Engineering AI into the product development lifecycle
AI is already changing how software is built. Google Cloud’s DORA research , based on nearly 5,000 technology professionals, found that 90% now use AI at work, spending a median of two hours a day with it, which translates to roughly a quarter of the working day. In many organizations, the focus is on what happens at the end of the lifecycle: how much code is generated, how many steps are automated, how quickly code is shipped. While those are visible signals of progress, they can be divorced from actual value. Google’s DORA research found that while AI adoption lifts delivery throughput, it also increases instability: more software shipped less predictably. The more impactful change is happening earlier in the lifecycle. Requirements, design and test strategy shape everything that follows. When those stages are structured correctly, downstream execution becomes faster, more consistent and easier to control. When they are not, issues tend to carry through the entire system, regardless