Industrial AI — Where knowledge (management) is power
作者：By Calvin Hennick
Imagine a routine equipment alert on your production line. A seasoned maintenance engineer rushes to the machine, guided by an AI co-pilot – a digital entity armed with every manual, every schematic, every byte of operational data your company possesses. Together, they pull up a 25-step runbook. The AI shines at first, correctly identifying a hard-to-find oiling inlet, saving the engineer precious time.
But then, in a fraction of a second, the system falters. The digitized manual is missing a single, critical detail: the specific grade of industrial grease required. To bridge this gap, the AI – powered by a world-class large language model (LLM) – doesn’t admit what it doesn’t know. Instead, it hallucinates, confidently suggesting WD-40, a “lubricant” it learned about from public internet data. This moment of internal failure is completely invisible; the AI presents its fabricated answer with the same authority as a fact from the manual.
The engineer freezes. He knows WD-40 is a solvent, not the high-pressure grease that’s required. Using it would be disastrous, leading to catastrophic equipment seizure, millions in damages, and a prolonged shutdown. He manually overrides the AI, wondering: what would a junior engineer, trained to trust the system, have done?
This isn’t a hypothetical situation. It’s a failure my team uncovered during early proof-of-concept tests with equipment maintenance manuals for a prospective customer in manufacturing. And it served as a stark warning: the probabilistic guessing of generative AI (GenAI) is fundamentally unsuited for high-stakes industrial operations.
However, there is a solution to this foundational crack in “AI 2.0” and it’s about more than simply better data – it’s about transforming data into verifiable and actionable knowledge.
Probability vs. actuality — The anatomy of an AI failure
Consider the near-miss with the lubricant. That wasn’t a bug. In fact, the LLM did what it was supposed to do – be helpful. These models are masters of correlation, not causation. When faced with a knowledge gap, an LLM doesn’t “know” it’s missing information. Instead, it predicts the most statistically probable next word or phrase based on its training and the context from the manual provided in its prompt. “Lubricant” correlates strongly with “WD-40” in its vast dataset scraped from the web. The model isn’t reasoning; it’s pattern-matching.
For industrial applications, where precision and safety are paramount, this is an unacceptable risk. We cannot build the future of autonomous operations on a foundation of “most probable.” We need a system grounded in actuality – one that not only understands what is in the manual but, critically, recognizes what is not. This means building a system that, when it finds no answer, immediately states, “I don’t have this information,” and escalates the query to a human expert or another designated system.
To do this requires the sophisticated blending of the appropriate AI and data tools into a strategic knowledge management system that exploits the best of LLMs and deterministic, logic-based systems.
Building knowledge management into AI early
The core challenge isn’t a lack of data, but the fact that the data is typically fragmented, disorganized, and unstructured. Industrial enterprises are swimming in diagrams, manuals, and tribal knowledge that machines cannot reliably understand without context. This is where a robust knowledge management strategy becomes the most critical pillar of any serious industrial AI initiative. Before we can achieve reliable autonomy, we must first:
- Make data AI-readable, not just digitized. We need to move beyond simple document ingestion. Tables, scanned diagrams, and color-coded safety manuals are subject to machine misinterpretation. Even the most advanced multimodal models struggle to consistently identify semantic details in complex industrial diagrams. We need the AI to know, not guess, that a specific pump (P-101) is connected to a motor (M-101), requires a specific lubricant (ISO VG 460), and has a maintenance schedule tied to runtime hours. A shared ontology – a knowledge “dictionary” – becomes essential, ensuring every term has one unambiguous meaning, traceable across multiple languages. The AI community often refers to this structured, interconnected knowledge base as a “knowledge graph.” Every table becomes a set of complete statements, every diagram – a structured text file, every chart – its description.
- Incorporate formal reasoning. Once this structured knowledge is in place, the AI can use formal logic, not just statistical probability. If a procedure calls for a lubricant, the AI can query its knowledge base for the exact specification linked to that exact piece of equipment. If the information is missing, it doesn’t guess. It flags the datapoint and its response becomes: “I have identified the lubrication point, but the required grease specification for this component is not in my knowledge base. Please verify from an approved source.” This is a safe, explainable, and trustworthy interaction.
This two-step process forms the basis of a new knowledge management system currently under active development at GlobalLogic, a Hitachi Group Company. And its potential role in the realm of industrial AI couldn’t be timelier. The necessity for this level of factual grounding is most critical in environments where precision is paramount. For instance, in the semiconductor industry, maintaining complex equipment within fabrication plants leaves no room for error. This is a point emphasized by one of our pilot customers, Hitachi High-Tech America, also a Hitachi Group Company, specializing in semiconductor manufacturing equipment, analytical systems, and electron microscopes.
Alexander Zhivotovsky, Associate GM, Metrology, and Analysis Systems Division at Hitachi High-Tech America, Inc., said it best recently, when asked about what aspect of AI is critical in his business. “In maintaining our complex semiconductor metrology systems, there is no room for ambiguity,” he said. “Grounding AI in verifiable facts from our own engineering documents is a fundamental requirement for reliability. We look forward to our collaboration with GlobalLogic to build a system where all guidance is traceable and trustworthy.”
GenAI: The ultimate human-machine interface
Within our industrial knowledge management system, GenAI’s vital role will not be as a decision-maker, but as the ultimate human-machine interface – a universal translator making deep institutional knowledge accessible without sacrificing reliability, as well as the tool to help maintain the structured knowledge. It will excel at bridging the gap between human intuition and machine logic:
- From unstructured to structured: An engineer will be able to upload a grainy photo of a part number, and GenAI’s multimodal capabilities will identify it, find the corresponding entity in the knowledge base, and pull up all associated documentation and operational history.
- From query to action: A technician will be able to ask in natural language, “What’s the standard procedure for replacing the primary bearing on the main conveyor motor?” The GenAI will parse this query, translate it into a formal query for its reasoning engine, and then present the precise, step-by-step procedure in clear, human-readable language.
The path forward
This knowledge-first approach carries another crucial advantage for any CIO: efficiency. By reserving the computationally intensive GenAI for the human interface and relying on a lean, deterministic reasoning engine for core logic, our system becomes significantly more energy efficient. This isn’t just a cost-saving measure; it’s what makes the vision of embedding intelligence directly into the equipment on the factory floor – true edge AI – achievable and scalable.
The next time our engineer from the opening story approaches that equipment, the interaction will be fundamentally different. The AI co-pilot, grounded in a deterministic knowledge base, won’t just provide the procedure; it will state, “The required lubricant is ISO VG 460, as specified in maintenance document #7B-4 for this component.” That junior engineer, now on the job, isn’t faced with a dangerous guess; they are given a verifiable, traceable fact.
This is how we build trust. The journey from a helpful but flawed co-pilot to a truly autonomous operational system isn’t a leap of faith into a black-box algorithm. It’s a deliberate process of building a verifiable knowledge foundation, ensuring every automated decision is one we can stand behind, explain, and trust. The future of industrial AI isn’t just intelligent; it’s intelligible.
For more on GlobalLogic’s approach to AI, check out: https://www.globallogic.com/enterprise-ai/.
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Yuriy Yuzifovich is Chief Technology Officer at GlobalLogic, a Hitachi Group Company. GlobalLogic is a trusted partner in design, data, and digital engineering for the world’s largest and most innovative companies. Since its inception in 2000, it has been at the forefront of the digital revolution, helping to create some of the most widely used digital products and experiences.
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