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Contemporary management practices are undergoing rapid transformation due to significant advancements in artificial intelligence technologies. and intelligent automation technologies, fundamentally reshaping organizational decision-making processes. The central question is no longer whether these technologies should be adopted, but rather which functions should be automated and to what extent intelligent systems should be entrusted with decision-making authority in a manner that balances technological efficiency with managerial accountability Against this backdrop, the Adaptive Digital Maturity Model for Administrative Decision-Making in Hybrid Environments (ADM-HDM) emerges as an original theoretical framework that explains the gradual path organizations follow as they transition from complete reliance on human decision-makers toward advanced integration between humans and intelligent machines. The model extends beyond the traditional concept of digital transformation as merely adopting technology, redefining it instead as a transformation in organizational awareness regarding the allocation of responsibilities and decision-making tasks between human intelligence and artificial intelligence The model is grounded in the Socio-Technical Systems (STS) perspective, integrating technological, organizational, and philosophical dimensions within a comprehensive conceptual framework. It is built upon three primary theoretical foundations The first foundation is the model developed by Parasuraman, Sheridan, and Wickens (2000), which identifies four principal decision-making functions that can be automated: information acquisition, information analysis, decision selection, and action implementation. These functions are distributed across ten levels of automation, ranging from complete manual control to full autonomy of intelligent systems The second foundation is the Technology–Organization–Environment (TOE) Framework, which explains organizational movement across digital maturity levels through three interconnected contexts: the technological context, the organizational context, and the environmental context. Within the proposed model, this framework is employed not only to explain technology adoption but also to interpret the sequential stages organizations undergo throughout their digital transformation journey The third theoretical foundation is Herbert Simon’s Theory of Bounded Rationality (1947), which argues that human decision-makers, due to cognitive limitations and incomplete information, generally seek satisfactory rather than optimal decisions. From this perspective, intelligent automation becomes a mechanism for expanding organizational rationality by providing decision-makers with richer information and advanced analytical capabilities that support more accurate and efficient decisions The scientific contribution of the ADM-HDM model lies in transforming the concept of automation levels from a framework describing interactions between an individual and an automated system into a comprehensive model that explains the digital maturity of an entire organization over time. Furthermore, it reinterprets the TOE framework as an instrument for explaining progressive organizational transformation while providing a philosophical foundation linking the need for automation to overcoming the cognitive limitations faced by decision-makers The model demonstrates that organizational progression through digital maturity stages depends upon several fundamental drivers. Technological infrastructure constitutes the essential prerequisite for initiating digital transformation by providing the environment necessary for digitizing organizational data and processes. Organizational culture, however, plays a pivotal role in determining the speed of progression between maturity stages, as resistance to change may significantly delay transformation despite the availability of advanced technologies Training and digital competency development also represent critical requirements for achieving genuine human–AI integration by ensuring that employees possess the capabilities necessary to supervise intelligent systems and effectively manage their outputs. Simultaneously, external competitive pressure serves as a continuous catalyst encouraging organizations to accelerate digital transformation in order to maintain competitiveness and adapt to the rapidly evolving business environment The model further reveals that the organizational dimension—represented by institutional culture and human competencies—is the most influential and complex factor because it constitutes the critical link connecting technological capabilities with practical implementation. Consequently, successful digital transformation depends as much upon people as it does upon technology From a practical perspective, the model offers several valuable applications. It can be utilized to diagnose an organization’s level of digital maturity, determine its current stage within the digital transformation journey, and guide investment decisions toward the technological, organizational, or environmental dimensions that exert the greatest influence on accelerating transformation. Moreover, the model provides a scientific foundation for developing quantitative assessment instruments, such as standardized questionnaires, capable of objectively measuring organizational digital maturity Despite its theoretical significance, the model remains a conceptual framework requiring extensive empirical validation to verify the stability of its maturity stages and its applicability across diverse organizational environments. Furthermore, it assumes a degree of consistency between individual and organizational behavior—an assumption that may not fully hold in all organizations. In addition, the complex interactions among technological, organizational, and environmental dimensions may result in transformation pathways that are more dynamic and less linear than the model currently assumes Accordingly, proposes the development of a standardized measurement instrument based on the model’s indicators to assess organizational digital maturity. The researcher also recommends conducting comparative empirical studies across diverse administrative organizations to examine the applicability of the proposed maturity stages and their driving factors in real-world settings, thereby contributing to further refinement and enhanced explanatory power of the model In conclusion, the ADM-HDM Model provides a comprehensive scientific perspective for understanding digital transformation within administrative organizations. It emphasizes that the future of managerial decision-making will not be characterized by replacing humans with machines, but rather by establishing a complementary partnership between them, in which artificial intelligence enhances human expertise to produce more accurate, flexible, and sustainable decisions while strengthening organizational capability to address the challenges of an evolving managerial environment and achieve institutional excellence Assistant Lecturer Mustafa Adnan Madlol Al-Bakri