Bridging the Intelligence Gap: A Conceptual Framework for Scaling Edge AI Across Heterogeneous Hardware

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 AIRIcertified 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.