Alikhani conducts a PRISMA-guided systematic review of 24 Q1-ranked journal articles from 2010 to 2025 on AI in venture capital, drawing from entrepreneurship, finance, management, and computer science to map a fragmented, early stage body of research. The review classifies studies by who uses the AI, VCs, startups, or researchers themselves, which VC process stage is affected, deal sourcing, investment selection, valuation, deal structure, value-added support, or exit, which AI technology is involved, machine learning, natural language processing, network analytics, and which methodological approach is used. The central finding is that existing research clusters heavily in pre-investment stages, especially deal sourcing and investment selection where AI is framed mainly as a screening and prediction tool, while valuation, deal structuring, post-investment governance, and exit remain underexplored, and quantitative machine-learning studies dominate over theory-driven, process-oriented work.

Researchers, doctoral students, and product teams building AI tools for VC get a single map of a fragmented literature, with the specific finding that deal sourcing and screening are heavily studied while valuation, deal structuring, and exit are comparatively neglected, pointing to where genuinely novel research or product contributions could add value rather than replicate existing work. The note that AI in VC is framed mainly as a prediction and screening technology in existing research is a useful corrective for anyone assuming AI tools already meaningfully assist with post-investment governance or exit timing, since the literature has not caught up to those use cases yet. Because the review follows PRISMA methodology and limits itself to Q1-ranked journals, it also offers a more rigorous starting bibliography than the informal blog posts and vendor whitepapers claiming to survey AI's role in venture capital.