The humanization of entrepreneurship, aligning with Sustainable Development Goal 8 (SDG 8: decent work and economic growth), is a growing trend. Corporate social responsibility (CSR) is crucial for achieving this, focusing on favorable conditions for employee development and potential realization. Manifestations include creating jobs (combating unemployment), knowledge-intensive roles, high-productivity positions, and career opportunities. However, the AI economy presents a challenge: while "smart" automation using AI increases efficiency (SDG 9: industrialization, innovation, and infrastructure), it can also lead to job displacement, conflicting with SDG 8. The COVID-19 pandemic further highlighted this tension, forcing businesses to choose between automation and job preservation. Outsourcing, the transfer of business operations to external services, offers a potential solution, reducing HRM costs and allowing flexible staffing. However, traditional outsourcing may not always align with CSR principles. This article addresses the lack of research on outsourcing's specifics in the AI economy and aims to study outsourcing's role in humanizing entrepreneurship in this context.

The research is grounded in Human Resource Management (HRM) theory. Humanizing entrepreneurship, according to this theory, involves responsible HRM, encompassing job preservation and creation, job optimization (comfort and safety), and career development opportunities (knowledge-intensive, creative, and high-productivity roles). SDG 8 provides the principles for this. In the pre-digital era, CSR provided a competitive advantage through attracting and retaining talent. The AI economy shifts this, emphasizing high-tech solutions through "smart" automation. However, the impact of automation varies across sectors: B2C may see partial automation, B2B may see fully autonomous systems, and high-tech sectors rely heavily on human expertise. Outsourcing balances CSR, economic efficiency, and digital competitiveness across these scenarios. However, it risks weakening the employee-business connection, hindering humanization. This study aims to address the literature gaps concerning outsourcing's demand and the role of AI in optimizing outsourcing by proposing “smart” outsourcing as a solution.

The study employs a mixed-methods approach, combining quantitative and qualitative methodologies. To answer the first research question (RQ1: Can outsourcing be massively applied in entrepreneurship in the AI economy?), regression analysis was used to model the relationship between revenue (rev), profit (pft), and the number of employees (ne) for 2022 Global-500 companies. The model is represented by the equations: rev = arev + brev*ne and pft = aprt + bprt*ne. The hypothesis (H1) is that brev will be significantly larger than bprt, indicating that human resources contribute more to revenue than profit, making outsourcing beneficial. To address RQ2 (How does artificial intelligence improve outsourcing?), a case study method was used, systematizing successful examples of smart outsourcing and comparing its advantages to traditional outsourcing via comparative analysis. Hypothesis H2 posits that AI-powered "smart" outsourcing offers superior efficiency and flexibility.

The regression analysis (using data from Global 500 companies in 2022) yielded the equations: rev = 15,976.76 + 0.27*ne and pft = 15,976.76 + 0.022*ne. These results, supported by robust regression statistics (significant at p<0.01), confirmed H1, showing that human resources contribute significantly more to revenue than profit. This supports the mass application of outsourcing to retain revenue while reducing costs. The comparative analysis (Table 2) and case studies confirmed H2, highlighting the advantages of "smart" outsourcing over traditional methods. "Smart" outsourcing offers: 1) ESG-based HRM (integrating environmental, social, and governance factors); 2) AI-driven rational outsourcing decisions; 3) comprehensive market analytics for provider selection; 4) flexible and adaptive organizational design; 5) individualized employee motivation via machine vision; and 6) accessible AI outsourcing, making "smart" technologies available to a wider range of businesses. Examples such as IQITO (for "rented IT directors"), Prodexy (for optimal outsourcing conditions), and Sever AI (for smart personnel management) illustrate these advantages.

The findings demonstrate that outsourcing, particularly "smart" outsourcing, is not merely a cost-cutting measure but a strategic tool for humanizing entrepreneurship within the AI economy. The results extend HRM theory by emphasizing the role of "smart" outsourcing in achieving SDG 8 in the context of AI, detailing its organizational and technological aspects. The study contrasts with previous literature that focused on isolated outsourcing instances or AI's role in internal management. This research shows that AI can be effectively used in external management, particularly within outsourcing, and proposes "smart" outsourcing as a way to make AI accessible for all businesses, not just larger corporations. It also highlights that outsourcing isn't simply about reducing human capital but about optimizing it and making it a source of competitive advantage.

This study addressed the literature gaps regarding outsourcing in the AI economy and confirmed the hypotheses. It demonstrated the widespread applicability of outsourcing and the advantages of "smart" outsourcing, driven by AI. "Smart" outsourcing, by integrating ESG principles and AI, offers superior flexibility, rationality, and efficiency, contributing to the humanization of entrepreneurship. Future research could explore the technical aspects of smart outsourcing implementation and its societal impacts in more depth. The study's significance lies in its implications for policy-makers and businesses alike, illustrating how a strategic approach to outsourcing can help achieve the SDGs in the context of a rapidly evolving technological landscape.

The study focuses primarily on the organizational and social aspects of "smart" outsourcing related to CSR within the context of humanizing entrepreneurship in the AI economy. The technical aspects of implementing "smart" outsourcing and the challenges associated with data privacy, algorithmic bias, and the digital divide were not thoroughly explored and represent potential avenues for future research.