Scalable and Interoperable AIoT Ecosystems: Market-Driven Adoption of Large-Scale AI Models for Context-Aware Intelligent Infrastructure

The convergence of Artificial Intelligence of Things (AIoT) and large-scale artificial intelligence models is reshaping intelligent infrastructure across industries. While advances in deep learning, foundation models, and edge computing have expanded the technical capabilities of AIoT systems, large-scale adoption remains constrained by scalability, interoperability, and market readiness. This study investigates how scalable and interoperable AIoT ecosystems enable the market-driven adoption of large-scale AI models for context-aware intelligent infrastructure. A conceptual architecture is proposed that integrates cloud–edge–device intelligence, interoperable data standards, and modular AI services to support real-time contextual reasoning. Using a market-oriented analytical framework, the study examines adoption drivers, including cost efficiency, operational resilience, regulatory compliance, and ecosystem maturity. Synthetic scenario-based simulations are used to demonstrate how large-scale AI models enhance situational awareness, predictive decision-making, and adaptive automation in smart infrastructure environments. The findings highlight that interoperability and scalability are not merely technical requirements but strategic enablers that determine economic viability and long-term sustainability. This research contributes a systems-level perspective that bridges technical design with market dynamics, offering practical insights for policymakers, system architects, and industry stakeholders seeking to deploy large-scale, context-aware AIoT infrastructures.