OPERATIONALIZING ORGANIZATIONAL ADAPTATION IN DEAM: AN EVENT-LEVEL MEASUREMENT PROTOCOL FOR AI-AUGMENTED SOFTWARE DELIVERY

Abstract

The growing integration of artificial intelligence into software delivery increases AI-tool usage, operational signals, and feedback speed, but technological adoption alone does not establish organizational adaptation. Previous research on the Data-Empirical Agility Model (DEAM) conceptualized adaptation latency as the temporal gap between an operational signal and coordinated organizational response but did not provide a reproducible event-level measurement protocol. The object of the study is organizational adaptation in AI-augmented multi-team software delivery. The study aims to develop a context-specific protocol for measuring the interval from a qualifying trigger to durable organizational reconfiguration and to specify the conditions required for its subsequent empirical evaluation. The methodology combines construct and prior-art analysis, event-level operationalization, retrospective calibration using a longitudinal five-Scrum-team case, and prospective validation design. The protocol represents an adaptation episode through T₀–T₄: qualifying trigger, recognition of the need for response, response commitment, implementation onset, and durable organizational reconfiguration. T₄ is assigned only when five conditions are jointly satisfied: observable change, operational enactment, persistence, traceable provenance, and independence from subsequent performance outcomes. On this basis, the study operationalizes L₀₄=T₄−T₀ as a candidate temporal measure rather than a validated new construct. Subsequent evaluation requires inter-rater reliability, robustness of the T₄ persistence criterion, discriminant validity, temporal validity, and incremental validity. Retrospective application of the protocol to one organizational adaptation episode within the five-team case demonstrates the practical separation of the initial trigger, local piloting, commitment to a scaled response, implementation onset, and durable change in organizational practice. For this episode, the interval from the approximately dated T₀ to T₄ was about 184 days, while the T₁ boundary had lower temporal precision because of the retrospective nature of the evidence. The example illustrates application of the protocol but does not constitute empirical validation of L₀₄ or establish its causal effect on software delivery outcomes. The practical value of the proposed approach lies in providing a structure for prospective data collection that separates AI exposure, organizational response, durable reconfiguration, and subsequent outcomes. Future empirical evaluation may support, refine, or reject the proposed operationalization.