The democratization of artificial intelligence (AI) involves extending access to AI technologies beyond specialized technical experts to a broader spectrum of users and organizations. This paper provides an overview of AI’s historical context and evolution, emphasizing the concept of AI democratization. Current trends shaping AI democratization are analyzed, highlighting key challenges and opportunities. The roles of pivotal stakeholders, including technology firms, educational entities, and governmental bodies, are examined in facilitating widespread AI adoption. A comprehensive framework elucidates the components, drivers, challenges, and strategies crucial to AI democratization. This framework is subsequently applied in the context of scenario analyses, offering insights into potential outcomes and implications. The paper concludes with recommendations for future research directions and strategic actions to foster responsible and inclusive AI development globally.

Generative artificial intelligence (AI) systems have transformed various industries by autonomously generating content that mimics human creativity. However, concerns about their social and economic consequences arise with widespread adoption. This paper employs agent-based modeling (ABM) to explore these implications, predicting the impact of generative AI on societal frameworks. The ABM integrates individual, business, and governmental agents to simulate dynamics such as education, skills acquisition, AI adoption, and regulatory responses. This study enhances understanding of AI's complex interactions and provides insights for policymaking. The literature review underscores ABM's effectiveness in forecasting AI impacts, revealing AI adoption, employment, and regulation trends with potential policy implications. Future research will refine the model, assess long-term implications and ethical considerations, and deepen understanding of generative AI's societal effects.