Why is the adoption of AI in manufacturing stalled?

There is currently a growing need to utilize AI in manufacturing sites.

According to a survey by Global Market Insights, the AI market for manufacturing is estimated to be worth US$ 4.2 billion in 2024 and is expected to reach US$ 60.7 billion by 2034 (*1).

Supporting this growth is the need for automation in operations such as production management, inspection, and inventory management.

* 1Source: Global Market Insights"AI in Manufacturing Market Opportunity, Growth Drivers, Industry Trend Analysis, and Forecast 2025-2034" (Provided by Global Information)
AI Market in Manufacturing | Market Size, Trends, and Forecast 2025-2034

However, in actual practice, the following challenges are hindering the introduction of AI:

・Even if PoC can be done using the cloud, it cannot be installed on the device.

・Heat dissipation and power limitations make it difficult to introduce AI

・There are no AI engineers in the company, so operation and maintenance are not possible

・The cost is not justified in a multi-product, small-lot production environment.

As a result, while expectations for the use of AI are rising in manufacturing sites, the reality is that technological, human, and economic constraints prevent AI from being introduced.

AI is not just about the cloud; it's also about using AI on-site

Traditionally, AI has mainly focused on learning and inference in a cloud environment.

Meanwhile, a new option known as "physical AI" has been gaining attention in recent years.

Physical AI is a technology that recognizes and understands the real world through sensors and actuators, and physically executes the results of AI decisions.

So, what are the differences between physical AI and traditional cloud-based AI? The table below summarizes the differences.

By using physical AI and cloud-based AI depending on the application and environment, it is possible to make the most of the strengths of each.

Physical AI is small and easy to incorporate into the workplace, and because processing is done locally, it has low latency and excellent security. There is little difference in the environment from PoC to mass production, making implementation smooth.

While cloud-based AI can utilize large-scale computing resources, it relies on communications, which can lead to delays and risks of information leaks, and may require restructuring when deployed in production.

What are the areas of AI utilization in manufacturing?

The use of AI in manufacturing is not necessarily introduced to all processes at once. It is more realistic to start with areas where the effects are easy to measure and where it does not pose a major risk to existing processes.

<Specific examples>

・Quality inspection: visual inspection using image recognition, detection of defective products, automatic sorting

・Facility maintenance: Anomaly detection and predictive maintenance using sensors and sound/vibration analysis

・Supply and demand/inventory management: Optimizing production plans, reducing inventory, and improving logistics efficiency

・Worker support: Education through motion analysis, safety confirmation, and transfer of skilled techniques

By starting with areas where we can demonstrate effectiveness without placing a burden on the workplace, we can expand the use of AI in a realistic and steady manner.

The features of physical AI and SiMa.ai, the platform that makes it possible

Physical AI has the advantage of being easy to use in the field, something that conventional cloud-based AI does not have.

• Size: Mainly small modules, allowing direct integration into equipment or on-site
• Real-time performance: Local processing makes it easy to realize decisions and control with low latency
• Ease of implementation: The same environment can be used from development to mass production, allowing for a smooth transition

​SiMa.ai is an actual product that embodies these features.

The company's platform has the following features:

1. High-efficiency AI chip: Achieves approximately 50 trillion calculations per second with power consumption equivalent to 5W
2. High real-time performance and security: No need for cloud communication, resulting in low latency and reduced risk of information leaks
3. Development environment: GUI tools allow drag-and-drop operation, even for non-experts
4. Integrated operation: Providing a comprehensive platform of optimized hardware and software

​SiMa.ai offers practical solutions to the challenges faced when implementing AI in manufacturing.

Summary: "Physical AI" is a new option for manufacturing sites

For manufacturing sites that have been struggling with the "reason for not being able to introduce AI," "physical AI" is gaining attention because it is centered around small modules, has low latency, and enables smooth implementation.

In particular, in areas such as quality inspection and equipment maintenance, the process from PoC to mass production can be smoothly expanded, and the barriers to implementation are low.

SiMa.ai 's highly efficient AI chips and GUI development environment make on-site AI a reality. As your first step in introducing AI to your manufacturing industry, why not consider the new option of physical AI?

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