Putting Generative AI to Work in the Plant

In the past, we’ve framed Industrial AI as a comprehensive toolbox filled with specialized instruments. We argued that generative AI, for all its power, is the newest tool in this box, not a replacement for the entire workshop. Now, it’s time to examine that new tool more closely. To truly leverage its potential, we must move beyond the generalized hype and understand its specific strengths and weaknesses in the demanding industrial environment.

From our research and conversations with manufacturers worldwide, two primary high-impact applications have emerged for generative AI. The first is its role as a new type of user interface. The second is its ability to unlock knowledge from all the unstructured data saturating every factory.

AI as a universal translator

Perhaps the most immediate and profound impact of generative AI in industry is its function as a "generative user interface" or "Gen UI." For decades, interacting with complex industrial software and data systems required specialized training. Engineers needed to learn specific query languages to pull data from a historian; operators had to navigate complex, menu-driven screens on a human-machine interface (HMI); maintenance staff had to know exactly where to find a specific manual in a labyrinthine document management system.

The Gen UI provides a conversational, natural language layer that sits between the human user and these complex backend systems. It acts as a universal translator, radically lowering the barrier to entry for accessing critical information.

The pro: radical accessibility. With a Gen UI, a process engineer can simply instruct: "Show me pressure and temperature trends 4 during the last production run of Product XYZ and flag any anomalies." A junior maintenance technician can ask their handheld device, "Walk me through the standard lockout-tagout procedure for the main conveyor belt motor." Democratization of data and knowledge is a paradigm shift, empowering a much broader range of employees to make faster, better-informed decisions.

The con: the persuasive lie. Large language models (LLMs) are designed for fluency and are masters of probability, not truth. They can "hallucinate"—producing an answer that is grammatically perfect, highly confident and completely wrong. In a consumer setting, this is an annoyance. In a factory, a confidently delivered but incorrect answer about a safety procedure, an asset's operating limit or a chemical mixture could be catastrophic.

The solution: grounding in reality. A Gen UI cannot be deployed in an industrial setting without being strictly "grounded" in a company's own factual data. Using a technique called retrieval-augmented generation (RAG), the system is architected so the LLM does not invent answers. Instead, it first retrieves verified information from trusted enterprise sources—a data historian, a maintenance database or an approved document library. Then, the LLM's role is limited to translating the user's question, understanding the retrieved facts, and formatting the correct answer in natural language. This grounding in a factual data architecture, like an industrial data fabric, is the essential safety rail that makes the Gen UI viable for industry. Even then, LLM is not 100% accurate. Language nuances can be misinterpreted and lead to inaccurate responses in the process.

Taming the document tsunami

The second game-changing application for GenAI is taming the document tsunami. Our research shows that for many enterprises, as much as 80% of their data is "unstructured"—locked away in formats that are difficult for traditional analytics to parse. Factories run on this data: PDF operating manuals, P&ID schematics, environmental compliance reports, maintenance work orders, and operator logbooks. For decades, the immense knowledge trapped in these documents has been largely inaccessible at scale.

Pro: unlocking trapped knowledge. LLMs are uniquely suited to ingest, index, and understand this massive corpus of text. This unlocks decades of invaluable, hard-won operational knowledge. For the first time, organizations can ask complex questions across their entire document library: "Analyze all maintenance comments from the last five years and identify the most common precursor to failure." Or, "Does our current operating procedure comply with the environmental regulations outlined in this 200-page permit?"

Con: the governance nightmare. How do you guarantee the AI is referencing the latest approved engineering drawing and not an obsolete draft? The system's knowledge base must be rigorously managed to prevent outdated information from causing errors or safety incidents.

Using public LLM APIs could mean sending sensitive, proprietary operational data or product information to a third-party cloud. For most industrial companies, this is a non-starter. The solution requires deploying models within a private, secure cloud or on-premise environment.

Also, not every employee should see every document. The GenAI system must be integrated with existing enterprise access controls to ensure that users can only get answers from data they are authorized to view.

Generative AI is not a magic bullet, but it is a profoundly valuable addition to the Industrial AI toolbox. Its true power today is as an interface that makes other systems easier to use, and as a processor for unlocking the value of unstructured text. When implemented thoughtfully, with the guardrails of grounding and governance, it bridges the gap between complex systems and human ingenuity.