A Competency Framework for Training of AI Projects Managers in the Digital and AI Era
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".