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

Paper

Darby (Student), R. and Lane, M.. 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. Canberra ACT 04 - 06 Dec 2024 Canberra, ACT, Australia.
Paper/Presentation Title

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

Presentation TypePaper
AuthorsDarby (Student), R. and Lane, M.
EditorsBeydoun, G., Sutanto, J., Wickramasinghe, N. and SARBAZHOSSEINI, H.
Journal or Proceedings TitleProceedings of 35th Australasian Conference on Information Systems
Article Number141
Number of Pages8
Year2024
Place of PublicationCanberra, ACT, Australia
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
Event Details
35th Australasian Conference on Information Systems
Delivery
In person
Event Date
04 to end of 06 Dec 2024
Event Location
Canberra ACT
Event Venue
University of Canberra
Event Description

The Australasian Conference on Information Systems (ACIS 2024) was hosted by the University of Canberra in Canberra, the capital of Australia, from 4 December to 6 December 2024. In its 35th year, the Australasian Conference on Information Systems is the premier conference on the Information Systems discipline in the Australasian region.

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.

KeywordsArtificial Intelligence, Project Management, Prediction, Random Forest, Project Status
ANZSRC Field of Research 2020460902. Decision support and group support systems
460209. Planning and decision making
Byline AffiliationsSchool of Business
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