01The AI conversation has inverted
Two years ago, companies asked 'should we use AI?'. Today they ask 'where is AI going to hurt us if we don't?' — and the second question produces worse decisions than the first. Fear-driven AI adoption creates expensive, abandoned projects.
02Where AI actually pays
In our work, AI earns its place in a handful of patterns: extracting data from documents, summarising unstructured text, routing and answering routine requests, and recommending next actions from history. Each of these removes real, measurable human hours.
03Data is the real investment
The cheapest way to make AI work is to have clean, structured data — and the most expensive mistake is adopting AI before data exists. That's why we design systems with AI-readiness in mind even when AI isn't in the first release. The data model is the moat.
04Boundaries are features
Enterprise AI needs guardrails: what it can access, what it can decide, and what always goes to a human. We treat these boundaries as core engineering — the difference between an assistant and a liability.