EY Insights_AI in Mining is the next frontier

AI in Mining is the next frontier

Finding value from scale, not pilots

AI in Mining is the next frontierContents

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Mining and metals companies have steadily built digital capabilities over the past two decades, often in isolated parts of the value chain. The industry is now entering a new phase, one where artificial intelligence (AI) shifts from being a peripheral enabler to becoming embedded in core operations, planning and decision‑making.

The EY Mining and Metals Risks and Opportunities 2026 report shows this shift is well underway: 21% of leaders plan to allocate more than 20% of incremental budget toward AI in the next year. Yet many organisations remain stuck in a cycle of experimentation, generating proofs of concept that demonstrate local value but fail to scale across the enterprise.

Africa: scaling AI with discipline and control

African mining operations span remote geographies, complex jurisdictions and highly variable operating environments. These realities magnify challenges already faced globally, including data fragmentation, uneven connectivity, governance gaps and resource constraints. For the region, successful AI transformation must prioritise:

A unified data and AI backbone
Clear accountability across corporate and site levels
Responsible AI, risk, and governance controls
Scalable platform patterns that prevent duplicated effort

This context underscores a global truth: the differentiator is no longer who experiments with AI, but who scales it with discipline, governance and measurable value.

A compelling case for AI in mining

Mining organisations face growing complexity, volatile markets and rising expectations around safety, sustainability and productivity. These conditions create a strong business case for AI:

AI can remove real operational constraints
It augments human decisions in fast‑changing environments
It reduces the cost of delayed or inconsistent actions

However, EY’s global mining experience shows that the next wave of value will not come from isolated digital wins. Instead, competitive advantage will shift to companies that adopt an enterprise‑scale transformation mindset, enabled by unified platforms, strong data foundations and consistent governance.

What “good” looks like: insights from global mining leaders

1

Anchor AI to business‑aligned use cases

High‑impact AI portfolios focus on strategic priorities such as improving recovery, reducing downtime, lowering energy intensity and strengthening safety. Successful programmes solve problems the business feels most acutely.

2

Build a unified platform for scale

A shared data and technology platform is fundamental. Without shared patterns and standards, individual sites build independently, resulting in duplicated effort, inconsistent risk controls and fragmented data. EY’s platform architectures emphasise reusability, interoperability and secure scale.

3

Break down entrenched data silos

AI requires clean, trusted and connected data across operations, maintenance, supply chain and workforce. A unified data backbone with automated lineage, ingestion, quality and metadata management creates the conditions for models to perform reliably and for teams to trust AI‑driven recommendations.

4

Create a management operating system for AI transformation

Scaling AI is an enterprise change, not a technology upgrade. It requires executive sponsorship, product‑based operating models, clear decision rights and a governance framework that balances corporate standards with site‑level innovation. EY’s transformation approaches emphasise behavioural change and measurable value realisation.

AI governance is now a strategic performance issue

As miners move from augmented to increasingly autonomous systems, governance becomes inseparable from performance. Responsible AI frameworks, covering transparency, safety, security, fairness, accountability and human‑in‑the‑loop principles, form the foundation for safe scaling. Governance is no longer optional; it is what ensures AI enhances decisions rather than creates new risk.

Talent: The ultimate constraint on AI maturity

Technology is only one part of the challenge. EY research shows 69% of miners cite technology skills as their biggest workforce gap. Scaling requires not only specialists but enterprise‑wide capability uplift:

Digital literacy for frontline and technical teams
Product ownership and platform operating skills
Strong data engineering and architecture capability
AI governance and risk management expertise
Leaders who can translate operational needs into scalable, cross‑functional solutions

AI maturity will ultimately be determined by people, not tools.

Implications for 2026 and beyond

Industry leaders will not be the companies running the most pilots, they will be those who scale AI consistently across operations, with:

Disciplined platforms
Trusted data
Strong governance
Aligned incentives
Measurable value delivery

The next frontier of productivity in mining will be shaped not by experimentation, but by execution at scale. With the Africa Mining Indaba approaching, these insights are already influencing the conversations we’re having with mining leaders across the continent.