How ABB Harnesses the Power of AI for Industrial Automation

Industries are increasingly turning to AI to modernise their automation systems while maintaining operational continuity.

ABB's Automation Extended programme represents a shift towards AI-integrated industrial ecosystems that could reshape how mining and other sectors approach digital transformation.

The programme builds on existing distributed control systems and introduces advanced analytics, AI and Internet of Things (IoT) integration. According to ABB, the approach allows technologies to be adopted at customers' pace without operational risk, preserving system integrity while enabling the flexibility needed for modern industrial operations.

Peter Terwiesch, President of ABB's Automation business area, says: "In industries we serve – many operating large and complex infrastructures that deliver essential resources – our customers rely on modernisation without disruption.

"Automation Extended delivers exactly that: bringing future-ready capabilities into the systems they know and trust, with security and interoperability at the core."

AI addressing workforce challenges

The integration of AI in industrial automation comes as operations face multiple pressures, including volatile markets, cybersecurity challenges, regulatory demands and a rapidly-changing workforce. AI technologies could directly address the industry's needs to retain, share and augment workforce knowledge.

ABB suggests the tool addresses these realities by enabling innovation without disruption to production, supporting advanced analytics and IoT integration, and simplifying operations for diverse skill levels.

The system is designed to pave the road to autonomy through AI technologies while providing operators with contextualised information and support to ensure effectiveness and plant productivity.

The programme aims to fuel the energy transition and enable remote operations. According to ABB's Mining's Moment survey, 77% of mining leaders see integrated electrification, automation and digitalisation as key enablers of sustainable transformation in the industry.

Machine learning for decision support

The architecture separates control and digital environments to enable AI deployment without compromising operational stability. The control environment provides a software-defined domain that ensures robust and reliable control for critical processes.

The digital environment connects securely to the control layer, enabling advanced applications, edge intelligence and real-time analytics.

This digital layer uses AI and machine learning for decision support without disturbing control structures. The separation allows AI to enhance operations while maintaining the integrity of critical systems that industries depend upon.

A unified automation service approach manages and maintains these diverse technological environments throughout their lifecycle. The ecosystem integrates technologies, including an Open Platform Communications Unified Architecture backbone and cloud-native architecture for managing both environments.

Proactive intelligence and optimisation

The AI-enabled ecosystem could deliver several operational enhancements. These capabilities include proactively detecting and correcting process anomalies before they impact production, optimising maintenance strategies through continuous condition monitoring of critical assets and elevating engineering with efficient modular approaches ready for deployment across diverse hardware platforms.

For the mining industry specifically, the programme helps connect systems and data across silos, enabling interoperability of solutions from mine to port. This integration allows artificial intelligence to analyse data across entire operations rather than isolated systems.

The approach reflects broader industry trends towards AI adoption in industrial settings. Mining companies are exploring how AI can enhance resilience and growth in safety, productivity and sustainability while addressing the challenge of knowledge transfer as workforces evolve.

By enabling progressive introduction of AI capabilities into existing systems, programmes like Automation Extended could allow industries to adopt AI at a pace that matches their operational requirements and risk tolerance. This measured approach to AI integration may prove critical for sectors where operational continuity is essential.