On AI strategy, innovation, and building things that work.
AI coding tools promised a revolution in software delivery. The tools are real, but the 10x productivity gains remain elusive. The bottleneck was never typing speed.
The biggest leap in AI model capability over the past year didn't come from more data or bigger clusters. It came from a routing decision. Here's why Mixture of Experts matters and what it means for what comes next.
The five structural failure modes that kill data strategies — and why better communication isn't the fix.
The way you structure your AI team determines whether AI becomes a strategic capability or an expensive research lab. Here's what I've seen work.
Data culture isn't a project you launch or a tool you buy. It's a byproduct of how your organisation actually makes decisions.
The problem with most enterprise AI strategies isn't the AI part. It's the strategy part. Here's what goes wrong and how to build one that actually works.
The transition from technical contributor to data leader is an identity crisis disguised as a promotion. Here's what actually changes and how to survive it.
Retrieval-Augmented Generation looked straightforward in the tutorial. In production, it was anything but. Here's what we learned the hard way.
We adopted data mesh principles with high hopes and a big budget. Here's an honest accounting of what worked, what didn't, and what we'd do differently.
Every AI team faces the build vs. buy decision repeatedly. Most get it wrong because they're optimising for the wrong variable.
That impressive AI prototype is probably 18 months and three rewrites away from running reliably at scale. Here's what actually breaks.
No articles match your search.