In 2016, the Industrial Minerals Association North America estimated that the average American will use 24 tons of industrial minerals over the course of a year—and that number has presumably only risen since then. From the gravel and limestone in the foundation of our built environments to the lithium and silicon in the computer chips and batteries powering our digital worlds, we depend on these little minerals in a big way.
Ironically enough, the minerals that make up so much of our modern world are cultivated through one of the oldest industries: mining. A historically manual, methodical, and labor-intensive industry. And now, in the age of AI, this very nature of industrial minerals mining (IMM) has made digital transformation all the more challenging. “The longer you’ve been in operation, the harder it becomes to move into the digital age,” says Tristan Kleinschmidt, lead data scientist at Boston Consulting Group (BCG). “But operations need to move away from traditional ways of working, where the reliance on experienced humans is paramount.”
That’s exactly why BCG believes the IMM industry has a massive opportunity with AI. “At every plant, there are opportunities to improve the capability, cost effectiveness, quality, safety, and environmental stewardship of that operation through a combination of AI and classical manufacturing levers,” says Adam Rothman, managing director and senior partner at BCG. “If mining companies aren't thinking about how AI fits in, they should be.”
Facing Challenges in IMM Today
Many of the industry’s biggest challenges that AI can help solve are surprisingly human—like attracting and retaining talent. Gaining and maintaining a steady workforce in IMM is difficult in part because IMM’s operations are much smaller and less resourced than those in large-scale conventional mining: Whereas a gold mine could easily have hundreds of workers on any given shift, IMM operations typically have between 20 and 100 workers across all shifts. And there’s a direct line between operational scale and the kind of tech and infrastructure miners can afford to finance.
On top of that, these labor-intensive—and at times dangerous—jobs are situated at mining sites typically established in remote locations. And that’s not even getting into the industry’s fraught environmental reputation. All this can amount to a sparse resource pool, ultimately creating a sizeable gap between talent and demand.
Then there are the processes these teams are managing, which are inherently complex and uncertain.
A Savvy Place to Start
The team at BCG believes that the key to taking on these challenges is to start by focusing on the wealth of data and technology IMM companies already use. “Any existing asset you can leverage is probably going to have more impact than [building a new tool],” Kleinschmidt says. “We’ve seen how quickly operators can get on board with work that’s being done in the digital and AI space because it was an extension of things they were already doing that made their jobs easier.”
Malinovsky thinks of it this way: What sets a best-in-class excavator operator apart is that they use the machine so naturally that the excavator seems like an extension of themselves. The same goes for AI: It should be naturally embedded in their day-to-day work.
But what does that look like in practice? Malinovsky notes that there are plenty of ways to leverage AI in IMM, from increasing ore body knowledge to improving mining processes and operations with data. Take the surveillance cameras and sensors, for example. Layering AI onto traditional tools or technology like computer vision can drive efficiency and accuracy across the entire production chain—and stop performance leakages before they start.
Understanding the Impact: Remote Site-Wide Monitoring
If a supervisor is making their rounds for routine checks, they might cover the entire mining site and processing plant just a few times in their 12-hour shift—meaning a few hours could go by before they notice and act on a performance issue, like an uncalibrated piece of equipment or a blocked screener that can’t separate particles accurately. That could be hours’ worth of valuable materials slipping through the process cracks, which AI could have prevented by quickly identifying the issue and alerting the operator in real time.
For mining operations that can span tens of square miles, there’s also a breadth of coverage AI can offer that’s difficult for a single operator or maintenance crew to match with the same rigor and regularity. “There’s no way one single person can be accountable for keeping an eye on everything at once,” says Malinovsky. “AI can extend human capabilities and help operators monitor 24/7, at every angle.”
That’s where remote site-wide monitoring comes in, and where AI can help uncover critical insights. When mining companies use drones to check inventory levels or monitor environmental changes around a mining site, they can also use that data to detect patterns and make better decisions. “Some of those changes can be so subtle that the naked eye may not even notice them,” says Kleinschmidt, “but the AI can tell you something’s happening here that could lead to a problem in two or three days.”
Beyond bringing innovation to one of the world's oldest occupations, there's a critical opportunity for AI to help make IMM an even safer industry for its workforce—and for the environment. With remote site-wide monitoring, companies can protect frontline workers by minimizing their exposure to hazardous elements and harsh conditions. They can also better monitor potential dangers and risks at a site, like a collapsed berm; or if a camera shows an operator is too close to a piece of equipment, it can instantly send an alert or even shut down production. And in an industry where minimizing environmental impact is top of mind, AI can play a key role in automating compliance reporting and monitoring during site rehabilitation.
The Bigger Picture
As the IMM industry continues to push toward digital transformation, mining operations won’t just become more focused and precise—they’ll also become more sustainable. And they have to be: There are only so many ore deposits in the world, and the quality of what’s left is only going to decline. But the demand for these minerals—and all that they power—is only going to rise.
“It's even more crucial that the mining process extracts the most high-quality minerals to avoid losses across the value chain,” says Malinovsky. “Ten years ago, waste material from the mining process was just that: waste. Now, we consider this to be available material we can reprocess to extract more.”
To Rothman, this is where AI has the biggest opportunity to change the trajectory of IMM. By combining data science with their knowledge of geology and the markets, mining companies can make smarter decisions about extracting, processing, and reprocessing ores. “These technologies are going to be essential to solving the supply and demand problem for many of these minerals,” he says. “It's really exciting and important—from an overall energy transition and geopolitical perspective—that these technologies are coming to life to improve the capability of industrial mineral operations.”
But in the end, it’s not so much about technology as it is about what people can achieve with it. “In these smaller operations,” adds Rothman, “what’s just as important as the technology itself is, How do we enable people to make consistent decisions to better manage the process across every shift, every day, every week?”
With that question as their North Star, IMM organizations will naturally be able to prioritize the people behind the operations. By leaning into digital transformation and equipping employees with the right technology and skills, these companies have the power to advance the mining occupation across the board—from productivity and talent acquisition to safety and sustainability—and transform the mining industry from the ground up.
This article was written by Adam Rothman, Managing Director and Senior Partner, BCG; Ilia Malinovsky, Managing Director and Partner, BCG; Mikhail Moguchev, Managing Director and Partner, BCG; and Tristan Kleinschmidt, Lead Data Scientist, BCG
