Why AI Won't Speed Up Your Development Processes – Focusing on Bottlenecks

Frederick Vanbrabant takes a critical look at the hype around AI for process optimization, drawing on classics like The Toyota Way and The Goal. His core point: throwing AI at the development phase misses the real bottleneck—often upstream ambiguity in requirements.

The Visual Bottleneck

Most project timelines show a long software development block. The instinct is to optimize there, but Vanbrabant argues that long duration doesn't mean the problem originates there. Using a Gantt chart, he illustrates a typical project: scoping (10d), budget scoping (3d), legal (10d), documenting (5d), then development (70d). The obvious target is development, but the real issue is upstream.

Upstream Issues

Software development isn't about typing faster; it's about understanding the problem. Vague requests like "send mail to user once sale is completed" require clarification: What is a sale? What if there's an error? Which mail content? This ambiguity is what slows developers down.

AI Won't Fix It

Vanbrabant presents the common naive projection: AI reduces development from 70d to 3d. But the reality is that AI still needs detailed specifications. The real timeline looks like: scoping (10d) + legal (10d) + documenting (40d) + AI development (40d). The documenting phase expands because domain experts must write every detail to get correct code from AI. He notes: "If you were to give human developers the same amount of feature/scope documentation you would also see your productivity skyrocket."

Takeaway

The article challenges the simplistic view that AI automatically accelerates processes. Instead, focus on the entire value stream and address upstream bottlenecks—better requirements, closer collaboration with domain experts—before expecting AI to deliver gains. For developers working with AI coding agents, this is a practical reminder to invest in specification quality.