AI infrastructure construction is driving data centers from equipment deployment projects to highly coupled engineering systems. Technical interface issues can sometimes be resolved through clarification early on, but sometimes they are delayed until near the deadline or construction preparation stage, leading to delays, document reissues, or adjustments to liability boundaries. Based on publicly available procurement documents from 2021 to 2025, this study distinguishes between centralized contractual responsibility, institutionalized cross-phase joint organizations, and interface closure requirements. It encodes the timing and observable consequences of technical issues in 44 data chain complete projects and employs negative binomial regression, robust linear models, and efficacy analysis. Results show that neither EPC nor individual joint organizations exhibit a stable association. The estimated late-stage disturbance correction point for complete structure-mechanism configurations is low, with an earlier average correction time, but a wider confidence interval. Alternative samples and weighted estimates still do not provide confirmatory evidence. Technical complexity is associated with increased late-stage disturbances. Publicly available document evidence is insufficient to confirm the performance effect of collaborative configurations; future research should shift its focus from "reducing all changes" to "controlling late-stage disturbances and moving problems forward."

Chenjie Gao. Organizational Change and Management Innovation in the Construction of Ai Data Centers under the Background of "Artificial Intelligence+". International Journal of Business Management and Economics and Trade (2026), Vol. 7, Issue 2: 92-101. https://doi.org/10.38007/IJBMET.2026.070210.

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