Original Research

The algorithmic mine: Enhancing managerial effectiveness and organisational agility in the mining industry through artificial intelligence – A spatially aware predictive framework

Talent Gosho, Mubanga Mpundu
South African Journal of Economic and Management Sciences | Vol 29, No 1 | a6538 | DOI: https://doi.org/10.4102/sajems.v29i1.6538 | © 2026 Talent Gosho, Mubanga Mpundu | This work is licensed under CC Attribution 4.0
Submitted: 11 September 2025 | Published: 23 January 2026

About the author(s)

Talent Gosho, Institute of Distance Education, Graduate School of Business, University of Zambia, Lusaka, Zambia
Mubanga Mpundu, Department of Economics, Faculty of Economics and Management Science, University of the Western Cape, Cape Town, South Africa

Abstract

Background: This research critically examines the integration of artificial intelligence (AI) within the mining industry, focusing on their capacity to enhance both managerial effectiveness and organisational agility.
Aim: This article addresses the existing literature’s limitations by introducing a novel, spatially aware predictive framework tailored to the unique challenges of mining operations.
Setting: While existing literature acknowledges the transformative potential of AI in mining, it often lacks concrete strategies for implementation and fails to address the inherent spatial variability of mining operations. This study proposes the spatially aware predictive framework, leveraging AI to optimise resource allocation, predictive maintenance and environmental management.
Method: A systematic literature review was conducted, employing Boolean logic across Web of Science, Scopus and IEEE Xplore databases, focusing on publications from 2019 to 2025.
Results: Managerial effectiveness and organisational agility are paramount for success in the increasingly complex and dynamic mining industry. The integration of advanced technologies such as AI offers a powerful means to enhance operational efficiency, improve decision-making and achieve sustainable growth. The spatially-aware predictive framework provides a practical roadmap for implementing these technologies, realising their full potential and moving beyond fragmented and spatially unaware applications.
Conclusion: This study proposes the spatially aware predictive framework, leveraging AI to optimise resource allocation, predictive maintenance and environmental management creating an AI-circular business model (AI-CBM).
Contribution: This study proposes a novel spatially aware predictive framework, leveraging AI to optimise resource allocation, predictive maintenance and environmental management, which creates an AI-CBM.


Keywords

artificial intelligence; machine learning; organisational agility; managerial effectiveness; mining industry; spatial analysis; predictive maintenance; optimisation algorithms; spatio-temporal regression; AI-circular business model

JEL Codes

L71: Mining, Extraction, and Refining: Hydrocarbon Fuels; M11: Production Management; O14: Industrialization • Manufacturing and Service Industries • Choice of Technology; O31: Innovation and Invention: Processes and Incentives; O32: Management of Technological Innovation and R&D; O33: Technological Change: Choices and Consequences • Diffusion Processes; Q32: Exhaustible Resources and Economic Development

Sustainable Development Goal

Goal 9: Industry, innovation and infrastructure

Metrics

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