AI-Enabled Strategic Frameworks for Supply Chain Innovation: A Study of Balancing Efficiency, Risk Management, and Local Responsiveness in the United Kingdom

Abstract

This dissertation explores the use of Artificial Intelligence (AI) in driving innovation within supply chains across the UK, their efficiency, resilience, and responsiveness to locality. It discusses how AI enables these dynamic capability elements to sense-seize-reconfigure and evaluates the strategy frameworks being used by the Big Four consulting firms (Deloitte, PwC, EY, and KPMG) towards integrating AI into supply chains. This research also tries to find out which governance mechanisms are most critical in encouraging sustainable, AI-driven supply chain innovation within the context of the UK. By means of a mixed-methods design, this study applied Structural Equation Modelling (SEM) on the quantitative side and in-depth insights from 21 semi-structured expert interviews for its qualitative component. The quantitative results above have empirically validated that AI augments dynamic capabilities, firstly by enhancing sensing capacity, which improves firms’ abilities to recognise shifts in markets as well as possibilities to better utilise their resources. While AI increases efficiency as well as resilience toward dynamism, it does not directly contribute to place sensitivity because the logic of global optimisation driven through AI may run parallel with the logic of region adjustment under new post-Brexit regulations. Qualitative findings solidify perceptions of AI as a strategic enabler, thereby allowing for real-time monitoring and predictive analytics. Challenges in the legacy systems, data fragmentation, and cultural resistance to the efforts of imposition were also noted in implementing AI. In critical terms, the study evaluated the adequacy of the Big Four AI strategic frameworks in intervening in the complex UK regulatory environment post-Brexit. The argument that this research advances is that there should be reforms to those frameworks so that intervention could be more effective by localising compliance-regulatory considerations. This research emphasises the need for flexible governance mechanisms through collaboration and digital management practices that would update AI adoption to local regulatory requirements. The findings of this study add value to existing literature regarding the role of AI in supply chain innovation and further provide a set of practical recommendations that would assist organisations trying to implement AI successfully into their supply chains. It, therefore, opens up more avenues for future research and investigation into aspects such as the effect of AI on local responsiveness and the evolving frameworks of governance about AI.