AI-Enabled Organizational Dependency: Assurance, Capability, and the Case for Accepted Frameworks

Abstract Organizations are rapidly adopting artificial intelligence because they expect it to create value. As reliance grows, they redesign work, reallocate resources, and change organizational capabilities. Some of those capabilities may later be needed to recognize problems, exercise judgment, intervene, and recover when AI-enabled systems are unavailable, inadequate, or confronted by unexpected conditions. This paper argues that assurance should develop alongside or closely following adoption rather than waiting for failure to expose the need. Technical performance alone cannot determine whether an AI-enabled capability is sufficient. That judgment depends on organizational objectives, operating context, consequences of failure, and the capabilities required to sustain performance. Earlier technology transitions show that assurance practices often develop as dependencies expose weaknesses. AI may alter that sequence because organizational capabilities can change before their continuing importance becomes clear, leaving organizations to rebuild capability after the need has already emerged. Assurance and governance are complementary mechanisms for managing that dependency. Proportionate, accepted assurance can support responsible reliance without imposing a single universal framework. The objective is to help organizations capture and sustain the value of AI while retaining the capability and authority needed to govern the dependencies created by successful adoption.