• From retrospective analysis to predictive management • From generalized decision-making to data-driven, targeted actions • From delayed response to preventive planning Overall, these results demonstrate that the integration of artificial intelligence into geochemical data management not only enhances operational efficiency but also significantly strengthens the capacity of mining operations to manage environmental risks in a proactive and sustainable manner. 4. CONCLUSIONS The development and implementation of an artificial intelligence–based platform for geochemical data management demonstrate that automation of the data pipeline is a key enabler for transforming environmental management in mining, shifting from a reactive approach to a predictive, evidence-based framework. As a primary transferable insight, the integration of historical data through automated processes, combined with analytical and predictive models, not only improves operational efficiency but also generates actionable knowledge to anticipate risks associated with acid rock drainage (ARD) and to optimize the planning of management and mine closure strategies. This approach is replicable across different contexts where large volumes of heterogeneous environmental data are present. Regarding future development needs, the incorporation of hybrid models that integrate physicochemical approaches with machine learning techniques is proposed, along with the connection to real-time monitoring systems to strengthen adaptive management of geochemical risk. Additionally, expanding the platform to other environmental components would enable the consolidation of a comprehensive data management framework in mining. Overall, this work demonstrates that the strategic application of artificial intelligence in geochemistry not only enhances data processing efficiency but also enables more proactive, accurate, and sustainable decision-making within the mining industry. 5. ACKNOWLEDGMENTS The authors would like to thank WES Perú for their continuous support during the preparation of the technical abstract for the World Mining Congress 2026.
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