From Challenge to Change: A Human- Centric Framework of Actionable Guidelines for AI Transformations

Abstract

The rapid growth of Artificial Intelligence (AI) is reshaping Software Engineering (SE), offering new opportunities but also introducing human-centered challenges. While existing research highlights behavioral barriers in AI integration, most studies focus on technical roll-out, neglecting how teams adapt and trust AI. This study aims to address this gap by proposing a Behavioral Software Engineering (BSE)-informed framework to support early-stage AI incorporation in SE organizations. To achieve this, a mixed-methods approach was applied. Initially, we conducted a literature review to explore the degree at which former change management models address the behavioral challenges of the AI integration, forming the core of the proposed framework. Then, we performed thematic analysis to enrich this core, using an existing qualitative dataset. Finally, we employed a systematic way to supplement actionable steps per guideline and form the final solution.

The proposed 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 guidelines and actionable steps to ensure their applicability. Quantitative evaluation involved a survey and two expert review workshops. The findings of the survey data analysis suggest that AI Strategy Design, Leadership, and Up-skilling are the most critical dimensions for seamless AI integration-results, aligning, to a significant extent, with prior change management initiatives. Specifically, survey respondents allocated on average 15.4% of their budget to AI Strategy Design, 14.9% to Leadership, and 12.9% to Up-skilling, highlighting their priority in early AI adoption. Age- and domain-related patterns also emerged. Despite the small sample size of the expert review sessions, the workshop data analysis indicates the need to focus more on real-life industrial insights. These insights provide a structured, humancentered foundation for AI adoption and offer guidelines for future research studies to contribute to the research body of human-AI integration.