Read the e-book<\/a><\/div>\n<\/div>\n\n\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t<\/div>\n<\/div>\n<\/div>\n\n\n\nWhat agentic AI for plant operations means\u00a0in practice<\/h2>\n\n\n\n
\n In process manufacturing, agentic AI cannot mean black-box autonomy. Plants run on physics, safety standards, and regulatory requirements that do not bend. In practice, agentic AI means human-agent teams: systems that observe, reason, and recommend\u2014and in some cases initiate workflow steps\u2014with the right approvals and guardrails in place. \n<\/p>\n\n\n\n
\n- Bring operational and engineering context together<\/strong>: Connect OT data, engineering documents, maintenance history, alarms, work orders, and shift logs so teams can see what is happening in the right plant context. <\/li>\n\n\n\n
- Guide decisions within real plant constraints<\/strong>: Recommendations need to reflect safety, quality, process limits, and operating conditions so they can be trusted in production. <\/li>\n\n\n\n
- Help the right teams act faster<\/strong>: The value is not just insight\u2014it is helping operations, maintenance, reliability, and quality teams move on the next step with less delay and less guesswork. <\/li>\n\n\n\n
- Capture what the best operators already know<\/strong>: Over time, agentic systems should help turn tribal knowledge into repeatable practices that scale across shifts and sites. <\/li>\n<\/ul>\n\n\n\n
\n This matters most in high-pressure moments\u2014such as troubleshooting, abnormal situation management, reliability planning, outage recovery, energy optimization, and quality response\u2014where minutes matter and context is often scattered across systems. In plant operations, AI only matters when it helps teams act faster, safer, and with more confidence. \n<\/p>\n\n\n\n
What manufacturing leaders are prioritizing in agentic AI<\/h2>\n\n\n\n
That is why the conversation with manufacturers is changing. At Hannover Messe<\/a> and ManuChem, leaders were no longer asking for definitions of generative AI. They were asking a harder question: where can this create measurable value in a live operating environment? Across those discussions, six market signals came through consistently. <\/p>\n\n\n\n\n- Focus on outcomes and guardrails.<\/strong> The strongest conversations were not about abstract AI potential. They were about measurable operational value and a governance model where humans remain accountable for judgment, escalation, and approvals. <\/li>\n\n\n\n
- Start with operational friction, not architecture.<\/strong> The best use cases begin with real pain points\u2014lost time finding the right drawing, inconsistent troubleshooting across shifts, slow outage recovery, or low trust in legacy system data. Technology matters, but the entry point should be a business problem that teams already feel. <\/li>\n\n\n\n
- Brownfield reality is the default.<\/strong> Most process manufacturers are not starting from scratch. Winning approaches respect legacy systems, edge constraints, and the need to modernize without stopping the plant\u2014making a flexible operating model across edge, cloud, and AI critical. <\/li>\n\n\n\n
- Trust is the adoption lever.<\/strong> In industrial environments, trust goes beyond compliance\u2014teams need identity, access control, auditability, lineage, and explainability. If a recommendation cannot be traced and bounded, it will not be used in production. <\/li>\n\n\n\n
- Design for scale across shifts and sites.<\/strong> The most valuable scenarios are often not the flashiest demos\u2014they are the ones that reduce variability, capture institutional knowledge, and help teams work more consistently from one shift, line, or site to the next. <\/li>\n\n\n\n
- Sovereignty and ontologies are rising priorities.<\/strong> Customers increasingly care where data resides, who can access it, and how policy applies across environments. At the same time, semantic models and ontologies are becoming critical for connecting operational, engineering, and business context\u2014enabling AI to reason more effectively. <\/li>\n<\/ol>\n\n\n\n
\n One story that captures these themes well is the work Yara and Kongsberg Digital have done together. \n<\/p>\n\n\n\n
Real-world agentic AI: How Yara and Kongsberg Digital built an operational intelligence layer<\/h2>\n\n\n\n
\n Yara\u2019s Porsgrunn fertilizer plant offers a good example of what this looks like in practice. The challenge was familiar: critical plant data existed, but it was spread across systems and difficult to use in real time. Engineering documents, 3D models, maintenance history, and live operational information were not coming together fast enough to support troubleshooting and day-to-day decision-making. The result was slower response times and more field work than necessary. \n<\/p>\n\n\n\n
\n Working with Kongsberg Digital, Yara built an Azure-based operational digital twin experience<\/a> that brought these sources together into a usable operating environment. That matters because, in process manufacturing, better decisions rarely come from more data alone\u2014they come from putting the right context in front of the right people at the right moment. The goal is not more intelligence in the system\u2014it is better decisions on the plant floor. Once that foundation is in place, copilots and agents can do something practical: help teams understand situations faster, guide the next action, and reduce the time lost moving between disconnected tools. \n<\/p>\n\n\n\n\n The business impact is what matters\u2014and Yara\u2019s results are a strong signal of what becomes possible when you remove operational friction: \n<\/p>\n\n\n\n
\n- Up to 70% faster shutdown recovery<\/strong> by consolidating operations onto a unified, actionable platform. <\/li>\n\n\n\n
- 60% fewer field trips<\/strong> through improved remote visibility and diagnostics. <\/li>\n\n\n\n
- 50% efficiency gains<\/strong> in targeted engineering tasks\u2014helping teams resolve issues faster and operate with greater precision. <\/li>\n<\/ul>\n\n\n\n
\nAccess to the right data at the right time is how we define success, and our digital twin gets us there. We\u2019re building something that can scale safely and deliver value over time.<\/em><\/p>\nRoar Nilsen, Program Manager, Digital Engineering, Yara<\/em> <\/cite><\/blockquote>\n\n\n\n\n The broader lesson is important. Before AI can guide work in a plant, teams need a trusted operational foundation that connects data, documents, assets, and process knowledge in the flow of work. Yara\u2019s results show what happens when that foundation is in place: operational knowledge becomes easier to access, decisions become faster, and expertise scales more effectively across the organization. For manufacturers asking what to do next, the implication is straightforward. \n<\/p>\n\n\n\n
A pragmatic playbook for agentic plant operations <\/h2>\n\n\n\n
\n For teams asking where to begin, the next step is not to copy every emerging use case\u2014it is to build the conditions that allow a few high-value use cases to succeed, earn trust, and scale. In other words, the opportunity is not to experiment more\u2014it is to operationalize what works. In my view, that starts with six practical moves. \n<\/p>\n\n\n\n
\n- Build the context layer.<\/strong> Unify operational, engineering, and maintenance knowledge so systems can answer basic but essential questions about asset state, process context, constraints, and history. <\/li>\n\n\n\n
- Use ontologies where they add business meaning.<\/strong> Unified data alone is not enough. Manufacturers increasingly need semantic models that connect systems, assets, documents, and work patterns in ways AI can understand. <\/li>\n\n\n\n
- Prioritize a high-friction use case.<\/strong> Start where value is clearly being lost\u2014such as troubleshooting, shutdown planning, work preparation, reliability triage, or quality response\u2014and pilot with the expectation that successful patterns will scale. <\/li>\n\n\n\n
- Design the guardrails early.<\/strong> Be explicit about permissions, approvals, explainability, and which actions an agent can recommend versus execute. <\/li>\n\n\n\n
- Embed intelligence in the flow of work.<\/strong> The goal is not to force people into another portal\u2014it is to support productivity and decision-making where teams already operate. <\/li>\n\n\n\n
- Scale through repeatable playbooks.<\/strong> Capture what the best teams do, standardize it across shifts and sites, and improve it through feedback over time. <\/li>\n<\/ol>\n\n\n\n
\n The north star is not how many models are deployed\u2014it is fewer hours lost to data hunting, faster recovery after disruptions, shorter resolution times, fewer unnecessary site visits, lower variability across shifts, and a safer environment where teams can focus on judgment instead of searching for information. \n<\/p>\n\n\n\n
What comes next\u00a0for manufacturers<\/h2>\n\n\n\n
\n Across customer conversations this season, one point has become clear: manufacturers will not realize the full value of AI through pilots alone. The real opportunity is to build an operating model where intelligence is trusted, governed, and embedded in the workflows that keep plants safe, efficient, and resilient. \n<\/p>\n\n\n\n
If you are exploring agentic AI for plant operations<\/a>, start where value is being lost. Choose a workflow, unify the operational context around it, establish the right guardrails in place, and scale what works. That is how process manufacturers move from experimentation to execution\u2014and from dashboards to decision-making. <\/p>\n\n\n\n