From Challenge to Change: Design Principles for AI Transformations

TL;DR

This study develops a Behavioral Software Engineering–informed framework to guide early-stage AI adoption in software engineering. By integrating a comprehensive literature synthesis, thematic analysis of practitioner data, and quantitative surveys plus expert workshops, it identifies nine dimensions organized into Direction, People, and Guardrails, with concrete design principles and actionable steps. Key findings show practitioners prioritize Up-skilling and AI Strategy Design, while governance and ethics remain underfunded despite recognized importance, highlighting a gap between awareness and implementation. The work offers a pragmatic roadmap for socio-technical AI transformations in SE and sets the stage for further empirical validation and domain-specific refinements.

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.

Figures (7)

• Figure 1: Research Method Overview
• Figure 2: PRISMA statement overview
• Figure 3: Timeline of Change Management Models
• Figure 4: Dimensions' prioritization per group of working experience
• Figure 5: Statistically distinct clusters of framework dimensions based on Scott-Knott ESD test results