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Pairing industrial AI autonomy and human control

In partnership with AVEVA Industrial intelligence. Pairing industrial AI autonomy and human control. Living digital twins are helping industrial plants run more like living systems, and setting the stage for industries that work in sync.

与 AVEVA 工业智能合作。将工业人工智能自主性与人类控制相结合。活体数字孪生技术正在帮助工业工厂更像生命系统一样运行，并为实现同步协作的工业奠定基础。

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.

大多数人将工业环境与冰冷的钢铁和发条机械联系在一起。然而，从功能上讲，工厂更像是一个生命体：它有脉搏，甚至有新陈代谢。它会随着时间的推移而磨损，健康状况恶化的迹象往往表现为难以察觉的不规则现象。

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.

数字孪生技术长期以来一直旨在反映这种动态性，并对可能预示更大问题的异常情况进行建模。但早期的实现方案比物理现实慢了几拍；它们无法进行实时分析，也无法主动解决检测到的任何问题。它们更多地关注模拟、观察和假设情景规划，而不是闭环执行。

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.

活体数字孪生不再仅仅是现实的数字镜像，它能够发现异常或建议优化，而是将复杂操作的动态、高保真数字表示与通过操作和控制系统干预物理世界的能力结合起来。人工智能是这一变革的核心。

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.

AVEVA 首席产品官 Rob McGreevy 将较新的、注入人工智能的数字孪生比作生长和进化的有机体。这些“活体”版本是工厂资产和流程的自适应、常青模型。由于它们能跟上实时状况并连接曾经孤立运行的系统，它们提供了一个以前缺失的可见性和编排层，能更好地反映设施本身的有机节奏。

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.

现在，代理式人工智能（Agentic AI）也使工业系统能够超越诊断或建议，实现自主行动。随着技术向更高的自主性发展，它开始形成一种工业稳态的反射机制，通过持续的小幅调整来保持工厂的健康和高效运转。

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.

尽管如此，人类的判断和操作员的监督仍然发挥着关键作用，因为他们首先定义了什么是“健康”的状态，并在人工智能系统出现偏差时发出警报。活体数字孪生为人类工程师提供了监控人工智能行为并确保执行步入正轨的手段。

The technologies that keep digital twins in sync. Living 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.

保持数字孪生同步的技术。活体数字孪生通过数字主线将工程和设计信息与实时操作数据连接起来，即资产在其生命周期内的一条连接记录。一些显著的技术转变帮助串联起了这条主线：传感器（近年来变得更加经济实惠）现在可以从物联网设备和工业控制设备传输遥测数据，而软件平台则将这些信息带入用户友好的界面中。预测模型和基于物理的模型也可以为机器或流程的行为方式提供人工智能增强的见解。

The 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?”

操作员观察数字孪生的视角也随之演变。McGreevy 说：“你可以通过可持续发展的视角来看待数字孪生：我的排放量是多少？或者通过维护的视角：该资产的运行状况是否健康？或者通过性能指标：它生产的小部件数量是否满足我的需求？”

Setting boundaries for semi-autonomous systems. The 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, 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, 65% of executives ranked operational risk, including the potential for business disruption, among their top concerns.

为半自主系统设定界限。下一阶段是让人工智能根据这些视角所揭示的特定见解采取行动。制造商们对这一篇章充满期待：在 2026 年制造业领导委员会对制造商的调查中，66% 的受访者表示他们正在或计划在运营中使用代理式人工智能，40% 的受访者预计到 2030 年将实现一定程度的工厂自主化。与此同时，他们也对潜在风险保持警惕：在德勤 2025 年智能制造调查中，65% 的高管将运营风险（包括业务中断的可能性）列为他们最关心的问题之一。

“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.

关于实现人工智能自主化的持续过程，McGreevy 表示：“还有很多改进工作要做，需要更多的证据点、测试和验证。”在短期内，McGreevy 预计代理式人工智能将遵循其他工业技术（如 SCADA 系统）的发展路径。他说：“SCADA 中的‘S’代表监督，而监督意味着中间有人类参与。”他认为在可预见的未来，人工智能主要以咨询模式工作，完全自主仅限于狭窄、高度重复的任务。

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.

至于人类操作员的角色，日常工作正在演变。曾经专注于单一生产线或维护任务的工人现在可以承担多种学科的工作。他们可以减少逐一批准常规操作的时间和精力，转而更多地关注定义可接受的操作限制。由于人工智能可以强化其接收到的数据模式，尽早发现失误是工作的核心部分。人类在解读人工智能所见所为方面的知识和经验至关重要。

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.

公司和行业层面的治理也需要与时俱进。组织必须建立明确的护栏，规定人工智能可以自主做出哪些决策，以及如何监控和审计其行为。尽管如此，许多组织尚未建立全面的保障措施。