Internal AI platforms fail when they are treated as infrastructure projects rather than products with customers, outcomes and an intentional service experience. A platform PM sets the mission, prioritises capabilities and makes adoption measurable.
Define the customer. Your users are the product teams, data scientists and engineers who need safe, fast paths to deliver AI features. Interview them, map their journeys and quantify friction points before building another API or feature flag.
Focus the roadmap on a few clear value levers: time-to-first-use-case, cost per inference, safety posture and developer experience. Tie each capability (feature store, eval harness, orchestration, prompt registry, observability) to one of those levers.
Adoption is a product metric. Instrument onboarding, deployment frequency, guardrail coverage and incident rates. Offer enablement packs—reference architectures, sample pipelines, blueprints—that shorten the path from idea to production.
Funding should reflect platform maturity. Early stages benefit from product-line sponsorship and co-funding of lighthouse use cases. As adoption grows, move to usage-based cost recovery with transparent unit economics so teams make informed trade-offs.
Finally, run the platform as a service. Publish SLAs, a support model and a change calendar. Treat the platform as a living product with regular releases, deprecation policies and a strong voice-of-customer loop. That is how AI capabilities become repeatable at enterprise scale.