Rethinking Software Sourcing for AI-Native Engineering

AI-native engineering may increase software output while reducing the external effort required per outcome, challenging sourcing models built around team size and billable hours. The article argues that buyers should reassess which capabilities remain internal, segment work by complexity and risk, and evaluate whether providers have changed their operating and commercial models—not simply adopted AI tools. It also highlights architecture, security, modernization, assurance and specialist expertise as areas where external value may persist. Technology leaders should reconsider the sourcing boundary before procuring a more efficient version of the existing delivery model.