Mining operations accumulate vast amounts of maintenance andcondition data, yet decision-making often relies on late signals orlabour-intensive analysis, even when comprehensive life cyclesystems are used.This presentation shows how RPMGlobal is enhancing itsproprietary DLCC (Dynamic Life Cycle Cost) engine by surroundingit with a new layer of intelligent context through a new initiativecalled AMT Insights. Rather than replacing the DLCC, whichresides at the core of AMT, Insights analyses historical behaviour,detects anomalies, highlights emerging risk patterns and providesexplainable insights that feed directly into life-cycle planning.Field deployments have demonstrated easier fault detection andfaster adjustments to maintenance strategies, reducing the timeengineers spend diagnosing issues.The result is a more transparent and responsive cost-modellingenvironment, where AI-driven interpretation strengthens, ratherthan automates, the core engineering logic behind the DLCC