Because circumstances can change so rapidly in mining operations, creating and adhering to accurate plans and schedules that span the value chain is challenging. But machine learning, which supports new ways of analyzing data, will create a more responsive and accurate way to plan and manage operations. A virtual environment (or “digital twin”) updating itself in real time and recommending next best actions, will realize huge benefits.
Three overlapping lenses
Some mining companies are already making significant use of artificial intelligence (AI) and machine learning. Recent Accenture research reveals that 70 percent of them are using machine learning in at least one business process.
As explained further in the book Human + Machine: Reimagining Work in the Age of AI written by Accenture’s Paul Daugherty and H. James Wilson, leaders are harnessing three interrelated dimensions of AI. They are: reinventing processes, utilizing data and rethinking human-machine collaboration. Fifteen percent of the mining companies interviewed are systematically bringing together these three lenses. But, they’re developing these capabilities at markedly different rates:
- 28 percent are systematically applying AI to reimagine processes and process change.
- 34 percent are harnessing data plus AI to capture exponential improvements in agility and KPIs.
- 46 percent are rethinking how humans and machines work together.
In terms of reimagining processes, one company is harnessing analytics and AI to create a reimagined mine of the future. Others are applying machine learning to discover new areas of opportunity and to reinvent processes. The most widespread activity, however, is applying machine learning to transform the human-machine work relationship.
Safety first: The irrefutable argument for AI in mining
Mining companies are investigating AI’s safety benefits. For example, instead of having to manipulate machinery and record data at the same time, workers can now rely on a chatbot. As such, what was a complex and risky manual process becomes a seamless, distraction-free activity.
However, AI’s real benefits will really start to flow once decision-making can be automated. But that means addressing some key challenges.
Confronting the challenges
At a foundational level, there’s a reluctance to relinquish control of the countless interrelated processes and decisions on which effective operations depend. For a machine to be trusted, how it makes decisions must be both transparent and understood.
Establishing a trusted relationship between machines and people is one of the key characteristics of what the authors of the book, Human + Machine, call the “missing middle:” how people and machines work together in a mutually beneficial way.
Prioritizing the missing middle will be vital to secure trust and drive progress. Right now, few companies excel in this area. But while less immediately exciting than the latest cool technology, the framework within which AI operates can’t be ignored.
Another challenge is the heterogeneous nature of global mining locations—thousands of meters up in the Andes, at sea level in the United States or under the ground in Mongolia. Developing an algorithm that can assimilate this extreme variability is massively complex. In other words, there’s no one-size-fits-all solution.
There’s also a big question mark over the mining industry’s ability to attract digital talent. In response, some companies are developing programs that position them as viable competitors to leading tech businesses.
Hitting a new seam of opportunity
But mining has an outstanding opportunity to move fast and harness the full power of AI. The possibilities for innovation are huge: unencumbered by technology accretion, mining companies can leapfrog into a machine-learning powered future.
So how to start? In the end, it all comes down to people. The experience, knowledge and understanding that machines need is stored in the heads of the workforce. What’s needed, therefore, is a platform to make the collection, management and dissemination of that information as seamless and simple as possible.
With that foundation, mining companies will be able to give machines the knowledge they need to automate, define next best actions and, ultimately, make decisions to drive real business value.