AI: Opportunities and Challenges in Strategic decision making using bibliometric analysis: A systematic Review

Abstract

Background: AI has been at the forefront of current debates concerning strategic decision-making due to the possibility of furthering more accurate and efficient decisions. On the other hand, it offers opportunities and challenges for efficient application in the realm of AI for this domain. This systematic review aggregates the research found available on the role of AI in processes involving strategic decision-making.

Objectives: Investigate and debate opportunities and challenges of AI use in strategic decision-making and gap in the existing literature.
Methodology: A wide literature search was carried out on the databases listed below: [list all databases: PubMed, Scopus, IEEE Xplore, etc.], from [2015] to [2023]. Only those peer-reviewed articles in English that particularly deal with AI applications have been considered for this review. Eligibility criteria were set at the time of studying selection process. Data extraction was performed according to a standardized procedure. Quality of the included studies was assessed using the Specify Quality Assessment Tool; for example, Cochrane Risk of Bias Tool.
Results: Out of the baseline records [450], [27] studies passed the inclusion criteria. A few key areas where AI could be useful in strategic decision-making were identified, such as improved capabilities in analyzing data, greater accuracy in predictions, and improved operational efficiency. The cons included data privacy issues and biases brought about by algorithms among others. There were also requirements for a lot of good quality data. Notably, although AI seems to offer a lot of positive features, its implementation is only possible if those challenges are met:
Conclusion AI provided tremendous scope for improvement in strategic decision-making processes but simultaneously throws massive challenges before human beings that act as huge impediments in its adaptation. The future work will, therefore, be to behold strategies on how to mitigate these challenges, such as improvement in data governance keeping in view the steps to be taken with regard to dealing with the algorithmic bias that this technology embodies. This would be taken into account by the policymakers and the organizations as they include AI into their strategic decision-making frameworks.

DOI - https://doi.org/10.65525/SVUP.9788199651586.2026.18-36