Figure 3 - Resource distributions for the pre-drilling model, AI-targeted drilling model, and conventionally targeted drilling model. CONCLUSIONS AND FUTURE WORK This paper examined the application of AI to mineral exploration, with a focus on data fusion, uncertainty modeling, and decision optimization, and demonstrated their impact through a largescale copper porphyry case study. The results show that AI-driven, probabilistic subsurface modeling can produce materially more precise resource estimates than conventional expert-based uncertainty analyses, while maintaining geological plausibility and consistency with available data. In addition, sequential, AI-guided drilling optimization achieved resource models statistically comparable to those derived from conventional drilling campaigns using approximately 45% of the drilling meters, indicating substantial potential for accelerating resource characterization and reducing exploration cost and risk. At the system level, these improvements translate into meaningful implications for project economics, development timelines, and long-term copper supply constraints. Despite these encouraging results, AI in exploration remains at a relatively early stage of maturity. Subsurface systems are complex, data are sparse and heterogeneous, and outcomes are strongly conditioned by geological context. As a result, realizing the full potential of AI will require continued bi-directional learning between the AI research community and the resource sector. Advances in model architecture, uncertainty quantification, and decision frameworks must be informed by geological realism and operational constraints, while domain practices must evolve to effectively integrate probabilistic outputs and adaptive decision-making into established workflows. Future work spans several important directions. At regional scales, two-dimensional prospectivity mapping remains an active area of research, with opportunities to improve robustness,
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