Companies do not lack AI ideas, they lack the nerve to spend on them. That is the uncomfortable finding underneath most failed AI programmes: the technology works in the pilot, the pilot saves real money, and then the organisation quietly shelves it. The pattern repeats because the problem was never the model, it was the…
Most technology pilots work. Most technology rollouts fail. An airline can run a flawless drone delivery pilot with a 100% success rate and still have no drones delivering anything three years later, and a bank can prove an AI credit model on 50,000 customers while the other 5 million never see it. The pattern is…
Why do AI transformation programmes fail? Most fail for the same reason legacy IT projects failed before them: technology gets bought before the operating model is redesigned, executive attention drifts after the launch, and nobody owns the change long enough to make it stick. The AI layer adds new failure modes on top of the…
Most digital transformation projects disappoint, and almost never for the reasons named in the post-mortem. Why it is a people problem wearing a technology costume, and what the successful ones do differently.
The smarter the team, the slower some decisions get. Why intelligence and slowness arrive together, how analysis becomes a hiding place, and what actually speeds decisions up.
Every productivity tool promises to save you time. The Jevons Paradox suggests the opposite happens. What it means for how we should actually use AI.
What you knew five years ago may no longer be reliable today. The problem is that the confidence often does not depreciate at the same rate as the knowledge.
Organisations with excellent project tools still deliver late. The dashboard shows green. The project is red. This is never a tooling problem.
Most meetings are not about making decisions. They are about deferring them. The demand for more analysis is often a way of deferring something uncomfortable without saying so.
The best way to have a good idea is to have lots of ideas and throw away the bad ones. Why volume thinking beats quality filtering.