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Building a safer path to autonomous industrial AI

Sponsored In partnership with AVEVA Industrial AI is entering a new phase. After decades of predictive analytics and other specialized applications, advances in foundation models, physical AI, and agentic AI are making it possible to automate more complex tasks across industrial environments. But unlike AI that operates purely in the digital world, industrial AI can interact directly with physical systems, where an unexpected decision can have consequences for safety, reliability, and critical infrastructure. That makes responsible deployment central to the next wave of industrial automation.

工业人工智能正在进入一个新阶段。经过数十年的预测分析和其他专业应用，基础模型、物理人工智能和代理人工智能的进步使得在工业环境中自动化处理更复杂的任务成为可能。但与纯粹在数字世界中运行的人工智能不同，工业人工智能可以直接与物理系统交互，而意料之外的决策可能会对安全、可靠性和关键基础设施产生影响。这使得负责任的部署成为下一波工业自动化的核心。

“How do we leverage these technologies while maintaining safety, while maintaining reliable operations, while still being able to deliver on the promises of the new capabilities?” asks Arti Garg, chief technologist at AVEVA. The challenge is particularly acute as newer AI systems become more capable but also harder to predict and explain. One foundation for making that transition work is data. Industrial systems often contain information across telemetry, service logs, engineering documents, and other disparate sources. Newer technologies can help connect and correlate that information more quickly, giving operators real-time support when diagnosing problems.

AVEVA 首席技术专家 Arti Garg 问道：“我们如何在保持安全、维持可靠运营的同时，利用这些技术并实现新功能所带来的承诺？”随着更新的人工智能系统变得功能更强大，但也更难预测和解释，这一挑战显得尤为突出。实现这一转变的基础之一是数据。工业系统通常包含来自遥测、服务日志、工程文档和其他不同来源的信息。新技术可以帮助更快地连接和关联这些信息，为操作员诊断问题时提供实时支持。

AI-powered robots could take that a step further by gathering information in hazardous environments without requiring workers to enter them. But greater autonomy also requires new approaches to governance. AVEVA’s framework for responsible AI emphasizes security, efficiency, and human safety and oversight. Garg argues that AI should augment rather than replace people in critical decision loops, with guardrails determining where automated systems can act and where human supervisors remain responsible.

人工智能驱动的机器人可以更进一步，在危险环境中收集信息，而无需工人进入。但更高的自主性也需要新的治理方法。AVEVA 的负责任人工智能框架强调安全性、效率以及人类的安全与监督。Garg 认为，人工智能应该在关键决策环节中辅助而非取代人类，并通过护栏来确定自动化系统可以在何处采取行动，以及人类主管在何处仍需承担责任。

Sustainability is another part of that equation. AI can help manage complex power systems as renewable generation grows, while organizations also need better ways to understand AI’s own environmental footprint. Garg is involved in an IEEE working group developing a standard methodology for measuring that impact across electricity, energy, resources, water, and carbon. The next phase could bring industrial AI further into the physical world, from autonomous robots and drones to AI-assisted coding that allows domain experts to build new applications.

可持续性是这一等式的另一部分。随着可再生能源发电的增长，人工智能可以帮助管理复杂的电力系统，同时组织也需要更好的方法来了解人工智能自身的环境足迹。Garg 参与了一个 IEEE 工作组，该工作组正在开发一种标准方法，用于衡量电力、能源、资源、水和碳方面的相关影响。下一阶段可能会将工业人工智能进一步带入物理世界，从自主机器人和无人机，到允许领域专家构建新应用程序的人工智能辅助编码。

But realizing that potential will require more than deploying new technology, says Garg. Organizations will need to rethink business processes, establish appropriate safeguards, and give experienced workers new ways to apply their expertise, creating a model of automation that is not only more autonomous, but safer, more efficient, and more sustainable. “Autonomous systems, whether they’re robots or drones, are really going to change the way that we work in plants, in power systems, on mining sites,” says Garg. “In a way, that will make these types of operations more efficient, much safer for the human beings involved and more productive.”

但 Garg 表示，实现这一潜力需要的不仅仅是部署新技术。组织将需要重新思考业务流程，建立适当的保障措施，并为经验丰富的工人提供应用其专业知识的新方法，从而创造一种不仅更自主，而且更安全、更高效、更可持续的自动化模式。Garg 说：“自主系统，无论是机器人还是无人机，都将真正改变我们在工厂、电力系统和矿区的工作方式。在某种程度上，这将使这些类型的运营更高效，对相关人员来说更安全，生产力也更高。”