TY - GEN
T1 - An AI-Enhanced Framework for Corporate Strategy on Ecosystems' Creation
AU - Rodriguez Garcia, Patricia
AU - Lopez-Lopez, D.
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025/8/25
Y1 - 2025/8/25
N2 - In an era where interconnectedness and collaboration define corporate strategy, the effective management of ecosystems has emerged as a critical driver of competitive advantage. This research presents the conceptual development of a multi-objective optimization framework for corporate ecosystem creation enhanced by artificial intelligence (AI). The framework progresses through three key phases. First, it establishes a foundational approach that integrates strategic dimensions (desirability, feasibility, and sustainability) into traditional portfolio optimization, moving beyond the sole focus on financial risk-return. Second, it extends this foundation by introducing an AI-optimization hybrid model that predicts and incorporates synergies generated by interdependent projects, using machine learning techniques and rigorous optimization tools. This integrated approach aims to provide CEOs with new strategic tools to align ecosystems while balancing financial performance and broader sustainability goals. By combining precision and adaptability, this research proposes a robust methodology for decision-making in complex, dynamic corporate ecosystems. Future work will focus on empirical validation through real-world case studies, the incorporation of temporal dynamics of synergies, and the enhancement of model interpretability to foster greater executive trust in AI-driven insights.
AB - In an era where interconnectedness and collaboration define corporate strategy, the effective management of ecosystems has emerged as a critical driver of competitive advantage. This research presents the conceptual development of a multi-objective optimization framework for corporate ecosystem creation enhanced by artificial intelligence (AI). The framework progresses through three key phases. First, it establishes a foundational approach that integrates strategic dimensions (desirability, feasibility, and sustainability) into traditional portfolio optimization, moving beyond the sole focus on financial risk-return. Second, it extends this foundation by introducing an AI-optimization hybrid model that predicts and incorporates synergies generated by interdependent projects, using machine learning techniques and rigorous optimization tools. This integrated approach aims to provide CEOs with new strategic tools to align ecosystems while balancing financial performance and broader sustainability goals. By combining precision and adaptability, this research proposes a robust methodology for decision-making in complex, dynamic corporate ecosystems. Future work will focus on empirical validation through real-world case studies, the incorporation of temporal dynamics of synergies, and the enhancement of model interpretability to foster greater executive trust in AI-driven insights.
KW - Artificial Intelligence
KW - Corporate Ecosystems
KW - Strategic Decision-Making
UR - https://www.scopus.com/pages/publications/105015367766
U2 - 10.1109/ICE/ITMC65658.2025.11106671
DO - 10.1109/ICE/ITMC65658.2025.11106671
M3 - Conference contribution
VL - 2025
T3 - Proceedings of the 31st ICE IEEE/ITMC Conference on Engineering, Technology, and Innovation: AI-Driven Industrial Transformation: Digital Leadership in Technology, Engineering, Innovation and Entrepreneurship, ICE 2025
SP - 1
BT - Proceedings of the 31st ICE IEEE/ITMC Conference on Engineering, Technology, and Innovation
PB - ieee Xplore
ER -