Validation-Centric AI-Native Decision Systems: Integrating the GAVA Loop with PRIME–INSPECT Governance

AI-native decision systems — in which generative artificial intelligence generates, executes, and adapts decisions in real time — are shifting the centre of gravity of analytical effort. Whereas the principal challenge used to be the extraction of insight, it is now becoming the validation of AI outputs while action is still being taken upon them. Existing approaches address this problem only partially: explainable artificial intelligence and governance frameworks treat validation as an ex post layer; reinforcement learning reduces it to a single scalar reward; control theory and the broader cybernetic tradition formalise the closed loop but neglect its sociotechnical constraints; while the PRIME–INSPECT framework establishes a governance foundation, yet leaves real-time validation implicit. This paper proposes a validation-centric architecture that links the GAVA decision loop (Generate–Act–Validate– Adapt), introduced here, with the PRIME–INSPECT framework and elevates validation to a central analytical function comprising four subdimensions: statistical, operational, cognitive, and governance-related. The proposed architecture is empirically examined on a dual sample of IT professionals and top-management representatives, using descriptive statistics, multiple regression, mediation, moderation, and structural equation modelling. The results confirm the central role of trust, the negative effect of perceived risk, and the importance of top management support, while robustness checks separated by the IT and TMT subsamples further strengthen the structural model. Taken as a whole, the findings support the view that validation should be treated as a first-order organisational stage in AI-native decision systems.