Integration of AI-Assisted Development Tools into Modern Software Delivery Workflows and Their Impact on Engineering Productivity

Abstract

AI-assisted development tools now enter routine software delivery through code completion, repository-aware coding assistants, automated pull request (PR) comments, and AI-supported testing. Their effect on productivity depends on the workflow stage in which engineers use them, the quality controls around generated output, and the way senior developers preserve ownership of design decisions. This article develops an analytical model for integrating AI code generation and AI-driven pull request review into modern software delivery. The study uses comparative source analysis, conceptual synthesis, typologization, and analytical generalization based on ten recent academic and industry-facing publications on large language models in software engineering, GitHub Copilot, automated review, hallucinations, package risks, and security attacks. The article separates measured evidence from practitioner-reported operational estimates. It identifies where AI shortens implementation work, where it adds review burden, and which governance mechanisms protect code quality, onboarding, mentoring, dependency control, and release stability.