Artificial intelligence promises rapid information processing, analysis and decisions. Yet, guidance on what to automate, when, and to what degree, remains limited for Human-AI teams. Case-based empirical studies provide rich context, but a framework for systematic exploration of Human–AI team performance is missing. This paper introduces a conceptual model for Human–AI teaming that integrates levels of automation, trust dynamics, and organizational functions within a social networked, agent based perspective. Building on the crisis information management cycle, it models sensing, analysis, sharing, and decision-making as an iterative loop in which automation shapes latency, reliability, and trust. As a proof of concept, we developed a minimal model with results showing how automation regimes, forecast horizons, and trust configurations affect performance through the concept. The model provides a starting point for users to explore cascading effects, authority shifts, and trade-offs between performance and meaningful human control. ... Read more