In partnership withAVEVA Industrial intelligencePairing industrial AI autonomy and human controlLiving digital twins are helping industrial plants run more like living systems, and setting the stage for industries that work in sync.Most people associate industrial environments with cold steel and clockwork machinery. Functionally, though, a plant or factory is more like a living thing: It has a pulse, even a metabolism. It goes through wear and tear as it ages, and signs of deteriorating health often show up as barely perceptible irregularities.Digital twin technology has long aimed to mirror that dynamism and model anomalies that may be symptomatic of larger problems.
But earlier implementations were a few beats behind physical reality; they were incapable of real-time analysis and unable to proactively address any issues detected. They focused more on simulation, observation and what-if scenario planning, rather than closed-loop execution. Rather than serving as a digital mirror of reality that can spot anomalies or suggest optimizations, a living digital twin combines a dynamic, high-fidelity digital representation of complex operations with the capacity to intervene in the physical world through operations and control systems.AI is at the center of this change.
Rob McGreevy, chief product officer at AVEVA, likens newer, AI-infused digital twins to organisms that grow and evolve. These “living” versions are adaptive, evergreen models of a plant’s assets and processes. Because they keep pace with live conditions and connect systems that once ran in isolation, they provide a previously missing layer of visibility and orchestration that better reflects the organic rhythms of the facility itself.Now, agentic AI is also enabling industrial systems to move beyond diagnostics or suggestions and act of their own accord.
As the technology progresses toward greater autonomy, it’s starting to develop the reflexes for a kind of industrial homeostasis, in which small, constant adjustments keep a plant or factory healthy and humming.All that said, human judgment and operator oversight still play an instrumental role by defining what healthy looks like to begin with—and flagging when AI systems are falling out of alignment. Living digital twins provide the means for human engineers to monitor what AI is doing and ensure execution is on track.The technologies that keep digital twins in syncLiving digital twins link engineering and design information with real-time operational data through a digital thread, or a connected record of an asset that follows it through its lifecycle. A couple of notable technological shifts have helped string this thread together: Sensors (which have gotten more affordable in recent years) now stream telemetry from IoT devices and industrial control equipment, and software platforms bring that information into user-friendly interfaces.
Predictive and physics-based models can also provide AI-enriched insights into how a machine or process might behave.“You can look at the digital twin through the lens of sustainability: What are my emissions? Or, through the lens of maintenance: Is this asset performing in a healthy way? Or, through a performance metric: Is it producing the number of widgets I need?”Rob McGreevy, Chief Product Officer, AVEVAThe lenses through which operators view digital twins have evolved in turn. “You can look at the digital twin through the lens of sustainability: What are my emissions?” says McGreevy. “Or, through the lens of maintenance: Is this asset performing in a healthy way?
Or, through a performance metric: Is it producing the number of widgets I need?”Setting boundaries for semi-autonomous systemsThe next phase is letting AI act on select insights those lenses illuminate. For their part, manufacturers are eager for this chapter: In a 2026 Manufacturing Leadership Council survey of manufacturers (opens in new tab), 66% said they are using or plan to use agentic AI in their operations, and 40% expect some degree of factory autonomy by 2030. At the same time, they’re wary of the potential risks: In Deloitte’s 2025 smart manufacturing survey (opens in new tab), 65% of executives ranked operational risk, including the potential for business disruption, among their top concerns.Manufacturers are accelerating toward agentic AI and greater factory autonomy0%66 % of manufacturers say they are using or plan to use agentic AI in their operations0%40 % of manufacturers expect some degree of factory autonomy by 2030Source: MIT Technology Review Insights with data from Manufacturing Leadership Council (opens in new tab), 2026“There is a lot more refinement to be done, a lot more proof points, testing, and validation needed,” McGreevy says about the ongoing process of making AI autonomous.
In the near term, McGreevy expects agentic AI to follow the path seen in other industrial technologies like SCADA systems. “The ‘S’ in SCADA means supervisory, and supervisory means there’s a human in the middle,” he says. He sees AI working mostly in that advisory mode for the foreseeable future, with full autonomy limited to narrow, highly repetitive tasks.As for the role human operators play, the day-to-day is evolving. Workers who once specialized in a single process line or maintenance task can now take on multiple disciplines.
They can spend less time and effort approving routine actions one by one, and more on defining acceptable operating limits. Because AI can reinforce patterns in the data it receives, catching missteps early is a core part of the job. Human knowledge and experience in interpreting what AI is seeing and doing is instrumental.Governance on a company- and industry-wide scale, too, needs to adapt to the times.
Organizations must establish clear guardrails for which decisions AI can make on its own and how its actions are monitored and audited. Still, many have yet to set up comprehensive safeguards: In a recent study from AVEVA and IMD (opens in new tab), 43% of organizations said AI has changed decision-making and accountability across ecosystem partners only slightly or not at all, while just 23% reported a significant or fundamental change. (Only 27% reported sharing data substantially or extensively with partners.)In the meantime, regulators are starting to define some of the rules. The EU AI Act classifies certain AI safety components used in critical infrastructure as high-risk and imposes requirements for human oversight.
Companies are setting up their own structures as well; AVEVA, for one, has a responsible AI governance board and a published set of principles for how AI is developed and deployed.Agentic AI and living digital twins: Real-world case studiesInside the semi-autonomous systems already running, that oversight looks different from one deployment to another. In some cases, an operator approves every action; in others, AI acts on its own while people oversee and define the boundaries it can operate within. McGreevy cites several examples of what machine autonomy and human-plus-machine collaboration looks like among AVEVA customers:At Saudi Aramco’s Yanbu refinery, optimization algorithms adjust production in a closed loop, with operators overseeing as needed.
At its Ras Tanura refinery, the algorithms recommend changes and operators decide whether to implement them.Nestlé uses AI optimization at Nesquik plants to tune the industrial dryers used to produce powdered beverage ingredients. In a landmark test, this tuning reduced energy use by 16%, while improving quality and yield.At Thailand’s SCG Chemicals, 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 ninefold return on investment within six months.The National Laboratory of the Rockies in the U.S. has built a generative AI model for grid control rooms to provide oversight at scale.
The model supports operators with renewable energy forecasting and estimates of grid conditions.Looking to the futureThe same synchronization that was once missing from individual plants’ technology systems has also been lacking on a larger scale. “Manufacturing and critical infrastructure have operated as siloed systems for the last 50 years,” says McGreevy. But certain emerging technologies, he suggests, are creating opportunities for more holistic coordination.On an enterprise level, this might involve the integration of physical AI: systems that pair robotics and sensing technologies with AI-powered decision-making. Drones that fly over pipelines and power lines, for instance, can feed visual inspection data to algorithms that spot gas leaks and other hazards.
Not only does this create a more accurate and interconnected flow of information, but it spares humans from needing to physically enter potentially dangerous environments. “The robotics-meets-software convergence will mean fewer incidents, fewer outages, and a more sustainable way of operating,” McGreevy predicts.“The robotics-meets-software convergence will mean fewer incidents, fewer outages, and a more sustainable way of operating.”Rob McGreevy, Chief Product Officer, AVEVAA bigger-picture version extends beyond any one plant or process to entire industrial networks, something McGreevy dubs the “integrated value chain.” “Living digital twins make digital manifestations of the entire value chain possible,” he says. “This allows companies to react quickly to supply chain changes, demand changes, tariffs, and material availability.”Amid today’s realities of turbulent geopolitics and shifting regulatory demands, such resilience will be “an absolute necessity,” adds McGreevy. “Without it, it will be very hard to run an effective business.”