Tag: Human skills

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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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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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The Magic in the White Space: why what we can’t measure what matters most

(This post originally appeared on my LinkedIn profile.)

We’ve optimised ourselves into a corner.

Somewhere along the way, we started measuring everything. Click-through rates. Response times. Conversion metrics. Engagement scores. Customer lifetime value. Net Promoter Scores. And in doing so, we forgot something fundamental: people aren’t data points.

I’ve been thinking a lot lately about how our obsession with technology and analytics has made us deeply, painfully transactional. Every interaction has become an opportunity to capture, quantify, and optimise. We’ve replaced conversations with touch-points. Relationships with customer journeys. Trust with sentiment analysis.

The cost? We’ve lost our ability to think holistically. And we’ve forgotten that the most important things that happen in organisations—and in life—happen in the spaces between the things we measure.

The Tyranny of the Measurable

There’s a management axiom that gets trotted out with alarming frequency: “What gets measured gets managed.” It’s usually attributed to Peter Drucker, though he probably never said it in quite that way. Regardless of its provenance, we’ve taken it as gospel and built entire industries around it.

But here’s the problem with making measurement the centre of everything: we start to believe that if something can’t be measured, it doesn’t matter. Or worse, it doesn’t exist.

Consider what happens when everything is reduced to discrete, measurable transactions. We stop seeing patterns. We stop understanding context. We stop recognising that the person on the other end of that support ticket is the same one who attended our webinar, recommended us to a colleague, and has been quietly championing our work for years. Their “customer journey” looks like a series of disconnected events in our CRM, but their actual relationship with us is something far richer—and far more valuable—than any dashboard can capture.

Strategic thinking requires us to hold complexity. To see connections that don’t fit neatly into a spreadsheet. To value things we cannot easily quantify—like loyalty, goodwill, institutional memory, and the trust that accumulates through countless small moments of being seen and valued.

What Lives in the White Space

In design, white space—the empty areas around and between elements—isn’t wasted space. It’s what makes the rest of the design work. It provides breathing room, creates emphasis, and allows the eye to rest. Without white space, everything blurs together into noise.

Organisations have white space too. It’s the corridor conversation that sparks an unexpected idea. The coffee break where two people from different departments discover they’re working on complementary problems. The offhand comment in a meeting that someone remembers six months later and applies in a completely different context. It’s the trust that accumulates not through formal processes but through countless small moments of showing up, following through, and treating people as whole human beings rather than inputs in a workflow.

None of this fits in a spreadsheet. None of it shows up in your quarterly metrics review. And yet—ask anyone where their best ideas came from, and they’ll almost never point to a scheduled brainstorming session or a formal innovation programme. They’ll tell you about a conversation. A chance encounter. A moment of connection that couldn’t have been predicted or planned.

This is where the magic happens. And we’re systematically eliminating it.

The Efficiency Trap

In our pursuit of efficiency, we’ve optimised away the very conditions that allow innovation, creativity, and genuine human connection to flourish. Every minute is accounted for. Every interaction has a purpose. Every meeting has an agenda and a hard stop.

We’ve mistaken activity for progress and transactions for relationships.

The irony is profound. We invest millions in innovation programmes while simultaneously creating cultures that leave no room for the unstructured thinking that innovation requires. We talk about the importance of relationships while treating every customer interaction as a data point to be captured and processed. We celebrate “human-centred design” while building systems that reduce humans to behavioural patterns and conversion funnels.

You can’t build a relationship in a transaction. You build it in the space between transactions. In the follow-up that wasn’t required. In remembering what matters to someone. In showing up consistently, not just when the metrics demand it.

A Different Way of Seeing

So how do we measure the magic that happens in the white space?

I think the uncomfortable answer is: we don’t. Not directly, anyway. The white space is where serendipity lives, and serendipity resists quantification. That might be precisely the point.

But that doesn’t mean we’re helpless. It means we need to shift our focus from measuring the magic itself to measuring—and deliberately cultivating—the conditions that allow magic to happen.

We can ask different questions. Do people have unstructured time to think and connect? Are there spaces—physical or virtual—where unexpected collisions can occur? Is there psychological safety to share half-formed ideas without them being immediately evaluated against KPIs? Do we reward relationship-building, or only task completion?

We can notice the outcomes, even if we can’t trace the path. Innovation doesn’t announce its origins. But we can ask: where did that breakthrough actually come from? When we bother to trace these things back, the answer is almost never “the innovation committee met and decided.” It’s “I had a conversation with someone in a completely different department” or “I remembered something a client mentioned offhand three months ago.”

We can listen for stories instead of statistics. Narrative is how humans have always captured what metrics cannot. The organisations that understand the value of white space are the ones that collect and share these stories deliberately—not as marketing material, but as genuine organisational knowledge about how value actually gets created.

The Human Imperative

This matters more now than ever. As artificial intelligence takes over more of our transactional work—answering routine queries, processing standard requests, analysing data at scale—what remains distinctly human is precisely the stuff that happens in the white space. The intuition. The empathy. The ability to hold ambiguity and see connections across domains. The relationships built on genuine understanding rather than algorithmic prediction.

If we continue to optimise humans out of the white space, we’ll have nothing left to offer that machines can’t do better. But if we recognise that the white space is where our uniquely human value lives, we can build organisations that amplify rather than replace what makes us irreplaceable.

Technology should serve relationships, not replace them. Data should inform human judgement, not substitute for it. Efficiency should create space for what matters, not consume every moment in pursuit of marginal gains.

An Invitation

Perhaps it’s time we measured what actually matters—even if that means accepting that some things can’t be measured at all.

What would it look like to run an organisation that valued the white space? That deliberately protected time for unstructured connection? That evaluated leaders not just on what they delivered but on the relationships they built and the conditions they created for others to thrive?

What would it look like to approach each interaction not as a transaction to be completed but as a relationship to be nurtured? To see the person behind the data point, the story behind the metric, the magic behind the measurement?

The magic is in the white space. It always has been.

Maybe it’s time we stopped trying to fill every inch of it with something we can count.

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Beyond Prompt Engineering: Strengthening the Uniquely Human Skills AI Can’t Replace

(This post originally appeared on my LinkedIn Profile.)

As artificial intelligence transforms workplaces across every industry, the conversation often centres on technical preparedness: learning to write effective prompts, understanding AI tools, or adapting to new technologies. But there’s a more fundamental question we should be asking: What uniquely human capabilities should we be strengthening to remain indispensable in an AI-augmented workplace?

The answer isn’t about competing with AI—it’s about complementing it by doubling down on the skills that make us irreplaceably human.

The Context Revolution: Where Human Intelligence Shines

AI excels at processing information and recognising patterns, but it struggles with something humans do intuitively: understanding context that isn’t explicitly stated. When a team member says they’re “fine” with a decision. Still, their body language suggests otherwise. When a client’s straightforward request masks a more profound concern, or when market data fails to capture the cultural nuances affecting a product launch, these are moments where human insight becomes invaluable.

Strengthen your contextual intelligence by:

  • Practising active listening that goes beyond words to understand underlying concerns
  • Developing cultural competency and awareness of unspoken dynamics
  • Learning to read between the lines in communications and situations
  • Building your ability to synthesise information from multiple, often contradictory sources

Ethical Reasoning in Complex Situations

AI systems can be programmed with rules, but they struggle with the grey areas where ethical considerations intersect with practical realities. Human professionals who can navigate complex ethical dilemmas—balancing stakeholder interests, considering long-term implications, and making principled decisions under pressure—will become increasingly valuable.

Develop your ethical reasoning by:

  • Studying case studies in your industry where ethical considerations shaped business decisions
  • Engaging with diverse perspectives on moral and ethical issues
  • Practising scenario-based thinking about the potential consequences of decisions
  • Learning frameworks for ethical decision-making in professional contexts

Relationship Building and Trust Creation

Trust is built through consistent human interaction, emotional connection, and shared experiences. While AI can facilitate communication, the deep relationships that drive business success—whether with clients, team members, or partners—remain fundamentally human endeavours.

Strengthen your relationship-building skills by:

  • Developing genuine curiosity about others’ perspectives and experiences
  • Practising empathy and emotional intelligence in professional settings
  • Learning to build rapport across different communication styles and cultural backgrounds
  • Mastering the art of difficult conversations with sensitivity and tact

Creative Problem-Solving and Innovation

True innovation often emerges from connecting seemingly unrelated ideas, challenging fundamental assumptions, or approaching problems from entirely new angles. While AI can generate variations on existing themes, breakthrough thinking typically requires the kind of creative leaps that come from human intuition and imagination.

Enhance your creative problem-solving by:

  • Exposing yourself to disciplines outside your expertise area
  • Practising brainstorming techniques that encourage unconventional thinking
  • Learning to suspend judgment and explore “what if” scenarios
  • Developing comfort with ambiguity and uncertainty

Strategic Thinking and Long-Term Vision

AI is excellent at optimising for defined parameters, but setting those parameters—determining what success looks like in a complex, changing world—requires strategic thinking that considers multiple stakeholders, uncertain futures, and competing priorities.

Build your strategic capabilities by:

  • Studying how successful leaders have navigated industry transformations
  • Practising systems thinking that considers interconnected effects
  • Developing comfort with making decisions based on incomplete information
  • Learning to balance short-term pressures with long-term objectives

Adaptability and Continuous Learning

Perhaps most importantly, the ability to adapt quickly to new situations, learn from failures, and pivot when circumstances change is a fundamentally human trait. While AI systems require retraining to handle new situations, humans can draw on experience, intuition, and creativity to navigate uncharted territory.

Cultivate your adaptability by:

  • Embracing new challenges outside your comfort zone
  • Learning from setbacks and viewing failure as valuable data
  • Developing mental models that can be applied across different contexts
  • Staying curious about emerging trends and their potential implications

Leadership in Times of Change

As organisations implement AI, they’ll need leaders who can guide teams through transformation, address concerns about job security, and help people find meaning in their evolving roles. This requires emotional intelligence, practical communication skills, and the ability to inspire confidence during times of uncertainty.

Develop your leadership skills by:

  • Practising transparent communication about change and its implications
  • Learning to address fears and concerns with empathy and honesty
  • Building skills in change management and organisational development
  • Developing your ability to articulate vision and purpose

The Integration Imperative

The most successful professionals won’t be those who ignore AI or those who simply learn to use AI tools—they’ll be those who understand how to integrate AI capabilities with uniquely human strengths to create value that neither could generate alone.

This means developing what I call “integration intelligence”: the ability to understand what AI does well, what it doesn’t do well, and how human capabilities can best complement artificial ones. It’s about becoming the bridge between technological capability and human need.

Practical Steps to Start Today

  1. Audit your current role: Identify which tasks could potentially be automated and which require distinctly human judgment.
  2. Invest in relationships: Spend more time building genuine connections with colleagues, clients, and industry peers.
  3. Seek diverse experiences: Volunteer for cross-functional projects or take on challenges outside your usual scope.
  4. Practice difficult conversations: Develop your skills in navigating conflict, delivering difficult news, or facilitating challenging discussions.
  5. Study other industries: Look for insights and approaches from fields completely different from your own
  6. Embrace ambiguity: Take on projects where the path forward isn’t straightforward and success metrics are still being defined.

The Future Belongs to Human-AI Collaboration

The organisations that will thrive in the AI era won’t be those that replace humans with machines, but those that create powerful collaborations between human insight and artificial intelligence. By strengthening our uniquely human capabilities—our ability to understand context, navigate complexity, build relationships, think creatively, and lead through change—we position ourselves not as competitors to AI, but as essential partners in creating value that neither humans nor AI could achieve alone.

The question isn’t whether AI will change our work—it already is. The question is whether we’ll be ready to contribute our irreplaceable human value to that transformation. Now is the time to start strengthening those muscles.