Author(s)

Valéry Psyché¹, Diane-Gabrielle Tremblay², Fatma Miladi¹, Amina Yagoubi¹

¹Department of Education, Université TELUQ, Montréal, Canada.
²Business School, Université TELUQ, Montréal, Canada.

In the context of a research project supported by the Montreal Pole of Higher Education in Artificial Intelligence (PIA), we have developed a competency framework for artificial intelligence (AI) project manager in the context of Industry 4.0. This framework aims at informing organizations on the state of the art of the competencies needed by any AI project manager and thus facilitate tasks such as recruitment or performance evaluation of managers. In parallel, it also aims to guide the AI management training strategies of educational institutions and training organizations in order to design training adapted to the reality of the workplace at all levels (college, university or professional). This article reports on the methodological research process that led to the co-construction of the competency framework, the resulting competencies and the resulting discussion due to the surprising findings on the emerging skills needed in the digital and AI era. Specifically, we employed a qualitative methodology that involves conducting a strategic survey, systematic literature review, and engaging experts through interviews and focus groups. We leveraged the DACUM method to construct the competency framework, which enabled us to facilitate exchanges between participants and capture the key competencies essential for an AI project manager. The main competencies and sub-competencies identified are also presented. We conclude with a discussion of the findings and recommendations for companies and training organizations.

Competencies, Artificial Intelligence, Digital Transformation, Technological Change, Competency Framework, DACUM Methodology, Project Manager

Psyché, V. , Tremblay, D. , Miladi, F. and Yagoubi, A. (2023) A Competency Framework for Training of AI Projects Managers in the Digital and AI Era. Open Journal of Social Sciences, 11, 537-560. doi: 10.4236/jss.2023.115032.