Perspectives on technology strategy, delivery, and the decisions that shape how organisations build.
Every team using AI in development thinks they are ahead of where they are. Here is a framework for knowing where you actually stand — and what it costs to mistake one level for another.
Most technology assessments end with a document. A good technology assessment ends with a decision.
Every importer approaching CBAM as a regulatory exercise is setting up for a harder time than necessary. The organisations that will manage it well are treating it as a data infrastructure problem.
The most revealing things about a software team are not in the codebase. They are in how the team talks about its work.
Before you can fix anything, you have to understand why it is broken. And why it is broken is almost always structural, not technical.
The visible cost of a CTO departure is a recruitment process. The invisible cost is what happens to the organisation while the seat is empty.
Most AI projects underinvest in data infrastructure and overinvest in models. The model is never the limiting factor.
The cost of non-compliance gets discussed at length. The cost of compliance done badly does not. They are often comparable.
The graveyard of AI projects is full of successful pilots. The problem is not the technology — it is what happens after the demo.
When a client goes quiet mid-engagement, the instinct is to assume they are busy. They are usually not busy. They have lost confidence.
AI has changed the economics of validation. It has not changed the underlying logic of why starting small produces better outcomes. It has simply removed the last credible excuse for not doing it.
The post-mortem examines what happened during delivery. The failure happened before the first meeting.