AI-Driven Decision-Making for Sustainable Industrialization in Africa

Abstract

Artificial Intelligence (AI) is rapidly reshaping industrial systems worldwide, offering unprecedented opportunities for sustainable development in Africa. This study examines how AI-driven decision-making can enhance sustainable industrialization across key African economies by improving efficiency, reducing waste, and supporting environmentally responsible practices. Using a mixed-methods approach, data were collected from 40 firms across Kenya, Nigeria, South Africa, and Zimbabwe, revealing that AI adoption remains moderate but uneven across sectors and countries. Agriculture and energy sectors demonstrate relatively higher adoption levels due to targeted innovation programs and sustainability-driven imperatives such as precision farming and energy optimization, while the manufacturing sector lags because of high costs, limited infrastructure, and a shortage of skilled professionals. The study finds that organizations using AI reported measurable benefits, including a 20% reduction in material waste, a 15% increase in productivity, and forecasting accuracy improvements up to 87%. However, adoption is constrained by persistent challenges such as inadequate digital infrastructure (64%), high implementation costs (63%), limited human capital (58%), and weak policy support. The research extends Decision Theory and the Resource-Based View (RBV) by demonstrating that AI serves as both a strategic resource and a sustainability enabler within volatile African markets. It further aligns AI integration with the Sustainable Development Goals (SDGs), particularly SDG 9 and SDG 12, underscoring AI’s role in promoting innovation, resource efficiency, and sustainable production. The paper concludes that for Africa to leverage AI as a driver of inclusive industrial growth, it must prioritize infrastructure investment, develop AI-related human capacity, and establish coherent regulatory frameworks that foster ethical and context-relevant innovation.