The Tech-Scenario-Governance Triangle: A Synergistic Framework for AI Cross-Domain Innovation (2022-2026)

Fan Yuan

special issue

Vol. 2 No. 3 (2026): International Journal of Multidisciplinary Research

The Tech-Scenario-Governance Triangle: A Synergistic Framework for AI Cross-Domain Innovation (2022-2026)

Fan Yuan

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Submitted: March 25, 2026
Published: 2026-04-02

Abstract

Artificial intelligence (AI) cross-domain innovation is not driven by single-factor advancement but by the synergistic interaction of three core elements: technical feasibility, scenario relevance, and governance compliance. This review synthesizes 9 key studies (2022-2026) to propose a "Tech-Scenario-Governance (TSG) Triangle" framework, analyzing how the dynamic balance of these three elements enables AI success across healthcare, quantum science, digital commerce, cybersecurity, and finance. The framework reveals that: technical elements provide foundational capabilities (e.g., hybrid architectures, unsupervised learning), scenario elements define practical relevance (e.g., clinical diagnosis, cross-channel marketing), and governance elements ensure sustainable adoption (e.g., privacy protection, regulatory alignment). Findings demonstrate that breakdown in any single element leads to innovation failure, while tight synergistic coupling drives impactful cross-domain AI. This framework offers a holistic tool for researchers, practitioners, and policymakers to evaluate and design AI solutions that are technically robust, practically applicable, and ethically compliant.

References

Chang, C., Fu, M., Chen, X., et al. (2025, November). Research on PDU-Net Lung Nodule Segmentation Algorithm Based on Path Aggregation and Dual Attention. In 2025 4th International Conference on Image Processing, Computer Vision and Machine Learning (ICICML) (pp. 1897-1900). IEEE.
Wu, A. K., Primeau, L., Zhang, J., et al. (2025). Modeling Quantum Geometry for Fractional Chern Insulators with unsupervised learning. arXiv preprint arXiv:2510.03018.
Yi, X. (2026). A Federated and Differentially Private Incentive–Marketing Framework for Privacy-Preserving Cross-Channel Measurement in AI-Powered Digital Commerce.
Zhou, D. (2025, December). M-VP2: Microservice-Oriented Vulnerability Patch Planning-A Cost-Aware Approach using Multi-Agent Reinforcement Learning. In 2025 5th International Conference on Computer, Internet of Things and Control Engineering (CITCE) (pp. 248-254). IEEE.
Zhou, D. (2026). AI-Driven Hybrid SAST–DAST–SCA–IAST Framework for Risk-Based Vulnerability Prioritization in Microservice Architectures.
Liu, T. (2022, December). Financial Constraint’Impact on Firms’ ESG Rating Based on Chinese Stock Market. In 2022 4th International Conference on Economic Management and Cultural Industry (ICEMCI 2022) (pp. 1085-1095). Atlantis Press.
Yi, X. (2026). Trusted AI Commercialization Infrastructure for SMBs: A Unified Multi-Tenant Architecture Integrating Incentive Systems, Content Governance, and Standardized Recommendation APIs.
Yi, X. (2026). Privacy-Enhanced Ad Targeting for Social E-Commerce: A Federated Learning Framework with Zero-Knowledge Verification for Creator Monetization. Frontiers in Business and Finance, 3(1), 102-113.
Li, H., & Liu, T. (2023). Portfolio optimization based on the LSTM forecasting model. In Proceedings of the 2nd International Conference on Financial Technology and Business Analysis (Vol. 48, No. 1, pp. 97-106).

Keywords

Tech-Scenario-Governance
Artificial intelligence

How to Cite

Yuan, F. (2026). The Tech-Scenario-Governance Triangle: A Synergistic Framework for AI Cross-Domain Innovation (2022-2026). International Journal of Multidisciplinary Research, 2(3). Retrieved from https://ijmrscience.org/index.php/ijmr/article/view/159

This work is licensed under a Creative Commons Attribution 4.0 International License.

[1] Chang, C., Fu, M., Chen, X., et al. (2025, November). Research on PDU-Net Lung Nodule Segmentation Algorithm Based on Path Aggregation and Dual Attention. In 2025 4th International Conference on Image Processing, Computer Vision and Machine Learning (ICICML) (pp. 1897-1900). IEEE.

[2] Wu, A. K., Primeau, L., Zhang, J., et al. (2025). Modeling Quantum Geometry for Fractional Chern Insulators with unsupervised learning. arXiv preprint arXiv:2510.03018.

[3] Yi, X. (2026). A Federated and Differentially Private Incentive–Marketing Framework for Privacy-Preserving Cross-Channel Measurement in AI-Powered Digital Commerce.

[4] Zhou, D. (2025, December). M-VP2: Microservice-Oriented Vulnerability Patch Planning-A Cost-Aware Approach using Multi-Agent Reinforcement Learning. In 2025 5th International Conference on Computer, Internet of Things and Control Engineering (CITCE) (pp. 248-254). IEEE.

[5] Zhou, D. (2026). AI-Driven Hybrid SAST–DAST–SCA–IAST Framework for Risk-Based Vulnerability Prioritization in Microservice Architectures.

[6] Liu, T. (2022, December). Financial Constraint’Impact on Firms’ ESG Rating Based on Chinese Stock Market. In 2022 4th International Conference on Economic Management and Cultural Industry (ICEMCI 2022) (pp. 1085-1095). Atlantis Press.

[7] Yi, X. (2026). Trusted AI Commercialization Infrastructure for SMBs: A Unified Multi-Tenant Architecture Integrating Incentive Systems, Content Governance, and Standardized Recommendation APIs.

[8] Yi, X. (2026). Privacy-Enhanced Ad Targeting for Social E-Commerce: A Federated Learning Framework with Zero-Knowledge Verification for Creator Monetization. Frontiers in Business and Finance, 3(1), 102-113.

[9] Li, H., & Liu, T. (2023). Portfolio optimization based on the LSTM forecasting model. In Proceedings of the 2nd International Conference on Financial Technology and Business Analysis (Vol. 48, No. 1, pp. 97-106).