AI in Chilean Mining Moves From Pilot to Live Operation at Scale

At the 13th International Mining Automation, Digitalisation, AI and Electrification Congress in Santiago, ABB did not present a roadmap or a proof of concept. It demonstrated software already running at Chilean mines, including a conversational AI copilot configured in real time that lets a plant supervisor ask a natural-language question and receive an answer drawn from 25 integrated operational systems. Chile anchors global copper supply, and its mining industry faces sustained pressure to improve recovery rates, reduce energy intensity, and extend asset life without wholesale infrastructure replacement. ABB’s two platforms, IMP and Genix, are designed to layer onto existing control systems rather than replace them, lowering the barrier to AI adoption at operating sites. This article covers what IMP and ABB Ability Genix actually do in a mining context, what the Gold Fields Salares Norte deployment reveals about real-world integration complexity, and what the shift from pilot to live operation signals for operators and investors tracking AI adoption across Latin American mining.

ABB chose live deployments over future promises at Minería Digital 2026

Minería Digital 2026, held 5-7 August 2026 at the Sheraton Santiago Hotel, drew more than 600 national and international companies from across the mining and energy sectors. Against that scale, ABB made a deliberate editorial choice: every demonstration featured software already in production at Chilean operations, not future-state concepts or theoretical architectures.

The distinction matters. Vendor conferences in mining technology tend toward aspirational presentations. ABB’s decision to show running deployments of both IMP and Genix, including live configuration of a conversational AI copilot, positioned its platforms as operational tools rather than procurement pitches.

According to Víctor Chávez, ABB’s Senior Digital Solutions representative for Chile, the company supports clients through multidisciplinary teams operating both within Chile and internationally. ABB’s assessment of its congress participation was highly favourable, noting that concrete results were effectively communicated to attendees, as reported by Reporte Minero on 14 August 2026.

For investors tracking AI adoption in Latin American mining, a vendor demonstrating running software at active sites rather than future-state concepts is a meaningful signal of platform maturity.

How IMP removes the data science bottleneck for plant engineering teams

Mining environments are hard on physical instruments. Sensors that measure ore grade, slurry density, or flotation performance can be difficult to install, expensive to calibrate, and prone to failure in corrosive or remote conditions. When a sensor goes offline, operators lose visibility into a critical process variable until maintenance can reach the site.

ABB’s Inferential Modeling Platform (IMP) addresses this gap by using existing data streams from the control system to infer key variables that cannot be measured directly or continuously by hardware. Rather than installing new physical instruments, IMP builds virtual sensors, sometimes called soft sensors, from data the plant already collects.

The application of soft sensor architectures in mineral processing is well established in technical literature, with comminution and flotation circuits identified as the highest-value targets because laboratory measurement latency in those stages can directly suppress recovery rates before control systems can respond.

The platform supports four modelling approaches:

• Neural networks
• Statistical regressions
• Multivariate statistical analysis
• Custom equation-based models

This flexibility allows engineers to match the modelling technique to specific process behaviours. Models integrate via standard OPC protocols to deliver inferred values directly into the control system or data acquisition stack.

IMP-SV: validating what the sensors report

IMP also includes IMP-SV, a dedicated module for sensor validation. It allows operators to assess readings from physical instruments and distinguish reliable values from faulty ones, supporting data integrity across the plant. The architecture combines an offline Model Builder environment for development and validation with a real-time server for online deployment and monitoring.

Critically, IMP is designed for process engineers and plant personnel, not data scientists. ABB has emphasised that enabling non-specialist site teams in Chile to configure and maintain virtual sensors themselves is central to its adoption strategy, removing the need for large internal data science groups. For operators, this reduces capital expenditure and downtime. For investors, the non-specialist accessibility model signals that adoption can scale without proportional growth in specialist headcount.

What ABB Ability Genix does at an active Chilean mine

ABB Ability Genix is an enterprise-grade but modular Industrial Analytics and AI Suite that integrates operational, engineering, and IT data into a single analytics environment. Its defining design choice is technology-agnostic connectivity: Genix connects with data from varied and existing systems regardless of origin, sitting on top of installed infrastructure rather than replacing it.

For Chilean mines with decades of legacy control systems, historians, ERPs, and specialised mining software, that architecture removes the requirement for wholesale replacement before AI analytics become viable.

Genix includes prebuilt applications targeting the full mining value chain:

At the edge, ABB Ability Edgenius Operations Data Manager connects, collects, and analyses operational technology data at the point of production, feeding real-time information into the central Genix analytics platform.

Gold Fields Salares Norte: integrating 25 systems in the Atacama

The Gold Fields Salares Norte project in Chile’s Atacama region is the concrete proof point. Genix integrates data from approximately 25 different engineering, operational, and information systems, spanning mine operations, processing, geology, asset management, finance, and HR, into one contextualised analytics environment. The order dates to 2022, with deployment ongoing and live demonstrations featured at the 2026 congress.

Gold Fields, now a larger producer following its A$3.7 billion acquisition of Gold Road Resources, is simultaneously running one of the mining sector’s most complex technology integrations at Salares Norte, where Genix connects 25 heterogeneous operational systems across an extreme-environment site.

The scope is significant. Twenty-five heterogeneous systems, none of them replaced, all feeding a single analytics layer in one of the most operationally extreme environments on earth. For investors assessing the practical limits of technology-agnostic integration, Salares Norte is the reference case.

The conversational copilot and what it changes for frontline operators

At Minería Digital 2026, ABB configured a conversational AI copilot embedded within Genix in real time during the congress. This was not a pre-recorded walkthrough. The copilot was set up live, demonstrating the platform’s ability to accept natural-language queries against complex, integrated operational datasets.

A plant supervisor could ask, for example, “Which flotation circuit has shown the largest drop in recovery over the last three shifts?” and receive an answer drawn from the same contextualised data foundation that Genix assembles from dozens of connected systems.

What the copilot removes is specific and measurable: the need to navigate multiple dashboards, cross-reference separate data sources, or write SQL queries to surface an insight from integrated operational data. It extends AI access beyond specialists to operators, supervisors, and managers across skill levels, building on Genix’s integrated data layer rather than operating as a standalone tool.

For mining operators, this reduces dependency on specialist analysts and speeds decision-making at the point where it directly affects production. The practical effect is a productivity lever that scales with the number of users who can now interrogate operational data without technical intermediaries.

Why Chilean mining is the right environment to test these tools at scale

Chile is not simply a geography where these platforms happen to be deployed. It is a specific set of conditions that stress-test exactly the capabilities IMP and Genix were designed to deliver.

Chilean copper production operates under conditions that leave almost no margin for unplanned disruption, and incidents at major operations like Codelco’s El Teniente have demonstrated how quickly operational events can ripple into national output figures and global supply projections.

The characteristics that drive adoption are also the characteristics that make implementation difficult:

• Remote mine sites, often hours from major urban centres
• Harsh desert conditions, including the Atacama, that degrade physical sensors and limit maintenance access
• Decades of installed control infrastructure from multiple vendors and eras
• Continuous production pressure that leaves little tolerance for system replacement downtime
• No appetite among operators for wholesale infrastructure overhaul

These conditions explain the specific design choices in both platforms. IMP’s non-specialist accessibility and OPC integration address the reality that remote sites cannot rely on fly-in data science teams. Genix’s technology-agnostic connectivity addresses the reality that no Chilean mine runs a single vendor’s systems end to end.

Chile’s role as a global anchor for copper production gives these deployments significance beyond the country’s borders. Successful AI adoption here, under the most demanding operational conditions the industry offers, is the most meaningful early indicator of where mining’s digital transition is heading across Latin America and comparable jurisdictions.

How ABB structures long-term client engagements around sustained digital outcomes

ABB’s positioning in Chile goes beyond software delivery. The company frames its engagements as long-term digital transformation partnerships, a distinction with practical consequences for how deployments are sustained and expanded.

The continuous accompaniment approach follows a cumulative sequence:

1. Customised roadmaps anchored in each site’s specific process flows, sensor infrastructure, and organisational capabilities
2. Multidisciplinary teams operating locally in Chile and globally, covering process engineering, control systems, data science, and change management
3. Ongoing support and iteration, with IMP models and Genix applications refined as more operational data and feedback accumulate
4. Pre-deployment services that help customers identify which assets, processes, and risk profiles stand to benefit most before analytics are designed and deployed

Víctor Chávez explicitly positioned ABB as a strategic long-term partner capable of addressing future challenges within the regional mining industry, as reported by Reporte Minero. The methodology ties ABB’s commercial success to its clients’ improving outcomes over time, reducing the risk of failed one-off implementations.

For operators evaluating vendor credibility, the partnership structure provides continuity. For investors assessing the durability of digital transformation in mining, it signals that these deployments are designed to compound in value rather than stall after initial installation.

From live demos to operational baseline: what this signals for mining’s AI transition

The demonstrations at Minería Digital 2026, backed by active deployments at Chilean sites including Salares Norte, collectively signal a transition. AI-enabled mining in Chile is moving from early-stage experimentation to a recognisable operational baseline.

The practical implications for mining operators centre on four concrete performance levers:

• Ore recovery rates
• Energy efficiency
• Equipment uptime
• Regulatory compliance

All four are addressable by layering IMP and Genix onto existing plant infrastructure, without hardware replacement and without building large internal data science teams. The platforms move operations from reactive process management, responding to failures after they occur, toward predictive approaches that anticipate degradation, inefficiency, and compliance risk before they affect production.

The structural argument for how miners outperform the metal over a cycle rests partly on operational leverage, and AI-driven improvements to recovery rates and equipment uptime are becoming a measurable component of that leverage at sites running platforms like IMP and Genix.

For investors tracking automation and AI adoption across Latin American mining, the Minería Digital 2026 presentations represent something specific: deployments already generating operational data at active sites, not projections or pilot results. IMP and Genix are both operational at Chilean mines as of August 2026.

Copper mining equities are increasingly valued not only on resource quality and production costs but on the demonstrated capacity of operators to deploy AI tools that protect throughput and reduce energy intensity across the asset base.

The combination of accessible tooling, technology-agnostic integration, and live reference deployments positions AI adoption in Chilean mining as a structural shift with implications for operators and investors across the sector.

This article is for informational purposes only and should not be considered financial advice. Investors should conduct their own research and consult with financial professionals before making investment decisions.