June 2026 – Entelechy

(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.

(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.

Month: June 2026

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