Physical artificial intelligence (AI) is gaining traction in industries long seen as resistant to advanced technology, not because those sectors lacked interest in innovation, but because failure was not an option. Mining, agriculture and energy have historically operated in harsh, high-risk environments where downtime, safety incidents and system errors carry serious consequences.
Now, a fundamental shift is occurring as AI is being embedded directly into the “muscle” of machinery, turning AI from an experimental software layer into critical operational infrastructure.
From Aversion to Acceptance
In traditionally technology-averse industries, AI adoption looks fundamentally different from what has unfolded in offices or consumer platforms. Physical AI refers to systems that combine machine learning, sensors and automation to perceive and act in the real world under strict safety and reliability requirements. These systems are designed for narrow, well-defined tasks, where predictability matters more than flexibility.
This is the move toward edge intelligence. By prioritizing predictability over flexibility, these systems enable machines to make micro-decisions without waiting for a ping from a distant server. Consequently, AI is no longer a “plugin” — it is becoming the nervous system of the global supply chain.
Mining: Optimizing the Heavy Lifting
Take Rio Tinto’s Pilbara operations in Western Australia. Here, the challenge isn’t just digging; it’s the orchestration of one of the world’s most complex iron ore supply chains. AI-enabled scheduling systems have been deployed to modernize mine, rail and port planning.
AI tools improve scheduler productivity, shorten planning cycles and help planners adapt more quickly to changing conditions, while humans retain control over critical decisions. The technology augments decision-making at scale rather than automating it away.
Rio Tinto has also deployed autonomous haul trucks, drilling systems and the AutoHaul rail network across its Pilbara operations. These systems rely on sensors and localized decision-making to operate continuously in remote and hazardous environments, reducing human exposure to risk while generating data that feeds back into planning and optimization models.
Agriculture: The CFO in the Tractor
In agriculture, the barrier to entry has always been biological variability. John Deere embeds AI directly into equipment to operate in constantly changing field conditions. Autonomous tractors, sprayers and harvesters rely on computer vision and machine learning to perceive crops, weeds and field boundaries in real time, enabling machines to make precise, localized decisions at scale. Deere’s See & Spray system uses multiple cameras and AI models to apply herbicide only where weeds are detected.
The emphasis is not on removing farmers from the process, but on giving them better tools to manage increasingly complex operations. As Justin Rose, president of Lifecycle Solutions, Supply Management, and Customer Success at John Deere, said, “At the core, it’s about giving farmers something invaluable — time. They’re not just farmers; they’re CEOs, CFOs and CTOs, all while physically working the land.”
In that context, physical AI functions less as automation and more as decision support, handling perception and micro-adjustments so farmers can focus on higher-level operational choices.
Energy Infrastructure and Predictive Operations
The energy sector companies, like Saudi Aramco, are applying high-performance computing to decades of seismic data. Saudi Aramco is deploying AI across its exploration, drilling, production and maintenance divisions. AI models trained on decades of seismic and operational data improve subsurface imaging, optimize drilling decisions and support predictive maintenance across refineries, pipelines and processing facilities.
Aramco’s use of supercomputing allows massive industrial datasets to be processed quickly, enabling earlier detection of equipment issues and reducing unplanned downtime. These systems are embedded into core operational processes rather than used as standalone analytics tools.
The Bottom Line
Across these three sectors, the economics of AI are being redefined. The metric of success is durability, shown through increased through increased throughput, higher yields and enhanced safety. As these systems mature, physical AI will likely become the silent engine of the global economy — less visible than a chatbot, but infinitely more consequential to the bottom line.
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