Leveraging Generative Adversarial Networks and Reinforcement Learning for Business Model Innovation: A Hybrid Approach to AI-Driven Strategic Transformation

Abstract

This research paper explores the integration of Generative Adversarial Networks (GANs) and Reinforcement Learning (RL) to drive business model innovation, presenting a novel hybrid approach for AI-driven strategic transformation. While traditional business model innovation is increasingly challenged by rapid technological advancements, this study proposes a framework that employs GANs to generate diverse business model scenarios and RL to optimize decision-making processes. The paper describes how GANs, with their dual-network architecture, can be utilized to simulate market dynamics and competitive landscapes, producing viable business model variants. Meanwhile, RL is deployed to evaluate and refine these simulated models, reinforcing successful strategies through iterative learning and feedback loops. Through a series of experiments across various industries, this hybrid approach demonstrates enhanced adaptability and strategic agility, allowing businesses to preemptively adapt to market shifts and technological disruptions. The study also identifies key challenges and opportunities in implementing this AI-driven methodology, highlighting the potential for reduced innovation cycle times and increased strategic foresight. This paper concludes by discussing the implications for future research and the potential transformative impact on organizational competitiveness and resilience.