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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 Sciences2026https://doi.org/10.4102/sajems.v29i1.6538article
AJG 1ABDC C
Weight
0.50

What the paper says

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.

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https://doi.org/https://doi.org/10.4102/sajems.v29i1.6538

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@article{talent2026,
  title        = {{The algorithmic mine: Enhancing managerial effectiveness and organisational agility in the mining industry through artificial intelligence – A spatially aware predictive framework}},
  author       = {Talent Gosho & Mubanga Mpundu},
  journal      = {South African Journal of Economic and Management Sciences},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.4102/sajems.v29i1.6538},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.