Over the past two years, I’ve worked with many customers using generative AI to transform their organizations. Most stall at experimentation, because costs stack up and timelines extend before delivering demonstrable value. A 2023 AWS MIT Chief Data Officer (CDO) Symposium survey backs this up, reporting that while 71% of Chief Data Officers were experimenting with generative AI, only 6% had successfully deployed it in production.
Successful adopters use platform engineering concepts to avoid this trap by building reusable components to accelerate development and control costs. In this post, I will illustrate how applying platform engineering principles to generative AI unlocks faster time-to-value, cost control, and scalable innovation.
Why platform engineering?
Platform engineering isn’t a new concept. In traditional software development, teams have long invested in building functional tooling to accelerate application development. This…
