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Research Summary: Why Organizational Rules Fail AI: O-I-B-A-R and the Externalization of Decision Boundaries
- Original authors
- Attribution requires verification
- Original source
- arXiv — Computers and Society
- Summary & Analysis prepared by
- Aziz Shuaib Ausi
- Resource type
- Research Summary / Knowledge Resource
- Resource published on AZIZ OS
- 1 September 2026
- Last updated
- 22 September 2026
- Reading time
- 1 min
- Publication type
- Knowledge Resource
- Availability
- Open access
About this Summary & Analysis
AZIZ OS provides independently prepared summaries and analytical interpretations of externally published research and knowledge sources. The underlying works remain attributable to their original authors and rights holders. This resource is intended to improve accessibility and understanding and does not replace the original publication.
Research identifies a recurring class of organizational AI failures stemming from a fundamental mismatch between how AI systems receive formal procedures and how human organizations execute them. AI systems are provided with explicit procedures, but human practice incorporates unstated elements such as negative boundaries, runtime judgments, responsibility assignments, and learning history. This discrepancy in knowledge representation at the sociotechnical interface leads to suboptimal AI integration and performance. The O-I-B-A-R framework (OPEN, IS, BUT, ACTION, RESULT) is introduced as a method to externalize these critical, often implicit, decision boundaries, thereby enhancing AI's operational effectiveness.
Why it matters
This analysis highlights a critical challenge in integrating AI systems into organizational processes, emphasizing that technical AI deployment is insufficient without addressing the nuances of human operational knowledge. Successfully bridging the gap between formal procedures and situated practice is vital for realizing the full strategic benefits of AI, avoiding costly failures, and ensuring AI systems perform as intended within complex organizational contexts.
Key insights
- Organizational AI failures frequently arise from a knowledge representation problem at the sociotechnical interface.
- AI systems primarily receive explicit procedures (policies, prompts) while human organizations operate on these procedures augmented by implicit contextual factors.
- Critical implicit factors include negative boundaries, runtime judgments, responsibility assignments, and learning history, which are often absent from AI's operational understanding.
- The discrepancy between formal descriptions of work and situated practice contributes significantly to AI deployment challenges.
- The O-I-B-A-R framework is proposed as a scaffold to externalize these missing decision boundaries, improving AI's alignment with organizational practice.
Source
arXiv — Computers and Society — https://arxiv.org/abs/2608.29055
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- Verification ID
- ASA-EXE-2026-00077
- Version
- v1.0 · r0
- Issued
- 1 September 2026
- Resource prepared by
- Aziz Shuaib Ausi
- Resource status
- Research Summary / Knowledge Resource
- Underlying work
- Why Organizational Rules Fail AI: O-I-B-A-R and the Externalization of Decision Boundaries
- Original authors
- Attribution requires verification
- Original source
- arXiv — Computers and Society
- Provenance status
- Attribution requires verification
- Rights
- Underlying publication rights remain with the respective copyright holder(s). Refer to the original source for authoritative publication and licensing information.
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