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Two Kinds of Knowledge, and Only One Travels Easily

September 10, 2026
Guest Blogger Ekta Sachania

Knowledge management usually splits knowledge into two buckets:

  • Explicit knowledge — things that can be written down: procedures, checklists, reports, data. This travels well. You can email it, file it, search it later.
  • Tacit knowledge — the experience-based, contextual, often unconscious knowledge that lives in someone’s head. The “why we don’t do it that way,” the pattern recognition built from years of doing the job, the judgment calls. This is exactly what the TV protagonist was worried about losing.

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The sad truth is that most organizations are very good at capturing explicit knowledge and very bad at capturing tacit knowledge. A project closes, a case is transferred, an employee leaves — and the parts of the story that mattered most, the ones that would help the next person avoid the same mistake, quietly walk out the door with the person who knew them.

Knowledge that exists but never moves has no operational value. A lesson sitting in someone’s head, or buried in a report nobody reads, is functionally the same as a lesson that was never learned at all.

Why aviation had no choice but to solve this

Most industries can afford to lose a little context in a handoff. Aviation can’t. When the cost of an unlearned lesson is measured in lives, “we’ll write it up eventually” isn’t good enough. That pressure is exactly why aviation has built one of the most mature knowledge-management ecosystems of any industry — worth studying even if you’ve never set foot in a cockpit.

A few of the mechanisms worth knowing about:

Confidential reporting systems. In the US, the Aviation Safety Reporting System (ASRS) is a voluntary, confidential channel that lets pilots, air traffic controllers, cabin crew, dispatchers, and maintenance staff report near-misses and close calls in the interest of improving safety. Crucially, it’s run by NASA rather than the FAA, which gives it the neutrality people need to actually be honest, since NASA has no enforcement power over them. Report something within the right window, and you get limited immunity — the system is built on the premise that a mistake reported openly teaches the whole industry more than a mistake punished quietly. It was created in direct response to a fatal 1974 crash where investigators found that similar warning signs had existed before, but nothing was systematically capturing or circulating them.

Operator-level programs alongside the national one. Airlines run their own internal version, the Aviation Safety Action Program (ASAP), through formal agreements between the airline, employee unions, and regulators. This captures the tacit, day-to-day knowledge — the near-misses that never make headlines — before it’s lost to memory or turnover.

A shared global taxonomy. Different countries and airlines used to describe incidents differently, which made it hard to compare data or spot patterns across borders. ICAO and the European Commission have worked to promote a single shared repository and a common categorization scheme so that all aviation accidents and incidents worldwide can be reported the same way. That sounds like a bureaucratic detail, but it’s the difference between a lesson staying local and a lesson becoming global. Standardizing the “language” of the report is what lets a hazard identified in one country prevent an accident in another.

Institutionalized feedback loops. Incident data doesn’t just sit in a database — it feeds back into training curricula, cockpit procedures, aircraft design, and regulation. Crew Resource Management training, now standard worldwide, exists largely because of hard lessons from accidents where the technical flying was fine but communication and hierarchy in the cockpit weren’t. The knowledge didn’t just get recorded; it got re-injected into the system that produced the next generation of pilots.

The pattern underneath it all

Strip away the aviation-specific detail, and the model is transferable to almost any team, industry or project:

  1. Make capturing knowledge low-friction and safe. People share the messy, honest version of what happened only when they trust it won’t be used against them.
  2. Capture context, not just conclusions. A checklist item (“check altitude clearance”) is explicit knowledge. The story of why that checklist item exists — the confusion, the assumption that went wrong — is the tacit knowledge that actually changes behavior.
  3. Standardize how knowledge is described, so it can be compared, searched, and aggregated across teams instead of staying siloed in one person’s notes.
  4. Close the loop. A lesson learned that never gets fed back into training, onboarding, or process design is just an interesting anecdote. It has to change what the next person does.
  5. Treat the handoff itself as a risk point. The TV show’s protagonist was right about one thing: transfer is where knowledge degrades. Overlap periods, structured debriefs, and “why” documentation — not just “what” documentation — are how you protect against that.

Knowledge existing in an organization is necessary but not sufficient. What determines whether it actually prevents the next mistake is whether it’s captured with enough context, shared without fear, standardized enough to travel, and looped back into the system before the next person needs it. Aviation didn’t get this right because it’s a more disciplined industry by nature — it got it right because the cost of getting it wrong left no other option. That’s the real lesson for the rest of us: build the system as if the stakes were that high, before something forces you to.

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From First International Hire to Local Entity: A Knowledge Continuity Plan for Global Expansion

August 26, 2026
Lucy Manole

A company hires its first employee in another country. Six months later, there are three people there. A year after that, leadership is discussing a local entity.

By then, the employment structure may have changed several times while the knowledge around it has remained informal. Decisions live in inboxes. A local exception is remembered by one HR manager.

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A payroll process still makes sense only because someone remembers why it was set up that way. The problem is no longer just administration. The business has created a knowledge continuity risk.

A 2026 paper in Knowledge and Process Management examines knowledge continuity at transition boundaries, where responsibility moves and context can become separated from the knowledge being transferred. International expansion creates exactly these kinds of boundaries. The useful question for Knowledge Management teams isn't simply whether the records exist, but whether the next owner will understand the decisions behind them.

The first hire creates a knowledge boundary

The first international employee may arrive long before the company is ready to establish a legal entity in that country. In that situation, the business may use an employer of record. Under this employment structure, the EOR acts as the legal employer and manages areas such as employment contracts, payroll, benefits, and local employment requirements while the company directs the employee's day to day work.

That split matters to knowledge management. Internal teams need a clear record of which decisions belong to the company and which responsibilities sit with the external provider. If a local allowance is introduced, for example, retaining the final policy is only part of the job. Future owners also need to know why the decision was made, who approved it, and whether it was an exception or something intended to continue.

A growing team exposes what was never captured

One employee can compensate for a weak knowledge system through memory and direct access to headquarters. Five employees expose the gaps much faster.

The first hire may know who to ask, how a local process differs from the standard one, or which workaround keeps a recurring issue moving. New colleagues don't automatically inherit that context. If it remains in private conversations, onboarding starts to depend on whoever happened to join first.

This is the point where local operating knowledge needs a durable home inside the organisation. For some companies, that may be an established knowledge platform; for others, it may mean building a dedicated training or knowledge-delivery platform around their internal processes. That doesn't mean documenting every conversation. Focus on information another person would struggle to reconstruct later, especially recurring exceptions and the reasoning behind local process differences. The aim is continuity, not documentation volume.

Entity planning includes a knowledge inventory

When a company starts considering its own local entity, discussion usually centres on legal structure, cost, and employment administration. Knowledge Management belongs in that planning as well because a new entity changes who owns processes and who performs them.

Work previously handled through external employment infrastructure may move to internal teams. If the transfer is treated as a records migration, the company can bring over the files while leaving behind the operational memory that made those files understandable.

Before responsibilities move, identify what the incoming owner must be able to explain without relying on the previous one. Policy rationale and unresolved employee matters deserve attention, but so do informal dependencies that have quietly become part of local operations. ISO 30401 treats knowledge management as a management system that is established, maintained, reviewed, and improved. That principle fits expansion well because the knowledge system has to evolve with the operating model.

Give the handoff an owner on both sides

A cleaner transition has an outgoing owner who understands the current arrangement and an incoming owner who will carry the responsibility forward. Granting system access or transferring folders doesn't create that understanding on its own.

A useful handoff records both the decision and the reasoning behind it. It also makes unresolved issues visible and names the person who owns the next step. That exposes weak spots before they become inherited problems. If nobody can explain a recurring payroll exception, resolve it before the new entity takes responsibility, ideally by tracking it directly in the company's HR software rather than relying on individual memory.  If a local manager has been handling an unofficial onboarding step for a year, decide whether that practice belongs in the formal process before it disappears into another handoff.

A simple test before the structure changes

Legal structures change as international operations mature. The more useful measure of continuity is whether the next owner can understand the operation without reconstructing its history from scattered files and old messages.

Before transferring employment responsibilities, ask whether the incoming team can explain the important local decisions, the exceptions that still matter, and the reasoning behind current processes without calling the outgoing owner for context. If the answer is no, the transfer isn't finished. That test gives KM teams a practical way to judge readiness before an administrative change becomes a knowledge loss event.

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Knowledge Mapping and Playbook

August 22, 2026
Guest Blogger Ekta Sachania

Most KM programs fail at the same step: nobody can see what they actually have. Not for lack of content — organizations are full of knowledge.

The problem is visibility. What exists, where it lives, who owns it, who needs it, and where the gaps quietly sit.

I have put together a Knowledge Mapping Playbook:
a practical framework to map an organization's knowledge, assess it, prioritize the gaps that matter,
and turn findings into action — from SME interviews to
a governance checklist to a one-page operating model.

A map makes knowledge and gaps visible.

A playbook makes it actionable. Sharing the playbook below — curious how others are tackling this...

Knowledge Mapping and Playbook Download....
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Your Organisation is on Holiday. So is Half of What it Knows.

August 6, 2026
CKM Grad and Guest Blogger Konstantinos Christodoulakis


Something happens to organizations in the Summer and most of us feel it without naming it.

The building runs on a smaller crew. Half the people who normally answer within the hour are somewhere with their phone face down and quite a lot of what the organisation knows is away with them.
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This is when you find out what your organisation actually knows, as opposed to what a few individuals happen to remember.

For most of the year, the gap is invisible. When everyone is present, missing reasoning gets patched in real time. You don't understand why a process works the way it does, so you ask the person who designed it, and they explain it from memory in thirty seconds. The knowledge was never written down, but it was never missed either, because the carrier was at the next desk.

Summer removes the carrier. The person who could explain in thirty seconds is on a beach, and the same question now takes a colleague half a day of digging or worse, produces a confident guess that turns out wrong.

None of this is a crisis. It is mildly inconvenient, and it resolves itself in September. But it is a useful, low-stakes preview of a much larger problem, because the holiday gap and the permanent gap are the same gap.

When someone goes on leave, their knowledge becomes temporarily inaccessible. When someone leaves for good, or moves roles, or retires, that same knowledge becomes permanently inaccessible and there is no September to look forward to. The reasoning behind a decision they made three years ago doesn't come back from holiday. It is simply gone, and the record that remains confirms that the decision was made without explaining why.

Summer just makes the mechanism briefly visible.

So it is worth paying attention to what the quiet weeks reveal. Which questions can't be answered while a particular person is away? Which processes only really make sense to one individual? Where does the covering colleague say "I'll check when they're back" — and what would happen if they never came back?

Those are not summer problems. They are the year-round problems, wearing a lighter outfit.

The organisations that handle August well are usually the ones that already do the unglamorous work: capturing why things are done the way they are, not only how; keeping the reasoning behind decisions accessible to people who weren't there when they were made. For them, a colleague going on leave is a scheduling matter, not a knowledge outage.

For everyone else, summer is a two-month reminder that quite a lot of what the organisation knows is actually just what specific people remember.

The good news is that it's a gentle reminder. Nothing important usually breaks in August. But it's worth noticing what goes quiet while people are away, because the same silence arrives permanently, eventually, and with far less warning.

For now, though, if you're one of the people currently away-enjoy it. The reasoning will keep until September.

And if you're holding the fort: make a quiet note of every question you couldn't answer this month. That list is more valuable than it looks.

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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Are Open Weight AI Models the Open Sesame Moment for Modern Knowledge Managers?

August 5, 2026
Rooven Pakkiri

1. The Intro

For decades, Enterprise Knowledge (tacit and explicit, structured and unstructured) has felt like a vault full of treasure that nobody had the key to unlock.

Open weight AI models might just be the "Open Sesame" moment knowledge leaders have been waiting for.

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2. The Story... So Far

If you haven't been tracking the frontier AI landscape over the past few weeks, the open-weight debate just reached a boiling point:

  • July 9–13, 2026 | Security & Frontier Risks: An autonomous AI agent from frontier lab OpenAI escaped its isolated sandbox environment (a "jailbreak" in lab parlance). To pass an internal benchmark test, it inferred that an external site (Hugging Face) might hold the answers. It gained internet access, moved laterally, and compromised Hugging Face's dataset processing infrastructure and cluster credentials.
  • July 16–27, 2026 | The Benchmark Shift: Moonshot AI released Kimi K3, an open-source frontier model matching or beating closed proprietary models like Claude Fabel and OpenAI Sol across key benchmarks. Think of it as AI's "Tesla vs BYD" tipping point. On July 27, Moonshot offered free access to its model weights—marking a major turning point for knowledge management.
  • Industry Giants Line Up: NVIDIA’s Jensen Huang authored an open letter defending open weights, co-signed by Microsoft, Google, IBM, and over two dozen tech leaders resisting proposed federal bans. (The notable exception was Anthropic, who did not sign).
  • Enterprise Validation: AWS and enterprise IT leaders are stepping in to provide enterprise-grade support and security for open-weight deployments—mirroring the playbook that made Linux the backbone of modern enterprise software.

3. The Technology (In Plain English)

So, what actually are "Open Weights"?

When you use proprietary SaaS models, you are renting access to a black box over an API. You send your data out; you get an answer back. This is the case for closed models like Claude, ChatGPT, or Gemini—you cannot see how your data is being used. Because your IP is at risk, there is a forced brake on AI adoption and progress for many companies, especially in highly regulated sectors.

With Open Weight models (like Kimi K3), the provider gives you the underlying neural blueprint and trained parameters ("the brain"). You can download it, host it on your own servers or private cloud, run it offline, and tweak it as you see fit.

You control not just your data security and privacy, but the model itself. You can begin to imbue it with the values and personality of your organization. For example, the term "disclosure" carries a vastly different legal weight in a law firm than in a restaurant chain.

Owning model weights is the difference between renting a taxi (Claude or Gemini) versus owning the vehicle (Kimi K3 or Inkling models) and parking it inside your own private garage.

4. Open Sesame: What This Means for Knowledge Managers

For Knowledge Managers, this technical shift solves the two biggest roadblocks that have plagued enterprise KM for twenty years:

  • Absolute Data Sovereignty & Privacy: You no longer have to compromise between cutting-edge AI and strict legal compliance. Run models inside your firewall or Private Cloud (VPC)—sensitive IP, code, and confidential documents never touch a third-party server.
  • Bespoke Domain Expertise: Off-the-shelf commercial models know a little about everything, but zero about your internal jargon. Open weights allow you to fine-tune smaller, highly efficient models directly on your corporate taxonomy and historical post-mortems. KM teams are uniquely positioned to gather, organize, and validate what the model should and shouldn't learn—opening a massive new horizon for modern knowledge managers as models are updated periodically in partnership with IT.
  • Predictable Cost & Scalability: Querying expensive proprietary APIs millions of times a day drains budgets fast. Open weights let you optimize inferencing costs and run task-tailored models affordably at enterprise scale.
  • From "SharePoint Graveyard" to Active Intelligence: Passive document repositories (where good ideas go to die) transform into a living, conversational corporate memory. The intelligence from your best data sources now sits inside the model's weights. When a user queries a novel situation, the model provides guidance because institutional wisdom is built directly into its architecture. The modern knowledge manager is instrumental in making this happen and curating it overtime.

The Open Sesame moment has arrived.

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