At the Fifth China Original Economics Forum and Economists & Entrepreneurs Summit held on September 6, Wang Junxuan, head of a Hong Kong family office, participated in a roundtable discussion themed "Finding New Growth Drivers for Enterprises in the Context of New Quality Productive Forces." He highlighted three core bottlenecks hindering AI transformation for traditional industries and small and medium-sized enterprises (SMEs).

First, severe data silos prevent tacit industry knowledge and the hands-on experience of veteran workers from being converted into standardized, trainable data. Second, a talent gap exists, with a shortage of professionals who combine industry-specific operational expertise with AI technical skills. Third, the cost of trial and error is prohibitively high; traditional manufacturing profit margins are only 3% to 5%, while AI projects require substantial investment with payback periods exceeding three years, making transformation costs unaffordable for most SMEs.

Wang emphasized that general-purpose large models struggle to meet the refined needs of specific industries, while developing customized models for niche sectors remains costly and is only feasible for leading large corporations. This creates a paradoxical industry dilemma: "The SMEs that need AI empowerment the most are the least able to afford it." He argued that this problem is fundamentally not a technological shortfall, but rather an imbalance in macro-level profit distribution and financial resource allocation, which leaves SMEs in the real economy without sufficient funding support and constrains the pace of their intelligent transformation.

Wang pointed out that the core vitality of innovation is concentrated in SMEs and the tail-end of the market. He stressed that solving the AI transformation challenges for SMEs through mechanism optimization, resource decentralization, and technology democratization is the key to the comprehensive implementation of new quality productive forces.

Furthermore, Wang noted that in the AI era, the core assets are no longer traditional fixed assets like factories and equipment, but rather structured industry data and replicable operational expertise. SMEs can deepen their focus within their own niche tracks, transforming years of accumulated tacit industry experience and operational logic into standardized datasets to build proprietary models for their specific fields, thereby creating core barriers that large corporations find difficult to replicate.

Wang believes that SMEs need not follow large enterprises in deploying full-chain, full-scenario intelligence or pursuing universal, all-purpose systems. Instead, they should concentrate on the specific scenarios they have deeply cultivated, relying on lightweight, customized small models to achieve precise empowerment and maximum efficiency gains. In niche tracks and vertical fields where industry giants do not reach, they can capture market gaps through refined data, professional services, and intelligent capabilities, forming differentiated competitive advantages.

He emphasized that in the era of new quality productive forces, the path to breakthroughs for SMEs lies not in scale competition, but in niche focus, data accumulation, and targeted intelligence. By leveraging the precision advantages of vertical tracks, SMEs can achieve corner overtaking and sustainable growth.