Economic Observer: Nearly Half of 'AI+' Scenarios Stuck at Pilot Stage, Blocked by Cost-Capability Mismatch
Economic Observer author Xie Ruolin points out that two State Council executive meetings within four months have addressed artificial intelligence, yet nearly half of 'AI+' application scenarios remain at the pilot stage. She identifies the core difficulty as a mismatch between deployment costs and application capabilities, noting that small and medium enterprises face high investment requirements, scarce talent, and deployment challenges; some enterprises exhibit an 'AI for the sake of AI' pattern, treating large model integration as the endpoint of digital transformation. The article argues that application should serve as the starting point of technological innovation rather than the 'last mile.'
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Two State Council executive meetings within four months have addressed artificial intelligence, with policy direction shifting from strategic positioning to implementation effectiveness. The June 29 meeting called for deepening the 'AI+' initiative and promoting scaled commercial application of intelligent products and services; the October 16 meeting further proposed using deployment applications to drive technological innovation. Economic Observer author Xie Ruolin, in a commentary published on October 19, noted a clear gap between policy enthusiasm and actual adoption: nearly half of 'AI+' application scenarios remain at the pilot stage.
Her diagnosis is not that the technology is inadequate, but that deployment costs and application capabilities are mismatched. For small and medium enterprises, high investment, scarce talent, and difficult deployment are real constraints; supply of inclusive computing power, models, software, and services remains insufficient; industry data, standards, and security governance are incomplete; and rapidly evolving technology roadmaps mean that a single model or product cannot cover all needs. More specifically, she identifies an 'AI for the sake of AI' behavioral pattern among some enterprises: treating large model integration as the endpoint of digital transformation, being enthusiastic about concept products and demonstration scenarios, and pursuing short-term traffic without finding genuine business needs.