Category: knowledge workers

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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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Trust Isn’t Built on Fear: Why Bullying Destroys Knowledge-Sharing Culture

(This post originally appeared on my LinkedIn Profile.)

Trust is the currency of any high-performing organisation. Yet I’ve watched it drain away in cultures where bullying—overt or subtle—goes unchallenged. What strikes me most isn’t the immediate harm: it’s the cascade effect, the quiet unravelling of ethics and the slow death of knowledge sharing.

How Bullying Erodes Trust

Bullying thrives in silence. When someone is undermined in a meeting, excluded from decisions they should shape, or publicly ridiculed for a mistake, a message travels fast: this is not a safe place. Others pull back. They stop speaking up. They stop sharing ideas. Trust—the belief that you’ll be treated with respect and dignity—evaporates.

The damage isn’t confined to the bullied individual. Colleagues who witness it withdraw too. They’re calculating risk. They’re protecting themselves. The whole system shifts from cooperation to self-preservation.

The Moral Toll

Here’s what’s rarely said plainly: organisations that tolerate bullying develop moral blind spots. When leaders ignore it, minimise it, or worse—enable it—they send a signal about what truly matters. Ethics become situational. Right and wrong depend on hierarchy, on whether you’re the perpetrator or the target.

Over time, this corrodes the foundation. Integrity becomes transactional. People stop believing the values on the website. Policies feel performative. And once that happens, recovery is slow.

The Knowledge-Sharing Crisis

A knowledge-sharing culture requires vulnerability. It asks people to admit gaps, share half-formed ideas, ask for help, and learn from mistakes without punishment. It requires trust.

In cultures where bullying is tolerated, this dies. People hoard information as armour. They hide failures instead of discussing them as learning opportunities. Mistakes become something to conceal rather than dissect and understand. Junior staff don’t ask questions. Expertise becomes gatekept.

The irony: organisations that most need collaborative learning—those navigating complexity, scaling, disrupting—are often the ones undermining it through cultural toxicity.

It Starts with Accountability

Building a knowledge-sharing culture isn’t about more training or better platforms. It’s about choice. Choosing to act when you see bullying. Choosing leadership that models respect. Choosing to investigate concerns seriously rather than dismissing them.

It’s about saying: here, we treat each other with dignity. That’s non-negotiable.

When that’s genuine—when accountability is real—everything shifts. People contribute ideas. They admit what they don’t know. They help each other. And organisations move faster, adapt better, and attract people who want to build something that matters.

Trust isn’t built on fear. And knowledge isn’t shared in silence.

Is your workplace psychologically safe? What does knowledge sharing look like where you work?

P.S. this article was inspired by the discussion in our podcast on July 28, 2026, if you missed it you can find it here: How to Thrive as a Human in an AI World: S2 E9. Thank you Simo Routarinne and Melanie Byrne Adams for such a wonderful, inspiring discussion.

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

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

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

The research says we’re right.

The social capital haemorrhage

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

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

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

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

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

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

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

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

We optimised them out of existence.

High productivity is masking exhaustion

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

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

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

More activity. Less capability.

This is what efficiency culture does

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

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

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

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

What this means for knowledge management

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

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

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

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

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

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

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

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

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Serious KM Game article published

Just in case you missed it in my social media activities, the World Journal of Science, Technology and Sustainable Development has published my article about Knoco’s Bird Island. The article is called, “Serious game: Knoco’s Bird Island, making the point for KM” and will be published this spring, but is already available online, https://linkis.com/emeraldinsight.com/ijdJ4

I am in the process of setting up a session to do with the KM Meetup Group here in Berlin and a virtual session with the KM group in Toronto that I used to co-facilitate with Connie Crosby and Martin Cleaver. Both sessions will happen in May 2017.

If you have any questions about it, would like to participate in one of the upcoming sessions or have something specially set-up for your organization, don’t hesitate to get in touch.

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Importance of a KM Strategy

Why do I need a knowledge management strategy? Why can’t I just implement some technology and be done with it? Why can’t I just implement Communities of Practice or Lessons Learned and be done with it?

I hear this sometimes from managers who want a quick fix, who are under a lot of pressure from time and resources (money and people).

The answer is, you can. I have worked with many organizations that have done just that, jumped in with both feet and “just done something”. I am usually there to fix it. Fix the technology because no one understood what it really needed to do to support knowledge work within the organization; fix the process because no one understands it and it’s not aligned with the rest of the activities in the organization and it’s created extra work for already over-worked staff.

Why do you need a strategy?

Would you jump in the car and set out on a journey of 5000km/3000miles without having some idea of where you were going and how you going to get there? Making sure that you had selected the right vehicle to get you there in time and a map to help direct you along the way?

A KM strategy does just that. It helps you figure out where you’re going, and the things you need to do along the way, the processes you need to support you and that need to be supported.

That’s not to say that you won’t make adjustments along the way, just like a friend of mine who drove from Toronto to Vancouver in September 2015, who ended up “detouring” through the United States, so that she could see some different sites, but she still knew where she was going and when she had to be there by—she made it with time to spare.

Isn’t that what you want from your KM strategy? To know where you’re going and how you want to get there, to meet the goals and objectives of the organization?

Why would you put a toaster over in the car for your trip, when you really needed a camp stove?

Why would you choose one technology because “everyone else is” when another technology is cheaper and better meets the needs of the organization because it requires less customization than the more popular software?

Isn’t it time you created (or updated) your KM strategy?

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Agile and Knowledge Management, part 1

At our Knowledge Worker Toronto event on January 23, 2013 our speaker, Gil Broza, spoke about the human side of Agile. Now, Agile, for those of you who don’t regularly interact with software developers, which I imagine are many of you who read this blog, is about iterative and incremental design and development of software applications. Gil was speaking about lessons that could be learned from the experience of software developers in this area and transferred to other areas of the organization. That activity in itself is a knowledge management activity: knowledge transfer of lessons learned, but I digress.

Gil spoke about 10 lessons that the rest of the organization could learn and apply:

  1. People are not resources
  2. Focus
  3. Nurture the joy of delivering value
  4. Take small, safe feedback-rich steps
  5. Mind the physical environment
  6. The social environment matters too
  7. Want high-performance teams? Be ready to invest
  8. Manage less, lead more
  9. Collaboration rocks
  10. Human conduct trumps “best practices”

There was a discussion after the presentation and Q&A ended about how this talk fit in with Knowledge Workers/Knowledge Management, this is what I contributed to the discussion: these 10 lessons are about how knowledge workers like to work. In the KM consulting that I do, I often have a section in the report about knowledge workers, especially when I’m working with an organization that is hierarchical. Knowledge work and knowledge management thrives in a flatter organization model, one where sharing and working together is expected, and the silos of a hierarchy are detrimental to achieving the goals of the organization.

Anyone else have any thoughts?

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Knowledge is the network

One of the themes at KM World in October 2012 was that the value of knowledge management is in the network, i.e. the value comes from the connections and the collective whole, rather than individual people, activities, processes, or technology. This was a shift from previous years where there was more focus on technology.

That the value of knowledge is in the network, is something we have known for a long, long, time. There has long been acknowledgement that “it’s who you know,” in business and in life. What has changed in the last 10 years is the ability to stay connected to people and to connect with people in geographically diverse locations through the use of technology, but it’s still about, “who you know.”

Our networks provide access to opportunities that we might not have been able to discover on our own. They pass along interesting articles, books, and other pieces of knowledge and information. Someone says something and that makes us think of something else or ask a question that’s not been asked before. Someone else builds on our ideas, it becomes an iterative process and suddenly we have created something new, some innovation that didn’t exist before.

When someone in our work network moves to another company or role, we all-of-a-sudden have to fill the void left in our knowledge network: who else knows what that person knew, how long will it take their replacement to learn the things we need them to know, what do we do until the gap is filled?

Organizations that go through down-sizing/right-sizing/lay-offs/retirements all have to figure out what to do about the impact on the knowledge networks of their organizations. Those that don’t take the loss of knowledge and the disruption to the network into consideration are negatively impacted by the loss/turn-over.

So what can organizations do to try to keep some of that knowledge when people leave the organization or create opportunities for innovation? Knowledge management activities like communities of practice, mentoring programs, lessons learned processes, after action reviews, expertise location activities, to name a few, and the technology that supports them all help to capture and share knowledge as well as make connections that might not happen otherwise. Knowledge management activities also give the knowledge longevity that it might not have otherwise.

Once the knowledge management practices are in place there is a need to make sure that it remains relevant through regular review and updating processes. This relevancy check could be as simple as reviewing documents and knowledge bases, or sending staff to conferences and training courses. It all becomes part of the learning and continuous improvement that the organization desired by implementing knowledge management in the first place.

This was also published in the Knoco January 2013 newsletter, which can be accessed here https://www.knoco.com/Knoco%20newsletter%20Jan%2013.pdf

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Topics and Trends from KM World 2012

I attended KM World 2012 in Washington, DC last month, for the first time since 2006 (when it was still in San Jose, California). Let me first just say that I enjoyed the new location very much, not just because it’s a much shorter flight for me, but it seemed more intimate–easier to meet and talk to people and find my way around. I did miss being able to visit all my friends in the Bay Area, but I will get out there again.

Okay, so on to what I learned and observed at KM World 2012…

I think one of the big things I observed was a shift away from all the talk of technology, don’t get me wrong, people still talked tech, but I found less of an emphasis on it this year and much more emphasis on the value of the network, i.e. the people-to-people connections. Certainly any of us who have been doing KM for a while know that this is the case, that technology just enables and supports the activities of the network, but for most of the last 15-20 years we have had to fight against the idea that technology was the silver bullet in KM, that if an organization implemented the right technology they would find a pot of gold at the end of the rainbow.

Other themes:

  • Mobility and Internal Social Media
  • Internal Communications (consistent and repetitive communications via multimedia channels)
  • Strategic Alignment (km must always align with the business strategy of the organization to be successful)
  • Measurement and Value (everyone is measuring trying to determine value, but everyone is also still measuring things differently, but that’s OK.)
  • The importance of
    • Governance
    • Serendipity
    • Complexity/interconnectedness of KM
  • The DIKW pyramid is dead. Or is it?
  • Don’t fall prey to echo chambers in your organization
  • People’s knowledge goes beyond their job description which is untapped capital
  • The power of influence by friendship through peer networks is real
  • Seek forgiveness, instead of permission
  • Ask yourself daily what your km clients would answer to the question, “What’s in it for me?”

Some of the presentations and keynotes are posted on the KM World website, https://kmworld.com/Conference/2012/ and Dave Snowden’s closing keynote is on his website, https://cognitive-edge.com/library/more/podcasts/km-world-2012-washington-dc-closing-keynote/

Finally, I have to thank Daniel Lee for his notes/thoughts on KM World, which I have incorporated in this post as well as in a presentation I did for Knowledge Workers Toronto and is posted on Slideshare.