Why does manufacturing AI get harder the more data there is?

Many manufacturing enterprises are talking about AI.
But there is a very interesting phenomenon:
the more data a factory has, the harder AI sometimes becomes.

Why?
Because between equipment data and the data AI truly needs, there is actually a long distance.

For example, a factory has a large amount of PLC data, sensor data, and MES data.
It looks like it has everything.
But when you actually take this data to train models, you quickly discover:

What does this variable actually represent?
Do different equipment data mean the same thing?
Are the units the same?
Are the sampling frequencies the same?
Can the timestamps be aligned?
Is this data point a normal state or an abnormal state?
If it is abnormal, what caused it?

Many times, the problem is not that AI cannot compute,
but rather:

that even humans have not yet explained this data clearly.

01The Most Easily Overlooked Step in Industrial AI: Data Production

So there is one particularly easily overlooked step in industrial AI:
not model training, but data production.

Collecting equipment data, cleaning out anomalies and dirty data, unifying data standards, aligning data from different times and different equipment, then segmenting and labelling it in combination with actual operating conditions, and finally forming a dataset that can truly be used for model training.

This process may not sound as "sexy" as large models, but on the industrial site, it may be precisely the most fundamental and also the most difficult step.
Because internet data is largely text, images, and video.
Industrial data is different.
Behind it often corresponds to:

equipment, operating conditions, processes, time, faults, and human experience.

Without any one of these contexts, the value of the data may be completely different.

02What Is Truly Lacking Is Not a Bigger Model, but High-Quality Data

So what manufacturing AI truly lacks, in many cases, is not a bigger model, but a batch of truly high-quality industrial data.

Models can become stronger and stronger, but if the data fed into them is itself chaotic,the final results may still be unreliable.

So if a factory is truly preparing to do AI,it is advisable to first ask one question:

Is our equipment data actually ready now?

Once this question is thought through, many things behind it – models, agents – truly begin to make sense.

03Turning Raw Data into Data AI Can Truly Use

Recently, there has also been ongoing attention and practice on this matter:
how to turn raw data from the equipment site, step by step, into data that AI can truly use.

Perhaps,this is the first step for many manufacturing enterprises moving toward AI.