Why Most AI Pilots Fail Before They Begin
The problem is rarely the technology. It's the mismatch between what the business asks the pilot to prove and what a pilot is structurally capable of demonstrating.
Essays, frameworks, and field notes from our work at the frontier of enterprise AI. Practical. Opinionated. No filler.
The problem is rarely the technology. It's the mismatch between what the business asks the pilot to prove and what a pilot is structurally capable of demonstrating.
Retrieval-augmented generation is now table stakes. The quality of your retrieval pipeline — chunking strategy, embedding model, reranker — is where the differentiation lives.
The organisations that are getting real value from AI agents aren't treating them like software. They're treating them like new team members with well-defined scopes of authority.
The most expensive AI mistakes we see come from organisations that underestimated their data infrastructure debt. A practical framework for assessing and closing the gap.
Caution feels responsible. But in a market where AI capability is compounding monthly, delayed adoption has measurable costs that accumulate invisibly.
The risks that boards worry about — hallucinations, bias, job displacement — are real but manageable. The risk they rarely discuss is the one that keeps us up at night.
Let's talk about what they mean for your organisation.