From Adoption to Mission Value: A Governance-Gated Framework for Generative AI in Nonprofit Organisations

Why might a nonprofit organisation support generative artificial intelligence yet fail to obtain defensible mission benefits? Building on Shi and Azevedo’s study of adoption in Florida nonprofits, this conceptual paper distinguishes favourable attitudes, authorised deployment, sustained use and mission value. It proposes a governance-gated adoption framework in which leadership communication and organisational readiness support experimentation, but progression depends on task-specific assurance. Assurance comprises an explicit mission rationale, an accountable decision owner, proportionate evaluation, meaningful routes for challenge and feasible withdrawal. Drawing selectively on technology–organisation–environment theory, technology acceptance research, absorptive capacity and dynamic capabilities, the paper develops six propositions concerning adoption transitions, learning, verification burdens and the interpretation of discontinuation. The framework challenges the assumption that more use necessarily indicates greater organisational maturity. It also separates the normative requirement to protect service users from the empirical question of whether particular controls improve performance. A proposed longitudinal research design combines decision records, staff experience and service-user outcomes to test the framework without treating executive enthusiasm as a proxy for impact. Practical implications include task-level approval records, protected learning time and explicit stopping rules. No original empirical data are presented; the propositions require evaluation across nonprofit missions, organisational sizes and regulatory contexts.