"Hasta la vista, baby" - will machine learning terminate human literature reviews in entrepreneurship?

Can, and should, artificial intelligence (AI) and its machine learning (ML) variant be applied to study scholarly literature? With AI and ML rapidly disrupting industries, we investigate how scholars in entrepreneurship and small business management can capitalize on AI and ML to support their scholarship and comprehensively review, catalog, and analyze the literature. We examine various ML tools and deploy these tools against a published literature review to consider whether ML complements or substitutes scholars' agency. We show that ML can reinforce human findings to support replicability and robustness, adding additional layers of transparency and validity to conclusions from human-derived systematic reviews. Our contributions provide scholars with valuable guidance and a blueprint for adopting ML into their scholarship and not replacing their scholarship.