Productizing expertise with AI means converting repeatable expert judgment into an installable product buyers can run on their own context.
The path can start as a PDF, course, community, or productized service, but a working AI product adds skills, agents, workflows, memory, licensing, payments, GitHub delivery, and updates around the method.
The best source assets are frameworks, playbooks, templates, calls, examples, and docs that reveal how the expert makes decisions and judges good output.
It means turning a repeatable expert method into an AI-native product that can apply the method to a buyer's context after checkout. The product may include skills, agents, workflows, memory, examples, delivery, licensing, payments, and updates.
No. A course can help, but the strongest inputs are frameworks, playbooks, templates, call transcripts, examples, docs, and proof that the method works for real buyers.
Stop earlier when buyers mainly need education, community, or direct expert judgment. Build a working AI product when the repeatable layer can run on buyer context and create useful outputs without every step requiring a live call.
Factory packages the method into installable AI product infrastructure: skills, agents, workflows, memory, licensing, Stripe Connect payments, GitHub delivery, customer onboarding, and update paths.