Livestream Recap: Where Is the Next Opportunity for Industrial AI?

Visual inspection, quality control, equipment maintenance, process optimization... more and more AI capabilities are beginning to enter production lines, directly tied to industrial metrics such as yield, OEE (Overall Equipment Effectiveness), and production capacity.

In this new episode of the QuantumBit talk show, we invited Gu Xin, Head of Sales and Ecosystem Success at Siemens Xcelerator China, and Huang Yao, founder and CEO of AQROSE Technology, to walk through the entire chain of industrial AI from implementation to scale-up, starting from real cases.

If you missed the livestream, feel free to follow this recap and relive the show.

Huang Yao opened with a real case from the semiconductor industry.

Wafers need to be cut into individual chips or chiplets, but at the micro/nano scale, incoming material variations, surface textures, and model changes all affect equipment performance. On top of that, different customers have different requirements, so engineers often need to step in during equipment operation to handle alerts, downtime, and parameter adjustments.

"If recognition is inaccurate and the wafer gets cut badly, the loss is enormous," Huang Yao said.

In the end, what they looked at was not how many more points the model scored.

Huang Yao anchored the measurement standard on a very industrial metric: OEE, or Overall Equipment Effectiveness.

In the case Huang Yao described, after introducing agent technology, equipment OEE and production capacity could increase by about 25%. In his own words:

It can make the equipment more capable and do more work.

Gu Xin then made this distinction clearer: "Its most critical point is that it must have industrial-grade reliability."

But on the industrial floor, a wrong judgment will affect production efficiency.

Rather, it needs to be trustworthy, traceable, and quantifiable.

Recognition must be accurate, judgment must be stable, problems must be traceable, and ultimately you must be able to calculate exactly how much value was created.

But once these metrics were actually achieved, a new problem quickly emerged: can a method that works in one factory still hold in another?

"Industrial applications are too fragmented," Huang Yao said bluntly.

In his view, industry looks like a huge market, but once you really break it down, it is sliced into pieces by different categories, scenarios, and processes.

Even within the same company, equipment, data, processes, and standards may differ across factories.

This is also why industrial AI finds it hard to copy the internet's scaling path.

Internet data can be collected centrally, but industrial data is often scarce, hard to obtain, and involves process and trade secrets; when a model reaches another site, it must be re-adapted to new equipment, parameters, and systems.

Gu Xin agreed that industry is not a naturally fully standardized world. Even different factories of the same company may differ.

And in real implementation, the problems extend further down.

Algorithms are only one layer.

Below are equipment, data, and computing power; above, it must connect to processes, control systems, business workflows, and delivery systems.

So a project running successfully only proves that the solution works in one specific environment.

When it truly reaches the scale-up stage, what must be replicated is actually a whole methodology for entering industrial sites.

For industrial AI to go from 1 to 100, the test is no longer just model generalization capability, but whether a highly non-standard system can gradually be turned into a product that can be delivered repeatedly.

And when the question shifts from "can the technology be used" to "how to deliver repeatedly," the boundaries of a single company begin to show.

The industry urgently needs a platform that can step up and bring the players together.

On the show, Gu Xin expressed his expectations for Siemens Xcelerator:

We hope to be a good connector, a good amplifier.

A connector connects good technology to real customer needs and application scenarios; an amplifier, once a solution has been validated, continues to bring in integration, delivery, and more ecosystem resources.

The key here is not for the platform to do everything for companies, but to organize the parts that a single company finds hard to accomplish alone.

The collaboration between Siemens' Chengdu factory and AQROSE Technology is one example.

The factory already has a relatively high digital foundation, but certain high-value inspection steps still needed further optimization, so the platform connected the scenario and requirements with AQROSE's industrial AI vision capabilities, and validated them through PoC (proof of concept) co-creation.

If the validation holds, the next step is no longer just a matter of one factory.

Who will find more similar customers? Who will do system integration? Who will be responsible for final delivery?

These questions, which originally tended to fall on a single company, need more roles to take on together at the scale-up stage.

Next, what to look for is who can help find business opportunities, who can do system integration, and who can complete final delivery.

"0 to 1 proves it can be implemented in the industry; 1 to 100 is what makes it a commercial success," Gu Xin said.

In his view, this is also the position that platforms and ecosystems truly need to fill.

Technology companies continue to deepen their products, the platform is responsible for connecting scenarios, customers, partners, and delivery resources, and then through repeated real-world validation, gradually amplifies single-point capabilities into replicable industrial capabilities.

The replication path is gradually being figured out, but with so many industrial scenarios, who will actually break out first?

Huang Yao's judgment on this question is clear: "In the first half, high-value applications will definitely rise first; in the second half, it will be a hundred flowers blooming."

He summarized these three conditions into an intersection:

High value, data ready, technically feasible.

In industry, the most typical batch is single-point high-value applications built around yield, process, and OEE.

In high-end manufacturing industries such as semiconductors and PCBs, a change of one or two percentage points can correspond to very considerable profits.

Gu Xin also mentioned that if the OEE of semiconductor equipment can be improved by a few points, for customers it means "enormous economic value."

It is not likely to sweep the market instantly with one hit feature; it is more likely to grow first from those scenarios where "the accounts are calculated most clearly."

From a single wafer, to a factory, and then to more factories.

In the full episode, there are more specific cases, judgments, and clashes of views worth watching.