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.

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.

Sustainable Leadership

At the end of February 2020, I did two talks on Sustainable Leadership. The first was with Janus Boye from Boye Company, you can find his blog post and a link to the recording and slides here.

The second was with Glenn Behenna for him MBA class. In that video Glenn interviewed me about my thoughts and experience with sustainability and leadership. Glenn has graciously allowed me to share the recording of that interview with you. You can find it on my YouTube channel, here.

Reflections on HBR Technology Must Reads, Stitch Fix Case Study

Reference Case Study: Stitch Fix’s CEO on Selling Personal Style to the Mass Market

I’m not going to re-hash the article, you can go read it yourself, that’s why am providing the link, what I am going to do is highlight the things I found interesting, the first two weren’t technology related, but the third one was and the fourth one was more personal.

My first thought was who shops that much and/or needs that many clothes? Now, to be fair, I’ve never been much of a shopper, I typically only buy things when I need to replace something that I’ve worn out. There were a few exceptions to this after I moved to Berlin and realised I’d gotten rid of a few things that I really should have kept, and bought things to replace them, but mostly, I just replace things that have worn out.

My other thought was around the fit/sizing. I have a terrible time finding things that fit the way I want them too, even when I know my measurements and buy according to the sizing chart, so I end up in a store/stores trying things on, who needs the hassle of online shopping and sending stuff back?

But, turning to the technology, that part was interesting, allowing for the fact that I am not even remotely in the target market for such a service. The fact that they have used AI/Machine learning to bring the fashion industry into the 21st century is interesting. It is all about understanding the customer-base/target market and the nuances of the sector and knowing what to do with the data once it’s collected. 

Finally, and more personally, I liked that Katrina Lake, the founder and CEO of Stitch Fix, when she didn’t see anyone else doing anything to address what she perceived (rightly) as an opportunity in the market place, decided to do it herself. This sounds like something I would do, and in fact have done, not that I have started a successful online business, but I have stepped into the void and provided leadership and guidance when there was none; I have done things that I thought “someone else” should be doing/responsible for but they weren’t interested or didn’t perceive the need. 

Those are my thoughts (quick and cursory as they may be) on the second article/case study in the HBR book, “On AI, Analytics, and the New Machine Age”.

I am Stephanie Barnes and I am … magic


(I am like a Tardis—I look ordinary, but contain powers beyond your imagination)

I don’t fit in a box
I work in the white space between the boxes

I don’t conform
I am a leader and a follower
I am analytical and creative
I am process-driven and flexible
I do digital transformation and culture change
I am a program and project manager and a performer of tasks
I get things done and I leave them until it is time to do them
I balance people, process, and technology
I balance business and technology
I am a bridge builder and an island
I like data and I think it’s pointless
I like theories and practicality
I create strategies, I implement them, and I go with the flow
I like the simplicity of black and white, while loving the energy, chaos, and emotions of layers of colours
I am patient and impatient
I rush ahead and I wait and see
I accept the things people tell me as true, and I ask lots of questions
I love silence, jazz concerts, and the symphony
I love the stillness of nature and the buzz of city life
I like order while finding it boring
I like chaos while finding it overwhelming and requiring order
I am a twin and an individual

I am organised chaos

Why do I do these things?

Because I am an expert and a novice
Because I am experienced and skilled, collaborative and curious
Because I am searching and challenging boundaries
Because I wonder what happens if…