Industrial AI is moving toward greater autonomy, but current deployments still rely on human oversight, defined operating limits and ongoing validation. AVEVA Chief Product Officer Rob McGreevy says AI is most likely to remain advisory in the short term, with full autonomy reserved for narrow, repetitive tasks. Examples from refineries, factories, chemical plants and power-grid research show how agentic AI, digital twins and human operators are already working together across industrial systems.
“There’s a lot more work to do, more proof points, more testing, more validation,” McGreevy said of the industry’s progress toward AI autonomy. In the short term, he expects agentic AI to follow a path similar to other industrial technologies, including SCADA systems.
“The ‘S’ in SCADA stands for surveillance, and surveillance means there is a human in between,” he said. McGreevy expects AI to operate primarily in an advisory role for now, while fully autonomous operation remains limited to narrow and highly repetitive tasks.
Human oversight remains central to industrial AI
The role of human operators is evolving as AI takes on more routine monitoring and decision support. Workers who once focused on a single process line or maintenance task can increasingly cover multiple areas. Instead of approving routine actions one at a time, they can spend more time defining acceptable operating limits.
AI can identify patterns in the data it receives, making early error detection a central part of its role. Human knowledge and experience remain important for helping AI interpret what it observes and determine how to respond.
Governance at both the enterprise and industry-wide levels must also adapt. Organizations need clear guardrails defining which decisions AI can make independently, as well as systems for monitoring and auditing its actions.
However, comprehensive safety measures are not yet in place at many organizations. According to research from AVEVA and IMD, 43% of organizations said AI had slightly changed or had not changed decision-making and accountability across ecosystem partners. Only 23% reported significant or fundamental changes. Just 27% said they share data substantially or extensively with partners.
Regulators are beginning to establish requirements. EU AI legislation classifies certain AI safety components used in critical infrastructure as high risk and imposes human-oversight obligations. Companies are also developing internal frameworks. AVEVA, for example, has a Responsible AI Governance Committee that has published principles for developing and deploying AI.
Agentic AI and living digital twins in industrial systems
Monitoring practices vary among semi-autonomous systems already in operation. In some deployments, operators approve every action. In others, AI operates independently while humans monitor performance and define the boundaries within which it can act.
McGreevy highlighted several examples among AVEVA customers:
- At Saudi Aramco’s Yanbu refinery, optimization algorithms adjust production in a closed loop and are monitored by operators as needed. At the Ras Tanura refinery, algorithms recommend changes, while operators decide whether to implement them.
- Nestlé is using AI optimization at its Nesquik factory to adjust industrial dryers used to make powdered beverage ingredients. In testing, the adjustment reduced energy use by 16% and improved quality and yield.
- At SCG Chemicals in Thailand, an AI-supported digital twin spanning engineering, operations and maintenance monitors asset health and flags potential failures. The company reports 99% plant reliability and a nine-fold return on investment within six months.
- Rocky Mountain National Laboratory in the US has built a generative AI model for grid control rooms that provides large-scale monitoring. The model supports operators with renewable-energy forecasting and estimates of grid conditions.
Connecting previously siloed industrial systems
The lack of coordination that once affected the technical systems within individual plants also exists at a larger scale. “Manufacturing and critical infrastructure have operated as siled systems for the past 50 years,” McGreevy said. He argues that emerging technologies are creating opportunities for more comprehensive coordination.
At the enterprise level, this could involve integrating physical AI: systems that combine robotics and sensing technology with AI-powered decision-making. Drones flying over pipelines and power lines, for example, can provide visual inspection data to algorithms that identify gas leaks and other hazards.
Such systems could create a more accurate and interconnected flow of information while reducing the need for people to enter potentially dangerous environments. “The convergence of robotics and software will lead to fewer incidents and outages, and a more sustainable way of operating,” McGreevy predicted.
“The convergence of robotics and software will lead to fewer incidents and outages, and a more sustainable way of operating.”
Rob McGreevy, Chief Product Officer, AVEVA
Living digital twins and the integrated value chain
The broader vision extends beyond an individual plant or process to an entire industrial network, which McGreevy calls an “integrated value chain.”
“A living digital twin enables a digital representation of the entire value chain,” he said. “This allows companies to quickly respond to changes in supply chains, changes in demand, tariffs, and material availability.”
In an environment shaped by geopolitical turbulence and changing regulatory demands, McGreevy said this resilience is “absolutely necessary.” “Without that, it becomes very difficult to run an effective business,” he added.
Source: www.technologyreview.com