Cengiz Özemli
Academic
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🏭 The AI Storm in Manufacturing: Expectations and Realities
The wind of artificial intelligence (AI) is blowing through the manufacturing sector. Recent advancements in speech, language, code generation, and content creation have increased interest and demand for AI/Machine Learning (ML) applications.
Engineers, factory operators, and technology leaders are keenly watching how AI can improve quality, reduce rework rates, and boost efficiency. However, despite significant investments, many organizations struggle to translate AI demonstrations into business value. It has become clear that deploying these systems in manufacturing environments is far more complex than adding a chat interface or connecting a language model to existing data sources.
⏳ Lessons from the First AI Wave
More than a decade ago, the first major wave of industrial data science and machine learning (DS/ML) spurred Industry 4.0 initiatives. Initial expectations were very high. Significant investments were made in data and connectivity platforms to build the data layer. Descriptive analytics, exploratory data analysis, and predictive analytics projects spread rapidly.
However, when DS/ML was first applied to industrial datasets, a large percentage of projects failed to achieve operational value. The primary reason was that first-generation algorithms were designed primarily for probabilistic internet behaviors, consumer interaction, advertising, and recommendation systems, not for deterministic industrial environments requiring safety, physical validity, and repeatable results.
The current wave of AI projects could follow a similar trajectory, and the first signs are already emerging. In industrial manufacturing, Generative AI (GenAI), co-pilots, foundational models, and industrial agents are simplifying some aspects of automation and expanding access to advanced capabilities. However, their adoption resembles early ML, but on a much larger scale.
While modern AI excels at language, summarization, and generating plausible responses, factories require real-time validation, process physics, safety guarantees, and regulatory compliance.
Manufacturers are increasingly asking, "We have all the data; now tell us what to do."
🌉 Automation Intelligence: Bridging the Gap
Understanding how AI can be successfully implemented requires grasping the technology itself, its relationship to the broader field of data science, and the lessons learned from the first wave of Industry 4.0. These lessons, combined with current AI tools, formed the foundation of automation intelligence—a technically grounded framework that enhances success when applied to challenging industrial problems.
Contemporary advancements in AI are largely driven by large language models (LLMs). LLMs learn statistical patterns in language to predict what comes next in a sequence. Combined with advancements in computational power, they can generate highly coherent and contextually relevant responses. However, in many industrial settings, these systems act more like advanced search and synthesis engines unless properly grounded. Practitioners must understand these limitations to appropriately position AI applications with realistic expectations.
AI is prone to hallucination, a common term used to describe incorrect or erroneous responses. This limitation should remind practitioners of the first wave of industrial DS/ML, where successful applications emerged by introducing engineering constraints, domain rules, and methods to adapt algorithm outputs.
Automation intelligence bridges this gap. By applying engineering-derived constraints to the inputs and outputs of AI, it ensures that actions derived from AI output can be effectively implemented into today's industrial systems. This approach creates immediate value while preparing organizations to move their current AI applications to the next frontier of vision-language-action (VLA) models.
🚀 Automation Intelligence in Practice
Consider a simple example: We ask an AI, "How fast is the car going?" The answer will almost certainly be a speed estimate or a method to calculate speed, and it is unlikely to return an irrelevant quantity. Even this output is valuable: it can reduce commissioning time, narrow down root cause investigations, and aid in training new operators.
However, a fundamental understanding of AI reveals that the response is generated from learned language patterns rather than direct awareness of the physical system. While automation intelligence is not part of existing off-the-shelf AI techniques, it can be combined with current AI methods to provide essential contextual constraints to improve reliability. Examples include:
[]Speed limits: A moving car typically operates within a range near a defined limit.
[]Distance to the car ahead: Assuming the car ahead adheres to the speed limit, changes in following distance constrain our possible speed.
- Physical vehicle limits: Speed is limited by the mechanical limits of the vehicle.
These rules represent process context and provide constraints for AI. While output validation remains important, post-processing approaches like agent-based workflows come into play afterward and come with increased computational load. Automation intelligence instead applies engineering constraints to AI, allowing it to act as a disciplined layer integrated with industrial control systems.
💡 Unlocking the Potential of Industrial AI
Automation intelligence can accelerate AI adoption across various sectors, including food and beverage, automotive and tire, semiconductor, oil and gas, packaged consumer goods, and pharmaceuticals. Common applications in these verticals include discrete and continuous processes such as drying, chemical synthesis, assembly, extrusion, packaging, rolling/winding, purification, and mixing.
While many of these processes have been optimized for decades, AI creates new opportunities for value. Automation intelligence accelerates the path to industrial value while also increasing deployment success.
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