A common theme among qualitative results indicates that industry preparedness is two-fold: universities must provide foundational technical knowledge, while employers must develop applied competencies and operational judgement. Practical experience, such as field exposure, was cited as an essential component to achieving professional independence and co-operative education programs were widely described as essential mechanisms to bridging the gap between academic preparation and industry expectations. 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 on-the-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. 104
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