This investigation paves the way for a comprehensive understanding of how AI is perceived by those who directly manage the introduction of these tools into traditional software development workflows, revealing a road map for future endeavors for the software development community.
Abstract
Artificial intelligence is rapidly changing the landscape of software development. With the unique ability to quickly generate code and the potential to disrupt traditional workflows, AI tools have found growing adoption within the software development process. Subsequently, this topic has been the focus of academic work, including research examining qualitative impacts to productivity and the analysis of sentiments from the developers who utilize AI tools. While this material is extensive, our research team identified a gap within existing literature: what do software managers have to say? The overarching goal of this study is to examine the views of software managers on how AI tools have affected software development. We seek to understand how managers, who leverage a top-down view of the development process, perceive the influence of AI on developers, their own roles, and the broader labor market. To answer these questions, we conducted an empirical study by releasing an online questionnaire containing both qualitative and quantitative questions, sampling software managers employed across both tech-focused and non-tech-focused companies. Through a survey of 42 managers, we found that managers hold nuanced views on the introduction of AI into software development. They encourage developers to use AI, perceive it as valuable for testing, and apply it themselves for knowledge work. At the same time, they raise concerns about privacy, responsibility, transparency, and over-reliance. Many also predict a loss of jobs within the software development market due to consolidation driven by AI. Overall, AI is seen by managers as both a powerful productivity tool and a source of new ethical challenges. Our investigation paves the way for a comprehensive understanding of how AI is perceived by those who directly manage the introduction of these tools into traditional software development workflows, revealing a road map for future endeavors for the software development community.
The findings suggest that Expertise-Building serves as a sufficient proxy for predicting user behavior, as the action cannot be completed without the essential Expertise-Building "gatekeeper", and contributes to both scholarly discourse and industry practice.
An enormous course in scholarly output from 2023 onwards is revealed by the findings, and this growth is driven by the industrial adoption of Large Language Models alongside autonomous agentic systems.
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It is concluded that AI meaningfully augments developer productivity but does not yet demonstrably improve satisfaction or earnings, and that a hybrid human-AI model, supported by governance and training, remains the most defensible direction for application development.
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