Article [5 min read]
Guiding rules
First, we need to establish some basic principles:
- AI should be seen as a tool to assist industrial teams, not replace them.
- AI makes mistakes. Even if AI is a trillion times smarter than a calculator (which is always right), it still requires careful oversight.
These challenges and risks must be considered when developing or implementing AI in industrial settings, especially in industries that produce mass goods, generate waste, and impact the environment.
Where are we with AI in 2025?
Artificial Intelligence has become a buzzword. I’ve even been advised to remove "AI" from our product description because it often carries the weight of unrealized expectations.
Yes, AI is a powerful technology. It can do incredible things—understanding people (and even animals), writing on any topic, creating images, music, and videos, analyzing data, solving engineering problems, conducting research, coding, and much more. There’s no doubt it’s a groundbreaking achievement.
But has AI actually made a significant impact in industrial fields like manufacturing, raw material extraction, or professional industrial services? While many AI-driven solutions are being implemented or are in development, let’s explore one that has the potential to merge AI with a real industrial use case.
What Matters When Integrating AI into Industrial Environment?
From AI perspective
AI needs high-quality data and well-structured prompts to generate useful results. These are fundamental requirements for any application, not just industrial. If given the right input, AI can provide significant benefits.
From a user perspective
Industrial operations are complex in many aspects. They involve highly technical environments where value is created on the shop floor. People interact with machines, engineering systems, vehicles, tools, and more.
Many production lines are already automated, generating massive amounts of data from sensors, IoT devices, and various inputs. While operational teams use this data effectively, two key challenges remain:
- Different data sources don’t always interact (e.g., machine measurements, production plans, and maintenance records are often separate).
- A critical element is often missing: detailed descriptions of process events, their causes, and consequences.
This is the gap - AI needs data, but industrial teams often have limited, scattered, or incomplete information. And that can be a major roadblock.
A Real-World Example
Imagine a production process suddenly becomes unstable, leading to a high rejection rate. Process engineers can access readings from control devices and may know what operators or maintenance teams have done with the equipment and fix the issue. However, this information is often fragmented and difficult combine and analyze.
A possible solution.
The solution sounds simple - organizations need a system where employees can document and share their experiences. This information would then connect process data, production data, and real-world observations.
But in reality, it’s not that simple. The industrial environment is tough. As was mentioned above, it’s a mix of complex engineering systems, dynamic logistics, and high-risk conditions. Operations involve diverse domains like electricity, mechanical systems, utilities, instrumentation, extreme temperatures, high pressure, motion, and speed. Everyday, something happens that affects normal operations and output.
Yet, capturing detailed event data systematically is rarely a top priority for management teams. The main reason for that is that such system would involve a significant amount of work, while the operations require fixing as quick as possible.
The solution should be practical and beneficial for all stakeholders. It must integrate seamlessly into daily workflows, offering value to workers and managers alike. It should serve as an interface where teams collaborate on troubleshooting, document best practices, and store crucial knowledge about machines and processes.
Most importantly, it should be a tool that helps resolve process issues permanently—improving productivity, quality, and reliability—factors critical to senior management and organizational growth. AI makes this goal achievable for a broader range of companies, regardless of size, structure, or level of engineering expertise.
Another Perspective where AI will help
Automation has transformed entire industries. Many production operations have reduced the number of workers needed by implementing technical improvements. This shift is generally positive, as it eliminates hard, manual, and unsafe tasks, transferring these burdens to robotic systems and advanced technology.
Today, a single operator can manage a vast array of machines and complex process flows—a hallmark of Industry 4.0. But there’s a challenge: increasing technological complexity demands greater specialization and ongoing training. Operators need assistance, and an AI Agent is an ideal solution.
Conclusion [promo]
AI should be seen as a tool that supports industries by assisting workers. It can be implemented through knowledge platforms and interactive interfaces. In-crew is one of them.
