The same problem, thirty years later
From early websites to AI assistants, the core challenge has never changed: how do you make what an organisation knows available to the people who need it?
The first websites I built for organisations were essentially knowledge problems. Someone in the building knew something useful. The question was how to get it out of their head, into a format a system could hold, and back to someone who needed it. We called it content. We called it information architecture. We called it knowledge management, depending on which consultant was in the room.
The terminology changed every few years. The problem didn't.
The pattern
I've watched this cycle play out across three decades of working at the intersection of organisations and technology. In the mid-nineties, the web arrived and suddenly there was a place to put things. The challenge was: which things, structured how, maintained by whom? Content management systems emerged to answer that. They helped. They also introduced new layers of complexity that made the original problem harder to see.
Intranets came next. Then wikis. Then enterprise search. Then SharePoint, in all its configurations. Each generation of tooling carried the same implicit promise: this time, knowledge will flow. And each time, the same thing happened. The tool got adopted. The underlying question got deferred.
What organisations actually struggle with is not tooling. It's the discipline of treating knowledge as something that needs to be captured, maintained and made available, not as a byproduct of work but as work itself.
What AI reveals
AI hasn't changed this. It has made it harder to ignore.
When you put a language model to work in an organisation, it works with what the organisation has. Documents, policies, case files, procedures. If those are well-structured, current and findable, the model is useful. If they are scattered across drives with inconsistent naming and no clear ownership, the model reflects that back, fluently and confidently. That is the wall every AI deployment eventually runs into: not the model's limitations but the organisation's information underneath it.
I've described this elsewhere as the difference between content as decoration and content as knowledge. Organisations have spent years publishing things. Far fewer have spent that time structuring what they know in a way that's durable and reusable.
The constant
What strikes me now, looking back, is how consistent this has been. The organisations that got the most out of the web were the ones that thought carefully about what they knew and how to represent it. The ones that got the most out of search were the ones with clean, maintained, findable content. The pattern holds for AI.
The technology shifts. The underlying requirement stays fixed.
This is not a pessimistic observation. It is a clarifying one. The question an organisation needs to answer before any AI project is not "which model should we use" or "which tool should we buy." It is the same question I was sitting with in 1995: what do we actually know, who owns it, and how do we keep it alive?
Three decades later, that question is worth more than ever. The systems that can use a good answer to it are considerably more powerful than a static webpage. But the answer still has to come from the organisation itself.