to escalate; how much faster an issue gets resolved when the article is used; “zombie content” — articles that take effort to maintain but barely get used or barely help — as candidates to retire; whether content usage connects to real outcomes, like fewer tickets, faster onboarding, or lower churn

No, AI can never replace knowledge managers. Because they are the ones who feed AI knowledge and information that it requires to do its job of keeping the KB updated.

All what it does is— it frees us from being full-time content archaeologists, digging through what already exists, and lets us focus on what KM was always meant to do: getting the right knowledge to the right person, at the right time, without them having to go looking for it.

AI doesn’t fix KM by doing the writing for us. It fixes KM by finally giving us visibility into the health of what we’ve already built — and the foresight to know what’s missing before it becomes someone else’s bad day.

That’s not automation for its own sake. That’s KM finally working the way it was always meant to.

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Our house was divided long before the final. My son follows the French talent, my daughter has a soft spot for the Scandinavian teams, my wife supports Spain because half our friends and colleagues are Spanish.

I wanted Spain to win, and not for any of those reasons.

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I have spent twenty years working on how organisations hold on to what they know. So I was not watching a final that night. I was watching two theories of success play each other, and I badly wanted one of them to hold.

Argentina's theory is the one most of us secretly believe in: find someone extraordinary and let them decide the night. Spain's theory is slower and far less romantic. Teach the same ideas to everyone for years, at youth level and senior level, until nobody has to be told what to do.

Argentina came at them hard: physical, direct, trying to break the rhythm and force a mistake. It is a good strategy against a team that improvises.

Spain didn't improvise. What struck me was how little visible communication there was. Nobody shouting instructions, nobody reorganising anything. Everyone already knew where everyone else would be. My wife asked, around the hour mark, why nobody was doing anything spectacular. That was the point. Nobody needed to.

Remove one Spanish player and the shape held. Remove Argentina's best and the theory collapses. That difference is not luck. It is twelve years of the same knowledge being passed to the next group, and the next.

I have watched organisations lose that same match. Not on a pitch, but in a meeting room, eighteen months after someone resigned. You know the person: Tom. The one who knows why the framework was designed that way, who remembers what was agreed with the regulator in 2019 etc etc. They are not listed anywhere as critical infrastructure. They just are.

Three things worth doing before that Tuesday:

Write down why, not just what. Most decision records confirm something was approved. Almost none explain what made it sensible at the time.

Name your single points of knowledge. If this person were unavailable for three months, what stops? Most teams have never made that list.

Debrief the near-misses. They hold the most useful knowledge and the least defensiveness.

Knowledge Management is not about producing heroes. It is about building organisations that can afford to lose them.

Every organisation loses its best people eventually.

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My co-Instructor David Gurteen's recent article (referenced here) beautifully reminds us that organizations don’t simply transfer knowledge,
they create the conditions for it to emerge through conversation. I couldn’t agree more. Let’s explore this even further.

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From an Organization Development (OD) and Gestalt perspective, knowledge doesn’t simply live in documents or even in conversations. It emerges in the relationships between people.

A conversation is not merely an exchange of information. It is an encounter. It is where people make meaning and sense together. What spreads is rarely just an idea; what spreads is confidence, trust, possibility, identity, and the willingness or opportunity to see a situation differently.

This is why “use of self” matters. Use of Self is conscious choice of who you are in any given situation.

Someone can ask you the exact same question on two different days and you’ll likely respond in two different ways depending on how you choose to show up in that moment. You might show up with curiosity one day and certainty the next day. Curiosity invites. Certainty closes. Presence creates safety. Judgment creates caution. Our awareness, our assumptions, our emotional state, and even our body language become part of the knowledge-sharing system.

In Gestalt we often say that awareness is curative. I believe awareness is also generative. The more aware we become of ourselves and one another and our emergent situation, the greater our capacity is to notice opportunities that previously remained invisible. In KM we mention serendipity quite a bit. Serendipity is not simply luck, it is often heightened awareness meeting meaningful connection.

This also shifts the role of leadership.

Leaders are not simply people with a certain job title. They are architects of conversational spaces and they are participants in the relational field they create. Every interaction has the possibility to expand or contract the possibility for learning. Every response to a question informs people whether curiosity is welcome. Every reaction to uncertainty shapes whether people will bring forward unfinished ideas or keep them hidden.

That is why psychological safety and psychological courage is not a training program. It is something continuously co-created in thousands of everyday interactions.

I also appreciate David’s reference to “way shaping.” In OD we often describe this as designing conditions rather than designing outcomes. We cannot manufacture innovation, trust, or learning, but we can cultivate environments where they become more likely to emerge.

Perhaps the next evolution of this conversation is moving beyond individual conversations toward a newer concept currently being called “communityship.”

Communityship asks us to stop thinking primarily about individual leaders and begin thinking about collective responsibility. Knowledge does not belong to experts. It is a temporary gift to them. Expertise belongs to communities that continually create, refine, challenge, and apply it together.

When communities become healthy, knowledge flows almost effortlessly because people rarely ask, “who owns this?” Rather, they more often ask, “how can we make each other more successful?”

In that sense, Communities of Practice, Knowledge Cafés, peer assists, after-action reviews, and informal conversations are not simply KM techniques. They are practices that strengthen the relational fabric of an organization.

Perhaps that is the real competitive advantage, not having more knowledge than everyone else (not to be confused with all the information that AI “has”), but having stronger relationships through which knowledge can continually emerge and evolve.

As AI accelerates the creation and distribution of explicit knowledge (aka information), the uniquely human advantage becomes even more valuable. AI can generate information at extraordinary speed. It cannot replace the lived experience of making meaning together, sensing what matters in context, building trust, or helping another person discover something for themselves.

The future of KM may therefore be less about managing knowledge and more about convening conversations where awareness, conversation, and relationships enable knowledge to flow naturally.

Because in the end, conversations don’t just spread knowledge, they create the people and communities capable of generating it.

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Organizations invest heavily in formal knowledge sharing methods. Yet many of the most valuable insights spread through informal conversation. By creating the conditions for people to meet, talk, and exchange ideas, we increase the chances that knowledge findsthe person who needs it, when they need it.

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We often assume that knowledge spreads best through formal channels. We create training programmes, publish documents, build knowledge bases, and schedule presentations. All of these have their place, but they are only part of the picture.

Much of what we really know is shared in conversation.

A passing remark over coffee. A question asked after a meeting. An unexpected discussion between people from different teams. These are often the moments when a useful idea connects with a real problem.

Knowledge rarely moves in a straight line. It spreads through networks of relationships and conversations. The more opportunities people have to interact across teams, disciplines, and levels within an organization, the more likely valuable knowledge will reach the people who can use it.

This idea has echoes of James G. March's Garbage Can Model of organizational decision making. Under conditions of uncertainty, problems, solutions, people, and opportunities often come together in ways that cannot be planned. Chance plays a larger role than we sometimes like to admit.

Creating the Conditions for Serendipity

Good management cannot create serendipity on demand, but it can make it more likely. It can create opportunities for people to meet, encourage curiosity, make it safe to ask questions, and leave enoughspace for conversation rather than filling every minute with planned activity.

Artificial intelligence is a particularly good example. The technology is developing so quickly that no single person can keep up. Most of us learn about new tools, useful prompts, unexpected applications, and practical limitations from colleagues rather than from formal training. One person's small discovery can solve another person's immediate problem, but only if the conversation happens.

Creating More Opportunities for Conversation

If valuable knowledge often spreads through unexpected conversations, the obvious question is how organizations can create more opportunities for those conversations to happen.

The answer is probably less about introducing new knowledge management systems and more about paying attention to the everyday conversational life of the organization. Do people from different teams regularly meet? Is there time for informal discussion before and after meetings? Are new joiners quickly connected to networks beyond their immediate colleagues? Are people encouraged to ask questions, share half formed ideas, and admit what they do not know?

Some organizations deliberately create these opportunities through communities of practice, cross functional projects, lunch and learn sessions, or simple conversation spaces. A Knowledge Café, for example, brings people together to explore a topic such as AI, not to reach a decision or produce a report, but to share experiences, ask questions, and think together. These conversations are planned, but what emerges from them isnot. Their purpose is not to control the outcome but to increase the likelihood that people, ideas, and problems will connect in useful ways.

Way Shaping Rather Than Directing

This is where the idea of way shaping becomes important. Rather than trying to direct how knowledge should spread, leaders shape the conditions in which it can spread more naturally. They pay attentionto relationships, trust, curiosity, diversity of connections, and opportunities for people to interact across organizational boundaries.

None of this guarantees that a valuable conversation will happen. Serendipity cannot be managed. But it can be mademore likely. By shaping the environment rather than directing the outcome, organizations increase the chances that the right conversation will happen atthe right moment between the right people. That is where some of an organization's most valuable learning takes place.

If we want knowledge to spread more freely, we should spend less time trying to control it and moret ime shaping the conditions in which good conversations can happen. We cannot plan every valuable exchange, but we can make it far more likely that the right people meet, talk, and learn from one another.

Note: This post The Power of Random Conversations first appeared in my online blook on Conversational Leadership.

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KM is more often than not treated as a support function instead of being recognized as a strategic enabler of growth, innovation, and operational efficiency because its value is not always reflected through clear and measurable ROI.
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Something upgrading or automating KM libraries has to wait until budgets are bigger. But the truth is stark: the wake-up call usually comes too late. By the time companies realize KM was the missing link, the damage is already done — lost expertise, repeated mistakes, and wasted opportunities.

Healthcare: Knowledge That Saves Lives

In healthcare, discoveries happen every hour. New research, updated treatment protocols, revised drug interactions — all of it must be captured, updated, and shared seamlessly. KM here isn’t about efficiency; it’s about survival.

Case Study – LV Prasad Eye Institute (India):  

LVPEI implemented the eyeSmart Electronic Medical Record (EMR) system to manage millions of patient records across multiple centers. Before KM practices, critical patient data was siloed, leading to delays in diagnosis and treatment. With a centralized KM-driven EMR:

• Doctors across locations accessed real-time patient histories, reducing test duplication.
• Research teams leveraged aggregated data to identify patterns in eye diseases faster.
• Operational efficiency improved, reducing patient wait times and enhancing the quality of care.

This initiative transformed LVPEI into a model for digital healthcare, proving that knowledge sharing can save lives and scale impact. (Source: LV Prasad Eye Institute case study, eyeSmart EMR project)

Aviation: Safety in Shared Wisdom

In aviation, every incident report, maintenance log, and pilot’s experience contributes to collective intelligence. If KM fails, lessons learned in one cockpit never reach another. The result? Repeated mistakes, compromised safety, and erosion of trust in an industry where trust is everything.

Case Study – Global Incident Reporting Systems:  

Airlines and regulators use shared databases of flight incidents and near-misses.

• Pilots and engineers log issues into repositories.
• Patterns are analyzed globally, ensuring lessons learned in one airline prevent accidents elsewhere.
• Safety protocols evolve continuously, reducing repeat errors and strengthening passenger trust.

Without KM, these insights would remain isolated, and mistakes would recur across fleets. (Source: International Civil Aviation Organization – ICAO Safety Reporting Systems)

The Critical Human Factor

In corporates, the challenge is human, not technology. Employees leave, or worse, they’re laid off. Do we really expect them to willingly share their hard-earned wisdom on the way out? The truth is, much of that knowledge walks out the door with them.

Picture this: a team in India has mastered a niche skill through repeated projects. Meanwhile, a team in the UK is struggling with the same challenge. They’re not connected, so the struggle continues. KM is the bridge that should have existed but didn’t. Without it, organizations waste time reinventing the wheel, while expertise sits untapped elsewhere.

The Need of the Hour

So, when do we know KM is no longer optional? The answer is simple: when the cost of not having it outweighs the effort of building it. In healthcare, that cost is measured in lives. In aviation, in safety. In corporates, in lost innovation, and wastes potential.

KM is not about storing documents in a repository. It’s about creating a living, breathing ecosystem where knowledge flows freely — across geographies, hierarchies, and time zones. It’s about ensuring that wisdom doesn’t die with attrition, but lives on to guide the next decision, the next project, the next breakthrough.

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