Articles and Thinking

You can build technically strong containers for context, smartly connected, but still have nothing meaningful to full them.

80% of respondents said AI had improved their individual productivity. Only 37% said it had contributed positively to their organisation's bottom line, a share essentially unchanged from the year before. And only around 6% attribute 5% or more of their EBIT to it. Those figures point to something important: individual gains don't necessarily become organisational gains.

In a world that is changing as quickly as this one, and continuing to accelerate as it does so, perhaps the strongest organisations will be those most capable of adapting well.

Your company brain is only as good as the knowledge your people put into it. We learned this once with data. It took years and it cost a fortune.

Information accumulates. Knowledge is chosen. On what the connect-everything AI pipeline quietly hands over, at both ends.

What sits under the choices your teams make, and how much is lost.

They sound like the same thing. They are not, and almost everything that matters sits in the gap between them. Redesigning a process increases capacity. It finds the parts a machine can carry and hands people back the hours, and there is genuine value in that. But underneath it sits an assumption that is rarely said out loud: that time was the thing standing between your people and better work. Free the capacity, and the capability will rise on its own. It won't. A person

Most organisations think their knowledge lives in their systems. It usually doesn’t.

When designing a knowledge environment there is a critical question to ask, how does this organisation actually think, and it is answerable across five observable dimensions. The answer is what a knowledge structure should be derived from. This piece lays out the five.

A lot of commentary on AI sounds more certain than it should. We do not know exactly how AI will reshape work, roles, teams, or organisations. We do not know which operating models will endure, which skills will matter most, or how quickly different types of work will be reshaped. But we do know this: when the nature of work changes, organisations need new foundations.

A knowledge flywheel where people use what came before, engaged, built new context, and documented it, ready to distribute for the next engagement, whether themselves or those that come after.

Context is not volume. It is relevance, chosen with intent.

Why organisations lose what their people create, and the architecture that stops the loss.

Satya Nadella (Microsoft CEO) wrote something last week worth sitting with. A key principle in his post was that as AI grows inside a company, he argued, human capital does not become less valuable. It becomes more. The people, their judgement, their relationships, the patterns they notice and the dots they connect, are what give the machinery direction. Without them, you have compute running in circles. He's right. And I think the way he's right matters more than the line it

Knowledge architecture rests on four pillars. Three of them, once built, hold. The fourth only stands when people choose it, which is the part most organisations skip.