Why do many industrial AI projects fail?
Artificial intelligence is advancing rapidly. More and more manufacturing companies are exploring intelligent assistance systems, predictive maintenance, automated quality assessment, and data-driven process optimization.
Often, the initial focus is on the AI technology itself: Which platform is suitable? Which model delivers the best results? Which solution offers the greatest added value? However, the crucial prerequisite is frequently overlooked. Artificial intelligence can analyze millions of data points and identify patterns. Whether these insights are reliable, however, depends on whether the underlying data is complete, consistent, and trustworthy.
That is why many industrial AI projects fail not because of the algorithms, but because of an inadequate data foundation. A successful AI strategy begins with the question: Can artificial intelligence even trust the existing production data?
What data does artificial intelligence really need?
Many companies are already generating enormous amounts of data. Machines continuously provide measurement data, ERP systems manage production orders, and quality data is collected from a wide variety of sources.
However, large amounts of data alone are not enough. Above all, Artificial Intelligence requires data that is reliable, complete, and interconnected. Only then can correlations be identified and reliable recommendations for action be derived.
It is not the volume of data that determines the success of Industrial AI, but its quality.
Why the production context is more important than the volume of data
A single measurement value has only limited informative value. Amachine temperature or a cycle time may seem unambiguous at first glance. However, a complete picture only emerges when viewed in the context of additional information.
Key factors include, for example:
- the production order
- the manufactured product
- the materials used
- the active machine parameters
- the results of the quality inspection
- the production output
It is only through these connections that artificial intelligence can recognize patterns and derive reliable recommendations from them. The difference between raw data and valuable information therefore lies in the context.
Industrial AI does not learn from individual numbers, but rather from the relationships between processes, events, and production conditions.
Only Production Context Makes Data Valuable
- Production order
- Material and batch information
- Machine parameters
- Quality data
- Traceability
- Production events
How does an MES lay the foundation for Industrial AI?
A Manufacturing Execution System is not an artificial intelligence platform. However, it creates precisely the data foundation that modern AI applications rely on.
An MES integrates information from production, machines, quality, traceability, and detailed planning into a single database. This creates a complete, real-time view of the shop floor, enabling both employees and AI applications to make informed decisions. Instead of isolated data silos, consistent information is available to all stakeholders.
At the same time, AI applications receive the necessary production context to correctly interpret relationships and derive well-informed recommendations.
A modern MES thus forms the foundation for numerous industrial AI applications—from predictive analytics tointelligent assistance systems such as MAIK and the continuous optimization of production processes. This makes it clear: Artificial Intelligence does not replace the MES. It builds on a high-quality, networked database provided by the MES.
An MES Connects, Among Other Things:
- Production orders
- Machines and equipment
- Materials and components
- Process parameters
- Quality inspections
- Performance data
- Traceability information
How are companies preparing for Industrial AI?
Before companies implement artificial intelligence, it’s worth taking a critical look at their own data infrastructure.
Some helpful questions to ask include:
- Can we trust our production data?
- Do all departments work with the same information base?
- Can production processes be fully traced?
- Is our data linked across systems?
- Is information available in a timely manner to support decision-making?
If weaknesses are identified here, the data foundation should be improved first.
In practice, a step-by-step approach has proven effective.
As part of the “Böhme & Weihs MES-Walk” potential workshop, we work together to analyze existing processes, identify data disconnects, and develop a roadmap for a robust data foundation. This lays the groundwork for Industrial AI—without placing an unnecessary burden on production.
