Truth 2 in Practice: Intelligence Is a Commodity Infrastructure Layer

Before: AI is a "data science project" with its own budget and team. Models are trained on laptops and deployed via email. Data is an afterthought, cleaned only when needed. Generative AI is a pilot in the innovation lab. The organization buys AI from vendors.

After: AI is a platform capability available to all product teams. MLOps pipelines automate training, validation, and deployment. Data is treated as a product, with curated domains and quality standards. Generative AI is available through internal APIs, embedded in every workflow. The organization builds proprietary intelligence on its own data.

Application:

Build an ML platform, not an ML team. The platform team provides APIs, feature stores, model registries, and serving infrastructure. Product teams consume them.
Treat data as a product. Assign domain owners. Enforce schemas, quality metrics, and cataloging. If data is not discoverable, it does not exist.
Make AI self-service. If a team needs a model, they should not file a ticket. They should call an API or use a tool.
Invest in proprietary data. The models you build on your own data are defensible. The models you buy from vendors are commodities.