Balancing AI tensions: Moving from AI adoption to effective SDLC use
Abstract
Using AI in the software development life cycle (SDLC) is quickly becoming an industry standard. The 2025 DORA State of AI-assisted Software Development report found that 90% of technology professionals now use AI at work, and over 80% believe it has increased their productivity.
Beneath these impressive top-line numbers, a more complex reality is emerging. Extensive qualitative analysis of enterprise software engineers reveals that AI's impact on the SDLC is not a simple linear improvement. Instead, it presents a series of profound tradeoffs. While AI successfully accelerates initial code generation and reduces the friction of starting new tasks, the time saved in creation is frequently re-allocated to a new hidden tax: high-scrutiny auditing and verification. This article explores four key tradeoffs of AI adoption, and provides actionable strategies to balance them.
Beneath these impressive top-line numbers, a more complex reality is emerging. Extensive qualitative analysis of enterprise software engineers reveals that AI's impact on the SDLC is not a simple linear improvement. Instead, it presents a series of profound tradeoffs. While AI successfully accelerates initial code generation and reduces the friction of starting new tasks, the time saved in creation is frequently re-allocated to a new hidden tax: high-scrutiny auditing and verification. This article explores four key tradeoffs of AI adoption, and provides actionable strategies to balance them.
