AI-Assisted Software Engineering Productivity: Evidence, Measurement, and Enterprise Adoption

Abstract

This paper analyzes how AI assistance changes software engineering productivity and how the change should be measured in real engineering environments.The key claim is that productivity gains from AI are real but conditional: the gains are strongest on bounded, repetitive, or search-heavy work, and much less predictable on high-context maintenance, quality-sensitive code, and mature repositories with strict review expectations.Rather than relying on one metric, the paper integrates the SPACE framework for multidimensional developer productivity, DORA delivery metrics, and the DevEx perspective on feedback loops, cognitive load, and flow state.The paper synthesizes published findings from controlled experiments, enterprise studies, surveys, and software engineering research from 2021 through 2026.The evidence shows both positive and negative outcomes: in one controlled task study, developers using a coding assistant completed work 55 percent faster, while a randomized controlled trial with experienced open-source maintainers found that AI increased completion time by 19 percent in familiar repositories.The paper argues that these results are not contradictory once task structure, repository familiarity, and verification cost are considered.To move from anecdote to operational decision making, the paper proposes a practical enterprise evaluation design that combines telemetry, pullrequest and incident data, code-quality measures, and developer-experience surveys.It also presents a reference architecture and workflow pattern for using AI assistance in coding, review, testing, and release activities while preserving human accountability.The paper contributes a publication-ready synthesis that treats AI-assisted productivity as a systems problem rather than a prompt engineering problem, and offers a measurement model suitable for organizations that need rigor rather than hype.

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