Does ChatGPT Reconstruct a Company into a Different Company? Risks of Losing Corporate Specificity in Generative AI Adoption: A GPT-5.6 Behavioral Analysis and Pre-Deployment Sales Workflow Stress Test

Engineering notes

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Chinese explanation / 中文解读

中文解读待补充：本站会优先为大语言模型、生成式AI、ChatGPT相关技术、计算机视觉、深度学习等高价值论文补充中文说明。

Original abstract

This Japanese-language technical white paper examines the risk that generative AI may not merely omit company-specific information, but reconstruct a company’s operational identity into a more generic and stable business template. The study uses pre-outage observations of GPT-5.6 as a baseline and compares them with behavioral differences subsequently observed in the same user environment. The principal change examined is not an inability to generate a valid output. Rather, it is the instability of preserving an already accepted state while applying a localized correction. In the observed image-generation cases, a limited correction could trigger broader reinterpretation, replacing previously accepted relationships, conditions, and structural elements with a different stable template. The paper transfers this observed structure into a pre-deployment sales workflow stress test using Nikko Seiki, a fictional precision-manufacturing company created solely for simulation. Its corporate core is defined through the relationship among field-based technical judgment, organizational systems, credibility, sales, manufacturing, quality assurance, and the manner in which commitments are made to customers. The simulation evaluates whether ChatGPT can preserve customer requirements, manufacturing and quality reservations, unresolved conditions, departmental authority, and company-specific decision principles throughout a long conversation and after localized revisions. It also examines whether “open for consideration” is silently transformed into “available,” whether unsupported capabilities or experience are introduced, and whether the resulting document becomes interchangeable with the sales language of another company. The organizational accident pathway is modeled as: AI output → human approval → customer communication → CRM registration → conversion into confirmed manufacturing conditions → shop-floor execution → quality or delivery failure. This pathway is interpreted through Stability Substitution Effect, Template Absorption, Template Activation, Template Evaluation Substitution, and Operational SSE. A document that appears stable may not preserve its underlying structure, and a workflow that continues to advance may not be under effective control. The paper therefore proposes a second-generation defensive workflow based on an externally fixed case-core register, condition tables independent of conversational memory, version-controlled approved states, core reinjection before final tasks, reverse verification against source conditions, a corporate-core preservation gate, full-document revalidation after localized edits, reconstruction from an approved version rather than conversational rollback, prohibition of unsupported supplementation, and explicit stopping criteria. The paper does not claim that a communications outage directly caused the observed behavioral differences. It records a chronological difference between previously published GPT-5.6 observations and later outputs observed in the same operating environment. The full text is written in Japanese in order to preserve the precise meanings of the fictional company’s historical and operational concepts, which could otherwise be weakened through premature translation into generic branding terminology.

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