Rajiv Chandramohan, Ausenco’s Global Director of Process Excellence and Optimization, shares insights into the opportunities and challenges of applying artificial intelligence (AI) in mining, emphasizing the importance of reliable data, and strong infrastructure to successfully implement AI.
Key takeaways
1. Reliable data is critical to AI
AI models are only as reliable as the data behind them. Rajiv highlights sensor calibration as a key challenge, noting that calibration drift can eventually make AI models unreliable and undermine operational confidence.
2. Build a strong data foundation first
Rajiv recommends establishing a reliable ‘single source of truth’ with calibrated sensors, integrated geological and mining data and well-structured secure information. A systematic audit can help identify unreliable instrumentation, validate data and ensure information reaches the systems that need it.
3. The technology exists, integration is the challenge
Sensors and systems capable of supporting fully integrated mine-to-mill optimization are already available. Rajiv notes that the biggest bottleneck is often organizational rather than technical, with data integration, instrumentation maintenance and operational processes still limiting AI adoption.
For mining companies looking to realize the potential of AI, Rajiv’s message is clear: build a strong data foundation, maintain reliable instrumentation and address the organizational barriers that stand between data and actionable insight.
