What question did this study set out to answer?

The aim is to explore how AI decision architectures perform under uncertainty, proposing new governance approaches.

June 27, 2026Open Access

From Poka-Yoke to Decision Stability: Why AI-Supported Decision Architectures Require Diagnostic Methods Beyond Traditional Quality Assurance

Key Points

The aim is to explore how AI decision architectures perform under uncertainty, proposing new governance approaches.
Introduces a conceptual framework of Decision Stability based on Lean Manufacturing and AI governance research.
Discusses the role of replay-based governance diagnostics for enhancing observability of decision architectures.
Proposes that traditional quality assurance may be insufficient for AI systems.
Suggests stability and governability as critical factors for AI-supported decision-making frameworks.

Abstract

Artificial intelligence is increasingly embedded into operational decision processes where correctness alone may no longer provide sufficient governance evidence. Building on principles from Lean Manufacturing, system safety, and emerging AI governance research, this paper introduces Decision Stability as a conceptual framework for understanding how AI-supported decision architectures behave under operational uncertainty. Rather than focusing solely on correctness, compliance, or model performance, the paper argues that future governance capabilities may increasingly depend on the stability, observability, and governability of decision architectures. Replay-based governance diagnostics are discussed as one possible architectural response for making emerging instability observable before visible operational failures occur. The paper is conceptual and proposes a research agenda for empirical validation.

From Poka-Yoke to Decision Stability: Why AI-Supported Decision Architectures Require Diagnostic Methods Beyond Traditional Quality Assurance

Key Points

Abstract

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