Artificial intelligence and firm governance: toward a predictive control model in the viable systems approach

Artificial intelligence (AI) is profoundly transforming organizational and decision-making processes. However, much of the management literature still interprets its impact primarily in terms of operational efficiency or performance improvement. Less explored is how AI affects the conditions of firm governance in contexts characterized by high levels of complexity and environmental dynamism. This study addresses this gap by developing an interpretative model of systemic predictive control within the theoretical framework of the Viable Systems Approach. In this perspective, artificial intelligence is interpreted as a cognitive lens that expands the informational variety available to decision-makers and contributes to the construction of the observed context, thereby influencing the relationship between deliberate and emergent strategy. The model highlights that AI does not generate value in a direct or deterministic way, but rather through the mediating role of the control system, which translates operational improvements into economically sustainable configurations consistent with strategic objectives. From this perspective, control evolves from a retrospective measurement tool into a predictive mechanism of systemic integration capable of supporting recursive processes of adaptation, learning, and strategic realignment. The theoretical contribution of the study lies in repositioning the debate on AI in Management within the broader theory of firm governance, proposing a conceptual architecture that connects artificial intelligence, complexity management, and systemic viability in the digital era.