Journal article
Extending the Participant’s Voice to Guide Artificial Intelligence Transformation Using Futures Methodology and Layered User Story Analysis
World Futures Review, Vol.13(3-4), pp.214-236
2021
Abstract
Artificial intelligence (AI) is a vital driver of the next wave of automatisation of Industry 4.0. It impacts product-based and service-based organisations and is becoming an investment stream in organisational transformation strategies. The transformation teams that deploy AI use agile incremental methodologies that ideally match the learning and adaptation requirements for the machine, as well as the human user who is the source of data and requires a service response. This research outlines a layered analysis process of 65 user stories (a common agile method of obtaining user requirements) generated via a participatory process, involving 110 participants in three workshop settings, in what they determined AI would not do. The results outline the workshop approach undertaken to generate user stories and the analysis of user stories via persona and futures methodology causal layered analysis (Inayatullah S. 1998. Causal layered analysis. Futures 30(8): 815-829). The final component of the analysis generated a futures focussed set of guiding principles that can be used as a lens to broaden the transformation teams perspective in AI deployment. Concepts also consider the importance of futures literacy as a key competency of AI creation teams.
Details
- Title
- Extending the Participant’s Voice to Guide Artificial Intelligence Transformation Using Futures Methodology and Layered User Story Analysis
- Authors
- Elissa Farrow (Author) - University of the Sunshine Coast, Queensland, Faculty of Arts, Business and Law
- Publication details
- World Futures Review, Vol.13(3-4), pp.214-236
- Publisher
- Sage Publications, Inc.
- DOI
- 10.1177/19467567211030377
- ISSN
- 2169-2793
- Organisation Unit
- University of the Sunshine Coast, Queensland; School of Law and Society
- Language
- English
- Record Identifier
- 99578907902621
- Output Type
- Journal article
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