“Smart” Outsourcing in Support of the Humanization of Entrepreneurship in the Artificial Intelligence Economy

Humanization, closely linked to the practical implementation of SDG 8 (decent work and economic growth), centers on creating conditions that develop and unlock human potential in workplaces, corporate culture, and organizational structures. Yet, in the AI-driven economy, there is tension between digital competitiveness and employment: enterprises pursue smart automation (supporting SDG 9), which can reduce staff needs, while workers, society, and the state prioritize combating unemployment. During crises (e.g., COVID-19), firms face pressure to reduce costs and maintain productivity, balancing automation, remote work, and social responsibility. Outsourcing is proposed as a promising solution: transferring business operations to external providers helps preserve jobs (outside the permanent staff), reduce HRM costs, and increase flexibility in workforce size and composition. However, outsourcing may conflict with corporate social responsibility and requires special management to support humanization. Existing literature covers outsourcing extensively but is unclear on its features in the AI economy, leading to the article’s purpose: to study the role of outsourcing in the humanization of entrepreneurship in the AI economy. The literature review identifies two gaps and formulates research questions: RQ1: Can outsourcing be massively applied in entrepreneurship in the AI economy? Hypothesis H1: outsourcing is in demand and its mass application is advisable due to its flexibility. RQ2: How does artificial intelligence improve outsourcing? Hypothesis H2: AI enables 'smart' outsourcing that is more efficient and flexible, unlocking outsourcing’s potential for humanization. The article proceeds with methods, results, discussion, and conclusion addressing these questions.

Grounded in Human Resource Management (HRM) theory, humanization entails preserving and creating jobs to support employment; optimizing and organizing jobs for comfort and safety; and enabling career development and human potential through knowledge-intensive, creative, innovative, and highly productive jobs, including decent digital work. In the pre-digital era, responsible HRM and corporate social responsibility (CSR) enhanced competitiveness via employer reputation. In the AI economy, competitive advantage increasingly stems from high-tech and smart automation; however, the degree and manner of substituting human labor vary by context (B2C operations retaining human-centered communication; B2B autonomous smart production; high-tech segments where human resources are strategic). Outsourcing can balance CSR, economic efficiency, and digital competitiveness across these cases by outsourcing customer communication, technical maintenance, or assembling high-skill innovative teams. Yet outsourcing may weaken employee-business ties, risking de-evolution from individualized social capital to impersonal labor, complicating humanization. 'Smart' outsourcing is proposed as a remedy, but the scientific foundation remains underdeveloped. Two literature gaps are identified: (1) the extent to which outsourcing is broadly demanded versus limited to exceptional cases (leading to RQ1/H1); and (2) how to organize outsourcing in the AI economy, as prior works focus on internal management uses of AI, not external management like outsourcing (leading to RQ2/H2). The article aims to fill these gaps by systematizing and discussing practical experiences of outsourcing in entrepreneurship within the AI economy.

A mixed quantitative and qualitative methodology is used at the micro-level of the AI economy. For RQ1, regression analysis models the dependence of revenues (rev) and profit (prt) on number of employees (ne) via simple linear regressions using the Fortune Global-500 (2022) dataset: rev = a_rev + b_rev*ne; prt = a_prt + b_prt*ne. Reliability is evaluated by R-squared and Significance F. H1 is considered supported if b_rev is significantly larger than b_prt, indicating human resources contribute more to revenue than profit, suggesting outsourcing can retain revenue contributions while reducing personnel costs. For RQ2, the case study method identifies successful 'smart' outsourcing examples across business operations, and comparative analysis evaluates advantages of 'smart' versus traditional outsourcing. H2 is considered supported if 'smart' outsourcing proves preferable and advantageous for key business operations.

- Regression results (Global-500, 2022; n=500) show that each additional employee increases revenue by approximately $0.27 million (b_rev ≈ 0.27; t-stat 23.22), but profit by only about $0.02 million (b_prt ≈ 0.02; t-stat 7.03). Intercepts: a_rev ≈ 15976.76; a_prt ≈ 2579.66. Model fit: R-squared ≈ 0.552 (revenue) and 0.0903 (profit). Both models are significant at p<0.01 (Significance F ~ 2.26e-81 for revenue; ~6.8e-12 for profit). - These results confirm H1: human resources contribute more to revenue than to profit, supporting the advisability of mass outsourcing to preserve revenue contributions while reducing HR costs and increasing profit contributions. - Comparative analysis indicates 'smart' outsourcing offers advantages over traditional outsourcing for humanization of entrepreneurship: ESG-oriented HRM; AI-supported rational outsourcing decisions; comprehensive 'smart' market analytics for provider selection; flexible 'smart' organizational design (e.g., support for remote work); individualized performance-based motivation using machine vision and analytics; and the possibility to outsource AI itself, lowering barriers to adoption. - Case studies/examples: IQITO 'smart outsourcing' services including rented IT directors, intelligent decision support ('Prodexy') and IQITO-360 for remote employment; Leader Team solutions using ML for resume evaluation, computer vision for individual performance tracking, SFA systems and gamification; Smart service providing IT infrastructure outsourcing with AI/smart technologies used by major companies (e.g., Lenta, Dixie, Lamoda, Detsky Mir, Leonardo, Okey, Magnit, Russian Post). - These findings support H2: AI-enabled 'smart' outsourcing is preferable due to increased flexibility, rationality, and efficiency, contributing to the humanization of entrepreneurship.

The study advances HRM theory by demonstrating that implementing SDG 8 in the AI economy benefits from adopting 'smart' outsourcing founded on ESG principles and enabled by AI/ML, which offers distinct organizational and technological advantages over traditional outsourcing. It extends prior literature by showing outsourcing’s applicability beyond isolated cases to mass use for HRM efficiency, and by highlighting AI’s role not only in internal but also external management processes, including outsourcing. The study also challenges the notion that firms must rely on proprietary AI by evidencing the feasibility and benefits of outsourcing AI, making 'smart' outsourcing broadly accessible. Overall, outsourcing is reframed as both a tool and an object of automation, with 'smart' outsourcing enabling humanization through maintained and new ties between employees and firms, individualized personnel management, and socially responsible yet efficient practices.

The article addresses identified literature gaps, answers RQ1 and RQ2, and supports H1 and H2. For RQ1, analysis of Global-500 (2022) data shows outsourcing is in demand in the AI economy and its mass application is advisable, as it can increase the contribution of human resources to profit versus maintaining the same contribution via permanent staff. For RQ2, AI enables 'smart' outsourcing that is more flexible and efficient, unlocking outsourcing’s potential to contribute to the humanization of entrepreneurship. 'Smart' outsourcing enhances HRM through ESG-based practices, intelligent decision support, comprehensive market analytics, smart organizational design, individualized performance management, and AI outsourcing, making these capabilities accessible to firms of all sizes. The key takeaway is that in the AI economy, traditional outsourcing should be supplanted by 'smart' outsourcing to most effectively and rapidly humanize entrepreneurship, implement SDG 8, strengthen digital competitiveness, and support high-tech growth. Theoretical significance includes clarifying outsourcing specifics in the AI economy and providing a methodology for 'smart' outsourcing; practical significance lies in recommendations that improve entrepreneurial efficiency and resilience; social significance stems from outlining balanced implementation of SDG 8 and SDG 9.

The study focuses on the social and organizational aspects of 'smart' outsourcing from the perspective of the humanization of entrepreneurship in the AI economy. Technical implementation issues are outside the scope and should be addressed in future research.