ARTIFICIAL INTELLIGENCE-ENABLED DIGITAL TWIN ARCHITECTURE FOR STRATEGIC INNOVATION MANAGEMENT AND REAL-TIME DECISION OPTIMIZATION IN ENTREPRENEURIAL ECOSYSTEMS
ARTIFICIAL INTELLIGENCE-ENABLED DIGITAL TWIN ARCHITECTURE FOR STRATEGIC INNOVATION MANAGEMENT AND REAL-TIME DECISION OPTIMIZATION IN ENTREPRENEURIAL ECOSYSTEMS

Summary/Abstract: Entrepreneurial ecosystems entail dynamic and intricate interactions with start-ups, investors, and market forces, which results in fragmented decision-making processes and lack of real-time visibility into the innovation process. Current methods based on the application of artificial intelligence (AI) and digital technologies do not have a full-fledged framework that combines strategic management of innovation with optimization of decisions in real time. To fill this gap, this study suggests an AI-enabled Digital Twin architecture that can dynamically and data-driven represent the business ecosystem. The system is based on machine learning models, real-time data acquisition, and simulation-based feedback loops to continually monitor, predict and optimize the decision results. The developed approach enables learning and scenario analysis processes, facilitating timely and informed decision-making. Experimental results suggest the methodology dramatically boosts the speed of decision-making, enhances innovation effectiveness and risk-sensitive planning. This paper adds the integrated architecture that acts as a bridge between AI and digital twin technology and innovation management to provide a scalable solution to the problem of making strategic agile and decision-intelligent decisions in complex entrepreneurial settings.

• Details
• Contents