The mounting pressure on software engineering teams to deliver more with less has created a perfect storm for AI adoption. 95% of developers are already embracing AI coding assistants, unlocking newfound efficiencies. But with only 30% of the delivery cycle spent on coding, organizations are also looking to leverage AI for improvements for other tasks in the software delivery cycle.

Harnessing AI from feature inception to operations promises improvements in efficiency, quality and speed. But while the potential is vast and evolving rapidly, the tooling landscape is still in its early days. For leaders, it’s challenging to know where to begin and how to invest.

This article will provide a blueprint for how to think about tools for AI assistance in a software engineering organization; it’s intended as a mental model for keeping up with a fast-moving and changing market.