Abstract

This study investigates the determinants of readiness to adopt Artificial Intelligence (AI) tools within Agile management environments by integrating machine learning analysis with managerial insights. Using survey data collected from Agile practitioners, an optimized XGBoost model was developed to predict willingness to adopt AI tools based on experiential and perceptual factors. The model achieved a high classification accuracy of 92.86 percent, demonstrating its reliability in identifying readiness patterns among respondents. The feature importance analysis revealed that previous experience using AI and familiarity with AI tools are the most influential predictors, indicating that experiential learning and direct exposure play central roles in shaping readiness. Conversely, professional role and Agile experience exhibited weaker influence, suggesting that hierarchical position and procedural familiarity alone are insufficient to drive adoption behavior. Complementary qualitative analysis of participants’ open-ended responses identified decision-making support, process efficiency, and product improvement as key perceived benefits of AI integration. These findings collectively suggest that readiness for AI adoption in Agile environments is an experiential and perception-driven construct, reinforced by organizational culture and learning orientation. The study contributes to the theoretical discourse by extending the Technology Acceptance Model (TAM) and Diffusion of Innovation (DOI) frameworks to include experiential familiarity as a mediating factor, while offering practical guidance for managers seeking to foster AI readiness through exposure-based learning, capability building, and cultural reinforcement.

Keywords: Agile Management, Artificial Intelligence Adoption, Machine Learning Prediction, Technology Readiness, Experiential Learning

How to Cite:

Pratama, S. & Fahmi, G. M., (2026) “Assessing Organizational Readiness for AI Integration in Agile Management Using Predictive Analytics”, Agile Management 1(1), 59-74. doi: https://doi.org/10.63913/am.v1i1.127