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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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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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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