Bridging The "Adoption Gap": Why Supply Chain AI Fails Without Product Marketing Principles

Abstract

Enterprise investment in artificial intelligence and autonomous systems across logistics networks has reached unprecedented heights, yet operational realization remains remarkably low. Historically, organizations have treated supply chain AI as a purely quantitative mathematics or engineering problem, optimizing for algorithmic accuracy while neglecting front line operational workflows. The result is a widening distance between what predictive systems can do in principle and what they actually accomplish on the warehouse floor, in the procurement office, and across the multi-tier supplier network. This paper posits that the root cause of systemic supply chain technology failure is not algorithmic deficiency but an "Adoption Gap" characterized by frontline worker resistance, vendor fragmentation, and rigid user interfaces. To resolve this, we propose an interdisciplinary paradigm shift: applying core product marketing and Customer Value Management (CVM) frameworks to internal enterprise software deployments. By restructuring complex predictive metadata into user centered, high incentive, and steerable workflows, organizations can transition AI from isolated software pilots to high yield operational realities. The argument is developed across four moves: a diagnosis of the realization gap, a synthesis of the adoption, diffusion, and value disciplines that explain it, an application of product marketing and customer value methods to the internal and inter firm adoption problem, and a Lean Six Sigma blueprint that operationalizes the synthesis. We close by drawing out the implications for management practice, organizational design, talent strategy, and industrial resilience, and by marking the boundaries of the argument and an agenda for empirical work