Artificial Intelligence (AI) as a revolutionizing investment strategy improves administration through forecasted analytics and data-centric insights. This study investigates the effectiveness of AI-augmented investment decisions using a determinant-based approach, focusing on factors such as technological literacy, improved decision-making, performance and accuracy, real-time analysis, and hedging and stabilizing. Drawing on empirical evidence from active retail investors who use AI tools, the research evaluates how AI tools integrate various indicators to improve decision accuracy. The study adopts jumbled approach, combining quantitative examination of market performance with qualitative aspects using responses received from retail investors. Key findings indicate that AI-augmented tools not only enhance decision-making efficiency with reduced human biases but also fosters more dynamic responses to market volatility. The efficacy of AI-driven strategies depends significantly on the quality of input data, time, and human oversight. This research contributes to the growing literature on AI in finance by offering a structured framework for assessing the determinants of AI’s success in investment decisions. The findings underscore the importance of aligning AI capabilities with strategic investment goals, emphasizing a symbiotic strong relationship between human expertise and machine intelligence. This determinant-based approach provides actionable insights for investors and financial institutions aiming to harness AI’s transformative potential in a rapidly evolving market landscape.