Technological and Infrastructural Challenges in AI-Based Workplace Safety
Abstract:
Artificial intelligence (AI) has emerged as a transformative tool for improving workplace safety through pre dictive monitoring, real-time hazard detection, and data-driven decision-making, particularly in high-risk in dustries such as mining, construction, oil and gas, and manufacturing. While AI-based safety systems have demonstrated significant success in developed economies, their adoption and effectiveness in Sub-Saharan Africa remain constrained by persistent technological and infrastructural challenges. This study critically examines the technological, infrastructural, human, and organizational barriers affecting the implementation of AI-driven workplace safety systems in high-risk industrial settings within Sub-Saharan Africa. Drawing on a comprehensive review of existing literature and comparative global practices, the paper highlights key constraints including unstable power supply, poor digital connectivity, limited data management capacity, lack of localized datasets, inadequate technical skills, and logistical and maintenance bottlenecks. The anal ysis further explores infrastructural readiness across physical, digital, and human dimensions, emphasizing how systemic weaknesses undermine the reliability and sustainability of AI-based safety interventions. The study also identifies emerging opportunities such as edge computing, hybrid cloud–edge architectures, offline analytics, and low-bandwidth AI models as context-sensitive solutions to overcome resource limitations. The f indings underscore the need for ecosystem-based, locally adaptive strategies that integrate infrastructure development, workforce capacity building, and sector-specific deployment models to enable effective and sus tainable AI-driven workplace safety in Sub-Saharan Africa.
