Operationalisation of Artificial Intelligence: How Organisations are Managing the Artificial Intelligent Technologies that are Deployed within their Organisation

Dinklo, Jacqueline N.

Operationalisation of Artificial Intelligence: How Organisations are Managing the Artificial Intelligent Technologies that are Deployed within their Organisation

Dinklo, Jacqueline N.

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Publication Type:: Thesis
Issue Date:: 2023

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Field	Value	Language
dc.contributor.author	Dinklo, Jacqueline N.
dc.date.accessioned	2023-11-28T03:09:02Z
dc.date.available	2023-11-28T03:09:02Z
dc.date.issued	2023
dc.identifier.uri	http://hdl.handle.net/10453/173603
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_US.UTF-8
dc.description.abstract	The effective management of Artificial Intelligence (AI) technologies is a crucial aspect that accompanies the adoption of AI within organisations. As AI’s rapid and widespread integration continues, there is an increasing need to comprehend the management practices associated with these technologies. This understanding is essential to ensure that AI deployment benefits organisations without causing harm or detrimental effects. This research aims to investigate how organisations manage their AI technologies to leverage the benefits offered by AI. The study adopts a Grounded Theory approach to explore the management of AI technologies. Qualitative data is collected through unstructured interviews with individuals overseeing AI within their organisations. The research analysis was conducted on the results of 30 interviews with managers of AI within organisations across three levels of management. The analysis of the interviews identified 11 factors in managing AI and 11 relationships between these factors. The identified factors and their relationships informed the construction of the theoretically grounded theory framework concluded from this research. The framework, factors and relationships allowed for the eight propositions to be established from this research. The findings of this research indicate that existing management practices are applied to AI technologies to align them with the organisation’s goals and objectives. These practices encompass monitoring and measuring the outcomes of AI, reporting on the AI’s progress in achieving the organisation’s goals and objectives, and continuously improving the AI’s performance to enhance goal attainment. Furthermore, the management of AI within organisations involves interdisciplinary collaboration, drawing upon domain-specific expertise related to AI functions coupled with AI skills. The research also identifies the presence of Anthropomorphising, a human-like attribution, in managing AI technologies. Based on the research findings, this study proposes a theoretically grounded framework that represents how organisations effectively manage their AI technologies. The framework is a valuable contribution to understanding AI management practices within organisational contexts for academics and practitioners.	en_US.UTF-8
dc.format	Thesis (PhD)
dc.language.iso	en_US	en_US.UTF-8
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/173603/1/thesis.pdf
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	© 2023 Jacqueline N. Dinklo
dc.rights	au.edu.uts.lib/cph
dc.title	Operationalisation of Artificial Intelligence: How Organisations are Managing the Artificial Intelligent Technologies that are Deployed within their Organisation	en_US.UTF-8
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

The effective management of Artificial Intelligence (AI) technologies is a crucial aspect that accompanies the adoption of AI within organisations. As AI’s rapid and widespread integration continues, there is an increasing need to comprehend the management practices associated with these technologies. This understanding is essential to ensure that AI deployment benefits organisations without causing harm or detrimental effects. This research aims to investigate how organisations manage their AI technologies to leverage the benefits offered by AI. The study adopts a Grounded Theory approach to explore the management of AI technologies. Qualitative data is collected through unstructured interviews with individuals overseeing AI within their organisations. The research analysis was conducted on the results of 30 interviews with managers of AI within organisations across three levels of management. The analysis of the interviews identified 11 factors in managing AI and 11 relationships between these factors. The identified factors and their relationships informed the construction of the theoretically grounded theory framework concluded from this research. The framework, factors and relationships allowed for the eight propositions to be established from this research. The findings of this research indicate that existing management practices are applied to AI technologies to align them with the organisation’s goals and objectives. These practices encompass monitoring and measuring the outcomes of AI, reporting on the AI’s progress in achieving the organisation’s goals and objectives, and continuously improving the AI’s performance to enhance goal attainment. Furthermore, the management of AI within organisations involves interdisciplinary collaboration, drawing upon domain-specific expertise related to AI functions coupled with AI skills. The research also identifies the presence of Anthropomorphising, a human-like attribution, in managing AI technologies. Based on the research findings, this study proposes a theoretically grounded framework that represents how organisations effectively manage their AI technologies. The framework is a valuable contribution to understanding AI management practices within organisational contexts for academics and practitioners.

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http://hdl.handle.net/10453/173603