Dynamic strategic foresight using predictive business analytics: Strategic modeling of competitive advantage in unstable market and innovation ecosystems

Ridwan, Ishola Bayo (2025) Dynamic strategic foresight using predictive business analytics: Strategic modeling of competitive advantage in unstable market and innovation ecosystems. World Journal of Advanced Research and Reviews, 26 (2). pp. 473-493. ISSN 2581-9615

In a global environment characterized by technological disruption, geopolitical volatility, and accelerated innovation cycles, traditional strategic planning methods are often insufficient for maintaining long-term competitiveness. Enterprises increasingly require dynamic strategic foresight—a future-oriented capability that integrates real-time data, scenario modeling, and predictive business analytics to anticipate change and proactively shape strategic responses. This paper examines how organizations can use predictive analytics not merely as a descriptive or forecasting tool, but as a strategic modeling framework for building and sustaining competitive advantage in unstable markets and rapidly evolving innovation ecosystems. Drawing on principles from systems theory, market intelligence, and machine learning, the paper outlines a multi-layered foresight architecture. It emphasizes the role of time-series modeling, natural language processing, and simulation-based optimization in identifying emerging risks, opportunities, and innovation inflection points. Strategic foresight models are evaluated not only on predictive accuracy but also on adaptability, strategic optionality, and cross-scenario robustness. The research explores applications in various domains—such as R&D pipeline management, competitor behavior modeling, policy impact simulation, and venture capital allocation—demonstrating how predictive analytics can support decision-making under high uncertainty. Special focus is placed on feedback loop design between data signals, strategic hypotheses, and decision simulations, enabling continuous recalibration of enterprise strategies. The study concludes by proposing a framework for embedding foresight into core business intelligence systems, bridging the gap between operational analytics and board-level strategy. This approach equips firms to thrive not just through optimization, but through anticipation, resilience, and proactive adaptation in complex, competitive environments.