Reconceptualizing the Business Model Canvas for the AI-Augmented Solo Venture: A Theoretical Integration Framework

The Business Model Canvas (BMC) remains a prominent framework for describing how a venture creates, delivers, and captures value. Yet, its conventional use does not explicitly represent ventures in which a single owner-operator relies extensively on generative and early agentic artificial intelligence (AI) to perform or coordinate work. This conceptual paper asks why the solo context is theoretically distinctive and how AI changes the interdependencies among the nine blocks of the BMC. The paper uses a theory-driven conceptual synthesis and model-building approach, integrating research on business models, digital entrepreneurship, entrepreneurial bricolage, organizational AI capability, algorithmic management, and AI responsibility. A solo venture is a venture in which one individual combines ownership, strategic decision-making authority, and day-to-day operational responsibility, without a standing internal hierarchy of employees. However, external contractors and technology providers may be used. This configuration creates four analytically distinct conditions: extreme internal human capital constraint, role fusion, limited internal redundancy, and recursive self-governance. The paper proposes an AI-Integrated Business Model Canvas (AI-BMC) that retains the nine established blocks while adding two cross-cutting constructs. Algorithmic Value Logic (AVL) specifies how AI capabilities are allocated and coordinated across business-model activities to contribute to customer value, while Human–AI Governance Architecture (HAGA) specifies how human review, accountability, traceability, escalation, and fallback are configured. The framework positions the solo entrepreneur as an orchestrator of external technological and task-bounded human capabilities and identifies a single-human review bottleneck as a boundary condition of AI augmentation. Two constructed vignettes illustrate the framework’s application, and propositions are developed for future empirical testing.