(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 Feed it outdated policies, inconsistent documentation, and tribal knowledge that lives only in people’s heads — and you’ll get confident, fluent, wrong answers at scale.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.