AI-Enhanced Mentoring for Social Entrepreneurship: Catalyzing Regenerative Finance and Sustainability Transitions

Objectives: This research investigates the intersection of Artificial Intelligence (AI), DeepTech, and social entrepreneurship (SE) in driving financial innovation for sustainability transitions within the banking, finance, and insurance sectors. It also examines the role and potential of mentoring, particularly AI-driven approaches, in guiding social entrepreneurs to develop and scale regenerative finance solutions and attract impact investment. Methods: Employing a mixed-methods approach, a Systematic Literature Review (SLR) following PRISMA guidelines was combined with Bibliometric Analysis. A comprehensive search across major academic databases yielded 99 relevant peer-reviewed articles. Analysis identified publication trends, citation metrics, and key thematic connections. Results: Findings demonstrate that AI and Deep Tech are powerful catalysts for SE in finance, enabling data-driven innovation, enhancing operational efficiency, and facilitating transformative instruments. Digital SE improves scalability. Bibliometric analysis confirmed “social entrepreneurship” centrality, linked to “deep tech startups” and “social impact.” Mentoring is critical for navigating complexities, strategic planning, and networks. AI enhanced mentoring's specific potential is currently underexplored in the literature, but it shows significant promise. Conclusions: AI and Deep Tech catalyze SE financial innovation for sustainability. Mentoring (human and AI-enhanced potential) is vital for SEs to leverage these technologies effectively, scale impact, and attract investment. Developing ecosystems, tailored AI-mentoring models, and robust impact metrics is crucial for a regenerative financial future. This study identifies critical gaps and provides a framework for future research for academics and practitioners.