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Application of artificial intelligence in agile project management: a task-technology fit lens

Zhiheng Huang et al.

International Journal of Managing Projects in Business2026https://doi.org/10.1108/ijmpb-07-2025-0291article
AJG 1ABDC C
Weight
0.50

What the paper says

Purpose This study conducts a systematic literature review to examine the applications of artificial intelligence (AI) in agile project management (APM) (AI-in-APM). Guided by the task-technology fit (TTF) lens, it aims to move beyond descriptive summaries and critically analyze the congruence between AI technologies and the tasks of APM. Design/methodology/approach Mixed methods are employed in this study, which integrates bibliometric analysis of 361 papers on APM with systematic content analysis of 47 papers specifically focused on AI-in-APM. Findings The analysis maps the fit mechanisms of various AI techniques to core APM tasks and identifies their practical strengths. In addition, it reveals persistent fit gaps and socio-technical tensions (e.g. between algorithmic opacity and agile transparency) that define the future research directions of AI-in-APM. Originality/value This study provides an integrative APM framework through the TTF lens. It reveals fit strengths and critical misfits of AI-in-APM.

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Cite this paper

https://doi.org/https://doi.org/10.1108/ijmpb-07-2025-0291

Or copy a formatted citation

@article{zhiheng2026,
  title        = {{Application of artificial intelligence in agile project management: a task-technology fit lens}},
  author       = {Zhiheng Huang et al.},
  journal      = {International Journal of Managing Projects in Business},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1108/ijmpb-07-2025-0291},
}

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Application of artificial intelligence in agile project management: a task-technology fit lens

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.