A SYSTEMATIC REVIEW OF INTELLIGENT SUPPORT SYSTEMS FOR STRATEGIC DECISION-MAKING USING HUMAN-AI INTERACTION IN ENTERPRISE PLATFORMS

Abstract

This systematic literature review provides a comprehensive and methodologically rigorous synthesis of scholarly work on Intelligent Support Systems (ISS), focusing on their design architectures, strategic applications, ethical governance, and human-AI interaction (HAII) frameworks within organizational contexts. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines, the review evaluates 124 peer-reviewed journal articles published between 2013 and 2023, ensuring transparency, reproducibility, and academic rigor in article selection and synthesis. The findings reveal a significant shift in ISS development from traditional rule-based systems toward hybrid and neural network-driven architectures, which offer improved predictive capabilities, flexibility, and real-time responsiveness. However, this transition introduces new challenges, particularly in terms of model interpretability, trust calibration, and dynamic system transparency. The review also identifies a growing trend in the adoption of AI-augmented strategic decision-making tools, such as decision-tree learning and reinforcement learning, which support portfolio management, resource optimization, and scenario-based planning. Despite these advancements, there is a notable deficiency in longitudinal performance evaluation, with very few studies tracking system impact or user trust over extended periods. While regulatory and governance frameworks such as GDPR, NIST AI RMF, and ISO/IEC standards are frequently referenced, only a limited number of studies report concrete implementation in live systems. The study concludes that future research must adopt a multidisciplinary lens, incorporating ethical AI principles, culturally aware design, long-term performance tracking, and user-centric evaluation metrics to ensure that ISS technologies evolve in a responsible, equitable, and sustainable manner. This review contributes to bridging the gap between advanced computational capabilities and the ethical, strategic, and social imperatives that define effective decision support in contemporary enterprises.