AI in the SDLC: What Engineering Leaders Get Wrong
Dev.to / 6/18/2026
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Key Points
- AI is already increasing throughput in many SDLCs (e.g., more code and pull requests), but delivery performance often fails to improve because later stages become bottlenecks.
- The core issue is systemic: teams are feeding work into constrained parts of the delivery pipeline faster, without improving review, testing, release, and upstream clarity at the same rate.
- AI can improve end-to-end delivery when applied in the right conditions, including faster planning/requirements, earlier test generation, and more responsive CI/CD automation.
- Engineering leaders often mismeasure AI impact by focusing on outputs at the point of code generation rather than validating how AI affects flow, defect rates, rework, and delivery predictability across the whole system.
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