“Wholly new forms of encyclopedias will appear, ready made with a mesh of associative trails running through them…”
Matt Perdeaux founded Associative Trails in 2005. Since then the work has almost always come back to the same thing: helping professional services firms capture what they know and put it where people can actually use it.
Technical libraries where knowledge linked up instead of piling up. Intranets that became the place a firm's knowledge lived. Systems for curating, tagging and sharing expertise that would otherwise stay locked in individuals' heads.
That was the work before anyone called it AI readiness.
The organisations were architects, engineers, business schools, law and insurance practices, and the problem was always structural: the knowledge that mattered most was the hardest to reach.
AI has made the old problem urgent.
Firms are buying tools that depend on structured, accessible knowledge and discovering they don't have it. The technology works. The foundations often don't.
We've spent two decades building the layer those tools depend on. We know what it takes to get a firm's knowledge into usable shape, what that effort is worth, and, just as importantly, when it isn't worth doing.
That experience shapes how we advise clients today. Experience earned from building systems inside professional services firms and seeing, repeatedly, why some approaches succeed and others fail.
Many AI advisers start with the technology. We start with the people.
We know where expertise hides. We know why intranets fail. We know which workflows look broken but aren't, and which ones quietly drain time every day. Most importantly, we're comfortable saying: "AI won't help you here." That scepticism isn't a limitation - it's what makes the advice useful.
When you start formalising what people know, some of them get nervous, and they are right to. Knowledge capture has too often been a polite name for making someone replaceable.
That is not what this is. The aim is to take the parts of the job that drain time and judgement and give them back, not to hollow out the expertise and keep the shell. People resist when they sense the first thing. They engage when they can see it is the second. Getting that distinction right, and being honest about it, is a big factor in why these projects succeed or fail.
Arup · Ashridge Business School · Eckersley O'Callaghan · Ellipse · Heatherwick Studio · London Business School · Safetime · Scott Brownrigg · Sheppard Robson
That experience is what we bring to AI now. See what we've built.