Deploying AI Governance Practices: A Revelatory Case Study

Papagiannidis, Emmanouil; Enholm, Ida Merete; Dremel, Chirstian; Mikalef, Patrick; Krogstie, John

doi:10.1007/978-3-030-85447-8_19

Cited by 11 publications

(4 citation statements)

References 20 publications

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“…Companies are investing considerably in AI. However, they face challenges in realizing performance gains and creating suitable AI governance practices, which leads to disillusionment about the value generated and the process for obtaining it 69 . A resource orchestration approach is suggested for dealing with this gap.…”

Section: Review Results and Theoretical Backgroundmentioning

confidence: 99%

“…However, they face challenges in realizing performance gains and creating suitable AI governance practices, which leads to disillusionment about the value generated and the process for obtaining it. 69 A resource orchestration approach is suggested for dealing with this gap. Also, to differentiate the ideation of AI capabilities and their implementation and analyze the relevance to the AI deployment context by applying the resource orchestration theory.…”

Section: Digital Business Model Innovationmentioning

confidence: 99%

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Activating supply chain business models' value potentials through Systems Engineering

Nuerk

Dařena

2023

Systems Engineering

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New business opportunities, driven by smart digitalization technology and initiatives such as Industry 4.0, significantly change business models and their innovation rate. The complexity of methodologies developed in recent decades for balancing exploration and exploitation activities of digital transformation has risen. Still, the desired integration levels across organizational levels were often not reached. Systems thinking promises to holistically consider interdisciplinary relationships and objectives of various stakeholders across supply chain ecosystems. Systems theory‐based concepts can simultaneously improve value identification and aligned transformation among supply networks’ organizational and technical domains. Hence, the study proposes synthesizing management science concepts such as strategic alignment with enterprise architecture concepts and artificial intelligence (AI)‐driven business process optimization to increase innovation productivity and master the increasing rate of business dynamics at the same time. Based on a critical review, the study explores concepts for innovation, transformation, and alignment in the context of Industry 4.0. The essence has been compiled into a systems engineering‐driven framework for agile value generation on operational processes and high‐order capability levels. The approach improves visibility for orchestrating sustainable value flows and transformation activities by considering the ambidexterity of exploring and exploiting activities and the viability of supply chain systems and sub‐systems. Finally, the study demonstrates the need to harmonize these concepts into a concise methodology and taxonomy for digital supply chain engineering.

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Section: Review Results and Theoretical Backgroundmentioning

confidence: 99%

Section: Digital Business Model Innovationmentioning

confidence: 99%

Activating supply chain business models' value potentials through Systems Engineering

Nuerk

Dařena

2023

Systems Engineering

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“…The governance of AI plays a critical role in ensuring the responsible and ethical use of AI in financial institutions (Papagiannidis et al, 2021). By implementing effective governance strategies, financial institutions can achieve the proposed outcomes of intelligent finance and change management while also ensuring transparency, accountability, and regulatory compliance.…”

Section: The Agility Of Change Management With Corporate Financementioning

confidence: 99%

Intelligent finance and change management implications

Guo

Polák

2023

Humanit Soc Sci Commun

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Change management is the embodiment of an enterprise’s core competence. It provides competitive differentiation and effectively adapts to the ever-changing world. This paper explores the implications of intelligent finance on change management and provides insights into how organizations can effectively manage change to achieve the desired outcomes. The study examines the case of Ping An (Ping An Insurance (Group) Company of China, Ltd.), a leading insurance company in China that has successfully implemented intelligent finance and change management strategies. The paper begins with a literature review that provides an overview of the concept of intelligent finance, the relevance of change management in the context of intelligent finance, models, and frameworks for intelligent finance, and approaches to change management. The study then presents a case analysis of Ping An, including descriptive statistics, inferential statistics, regression analysis, and qualitative findings. The paper concludes with implications for practice and theory, contributions of the study, and recommendations for future research. Overall, this paper contributes to the growing literature on intelligent finance and change management and provides practical insights for organizations seeking to adopt intelligent finance.

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“…Ethical requirements or ethical requirements for AI are requirements for AI and AI-enabled systems derived from AI guidelines, principles, or ethical codes (norms) like legal requirements derived from laws and regulations to disseminate AI ethics practices [15]. Although researchers, practitioners, regulatory bodies, and other government agencies have made significant progress at the technical developmental levels [6,36], a gap still exists from portfolio management and higher management levels, likened to the middle and higher-level SE management [6,38,45].…”

Section: Introductionmentioning

confidence: 99%

Implementing AI Ethics: Making Sense of the Ethical Requirements

Agbese

Mohanani

Khan

et al. 2023

Proceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering

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Society's increasing dependence on Artificial Intelligence (AI) and AI-enabled systems require a more practical approach from software engineering (SE) executives in middle and higher-level management to improve their involvement in implementing AI ethics by making ethical requirements part of their management practices. However, research indicates that most work on implementing ethical requirements in SE management primarily focuses on technical development, with scarce findings for middle and higher-level management. We investigate this by interviewing ten Finnish SE executives in middle and higher-level management to examine how they consider and implement ethical requirements. We use ethical requirements from the European Union (EU) Trustworthy Ethics guidelines for Trustworthy AI as our reference for ethical requirements and an Agile portfolio management framework to analyze implementation. Our findings reveal a general consideration of privacy and data governance ethical requirements as legal requirements with no other consideration for ethical requirements identified. The findings also show practicable consideration of ethical requirements as technical robustness and safety for implementation as risk requirements and societal and environmental well-being for implementation as sustainability requirements. We examine a practical approach to implementing ethical requirements using the ethical risk requirements stack employing the Agile portfolio management framework. CCS CONCEPTS• Software and its engineering → Software organization and properties.

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Deploying AI Governance Practices: A Revelatory Case Study

Cited by 11 publications

References 20 publications

Activating supply chain business models' value potentials through Systems Engineering

Activating supply chain business models' value potentials through Systems Engineering

Intelligent finance and change management implications

Implementing AI Ethics: Making Sense of the Ethical Requirements

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