Tag: knowledge management

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ABIs Are Your Tools. They’re Not About Creating Art

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

For years (I’ve been working on this idea for 15+ years), when I mention arts-based interventions in KM conversations, I watch the same response play out. Eyes light up. Then — a gentle retreat. “That sounds lovely,” they say, “but we’re not really an arty organisation,” or “I work with a bunch of lawyers/engineers/accountants, they will never do this stuff”.

I understand the hesitation. We’ve been conditioned to think of arts-based work as something separate from serious business — creative indulgence rather than strategic leverage. But that’s not what ABIs are.

ABIs are tools. Specifically, they’re tools for doing the thing KM practitioners struggle with most: getting people to think.

In a world drowning in information and increasingly suspicious of what we’re told, our core challenge isn’t capturing knowledge, storing it better, or finding it when it’s needed. It’s reactivating the curiosity, critical thinking, and continuous learning that so many organisations have accidentally designed out of their cultures.

Here’s the paradox: you can’t mandate creativity. You can’t drive critical thinking with a policy. And you certainly can’t build psychological safety — the actual foundation of knowledge sharing — through process and conversation. These things live in the human layer, and they respond to something more subtle than corporate edicts.

That’s where creativity becomes a tool rather than a luxury.

When you ask someone to paint their response to “What does trust look like in our team?” you’ve created a constraint that forces novel thinking. They can’t fall back on corporate-speak. They can’t hide behind jargon. The unfamiliar medium — the act of making something — bypasses the rational filters and surfaces what people actually think and feel.

You’re not running an art class. You’re creating conditions for knowing to emerge.

ABIs work because they:

  • Disrupt habitual thinking. Familiar formats produce familiar responses. A different medium invites different pathways.
  • Create psychological safety through ambiguity. There’s no “correct” answer to a painting or a collage. That removes the performance anxiety that stifles honesty.
  • Make the tacit visible. The patterns in how someone uses colour, composition, or materials often reveal assumptions and thinking they hadn’t consciously articulated.
  • Invite full-system engagement. The hand, the eye, and the brain working together create a different quality of attention than sitting in a meeting watching slides.
  • Generate genuine conversation. When artefacts are at the centre — not abstractions — dialogue becomes richer, more grounded, and more memorable.

I’m not asking you to turn your people into artists. I’m asking you to consider: what if the tool you’re looking for to unlock knowing isn’t another platform, another framework, or another consultant-led workshop? What if it’s permission to engage your people’s creativity as a capacity rather than a personality trait?

Because here’s what I’ve learned: people don’t lose their creativity in organisations. They’re just trained not to use it where it matters most.

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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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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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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.

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Keeping Humans in the Loop: A Strategic Approach to AI Implementation

(this post originally appeared on my LinkedIn Profile)

As organisations increasingly adopt artificial intelligence technologies, a critical question emerges: how do we harness AI’s power whilst maintaining essential human oversight and judgment? The answer lies in implementing robust human-in-the-loop (HITL) systems that combine the efficiency of automation with the nuanced decision-making capabilities that only humans possess.

Understanding Human-in-the-Loop Systems

Human-in-the-loop refers to AI systems designed to incorporate human judgment at critical decision points. Rather than fully automated processes, HITL systems pause at predetermined moments to seek human input, validation, or correction. This approach acknowledges that whilst AI excels at processing vast amounts of data and identifying patterns, humans remain superior at contextual interpretation, ethical reasoning, and handling edge cases.

The concept becomes particularly relevant in Canadian organisations navigating the proposed Artificial Intelligence and Data Act, which emphasises the importance of human oversight in high-impact AI systems.

Why Human Oversight Remains Essential

Contextual Understanding AI systems, despite their sophistication, often struggle with context that humans intuitively grasp. A customer service chatbot might misinterpret sarcasm or fail to recognise when a routine inquiry masks a serious complaint requiring immediate escalation. Human agents can read between the lines and understand the broader context of interactions.

Ethical Decision-Making Complex ethical dilemmas require human judgment that considers values, cultural nuances, and long-term consequences. When AI systems encounter scenarios involving fairness, privacy, or potential harm, human intervention ensures decisions align with organisational values and societal expectations.

Accountability and Trust Maintaining human involvement in AI decision-making processes supports accountability frameworks and builds public trust. Stakeholders are more comfortable with AI systems when they know qualified humans are monitoring and can intervene when necessary.

Continuous Learning and Improvement Human feedback serves as a crucial training mechanism for AI systems. When humans correct AI decisions or provide alternative solutions, this information can be fed back into the system to improve future performance.

Practical Strategies for Implementation

1. Tiered Decision Authority

Implement a hierarchical system where AI handles routine decisions independently but escalates complex cases to human reviewers. For example, in healthcare AI systems used by Canadian hospitals, routine diagnostic suggestions might proceed automatically, whilst unusual cases require physician review.

Implementation Framework:

  • Level 1: AI handles standard cases (80-90% of volume)
  • Level 2: AI flags uncertain cases for human review (5-15%)
  • Level 3: Complex cases requiring specialist human judgment (1-5%)

2. Active Learning Loops

Design systems that continuously learn from human corrections and feedback. This approach is particularly effective in knowledge management systems where subject matter experts can refine AI-generated content recommendations or search results.

3. Confidence Thresholds

Configure AI systems to request human input when their confidence levels fall below predetermined thresholds. This ensures that uncertain decisions receive appropriate human scrutiny whilst maintaining efficiency for high-confidence scenarios.

4. Collaborative Interfaces

Develop user interfaces that facilitate seamless collaboration between humans and AI. Rather than treating AI as a black box, create transparent systems that show their reasoning and allow humans to easily modify or override decisions.

Overcoming Implementation Challenges

Resource Allocation One of the primary challenges in HITL implementation is determining the appropriate level of human involvement. Too much human oversight defeats the purpose of automation, whilst too little risks poor outcomes. Organisations should start conservatively and gradually reduce human intervention as systems prove reliable.

Training and Skill Development Staff need training not just on how to use AI systems, but on how to effectively collaborate with them. This includes understanding AI capabilities and limitations, recognising when to intervene, and providing meaningful feedback for system improvement.

Change Management Some employees may view HITL systems with suspicion, seeing them either as job threats or as additional burdens. Clear communication about the benefits of human-AI collaboration and how it enhances rather than replaces human capabilities is essential.

Sector-Specific Considerations

Healthcare Canadian healthcare organisations implementing AI diagnostics must maintain physician oversight for critical decisions whilst leveraging AI for initial screening and triage. The College of Physicians and Surgeons’ guidelines emphasise that clinical judgment cannot be fully delegated to AI systems.

Financial Services In compliance with Canadian banking regulations, financial institutions using AI for lending decisions must ensure human reviewers can understand and explain automated decisions to customers and regulators.

Government Services Public sector AI implementations require particular attention to transparency and accountability. Citizens have the right to understand how AI systems affect decisions about their benefits, services, or regulatory compliance.

Building Effective Human-AI Teams

The most successful HITL implementations treat humans and AI as complementary team members rather than competitors. This requires:

Clear Role Definition Establish clear boundaries between what AI handles independently and what requires human input. These boundaries should be regularly reviewed and adjusted based on system performance and organisational needs.

Feedback Mechanisms Create structured processes for humans to provide feedback on AI decisions, including both corrections and confirmations of good decisions. This data becomes valuable for system improvement.

Performance Monitoring Implement metrics that measure not just AI accuracy but also the effectiveness of human-AI collaboration. Track how often human interventions improve outcomes and use this data to optimise the balance.

Future-Proofing Human-AI Collaboration

As AI capabilities continue to evolve, HITL systems must be designed for adaptability. What requires human oversight today might be handled autonomously tomorrow, whilst new capabilities may create new needs for human involvement.

Organisations should regularly reassess their HITL configurations, staying informed about advances in AI technology whilst maintaining focus on their core mission and values. The goal isn’t to eliminate human involvement but to ensure it remains meaningful and value-adding.

Conclusion

Keeping humans in the loop isn’t about limiting AI’s potential—it’s about maximising the combined potential of human intelligence and artificial intelligence. By thoughtfully designing HITL systems, organisations can harness AI’s efficiency and analytical power whilst preserving the judgment, creativity, and ethical reasoning that humans bring to complex decisions.

The most successful organisations of the future will be those that master this collaboration, creating systems where humans and AI work together seamlessly to achieve outcomes neither could accomplish alone. In the Canadian context, with increasing regulatory attention to AI governance, human-in-the-loop approaches aren’t just good practice—they’re becoming essential for compliance and public trust.

As knowledge managers and organisational leaders navigate this landscape, the question isn’t whether to keep humans involved in AI systems, but how to do so most effectively. The answer lies in thoughtful design, continuous learning, and a commitment to maintaining the human elements that make organisations truly intelligent.

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AI-Ready

(this post originally appeared on my LinkedIn profile and has been edited slightly from the original)

I’ve been talking to many people about the importance of keeping humans in the loop and preparing organisations to be AI-ready. With that in mind, I thought I might do a short series on those themes. Today marks the first in a series focused on helping organisations become AI-ready. This has been a theme at the conferences I’ve attended over the last couple of years, so I’m sure many of you have thoughts about this necessity. Feel free to share your thoughts in the comments.

Knowledge managers play a crucial role in preparing organisations for AI adoption. Here are the key areas they need to focus on:

Data Governance and Quality Establish robust data governance frameworks that ensure information is accurate, consistent, and accessible. AI systems are only as good as the data they’re trained on, so knowledge managers must audit existing knowledge repositories, standardise data formats, and implement quality control processes. This includes creating metadata schemas and ensuring compliance with Canadian privacy legislation like PIPEDA.

Knowledge Audit and Documentation Conduct comprehensive audits of organisational knowledge assets – both explicit (documented) and tacit (experiential) knowledge. Map knowledge flows, identify critical knowledge gaps, and document processes that have previously relied on institutional memory. This foundation is essential for training AI systems effectively.

Change Management and Skills Development: Develop change management strategies that address employee concerns about AI, while building digital literacy. Knowledge managers should collaborate with HR and training departments to create programmes that help staff understand how AI will augment rather than replace their work. Focus on developing human skills that complement AI capabilities.

Technology Infrastructure Assessment: Evaluate current knowledge management systems and determine what upgrades or integrations are needed to support AI tools. This might involve migrating to cloud-based platforms, improving search capabilities, or ensuring systems can integrate with AI applications through APIs.

Ethical Guidelines and Bias Mitigation: Establish clear guidelines for ethical AI use within the organisation. Knowledge managers should work with legal teams to develop policies around AI transparency, accountability, and bias prevention. This is particularly important in Canadian organisations operating under evolving AI governance frameworks.

Pilot Programmes and Gradual Implementation Start with small-scale AI implementations in specific knowledge domains before organisation-wide deployment. This allows for testing, learning, and refinement while building internal confidence and expertise.

Cross-Functional Collaboration: Foster partnerships between knowledge management, IT, legal, and business units to ensure AI initiatives align with organisational objectives and compliance requirements.

The key is taking a strategic, human-centred approach that treats AI as a tool to enhance organisational knowledge capabilities rather than simply a technological upgrade.

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Creating a knowledge sharing culture

Are you wondering how to create a culture more conducive to knowledge sharing, collaboration, innovation, trust, and respect?

Radical Knowledge Management will do that for you.

Implementing Radical KM addresses a whole host of challenges being face in organisations and really is the silver bullet it appears to be.

ProblemSymptomArts-based Solutions
Uncertainty and exaggerated sense of urgency▸ People are stressed, worried, overwhelmed▸ Support mental health and well-being
▸ Creating space for reflection
No sense of purpose/meaning▸ Disengagement
▸ Connections have been broken
▸ No sense of belonging
▸ No empathy and compassion		▸ Build teams, connections, engagement
▸ Build a sense of belonging
▸ Build/develop empathy and compassion for self and others
Lack of Agency▸ Don’t know what they want, what they can do, what is possible
▸ People starting to think like AI/ computers		▸ Build sense of purpose and meaning, big picture
▸ Create/develop agency
Lack of critical thinking▸ Don’t know who/what to believe
▸ Lack of curiosity and creative problem solving		▸ Critical thinking to discern what is real and what is fake
▸ Support trial and error, and iteration

You can check out my recent ReWorked column for more information or watch this webinar I did with KMI.