Framework design reveals a standardized protocol for validating enterprise operational AI systems, indicating a pathway toward reproducible and auditable autonomous decision-making.
Artificial intelligence is increasingly moving from conversational assistance toward operational participation in enterprise systems, where AI components can invoke tools, execute workflows, modify data and create legally, technically or economically consequential outcomes. This paper introduces the Operational Integrity Benchmark (OIB), a technology-neutral framework for evaluating whether operational consequences arise through authorized, verified, governed, auditable and reproducible operational processes. The framework introduces Operational State Reconstruction, Canonical and Observed Operational States, Integrity Predicates, Operational Validation, benchmark scenarios, benchmark profiles, standardized metrics, an execution protocol and formal conformance requirements. The OIB is designed to enable reproducible and implementation-independent evaluation of heterogeneous operational AI systems without prescribing specific models, governance mechanisms or architectures.
No takes yet. Share an insight, caveat, or question.
Thomas Gessler (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: