Cross-Domain AI Evolution: A Framework for Capability Transformation and Value Enhancement

Abstract

Artificial intelligence (AI) is increasingly evolving from domain-specific automation toward reusable capabilities that can be transferred, recombined, governed, and commercialized across heterogeneous application environments. However, cross-domain AI adoption is not equivalent to simply migrating an existing model from one sector to another. Effective migration requires adaptation of technical capabilities, data structures, governance mechanisms, incentives, security controls, and value-generation processes. This research develops a conceptual framework for understanding cross-domain AI evolution as a progression from capability transfer to capability transformation and ultimately to value enhancement. The framework synthesizes evidence from studies addressing AI-enabled medical image segmentation, unsupervised learning, financial forecasting, ESG-related financial analysis, privacy-preserving digital commerce, AI commercialization, and AI-driven cybersecurity. The methodology employs comparative thematic synthesis and architectural abstraction to identify reusable capability layers and transformation mechanisms across domains. The analysis indicates that cross-domain AI value depends on four interdependent dimensions: capability portability, contextual adaptation, trustworthy governance, and measurable value realization. The resulting framework positions AI evolution as a continuous process in which technical capabilities are transferred, contextualized, integrated with domain constraints, and converted into economic, operational, scientific, or social value. The study contributes a structured theoretical model for researchers and practitioners seeking to design scalable AI systems capable of operating beyond their original application boundaries.