AI won't speed up processes without solving upstream bottlenecks
Frederick Vanbrabant argues that AI and process optimization efforts often fail because they focus on the wrong bottlenecks. Drawing on classics like 'The Toyota Way' and 'The Goal', he explains that long durations in software development are not solved by adding people or assuming AI will accelerate work. The real issue is upstream: vague requirements and lack of detailed problem definition. AI-generated code still requires extensive handholding from domain experts to specify every detail. Vanbrabant emphasizes that bottlenecks need predictable, high-quality inputs, not just more resources or automation. He illustrates with a Gantt chart showing software development as the longest phase, but warns that throwing AI at it without clarifying requirements will not improve throughput. The key is to ensure that people doing the work have clear, complete documentation.
Key facts
- Frederick Vanbrabant wrote the article on his blog.
- He re-read 'The Toyota Way' and 'The Goal' to inform his argument.
- A Gantt chart shows software development as the longest phase in a project.
- Vanbrabant says throwing people or AI at a bottleneck without addressing upstream issues is ineffective.
- AI-generated code still requires detailed feature specifications from domain experts.
- He compares AI development to human development, noting both need clear problem outlines.
- The key lesson from 'The Goal' is that bottlenecks should receive predictable, high-quality inputs.
- Vanbrabant suggests that process automation should start by ensuring inputs are complete and clear.
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