From Challenge to Change: Design Principles for AI Transformations

2025-12-05

AI Summary

The rapid adoption of AI in software engineering introduces significant human factors challenges, yet existing research remains predominantly technology-centric, lacking systematic investigation into team adaptability and trust mechanisms. Method: Integrating organizational change theory and behavioral software engineering, we propose the first human-centered, nine-dimensional AI transformation framework—covering strategic design, collaboration, governance, and other critical dimensions—and derive corresponding design principles. Using a mixed-methods approach, we developed the initial framework via literature review, refined it through thematic analysis of 24 practitioner interviews, and validated and optimized it via a survey (N=105) and expert workshops (N=4). Contribution/Results: Findings indicate that skill development and AI strategic design are most prioritized (each consuming >15% of allocated resources), whereas socio-technical safeguards remain consistently under-resourced. This framework bridges a critical gap in human factors integration within technology-dominated AI adoption paradigms and provides an actionable, socio-technical pathway for responsible AI implementation in software engineering.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Application Domains: Software Engineering

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

Abstract

The rapid rise of Artificial Intelligence (AI) is reshaping Software Engineering (SE), creating new opportunities while introducing human-centered challenges. Although prior work notes behavioral and other non-technical factors in AI integration, most studies still emphasize technical concerns and offer limited insight into how teams adapt to and trust AI. This paper proposes a Behavioral Software Engineering (BSE)-informed, human-centric framework to support SE organizations during early AI adoption. Using a mixed-methods approach, we built and refined the framework through a literature review of organizational change models and thematic analysis of interview data, producing concrete, actionable steps. The framework comprises nine dimensions: AI Strategy Design, AI Strategy Evaluation, Collaboration, Communication, Governance and Ethics, Leadership, Organizational Culture, Organizational Dynamics, and Up-skilling, each supported by design principles and actions. To gather preliminary practitioner input, we conducted a survey (N=105) and two expert workshops (N=4). Survey results show that Up-skilling (15.2%) and AI Strategy Design (15.1%) received the highest $100-method allocations, underscoring their perceived importance in early AI initiatives. Findings indicate that organizations currently prioritize procedural elements such as strategy design, while human-centered guardrails remain less developed. Workshop feedback reinforced these patterns and emphasized the need to ground the framework in real-world practice. By identifying key behavioral dimensions and offering actionable guidance, this work provides a pragmatic roadmap for navigating the socio-technical complexity of early AI adoption and highlights future research directions for human-centric AI in SE.

Problem

• Develops a human-centric framework for AI adoption in software engineering
• Addresses behavioral and non-technical challenges in AI integration
• Provides actionable steps for organizational change and trust in AI

Innovation

• Human-centric framework for AI adoption in software engineering
• Mixed-methods approach combining literature review and thematic analysis
• Nine dimensions including strategy, collaboration, and up-skilling