Perceived Enterprise Use of Agentic AI Capabilities and Strategic Business Development: The Mediating Role of Decision-Making Efficiency

This is the authors' abstract. We don't add key points for this paper.

This study examines whether perceived enterprise use of agentic AI capabilities is associated with strategic business development through decision-making efficiency. Cross-sectional survey data were obtained from 318 managers, analysts, and professionals working in enterprises of varying sizes and industries. The model was estimated using partial least squares structural equation modeling. Perceived agentic AI use was positively associated with decision-making efficiency (β = 0.624, p < 0.001) and strategic business development (β = 0.283, p < 0.001). Decision-making efficiency was also positively associated with strategic business development (β = 0.478, p < 0.001). The indirect association was significant (β = 0.298, p < 0.001; 95% CI [0.215, 0.387]), indicating partial mediation. By focusing on goal-directed, context-adaptive, and action-oriented capabilities, the study extends research that treats AI as a broad organizational capability and identifies decision-making efficiency as a plausible process mechanism. Because the data are cross-sectional and self-reported, the findings do not establish causality, verify a specific technical implementation, or demonstrate superiority over conventional or generative AI. Potential relevance to SDGs 8, 9, and 12 is discussed as a conceptual implication of responsible implementation; the survey did not measure SDG outcomes directly. The study recommends that enterprises integrate agentic AI into core decision-making functions through clear governance, workflow redesign, and sustainability-aligned strategic objectives.