Artificial intelligence is not new. What is new is its scale, ease of adoption and end to end deployment. In mining, value is being captured by combining predictive maintenance, critical inventory management and knowledge assistants with full traceability.
By Denisse Barnaby
Partner and Head of Engineering and Processes, Miebach Peru
Artificial Intelligence (AI) is often associated with misconceptions, ranging from viewing it as a new technology to assuming it is limited to conversational assistants or that it will replace people. It is essential to break free from these biases and knowledge gaps, build a clear mental framework around its potential, and reflect on how the challenges faced across the supply chain can be addressed through AI.
The first myth to dispel: AI did not emerge “out of nowhere”
The concept of “thinking machines” and technological developments designed to simulate human capabilities for problem solving have existed since the 1940s. Progress has accelerated from Alan Turing and early psychoanalysis chatbots, through milestones such as Deep Blue and Watson, the launch of Siri, and the first transformer model used in Google Translate, to today’s generative AI technologies such as ChatGPT, Gemini and Copilot.
What truly stands out is how AI capabilities have surged as a result of scale and democratization. On one hand, there is unprecedented computing power, advances in statistics and modeling, and the availability of more reliable data. On the other, there is greater ease of use, natural language interaction and access at significantly lower cost.
AI and today’s professionals: enhancing human value
Tools are only as good as the information that feeds them. This is particularly evident in generative AI. Models respond to prompts or questions, but the quality of the output depends on the quality of the instruction and, above all, the context provided. If users are unable to clearly define objectives or supply relevant data and criteria, responses lose accuracy and usefulness. This makes the role of the user critical, as the human element is what ultimately turns technology into something practical and applicable.
In this context, human skills become even more valuable. To ensure value capture from AI, organizations must overcome anxiety and fear, while closing skills gaps. The differentiator is no longer technical knowledge alone, but how that knowledge is applied and guided through human intervention.
In particular, professionals, especially supply chain leaders, are expected to prioritize the following:
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Act as integrators of strategy and vision, with a strong focus on resilience and risk management.
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Promote continuous learning and the habitual use of digital tools.
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Strengthen soft skills, particularly leadership, empathy, communication and change management, fostering a collaborative and innovative culture.
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Ensure responsible use, protecting sensitive information and competitive differentiators,
In other words, organizations are seeking augmented leaders, professionals capable of combining business and operational judgment with AI tools to drive improvements and accelerate transformation.
Mining Supply Chain 4.0 is already a reality
Banking and Finance, Telecommunications and Technology have led AI adoption since the 1980s. Mining, by contrast, has advanced at a slower pace since the early 2000s and today shows a medium level of adoption due to cultural barriers and change management challenges. Even so, the sector concentrates some of the most powerful and representative use cases within heavy industry.
In mining, AI initiatives with higher digital maturity are already delivering concrete and measurable value: improved regulatory compliance, greater operational continuity and more efficient working capital management.
On the regulatory front, where traceability and compliance are critical to safeguarding safety and sustaining operations, generative AI enables specialized assistants based on Retrieval Augmented Generation (RAG). These tools provide agile access to procedures, instructions, standards and technical documentation, while also incorporating business lessons learned. The objective is to enhance professional judgment by offering direct decision support for auditing, validation and timely action.
In parallel, maintenance teams apply AI to predictive maintenance for assets with high operational impact, including fleets, core process equipment and critical components. Using telemetry data such as vibration, temperature and pressure, models are developed to anticipate failures, plan shutdowns and minimize unplanned events, ensuring asset availability and reliability.
Finally, AI addresses spare parts inventory management, overcoming challenges associated with large item counts, intermittent demand and the high cost of stockouts. Applied AI enables criticality segmentation, demand modeling and policy optimization to balance availability and tied up capital, particularly in environments where lead times and variability add complexity.
The path toward digital transformation
The question is no longer whether AI should be adopted, but where and how to begin. The recommendation is to start with the most relevant pain points and challenges, where data already exists or can feasibly be built, and where decisions are repetitive, high impact, or where AI can enhance the human role, judgment and sensitivity. The invitation is to take an end to end view of processes and move forward with focus, responsibility and people at the center.
