Strategic Sensing under VUCA Conditions: A Critical Reflection on Forecasting, Anchoring Bias, and Agentic AI

Frame of the research. Contemporary organizations increasingly operate under persistent VUCA conditions, where volatility, uncertainty, complexity and ambiguity undermine predictive approaches to strategy. Despite advances in analytics and artificial intelligence, organizations continue to experience delayed adaptation, interpretive rigidity and systematic decision biases, particularly in relation to forecasting practices and strategic sensing. Purpose of the paper. This paper critically examines how the emergence of agentic AI reconfigures the relationship between forecasting systems, anchoring mechanisms and strategic sensing under persistent VUCA conditions, challenging dominant assumptions that advanced AI primarily improves prediction or mitigates cognitive bias. Methodology. The study adopts a theory-informed critical reflection approach, integrating and problematizing insights from strategic management, dynamic capabilities, behavioural strategy, forecasting research and emerging literature on agentic AI to develop a diagnostic conceptual framework. Results. The paper conceptualizes strategic sensing as a hybrid human–AI and tension-filled process in which agentic systems become constitutive elements of sensing. Rather than eliminating anchoring, agentic AI redistributes and reshapes cognitive and algorithmic constraints, generating persistent tensions between prediction and interpretation, speed and reflection, autonomy and control, and learning and lock-in. Research limitations. As a critical reflection, the study does not provide empirical validation or causal testing and focuses on organizational-level strategic sensing, leaving broader institutional and regulatory dimensions unaddressed. Managerial implications. Managers are encouraged to rethink decision architectures and governance arrangements, focusing less on predictive optimization and more on preserving interpretive flexibility and collective sensemaking within hybrid human–AI sensing systems. Originality of the paper. The paper offers a novel reframing of forecasting and agentic AI as interpretive infrastructures shaping strategic meaning, rather than as purely technical solutions to uncertainty.