AI-Driven Scenario Planning Versus Traditional Forecasting in Executive Strategic Decision-Making

Abstract Book of the 3rd Global Conference on Management and Economics

Year: 2026

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AI-Driven Scenario Planning Versus Traditional Forecasting in Executive Strategic Decision-Making

Prof. Dr. Azam Bejou

ABSTRACT:

This paper explores the transition from purely human-led strategic processes to AI-augmented frameworks, analyzing the cognitive constraints inherent in managing high-stakes, ambiguous long-term courses of action and exploring how AI-augmented frameworks may address them. This paper also examines the paradigm shift from traditional linear forecasting to AI-driven scenario planning, investigating how machine learning and generative tools enhance executive strategic decision-making by navigating uncertainty more effectively than historical trend extrapolation (Nweke, 2025), (Ködding et al., 2023), particularly through the use of generative AI to automate the construction of diverse future hypotheses and prioritize emergent trends (Picó et al., 2025), thereby allowing leadership teams to categorize these futures into a structured response spectrum that ranges from immediate priority actions to long-term monitoring (Shinkle et al., 2025). By integrating large-scale pattern recognition with probabilistic modeling, these advanced systems enable executives to transition from reactive, historical-based planning to proactive strategic foresight (Vudugula et al., 2023), ultimately redefining the core research inquiry to focus on how AI-augmented systems can evaluate the predicted consequences of irreversible strategic alternatives(Doshi et al., 2024). Research suggests that generative artificial intelligence can assist in this evaluative process by generating rankings for various business models that correlate with human expert assessments, providing a systematic mechanism for vetting high-stakes organizational paths (Doshi et al., 2024). Furthermore, this distinct shift from task-specific supervised learning to generative models allows for strategic framing in data-scarce environments where historical input-output pairs are absent or unique circumstances render past trends irrelevant (Doshi et al., 2024). By automating environmental scanning through natural language processing, AI-driven tools can detect weak signals and emergent risks that would otherwise remain hidden within vast datasets, enabling a “virtual strategy simulation” approach that allows leaders to test the resilience of specific policy options before they are implemented (Shachar, 2024), (Csaszar et al., 2024) ultimately resolving the inferential trilemma by helping executives distinguish between genuine breakthrough innovations and mere algorithmic hallucinations when navigating subjective tactical choices (Page & Kallapur, 2025). Consequently, these AI-driven decision support systems leverage reinforcement learning to adapt to real-time environmental feedback, ensuring that strategic pathways remain resilient and capable of rapid adjustment during unforeseen crises (Patil et al., 2024).

Keywords: AI Versus Traditional Forecasting, Strategic Decision Making