Platform strategy for OpenAI fine-tuning in the enterprise

Develop a product and platform strategy for OpenAI's model fine-tuning capabilities that drives enterprise adoption and builds long-term defensibility.

Context

Enterprises want models that know their domain, but fine-tuning is hard to do well, open-source models are catching up, and base models improve so fast that custom models can go stale.

Clarifying questions to ask first

Is the goal adoption, revenue or retention against open-source?
Which enterprise segments matter most?
Who are the alternatives (open-source, cloud providers)?

How to approach it

Tie the strategy to the company mission and the business goal (adoption, revenue, lock-in)
Segment enterprise buyers and say which you serve first and why
Lay out strategic options and where defensibility comes from
Make a clear bet with risks, metrics and what you would not do

Framework: Mission & goal -> Customers & their jobs -> Options -> Moats -> Bet, metrics & risks

Sample answer

Enterprises want results, not fine-tuning: evals, data prep and deployment are the hard parts
Defensibility comes from workflow and evaluation tooling, not the tuned weights
Regulated industries need privacy, isolation and auditability

Options: self-serve fine-tuning API, managed tuning service, vertical solutions with partners
Moats: eval and data tooling, upgrade path when base models improve, ecosystem of partners
Metrics: enterprise accounts with a tuned model in production, net revenue retention; risks: models going stale, data privacy, open-source price pressure

Bet on an end-to-end customisation workflow (data prep, evals, one-click upgrade to new base models) for regulated enterprises, with isolated data hosting. Measure tuned models in production and net revenue retention.

What interviewers look for

Mission and goal alignment
Enterprise segmentation
Defensibility (data, workflow, ecosystem)
Clear bet with risks