Leveraging AI to advance the competitiveness of African mining companies


In the ever-evolving landscape of the African mining and resources sector, the allure of cutting-edge technology often overshadows the fundamental challenges faced by businesses. However, amidst the clamor for innovation, it’s essential to recognize that technology should serve as a means to an end, rather than an end in itself. Artificial Intelligence (AI) emerges not merely as a buzzword or trend but as a potent toolset capable of addressing the persistent pain points that plague mining operations.

The African continent boasts some of the world’s most experienced and adept mining operators, given its abundant resources and deposits. From traditional commodities like coal, platinum, and gold to the burgeoning battery metal cluster comprising copper, nickel, cobalt, and lithium, the continent is poised for further investment in infrastructure and operations, provided its competitive position can be secured.

The pressing question on the minds of executives in these organizations is: What do we need to do to remain competitive against our peers—both locally and globally?

In numerous discussions, the answer often revolves around “Artificial Intelligence,” with executives pinning high hopes on productivity advances through advanced analytics. However, the reality on the ground reveals a frequent deployment of a ‘gadget’ and ‘backbone’ focused AI approach, with little emphasis on actual business value creation.

This scenario is akin to acquiring a hammer without first confirming the availability of nails. Instead, mining companies should prioritize a ‘value-first’ approach, considering whether Artificial Intelligence is the solution only after evaluating its potential impact.

Two notable examples are safety enhancements and improvements in shift changeover productivity. While AI can contribute to better performance in both scenarios, it is not the sole intervention. For instance, AI can detect unsafe behavior, but without addressing underlying behavioral aspects, it merely flags unsafe practices.

Similarly, for enhancing shift change-over productivity, AI can optimize movement patterns, but it cannot address issues like driving styles or lack of accountability.

The analogy of purchasing a hammer before searching for nails aptly captures the disconnect between executive enthusiasm for AI and its practical value generation. One of the reasons for this disconnect is the failure to engage the right people within the organization. True value emerges when operational roles, from general managers to frontline workers, are involved in AI strategy discussions.

Another common pitfall lies in adopting a “think big, do it right the first time” mentality, where companies invest significant resources in infrastructure and data collection without delivering immediate value. Instead, a nimble and focused approach is advocated, starting small and identifying value before scaling up.

A case study of an African gold mining business exemplifies this approach, where an AI implementation delivered a sustained recovery improvement without substantial investment by focusing on critical parameters.

The potential applications of AI are extensive, encompassing core processes as well as procurement, HR, finance, exploration, and marketing. However, to realize these benefits, mining companies must shift their mindset from expecting AI to solve all problems to seeking value first.

For African mining and processing businesses, adopting innovative approaches to leverage AI is essential for maintaining competitiveness in the global market. In a landscape where success hinges on efficiency and adaptability, embracing AI-driven strategies that unlock real value is crucial for sustained success.

Source: miningreview.com