从质检到出海增长 制造业 AI 开始走向行业深处

This article outlines the current development trend of AI in the manufacturing sector as it enters a deepening phase of maturity, and summarizes new industry models and applications. Key takeaways are as follows:

1. AI in manufacturing today is not synonymous with large language models. Most industrial value comes from the integration of mature algorithms, computer vision, industrial software and business processes. Enterprises of different sizes follow distinct implementation paths: large enterprises can develop their own infrastructure in-house, while small and medium-sized enterprises (SMEs) can adopt mature SaaS tools and external services. Currently, the most scarce resource in the industry is industry-specific judgment capabilities, rather than general-purpose AI tools.

2. A new division of labor has formed in the sector: platforms provide foundational capabilities, while industrial software manages process data. A new "OPC+" service model has also emerged, centered on small expert teams that combine industry experience with AI tools to deliver customized solutions for specific business problems.

3. AI has expanded from internal factory operations to external marketing, with mature implementations already driving growth for cross-border industrial goods enterprises, helping them secure higher-quality overseas orders.

This article sorts out new AI application trends and marketing growth opportunities for manufacturing brands, especially those operating cross-border businesses. Key insights are as follows:

1. Overseas B2B purchasing behavior has changed: surveys show 45% of buyers use generative AI to collect information about suppliers and products before purchasing, while 69% require human verification of AI-generated content. Buyers now prioritize verifiable, specific information such as model compatibility, parameter testing conditions, market certifications and delivery capabilities, and reject vague general quality claims.

2. Brands can choose an AI implementation path based on their scale: large brands can build their own data infrastructure and develop in-house models, while smaller brands can adopt mature SaaS solutions and external professional services, starting with specific problems to lower entry barriers.

3. Cross-border brands can leverage AI-powered growth services via the OPC+ model to align with buyer demand, organize verifiable product information to reduce information asymmetry, continuously monitor brand visibility and accuracy in AI-driven environments, and optimize based on inquiry signals to achieve high-quality overseas growth.

This article summarizes market changes, AI implementation paths and new growth opportunities for manufacturing sellers in the AI era. Key takeaways are as follows:

1. The market has shifted: AI has become part of the supplier discovery stage in overseas B2B purchasing, with nearly half of buyers collecting supplier information via AI. Traditional marketing logic no longer fits the AI-driven environment, so sellers need to adjust their customer acquisition strategies to capture new opportunities.

2. Small and medium-sized sellers do not need to invest heavily in developing proprietary AI models. They can leverage the mature capabilities of existing platforms and industrial software, and choose professional AI services under the OPC+ model starting from specific problems. This approach cuts the cost of communication and trial-and-error, and eliminates the need to build technical infrastructure from scratch.

3. Key risk note: AI-generated content should never be used directly as commercial or engineering conclusions, and all critical information requires human review. AI has expanded into marketing growth, and cross-border industrial goods sellers can leverage the AI-plus-expert service model to continuously optimize marketing performance and achieve stable growth.

This article sorts out AI-driven digitalization paths, production-side implementation cases and new business opportunities for manufacturing factories. Key insights are as follows:

1. AI already has mature production-side applications: for example, Haier's Kaosi COSMOPlat platform applies computer vision and deep learning to surface defect detection for rolled materials, covering copper foil, aluminum foil, photovoltaic materials and other scenarios. It supports the full process of defect identification, recording, analysis and traceability, and factories can adopt such mature solutions to improve quality inspection efficiency.

2. Factories of different sizes can choose different digitalization paths: large factories can build data infrastructure and develop proprietary AI models to推进 full-process intelligent transformation, while small and medium-sized factories can adopt mature SaaS tools and external professional services, starting with specific pain points to lower the barrier to AI adoption.

3. AI has brought new business opportunities to factories, expanding from production to overseas marketing. Factories looking to expand into overseas markets can leverage the AI-plus-expert OPC+ service to solve cross-border customer acquisition pain points, secure more high-quality orders, and achieve cross-border growth.

This article summarizes industry development trends, customer pain points and viable new business models for AI service providers in the manufacturing sector. Key takeaways are as follows:

1. AI in manufacturing has now entered a deepening phase of maturity. Competition has shifted from showcasing models and platforms to the ability to understand industry specifics, integrate into business processes and verify implementation outcomes. As AI tools become increasingly generalized, the most scarce resource is business-aligned industry judgment. The market needs a lightweight connection layer to convert mature technologies into implementable solutions for enterprises.

2. The core pain point for customers today is that SMEs lack the capability to assess problems: they do not know which problems to prioritize, how to verify AI outputs, or how to integrate AI into existing business processes.

3. Service providers can build out the new OPC+ service model: centered on small expert teams, the model combines mature AI tools to provide in-depth diagnosis, customized implementation and continuous operation for specific problems in niche industries. Core competitiveness comes from the multiplication of industry experience and AI tool capability, and providers can also enter the new blue ocean market of AI-driven growth for cross-border industrial goods.

This article summarizes changing industry division of labor, market demand and risk directions for AI-focused manufacturing platforms. Key insights are as follows:

1. A clear new division of labor has formed for AI in manufacturing. The core positioning of platforms is to build reusable foundational AI capabilities: for example, in quality inspection scenarios, platforms can integrate algorithms, optical imaging and industry knowledge to output standardized industrial capabilities, leveraging their own scale advantages. Platforms do not need to cross over to replace the roles of industrial software providers or small professional service teams.

2. The market now has clear tiered demand: large enterprises need foundational capabilities such as data infrastructure from platforms, while SMEs need lightweight, business-aligned solutions. Platforms can partner with industrial software companies and OPC+ service teams to output their own foundational capabilities, cover market demand across different tiers, and expand their business boundaries.

3. Key risk to avoid: platforms do not need to blindly compete on large model parameters and computing scale. The core of AI in manufacturing is to solve real business problems, so platforms should focus on building reusable foundational capabilities, collaborate with different players along the industrial chain, and avoid the high-cost trap of developing the entire value chain in-house.

This article sorts out the latest industrial developments in AI adoption in China's manufacturing sector, proposes a new business model, and outlines clear new directions for industrial research. Key points are as follows:

1. The industry has entered a new development stage. Competition in manufacturing AI has shifted from early competition over models and platforms to a deep-water phase focused on "understanding the industry, integrating into processes, and verifying outcomes", and value assessment has shifted from technology stacking to solving real problems. According to data from the *White Paper on AI Application Scenarios in China's Manufacturing Industry (2026)*, 62.3% of implemented AI cases are in production and manufacturing scenarios, while generative AI accounts for only 4.8%. This confirms that the value of manufacturing AI primarily comes from integrating mature technologies with business operations, and large models are not the only source of value.

2. A new OPC+ business model has emerged in the industry: this new model features small expert teams combining mature AI tools to deliver deeply customized services for niche industry problems. Characterized by lean teams, low costs, industry experience as core competitiveness, and continuous operational optimization, it fills the demand gap for AI implementation among SMEs and serves as a new supplement to the industrial service chain.

3. AI applications have expanded from internal factory operations to external marketing, giving rise to a new growth model for cross-border industrial goods that combines AI and expert services. It replaces the traditional one-off marketing delivery model with a continuously optimizable growth process, making it a new industrial direction worthy of research.