{"id":24927,"date":"2026-10-28T14:05:14","date_gmt":"2026-10-28T14:05:14","guid":{"rendered":"https:\/\/news678.top\/?p=24927"},"modified":"2026-10-28T14:05:14","modified_gmt":"2026-10-28T14:05:14","slug":"pairing-industrial-ai-autonomy-and-human-control","status":"publish","type":"post","link":"https:\/\/news678.top\/?p=24927","title":{"rendered":"Pairing industrial AI autonomy and human\u00a0control"},"content":{"rendered":"

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\u201cThere is a lot more refinement to be done, a lot more proof points, testing, and validation needed,\u201d 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. \u201cThe \u2018S\u2019 in SCADA means supervisory, and supervisory means there\u2019s a human in the middle,\u201d he says. He sees AI working mostly in that advisory mode for the foreseeable future, with full autonomy limited to narrow, highly repetitive tasks.<\/p>\n

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.<\/p>\n

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)<\/span>, 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.)<\/p>\n

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.<\/p>\n

Agentic<\/span> <\/span>AI<\/span> <\/span>and<\/span> <\/span>living<\/span> <\/span>digital<\/span> <\/span>twins:<\/span> <\/span>Real-world<\/span> <\/span>case<\/span> <\/span>studies<\/span><\/span><\/h2>\n<\/p>\n

Inside 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:<\/p>\n