{"id":21634,"date":"2026-05-18T13:23:05","date_gmt":"2026-05-18T11:23:05","guid":{"rendered":"https:\/\/mining-report.de\/english\/?page_id=21634"},"modified":"2026-06-11T10:50:25","modified_gmt":"2026-06-11T08:50:25","slug":"we-need-to-teach-ai-about-mining","status":"publish","type":"page","link":"https:\/\/mining-report.de\/english\/we-need-to-teach-ai-about-mining\/","title":{"rendered":"We Need to Teach AI About Mining"},"content":{"rendered":"\n\t\t
<\/div>\n\t\t
\n\t\n

Author: <\/strong>Stefan Ebert, Managing Director, Digital Mine GmbH, Aachen\/Germany
\nDOI: <\/strong>10.66356\/TKGJ4613<\/a><\/p>\n\n\t\t

<\/div>\n\t\t
\n\t\n

T<\/span>he human factor: How to truly bridge the gap between physical reality and the digital data world<\/strong><\/p>\n

Although the raw materials industry already employs a wide range of mature digital tools, isolated data silos and proprietary systems often prevent a holistic leap in efficiency. Neither purely technical IT integration nor the unstructured deployment of Artificial Intelligence (AI) solves this problem, as generic AI models lack an understanding of the specific physical and geological reality of a mining operation. To overcome this bottleneck, the operations engineer must become a bridge-builder and translate their intuitive, experience-based knowledge into precise decision-making parameters that algorithms can understand. As this is difficult to achieve in day-to-day operations, experts are needed as catalysts who master both the complex mining context and the model logic. Ultimately, success depends on the precise human formulation of the problem, because we must explain mining to AI, not the other way round.<\/p>\n

Please log in to read the full article.<\/strong><\/div>