Industrial manufacturing is undoubtedly a strong economic catalyst in both the United States and China. Artificial Intelligence represents an opportunity to not only revolutionize the industrial manufacturing space, but drive economic transformation and growth as well.
While both countries have integrated AI capabilities into their industrial manufacturing operations, the United States currently holds a slight edge over in China, despite a near equal number of installed bases of AI-enabled end devices in each market. Primarily, the U.S. is more incentivized to develop approaches and strategies that encourage greater adoption of AI in industrial manufacturing.
Different Paths Through the AI Journey
In the United States, labor has become increasingly expensive, creating an imperative to lower production costs while maintaining — or even enhancing — production. This has resulted in a boost to the ecosystem of AI in industrial applications, with several types of vendors developing partnerships to bring AI into the industrial fold: cloud service providers, smart manufacturing platform vendors, and chipset vendors among them. Domestically, optimizing operational efficiency, reducing bottlenecks in production and limiting factory resource consumption are key goals centered around AI in manufacturing.
The investments poured into AI and all its adjacencies, such as 5G and robotics, will create new opportunities for companies that focus on AI in industrial manufacturing.
Leveling the Playing Field
In China, although adoption is strong and remains prevalent, it is mostly driven by the Chinese government. The Made in China 2025 initiative, designed to drive China toward digital transformation within the industrial manufacturing sector, have sparked companies such as Alibaba, Baidu and Huawei to prioritize AI from a strategic standpoint. However, despite government-applied pressure, small-and medium-sized companies are less inclined to adopt AI into their operations — labor remains cheap and in abundance in China, so these operators still find performing back-end tasks manually to be more economically viable.
Therefore, building a pure-play industrial AI vendor ecosystem in China remains a challenge. Many startups are focused on AI as a connectivity layer, rather than as part of a larger digital transformation strategy. Cloud-based companies like Alibaba often lack the right connections and go-to-market channel to reach many provincial and municipal manufacturers where the majority of small- and medium-size manufacturers reside, and are still relying on existing relationships with legacy solution providers and local system integrators, in addition to cheap labor to support back-end operations.
Leveling the Playing Field
High-labor costs and a quicker time-to-market have prompted U.S. manufacturers to be more aggressive with the adoption of industrial AI solutions. This has paved the way for the development of pure-play AI players in the U.S., which keeps the U.S. as the global leader in industrial AI solutions. Over time, however, we at ABI Research expect China to catch up. The investments poured into AI and all its adjacencies, such as 5G and robotics, will create new opportunities for companies that focus on AI in industrial manufacturing, set to place China on a more level playing field.
Implementing AI in the manufacturing sector is still in a nascent stage. In order to fully enjoy the benefits of AI, manufacturers must look at their production processes, organization make-up, and human resources. Internal buy-in from senior management must be the first step before implementing any AI strategy, regardless of the market.
Lian Jye Su, Principal Analyst at ABI Research, is responsible for orchestrating research relating to robotics, artificial intelligence, and machine learning. He leads research in emerging and key trends in these industries, deep-diving into advancements in key components, regional dynamics in robotics and AI adoptions, and their future impacts and implications. Prior to joining ABI Research, Lian Jye worked in several healthcare organizations, in both the technical and business domain. He held various roles in quality management, operation reviews, and market research analysis.