Triadic Dynamic: Capability Alignment, Constraint Resolution, and Value Co-Creation in AI-Powered Business-Finance-Cybersecurity Synergy (2022-2026)

Ji Liu

special issue

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

Triadic Dynamic: Capability Alignment, Constraint Resolution, and Value Co-Creation in AI-Powered Business-Finance-Cybersecurity Synergy (2022-2026)

V.A.

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

Abstract

Artificial intelligence (AI) has redefined cross-domain collaboration among digital commerce, finance, and cybersecurity through a triadic dynamic: capability alignment (matching domain-specific core capabilities via AI-enabled integration), constraint resolution (addressing interdomain frictions and bottlenecks with targeted AI tools), and value co-creation (generating emergent cross-domain value that transcends single-sector outcomes). This review synthesizes 7 key studies (2022-2026) to unpack the interdependent logic of the triad: capability alignment lays the groundwork by integrating complementary strengths (e.g., digital commerce’s user-centric tools, finance’s predictive models, cybersecurity’s risk safeguards); constraint resolution removes barriers by mitigating critical frictions (e.g., SME resource scarcity, data privacy conflicts, risk-cost tradeoffs); value co-creation delivers transformative outcomes by amplifying collective potential (e.g., inclusive sustainable growth, proactive risk-resilient ecosystems). Findings reveal that: the triadic dynamic operates as a mutually reinforcing system—alignment identifies constraints, constraint resolution optimizes alignment, and both enable sustained co-creation; modular AI architectures and privacy-enhancing technologies (PETs) are the primary enablers of capability alignment; SMEs serve as the linchpin for inclusive constraint resolution and value diffusion. This framework offers a capability-centric perspective on cross-domain AI synergy, guiding researchers, practitioners, and policymakers to leverage complementary strengths for systemic value creation.

References

Yi, X. (2026). A Federated and Differentially Private Incentive–Marketing Framework for Privacy-Preserving Cross-Channel Measurement in AI-Powered Digital Commerce.
Yi, X. (2026). Trusted AI Commercialization Infrastructure for SMBs: A Unified Multi-Tenant Architecture Integrating Incentive Systems, Content Governance, and Standardized Recommendation APIs.
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.
Zhou, D. (2026). AI-Driven Hybrid SAST–DAST–SCA–IAST Framework for Risk-Based Vulnerability Prioritization in Microservice Architectures.
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.
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).
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.

Keywords

Capability Alignment
Constraint Resolution
Value Co-Creation

How to Cite

Liu, J. (2026). Triadic Dynamic: Capability Alignment, Constraint Resolution, and Value Co-Creation in AI-Powered Business-Finance-Cybersecurity Synergy (2022-2026). International Journal of Multidisciplinary Research, 2(3). Retrieved from https://ijmrscience.org/index.php/ijmr/article/view/160

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

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

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

[3] 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.

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

[5] 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.

[6] 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).

[7] 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.