AI in the Software Development Life Cycle
White paper overview
AI affects more than code generation. It changes how teams handle the software development life cycle: define requirements, design systems, test software, manage releases, and oversee delivery. This white paper shows where AI reduces effort across the SDLC, where it can add rework, and how curated project context, verification, and human approval let teams measure value before scaling.
Who this white paper is for
CIOs and technology leaders
Who need a defensible plan for AI investment across software delivery.
Engineering and delivery leaders
Who need faster delivery without trading away quality or maintainability.
Architecture, QA and DevOps leads
Who need AI workflows that fit existing systems, tools, and approval processes.
Why this white paper matters
AI can speed delivery, but the return depends on project context, verification, and a measured rollout.
60% of work time
Goes to coordination and related overhead, according to the Asana Anatomy of Work Index.
60–80% of lifetime cost
Can come after release, making maintainability and verification central to AI investment.
15–20% modeled gain
It is Andersen’s estimate for whole-project efficiency when AI connects delivery stages and controls.
Planning an AI-assisted delivery pilot?
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