This study investigates the integration of artificial intelligence (AI) and big data analytics within internal control frameworks, focusing on their impact on business risk mitigation and innovation among organizations listed on the Muscat Stock Exchange. Employing a mixed-methods approach, the research combined quantitative content analysis of annual reports from 100 companies with qualitative thematic analysis to discern patterns in technology adoption. The findings reveal that while AI and technology contribute to fostering innovation, the overall explanatory power of the model in terms of business risk mitigation and internal control effectiveness was limited, as evidenced by low R-squared values. A positive correlation was identified between technology adoption and innovation, indicating that organizations leveraging technological advancements significantly enhance their innovative capacities. However, a negative relationship between innovation and internal control effectiveness underscores the challenges of balancing innovation with strong internal controls. This study highlights the critical role of AI-driven predictive analytics for proactive risk management and emphasizes the need for organizations to cultivate a culture that embraces technological innovation while addressing concerns related to data privacy and skill gaps. Implications for practitioners and policymakers include the integration of advanced technologies into internal control frameworks to bolster risk mitigation efforts. Future research should investigate additional influencing variables and consider longitudinal studies to gain deeper insights into the interplay between AI, big data, internal control, and business risk mitigation.