From Capability Migration to Value Upgrade: The Evolutionary Path of AI Cross-Domain Innovation (2022-2026) - Yu, He (International Journal of Multidisciplinary Research, 2026)
Artificial intelligence (AI) cross-domain innovation follows a distinct evolutionary path: starting with technical capability migration (adapting AI methods from one domain to another), progressing through scenario reconstruction (redefining application contexts to fit cross-domain needs), and culminating in value upgrade (creating new value that transcends single-domain boundaries). This review synthesizes 9 key studies (2022-2026) to map this three-stage evolutionary path, analyzing how AI evolves across healthcare, quantum science, digital commerce, cybersecurity, and finance. Findings reveal that: capability migration lays the technical foundation (e.g., LSTM from NLP to finance, hybrid architectures from computer vision to healthcare), scenario reconstruction aligns AI with cross-domain contexts (e.g., redefining data sharing as compliant collaboration in digital commerce), and value upgrade delivers systemic impact (e.g., risk-cost balance in cybersecurity, inclusive innovation for SMEs). This evolutionary framework offers a dynamic perspective on AI’s cross-domain expansion, guiding researchers to design evolvable AI systems and practitioners to leverage AI’s transformative potential beyond single-domain applications.