I keep coming back to this idea with enterprise AI: The model does not need everything. It needs the right things. That sounds obvious, maybe. But I think it gets lost quickly once organizations start talking about large language models, internal chatbots, RAG models, and AI-supported knowledge experiences. Most organization's first instinct is to connect the model to as much as possible: Policies FAQs Product documentation Training materials Knowledge articles Old pages that

A lot of organizations are talking about AI readiness. The conversation usually starts with tools: Which model should we use?Should we build a chatbot?Can we automate this workflow?How fast can we launch a pilot? Those are reasonable questions. But they skip the one that matters first: Is our knowledge ready to be used this way? In one of my enterprise AI-readiness efforts, the most useful work did not start with a model. It started with a question set. Before asking whether

A little reflection on digital product work, shared decision-making, and the hidden cost of ambiguity. The quick version Most product teams do not need more meetings. They need fewer repeated decisions. Standards help teams move with less friction because they make expectations visible. The goal is not control. The goal is clearer, more humane operating conditions. I’ve been thinking a lot about why some digital product teams feel heavier than they should. Not because people

Why mature content organizations need ownership, decision rights, taxonomy, metadata, maintenance, and measurement — not just better writing standards. In one large, regulated organization, a content team was delivering a high volume of feature-level work across product, design, engineering, and business partners. The work was getting done. The backlog was moving. Individual screens and experiences were improving. But a pattern started to emerge. A taxonomy fix in one area so