A quiet shift is under way in global industry. Systems that learn from data are moving from pilot projects to operational infrastructure, changing how firms assess risk, allocate capital and run complex processes. The productivity gains are real. So are the institutional consequences. Mining offers a clear illustration. Faced with declining ore grades, more intricate geology and tighter environmental constraints, the sector is embedding machine-learning tools across exploration, planning, processing and monitoring. Algorithms refine geological models, optimise short-term schedules and anticipate equipment failure. Sensor networks and adaptive control systems improve safety and stabilize output. Processing plants increasingly adjust to feed variability through data-driven models rather than fixed operating assumptions. The implications reach beyond efficiency. When predictive systems influence reserve estimation, dispatch decisions and environmental oversight, they also reshape how uncertainty is measured and how responsibility is assigned. Questions of transparency, skills and data governance become central. The success of intelligent mining will depend less on technical sophistication than on institutional discipline. Properly integrated, these systems can strengthen resilience and resource efficiency. Poorly governed, they risk creating new forms of opacity. The challenge is not simply to deploy smarter tools, but to ensure they remain accountable.