Hakeem Owolabi

This study examines the influence of artificial intelligence (AI) on leadership in organisations and spotlights the emerging notion of automated leadership. Conventionally, leadership has been noted as human-focused, and thrives on human abilities like empathy, emotional intelligence, decision-making, and interpersonal skills. However, with an unprecedented rise in computer processing power and automation, AI is revolutionising leadership roles, with artificially intelligent agents now performing strategic-level functions hitherto conducted by human-leaders. However, despite the promised potentials and benefits, concerns have surfaced regarding the impacts of AI-automated leadership on team outcomes, particularly conflict management. Therefore, using phenomenology and case study design and focusing on UK organisations in the logistics and warehouse sector-where adoption of AI-powered automated leadership systems has surged-we examine the influence of AI-automated leadership agents on conflicts within working teams. After theoretically examining traditional leadership and team conflict models vis-à-vis AI-automated leadership, the study proceeded to hypothesise and operationalise efficient conflict management approaches for this emerging paradigm.

Purpose: Despite an enormous body of literature on conflict management, intra-group conflicts vis-à-vis team performance, there is currently no study investigating conflict prevention approach to handling innovation-induced conflicts that may hinder smooth implementation of big data technology in project teams.
Design/methodology/ Approach: This study uses constructs from conflict theory, and team power relations to develop an explanatory framework. The study proceeded to formulate theoretical hypotheses from task-conflict, process-conflict, relationship, and team power conflict. The hypotheses were tested using Partial Least Square Structural Equation Model (PLS-SEM) to understand key preventive measures that can encourage conflict prevention in project teams when implementing big data technology.
Findings: Results from the structural model validated six out of seven theoretical hypotheses and identified Relationship Conflict Prevention as the most important factor for promoting smooth implementation of Big Data Analytics technology in project teams. This is followed by Power-Conflict prevention, prevention of relationship disputes and prevention of Process conflicts respectively. Results also show that relationship and power conflict interact on the one hand, while Task and relationship conflict prevention on the other hand, suggesting the prevention of one of the conflicts could minimise the outbreak of the other.
Research Limitations: The study has been conducted within the context of big data adoption in a project-based work environment and the need to prevent innovation-induced conflicts in teams. Similarly, the research participants examined are stakeholders within UK projected-based organisations.
Practical Implications: The study urges organisations wishing to embrace big data innovation to evolve a multipronged approach for facilitating smooth implementation through prevention of conflicts among project frontlines. We urge organisations to anticipate both subtle and overt frictions that can undermine relationships and team dynamics, effective task performance, derail processes and create unhealthy rivalry that undermines cooperation and collaboration in the team.
Social Implications: The study also addresses the uncertainty and disruption that big data technology presents to employees in teams and explore conflict prevention measure which can be used to mitigate such in project teams.
Originality/Value: The study proposes a Structural Model for establishing conflict prevention strategies in project teams through a multidimensional framework that combines constructs like team power, process, relationship & task conflicts; to encourage Big Data implementation.

This chapter examines the use of artificial intelligence, data analytics and other digital technologies in the management of the supply chain decision-making. The study highlights the challenges faced by supply chain managers and how the application of AI and data analytics can help in making better and more informed decisions with respect to sustainability. Data analytics, AI techniques, such as machine learning, natural language processing and other digital technologies that include Internet of Things, Robotics and Cloud computing and their applications to different areas of supply chain management, such as demand forecasting, inventory management and logistics optimisation are discussed. Some of the challenges (initial cost of physical and cloud resources, change management, ethical and legal-related issues) that the supply chain managers need to put into consideration when adopting these technologies are also presented. The chapter concludes that continuous data collection and storage across all the stakeholders in the supply chain must be ensured to enable transparent and efficient use of AI algorithms to support quick and timely supply chain decision-making.

This book chapter explores the challenges associated with adopting Industry 4.0 technologies in the context of achieving a sustainable manufacturing supply chain. The chapter highlights both general and technology-specific hurdles that organizations encounter when implementing Industry 4.0, such as dealing with data accumulation and compatibility issues with legacy systems, data management complexities, data protection, privacy and cyber attack risks, cost considerations, and workforce upskilling and transition. The chapter emphasizes the importance of addressing these challenges to enable the effective incorporation of Industry 4.0 technologies for sustainability goals. It provides insights and recommendations for mitigating these challenges, including prioritizing sustainability considerations during technology selection and implementation, emphasizing energy efficiency and environmental impact assessments in technology design and deployment, incorporating ethical frameworks and guidelines for data usage, privacy, and fairness in AI and IoT systems, encouraging collaboration among stakeholders to develop industry standards and best practices for sustainable technology adoption, among a few others. By proactively addressing these challenges, organizations can leverage the transformative potential of Industry 4.0 while driving sustainability in their manufacturing supply chain.