Li Jiansi, a Practitioner of AI Adoption in Manufacturing: Why the Lean and Fragmented Nature of Small and Medium Enterprises Is an Advantage for AI Agents
Drawing on his experience driving AI adoption on the front lines of manufacturing, Li Jiansi argues that small and medium enterprises (SMEs), which lack white-collar staff, can use AI agents to directly fill functional gaps, gaining management capabilities faster than large corporations. Additionally, their moderate volume of unstructured data eliminates the need for expensive information restructuring.
When discussing corporate AI adoption, common prerequisites include having sufficient data, a complete IT architecture, and a professional team. In a column published by TechOrange, Li Jiansi takes a different approach. He recounts his experience promoting AI adoption on the front lines of manufacturing and observes a structural paradox: the long-standing shortage of white-collar support staff and management know-how in SMEs, along with their moderately sized and fragmented data, actually become advantages for using AI.
The first mechanism behind this argument lies in organizational structure. Many small and medium enterprises in Taiwan possess world-class factories and are hidden champions in their respective fields, but their management and support staff structures are highly streamlined—market analysis, legal affairs, human resources, and capital strategy are often concentrated among a few executives, sometimes with no one responsible at all. This approach worked reasonably well in a relatively stable globalized environment, but geopolitical economics and technology are undergoing drastic changes. World-class factories require world-class enterprise management. The traditional way to address this is to hire personnel and establish departments, bearing fixed costs over the long term; yet many companies cannot move up precisely because they lack these resources, creating a chicken-and-egg dilemma.
In the AI era, being lean may instead become an advantage. Compared to large enterprises with complete departmental divisions and responsibilities, SMEs have fewer burdens from existing staffing structures, making it easier for AI agents to directly fill functions that were previously understaffed. Li Jiansi gives an example: a factory that originally had no market research personnel can begin to track changes in its clients' industries and evaluate business opportunities it previously did not have time to study. With this support, executives also have more bandwidth to think further ahead for the company. The core judgment here is that SMEs can acquire previously lacking capabilities faster than large enterprises.