Abstract for: Societal Change in the Age of AI: Inclusion as Defense Against the Augmented Insider

Despite widespread expectations that artificial intelligence will drive productivity and growth, evidence shows persistent failure in scaling AI and realizing value. Organizations face a paradox of high investment but limited returns, alongside rising social inequality, security risks, and governance gaps. This raises the question of why AI adoption fails to deliver consistent outcomes. This study employs a qualitative system dynamics approach, combining literature review with six expert consultations across security, management, and inclusion domains. Through iterative model building, key variables and feedback loops are identified and structured into a causal system. The model emphasizes endogenous dynamics, capturing how interactions within economic, social, and security domains shape AI adoption outcomes. The analysis reveals that AI adoption is governed by interacting reinforcing and balancing feedback loops across four domains: investment dynamics, social inequality, security escalation, and the emergence of the “augmented insider.” These interactions produce systemic contradictions, where initial gains are offset by rising costs, distrust, and risks. AI adoption thus behaves as a complex, co-evolving system rather than a linear technological progression. The findings suggest that technical or isolated policy interventions are insufficient. Sustainable AI adoption requires integrated strategies addressing inclusion, governance, workforce transformation, and security simultaneously. Inclusion emerges as a critical stabilizing mechanism to counter reinforcing risks. Future research should focus on model quantification and empirical validation to support policy design and enable simulation of long-term system behavior. check spelling and structure, find papers