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Michelin Stars Are Cheap. The Grocery Run Will Kill You.
You wouldn't send a world-class chef shopping after every order. So why are you doing it to your AI?
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Notes on audience intelligence.
Writing on audience data, AI readiness, research operations, and the proprietary knowledge layer behind better AI systems.
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Latest thinking from Wick.
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The Elephant in the Dark
Every team has a read on the audience. The problem has never been a lack of data. It's never having a way to see all of it at once.
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Maine 2026 Senate Race: Tight Senate Race Between Collins and Platner
A survey of likely Maine voters finds a close contest, with sharp divides by age, gender, and party affiliation.
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The public web is not your audience strategy
When every AI system can read the same internet, the advantage shifts to what only your organization knows.
Read moreWhat AI needs before it can do audience work
A practical look at the context, evidence, and definitions audience-facing AI systems need before they can be useful.
Read moreWhy research should outlive the deck
The findings are only part of the value. The deeper asset is the structured intelligence research leaves behind.
Read moreAgent Dossiers and the end of context rebuilds
How packaged intelligence reduces repeated setup work and helps AI reason from a consistent starting point.
Read moreData Foundry: preserving what a dataset knows
A single source can carry more intelligence than a report ever captures, if it is cleaned and documented for reuse.
Read moreAudience data readiness before the workflow
Before AI can reason about an audience, the sources, definitions, cuts, and caveats have to be made legible.
Read moreTurning agency research into reusable intelligence
Research partners can create more durable client value when the work becomes an intelligence layer, not just a deliverable.
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