Existing studies predominantly focus on the technical capabilities and predictive performance of AI models

The gap

Evidence profile

Sourced from the limitations and future-work section and stated challenges and stated research gap of the source papers, classified as general, spanning 3 journals. Those papers have been cited 1 times in total.

Supporting evidence — 5 representative gaps

• Integrating Artificial Intelligence into Financial Investment Decision-making: Opportunities and Constraints (2026) · Minnesota Journal of Business Law and Entrepreneurship · doi

  Despite its contributions, the study has certain limitations. First, the findings are based on self-reported perceptions of investors and finance professionals, which may be subject to response bias and social desirability effects. Second, the cross-sectional nature of the study restricts the ability to capture changes in perceptions and adoption behaviour over time as AI technologies continue to evolve rapidly. Third, the study focusses primarily on perceived opportunities and constraints rather than objective performance outcomes, which may limit the generalizability of the results to actual investment performance. Fourth, contextual factors such as regulatory environment, technological infrastructure, and market maturity may vary across regions, thereby limiting the applicability of the findings beyond the study setting. Finally, the study does not differentiate extensively among types of AI tools or investment instruments, which may influence perceptions and adoption patterns differently.

  generallimitationsevidence 5/5
  Keywords: perceptions adoption performance investment despite contributions certain limitations first based self reported investors finance professionals
• IA em ação: uma análise comparativa entre algoritmos e especialistas na recomendação de carteiras de investimento (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi

  The study suggests that further research is needed to improve the performance of AI tools in forming investment portfolios. The study highlights the need for more detailed analysis of the AI tools used and their limitations. The study suggests that future research could explore the use of AI in other areas of financial markets.

  generalfuture-work sectionevidence 5/5
  Keywords: study suggests further research needed improve performance tools
• IA em ação: uma análise comparativa entre algoritmos e especialistas na recomendação de carteiras de investimento (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi

  The study notes that AI tools face challenges in predicting market trends, economic analysis, and company selection. The study highlights the need for further research to improve the performance of AI tools in forming investment portfolios. The study suggests that AI tools may not be able to replicate the expertise of human financial analysts.

  generalstated challengesevidence 5/5
  Keywords: study notes tools face challenges predicting market trends
• A Study on the Impact of Artificial Intelligence on Strategic Decision-Making in Indian Businesses (2026) · International Journal of Emerging Research in Science Engineering and Management · cited 1× · doi

  The shortage of skilled professionals remains a significant barrier to AI adoption in Indian businesses. Ethical concerns associated with AI adoption significantly influence organisational trust and decision reliability. The financial burden associated with AI implementation remains a significant obstacle for small and medium-sized enterprises.

  generalstated challengesevidence 5/5
  Keywords: shortage skilled professionals remains significant barrier adoption indian
• Integrating Artificial Intelligence into Financial Investment Decision-making: Opportunities and Constraints (2026) · Minnesota Journal of Business Law and Entrepreneurship · doi

  Existing studies predominantly focus on the technical capabilities and predictive performance of AI models. Prior research examines AI adoption primarily through technology-centric or acceptance-based frameworks. There is limited empirical research that integrates AI opportunities and constraints within a single framework.

  generalstated research gapevidence 5/5
  Keywords: existing studies predominantly focus technical capabilities predictive performance

Questions about this gap