The Future of Workplace AI

Citizen Developers, Dark IT and the Queue at the Gate Walk across any university campus or business park and you'll see them: thin brown tracks worn through the grass, cutting the corner the architect drew. Planners call them desire paths. They are the routes people actually take when the official one is longer than it needs to be, and they carry a useful lesson. You can put up a fence and a sign. Or you can notice where everyone is walking and pave it. I should declare an in

Every post in this series so far has taken the data organisations already hold as its starting point. The mess exists; the question has been what to do about it. This final post asks the question that sits upstream of all of it: why does the mess keep forming in the first place? The answer, more often than not, is that the data was never really collected at all. It just happened. It's a byproduct: the exhaust of systems and processes that were designed to do something else. T

Five posts into this series and the argument so far has mostly been about what's wrong: data nobody trusts, AI trained on data that was never fit to train anything, definitions that contradict each other across departments, analysts drowning in more of all of it than anyone can reasonably process, and, sitting underneath the lot, an unbudgeted risk nobody has ever billed to anyone. None of that is much use without an answer to the obvious next question. What do you actually d