Enterprises racing to adopt AI often create more risk than value. To mitigate these risks, AI needs the same treatment as every other capability in the internal developer platform (IDP). Provide it through the platform, complete with budgets, policies, and observability from day one. That is how adoption scales without chaos.

Independent research reinforces this. MIT’s 2025 State of AI in Business report found that despite tens of billions in spend, only 5% of custom enterprise AI tools make it to production. Enterprises lead in pilot volume yet lag in scale. The findings point to approach, not model quality, as the real limiter. This article explores three platform-engineering principles that consistently turn AI from hype into working enterprise systems.

According to the CNCF’s GitOps survey, observability is one of the biggest challenges GitOps adopters face . Platform teams often lack...