Artificial Intelligence as a Strategic Digital Resource

Extending the Digital Resource View Through a Layered Resource Architecture

Abstract

Artificial intelligence (AI) has become a central element of digital transformation, yet strategic-management research still lacks a sufficiently precise explanation of when AI constitutes a strategic resource rather than a widely accessible technology. This conceptual study extends the Digital Resource View (DRV) by theorizing AI as a configured, multi-layered digital resource architecture composed of AI infrastructure, AI capability, AI-driven decision systems, and AI-orchestrated digital platforms. Following a theory-adaptation design, the article integrates resource-based theory, resource orchestration, dynamic capabilities, digital transformation, organizational AI capability, platform, and institutional perspectives. The resulting framework specifies how the four AI layers are connected and how they activate three DRV transmission mechanisms—digital value creation, digital rareness, and uncertainty-driven imitation barriers—to generate competitive advantage. Normative digital pressure is introduced as a contextual condition that shapes the conversion of AI-enabled decision systems into realized digital value. Ten propositions clarify the causal logic and make the framework empirically testable. The study contributes by distinguishing AI resource layers from AI outcomes, replacing a direct technology–performance assumption with an orchestration-based mechanism, and clarifying how configurational uniqueness and learning uncertainty can sustain advantage even when core AI technologies are widely available. The framework also identifies boundary conditions for platform dependence, institutional context, and governance capacity, thereby providing a more disciplined foundation for future empirical research on AI-enabled competitive advantage.