Vale opened its first AI-powered model plant at the Conceição 2 facility in Itabira, Brazil, increasing iron ore productivity by 25% and raising premium pellet feed output by 40%. The 11.2-million-ton-per-year operation sets a new operational baseline for automated, water-efficient mining by integrating artificial intelligence directly into core industrial workflows.
Integrating AI into industrial workflows
The 1.5-year modernization project deployed 51 targeted solutions to eliminate operational bottlenecks. Engineers installed more than 100 monitoring cameras and automated approximately 7,300 instruments, including advanced measurement devices and sensors. This data intelligence layer now controls and optimizes over 400 variables across the ore processing workflow. For professionals overseeing similar industrial environments, understanding these integration strategies is a core component of the AI Learning Path for Plant Managers.
"The model plant is more than a project: it represents a new way of operating, based on the application of advanced technologies that redefine standards of efficiency, sustainability, and competitiveness in mining," said Carlos Medeiros, Vale's vice president of operations. He added that the system includes an artificial intelligence layer that supervises hundreds of variables and performs real-time adjustments to the production process.
Measurable gains in output and safety
Production of direct reduction pellet feed, a strategic product for steel decarbonization, grew by 40% following plant automation. The unit increased overall productivity by 25%, scaling output from 9 million tonnes in 2024 to its planned capacity of 11.2 million tonnes. Online ore grade analysis technology now allows immediate corrections to the mineral processing route, reducing waste generation. In 2026, the average iron content in waste dropped by 26%.
"The plant has reached a new level of digital maturity, with 100% of critical operational decisions supported by expert systems," said Rafael Bittar, Vale's technical vice president. He explained that this advancement combines technology and technical expertise to increase efficiency and enhance predictability.
Workforce development and remote operations
The facility reduced manual interventions through remote operation solutions, including robotic arms and the automation of electrical and mechanical equipment. All 122 operators, instrument technicians, and leaders at the plant completed over 2,800 hours of training. Upskilling teams to analyze data and support real-time decision-making is a central focus of the AI Learning Path for Operations Managers.
"Technology has added a lot. I didn't have much knowledge of digital systems, and I improved my skills during the training," said Ivo Castro, an equipment and plant installations operator with 17 years at Vale. "Today I work with more safety, information, and agility. Before, I was constantly in the field, checking installations and performing manual operations. Now I can monitor areas on my phone, in the palm of my hand."
Why this matters for operations professionals
Operations leaders should note that Vale's productivity gains did not come from a single software purchase. The 25% output increase resulted from mapping 400 distinct workflow variables, automating 7,300 instruments, and retraining the frontline workforce to manage exceptions rather than perform routine manual checks. Replicating this model requires treating AI as an operational layer that dictates workflow redesign, not just an IT upgrade.
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