Enterprise Cognitive Product Strategy Engineering for AI-Driven Retail Supply Chains

Abstract

Retail supply chains now operate cognitive systems capable of processing millions of demand signals per hour, yet the product strategy frameworks governing those systems were designed for human-paced decision cycles. This misalignment is not merely a speed problem; it is an architectural one. When AI systems outrun the governance models meant to oversee them, enterprises are forced into a binary that undermines the value of their investments: constrain cognitive agents to tasks narrow enough for human oversight cycles, or accept autonomous operation without adequate governance. This paper proposes the Cognitive Product Strategy Engineering (CPSE) Stack, a six-layer operational architecture that resolves this tension by embedding AI capability directly into the strategic product decision-making process. The six layers - Strategic Intent Formation, Enterprise Signal Capture, Cognitive Reasoning Engine, Product Orchestration, Adaptive Governance, and Outcome Learning - form an integrated architecture grounded in design science research methodology. The framework is evaluated through three scenario-based validation cases representing AI maturity progression in retail supply chain contexts. A five-level, five-dimension AI maturity assessment instrument accompanies the framework to enable practitioners to self-locate within the CPSE development pathway. Results confirm that the CPSE Stack provides a structured, governance-ready architecture for advancing enterprise AI product strategy beyond experimentation toward institutionalized intelligence. The framework is intended for researchers studying cognitive product systems, practitioners scaling AI in retail supply chains, and organizations seeking alignment between AI capability and strategic accountability.