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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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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  A lot of internal knowledge problems look small until
  they start slowing people down. A new hire cannot
  find the current onboarding guide. A manager keeps
  explaining the same approval process. A team follows
  an old workflow because the updated version sits in a
  private document. Training exists, but people still
  complete the same task in four different ways.

For KM teams, that is the real work. The goal is not to keep adding pages to a knowledge base. The goal is to make knowledge easier to collect, easier to trust, and easier to use when someone is trying to get work done.

That takes more than tidy folders. It takes a working system for turning scattered expertise into clear documentation, useful training, and internal processes people can actually follow.

Start by collecting better source material

Weak documentation usually starts before the writing begins.

If the source material is thin, rushed, or scattered across messages, the finished guide will feel the same way. It may explain the basic steps but miss the exceptions. It may describe what happens, but not who owns each part. It may sound clear to the subject matter expert and still leave a new employee guessing.

KM teams can avoid a lot of rework by treating knowledge capture as a separate stage.

Instead of asking someone to "send over the process," ask for the pieces that make the process usable. A good intake should pull out the trigger, owner, tools, approvals, common mistakes, exceptions, and update cycle.

A simple capture brief might ask questions like these.

• What starts this process?
• Who owns the decision or final approval?
• What tools, files, forms, or templates are involved?
• Where do people usually get stuck?
• What changes when the case is unusual?
• How often does the workflow need review?

That kind of structure helps experts share what they know without dumping a messy document into the KM team's lap.

A tool like Content Snare can fit here when teams need a cleaner way to collect documents, answers, and internal inputs from different people. For KM work, the benefit is less about gathering files and more about reducing the loose, forgettable back and forth that makes documentation harder than it needs to be.

Write for the person using the process

Many internal documents are written from the expert's point of view. That is why they often feel accurate but hard to use. The person reading the guide may not know the system, the team language, or the reason behind each step. They may be under time pressure. They may be trying to complete the task for the first time while a customer, manager, or colleague waits for an answer.

That changes how the document should be written.

A useful process page should explain what the person is trying to do, when the process applies, what needs to happen first, and what a good result looks like. The writing should be plain enough for someone outside the team to follow, but not so shallow that experienced employees have to chase missing details.

Good KM documentation often works in layers. The first layer gives the quick path. The next layer explains the steps. A deeper layer covers exceptions, edge cases, and ownership.

That structure keeps the page readable. It also respects the fact that not every reader needs the same depth at the same moment.

Connect documentation with training

Training and documentation often live in separate systems. One team owns the knowledge base. Another owns courses. Another owns the process itself.

Employees do not care about those boundaries. They just need to learn the task and do it correctly.

KM teams can help by building documentation that is easy to reuse in training. A clear process overview can become part of onboarding. A checklist can become a practice exercise. A set of common mistakes can become a short refresher lesson after the first month on the job.

This matters for recurring training needs such as compliance updates, system rollouts, customer handoffs, quality checks, and manager onboarding. If the source documentation is clean, the training team is not rebuilding the same material from scratch every time.

For organizations running structured learning programs, training administration software can help manage the operational side of that work. Sessions still need scheduling. Participants need tracking. Reminders need to go out. Certificates, attendance, and completion records need a reliable home.

KM teams may not run those systems directly, but their content feeds them. When the knowledge base is current and written in reusable pieces, training becomes much easier to maintain.

Make processes easier to follow in the moment

A process can be documented and trained, then still fail in daily use.

That often happens because the guidance is too far away from the work. Someone learns a workflow during onboarding, then needs it again three weeks later inside a tool they barely remember. If the guide is long or hard to find, they may ask a coworker, guess, or build their own shortcut.

For software based workflows, written instructions are useful, but screenshots and interactive guidance can remove a lot of friction. People often need to see the action, not just read the step.

That is where Supademo can support internal process adoption. KM or enablement teams can create walkthroughs that show employees how to complete common workflows inside the tools they already use.

This works well for approval requests, CRM updates, reporting steps, customer handoffs, account setup, or any task where the screen matters. The best walkthroughs stay narrow. One task. One user need. One clear outcome.

A broad tour of an entire system rarely helps much. A short walkthrough that shows exactly how to submit a request or update a record is far more useful.

Keep ownership visible

Knowledge gets stale when nobody is clearly responsible for it.

A process changes, but the guide stays the same. A form gets replaced, but the old link remains live. A team adjusts its workflow, but the training still shows last year's version.

KM teams can reduce this by making ownership part of every major knowledge asset. Each process page should have a named owner, a review date, and a simple way for employees to flag a problem.

The owner does not always need to be the KM team. Often, the subject matter expert should own accuracy, while the KM team owns structure, publishing standards, and clarity.

That split works well. Experts keep the details honest. KM teams make sure the knowledge stays readable, findable, and consistent.

Review timing should match risk. A compliance workflow may need a quarterly check. A low risk reference page may only need review twice a year. A tool walkthrough should be checked whenever the software changes.

Watch how people actually use the knowledge

Publishing a page is not the end of the work. KM teams need to know whether the guidance is helping. Page views can offer clues, but they can also mislead. A heavily viewed page might be useful. It might also be so confusing that people keep coming back because they cannot get what they need the first time.

Better signals come from daily friction. Repeated questions. Slow onboarding. Missed steps. Search terms that lead nowhere. Training completion with the same mistakes still showing up afterward.

Those signals tell the KM team where the system needs attention. Maybe the answer exists but uses the wrong language. Maybe the process is documented but not connected to training. Maybe the guide is clear, but the workflow itself has too many awkward handoffs.

The strongest KM teams treat these signals as feedback, not failure.

Build the loop, then keep it moving

Good KM work has a rhythm. Teams collect knowledge from the people closest to the work. They turn it into clear documentation. They reuse it in training. They support adoption with guidance people can follow during real tasks. Then they refine the system based on questions, errors, and feedback.

That loop is what keeps internal knowledge alive.

For KM teams, the opportunity is practical. Help people find the right answer faster. Help new employees learn without drowning in scattered documents. Help process owners keep guidance current. Help the business stop relearning the same lessons every quarter.

A knowledge base can store information. A strong KM system helps people use it.

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You probably remember someone like him: Tom, the senior policy expert who spent two decades at the institution. Tom remembered which decisions had been revisited three times before reaching their final form. Tom carried in his head the assumptions behind frameworks that now looked obvious but had been genuinely contested when they were designed.

Tom is "The Organisational Memory" :)

When he retired, the organisation held a warm farewell.Three months later, his former team faced a decision that depended on understanding why a key position had been taken eight years earlier. They found the approval. The minutes. The final document.They could not find the reasoning.It had retired with him.

‍The gap existing frameworks do not close
In a previous article, I introduced Structured Decision Continuity through the story of Sophia: every senior expert whose absence only becomes visible when the system that replaced her produces an answer that looks correct and is not.

The diagnosis is this: organisations do not fail because they lack knowledge. They fail because the continuity between their knowledge and their decisions is not governed.

SDC names that gap. At the centre of SDC sits the Control Layer, a practical model that examines whether the path from knowledge to decision is strong enough to survive over time.
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The Four Conditions
The Control Layer has four conditions. Each addresses a failure that senior professionals recognise but few organisations name explicitly

‍Validation
Was the knowledge behind this decision reliable and valid in context?

Validation ensures that information, analysis and expertise were sufficiently checked; not just accurate in isolation, but appropriate for the specific decision being made.

The failure mode is familiar: a recommendation sound in theory but built on data that no longer reflected organisational reality. Before closing any significant decision, one question is worth asking: what are we taking for granted that we have not actually tested.

‍Context
Will someone encountering this decision in three years understand the conditions that shaped it.

Context is the set of circumstances and constraints that existed at the moment a decision was made. It is why the decision made sense then; even if it looks different now.

Context is the component most commonly lost. It lives informally in the people who were present. When those people retire or move on, the context goes with them unless it has been deliberately preserved.‍

Alignment
Was this decision coherent with strategy and governance and is that coherence still visible?

The failure mode here is subtle. A decision can be fully aligned at the moment it is made and look disconnected two years later, not because it was wrong, but because the strategy moved and no one recorded what the decision was originally aligned to.‍

Traceability
Can someone follow the path from knowledge to decision and from decision to consequence?

This is the expert's failure. The decision was defended procedurally. But the reasoning left with the person who held it. Traceability ensures that reasoning does not have to leave when the expert does.‍

Four Questions Worth Asking

For any decision that will carry long-term consequences, four questions are enough:
- Was the knowledge behind this decision sufficiently checked?
- Is the context preserved?
- Is the alignment with governance and strategy visible?
- Can the path from knowledge to decision be followed later?

If the honest answer to any of these is uncertain, something is worth capturing before the people who hold the answer have moved on.‍

A Closing Thought

The expert who retired took something with him that no record system was designed to preserve. Not his knowledge; much of that existed in documents. What left with him was the reasoning that connected that knowledge to the decisions that had shaped the institution.

The SDC Control Layer does not prevent people from leaving. It ensures that when they do, the reasoning behind their most important decisions does not leave with them.

Structured Decision Continuity is a developing professional concept examining how organisations preserve the reasoning behind important decisions over time. This is the fourth article in a series contributed exclusively to the Knowledge Management Institute.

The views expressed in this article are my own and do not represent the position of my employer or any institution I am associated with.

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