protected system outcomes while avoiding material expenditure on low-value scope. Importantly, the capital freed through this process becomes available to advance other projects in the portfolio, directly supporting the pace at which new mineral supply can be delivered. 4.2 Broader insights ● Evidence changes the quality of the conversation. When system consequences are visible and testable, governance discussions shift from opinion-based scope debates to evidence-based capital decisions. The framework strengthens engineering judgement by providing the system-level evidence that judgement requires. ● AI expands the decision space. Reinforcement learning-driven scenario discovery identifies variable combinations and constraint interactions that manual analysis would typically miss, particularly in systems with high-dimensional solution spaces. ● MVS provides decision discipline. The Minimum Viable Solution framework converts simulation evidence into structured capital decisions with clear categories, making the decision logic transparent and auditable. ● The approach addresses a systemic issue, not just a technical one. Capital escalation is driven by feedback loops across the project system. Addressing individual scope items without addressing the system dynamics that produce them is unlikely to deliver sustained improvement. 5. SCALING AND APPLICABILITY The logic of this framework is repeatable wherever capital intensity, system complexity and uncertain operating assumptions intersect. Priority candidates include new infrastructure, debottlenecking studies, brownfield expansions, ramp-up critical projects and logistics or processing constraints. The approach is most valuable where multiple credible design options exist but their system-level consequences are difficult to compare, and where there is still time to influence scope before capital is committed. 4. CONCLUSIONS AND IMPLICATIONS FOR INDUSTRY Delivering the minerals the world needs, faster and more responsibly, requires the industry to allocate capital with greater discipline. Cost escalation in complex projects is frequently a systemic outcome of how uncertainty is managed: when system consequences cannot be tested, capital becomes the default mechanism for managing risk, and reinforcing feedback loops compound the effect. The framework presented in this paper, combining systems thinking, digital twins and AI, offers a structured alternative. Systems thinking identifies where and why capital is being added. Digital twins make the consequences testable under realistic conditions. AI accelerates insight generation and expands the scenarios that decision-makers can evaluate. The Minimum Viable Solution framework converts evidence into disciplined capital decisions.
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