The Hardest Part of Industrial AI Is Not "AI" – It's "Industry"

AI in factories never faces just a large model.
It faces a pile of real-world problems:
equipment, sensors, MES, ERP, process parameters, and the experience inside master technicians' heads.
If any one link fails to connect, AI may end up being nothing more than a "chatbot that looks clever."

For example, a factory wants to do equipment predictive maintenance.
Many people's first reaction is:
Give the equipment data to AI and let it predict failures.
It sounds simple.
But when you actually do it, you discover:
Where does the equipment data come from?
Do different equipment models have the same data format?
How do you correlate production data from MES?
Are there historical maintenance records?
Does AI know how master technicians judge failures?

In the end, you will find:

The model is just the beginning. The truly difficult part is fitting AI into the factory's existing operating system.

This is also why manufacturing may need FDEs even more than internet companies.
Because most of an internet company's data is already digitalised.
But manufacturing is not.
A lot of critical knowledge
is hidden in equipment,
hidden in processes,
and even hidden in the head of a master technician who has worked for twenty years.

01What Industrial FDEs Really Need to Do

So what industrial FDEs really need to do
is not simply deploy an AI.
Rather, it is to reconnect these things:
equipment data,
business systems,
process knowledge,
human experience,
and AI.
To form an intelligent system that can truly work.

At this point, you will find:

The hardest part of industrial AI is actually not "AI" – it's "industry."AI is just the final layer of intelligence.What truly determines the outcome is how well you understand this industry, this equipment, and this production line.

02The Future Competition of Industrial AI

So the future competition of industrial AI
may not be about whose large model has more parameters.
Rather, it is about who can go deep into 1,000 factories
and truly solve 1,000 on-site problems.
Because every problem solved
deposits another layer of industry experience.
What is ultimately formed
is not a model.
But an ever-deepening body of industrial know-how.

This is also where FDEs are most valuable:

They do not bring AI into the factory – they bring the factory's problems back to AI.The former is implementation.The latter is true product evolution.

So there is a trend worth watching:
in the future, industrial software companies
may increasingly resemble AI companies;
and AI companies
will increasingly resemble industrial software companies.
Whoever can understand both AI and the real shop floor
may hold the ticket to the next round of industrial intelligence.

But there is an even bigger question here:

If FDEs have to start from scratch every time they enter a factory, can this model ever scale?

In the next issue, the discussion continues:
The biggest trap for FDEs – why they easily become "premium outsourcing."