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Since the start of this year, artificial intelligence (AI) technology has been iterating at an accelerated pace, speeding up its empowerment of the manufacturing sector. A recent survey of multiple small and medium-sized enterprises revealed that AI still faces several practical difficulties in moving from the laboratory to the factory floor, which require attention from all parties and targeted solutions.
The security hurdle. For a long time, industrial production has relied on closed internal networks, operating independently with controllable risks, ensuring the safe and orderly operation of production chains. Deep AI deployment requires breaking down equipment barriers and promoting interconnection between internal and external networks to achieve data interoperability. As industrial systems shift from closed to open, security risks such as virus intrusion and data leaks increase significantly, causing concerns for many enterprises. In reality, the current industrial digital security system still needs improvement, small and medium-sized enterprises have weak prevention and control capabilities, and emergency response mechanisms are not sufficiently robust. Security must be embedded throughout the entire process of intelligent transformation, strengthening the industrial digital security barrier to safeguard AI's entry into the workshop.
The cost hurdle. The threshold for intelligent transformation is relatively high. Whether upgrading production equipment or building proprietary large models, substantial capital and talent costs are required. Large enterprises have abundant resources and obvious advantages, allowing them to seize the initiative in transformation. However, the vast number of small and medium-sized enterprises, facing market fluctuations and unstable orders, place greater emphasis on input-output efficiency. High transformation costs and uncertain return cycles make many SMEs hesitant, resulting in a structural imbalance in industrial AI applications that is difficult to fully popularize in a short time.
The technology hurdle. Industrial production standards are complex and processes are relatively precise, placing extremely high demands on the stability, adaptability, and verifiability of AI systems. Currently, large model technology iterates rapidly with continuously shortening update cycles. Some industrial models and intelligent systems that enterprises have invested in developing face technological updates and iterations before they are even fully deployed, sometimes resulting in situations where they become obsolete upon completion, causing idle resources and duplicated investment. Additionally, some industrial AI systems have algorithm black boxes, making results difficult to verify and technical stability insufficient, leading enterprises to hesitate in replacing traditional processes, which to some extent constrains the pace of digital transformation.
When technology thrives, manufacturing thrives; when digital capabilities are strong, the real economy is strong. AI should not be a decorative gimmick but an important lever for empowering industrial upgrading. The value of AI technology must ultimately be demonstrated on the factory floor. Only by leaving the laboratory and integrating into production scenarios can it truly generate value. Promoting deep integration of AI and manufacturing requires maintaining a results-oriented approach, continuously improving the industrial digital security system, refining long-term technology adaptation mechanisms, promoting lightweight transformation solutions, and persistently removing implementation bottlenecks and addressing development shortcomings, so that industrial AI becomes safer, more stable, and more practical, empowering the transformation and upgrading of manufacturing through digitalization and intelligentization, and injecting strong momentum into building a modern industrial system.