There is no shortage of AI hype aimed at mining, oil and gas right now. There is a real shortage of plain descriptions of what companies in this industry are doing with it today, as opposed to what a vendor deck says is coming next year. Here is the difference.
Why this matters now, not eventually
Mining productivity per worker declined by roughly half between 1997 and 2023, over the same period manufacturing productivity more than doubled, according to McKinsey’s analysis of the sector (McKinsey & Company, “Unearthing a new era of innovation in mining”). That gap is exactly why AI and automation are getting serious investment now rather than being treated as a future experiment: GlobalData analysis cited in recent industry reporting puts mining industry AI spending on track to rise from US$2.7 billion in 2024 to US$13.1 billion by 2029 (Mining Technology, August 2026).
What is deployed in mining today
Predictive maintenance is the most mature application by a clear margin. Machine learning models analyse vibration, temperature, pressure, electrical current and lubricant condition data from fixed and mobile equipment to flag degradation before it causes a failure, rather than waiting for a scheduled inspection or a breakdown.
Prescriptive maintenance is the next step up, already in use at more advanced sites: rather than just flagging a problem, the system estimates remaining useful life, assesses the consequence of failure, and recommends when to intervene, factoring in parts availability and crew scheduling.
Ore classification and flotation control use AI to make real-time processing decisions based on ore characteristics, improving recovery rates without a person manually adjusting settings on the fly.
Haulage optimisation applies AI to routing and scheduling for haul trucks and other mobile fleet, reducing cycle times and fuel use across a pit.
Environmental monitoring uses AI-driven sensor analysis to track emissions, water and tailings conditions continuously, rather than relying solely on periodic manual checks.
The common thread through all of these is that they are not replacing human judgement outright. Most sit in a progression from monitoring and prediction toward recommendation, and only the most advanced deployments move toward genuinely autonomous, bounded control.
What oil and gas is doing differently
Oil and gas organisations are, on average, further ahead on the technology itself. EY’s analysis found the sector already has advanced AI models, digital twins and analytics capabilities in place (EY, April 2026). In practice, that shows up as digital twins of wells, pipelines and process plant used to simulate scenarios before committing to a physical change, AI-assisted drilling optimisation that adjusts parameters against real-time downhole data, and predictive analytics applied to pipeline and wellhead integrity to flag corrosion or equipment risk earlier than a fixed inspection schedule would catch it.
EY’s research points squarely at legacy infrastructure and organisational silos, rather than the models themselves, as the barrier to getting enterprise-level value out of tools that, technically, already work.
The pattern behind both industries
In both mining and oil and gas, the honest state of play is the same: the individual use cases are proven, but the value stays trapped at the pilot or single-site level until the organisation around the technology, its data foundations, its governance, its people and its change management, catches up. That is a readiness problem, not a technology problem, and it is exactly what an AI readiness assessment is built to diagnose.
Move from isolated wins to enterprise value
If your organisation has one or two AI use cases working in isolated pockets but has not been able to scale them further, an AI readiness and maturity assessment identifies exactly what is holding enterprise-wide value back, across strategy, data, governance, people and culture. Get in touch to talk through where your organisation sits.