Tag: Humanity

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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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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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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.