Using Artificial Intelligence to Detect Underlying Issues in Projects: Seeing Beyond Current KPIs and Project Status Reports

Paper

Darby, Ryan and Lane, Michael. 2024. "Using Artificial Intelligence to Detect Underlying Issues in Projects: Seeing Beyond Current KPIs and Project Status Reports." Beydoun, G., Sutanto, J., Wickramasinghe, N. and SARBAZHOSSEINI, H. (ed.) 35th Australasian Conference on Information Systems (ACIS 2024). Canberra, Australia 04 - 06 Dec 2024 Australia. AIS Electronic Library.
Paper/Presentation Title

Using Artificial Intelligence to Detect Underlying Issues in Projects: Seeing Beyond Current KPIs and Project Status Reports

Presentation TypePaper
AuthorsDarby, Ryan and Lane, Michael
EditorsBeydoun, G., Sutanto, J., Wickramasinghe, N. and SARBAZHOSSEINI, H.
Journal or Proceedings TitleProceedings of the Australasian conference on information systems (ACIS 2024)
Article Number141
Number of Pages7
Year2024
PublisherAIS Electronic Library
Place of PublicationAustralia
Web Address (URL) of Paperhttps://aisel.aisnet.org/acis2024/141/
Web Address (URL) of Conference Proceedingshttps://aisel.aisnet.org/acis2024/
Conference/Event35th Australasian Conference on Information Systems (ACIS 2024)
Event Details
35th Australasian Conference on Information Systems (ACIS 2024)
Parent
Australasian Conference on Information Systems (ACIS)
Delivery
In person
Event Date
04 to end of 06 Dec 2024
Event Location
Canberra, Australia
Event Venue
University of Canberra
Event Web Address (URL)
Abstract

Project status reports traditionally are the primary source of project control. However, they offer an incomplete view relying on static snapshots with limited historical context for managing projects. It is difficult obtain a real-time status of projects, relying on project status reports alone. This study explores the research question that past project performance can inform future insights, the need for which is driven by increasing workloads and the rise of artificial intelligence (AI). Project professionals face an immense pressure to deliver increased business value with limited resources. This study attempts to understand the application of AI to reduce the burden of analysing a large data source that changes over time, and to identify potential upcoming challenges in delivering successful projects outcomes. Using a machine learning approach, this study offers insights into detecting patterns and relationships in project data indicating success/failure and outline the criteria for a successful AI-enabled project management system.

KeywordsProject Management; Artificial Intelligence; Project Status; Random Forest; Prediction
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020460902. Decision support and group support systems
460209. Planning and decision making
Byline AffiliationsSchool of Business
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