ILO Study Finds AI Boosts Productivity in Chinese Firms, but Skills Gaps and Job Displacement Risks Loom
<a href="https://bitcomme.com/does-trump-want-to-regulate-ai-amid-growing-concerns-over-artificial-intelligence/” title=”Does Trump want to regulate AI amid growing concerns over artificial intelligence?”>Artificial intelligence is generating notable efficiency improvements in companies across China, a fresh ILO research brief has found. The study, authored by Ekkehard Ernst from the ILO Research and Statistics Department alongside scholars at Renmin University of China, looks at corporate adoption patterns and raises flags about skill shortages, worker displacement and consequences for employees’ future earnings.
Evidence was gathered through detailed interviews at 21 businesses and a questionnaire completed by 1,591 professionals. The companies cover manufacturing, finance, business services, construction, education, media and travel, varying in size from a start-up with eight staff to a conglomerate with 270,000 workers. All interviewed firms either already deployed AI or had firm intentions to do so.
Within the surveyed professional group, 56 per cent regard AI uptake as an unavoidable development, and 47 per cent think the technology generates more positions than it eliminates, a rosier view than comparable polls in numerous OECD nations have recorded. Meanwhile, 39 per cent anticipate that AI will lower their earnings.
Enterprises that monitor AI’s effects document considerable efficiency gains. A single insurance firm saw 300 customer-service staff raise daily issue resolution from 6,000 to 15,000. A major insurance group cut hiring cycle duration from 30 days down to 13 days, a 57 per cent decrease. One smart manufacturing plant recorded a 30 per cent boost in production efficiency.
Even so, this kind of measurement is uncommon rather than standard. The majority of firms examined do not have systematic methods for evaluating AI’s effects, especially outside traditional productivity indicators. The brief stresses the importance of improved tracking of shifts in job quality, working conditions and the makeup of workers’ tasks.
The productivity numbers supplied by firms are self-reported and have not been independently confirmed. The study further observes that the 21 interviewed enterprises were chosen purposively, so the company-level results cannot be statistically extended to all Chinese enterprises.
Reported benefits are clustered in repetitive and data-heavy activities including document processing, handling customer queries, screening resumes and gathering data. Consequently, the brief concludes that displacement risks may be especially acute for routine clerical, administrative and customer-service positions.
Companies also cite difficulties involving employee pushback, skills, the quality of AI output, data security, regulation and integration with legacy systems. An ed-tech firm found a clear age-related gap in AI proficiency among staff aged over 40, while others pointed to wider deficiencies in AI literacy and the capacity to use new tools productively.
Notwithstanding these strains, the research concludes that human-AI cooperation, rather than complete automation, remains the dominant approach. Among the enterprises examined, AI is typically woven into hybrid workflows where human judgement and supervision continue to matter.
Ernst remarked that Chinese companies are not so much substituting workers with AI as restructuring work around hybrid human-AI workflows. He noted that the actual constraint is not technology but the skills and managerial ability to deploy it effectively, explaining why lifelong learning policies, transition assistance and accessible AI services for small and medium-sized enterprises will prove more consequential than the speed of technological advancement itself.
The brief identifies four domains for policy intervention: reinforcing AI skills and lifelong learning, especially for mid-career and older employees; assisting workers in moving toward higher-value tasks; creating improved frameworks for gauging AI’s impact on productivity, job quality and working conditions; and enabling smaller companies to obtain AI
According to the brief, such steps will be vital to guarantee that AI-driven productivity gains come with decent work and are spread more evenly among workers and enterprises.
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