Production lines have long been among the earliest adopters of AI, for example in quality control and predictive maintenance. Now, generative AI is opening up far broader opportunities to improve industrial processes. We began boosting industrial AI in 2018, and we’ve never before seen so many possibilities to raise overall productivity.

Industrial agents bring AI into workers’ hands

Agents are autonomous applications that are capable of learning, supporting human workers. They know how to combine data, the operational context, and natural language effectively. Industrial agents can, for instance, help operators respond to malfunctions, support communication between shifts, or facilitate planning of maintenance activities.

Tracing root causes of production problems using AI‑enhanced analysis is a good example of an industrial agent at work on the factory level. Identifying the causes of defects or component failures is labor‑intensive and slow, often relying on employees’ manual analysis. AI can interpret process instructions and draw conclusions from multiple data sources to figure out what went wrong. Industrial maintenance agents can detect problems proactively, even issues that people might overlook. Operators can react quickly as soon as problems surface, or ideally even before. As a result, downtime is both shorter and less frequent.

Another example comes from transport infrastructure. Inspecting critical components, such as switches or relays, has traditionally been a time-consuming and manual process. AI agent–based solutions can automate inspections by imaging and analyzing the components, enabling proactive identification of maintenance needs. This improves safety, allows maintenance to be scheduled optimally, and reduces downtime. At the same time, tasks that used to take weeks can now be completed in just a few days.

Industrial agents can also make use of unstructured data, such as PDFs from different sources, emails, maintenance logs and training materials. Previously, the data was hard to leverage, but now it has become useful: AI can aggregate scattered data into actionable process instructions. These can provide users with precise answers in their own language, regardless of the original language of the source material.

Generative AI + machine learning =  flexible power steering

In machine learning (ML), an AI model is trained to solve a specific problem, such as optimizing maintenance for a paper machine. The model handles that trained problem fairly reliably, but struggles with new challenges. Even a modest change in business rules may require retraining the model, making experimental business development difficult.

Generative AI (GenAI), best known through language models, works in the opposite way. Its base training gives it tools to understand language and reasoning, but it does not handle mathematical problems. As a predictor for paper‑machine maintenance, it would be quite unreliable.

The key lies in combining them – you need both. An agent supported by generative AI can understand natural language, so it can interpret organizational process instructions, maintenance‑scheduling requests, fault reports, and other ambiguous data sources. Using that information, it can act like an organizer for a machine learning model that, fed with GenAI‑enhanced input, adapts more flexibly to changes in working methods. At best, GenAI enables a process engineer to ask the model questions in plain language – questions that previously would have required a dedicated data‑science specialist.

When generative AI and machine learning are combined, you create power steering that anticipates change and adapts quickly to organizational needs. For example, in one case we combined GenAI‑interpreted process manuals with maintenance logs and machine usage diaries processed by a machine learning model.

On this foundation we built an AI agent. With its support, workers’ problem‑solving capabilities improved markedly: the AI assistant could explain, in natural language and in Finnish, the causes of downtime and their downstream effects.

High‑quality factory data is a must

In its simplest form, industrial AI can be a black box, for example a device bought for a production line that uses computer vision to detect manufacturing defects. But a broader AI targeting process‑level improvements is usually custom‑built for a factory. It relies on a holistic view of the factory’s operations, beyond just a single process step or machine. The core of that change is collecting high‑quality factory data.

Without unified and reliable data, AI agents cannot operate effectively. This is not only a technical requirement, it’s a competitive advantage. High quality data enables real‑time process optimization, automated responses to anomalies, operators’ situational awareness, and much more.

Bringing factory data together usually requires some kind of data platform. It typically aggregates information about production loads, shift scheduling – and of course the actual operations. Often the setup involves SCADA systems, OPC integrations, or even machine learning models acting as so‑called soft sensors. The integrated data platform can support control‑room reporting, AI agents, and even enterprise‑level production optimization.

At the core of successful AI enhancements lies better data. When data is unified, real‑time, and integrated, agents have many ways to boost Overall Equipment Effectiveness (OEE). With high‑quality data as foundation, AI improvements can scale swiftly. That matters, because AI innovation thrives on experimentation: the best results come from testing ideas and bringing successful experiments into production.

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