摘要
Abstract
The deep application of artificial intelligence technology has made traditional post setting and staffing models difficult to adapt to new organizational forms and work patterns,driving organizations into a new"human-machine collaboration"paradigm.Organizational structure,post forms,staffing logic,and competency requirements are undergoing systematic transformation.The core of work is shifting from"fixed positions"to"dynamic roles",from"personnel quotas"to"human-machine ratios",and from"static allocation"to"dynamic optimization".Addressing the shortcomings of traditional staffing methods,this article introduces two key variables-technology penetration rate and human-machine collaboration coefficient-to revise classical methods including labor efficiency-based staffing,equipment-based staffing,and ratio-based staffing,forming a staffing formula system adapted to human-machine collaboration scenarios.Simultaneously,the article proposes practical implementation points for the new staffing methods,including strengthening data support,establishing cross-functional teams,building predictive models,moderately maintaining human resource redundancy,and implementing phased pilot iterations.This research provides methodological references and pathway guidance for theoretical innovation and practical implementation of post setting and staffing under the new human-machine collaboration paradigm.关键词
人机协同/组织范式/定岗定员/劳动定员/动态配置/人机配比Key words
Human-machine collaboration/Organizational paradigm/Post setting and staffing/Labor staffing/Dynamic allocation/Human-machine ratio分类
社会科学
