Conference paper
Value Capture from Enterprise AI: A Process-Centric Analysis
ACIS 2025 Proceedings, pp.1-15
Australasian Conference on Information Systems (ACIS), 36th (Sunshine Coast, Australia, 01-Dec-2025–05-Dec-2025)
Australasian Association for Information Systems (AAIS)
2025
Abstract
The use of AI in enterprise systems, known as enterprise AI, has grown significantly recently. However, many organisations struggle to operationalise AI, resulting in cost overruns and challenges in demonstrating ROI. Furthermore, the inappropriate application of AI to tasks for which it is ill-suited can have unintended consequences, potentially undermining or diminishing value. The root cause is AI’s unique characteristics (e.g., opacity and autonomy) that make its value creation process distinct from traditional IT systems. In this paper, we analyse five successful case studies from various industries and sizes to develop a process model of how organisations can create and capture value from enterprise AI. We identify two types of AI use: internal use (e.g., improving operational efficiency) and external use (e.g., enhancing customer experience). This distinction helps contextualise the triggers. Moreover, our study identifies six episodes, both at the project and enterprise levels, as processes leading to value capture.
Details
- Title
- Value Capture from Enterprise AI: A Process-Centric Analysis
- Authors
- Nova Sepadyati - The University of QueenslandIda Asadi Someh - The University of QueenslandMarta Indulska - The University of QueenslandTianwa Chen - The University of QueenslandShazia Sadiq - The University of Queensland
- Publication details
- ACIS 2025 Proceedings, pp.1-15
- Conference details
- Australasian Conference on Information Systems (ACIS), 36th (Sunshine Coast, Australia, 01-Dec-2025–05-Dec-2025)
- Publisher
- Australasian Association for Information Systems (AAIS)
- Date published
- 2025
- Copyright note
- (c) 2025 Sepadyati,Someh,Indulska,Chen,Sadiq. This is an open-access article licensed under a Creative Commons Attribution-Non-Commercial 4.0 International License, which permits non commercial use, distribution, and reproduction in any medium, provided the original author and ACIS are credited.
- Organisation Unit
- School of Science, Technology and Engineering
- Language
- English
- Record Identifier
- 991246799602621
- Output Type
- Conference paper
Metrics
1 Record Views