Adding External Artificial Intelligence (AI) into Internal Firm-Wide Smart Dynamic Warehousing Solutions

John R. Hamilton (), Stephen J. Maxwell, Syeda Arfa Ali and Singwhat Tee
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John R. Hamilton: College of Business, Law, and Governance, James Cook University, Cairns, QLD 4878, Australia
Stephen J. Maxwell: College of Business, Law, and Governance, James Cook University, Cairns, QLD 4878, Australia
Syeda Arfa Ali: College of Business, Law, and Governance, James Cook University, Cairns, QLD 4878, Australia
Singwhat Tee: College of Business, Law, and Governance, James Cook University, Cairns, QLD 4878, Australia

Sustainability, 2024, vol. 16, issue 10, 1-23

Abstract: This study advances knowledge in the AI field. It provides deep insight into current industry generative AI inclusion systems. It shows both literature and practical leading industry operations can link, overlap, and complement each other when it comes to AI and understanding its complexities. It shows how to structurally model and link AI inclusions towards delivering a suitable sustainability positioning. It shows approaches to integrate external AI contributions from one firm into another firm’s intelligences developments. It shows how to track, and maybe benchmark, the progress of such AI inclusions from either an external or an integrated internal software developer perspective. It shows how to understand and create a more sustainable, AI-integrated business positioning. This study considers firm artificial intelligence (AI) and the inclusion of additional external software developer engineering as another AI related pathway to future firm or industry advancement. Several substantive industrial warehousing throughput areas are discussed. Amazon’s ‘smart dynamic warehousing’ necessitates both digital and generative ongoing AI system prowess. Amazon and other substantive, digitally focused industry warehousing operations also likely benefit from astute ongoing external software developer firm inclusions. This study causally, and stagewise, models significant global software development firms involved in generative AI systems developments—specifically ones designed to beneficially enhance both warehouse operational productivity and its ongoing sustainability. A structural equation model (SEM) approach offers unique perspectives through which substantive firms already using AI can now model and track/benchmark the relevance of their prospective or existing external software developer firms, and so create rapid internal ‘net-AI’ competencies incorporations and AI capabilities developments through to sustainable operational and performance outcomes solutions.

Keywords: artificial intelligence; assimilation; acquisition; digital network; generation AI system; transformation; innovation; sustainable performance; autonomous robots; deep machine learning; competitiveness; collective knowledge; strategic risk; productive capacities (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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