AI For Editorial Excellence
AI tools are now part of how I work. Here are the principles that guide the practice.
Knowledge Architecture
I run two dedicated AI projects: one for ongoing market intelligence and synthesis, one for active work. Each maintains its own context and purpose. The goal isn't to produce more content faster — it's to be better informed before I write anything.
Point of View
Generic content serves no one. I apply editorial taste to every AI output — cutting what's algorithmic, keeping what's true. Audiences trust distinct perspectives, not content that sounds like everyone else using the same prompt.
Intentionality
AI isn't for outsourcing decisions, values, or strategy. The discipline is learning how to use AI to enhance clear thinking without becoming distracted by the onslaught on information it provides.
Domain Authority
AI is only as good as the judgment I bring to it. I need to know what story to tell, who needs to hear it, and why it matters to them — before I prompt anything. The tool amplifies my thinking. It doesn't replace it.
Clear Guidelines
Organizations need clear AI governance: transparent policies about which tools are permissible and why, ongoing education for teams, and frameworks that align with company values and editorial standards.
Honesty
I'm transparent about how I use AI in my work. It accelerates production and research, but the responsibility for the story I tell remains mine.