Public Report sign-off is preserved under all CRIRSCO-aligned codes. The reliability of AI-assisted screening is also bounded by the quality and representativeness of input data: where governance is weak, and source records are incomplete, ambiguous, or inconsistently coded, AI-assisted outputs inherit those limitations. Improved data governance is therefore a prerequisite – rather than a consequence – of effective AI deployment in this domain. Within these bounds, the contribution of AI-assisted tools is best understood as augmenting expert review by handling repetitive, rule-based checks on scale, thereby allowing technical professionals to focus their effort on interpretation and decision-making Governance improvements also do not eliminate environmental and social timelines, which must remain robust to preserve project legitimacy and social license to operate (Noppé et al., 2025; Owen et al., 2022). Organizational factors represent the main barrier to implementation. Without management commitment, clear data ownership, and sustained governance practices, technical tools alone cannot generate durable improvements. In many organizations, technical disciplines still operate through disconnected workflows, limiting data reuse, delaying validation, and weakening accountability. A recurring obstacle is the lack of clearly defined data ownership, which allows inconsistencies to propagate across project stages and become apparent only during reporting or permitting. Governance and assurance activities are also frequently perceived as administrative burdens rather than enablers of project acceleration, reinforcing organizational resistance to change. Industry analyses, such as Whincup & Kroon (2025), similarly indicate that incomplete submissions, fragmented workflows, and insufficient early-stage risk integration frequently led to cascading delays in the permitting and financing stages. Overall, acceleration occurs only when governance practices, technological tools, and organizational culture evolve together. Responsible acceleration, therefore, depends on aligning technical reliability, regulatory robustness, and social legitimacy, ensuring that faster project development does not compromise transparency or stakeholder trust. 6. CONCLUSIONS AND INDUSTRY IMPLICATIONS This work presented the TIME TO MINE framework as an operational approach to accelerate mineral project development through improved data governance, technical assurance, and structured validation workflows, rather than through compression of regulatory or technical stages. The framework integrates GDQM-based data qualification and Mining Assessment governance mechanisms to address recurring delays in mining projects, particularly those related to data usability, workflow fragmentation, and late-stage technical corrections. The results presented demonstrate that measurable operational gains can be achieved without altering the physical characteristics of mineral assets or bypassing environmental and social safeguards. Instead, improvements arise from reducing informational uncertainty, improving traceability, and strengthening accountability across project phases. Case studies show that consolidating and qualifying legacy datasets reduces modeling instability, improves audit readiness, and enables technical teams to use existing information more effectively, often avoiding unnecessary additional drilling or repeated validation cycles. Similarly, structured internal compliance readiness assessments improve reporting preparedness, reduce rework during financing or regulatory submissions, and increase transparency for investors and stakeholders. For the mining industry, the main implication is that project acceleration should be understood not only as shortening development stages but primarily as reducing uncertainty earlier in project lifecycles. Many delays originate from fragmented data management and weak
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