ABB participated as a Gold Sponsor at ARC Advisory Group’s 24th Annual ARC Industry Forum in Bengaluru, titled How AI Is Driving the Future of Industrial Operations and Supply Chain, held on July 9–10, 2026. At the Forum, Rajesh Ramachandran, Global Chief Digital Officer, Automation, ABB, presented The Next Industrial Frontier: AI-Driven Autonomous Operations.
Ramachandran’s presentation can be viewed on YouTube or here:
Why Industries are Moving Toward Industrial AI and Autonomous Operations
Industrial organizations are facing increasing complexity in their operations, growing volumes of industrial data, and pressure to address workforce skills gaps, downtime and reliability, energy efficiency and sustainability. These challenges are increasing the need for more intelligent, adaptive and increasingly autonomous ways of operating.
Autonomous operations are emerging as the next stage of industrial digitalization. Rather than simply generating insights, AI-enabled systems can assist operators, execute defined workflows, and, within appropriate operational and safety boundaries, optimize processes with limited human intervention.
Within industrial operations, the priorities for adopting autonomy differ by industry. Mining companies may prioritize worker safety, marine operators may seek better fuel efficiency, and manufacturers may focus on productivity, asset reliability, and operational efficiency.
Autonomous operations are therefore not an end in themselves. They are intended to help industrial organizations achieve specific operational and business outcomes.
Moving from Automation to Autonomy
Traditional industrial automation senses conditions, analyzes information, and acts according to predefined instructions. It performs effectively when operating conditions and required responses are already known.
Autonomous systems must go further. They need to perceive their environment, understand changing conditions, and respond to unexpected situations that may not have been explicitly programmed.
However, industrial organizations cannot move directly from automation to complete autonomy. ABB describes this transition through different levels, beginning with systems that operate entirely under human control and progressing toward systems capable of largely autonomous operation.
Ramachandran identified Level 4 autonomy as the practical objective for many industrial applications. At this level, the system is mostly in control, while humans continue to supervise operations and intervene when required. Full autonomy, where humans are absent from the operational process, would also require appropriate legal, ethical, safety, and governance frameworks.
From Systems of Insight to Systems of Action
Industrial software has traditionally served as a system of insight, helping users understand equipment conditions, production performance, and operational events.
Generative AI has introduced a layer of assistive intelligence. Industrial copilots help users review equipment information, investigate problems, respond to service requests, and summarize operational activities.
These copilots are now evolving into AI agents. Instead of only communicating insights to users, agents can interact with machines, software applications, and industrial processes to execute defined workflows.
Organizations can initially require human approval before an agent takes action. Over time, selected lower-risk workflows can move toward greater automation, with the level of autonomy determined by operational risk, process integrity, and safety requirements.
Building the Foundation for Industrial AI
Collecting industrial data is no longer the only major challenge. Organizations must also make that data understandable within its operational context.
Industrial data may need to be interpreted according to an asset hierarchy, production process, operating condition, safety requirement, energy target, or financial objective. This contextual intelligence is essential when AI systems are expected to recommend or execute actions.
ABB presents the progression toward autonomous operations through six stages:
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Connect: Gather industrial data from assets and operations.
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Contextualize: Add industrial knowledge and operational context.
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Predict: Use analytical AI to anticipate issues and performance changes.
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Assist: Use generative AI to provide relevant insights.
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Act: Use agentic AI to execute defined workflows.
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Autonomous: Enable self-optimizing operations.
This approach moves organizations from data to insight, from insight to action, and eventually toward autonomous operations.
ABB’s approach progresses from connecting and contextualizing industrial data to predictive, assistive, agentic, and self-optimizing operations
Industrial AI in Practice
Ramachandran highlighted several examples of how these capabilities are being applied.
In asset performance management, AI copilots can provide maintenance engineers, operations personnel, reliability engineers, and asset strategy teams with relevant information without requiring them to navigate multiple applications. The copilots can summarize equipment conditions, support root-cause analysis, recommend actions, and help transfer information between shifts and operational roles.
ABB is also combining digital twins with conversational and agentic AI. In one example, a digital twin helps a maintenance or service engineer investigate a potential gas analyzer leak, review operating information, and access relevant instructions based on ABB’s service knowledge.
Industrial AI can also support technicians directly on the shop floor. A technician can scan the QR code on an industrial device to retrieve real-time information and connect with an AI-based service assistant. According to ABB, its generative AI-powered device management approach can resolve up to 80 percent of technical support issues, with remote visual assistance supporting many of the remaining cases.
These capabilities can reduce equipment downtime, improve the availability of critical assets, limit unnecessary travel by specialist personnel, and support safer and more sustainable operations.
Keeping Humans in the Loop
The move toward autonomous operations does not necessarily mean removing people from industrial environments. Instead, it changes the role humans play.
AI can perform repetitive analysis, retrieve information, coordinate workflows, and execute approved lower-risk actions. Operators and engineers can focus on supervision, exception management, operational improvement, and decisions that require experience and accountability.
The appropriate level of autonomy will depend on the industrial process, operating conditions, safety requirements, and the level of human oversight required. Organizations that establish a contextualized data foundation and clearly define where AI can assist, act, or optimize will be better positioned to progress from automation toward autonomous operations.
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