Category: Humans-in-the-loop

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The Automation Paradox: Why AI Makes Human Capacity More Valuable (Not Less)

(This post was originally shared on my LinkedIn Profile.)

We’re told AI will handle routine work, freeing humans for creativity and critical thinking. Sounds great. So why do leaders I speak with say their organisations feel less creative, not more? (Some have said they feel like AI is making them dumb.)

The problem isn’t AI. It’s that we’ve been systematically dismantling the conditions humans need to think, create, and judge well—and we did it before AI arrived.

The Post-mortem of Lost Capacity

Over the last 15+ years, organisations have optimised for efficiency: fewer meetings (no, we added more), faster decision-making (no, we added layers of approval), knowledge capture (no, we built systems nobody uses). The net result? Cognitive exhaustion. People are too busy processing information to actually think with it.

Add AI to this picture, and something shifts. Organisations realise they can automate tasks, but they can’t automate the parts of work that matter: understanding what problem actually needs solving, sensing when the data is misleading you, creating something genuinely new, making judgements that hold human values.

Suddenly, the capacity for critical thinking, intuition, and creativity isn’t a nice-to-have. It’s everything.

But here’s the trap: you can’t generate that capacity on demand. It requires conditions. Uninterrupted thinking time. Psychological safety to challenge ideas. Exposure to perspectives that collide with your own. Reflection. Sense and meaning-making.

Most organisations have actively eliminated these conditions in the name of efficiency.

Where Knowledge Management Has Failed (And Why It Can Recover)

Traditional KM treated knowledge like widgets: capture it, store it, retrieve it. The assumption was that better information architecture = better decisions. It didn’t account for the human dimension.

We lost the messy, generative stuff: the conversations where ideas collide, the storytelling that embeds wisdom, the reflection that turns experience into judgement. We built systems, not cultures.

So, when AI arrived, organisations had already stripped away the human conditions for thinking. The software wasn’t the problem. The culture was.

Radical KM: People-First, Not Technology-First

This is where the work shifts. If human capacity for creation, critical thinking, and judgement is the competitive advantage in an AI world, then KM isn’t about technology adoption. It’s about designing the conditions that allow humans to do what humans do best.

That means:

  1. Making space for thinking. Not as something to aspire to, something that is structurally embedded in the systems. Protected time. Simplified information. Reduction of noise.
  2. Reactivating curiosity through exposure. Cross-sector conversations. Diverse perspectives. Learning that isn’t bounded by function. This is where intuition develops—through pattern recognition across domains.
  3. Creating safety for dissent and iteration. Humans refine judgement through being challenged. Organisations that treat disagreement as a threat lose their smartest people.
  4. Embodying knowledge differently. Not everything knowable fits in a database. Wisdom about how to navigate ambiguity, where to apply intuition, when the data is lying to you—these are often learned through story, conversation, and shared sense-making.

Using arts-based approaches to do all of this.

I know that last line raises eyebrows. But arts-based interventions—guided visualisations, drawing, painting, storytelling, reflection practices—aren’t luxuries. They work because they interrupt the efficiency mindset, create psychological safety, and make knowledge shareable in ways that pure information architecture can’t reach.

A facilitated drawing exercise doesn’t seem like “knowledge work.” But it’s creating the conditions where people actually think together instead of just exchanging information.

The Choice

You can treat AI as a threat to human work, and race to automate more. Or you can treat it as a signal: the human work matters now more than ever. Which means investing in the conditions that allow humans to create, think critically, and exercise sound judgement.

That investment starts with culture, not code. With people, not platforms. With how you design the rhythm and psychology of work itself.

Organisations that get this right won’t lose to AI. They’ll use it to do what it does—handle volume, pattern-match, optimise routine—while reclaiming the uniquely human work their people are actually capable of.

The question for your leadership team: Are the conditions in your organisation supporting human capacity, or eroding it?

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Turning ROT into ART: Why Humans and Governance Are Your AI Readiness Foundation

(This post was originally shared on my LinkedIn Profile.)

We talk obsessively about AI readiness. Plug in the right tools, train the model, scale the adoption. But I keep hearing the same quiet concern in rooms full of knowledge and learning leaders: What happens when we feed the beast garbage?

The garbage has a name: ROT. Redundant, obsolete, trivial content. And if you’re serious about AI readiness—or frankly, about knowledge management at all—you need to stop treating ROT disposal as a nice-to-have technical task. It’s a governance problem. It’s a people problem. And it’s non-negotiable.

The ROT Problem Is Your AI Problem

Here’s the uncomfortable truth: the organisations struggling most with AI implementation are the ones drowning in ROT.

Redundant documents that say the same thing three different ways. Obsolete processes locked in PDFs from 2015. Trivial information cluttering search results so badly that finding what you actually need takes longer than just asking a colleague. Add multiple systems, no single source of truth, and inconsistent version control, and you’ve created a perfect petri dish for training data that’s incomplete, contradictory, or simply wrong.

Now feed that into an AI system. The model doesn’t know intent. It doesn’t know that the 2015 process was replaced by something newer. It can’t distinguish between the authoritative procedure and the three near-duplicates authored by well-meaning teams in different divisions. It learns from what you give it. Garbage in, garbage out—but faster, at scale, and with the veneer of algorithmic confidence.

That’s not an AI problem. That’s a governance problem. Which is a people problem.

Enter ART: Accurate, Reliable, Trustworthy

The antidote to ROT is ART: content that is accurate, reliable, and trustworthy.

Accurate means it reflects reality as it currently stands. The process described is the process actually followed. The policy hasn’t shifted. The template is current. Accuracy requires human judgment—someone who understands the business has to say, “This is true today.”

Reliable means it’s consistent and findable. Identical information doesn’t exist in five formats. Metadata is correct. Version history is transparent. There’s a clear path to the current, authoritative version. Reliability requires governance: standards, ownership, maintenance schedules.

Trustworthy means people believe it enough to act on it. That only happens when they’ve seen it stay accurate and reliable over time. Trustworthiness is built through culture—through demonstrating that the knowledge function takes stewardship seriously.

None of these are technical properties. They’re human commitments, enforced through governance structures.

The Governance Gap

Here’s where most organisations trip. They treat quality as a technical project: cleansing tools, metadata standards, taxonomy implementation. These are important. But without governance, they’re cosmetic.

Governance asks the hard questions:

  • Who owns this content? (Not who wrote it. Who’s responsible for keeping it true?)
  • Who decides what’s redundant, and by what criteria? (Delete the 2015 process, but who makes that call, and what if someone was relying on it for context?)
  • When does content become obsolete, and who’s checking? (That vendor partnership doc from 2012—is it history or a hidden liability?)
  • What happens when we find contradictions? (Two teams think they own the same process. Now what?)
  • Who’s accountable for trustworthiness? (Not a system. A person or team.)

Governance without people is theatre. But people without governance quickly become overwhelmed, inconsistent, or burnt out. You need both.

Keeping Humans in the Loop

This is where AI readiness becomes interesting. The temptation is to assume AI can automate the governance problem away—classifiers that identify ROT automatically, algorithms that spot inconsistencies, systems that flag obsolete content.

Some of that is useful. But it’s not sufficient. And if you try to make it sufficient, you’re back where you started: garbage in, garbage out.

Humans stay in the loop at the critical junctures:

Defining what matters. What counts as redundant rather than useful context? What’s a necessary variant rather than an inconsistency? These are judgement calls. Tools can flag; humans decide.

Ownership and accountability. An algorithm can tell you content needs updating. A human has to take responsibility for updating it, and for saying “I’ve reviewed this and it’s true.”

Interpretive consistency. Policies that sound the same might apply differently in different contexts. Humans who understand the business catch that. Algorithms amplify the confusion.

Escalation and exceptions. ROT-clearing sometimes reveals gaps or conflicts in your business logic, not just your documentation. Those need human attention, not automated resolution.

The goal isn’t to remove humans from governance. It’s to remove humans from the repetitive, low-judgment parts so they can focus on the parts that require discretion, accountability, and real understanding.

The Real ROI

Organisations that have tackled ROT properly—through committed governance, clear ownership, and sustained human attention—report something interesting: it’s not just cleaner data. They report faster decision-making, fewer duplicated efforts, lower training time for new staff, and better cross-team collaboration.

And when they do deploy AI, it works. Not because the tool is magic, but because the foundation is solid.

The quiet concern I keep hearing? It’s legitimate. You can’t automate your way out of a governance problem. But you can solve it. It just requires treating knowledge stewardship as seriously as you treat technology adoption.

That’s the difference between readiness and recklessness.

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Stop Talking About Knowledge Management. Start Showing Its Impact

(This post was originally shared on my LinkedIn Profile.)

Your organisation doesn’t have a knowledge management problem. It has a credibility problem.

You can launch the most elegant KM programme, design the cleverest framework, or build the sleekest knowledge platform. But if nobody sees how it changes the way work actually gets done, they won’t care. And why should they?

The organisations that genuinely embed knowledge management aren’t the ones with the loudest programmes. They’re the ones where people notice that knowledge moves, decisions get made faster, and reinvention stops happening.

Here’s how that actually occurs in practice.

1. Anchor to outcomes, not terminology

Stop leading with “knowledge management.” Lead with the outcome your organisation desperately needs: faster decision-making, reduced reinvention, onboarding that doesn’t take six months, compliance that’s actually defensible, or customer responsiveness that beats your competitors.

The frameworks work behind the outcomes. Make them invisible. When a senior leader notices that new hires are productive in weeks instead than months, or that a decision gets made because the right knowledge actually arrived, they start valuing what made it possible. They just don’t call it “KM.”

2. Build credibility through small, visible wins

Don’t launch a programme across the enterprise. Pick one team—preferably one where knowledge clearly gets lost or repeated—and work with them quietly.

Document what changes: time saved, mistakes avoided, ideas that moved between people. Then let their story travel.

Peer testimony is infinitely more powerful than a pitch from the programme office. People trust other practitioners. They’re suspicious of consultants.

3. Make trust the visible difference

Here’s what I’ve observed: organisations that fail at KM typically have a trust problem they don’t know they have. Information gets hoarded. Knowledge is buried behind permission structures. Sharing is punished or seen as losing advantage.

You can’t recognise knowledge management in that environment because the conditions for knowledge to actually travel don’t exist.

The organisations I’ve seen shift? They model something different. Generosity. Honesty. The willingness to say “I don’t know” without it costing you. Inquiry before judgment. These aren’t soft skills. They’re the infrastructure that makes knowledge move.

And if you’re driving KM, you have to model them first.

4. Celebrate the human story, not the system

People don’t care about your knowledge base. They care that they’re not reinventing the wheel at 3 p.m. on a Friday, or that someone remembered a client context that completely changed the conversation.

Recognition happens when you celebrate the person who shared as much as the knowledge that was shared. When you tell the story of how an idea travelled across teams and landed somewhere unexpected. When you highlight the person who documented their thinking so clearly that six months later, someone else could understand the reasoning behind a decision.

Make knowledge sharing visible as a human thing, not a compliance requirement.

5. Make it a leadership conversation

KM recognition happens when your executive team starts talking about knowledge as how we work here—not as a project sitting in a corner.

This means asking leadership questions:

  • Are decisions documented in a way that the logic is actually recoverable?
  • Do people have permission to ask questions without it being seen as weakness?
  • Is failure treated as learning, or is it buried?
  • Do you privilege speed over understanding, or vice versa?
  • Does your culture reward knowledge-hoarding or knowledge-sharing?

These aren’t KM questions. They’re leadership questions. But they’re the ones that determine whether knowledge management actually takes root.

6. Measure what actually matters

Forget knowledge base articles indexed. Forget training completion rates. Those metrics measure activity, not impact.

Track what actually shifts:

  • How much faster are people becoming capable?
  • Are the same questions still cycling through support?
  • Do ideas travel between teams that normally don’t connect?
  • Are decisions getting revisited repeatedly, or is the reasoning clear enough that people move forward with confidence?

Be rigorous about it. “We saved time” is vague. How much? For whom? What else did they do with the time they reclaimed?

Here’s the uncomfortable truth: Recognition follows credibility, and credibility comes from solving real problems quietly and consistently.

The organisations that genuinely “get” knowledge management didn’t get there through programme launches and change management initiatives. They got there because someone in a position of influence—often a team leader, sometimes a practitioner—demonstrated that knowledge actually matters to outcomes. And that became the way things worked.

Stop announcing KM. Start showing what happens when knowledge moves the way it should.

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Why Your Knowledge Management Strategy Needs the Arts

(This post was originally shared on my LinkedIn Profile.)

Most knowledge management strategies focus on systems, processes, and technology. But what if the missing piece is a scribble drawing?

Knowledge Work Has a Creativity Problem

We have built our organisations around efficiency metrics and linear processes — get from A to B to C in the most direct line possible. But knowledge work does not actually work that way. It requires curiosity, reflection, and iteration. It demands that people make unexpected connections, ask uncomfortable questions, and see the bigger picture.

Those capacities are not switched on by yet another technology upgrade.

This is the premise of Radical Knowledge Management — a framework developed to bring creativity and arts-based interventions (ABIs) deliberately into KM practice. The definition of KM at the heart of this approach is broader than the traditional one: rather than merely connecting people to documented knowledge, it focuses on enabling people to find and create the knowledge they need to do their jobs — including through continuous learning and new ways of thinking.

What Are Arts-Based Interventions?

Arts-based interventions are activities that use an artistic medium or discipline to improve a process or situation. In organisational settings, the most common artistic modalities include drawing, painting, photography, theatre, music, poetry, improvisation, and storytelling.

These are not team-building novelties bolted on to the end of a strategy day. Used deliberately, they are instruments for culture change, innovation, leadership development, problem-solving, and organisational transformation.

There are at least twelve use cases for ABIs in organisations:

  1. Culture change
  2. Innovation
  3. Wellness
  4. Getting unstuck
  5. Sustainable leadership
  6. Team building
  7. Collaboration
  8. Problem-solving
  9. Amplifying learning
  10. Transformation
  11. Creating community
  12. Employee engagement

Because of the interconnected nature of these activities, focusing on ABIs for one use case tends to positively affect the others as well.

Why ABIs Belong Inside Your KM Strategy

There is a tendency to think of creativity as a bolt-on — something for design or marketing, or perhaps for the occasional off-site. The argument for placing ABIs firmly within KM rests on something more fundamental.

Knowledge is created by people. It is intangible, complex, and deeply social. In our increasingly volatile, uncertain, complex, and ambiguous (VUCA) world, the knowledge we most urgently need cannot be captured in a database. It lives in the capacity of people to think differently, collaborate across silos, and remain curious even under pressure.

ABIs directly support those capacities. They help people tap into skills and behaviours that formal education and career pressures have pushed into dormancy. They encourage people to get out of their comfort zones, see the bigger picture, and ask different questions. In doing so, they create new knowledge — and they shift organisational culture towards one that supports knowledge sharing rather than knowledge hoarding.

As Albert Einstein observed, imagination is more important than knowledge; knowledge is limited, whereas imagination embraces the entire world. Arts-based interventions are one of the most reliable ways to fuel imagination in the workplace.

The MAGIC-SH Framework

For organisations ready to take this seriously, the Radical KM framework provides a practical structure organised around the acronym MAGIC-SH:

S & H — Sustainable and Human. This is the foundation. ABIs make organisations more sustainable because they help people see the interconnectedness of things and develop sustainable leadership behaviours. They also reinforce the humanity of work — critical at a time when technology threatens to crowd out the very qualities that make people valuable.

The Nine Cs. The middle layer of the framework encompasses the concepts that ABIs develop and evolve:

  1. Creativity — producing novel ideas and approaches
  2. Collaboration — building the trust that makes real knowledge sharing possible
  3. Communication — developing empathy and purposeful expression (improv theatre is particularly powerful here)
  4. Content — shaping what knowledge is captured and how
  5. Critical thinking — challenging the “that’s how we’ve always done it” assumption
  6. Conversation — opening new avenues for dialogue and insight
  7. Culture — shifting the organisation towards one that values learning and sharing
  8. Confidence — building the self-belief to ask for help, try new approaches, and share imperfect knowledge
  9. Change management — supporting the cultural shift that ABIs require

A, G, I — ABIs, Ideas, Innovation and the Guerrilla approach. These represent both the outputs sought (fresh ideas and innovation) and the practical approach recommended: starting small, positioning ABIs as experiments, and growing support iteratively.

M — Metrics. Measurement comes last — not because it is unimportant, but because defining metrics too early can constrain the very creative exploration that makes ABIs valuable.

How to Start: Two Practical Pathways

There is no need to overhaul your entire KM programme. Radical KM explicitly recommends starting small and iterating.

The Icebreaker stream begins at the individual or team level with short, low-stakes activities: a guided visualisation at the start of a meeting, a scribble drawing exercise during a workshop, an improvisational warm-up before a strategy session. These take as little as five minutes, but they shift the energy and engagement in the room, build psychological safety, and begin to normalise the idea that creativity belongs at work.

The Studio stream takes a larger, more structured approach — dedicated sessions, longer interventions, and formal integration into KM processes such as knowledge capture workshops, after-action reviews, or communities of practice.

In both cases, the implementation approach is the same: treat it as an experiment, start with early adopters, debrief afterwards, and iterate based on what you learn. Change management is essential — people have often been educated to believe that the arts are wasteful, and that belief takes time and consistent experience to shift.

The Hardest Part

The biggest obstacle to incorporating ABIs into a KM strategy is not logistics. It is the stories we tell ourselves about why we cannot — or should not — do it. What will my colleagues think? Are the benefits really there? Is this really my job?

Those doubts are understandable. Most of us had our creativity criticised or ignored somewhere around adolescence, and we learned to put it away as unprofessional.

But look around your team. Someone is doodling during calls. Someone uses role-play to prepare for difficult conversations. Someone plays an icebreaker game at the start of team stand-ups. The arts and creativity are already finding their way into your workplace — unconsciously. Radical KM simply asks you to be more deliberate about it.

The first time you try a scribble drawing exercise in a knowledge management workshop, you may be pleasantly surprised. The energy and engagement in the room shifts. Responses become more thoughtful and detailed. People make connections they would not otherwise have made. The knowledge that flows is richer.

A Final Thought

In a world shaped by AI, automation, and relentless change, the knowledge and capabilities that most need managing are the human ones: curiosity, resilience, empathy, imagination, and the willingness to keep learning. Those are not developed by better taxonomy.

They are developed through practice — and arts-based interventions are one of the most evidence-supported ways to practise them.

Your KM strategy does not need an overhaul. It needs a scribble drawing, and the courage to see what happens next.

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No, I Don’t Think AI Is the Enemy. I Just Know It Isn’t the Answer

(This post originally appeared on my LinkedIn Profile.)

A colleague said something to me recently that gave me pause.

“You’re a bit of a Luddite when it comes to AI, aren’t you?”

It was said with a smile,  the kind of gentle ribbing that comes from genuine curiosity rather than criticism. But it stuck with me, because I realised I haven’t been clear enough about where I actually stand.

So let me be direct: I think AI is remarkable. I use it. I find it genuinely useful. I’m curious (and also deeply concerned) about where it’s headed. And in the context of Knowledge Management specifically, I believe it has real and growing value.

What I don’t believe, and what I’ll continue to push back on, is the idea that AI solves Knowledge Management. Because that framing, however appealing, leads organisations down a very expensive and frustrating path.

The Seductive Logic of the Tech Solution

There’s a pattern I’ve watched repeat itself across organisations for years, and AI is simply the latest chapter in a long story.

It went like this: first, we were going to solve knowledge management with intranets. Then wikis. Then SharePoint. Then enterprise social networks. Then big data platforms. Each time, the pitch was essentially the same: “This technology will finally make your organisational knowledge accessible, searchable, and usable.”

And each time, organisations discovered the same uncomfortable truth: the technology worked. It was the knowledge management that didn’t.

Files sat untagged. Content became stale within months. People reverted to emailing each other, because the system was technically available but practically ignored. Adoption curves flattened. The investment quietly became shelfware.

AI doesn’t change this dynamic. It accelerates it, in both directions. Deploy AI on top of well-structured, well-governed, actively maintained knowledge assets, and it can be genuinely transformative. Deploy it on top of the organisational equivalent of a cluttered attic, and it will confidently surface the wrong answer, twice as fast. (Wrong answers at scale, as I recently commented to another colleague.)

Knowledge Management Is a People Problem First

Here’s the thing that no vendor wants to put on a slide: most knowledge management failures are not technology failures.

They’re culture failures. Process failures. Leadership failures.

When an organisation loses critical expertise because a senior employee retires, that’s not a software gap, it’s a knowledge transfer gap that nobody prioritised until it was too late. When two teams duplicate months of research because they had no way of knowing what each other had already done, that’s a collaboration and communication failure. When a new hire spends their first three months reinventing wheels, that’s an onboarding and documentation failure.

No AI tool, however sophisticated, fixes those problems at the root. It might paper over some of the symptoms and sometimes that’s genuinely useful, but it doesn’t address the underlying conditions that created the problem in the first place.

The Five Pillars That Actually Matter

Effective Knowledge Management rests on five interconnected foundations:

People: Knowledge lives in people’s heads first. Building a knowledge-sharing culture, incentivising contribution, and reducing the friction between “someone knows this” and “the organisation benefits from this” is fundamentally a human challenge. It requires trust, psychological safety, and genuine leadership commitment.

Process: How does knowledge get created, validated, stored, retrieved, and retired? Without intentional, well-designed processes, even the best technology becomes a dumping ground. Process is the architecture that gives technology its shape.

Technology: Yes, absolutely. The right tools, well-chosen and properly implemented, are a genuine enabler. AI included. But technology is an enabler of a good strategy, not a substitute for one.

Governance: Who owns the knowledge? Who decides what’s accurate, current, and trusted? Who has authority to retire outdated content? Without governance, knowledge systems decay. This is the unglamorous work that makes everything else sustainable.

Strategy: What are you actually trying to achieve? Knowledge Management without a clear strategic anchor is just tidying. It needs to connect to organisational goals: accelerating innovation, reducing risk, improving customer outcomes, developing capability. Strategy is what makes the whole system purposeful.

These five pillars don’t operate in isolation, they’re deeply interdependent. A brilliant technology choice undermined by weak governance will still fail. Strong processes with no cultural buy-in will still fail. Strategy without execution infrastructure will still fail.

There Are No Easy Answers and That’s Important to Say Out Loud

One of the most valuable things a Knowledge Management professional can do is resist the pressure to promise simplicity.

Organisations want easy answers. They want to hear that the new platform, the new AI integration, the new taxonomy framework will sort things out. And sometimes, in the short term, a well-chosen tool can create enough momentum to feel like progress.

But sustainable Knowledge Management, the kind that actually compounds in value over time, requires patience, iteration, and a willingness to do genuinely hard organisational work. It requires leaders who understand that knowledge is a strategic asset, not an IT problem. It requires investment in people and process, not just licences and implementations.

The organisations that get this right don’t treat Knowledge Management as a project with an end date. They treat it as an ongoing capability, like finance or HR, something that needs continuous attention, resourcing, and refinement.

So Where Does That Leave AI?

Exactly where good tools always belong as a powerful enabler within a thoughtful, well-structured Knowledge Management strategy.

Used well, AI can dramatically improve knowledge discovery. It can surface connections between documents that no human would have the bandwidth to find. It can reduce the cost of creating knowledge assets. It can personalise the knowledge experience for different roles and contexts. These are real benefits, and they matter.

But AI doesn’t replace the need for governance. It doesn’t create a culture of sharing where none exists. It doesn’t write your knowledge strategy for you, or decide which expertise is critical to capture, or ensure that the knowledge your organisation produces is accurate and trustworthy.

Those are human responsibilities. And until we’re honest about that, we’ll keep buying technology solutions to organisational problems and wondering why the needle doesn’t move.

I’m not anti-technology. I’m anti-magical-thinking.

The organisations that thrive in the long run are the ones willing to do both: invest in excellent tools and do the harder, slower, more human work of building the conditions in which those tools can actually deliver.

That’s not a pessimistic message. I think it’s actually a hopeful one. Because it means the answer is within reach, it just takes more than a software purchase to get there.

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AI Won’t Replace Knowledge Management. It Will Finally Force Us to Take It Seriously

(This post was originally shared in my LinkedIn Profile.)

For decades, knowledge management has been the organisational discipline that everyone agreed was important and almost no one invested in properly.

We built SharePoint graveyards. We created knowledge bases nobody searched. We ran “lessons learned” sessions whose lessons were never applied. And when things went wrong — when the expert retired, when the project team disbanded, when the same mistake happened for the third time — we shrugged and called it institutional memory loss, as if it were a natural disaster rather than a preventable one.

Then came AI.

And suddenly, knowledge management isn’t a nice-to-have. It’s the foundation everything else depends on.

AI is only as good as the knowledge it can access

Here’s what the AI vendors won’t tell you in their sales decks: a large language model deployed inside your organisation will reflect the quality of your organisational knowledge. Feed it outdated policies, inconsistent documentation, and tribal knowledge that lives only in people’s heads — and you’ll get confident, fluent, wrong answers at scale.

The organisations rushing to implement AI copilots and knowledge assistants are about to discover something KM practitioners have known for years: garbage in, garbage out is not a technology problem. It’s a knowledge governance problem.

AI doesn’t fix broken knowledge ecosystems. It amplifies them — for better or worse.

The sudden urgency is real — and welcome

I’ve sat in enough boardrooms to know that the argument “we need to capture institutional knowledge before people leave” rarely moved budgets. Neither did “we’re duplicating effort because teams can’t find what exists.”

But “our AI tool is producing unreliable outputs because our knowledge base is a mess”? That one lands differently.

AI is creating the business case for KM that practitioners have struggled to articulate for years. Not because AI is a KM solution — it isn’t — but because AI makes the consequences of poor knowledge management immediately visible and commercially painful.

That’s not a crisis. That’s an opportunity.

What AI actually makes possible

Used thoughtfully, AI tools offer genuine advances for knowledge work:

  • Surfacing what exists — AI-powered search can finally make organisational knowledge findable, cutting through the folder structures and file naming conventions that defeated traditional search.
  • Capturing tacit knowledge — Conversational AI can assist in eliciting and structuring knowledge from subject matter experts in ways that don’t require them to become writers.
  • Reducing the burden of contribution — One of the biggest barriers to knowledge sharing is the effort it takes. AI can lower that friction significantly.
  • Connecting dots across silos — AI can identify relationships between knowledge assets that no human would have the bandwidth to spot.

None of this happens automatically. All of it requires human judgement, governance, and strategy to work.

The human element isn’t optional

Here’s where I’d push back on the more breathless AI narratives: knowledge is not just information. It includes context, relationship, experience, and meaning — the things that make information useful rather than merely available.

AI can retrieve. It cannot always discern. It can summarise. It cannot always judge what matters. It can generate. It cannot replace the wisdom that comes from having lived through something.

The organisations that will succeed with AI-augmented knowledge management are those that treat the human and the machine as partners — where AI handles the retrieval, synthesis, and surface-level generation, and people contribute the judgment, the curation, and the culture that makes knowledge genuinely shared.

The moment KM has been waiting for

I started my career arguing that knowledge management deserved more strategic investment. I’m still making that argument — with AI more executives are “getting it”.

AI has done what years of best practice frameworks and maturity models couldn’t: it has made knowledge management urgent.

That urgency is an opening. The question is whether organisations will use it to build something sustainable — genuine knowledge cultures, sound governance, human-centred practice — or whether they’ll bolt AI onto the same neglected foundations and wonder why the results disappoint.

The technology is ready. The case is made. Now comes the harder work.

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The Answer to the Collaboration Crisis Isn’t Another Platform

(This post was originally shared on my LinkedIn Profile.)

Last week I wrote about the research evidence that our obsession with efficiency and technology has eroded the social capital, trust, and genuine collaboration that make organisations actually work. The response told me the article landed somewhere real.

But a diagnosis without a direction isn’t much use. So this week I want to talk about what I believe a genuine response looks like.

It isn’t another tool. It isn’t a workshop. It isn’t a culture deck with better values on it.

It’s a fundamentally different way of thinking about knowledge — one that starts with people rather than platforms, and with meaning rather than metrics.

What conventional KM gets wrong

Most knowledge management programmes are designed to solve an information problem. Information is scattered, so we build a repository. People can’t find what they need, so we improve search. Expertise walks out the door, so we create a capture process.

These are real problems. But they’re symptoms, not causes.

The actual problem — the one the research keeps pointing back to — is that the conditions for knowledge to flow have broken down. Trust has eroded. Networks have shrunk. Cross-boundary relationships have withered. The informal conversations that carry tacit knowledge, the collegial curiosity that generates new thinking, the relational safety that makes people willing to share what they actually know — all of it has been quietly deprioritised in the name of efficiency.

You cannot fix that with a better taxonomy.

A different starting point

Radical Knowledge Management — the framework I’ve been developing and practising for over a decade — begins from a different premise.

Knowledge is not content. Knowledge is human. It is created through experience, shaped by context, and moved through relationship. It lives in people, between people, and in the culture that connects them. Any KM strategy that doesn’t start there is working on the wrong problem.

Radical KM is built on what I call the MAGIC-SH model: seven conditions that together create an environment where knowledge genuinely flows, where people are willing to share what they know, and where organisations develop the kind of collective intelligence that no platform can replicate.

Those conditions are Meaning, Agency, Generosity, Inquiry, Community, Story, and Humanity.

Let me walk through why each one matters in the context of the collaboration crisis.

Meaning

People share knowledge willingly when they understand why it matters. Not because they’ve been told to fill in the knowledge base, but because they can see the connection between what they know, what others need, and the outcome they all care about.

Efficiency culture strips meaning out of work progressively. It reduces contribution to task completion and measures value in outputs. Radical KM inverts this — it starts by building shared understanding of purpose, so that knowledge-sharing becomes an act of contribution rather than compliance.

Agency

Knowledge hoarding is almost never malicious. It’s usually rational. In environments where expertise equals job security, where being the person who knows something is a form of power, people protect what they know.

Agency addresses this by designing for genuine autonomy and contribution. When people have real influence over how their knowledge is used and recognised, the incentive structure flips. Sharing becomes generative rather than threatening.

Generosity

Generous knowledge cultures don’t happen by accident. They are built through modelling, through recognition, and through the deliberate design of spaces — physical, virtual, and cultural — where people are expected and encouraged to give freely of what they know.

This is directly counter to the transactional logic that dominates most organisations, where knowledge exchange is a negotiation and every interaction has an implied cost-benefit calculation behind it.

Inquiry

The collapse of cross-boundary weak ties that the Microsoft research documented is, at its root, a collapse of curiosity. When people work only within their immediate team, they stop asking questions of the wider organisation. They stop being curious about what’s happening elsewhere, what others have learned, what problems are being solved in parallel.

Radical KM designs explicitly for inquiry — creating the conditions, the habits, and the structures that keep curiosity alive across organisational boundaries.

Community

This is where the research evidence and the framework converge most clearly. Social capital is not built through formal programmes. It is built through communities — genuine ones, where people gather around shared interests, shared challenges, or shared expertise, and where the relationships that form outlast any particular project or process.

Communities of practice, done well, are the single most powerful KM intervention available. Not because of the knowledge they capture, but because of the trust they generate.

Story

Data informs. Story moves. The knowledge that organisations most need to retain and transmit — the contextual, experiential, relational knowledge that can’t be fully codified — travels in narrative.

Story is also how culture is sustained and changed. The stories an organisation tells about itself, about what it values, about who its heroes are, shape what knowledge gets shared and what gets suppressed.

Humanity

This is the frame that holds everything else. Knowledge management that forgets it is working with human beings — with people who have emotions, relationships, histories, and needs — will always fall short of what’s possible.

Humanity in KM means designing for the whole person, not just the role. It means recognising that psychological safety is not a HR initiative; it’s a knowledge infrastructure issue. It means understanding that the reason knowledge walks out the door is almost always relational, not technical.

Why this matters now

The organisations that will navigate the next decade well are not the ones with the best AI tools or the most sophisticated knowledge repositories. They are the ones that have invested in the human conditions that make knowledge genuinely flow — trust, relationship, meaning, and community.

The collaboration crisis the research describes is real. But it is not inevitable, and it is not irreversible. What it requires is a different kind of intervention — one that takes human beings seriously as the infrastructure of organisational intelligence.

That’s what Radical Knowledge Management is designed to do.

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We Didn’t Lose Productivity. We Lost Each Other.

(The post was originally shared on my LinkedIn Profile.)

There’s a feeling many of us carry into work that’s hard to name. A sense that despite the tools, the platforms, the dashboards, and the metrics — something fundamental has gone missing. That we’re busier than ever, but less effective. That we collaborate more and connect less.

The research says we’re right.

The social capital haemorrhage

McKinsey surveyed over 5,500 workers on the state of their workplace networks. What they found was striking: more than three-quarters reported connecting with others less frequently, maintaining smaller networks, and spending less time building relationships than before.

But here’s the detail that matters. Of the connections people still maintain, 57% are for sharing work-related information. 48% for career advice. Only 29% involve any kind of genuine social engagement.

What passes for collaboration in most organisations has become almost entirely transactional. Information exchange has replaced relationship. Efficiency has displaced trust.

And trust, it turns out, is not optional. Research consistently finds that mutual trust, reciprocity, and genuine collegial connection are among the strongest predictors of innovation, knowledge-sharing, and organisational effectiveness.

The silos aren’t a bug. They’re a feature of how we work now.

The most compelling evidence here comes from a 2022 Nature Human Behaviour study of over 61,000 Microsoft employees. When the company shifted to firm-wide remote work, something happened to its collaboration networks: they became static and siloed, with fewer bridges between different parts of the organisation.

The share of collaboration time employees spent with cross-group connections dropped by 25%.

This matters because of what those cross-group connections do. Sociologist Mark Granovetter called them “weak ties” — the loose connections across different teams, functions, and disciplines. They’re not where your most frequent interactions happen. But they’re where new knowledge comes from. They’re the connections that carry different perspectives, unexpected information, and creative friction.

We optimised them out of existence.

High productivity is masking exhaustion

Here’s the dangerous part: standard metrics often won’t show you this is happening.

Microsoft’s Work Trend Index, covering 31,000 workers across 31 countries, flagged it explicitly: high productivity is masking an exhausted workforce, and shrinking networks are endangering innovation.

A separate study of over 10,000 technology professionals found that after the shift to remote work, hours worked went up, output declined slightly, and productivity fell by 8–19%. More meetings. More messages. More coordination activity. Less actual work done.

More activity. Less capability.

This is what efficiency culture does

This didn’t happen by accident. It’s the predictable outcome of decades of management doctrine that treats organisations primarily as efficiency machines — measuring outputs, eliminating waste, standardising process, and optimising everything that can be quantified.

The problem is that genuine collaboration, learning, trust, and tacit knowledge are hard to quantify. So they don’t show up on the efficiency scorecard. And what doesn’t show up on the scorecard doesn’t get invested in.

UK government research on organisational effectiveness puts it plainly: the literature points to the failures of managerialism and market mechanisms to address complex challenges — and argues that those very challenges require genuine collaboration and inter-organisational cooperation to solve.

We’ve built management systems that are structurally antagonistic to the capabilities we actually need.

What this means for knowledge management

I’ve been making this argument for a long time: knowledge is not a technology problem.

Knowledge lives in relationships. It moves through trust. It surfaces in the informal conversation after the meeting ends, the cross-team question that no one put on a ticket, the senior person who explains the context behind the decision.

None of that is produced by a better platform. All of it depends on social capital — the networks, norms, and trust between people that make knowledge genuinely flow.

And the evidence is clear: that social capital has been eroding, quietly, for years. Not because people stopped caring, but because the conditions for building it have been systematically deprioritised in the name of efficiency.

Knowledge doesn’t walk out the door because of inadequate tooling. It walks out because the human infrastructure that carries it was allowed to decay.

The good news is that this is reversible. But it requires treating human connection, relational trust, and genuine collaboration as strategic organisational assets — not as pleasant by-products of getting the processes right.

Because they’re not by-products. They’re the foundation.

References available on request. Key sources include Yang et al. (2022), Nature Human Behaviour; McKinsey Quarterly (2022); Microsoft Work Trend Index (2021); Gibbs, Mengel & Siemroth (2022), Journal of Political Economy Microeconomics.

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Your KM Problem Isn’t the Platform and AI isn’t going to fix it

(This post originally appeared on my LinkedIn Profile and received a lot of attention there, feel free to read the comments there.)

I’ve spent my entire career fighting the same battle.

Someone finds out I work in knowledge management, and within minutes the conversation has shifted to SharePoint, or Confluence, or whatever platform is currently being championed as “the solution.” These days, that platform increasingly has “AI-powered” somewhere in the marketing copy.

And I understand why. Technology is visible. It has a price tag, a vendor, a demo. You can point to it in a procurement document. Knowledge — the human, messy, relational kind — is much harder to put in a slide deck.

But here’s what two decades in KM have taught me: the platform is never the problem. And it’s never the solution either. Neither is the AI layer on top of it.

When a knowledge base goes stale three months after launch, that’s not a technology failure. It’s a culture failure. When frontline staff can’t find what they need in the moment of need, it’s usually not because the search function is broken — it’s because the knowledge was never captured in a way that reflects how those people actually think and work.

Now imagine pointing an AI at that same broken foundation. A large language model is extraordinarily good at generating fluent, confident-sounding responses. It is only as trustworthy as the content it draws from. Outdated articles, undocumented workarounds, tribal knowledge that never made it into the system, contradictory guidance left unresolved for years — AI doesn’t fix any of that. It amplifies it. Confidently.

This focus on people and relationships is the heart of Radical Knowledge Management.

Radical KM starts from a simple premise: knowledge lives in people, not systems. Systems can support its flow, but they cannot create it, sustain it, or replace the trust and relationships that make people willing to share what they know. If you haven’t invested in those human conditions first, you can implement the most sophisticated AI-powered platform on the market and still end up with an expensive, high-velocity misinformation engine.

Moving beyond “KM is a technology problem” requires three honest conversations most organisations are reluctant to have:

Who owns knowledge here — and who is excluded from that? Knowledge governance isn’t about permissions and taxonomies. It’s about power. Whose expertise is captured and valued, and whose is invisible? AI will simply encode and accelerate whatever answer your organisation has already given to that question.

What does your culture reward? If people are measured on individual output and knowledge-sharing is an afterthought, no tool will change that — and no AI will surface knowledge that was never shared in the first place.

What does “good” actually look like for the people doing the work? Not for leadership. Not for auditors. For the person answering their fifteenth call of the day, under pressure, needing to find an accurate answer quickly. AI can be a powerful ally for that person — but only if the knowledge behind it has been curated with their reality in mind.

Technology has an important role. I’ve spent considerable time helping organisations choose and implement platforms, assess readiness, and design governance frameworks. The tools matter. AI genuinely matters. But they’re the last conversation, not the first.

The first conversation is about people. What they know, how they share it, whether they feel safe doing so, and whether the organisation has created the conditions for knowledge to actually flow.

Until we’re willing to have that conversation — really have it — we’ll keep buying new platforms, adding AI on top, and wondering why nothing changes.

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Your Organisation Is Losing Its Most Valuable Knowledge — And You’re Looking in the Wrong Place

(This post was originally posted on my LinkedIn Profile.)

Most knowledge management programmes are built on a flawed assumption: that the knowledge worth capturing is the kind you can write down.

Databases. Repositories. Best practice libraries. Lessons learned registers. These are the tools we reach for, and they are not without value. But they systematically leave behind the knowledge that organisations need most — the tacit, relational, embodied, and culturally embedded knowledge that lives in people’s instincts, their ways of seeing, their ability to navigate ambiguity. And when those people leave, retire, or burn out, that knowledge goes with them.

I have spent years pulling at the thread of what it would take to build a genuinely different approach to knowledge management — one that takes seriously the full range of what humans know, not just the fraction that fits into a text field. The review of 70+ research studies that have been sitting on my hard drive has strengthened my conviction that the answer lies somewhere most KM practitioners have not looked: in the arts, in creativity, and in a fundamental rethinking of what knowledge really is.

Creativity Is Not a KM Output. It Is a KM Process.

Most frameworks treat creativity as something that knowledge enables — invest in KM, and innovation follows. The research suggests the inverse is equally true, and arguably more important: creative processes are themselves a primary mechanism for knowledge creation.

This is not a semantic distinction. It has structural implications. If creativity is a core KM process — not a downstream benefit — then knowledge management systems need to be architected around enabling creative application, not just knowledge access and storage.

The practical implication is this: your knowledge management programme will never perform to its potential if it optimises for retrieval while neglecting creation. Knowledge that is shared but not creatively applied circulates without transforming. It becomes organisational wallpaper.

What Neuroscience Is Telling Us About How Knowledge Is Actually Formed

Here is something that does not appear in most KM strategy documents: arts engagement simultaneously activates multiple brain systems — sensory, emotional, memory, and social — that purely linguistic knowledge exchange does not reach.

This is not a soft claim. It is a structural argument, supported by neuroscience research, for why arts-based processes generate different and richer knowledge than text-based alternatives. When we design knowledge processes that engage multiple sensory modalities — visual, auditory, kinaesthetic, tactile — we enhance memory formation, creative connection, and knowledge retention in ways that a well-formatted document simply cannot replicate.

The corollary is equally important: the knowledge that organisations most need to preserve and transfer — what experienced practitioners know through gesture, spatial sense, physical practice, and professional intuition — is precisely the knowledge that conventional KM systems are structurally unable to capture.

The Knowledge Your Organisation Cannot Afford to Keep Losing

Research by Root-Bernstein on Nobel laureates and highly innovative scientists reveals a striking pattern: artistic avocation and scientific or organisational innovation are strongly linked. This is not biographical curiosity. It points to something structural about cognition: the moves developed through artistic practice — observation, pattern recognition, aesthetic discrimination, creative synthesis — transfer directly into knowledge work and produce measurably better outcomes.

Meanwhile, a landmark meta-analysis of 268 publications and 205 documented cases of arts-based interventions in organisations identified 393 distinct positive outcomes. These were not soft or anecdotal: they mapped directly onto what we recognise as KM objectives — improved collaboration, enhanced sense-making, stronger learning cultures, greater capacity to navigate complexity.

The evidence base for arts-informed approaches to knowledge management is substantial. What has been missing is a methodology that makes it organisationally accessible.

The Problem with How We Measure Creativity

One of the most consistent findings in the creativity measurement literature is that organisations damage creative performance by applying innovation metrics to creative processes. The two modes require fundamentally different conditions, incentives, and approaches to evaluation.

The most common way organisations kill creativity is by applying business-case logic too early. Ideation phases — genuinely exploratory, divergent, high tolerance for ambiguity — need to be protected from premature evaluative pressure. This is not a cultural nicety. It is a design requirement.

The research recommends measuring creative process quality alongside outputs: psychological safety levels, diversity of perspectives in dialogue, quality of questioning, willingness to challenge assumptions. And it argues for tracking disposition indicators as leading measures — curiosity, comfort with ambiguity, eagerness to explore — because these predict creative output better than measuring outputs directly.

This is a genuinely different logic from most organisational measurement frameworks, and it matters because the wrong measurement approach actively undermines the thing you are trying to build.

The Rise of AI Makes This More Urgent, Not Less

Two threads in the research address the AI question directly, and both arrive at the same place: the risk is not competition between human and artificial intelligence. The risk is substitution — outsourcing creative processes to AI in ways that reduce human engagement in precisely the activities that generate authentic knowledge and genuine human connection.

The knowledge created through creative, arts-informed processes carries something that AI-assisted processes cannot replicate: the self-disclosure and authentic engagement of human beings working through complexity together. This is not sentimentality. It is a functional argument for why relational knowledge — the kind created in the interspace between people — remains irreducibly human, and why protecting it matters more as AI becomes more capable, not less.

AI belongs in knowledge management as infrastructure: search, synthesis, organisation. Human creative engagement belongs at the centre, not as a nice-to-have, but as the core activity that makes the rest of it worth doing.

What This Means for How Organisations Should Approach KM

The research points to several structural changes that would make a genuine difference.

Organisations need to create what researchers call interspaces — protected environments, physical and psychological, where conventional organisational norms are temporarily suspended so that genuinely different knowledge can emerge. The arts are particularly effective at creating these spaces because they carry cultural permission for experimentation and ambiguity that normal business contexts do not. But the research is clear that an interspace only delivers KM value if insights flow back into mainstream practice. The re-integration pathway matters as much as the creative space itself.

Demographic diversity is necessary but not sufficient. What the research points to is cognitive diversity — different thinking modes, expertise domains, and problem-solving approaches — as the primary driver of creative knowledge work. The research from Unilever’s innovation practice establishes this as the primary predictor of creative problem-solving performance. And it requires facilitation processes designed to harness the productive friction of different perspectives, rather than suppressing it in the name of social comfort.

Knowledge management needs to be framed as a wellbeing and human development practice, not merely an efficiency tool. The neuroscience literature establishes that arts engagement generates measurable benefits to stress resilience, emotional regulation, and cognitive flexibility — all of which directly affect the quality of knowledge work over time. Organisations that attend to the human experience of knowledge work generate more and better knowledge. This is not a values claim. It is a performance claim.

A Different Kind of Knowledge Management is not just Possible. It Is Necessary.

We are asking our organisations to navigate a world of genuine complexity — technological disruption, demographic change, shifting social expectations, and wicked problems that do not yield to standard playbooks. The knowledge management frameworks we inherited from the 1990s were designed for a different world. They optimised for efficiency, standardisation, and explicit knowledge. They have served their purpose.

What we need now is a methodology that takes seriously the full spectrum of what human beings know — including the knowledge that lives in the body, in relationships, in aesthetic sensibility, in the capacity to sit with ambiguity long enough to generate something genuinely new.

That is what Radical Knowledge Management is designed to do.