Navigating the Future of IT Project Management: From Global Crises to AI-Driven Transformation
Bibliographic record
Abstract
This study examines the evolution of IT project management, focusing on the pivotal role of agile approaches in addressing paradoxical tensions. Tracing the journey from the 1960s through the post-2020 and AI-driven eras, the paper explores how global crises like the Y2K bug, the COVID-19 pandemic, and the rise of AI have forced organizations to undergo rapid digital transformations, reshaping IT project management practices. Based on semi-structured interviews conducted over 15 years, the research highlights how agile methodologies have been instrumental in managing organizational tensions and facilitating digital transformations. These preliminary findings underscore the role of agile in navigating paradoxical challenges, especially in adapting to the new normal defined by heightened digital reliance and remote work. As AI continues to disrupt the landscape, further interviews will explore how these evolving paradoxes are being addressed, particularly in the context of AI’s increasing influence on IT project management practices.
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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 | 0.003 |
| 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".