Beyond Automation: A Conceptual Framework for Strategic AI Integration in Business Decision-Making Processes
DOI:
https://doi.org/10.54099/aijbs.v6i2.1951Keywords:
Strategic AI Integration, Human-AI Complementarity, AI Governance, Dynamic CapabilitiesAbstract
Purpose – Purpose – The purpose of this paper is to develop a conceptual framework for strategic artificial intelligence (AI) integration in organisational decision making. It deals with the fragmentation of existing research by differentiating AI-supported decision intelligence from automation-centric approaches and offers a systematic model to align AI capabilities with organisational strategy. Methodology/approach – The study adopts a conceptual research design using integrative review of literature. The proposed Strategic AI Integration Framework (SAIF) is developed by synthesizing Dynamic Capabilities Theory, the Resource-Based View, and Simon’s bounded rationality model. The framework is developed through a critical literature review on AI, strategic management, decision intelligence and organisational governance. Relevant literature on AI, strategic management, decision intelligence, and organisational governance is critically reviewed to establish the framework. Findings – The study proposes the Strategic AI Integration Framework (SAIF), comprising four dimensions: Decision Domain Mapping, AI Capability Alignment, Human–AI Complementarity, and Governance and Accountability Architecture. The framework is operationalised through six research propositions explaining how strategic AI integration enhances decision quality, organisational agility, and sustainable competitive advantage while ensuring effective governance and human oversight. Originality/value – This study contributes to the AI and strategic management literature by providing a theoretically grounded framework that integrates strategic management, decision-making, and AI governance into a unified model. The framework offers a foundation for future empirical research and practical guidance for organisations implementing AI strategically.
References
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99-120.
Berente, N., Gu, B., Recker, J., and Santhanam, R. (2021). Managing artificial intelligence. MIS Quarterly, 45(3), 1433-1450.
Borges, A.F.S., Laurindo, F.J.B., Spínola, M.M., Gonçalves, R.F., and Mattos, C.A. (2021). The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions. International Journal of Information Management, 57, 102225.
Braun, V., and Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101.
Brynjolfsson, E., and McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W.W. Norton and Company.
Brynjolfsson, E., Li, D., and Raymond, L. (2025). Generative AI at work. The Quarterly Journal of Economics, 140(2), 889-942.
Burton, J.W., Stein, M.K., and Jensen, T.B. (2020). A systematic review of algorithm aversion in augmented decision making. Journal of Behavioral Decision Making, 33(2), 220-239.
Collins, C., Dennehy, D., Conboy, K., and Mikalef, P. (2021). Artificial intelligence in information systems research: A systematic literature review and research agenda. International Journal of Information Management, 60, 102383.
Davenport, T.H., and Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108-116.
Dietvorst, B.J., Simmons, J.P., and Massey, C. (2015). Algorithm aversion: People erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology: General, 144(1), 114-126.
Duan, Y., Edwards, J.S., and Dwivedi, Y.K. (2019). Artificial intelligence for decision making in the era of Big Data: Evolution, challenges and research agenda. International Journal of Information Management, 48, 63-71.
Dwivedi, Y.K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., et al. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994.
Eisenhardt, K.M., and Martin, J.A. (2000). Dynamic capabilities: What are they? Strategic Management Journal, 21(10-11), 1105-1121.
Enholm, I.M., Papagiannidis, E., Mikalef, P., and Krogstie, J. (2022). Artificial intelligence and business value: A literature review. Information Systems Frontiers, 24(5), 1709-1734.
European Union (2024). Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Official Journal of the European Union, L series, 12 July 2024.
Fügener, A., Grahl, J., Gupta, A., and Ketter, W. (2021). Will humans-in-the-loop become borgs? Merits and pitfalls of working with AI. MIS Quarterly, 45(3), 1527-1556.
Fügener, A., Grahl, J., Gupta, A., and Ketter, W. (2022). Cognitive challenges in human-artificial intelligence collaboration: Investigating the path toward productive delegation. Information Systems Research, 33(2), 678-696.
Gartner (2024). Gartner predicts 30% of generative AI projects will be abandoned after proof of concept by end of 2025 [Press release]. Gartner, Inc.
Glikson, E., and Woolley, A.W. (2020). Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals, 14(2), 627-660.
Haefner, N., Wincent, J., Parida, V., and Gassmann, O. (2021). Artificial intelligence and innovation management: A review, framework, and research agenda. Technological Forecasting and Social Change, 162, 120392.
Haenlein, M., and Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, 61(4), 5-14.
Jaakkola, E. (2020). Designing conceptual articles: Four approaches. AMS Review, 10(1-2), 18-26.
Jarrahi, M.H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577-586.
Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux.
Keding, C. (2021). Understanding the interplay of artificial intelligence and strategic management: Four decades of research in review. Management Review Quarterly, 71(1), 91-134.
Kellogg, K.C., Valentine, M.A., and Christin, A. (2020). Algorithms at work: The new contested terrain of control. Academy of Management Annals, 14(1), 366-410.
Krakowski, S., Luger, J., and Raisch, S. (2023). Artificial intelligence and the changing sources of competitive advantage. Strategic Management Journal, 44(6), 1425-1452.
Lebovitz, S., Lifshitz-Assaf, H., and Levina, N. (2022). To engage or not to engage with AI for critical judgments: How professionals deal with opacity when using AI for medical diagnosis. Organization Science, 33(1), 126-148.
Lichtenthaler, U. (2020). Artificial intelligence and innovation management: A research agenda. Technology Innovation Management Review, 9(3), 4-10.
Logg, J.M., Minson, J.A., and Moore, D.A. (2019). Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes, 151, 90-103.
Makridakis, S. (2017). The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures, 90, 46-60.
Mäntymäki, M., Minkkinen, M., Birkstedt, T., and Viljanen, M. (2022). Defining organizational AI governance. AI and Ethics, 2(4), 603-609.
Mikalef, P., and Gupta, M. (2021). Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Information and Management, 58(3), 103434.
Mikalef, P., Conboy, K., Lundström, J.E., and Popovič, A. (2022). Thinking responsibly about responsible AI and ‘the dark side’ of AI. European Journal of Information Systems, 31(3), 257-268.
Ministry of Science, Technology and Innovation (MOSTI) (2024). National Guidelines on AI Governance and Ethics. Putrajaya: MOSTI.
Page, M.J., McKenzie, J.E., Bossuyt, P.M., Boutron, I., Hoffmann, T.C., Mulrow, C.D., et al. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71.
Papagiannidis, E., Mikalef, P., and Conboy, K. (2025). Responsible artificial intelligence governance: A review and research framework. The Journal of Strategic Information Systems, 34(2), 101885.
Raisch, S., and Krakowski, S. (2021). Artificial intelligence and management: The automation-augmentation paradox. Academy of Management Review, 46(1), 192-210.
Russell, S., and Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
Shrestha, Y.R., Ben-Menahem, S.M., and von Krogh, G. (2019). Organizational decision-making structures in the age of artificial intelligence. California Management Review, 61(4), 66-83.
Simon, H.A. (1960). The New Science of Management Decision. Harper and Row.
Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333-339.
Stanford HAI (2025). Artificial Intelligence Index Report 2025. Stanford, CA: Stanford Institute for Human-Centered Artificial Intelligence, Stanford University.
Teece, D.J. (1986). Profiting from technological innovation: Implications for integration, collaboration, licensing and public policy. Research Policy, 15(6), 285-305.
Teece, D.J., Pisano, G., and Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509-533.
Tio, L., Khan, S., and Yadewani, D. (2026a). AI-Personalized Green Marketing: Driving Eco-Conscious Consumer Behavior In Real-Time. Jurnal Menara Ekonomi: Penelitian dan Kajian Ilmiah Bidang Ekonomi, 12(1), 96-105.
Tio, L., Kulal, S. M., and Yadewani, D. (2026b). Sustainable influencer marketing: leveraging ai and ar tools to promote green lifestyles. International Journal of Islamic Business and Management Review, 5(2).
Torraco, R.J. (2005). Writing integrative literature reviews: Guidelines and examples. Human Resource Development Review, 4(3), 356-367.
Trunk, A., Birkel, H., and Hartmann, E. (2020). On the current state of combining human and artificial intelligence for strategic organizational decision making. Business Research, 13(3), 875-919.
van Eck, N.J., and Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538.
Weber, M., Engert, M., Schaffer, N., Weking, J., and Krcmar, H. (2023). Organizational capabilities for AI implementation: Coping with inscrutability and data dependency in AI. Information Systems Frontiers, 25(4), 1549-1569.
Wirtz, B.W., Weyerer, J.C., and Geyer, C. (2019). Artificial intelligence and the public sector: Applications and challenges. International Journal of Public Administration, 42(7), 596-615.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Lina Tio, Dorris Yadewani, Salamiah Muhd Kulal
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
This work is licensed under a 