Most research on generative AI in small and medium-sized enterprises (SMEs) asks whether firms adopt the technology. It rarely asks how they source it. Yet a small firm's main AI decision is often a governance choice: develop the capability in-house, buy a standardized software-as-a-service (SaaS) tool, or delegate the function to a managed service provider. This study argues that the best sourcing mode depends on the jurisdiction. Two parameters vary across countries: the opportunity cost of staff time, and the governance cost of handling data under national rules. We develop a Net Sourcing Value (NSV) model based on transaction cost economics and the task-based view of technological change. We apply it to stylized SME profiles in the United States and Switzerland. The two economies have similar per-capita income but very different wage levels, labor-market institutions, and data protection regimes. The illustrative analysis gives three findings. First, in-house development is the dominated mode in both countries at typical SME task volumes. It only becomes competitive above a scale threshold of roughly 5,000 automatable hours per year. Second, the preferred mode differs by country: standardized SaaS ranks first in the U.S. profile and managed delegation in the Swiss profile. Third, this reversal is driven mainly by the labor-cost parameter, and the switching wage falls within a narrow band of about 60–65 units per hour. Governance costs reinforce the effect but do not cause it. We discuss implications for SME owners, advisors, and policymakers and propose an agenda for empirical calibration.