This article and the accompanying slide deck are part of a series exploring how companies in specific industries can adopt the mindset, expertise, and ambition required to win in an AI-first world.
The mining and metals industry has made notable strides in applying artificial intelligence over the past year. It has gone from near the bottom to the middle of the pack, according to BCG’s Build for the Future research.
The industry has improved the flow of materials from extraction to processing and streamlined scheduling and planning. But there is more to do to stay ahead in a changing and challenging industry.
Why AI Matters
The mining and metals industry is being reshaped by multiple pressures that AI can help reduce.
- Experienced workers are retiring faster than new hires can replace them. AI captures institutional knowledge, automates tasks, and frees up capacity to cover the shortfall.
- The industry’s boom-and-bust cycles punishes slow movers. Leaders continually adjust their portfolios, control on costs, and tune day-to-day operations—areas where AI now plays a growing role.
- Operational and maintenance challenges. Generative AI and AI agents can help solve many of the challenges, such as finding the cause of machine breakdowns and suggesting fixes, with which older versions of AI struggled.
Mature, Emerging, and Cross-Industry AI Applications
Many mining companies already have some of the digital foundations—data connectivity, automation, and process control—to scale AI. But they also need an approach—a common vocabulary—to continue on their paths to become AI-first—to put AI at the center of how the operate and make decisions.
We have created three categories to illustrate how the technology is maturing (from traditional machine learning models to generative and agentic AI); where it’s taking shape; and where ideas from other sectors are helping the industry move faster.
Mature applications. Mining and metals companies have demonstrated success with AI tools that address operational bottlenecks. Traditional machine learning–based predictive models, set point optimization models, sales and operations planning via digital twins, and computer-vision safety systems are widely and profitably deployed. Sites report throughput improvements of 2% to 5%, margin improvements of 2 to 4 percentage points, and reductions in unplanned downtime
Emerging applications. Mining and metals companies are beginning to use newer AI applications—AI-enabled exploration, asset design, and mine planning—to speed up decisions and improve performance. These tools help teams react faster to changing ore quality, plan operations more precisely, and reduce manual work in areas like drilling, design, and sustainability. These applications are sketching the outline of the mining and metals company of the future.
Cross-industry applications. Mining and metals companies can tap into a new wave of applications already proven in other industries—such as procurement optimization, workforce productivity tools, AI-assisted commercial workflows, and smarter finance and planning systems. These may seem generic or less specialized than core mining use cases, but they consistently deliver meaningful savings and efficiency gains when adopted. Because they’re faster to deploy and easier to scale, they offer a practical way for mining and metals companies to capture value while building momentum for deeper operational transformation.
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The Payoff
AI’s value in mining and metals is coming into sharper focus through the experience of early movers. The leading companies are building enterprise programs that span years and reshape how the business works.
Consider two examples that highlight the range of what’s possible:
A leading global mining company has built an AI-enabled, end-to-end supply chain. Advanced optimization and AI now drive rail, port, and execution scheduling and stabilize operations amid weather and demand swings. The initiative achieved its ROI target within three months, achieved savings in cost and capital expenses of up to 5%, and improved productivity by up to five times.
A major European steel producer used predictive supply chain planning and AI‑enabled operations to raise EBITDA by 2 to 4 percentage points. The company can now simulate scenarios across sourcing, production, and distribution in real time, improving both delivery reliability and cost control.
These cases show how AI delivers tangible financial and operational impact when treated as a long‑term business transformation, not a set of quick wins.
How to Get There
The path forward requires more than technology. Success depends on aligning people, processes, and governance with AI’s potential. Leading mining and metals companies share several traits:
Business-led strategy. AI programs are viewed as business rather than tech initiatives. They are tied directly to productivity, cost, and sustainability outcomes rather than isolated use cases.
Strong data and digital backbone. Leaders understand the maturity of their data. They work with imperfect data, recognizing it can be improved as they roll out initiatives. Interoperable systems allow data to flow across exploration, operations, and commercial functions.
Empowered workforce. Upskilling, AI literacy, and new ways of working help teams collaborate with digital systems and drive adoption.
Integrated operating model. Engineers, planners, AI specialists, and even AI agents work together in cross-functional teams to rethink roles and achieve measurable results.
Responsible governance. Clear principles ensure safety, transparency, and accountability as they prepare for agentic systems to take on greater autonomy.
AI is beginning to deliver consistent, measurable improvements in how mining and metals companies plan, produce, and deliver results. Those who move decisively to integrate it across operations will write the next chapter of the industry’s long history.
