Category: AI

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

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

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

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

The Post-mortem of Lost Capacity

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

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

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

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

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

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

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

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

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

Radical KM: People-First, Not Technology-First

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

That means:

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

Using arts-based approaches to do all of this.

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

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

The Choice

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

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

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

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

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

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

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

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

The ROT Problem Is Your AI Problem

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

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

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

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

Enter ART: Accurate, Reliable, Trustworthy

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

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

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

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

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

The Governance Gap

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

Governance asks the hard questions:

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

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

Keeping Humans in the Loop

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

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

Humans stay in the loop at the critical junctures:

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

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

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

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

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

The Real ROI

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

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

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

That’s the difference between readiness and recklessness.

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Stop Talking About Knowledge Management. Start Showing Its Impact

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

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

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

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

Here’s how that actually occurs in practice.

1. Anchor to outcomes, not terminology

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

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

2. Build credibility through small, visible wins

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

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

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

3. Make trust the visible difference

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

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

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

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

4. Celebrate the human story, not the system

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

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

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

5. Make it a leadership conversation

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

This means asking leadership questions:

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

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

6. Measure what actually matters

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

Track what actually shifts:

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

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

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

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

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

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

(This post originally appeared on my LinkedIn Profile.)

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

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

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

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

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

The Seductive Logic of the Tech Solution

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

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

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

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

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

Knowledge Management Is a People Problem First

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

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

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

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

The Five Pillars That Actually Matter

Effective Knowledge Management rests on five interconnected foundations:

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

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

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

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

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

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

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

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

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

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

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

So Where Does That Leave AI?

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

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

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

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

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

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

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

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

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

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

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

Then came AI.

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

AI is only as good as the knowledge it can access

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

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

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

The sudden urgency is real — and welcome

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

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

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

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

What AI actually makes possible

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

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

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

The human element isn’t optional

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

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

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

The moment KM has been waiting for

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

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

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

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

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Your Organisation Has a Knowledge Management Problem. It Just Doesn’t Know It Yet

(This post originally appeared on my LinkedIn Profile.)

Ask most people what Knowledge Management means and they’ll mention a chatbot. Or a FAQ database. Or the system the contact centre uses to answer customer calls faster.

They’re not wrong. That is KM. But it’s one room in a very large house.

And while everyone’s focused on that room, the rest of the house is quietly falling apart.

The contact centre problem

Contact centre KM is visible. It has vendors. It has metrics. It has a clear ROI story — faster handle times, fewer escalations, happier customers. There are software platforms built specifically for it. Conferences dedicated to it. Entire consulting practices around it.

So when organisations say “we have KM,” they often mean “we have a tool that helps agents answer questions.”

That’s not KM strategy. That’s knowledge delivery at one endpoint.

What’s happening everywhere else

While the contact centre is meticulously managed, here’s what’s happening in the same organisation:

A senior leader retires and takes twenty years of institutional memory with them. Nobody captured it. Nobody thought to.

A project team spends three weeks solving a problem that another team solved eighteen months ago. The solution existed. It just wasn’t findable.

A proposal goes out with assumptions that contradict positions taken on a previous engagement. The left hand didn’t know what the right hand had written.

A new hire takes six months to reach full productivity because the knowledge they need lives in people’s heads, not in any system.

None of this shows up in a contact centre dashboard. All of it is a knowledge management failure.

The invisible KM problem

Contact centre KM is visible KM. It has a home, a budget, and a team accountable for it.

Everything else is invisible KM — the organisational knowledge that lives in people, relationships, past work, and institutional memory. It has no home. No owner. No metrics. And so, no urgency.

Until someone leaves. Until a bid is lost. Until a mistake is repeated. Until the organisation realises it keeps reinventing the wheel because nobody was minding the wheel.

KM is a whole-organisation discipline

The international standard for KM — ISO 30401 — doesn’t mention contact centres. It talks about strategy, culture, learning, leadership, and governance. It defines KM as a management discipline that helps organisations create, retain, share, and apply knowledge to achieve their objectives.

APQC, one of the most respected research bodies in the field, frames KM across five pillars: strategy, process, content, culture, and technology. Technology is one pillar. The contact centre sits inside technology. That’s a lot of building left unattended.

The questions that reveal the real problem

You don’t need a formal audit to see where your organisation’s invisible KM is failing. Just ask:

  • What happens when your most experienced person walks out the door?
  • How long does it take a new hire to become genuinely productive?
  • How do you capture what worked — and what didn’t — at the end of a major project?
  • Can you find your organisation’s best thinking on any given topic in under five minutes?
  • Do your teams build on each other’s work, or do they start from scratch every time?

If those questions make people uncomfortable, that’s not a contact centre problem. That’s a knowledge strategy problem.

What to do about it

Start by separating the tool from the discipline. Contact centre software is a tool. KM is a discipline that applies across every part of an organisation — from how you onboard people to how you run retrospectives to how you capture competitive intelligence to how you make expert knowledge accessible to everyone who needs it.

Then look at where knowledge is genuinely at risk. Who are your critical knowledge holders? What happens when they leave? Where does important knowledge live that isn’t written down anywhere?

You don’t need a massive programme to start. You need someone asking the right questions. And you need leadership that understands that knowledge isn’t just a contact centre asset — it’s the organisation’s most valuable and most under protected resource.

The contact centre KM is working fine. That’s not the problem.

The problem is the illusion of coverage it creates. The belief that because one type of knowledge is well-managed, the rest is too.

It isn’t. And the cost of that gap shows up slowly — in turnover, in repeated mistakes, in lost bids, in the quiet erosion of things that once worked.

KM done well is invisible too. You notice it in how fast people find what they need. In how smoothly transitions happen. In how teams learn from each other rather than around each other.

That kind of KM doesn’t live in a single tool. It lives in how an organisation thinks about what it knows — and what it can’t afford to lose.

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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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The AI Revolution in Call Centres: Elevating Customer Satisfaction Through Smart Technology

(This post originally appeared on my LinkedIn profile.)

The landscape of customer service is experiencing a transformational shift as artificial intelligence becomes the cornerstone of modern call centre operations. With the global call centre AI market valued at US$3.23 billion in 2024 and projected to reach approximately US$25.84 billion, organisations worldwide are recognising that AI isn’t just a technological upgrade—it’s a strategic imperative for delivering exceptional customer experiences.

The Business Case for AI in Customer Service

Recent research presents compelling evidence for AI adoption in call centres. Mature AI adopters reported 17% higher customer satisfaction scores, while human agent satisfaction increased by 15% when supported by intelligent systems. These aren’t merely incremental improvements; they represent a fundamental shift in how customer service organisations operate.

The scale of this transformation is remarkable. By 2025, 95% of customer interactions are predicted to be handled by AI, with 75% of customer inquiries now being resolved by AI tools without human intervention. This isn’t about replacing human agents—it’s about empowering them to deliver more meaningful, high-value interactions.

Key Benefits Driving Customer Satisfaction

Enhanced Response Times and Availability

AI-powered systems deliver round-the-clock service that modern customers demand. 77% of customers expect to reach someone right away when they contact a company, and 90% say a quick response is critical, with 60% expecting “immediate” to mean within 10 minutes. AI chatbots and virtual assistants meet these expectations by providing instant responses to common queries, eliminating wait times for routine issues.

Personalised Customer Experiences

Modern AI systems leverage comprehensive customer data to deliver highly personalised service. 66% of global customer service managers who are optimising AI use generative AI to increase personalisation. By analysing purchase history, past interactions, and real-time context, AI enables agents to provide tailored solutions that feel genuinely human.

Improved First Call Resolution

First Call Resolution (FCR) rates are now expected to reach 80% or higher, a significant improvement from previous benchmarks. AI assists agents by providing real-time suggestions, retrieving relevant knowledge base articles, and surfacing customer history instantly, enabling them to resolve issues more effectively on the first contact.

Sentiment Analysis and Emotional Intelligence

Advanced AI systems now incorporate sentiment analysis to gauge customer emotions in real-time. When speech analytics are used, customer satisfaction increases as much as 10%, whilst operational costs can decrease by up to 30%. This emotional intelligence allows for proactive intervention when customers become frustrated, preventing escalation and improving overall satisfaction.

Best Practices for AI Implementation

Start with Clear Objectives

Successful AI implementation begins with defining specific goals. Whether aiming to reduce wait times, improve resolution rates, or enhance agent productivity, clear goals that align with your business’s needs are essential for measuring success and guiding technology choices.

Adopt a Human-AI Collaborative Approach

The most effective call centres don’t replace human agents with AI—they enhance them. AI is not replacing human agents but enhancing them, providing real-time support and context that allows agents to focus on complex issues requiring empathy and critical thinking.

Ensure Comprehensive Training and Change Management

75 percent of CX leaders believe the skillsets of support teams will look drastically different in three years as agents become AI managers. Investing in comprehensive training programmes ensures agents can effectively collaborate with AI systems and evolve into more strategic roles.

Prioritise Data Integration and Quality

AI systems are only as effective as the data they access. Unified customer data from multiple touchpoints enables AI to deliver personalised, contextually relevant responses. This integration allows different systems to communicate effectively, ensuring seamless customer experiences across all channels.

Measuring Success: Key Performance Indicators

Organisations implementing AI in call centres should track several critical metrics:

  • Customer Satisfaction (CSAT) scores: Modern AI tools can analyse 100% of conversations to provide comprehensive satisfaction metrics
  • First Call Resolution rates: Target improvement from 70% to 80%+ with AI assistance
  • Average Handle Time: Balance efficiency gains with quality service delivery
  • Agent satisfaction scores: Measure how AI tools impact employee experience
  • Cost per interaction: Track operational efficiency improvements

The Canadian Context

Canadian businesses are particularly well-positioned to leverage AI in call centres, with several leading companies already demonstrating best practices. Canadian organisations should consider partnering with local AI specialists who understand the regulatory environment and can provide culturally relevant implementations.

The technology infrastructure in Canada, combined with strong privacy frameworks, creates an ideal environment for responsible AI deployment in customer service settings.

Looking Ahead: The Future of AI-Enhanced Customer Service

The trajectory is clear: AI will continue evolving from a support tool to an integral component of customer service strategy. It is expected that by the end of 2025, 80% of companies will have adopted or are planning to adopt AI-powered chatbots, making this transformation inevitable rather than optional.

Future developments will likely include even more sophisticated natural language processing, predictive customer service that anticipates needs before they arise, and seamless integration across omni-channel experiences. The organisations that invest in AI capabilities now will be best positioned to meet rising customer expectations and maintain a competitive advantage.

Conclusion

The evidence is overwhelming: AI in call centres isn’t just improving operational metrics—it’s fundamentally enhancing customer satisfaction whilst empowering human agents to deliver more meaningful service. With 40% of companies worldwide using AI, and with 82% either using or exploring AI in their operations, the question isn’t whether to adopt AI, but how quickly and effectively organisations can implement these transformative technologies.

The companies that embrace AI-powered customer service today will set the standard for customer experience tomorrow. By focusing on human-AI collaboration, comprehensive training, and customer-centric implementation, organisations can harness the full potential of artificial intelligence to create truly exceptional customer service experiences.

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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.