AI and Business Decision-Making: Opportunities and Challenges

Shubham Singh; Shubham Sharma; Shaksham Dixit; Sheetal Patel

doi:10.69968/ijisem.2026v5i36-12

Authors

Shubham Singh MBA Student, Prestige Institute of Management and Research, Bhopal
Shubham Sharma MBA Student, Prestige Institute of Management and Research, Bhopal
Shaksham Dixit MBA Student, Prestige Institute of Management and Research, Bhopal
Sheetal Patel MBA Student, Prestige Institute of Management and Research, Bhopal

DOI:

https://doi.org/10.69968/ijisem.2026v5i36-12

Keywords:

Artificial Intelligence, Business Decision-Making, Machine Learning, Predictive Analytics, Systematic Literature Review, Opportunities, Challenges, Ethical AI.

Abstract

The rapid proliferation of Artificial Intelligence (AI) has significantly transformed business decision-making across diverse domains such as finance, marketing, supply chain, human resources, and strategic management. This study presents a Review of twelve peer-reviewed articles published between 2020 and 2025, sourced from international journals and conference proceedings. Using a structured coding framework, the review synthesizes insights on AI applications, methodologies, tools, opportunities, challenges, and future research directions in organizational decision-making.

Findings indicate that AI tools—including machine learning, deep learning, natural language processing, predictive analytics, robotic process automation, and intelligent decision-support systems—enable organizations to improve efficiency, accuracy, and foresight while reducing human bias. Across the studies, AI is shown to support enhanced financial forecasting, customer engagement, supply chain optimization, HR talent management, innovation, and strategic planning. Opportunities identified include real-time predictive insights, process automation, risk mitigation, and the creation of competitive advantages in dynamic markets.

However, challenges remain in the areas of data quality, algorithmic bias, interpretability, ethical concerns, integration with legacy systems, high implementation costs, and regulatory uncertainties. Several studies also emphasize the social and organizational risks of AI adoption, such as workforce displacement and trust deficits in automated decisions. The review highlights that successful adoption requires not only technological readiness but also ethical frameworks, transparent governance, and human–AI collaboration.

Future research directions proposed include developing sector-specific AI models, advancing explainable and ethical AI frameworks, investigating adoption in emerging markets, and exploring human–machine integration for responsible decision-making. Overall, the SLR underscores that while AI offers transformative opportunities for business decision-making, its long-term impact depends on responsible implementation, alignment with human values, and continuous adaptation to evolving business environments.

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