A Strategic Framework for Trustworthy AI-Augmented Decision Intelligence
DOI:
https://doi.org/10.51137/wrp.ijada.693Keywords:
Artificial Intelligence (AI), Decision Intelligence (DI), DEMATEL–ISM–MICMAC, Ecosystem Governance, Explainable AI (XAI), Human–AI Collaboration, MLOpsAbstract
Organizations are rapidly adopting artificial intelligence (AI) to improve managerial decision-making; nevertheless, adoption often stumbles due to inadequate transparency, auditability, and alignment with strategy and ecosystem requirements. This study develops and critically assesses a multi-layer framework for AI-enhanced decision intelligence (DI), including five dimensions: Data and Infrastructure, AI and Analytics, Decision Intelligence Core, Strategic Alignment, and Ecosystem and Governance. A hybrid conceptual-analytical approach is used, combining literature-based thematic analysis with quantitative structuring and prioritization techniques to examine relationships among important enablers and determine implementation priorities. The results indicate that data governance, interoperability, MLOps/monitoring, and explainable AI are critical drivers with significant causal impact, while Human-in-the-Loop and Assurance/Auditability form the operational core that connects technological capabilities to decision-making processes. Strategic KPIs, ESG compliance, and risk appetite are downstream dependent outcomes influenced by upstream enablers. BWM prioritizes designating MLOps, Monitoring and Assurance, and Auditability as the key investment sectors. The research further presents a Decision Intelligence Preparedness Index (DIRI) to operationalize these findings, combining capability weights with maturity ratings for a practical evaluation of organizational preparedness. The proposed framework, therefore, contributes by providing an analytically grounded, multi-layer architecture that integrates technical, organizational, and ecosystem-level dimensions of AI-enabled decision-making, while also illustrating the applicability of hybrid analytical methods for structuring complex socio-technical systems. The analytical inputs are based on literature-derived evidence rather than primary empirical data or expert opinion; hence, the results should be seen as a conceptual-analytical synthesis that offers organized direction and a basis for future empirical validation.
References
Abirami, S. S. R., & Chitra, S. (2021). Data science and analytics: An overview from data-driven smart computing, decision-making and applications perspective. SN Computer Science, 2(5), Article 377. https://doi.org/10.1007/s42979-021-00765-8
Bao, Y., Gong, W., & Yang, K. (2023). A literature review of human-AI synergy in decision making: From the perspective of affordance actualization theory. Systems, 11(9), Article 442. https://doi.org/10.3390/systems11090442
Batool, A., Zowghi, D., & Bano, M. (2025). AI governance: A systematic literature review. AI and Ethics, 5(3), 3265–3279. https://doi.org/10.1007/s43681-024-00653-w
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Csaszar, F. A., Ketkar, H., & Kim, H. (2024). Artificial intelligence and strategic decision-making: Evidence from entrepreneurs and investors. Strategy Science, 9(4), 322–345. https://doi.org/10.1287/stsc.2024.0190
de Oliveira, F. A., Kluge, K., & Hennemann, K. M. (2023). Defining human-AI teaming the human-centered way: A scoping review and network analysis. Frontiers in Artificial Intelligence, 6, Article 1250725. https://doi.org/10.3389/frai.2023.1250725
Delen, D., & Zolbanin, H. M. (2018). The analytics paradigm in business research. Journal of Business Research, 90, 186–195. https://doi.org/10.1016/j.jbusres.2018.05.013
Fontela, E., & Gabus, A. (1976). The DEMATEL observer. Battelle Geneva Research Centre. https://www.scirp.org/reference/ReferencesPapers?ReferenceID=1847241
Godet, M. (2001). Creating futures: Scenario planning as a strategic management tool (2nd ed.). Economica.
Kalpokaite, N., & Radivojevic, I. (2019). Demystifying qualitative data analysis for novice qualitative researchers. The Qualitative Report, 24(13), 44–57. https://doi.org/10.46743/2160-3715/2019.4120
Liu, J., Gao, Q., & Zhu, L. (2025). Business ecosystem governance, digital capabilities, and enterprises' digital innovation performance: Empirical data from China. Journal of Engineering and Technology Management, 77, Article 101900. https://doi.org/10.1016/j.jengtecman.2025.101900
Munni, A. (2022). Artificial Intelligence in Project Management: A Systematic Review of Applications, Challenges, and Future Directions. Frontiers in Computer Science and Artificial Intelligence, 1(1), 68-78. https://doi.org/10.32996/fcsai.2022.1.1.8x
O'Connor, C., & Joffe, H. (2020). Intercoder reliability in qualitative research: Debates and practical guidelines. International Journal of Qualitative Methods, 19, Article 1609406919899220. https://doi.org/10.1177/1609406919899220
Papagiannidis, E., Mikalef, P., & Conboy, K. (2025). Responsible artificial intelligence governance: A review and research framework. The Journal of Strategic Information Systems, 34(2), Article 101885. https://doi.org/10.1016/j.jsis.2024.101885
Pappas, I. O., Mikalef, P., Giannakos, M. N., Krogstie, J., & Lekakos, G. (2018). Big data and business analytics ecosystems: Paving the way towards digital transformation and sustainable societies. Information Systems and e-Business Management, 16(3), 479–491. https://doi.org/10.1007/s10257-018-0377-z
Papri, N. K., Haque, M. K. A., Mouwa, Z. (2022). "Climate-Adaptive Sustainable Apparel Supply Chains: Integrating Machine Learning, Digitalization, and Circular Economy", Journal of Primeasia, 3(1), 1-16, 10730. https://doi.org/10.25163/primeasia.3110730
Raghupathi, W., & Raghupathi, V. (2021). Contemporary business analytics: An overview. Data, 6(8), Article 86. https://doi.org/10.3390/data6080086
Rahman, M., Forhad, M. R. (2021). "Sustainable Biomaterials for Next-Generation Textile Applications: Emerging Fibers, Biopolymers, and Advanced Fabrication Strategies – A Review", Journal of Primeasia, 2(1), 1-8, 10698. https://doi.org/10.25163/primeasia.2110698
Rezaei, J. (2016). Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega, 64, 126–130. https://doi.org/10.1016/j.omega.2015.12.001
Sarker, A., Haque, M. K. A., & Mouwa, Z. (2022). Artificial Intelligence for Sustainable and Climate-Resilient Apparel Supply Chains: A Narrative Review and Integrative Framework. Frontiers in Computer Science and Artificial Intelligence, 1(1), 79-92. https://doi.org/10.32996/fcsai.2022.1.1.9x
Sauer, C. R., & Burggräf, P. (2025). Hybrid intelligence—Systematic approach and framework to determine the level of human-AI collaboration for production management use cases. Production Engineering, 19(3), 525–541. https://doi.org/10.1007/s11740-024-01326-7
Schmutz, J. B., Outland, N., Kerstan, S., Georganta, E., & Ulfert, A.-S. (2024). AI-teaming: Redefining collaboration in the digital era. Current Opinion in Psychology, 58, Article 101837. https://doi.org/10.1016/j.copsyc.2024.101837
Schneider, J., Abraham, R., Meske, C., & vom Brocke, J. (2023). Artificial intelligence governance for businesses. Information Systems Management, 40(3), 229–249. https://doi.org/10.1080/10580530.2022.2085825
Simón, C., Revilla, E., & Sáenz, M. J. (2024). Integrating AI in organizations for value creation through human-AI teaming: A dynamic-capabilities approach. Journal of Business Research, 182, Article 114783. https://doi.org/10.1016/j.jbusres.2024.114783
Susha, I., van den Broek, T., van Veenstra, A.-F., & Linåker, J. (2023). An ecosystem perspective on developing data collaboratives for addressing societal issues: The role of conveners. Government Information Quarterly, 40(1), Article 101763. https://doi.org/10.1016/j.giq.2022.101763
Tasleem, N., Raghav, R. S., Ansari, M. N., & Sharma, A. J. (2024). A decision intelligence framework: Integrating human intuition with AI models. JAIGS, 7(1), 304–319. https://doi.org/10.60087/jaigs.v7i01.361
Toorajipour, R., Oghazi, P., & Palmié, M. (2024). Data ecosystem business models: Value propositions and value capture with artificial intelligence of things. International Journal of Information Management, 78, Article 102804. https://doi.org/10.1016/j.ijinfomgt.2024.102804
Warfield, J. N. (1974). Developing interconnection matrices in structural modeling. IEEE Transactions on Systems, Man, and Cybernetics, 4(1), 81–87. https://doi.org/10.1109/TSMC.1974.5408524
Wissuchek, C., & Zschech, P. (2025). Prescriptive analytics systems revised: A systematic literature review from an information systems perspective. Information Systems and e-Business Management, 23(2), 279–353. https://doi.org/10.1007/s10257-024-00688-w
Zaidan, E., & Ibrahim, I. A. (2024). AI governance in a complex and rapidly changing regulatory landscape: A global perspective. Humanities and Social Sciences Communications, 11(1), Article 1121. https://doi.org/10.1057/s41599-024-03560-x
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Copyright (c) 2026 Farzana Zannat, Tama Rani Kundu, Rebeka Islam Tohfa, Volha Khomich, Nafiz Imtiaz, Shadman Mahmud (Author)
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