AI in Natural Resources

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Most discussions about AI assume a digital world (software companies, platforms, high-margin services, fast iteration cycles).

But the real economy doesn’t work like that. And that disconnect is exactly why most AI commentary misses where the real value is actually being created.

Natural Resources businesses (mining, energy, chemicals, agriculture, etc.) operate under very different constraints: heavy assets, long investment cycles, thin margins, operational risk, volatile prices, and capital discipline that matters.

Many of these decisions are also irreversible: once capital is deployed, there is no “undo” button.

That’s precisely why AI is not a sideshow here.

In commodity businesses, AI is not about disruption or reinvention.
It is about efficiency, control, and decision quality under constraint.

And when deployed properly, its impact is often larger than in digital-native industries.

Why Commodities Are a Natural Fit for AI

Commodity businesses share three defining characteristics. Together, they create an environment where decision quality matters more than speed, and where small errors compound over long cycles.

1. High operational complexity

2. Price volatility outside management control

3. Small efficiency gains with outsized financial impact

Let’s pick Mining as an example.

In mining, a 1–2% improvement in recovery rates, uptime, energy usage, or working capital can move EBITDA materially.

AI does not change the geology.
It does not change the market.

What it changes is how well companies operate within those constraints.

That’s where the value is.

The Reality of AI in Mining (Not the Marketing Version)

AI in mining is often discussed in abstract terms: “smart mines”, “autonomous operations”, “digital twins”.

In practice, the most valuable applications are far more mundane, and far more effective.

They fall into two broad layers:

1. Financial and decision infrastructure

2. Operational optimisation and execution

Both matter.
But finance is usually where value appears first.

Layer 1: AI in the Financial Core (Where Leverage Starts)

Mining is capital-intensive and cyclical. That makes decision quality under uncertainty a competitive advantage.

Capital allocation in mining is not just about what to invest in, but when. AI materially improves sequencing decisions: delaying, accelerating, or resizing capital deployment as uncertainty evolves.

This is where AI quietly delivers real returns.

Scenario Analysis Under Volatility

Commodity businesses live with uncertainty:

• prices

• FX

• energy costs

• freight

• interest rates

AI enables faster, more granular scenario modelling across these variables simultaneously, not just point forecasts, but distributions.

This allows management teams to:

• stress-test capital plans

• evaluate downside resilience

• decide when not to invest

• avoid false precision in budgets

The advantage is not prediction. It is preparedness. And preparedness creates optionality, or the ability to act when others are forced to react.

Cash Flow & Working Capital Optimisation

In mining, liquidity matters as much as profitability.

AI can materially improve:

• receivables and payables forecasting

• inventory valuation under price volatility

• timing of cash inflows and outflows

• covenant headroom visibility

For CFOs, this translates into:

• fewer liquidity surprises

• better timing of financing decisions

• reduced reliance on static buffers

Small improvements here compound quickly. This is why, in practice, AI value in mining often starts with the CFO, not the CTO.

Cost Visibility and Variance Analysis

Mining cost structures are complex:

• energy

• consumables

• maintenance

• labour

• contractors

• logistics

AI-driven variance analysis allows management to:

• spot drift before it becomes embedded

• isolate controllable from uncontrollable inflation

This is not about cutting costs blindly.

It is about maintaining margin discipline in an environment where external variables move constantly.

Layer 2: AI in Operations (Where Margins Are Made)

Operational AI in mining works best when it targets bottlenecks, not entire systems.

Predictive Maintenance and Downtime Reduction

Unplanned downtime is one of the largest hidden costs in mining.

AI can:

• identify failure patterns earlier

• prioritise maintenance based on risk, not schedules

• reduce catastrophic failures

• extend asset life

The financial impact is often larger than headline efficiency metrics suggest, because downtime disrupts entire value chains.

Throughput and Yield Optimisation

AI can help optimise:

• ore blending

• plant throughput

• recovery rates

• energy usage per tonne

These improvements are incremental, but in high-volume operations, they move the needle.

The key point:
AI supports operators. It does not replace them. Human judgment remains central.

Mine Planning and Sequencing

This is where financial logic and operational reality intersect.

• short-term vs life-of-mine optimisation

• sequencing under price uncertainty

• balancing grade, recovery, and cash flow timing

Energy and Input Efficiency

Energy is often the largest variable cost in mining.

AI-driven optimisation can:

• smooth peak demand

• reduce waste

• improve energy intensity

• align operations with price signals

In a volatile energy environment, this becomes a structural advantage.

Why Many AI Initiatives in Mining Fail

Despite the potential, many AI programmes disappoint.

Not because the technology doesn’t work, but because of misallocation and poor governance.

Common failure patterns include:

• pilots without ownership

• tools layered onto broken processes

• unclear objectives

• success measured by activity rather than impact

• budgets approved without ROI discipline

The result is automation theatre. It looks sophisticated on a slide deck, but destroys value systematically over time.

In asset-heavy businesses, this is dangerous.
Bad automation decisions are expensive to unwind.

The Role of Discipline and Governance

The mining companies that benefit most from AI share a common trait:

They are disciplined.

They:

• start with decision quality, not tools

• tie AI initiatives to financial outcomes

• keep accountability explicit

• retain human judgment where risk is asymmetric

• scale only after value is proven

AI does not replace management discipline.

It amplifies it, for better or worse.

What This Means for Mining Executives

For CEOs and CFOs in mining and commodities, the implication is clear:

AI is not a transformation project.
It is an operating capability.

The questions to ask are not:

• “Do we have AI?”

• “Are we behind?”

But:

• Where does AI improve decision quality?

• Where does it protect margins?

• Where does it reduce risk under volatility?

• Where should judgment remain human?

These are not technology questions. They are capital allocation and risk management questions. And they sit squarely at the intersection of finance, operations, and strategy.

The winners will not be the most automated companies. They will be the ones that deploy AI selectively, financially, and deliberately.

Final Thoughts

AI is often portrayed as a technology story.

In Natural Resources, it is a management story. For companies that get it right, AI does not change the nature of the business, it sharpens it.

And in an industry where margins are earned through execution rather than narrative, that difference matters.

In commodities, AI doesn’t reward ambition. It rewards discipline, financial clarity, and operational realism.

Until next time,
Stay adaptive. Stay strategic.
And keep exploring the frontier of AI.

Fabio Lopes
XcessAI

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