An Empirical Study on the Impact of the Integration of AI and Big Data on Market Uncertainty in the Context of Economic Turbulence

This research investigates the impact of integrating Artificial Intelligence (AI) and Big Data analytics on market uncertainty specifically during periods of economic turbulence. It addresses the gap in existing literature concerning the empirical evidence of this relationship, particularly during economic downturns. The study employs a mixed-methods approach, combining quantitative analysis of market data with qualitative case studies of firms implementing AI and Big Data solutions. Findings reveal that the strategic integration of AI and Big Data can significantly reduce market uncertainty by enhancing predictive modeling capabilities and enabling proactive risk management strategies. However, the effectiveness of these technologies is contingent upon moderating factors such as industry type, the stringency of the regulatory environment, and the maturity of organizational data governance frameworks. Furthermore, the research identifies potential pitfalls, including algorithmic bias and over-reliance on data-driven insights without adequate human oversight, which can exacerbate uncertainty. The study culminates in a practical framework designed to guide businesses in effectively leveraging AI and Big Data to navigate market volatility. The implications extend to policy recommendations for fostering responsible AI adoption and data utilization to mitigate economic risks.