Abstract

This study examines the transformative impact of Artificial Intelligence (AI) on organizational decision-making processes, addressing both opportunities and challenges in contemporary business environments. Employing a systematic literature review and multiple case study analysis, this research synthesizes findings from academic literature and real-world implementations across various industries. The research reveals that AI significantly enhances decision-making accuracy, operational efficiency, and scalability. However, critical challenges include algorithmic bias, data privacy concerns, and organizational resistance to adoption. This paper contributes an integrated framework for AI implementation in decision-making, addressing technical, organizational, and ethical dimensions simultaneously. The findings underscore the necessity for a strategic, resource-based approach to AI adoption, moderated by a robust ethical governance structure to ensure long-term competitive advantage and responsible innovation. The study further emphasizes that the success of AI initiatives is predominantly determined by organizational factors, including strategic leadership, cultural alignment, and effective change management, rather than purely technological prowess, paving the way for a new era of human-AI collaboration.

References

• Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165-1188.
• Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108-116.
• Brynjolfsson, E., & McAfee, A. (2017). Machine, platform, crowd: Harnessing our digital future. WW Norton & Company.
• McKinsey & Company. (2025). Rethinking decision making to unlock AI potential. Retrieved from https://www.mckinsey.com/capabilities/operations/our-insights/when-can-ai-make-good-decisions-the-rise-of-ai-corporate-citizens
• Forbes. (2024). Will AI Save Or Harm Us? 3 Ethical Challenges For Businesses in 2025. Retrieved from https://www.forbes.com/sites/bruceweinstein/2024/12/11/will-ai-save-or-harm-us-3-ethical-challenges-for-businesses-in-2025/
• Simon, H. A. (1957). Models of man: Social and rational. Wiley.
• Shick, M., Johnson, N., & Fan, Y. (2024). Artificial intelligence and the end of bounded rationality: a new era in organizational decision making. Development and Learning in Organizations.
• Almutairi, W., & Almatrodi, I. (2025). Trust Under Bounded Rationality: Exploring Human-AI Interaction in Decision-Making Through Large Language Models. Sage Open.
• Schwarz, G. (2022). Bounded Rationality, Satisficing, Artificial Intelligence, and Decision-Making in Public Organizations. Public Administration Review, 82(6), 957-969.
• The Decision Lab. (2025). Bounded Rationality – The Decision. Retrieved from https://thedecisionlab.com/biases/bounded-rationality
• Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99-120.
• Nelson, J., & Liam, M. (2024). Revolutionizing Manufacturing and Finance: The Power of AI and Machine Learning Approaches. ResearchGate.
• Elwaked, D. K., Kassim, N. M., & Thurasamy, R. (2025). Enhancing Internal Integration Through Ai: A Resource-Based View With Data-Driven Culture As Moderator. Journal of Logistics, Informatics and Service Science, 12(2), 203-222.
• Day, S. W. (2025). The Role of Artificial Intelligence in Scaling Impact. Sustainability, 18(7), 341.
• Teece, D. J. (2018). Dynamic capabilities as a foundation for strategic management. Academy of Management Perspectives, 32(3), 329-351.
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
• Ibrahim, F. (2025). The technology acceptance model and adopter type analysis of artificial intelligence in healthcare. Frontiers in Artificial Intelligence.
• Kelly, S. (2023). What factors contribute to the acceptance of artificial intelligence (AI) in the workplace? A systematic review. Computers in Human Behavior.
• Marocco, S., Barbieri, B., & Talamo, A. (2024). Exploring facilitators and barriers to managers' adoption of AI-based systems in decision making: A systematic review. AI, 5(4), 123.
• Penske Logistics. (2025). The Benefits of AI in the Supply Chain. Retrieved from https://www.penskelogistics.com/solutions/supply-chain-management/ai-in-the-supply-chain/
• Patalas-Maliszewska, J., & Pająk, I. (2020). AI-based Decision-making Model for the Development of a Manufacturing Company in the context of Industry 4.0. IEEE International Conference on Fuzzy Systems.
• LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
• Hadjitchoneva, J. (2019). Efficient Automation of Decision-making Processes in Financial Industry: Case study and generalised model. CEUR Workshop Proceedings.
• DigitalDefynd. (2025). Top 20 AI in Finance Case Studies. Retrieved from https://digitaldefynd.com/IQ/ai-in-finance-case-studies/
• Aimultiple. (2025). Top 25 Generative AI Finance Use Cases & Case Studies. Retrieved from https://research.aimultiple.com/generative-ai-finance/
• Esteva, A., et al. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118.
• AMA. (2025). 2 in 3 physicians are using health AI---up 78% from 2023. Retrieved from https://www.ama-assn.org/practice-management/digital-health/2-3-physicians-are-using-health-ai-78-2023
• Zeb, S., et al. (2024). AI in healthcare: revolutionizing diagnosis and therapy. International Journal of Research in Medical Sciences.
• Kagermann, H., et al. (2013). Recommendations for implementing the strategic initiative INDUSTRIE 4.0. National Academy of Science and Engineering.
• Ma, L. (2025). The impact of regional artificial intelligence development on supply chain resilience. Transportation Research Part E: Logistics and Transportation Review, 185, 103521.
• Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260.
• Whatfix. (2025). Why AI Implementations Are Failing (Root Causes). Retrieved from https://whatfix.com/blog/ai-implementation-failures/
• McKinsey & Company. (2024). Harnessing the power of AI in distribution operations. Retrieved from https://www.mckinsey.com/industries/industrials-and-electronics/our-insights/distribution-blog/harnessing-the-power-of-ai-in-distribution-operations
• Future-Code. (2025). Case Studies in Transforming AI Process Automation. Retrieved from https://future-code.dev/en/blog/case-studies-in-transforming-ai-process-automation-across-sectors/
• Booyse, D., & Scheepers, C. B. (2024). Barriers to adopting automated organisational decision-making through the use of artificial intelligence. Management Research Review, 47(1), 1-20.
• RAND Corporation. (2024). Why AI Projects Fail and How They Can Succeed. Retrieved from https://www.rand.org/pubs/research_reports/RRA2680-1.html
• Fortune. (2025). MIT report: 95% of generative AI pilots at companies are failing. Retrieved from https://fortune.com/2025/08/18/mit-report-95-percent-generative-ai-pilots-at-companies-failing-cfo/
• Kane, G. C., et al. (2015). Strategy, not technology, drives digital transformation. MIT Sloan Management Review, 14(1), 1-25.
• [39]Harvard Law School Forum. (2025). Strategic Governance of AI: A Roadmap for the Future. Retrieved from https://corpgov.law.harvard.edu/2025/04/24/strategic-governance-of-ai-a-roadmap-for-the-future/
• Suljic, V. (2025). Strategic leadership in AI-driven digital transformation. SBS Journal of Applied Business Research, 1(1).
• Global Knowledge. (2025). Overcoming the Challenges of AI Adoption in Organizations. Retrieved from https://www.globalknowledge.com/us-en/resources/resource-library/articles/overcoming-the-challenges-of-ai-adoption-in-organizations/
• IBM. (2025). The 5 biggest AI adoption challenges for 2025. Retrieved from https://www.ibm.com/think/insights/ai-adoption-challenges
• The Decision Lab. (2025). Organizational Barriers to AI Adoption. Retrieved from https://thedecisionlab.com/reference-guide/management/organizational-barriers-to-ai-adoption
• Raftopoulos, M. (2024). Organizational Challenges in Adopting Artificial Intelligence. Tampere University Research Portal.
• Functionly. (2025). Decentralized Decision Making: Empowering Teams with AI-Driven Insights. Retrieved from https://www.functionly.com/orginometry/the-ai-revolution/decentralized-decision-making-empowering-teams-with-ai-driven-insights
• Brink, A., Benyayer, L. D., & Kupp, M. (2024). Decision-making in organizations: should managers use AI? Journal of Business Strategy, 45(4), 234-245.
• Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577-586.
• Eller Arizona. (2025). 10 Ethical Considerations Shaping the Future of AI in Business. Retrieved from https://eller.arizona.edu/news/10-ethical-considerations-shaping-future-ai-business
• Mehrabi, N., et al. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), 1-35.
• Harvard Professional Development. (2025). Building a Responsible AI Framework: 5 Key Principles for Organizations. Retrieved from https://professional.dce.harvard.edu/blog/building-a-responsible-ai-framework-5-key-principles-for-organizations/
• Globisinsights. (2025). The Ethical Challenges of AI and Their Actionable Solutions. Retrieved from https://globisinsights.com/career-skills/innovation/ethical-challenges-of-ai/
• Maiti, M. (2025). A study on ethical implications of artificial intelligence in business. Financial Business Journal, 4(1), 1-15.
• Birkstedt, T., et al. (2023). AI governance: themes, knowledge gaps and future agendas. Internet Research, 33(7), 2413-2438.
• Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
• Bhandari, R., & Bhandari, S. (2025). AI governance and global stability: Why US leadership matters. Journal of Current Social and Political Issues, 1(1).
• CTO Magazine. (2025). Decision Making Models in AI Leadership. Retrieved from https://ctomagazine.com/ai-leadership-decision-making-models-accountability/
• Netcom Learning. (2025). From Risk to Reputation: The Role of AI Ethical Leadership. Retrieved from https://www.netcomlearning.com/blog/how-ai-ethical-leadership-protects-your-Business
• Ninetwothree.co. (2025). AI Adoption That Works: 5 Enterprise Case Studies. Retrieved from https://www.ninetwothree.co/blog/ai-adoption-case-studies
• FPA-Trends. (2020). AI and ML in FP&A – Two Case Studies. Retrieved from https://fpa-trends.com/report/ai-ml-fpa-case-studies
• Hadjitchoneva, J. (2019). Efficient Automation of Decision-making Processes in Financial Industry: Case study and generalised model. CEUR Workshop Proceedings.
• McKinsey & Company. (2024). Harnessing the power of AI in distribution operations. Retrieved from https://www.mckinsey.com/industrials-and-electronics/our-insights/distribution-blog/harnessing-the-power-of-ai-in-distribution-operations
• Corsica Tech. (2025). AI Strategy: 7 Real-World Examples That Drive Business. Retrieved from https://corsicatech.com/blog/ai-strategy/
• Patalas-Maliszewska, J., & Pająk, I. (2020). AI-based Decision-making Model for the Development of a Manufacturing Company in the context of Industry 4.0. IEEE International Conference on Fuzzy Systems.
• AI Business Case Studies. (2024). Application of Artificial Intelligence in Business. Retrieved from https://online.nmhu.edu/resources/article/ai-case-studies-application-of-artificial-intelligence-in-business/
• Samuels, A. (2025). Examining the integration of artificial intelligence in supply chain management. Frontiers in Artificial Intelligence, 8, 1477044.
• Esteva, A., et al. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118.
• Jha, S., & Topol, E. J. (2016). Adapting to artificial intelligence: Radiologists and pathologists as information specialists. JAMA, 316(22), 2353-2354.
• Zeb, S., et al. (2024). AI in healthcare: revolutionizing diagnosis and therapy. International Journal of Research in Medical Sciences.
• Khalifa, M., & Albadawy, M. (2024). AI in diagnostic imaging: revolutionising accuracy and efficiency. Computer Methods and Programs in Biomedicine Update, 4, 100171.
• Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577-586.
• Wipfli. (2025). Automation vs. augmentation --- making the right AI move. Retrieved from https://www.wipfli.com/insights/articles/tc-ai-automation-vs-augmentation
• MIT Sloan. (2024). Humans and AI: Do they work better together or alone?. Retrieved from https://mitsloan.mit.edu/press/humans-and-ai-do-they-work-better-together-or-alone
• Sganalytics. (2025). Automation vs Augmentation – Will AI Replace Professionals. Retrieved from https://www.sganalytics.com/blog/automation-vs-augmentation/
• Wang, F. (2025). Collaborative Foresight in the Age of AI: A Framework for Evolving Human-AI Dynamics in Strategic Decision-Making and Futures Research. Journal of International DBA Studies-GGU, 1(1).
• Consultancy.eu. (2024). The future of decision-making: AI and human collaboration. Retrieved from https://www.consultancy.eu/news/10605/the-future-of-decision-making-ai-and-human-collaboration
• CTO Magazine. (2025). Decision Making Models in AI Leadership. Retrieved from https://ctomagazine.com/ai-leadership-decision-making-models-accountability/
• Jain, R., Garg, N., & Khera, S. N. (2023). Effective human—AI work design for collaborative decision-making. Kybernetes, 52(11), 3241-3258.
• Workday. (2025). The Future of Work Requires Seamless Human-AI Collaboration. Retrieved from https://blog.workday.com/en-us/future-work-requires-seamless-human-ai-collaboration.html
• Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101.