TOWARD DATA-EMPIRICAL ORGANIZATIONAL ADAPTATION: ADDRESSING FRAGMENTED ADAPTATION CAPABILITIES IN AGILE TRANSFORMATION MODELS

Abstract

The paper analyzes structural adaptation limitations of contemporary Agile transformation models under AI-augmented organizational conditions. Traditional Agile frameworks evolved within environments characterized by periodic empirical adaptation and bounded feedback cycles. However, continuous telemetry generation, AI-assisted workflows and accelerated operational feedback fundamentally change organizational learning conditions. The purpose of the study is to analyze adaptation limitations embedded within major Agile transformation approaches and to position the Data-Empirical Agility Model (DEAM) as a synthesis architecture for continuous organizational adaptation. The research applies comparative conceptual analysis, systems thinking and Design Science Research positioning to examine Scrum, Scrum@Scale, SAFe, LeSS, Kanban Maturity Model, Evidence-Based Management, Agile Operating Model and Agile Product Operating Model. The analysis demonstrates that existing Agile approaches evolved through progressive specialization of adaptation capabilities related to coordination, governance, maturity progression and empirical measurement. Nevertheless, these mechanisms remain structurally fragmented under continuous sensing conditions. The study introduces the concept of adaptation capability fragmentation describing the structural separation of sensing, governance, measurement, coordination and organizational learning mechanisms across Agile transformation models. The paper further conceptualizes adaptation latency as the temporal gap between operational signal generation and coordinated organizational response. Within this context, DEAM is positioned not as a replacement framework, but as a synthesis architecture integrating continuous sensing, adaptive feedback loops and data-empirical organizational learning into coherent adaptive systems.