Interactive Intelligence Systems: A Framework for Human-Orchestrated Intelligence Across Heterogeneous Systems v1.0

Contemporary AI systems increasingly demonstrate high capability yet fail to deliver reliable value when deployed in real organizational environments. This paper argues that such failures arise not from limitations in artificial intelligence itself, but from the absence of explicit frameworks for coordinating intelligence in use. Interactive Intelligence Systems (IIS) is introduced as a general, technology-agnostic framework that treats intelligence as an emergent property of structured human–machine interaction under uncertainty. IIS foregrounds human decision authority, disagreement adjudication, verification, and orchestration as first-class system components rather than auxiliary oversight mechanisms. The framework is developed in response to documented enterprise deployment failures, including recent empirical findings indicating that the majority of AI initiatives fail to reach sustained production or economic impact. These failures are interpreted as manifestations of a broader coordination problem affecting non-deterministic systems more generally. Adaptive Intelligence Systems (AIS) are defined as a behavioral class characterized by dependence on feedback, human judgment, and contextual adaptation. Within this framework, Individual-Scale AI Orchestration (I.S.A.O.℠) is presented as a rigorously documented operational methodology that instantiates IIS principles at individual scale. Although contemporary AI systems provide a salient empirical domain, IIS is not limited to artificial intelligence. The framework generalizes to any system in which intelligence emerges through interaction between human agents and non-deterministic processes. The contribution claimed is methodological rather than algorithmic, emphasizing reproducibility, accountability, and operational traceability over performance benchmarks alone.