For mining companies seeking to improve margins across complex, multi-site operations, the challenge is not simply deploying AI. It is identifying the right use cases, adapting them to site-specific conditions, and embedding intelligence into daily decisions without disrupting production. Variability in ore quality, high energy consumption, siloed data, and inconsistent operational practices can quickly limit performance and value realization.
This case study reveals how Cyient helped a global mid-tier mining company optimize operations across Australia and Southern Africa through a scalable, AI-augmented operational optimization suite. Discover how AI-driven ore blending, process set-point optimization, energy optimization, and intelligent resource planning enabled the company to improve metal recovery and yield by 1% to 2%, reduce smelter energy consumption by approximately 3%, unlock USD 15 million to USD 18 million in annual savings, and establish a scalable foundation for expanding AI across operations.