Digital tools such as artificial intelligence and machine learning are increasingly shaping mineral exploration workflows, although many companies are still in the early stages of adoption. Powered by VRIFY Technology.
recent report found it artificial recruitment Intelligence (AI) in mineral exploration I am getting strong momentum, around 77 cent of Respondent reportNG Some usability of A.I. tools of their exploration operation.
of 2025 Mineral Exploration Technology Report carried out By Ipsos, a global market research and opinion polling company on behalf of Vrefi TechnoroG. that Based on a global survey of 135 mineral exploration experts, we provide the following snapshot. how AI, machine learning and other digital technologies has been adoptEdited, of Perceived benefits, and barriers that continue to slow innovation.
“We hope this report serves as a practical benchmark for the mining industry, enabling teams to understand where they stand, learn from their colleagues, and make decisions with more confidence, while asking the increasingly important question of how implementing the right technology can make projects faster, smarter, and more competitive.” Steve de Jong, CEO and founder of VRIFY, said in an emailed statement. CIM Magazine.
Although industry interest in AI is high,, Usage remain Uneven And in the early stages: 56% of respondents reported that they sometimes use AI and machine learning tools. meanwhile Only 21% say they use it regularly and 10% said they did not use it at all.
report identified Geologists are the most skepticals AI and machine learning tools, then field or site managers, and then executive leadership. In contrast, functional experts demonstrate Use of best-in-class tools. THis report points to budget constraints and uncertain returns on investment. (ROI)and disbelief A.I. Model output as the main challenge to adoption. Organizations face preparedness challenges such as:he has not reportedare doing Advances in these areas intention probably depends Aiming to strengthen technical capabilities and internal structure Supporting effective AI adoption.
The report is further indicates that barriers to implementation vary depending on the size of the organization; Financial constraints weigh most heavily on small and medium-sized businesses Skills gaps, capacity limitations, and integration complexities is emerging This is a bigger concern for larger organizations. MID scale companies It is suggested that in the perfect position for adopt Operationalize AI and machine learning tools With sufficient resources and capabilities, retention Sufficient organizational agility and absorb higher levels of risk.
Despite challenges, the report found that: recognition AI and machine learning improved The company leveraged case studies from trusted peers along with insights from internal pilot initiatives, supported by more specific ROI expectations.
When evaluating the benefits of implementing AI and machine learning in mineral exploration, 36 percent Respondents highlighted faster decision-making and more efficient use of resources as the main benefits. This suggests that current applications are improving existing workflows rather than bringing innovative changes to exploration results. At the moment.
22 percent of participants shown No visible results have been achieved since the adoption.AI and machine learningthe report suggests that outcomes vary by organization and have measurable impacts.s influenced by both Implementation maturity and data quality.
Looking to the future, The industry is optimistictic About AI and machine learning, 84% of respondents believeare doing These tools are advantage they are nearby–semester. Report findings paint a complete picture of the industry Areas where innovation is happening but not yet progressing of early stage of trialIare doing technology and discovery dependable Practice. report Noted Further progress will depend on stronger data foundations and technical skills, as well as improved system transparency and a more open approach to sharing results. and inter-organizational best practices.
