To read this content please select one of the options below:

Competing with artificial intelligence – can the records and information management profession withstand the challenge?

Sherry Li Xie (School of Information Resource Management, Renmin University of China, Beijing, China; Center for Digital Records Management Research, Beijing, China and Key Laboratory of Data Engineering and Data Knowledge of the Ministry of Education of China, Beijing, China)
Li Siyi (School of Culture Heritage and Information Management, Shanghai University, Shanghai, China)
Ruohua Han (School of Information Sciences, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA)

Records Management Journal

ISSN: 0956-5698

Article publication date: 29 March 2022

Issue publication date: 1 June 2022




To report on a study that focused on the records and information management (RIM) profession’s competencies with respect to the development of AI.


Designed as deductive, the study distilled artificial intelligence (AI) insusceptibility indicators, creative intelligence and social intelligence, from the Oxford study and applied them to the current RIM core competencies developed by ARMA International. Manual coding and semantic analysis served as the primary inquiring methods, and both statistical and qualitative results are presented.


The RIM profession as a whole is currently AI-resistant, yet it is not AI-proof. To be AI-proof, the existent competencies model needs to be redesigned as the AI-resistant parts are mingled with AI-prone ones, and the prescriptions of some RIM theories and principles are not ready for AI judgements or adjustments. It requires also strategizing collaborations among all stakeholders so that we can be one step ahead of future unfavorable organizational decisions. If our professional nature renders us AI-resistant for now, then it is our professional unity that will ensure us AI-proof in the future.


To the best of the authors’ knowledge, this paper is first of its kind within the international RIM community. It provides detailed assessment data on AI insusceptibility and targeted suggestions regarding the RIM community as a whole.



Funding: This work was supported by the Fundamental Research Funds for Central Universities and the Research Funds of Renmin University of China [grant number 15XNL032].

This study is a supporting study to the Records and Records Management in Trustworthy AI project under the InterPARES Trust AI project (2021–2026;


Xie, S.L., Siyi, L. and Han, R. (2022), "Competing with artificial intelligence – can the records and information management profession withstand the challenge?", Records Management Journal, Vol. 32 No. 2, pp. 151-169.



Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

Related articles