Abstract: The scalability of Edge Artificial Intelligence (Edge AI) is fundamentally constrained by hardware heterogeneity and uneven compute capabilities across deployment environments. This "Intelligence Gap" represents a critical architectural barrier that prevents the democratized adoption of vision intelligence. This paper proposes a theoretical framework to address the Edge AI scalability problem through three interconnected innovations: (1) the AI Readiness Index (AIRI) for standardizing intelligence measurement; (2) a modular Appization architecture for decoupling intelligence from hardware; and (3) a Vertical Solution Stack implementing the 3Ps framework (Personalization, Platforms, Performance Analytics). Central to this framework, we introduce the EdgeBox as a "Legacy Redemption" artifact—a neural interface designed to retroactively apply the Solution Stack to non-intelligent infrastructure. This allows for the "Neural Scrubbing" of legacy systems, transforming "dumb" sensors into AIRI-certified intelligence nodes. This paper employs a design science methodology, drawing on established theories from platform economics and service-dominant logic to develop a structured approach to artifact creation. We present a comprehensive model that transforms hardware heterogeneity from a scaling constraint into a managed resource. We demonstrate how the EdgeBox mediates the transition from hardware-centric to intelligence-centric edge computing. The proposed framework provides a novel theoretical foundation for Edge AI scalability. The introduction of the EdgeBox and the 3Ps framework offers a pathway to bridge the intelligence gap, enabling modular, measurable, and trustworthy AI across heterogeneous environments and present a case study.

Keywords: Edge AI, Hardware Heterogeneity, Conceptual Framework, AI Standardization, Solution Stack, Platform Economics, Theoretical Model, Indoai, AI Cameras

Downloads: | DOI: 10.17148/IARJSET.2026.13202