2426 5.4 Productivity Timeline and Training Practices Even though most respondents felt that new graduates are not industry-ready upon hire, reported timelines for new talent to achieve independent productivity varied. Approximately 35.3% of respondents indicated that new hires reach independence within six months, while an equal proportion reported a timeframe of six to twelve months. A further 29.4% stated that more than one year is required, particularly in technically complex or operationally sensitive roles requiring site-specific knowledge. Figure 10 – Time it takes for Early-Career Hires to become Fully Productive Training-wise, workforce development was divided between internal mentorship systems and onthe-job experience. However, the results strongly indicate that structured mentorship under senior professionals is the overwhelming preference for early career progression, with 93.75% of respondents selecting this option. Notably, none of the surveyed respondents reported utilizing third-party training systems or private/company-developed digital programs. 5.5 AI Adoption and Knowledge Transfer Industry responses indicate cautious but growing openness to AI-supported knowledge systems. Most respondents (70.6%) indicated that AI could support training in geomodelling software, geological database management, and data interpretation. However, respondents consistently emphasized that AI should function as a decision support tool, complementing rather than substituting professional expertise and applied geological judgement. Despite this openness, only 41.2% of respondents reported actively using AI to address work-related tasks, primarily literature review, summarization, data organization, research assistance, and administrative drafting. Concerns were frequently expressed regarding the reliability of AI outputs, data quality, confidentiality, and the limitations of AI in complex geological contexts. Many emphasized that activities such as geological classification, resource modelling, and exploration targeting rely heavily on contextual judgment and field-based experience—capabilities that AI cannot consistently replicate. Nevertheless, AI was acknowledged as a significant technological development for the industry. Rather than rejecting its use, most indicated that the sector must clearly identify appropriate and well-defined applications where AI can enhance efficiency while maintaining professional oversight. 6. DISCUSSION The findings suggest that workforce capacity in the mining sector may be shaped by the interaction of pipeline contraction and institutional knowledge concentration, consistent with the structural logic of the Double Talent Gap framework proposed in this study. At the upstream level, a persistent disconnect appears between recognition of mining’s societal relevance and its perceived career attractiveness. Although students widely acknowledge mining’s 0-6 months 33% 12+ months 39% 6–12 months 28%
RkJQdWJsaXNoZXIy MTM0Mzk2