Integrating AI and IoT for Smarter Decision Support in Entrepreneurial Ecosystems

This paper looks at the applicability of AI and IoT in promoting Technology Business Incubators to improve entrepreneurial environments. TBIs are essential for giving access to new and fundamental resources such as mentorship, networking and infrastructure for entrepreneurs. However, many fail to allocate resources and manage operations and stakeholder's relations efficiently. This research aims to make an improved recommendation of AI for predictive analysis and IoT for real-time analysis to improve management decisions and optimise resource utilisation for better startup performance. Following a multi-method approach, the work employs descriptive analysis, correlation studies, and lastly regression modeling on varied data sets. The results presented in the work highlight high accuracy of the proposed framework for predicting the success of startups and an informative approach to the management of resources for improving the decision-making process. The suggested framework can be implemented as best practice for TBIs since it addresses the management and steering challenges, focuses on resource efficiency, and supports incubator managers and policymakers in establishing sustainable environments for entrepreneurship.