Selling expertise as an AI product means charging for execution: your judgment packaged as a working system buyers install and run on their own context, not more content.
The path has five steps: pick one buyer job, choose the judgment slice worth making executable, gather the source assets that prove how you think, package the method as skills, agents, workflows, and memory, and sell to the audience that already trusts you.
What survives when generic intelligence is cheap is the judgment layer: how you score situations, handle edge cases, sequence work, and hold outputs to a standard. That layer is what becomes the product.
Instead of charging for access to your knowledge (a course, a PDF, a call), you charge for a working system that executes your method on the buyer's own work: skills, agents, workflows, and memory delivered as an installable product with payments, licensing, and updates handled.
The judgment layer: how you score situations, what you do in edge cases, the order you do things in, and the standards you hold outputs to. Generic models can explain your field; they cannot ship your specific decision rules unless you package them.
The explanatory layer: definitions, overviews, generic frameworks, beginner lessons. Content-only products lose pricing power first, which is why the repeatable judgment inside your consulting or course is the part worth productizing.
It helps a lot. Factory handles the product layer, not distribution. This works best for experts whose audience already trusts their method and asks for implementation help.
A course explains your method and leaves execution to the student. An AI product runs the method: it takes the buyer's context, applies your rules, and produces work product, improving through memory and updates after purchase.