Position
Identify the buyer, the job to be done and the alternative already in use.
Commercializing AI means connecting a real buyer problem to product design, pricing, deployment economics, procurement and a reason to renew.
An AI product can impress in a demonstration and still fail to earn a production budget.
The commercialisation question begins with a specific workflow or decision. Who owns it? What costs or outcomes matter? What evidence would justify a contract? The answer shapes the product promise, the commercial model and the route to market.
For frontier models, that includes API versus enterprise distribution, differentiation against open and specialized alternatives, and the relationship between performance and inference cost. For enterprise AI, it includes integration, governance, reliability and the cost of a useful outcome.
Ashraf approaches these as connected commercial choices rather than separate product, marketing and sales tasks.
Identify the buyer, the job to be done and the alternative already in use.
Connect value, usage, compute cost and willingness to pay to a sustainable model.
Design evidence that survives security review, procurement and production conditions.
Build distribution, partnerships, account coverage and renewal discipline.
It is the system that turns AI capability into a product customers can understand, purchase, deploy and continue paying for.
A pilot can show capability without resolving budget ownership, integration, governance, operational support or an agreed measure of value.
Pricing should reflect customer value, usage behaviour, service levels and delivery cost. Token cost alone rarely describes the full commercial outcome.
A defined customer segment, credible product promise, procurement path, delivery model and account economics that can be reproduced beyond a few early wins.