73pc of HK workers have adopted AI at work: HKUST
Hong Kong University of Science and Technology on Monday announced they found that over 70 percent of workers across the SAR has adopted artificial intelligence at work – significantly higher than the global average. The university said the global average stood at 31 percent. Among the roughly 3,700 people it polled between March and April, more than 60 percent were aged between 30 and 49. The…
A recent survey conducted by the Hong Kong University of Science and Technology (HKUST) has revealed that over 73 percent of workers in Hong Kong have integrated artificial intelligence (AI) into their work routines, surpassing the global average of just 31 percent. Among the 3,700 individuals surveyed between March and April, more than 60 percent were aged between 30 and 49.
The university found that approximately 73 percent of these workers utilized AI daily or weekly, primarily for drafting documents, organizing information, summarizing data, and analyzing insights.
Business leaders, professionals, and management personnel were observed to be more frequent users of AI technology. Tam Kar-yan, HKUST's vice-president for administration and business, expressed concern that the widespread adoption of AI could potentially diminish the value of skills held by junior-level employees. Many respondents had not received adequate AI training, highlighting the need for tailored educational programs.
Tam emphasized that while individuals must self-improve through various online resources, organizations must also provide role-specific training.
The government and educational institutions are urged to collaborate in equipping the workforce with practical AI skills, accelerating the technology's adoption to bolster AI+ development. Despite the current lack of data on employment impacts on fresh graduates, Tam anticipates that certain job roles closely tied to AI will gradually diminish over time.
Written by urgent.news from RTHK News's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
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