A survey study on the adoption and perception of artificial intelligence in the mining industry
Abstract Artificial intelligence (AI) is increasingly viewed as a technology for resolving the mining industry’s long-standing challenges, from streamlining operations and cutting costs to protecting workers and safeguarding the environment. However, despite its potential, the mining sector has been...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-07-01
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| Series: | Discover Applied Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s42452-025-07342-1 |
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| Summary: | Abstract Artificial intelligence (AI) is increasingly viewed as a technology for resolving the mining industry’s long-standing challenges, from streamlining operations and cutting costs to protecting workers and safeguarding the environment. However, despite its potential, the mining sector has been slow to adopt AI on a large scale due to its cautious approach, high costs and resistance to change. To explore how professionals expect and perceive AI, this study surveyed 71 experts across managerial, engineering, research, and technical roles. The results reveal optimism about AI’s capacity to enhance mine planning, automate critical processes, and enable predictive maintenance, with cited benefits including better responses to complex geologies, improved safety protocols, and reduced expenses. However, respondents pointed out some obstacles, notably inadequate digital infrastructure, implementation costs, and social challenges such as workforce displacement and diminished human oversight. Addressing these challenges requires collaboration among industry and academia, supportive government frameworks, and specialized training to equip the workforce with digital competencies. By promoting a culture of responsible innovation and reskilling, the mining sector can fully use AI to create safer, more efficient, and more sustainable operations that benefit both workers and society as a whole. |
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| ISSN: | 3004-9261 |