The article examines how generative artificial intelligence can transform the economics of women’s entrepreneurship by converting existing digital skills into measurable business outcomes, with particular emphasis on productivity, operating costs, revenue growth, and access to specialized expertise. The aim of the study is to assess whether AI-enabled business practices can overcome structural constraints faced by women-led enterprises and provide a measurable framework for evaluating the economic effectiveness of entrepreneurship support programs. Drawing on international evidence from the World Bank, OECD, UNESCO, and ILO, alongside Georgian statistical microdata and empirical findings from a Mastercard–Business and Technology University (BTU) pilot program, the study investigates the relationship between digital inclusion, specialist skills, and entrepreneurial performance. The analysis demonstrates that Georgia has largely closed the gender gap in basic digital access and applied digital skills, with household internet access reaching 94% in 2026 and women matching or outperforming men in several digital competencies. However, limited access to affordable specialist expertise remains a significant barrier to business development. The findings indicate that generative AI can mitigate this constraint by supporting marketing, competitor research, customer communication, and routine administration without requiring advanced technical training. Empirical results from the Mastercard–BTU pilot show that participating women entrepreneurs saved an average of 14 hours per week, reduced startup and operating costs by 22%, and increased average monthly revenue by 15–20%. The program also generated approximately $2.5 in economic value for every $1 invested, while adoption of digital financial services increased by roughly 25%. These results suggest that AI can enhance business productivity, reduce operational barriers, and expand opportunities for women-led enterprises. Nevertheless, the study acknowledges that the findings are based on a single cohort observed over four months and therefore represent preliminary evidence rather than established long-term effects. The article further identifies connectivity, affordability, language accessibility, digital readiness, and human oversight as critical conditions for replicating such initiatives across different socioeconomic environments. Building on these findings, it proposes four principles for designing effective AI entrepreneurship programs: establishing baseline economic indicators, assessing participants’ digital readiness, delivering function-specific AI training, and evaluating the durability of business outcomes alongside appropriate safeguards. The findings highlight the potential of generative AI to support women’s economic empowerment, improve resource allocation within small businesses, and strengthen inclusive economic growth through evidence-based entrepreneurship development programs.

Keywords: generative AI, women’s entrepreneurship, digital skills, economic empowerment, business productivity, financial inclusion, inclusive economic growth.

JEL Classification (suggested): J16, L26, O33, D24, O15.

Enukidze, N., & Zhghenti, T. (2026). How AI can change the economics of women’s entrepreneurship. Business and Technology University. [Publication details to be confirmed.]