Thinking on transformation

Most RAG failures are retrieval failures, not generation failures. The architecture decisions that separate systems that get it right from ones that confidently get it wrong.

AI features break the normal product rules — they're probabilistic, they drift, and users have no frame of reference for what "good" looks like. Here's how to ship them anyway.

The hardest part of enterprise AI is not the model — it's the person sitting in front of it. Design principles that move organisations from reluctant users to confident adopters.

Eighty percent of AI pilots never reach production. The engineering and organisational decisions that determine whether your initiative scales or quietly disappears.

AI readiness is not about having the latest tools. It's a function of data quality, leadership alignment, and the willingness to rethink processes — not just automate them.

Teams consistently budget 20% of effort for data and 80% for modelling. In practice it's the reverse — and discovering this midway through a project is expensive.

Most AI strategies fail to get funded because they're written for the wrong audience. What it actually takes to get an AI initiative approved and resourced.

The five patterns that work in production versus the three that look good in demos. An architectural guide to integrating large language models into enterprise software.

No AI implementation has ever failed because the algorithm wasn't good enough. What real change management looks like in an AI transformation context.

Trust is not a feature you add at the end — it's an architectural decision made at the beginning. How to design AI systems that users believe in, rely on, and actually use.

A deep dive into how a 2,400-person operations team went from manual review processes to AI-augmented workflows — what worked, what didn't, and what we'd do differently.