Assessing Client and Consultant Readiness for Adopting AI-Driven Predictive Risk Mitigation in Commercial Construction Projects: Evidence from Lusaka, Zambia

The increasing complexity of commercial construction projects has intensified the need for proactive risk management approaches capable of improving project performance outcomes. Artificial intelligence (AI)–driven predictive analytics has emerged as a promising tool for enhancing early risk identification and decision-making in construction projects. However, limited empirical evidence has existed regarding stakeholder readiness to adopt such technologies within emerging construction economies. This study assessed the readiness of clients and consultants to adopt AI-driven predictive risk management in commercial construction projects in Lusaka, Zambia by examining existing risk management practices, stakeholder perceptions of AI applications, and factors influencing adoption. A qualitative research approach was employed using semi-structured interviews conducted with twenty-five purposively selected clients and consultants involved in commercial construction projects, and the data were analyzed using thematic analysis. The findings revealed that although stakeholders demonstrated strong awareness of the potential of predictive analytics to improve forecasting accuracy and decision-making efficiency, adoption readiness was constrained by limited digital infrastructure, inadequate technical skills, organizational resistance to change, and implementation costs. The study concluded that successful AI integration required socio-technical alignment between technological systems, professional expertise, and organizational processes. The findings provided practical insights for policymakers and industry stakeholders seeking to transition from reactive toward predictive risk mitigation strategies in emerging construction environments.