Entrepreneurship drives innovation, economic growth, and societal transformation, yet identifying and developing entrepreneurial potential remains a complex challenge. Traditional assessment methods often fail to capture the dynamic interplay of cognitive, behavioral, and skill-based traits that determine entrepreneurial success. The emergence of artificial intelligence (AI) offers transformative opportunities for predictive and developmental entrepreneurship analytics. This chapter presents a comprehensive framework integrating behavioral and skill analytics with AI techniques such as machine learning, deep learning, and natural language processing to predict entrepreneurial orientation and facilitate competency enhancement. Behavioral data, including decision-making patterns, emotional responses, and social interactions, are analyzed alongside skill metrics to uncover latent entrepreneurial traits. Explainable AI ensures interpretability and transparency, while ethical and privacy-preserving approaches safeguard sensitive information. The framework supports personalized developmental pathways through adaptive feedback, enabling early identification of high-potential individuals and targeted skill improvement. Case studies and simulated implementations demonstrate improved prediction accuracy, reliability, and scalability compared to traditional methods. This chapter contributes to the advancement of data-driven entrepreneurship ecosystems by providing actionable insights for educational institutions, incubators, and policy frameworks. The integration of AI-based behavioral and skill analytics establishes a scientifically grounded, ethically responsible, and scalable approach for cultivating entrepreneurial talent in diverse socio-economic contexts.
Entrepreneurship is a pivotal driver of innovation, economic growth, and societal transformation, shaping the evolution of industries and influencing global competitiveness [1]. The identification and development of entrepreneurial potential have become essential to fostering sustainable innovation ecosystems that respond effectively to rapid technological and market changes [2]. Traditional methods of entrepreneurship assessment, including psychometric evaluations, self-reported surveys, and observational studies, primarily capture explicit cognitive and personality traits, such as risk-taking, leadership, and creativity [3]. While these methods provide foundational insights, they are limited in their ability to assess complex behavioral interactions, adapt to changing contexts, and predict future performance accurately [4]. The static nature of conventional evaluations prevents timely identification of latent entrepreneurial talent and constrains the design of targeted development interventions [5]. This limitation underscores the need for dynamic, data-driven approaches capable of integrating multiple dimensions of entrepreneurial behavior and skill into predictive frameworks that support both identification and development [6].
Recent advancements in artificial intelligence (AI) have introduced transformative opportunities in the domain of entrepreneurship analytics [7]. Machine learning, deep learning, and natural language processing enable the extraction of meaningful patterns from diverse datasets encompassing cognitive, behavioral, and skill-based attributes [8]. Behavioral data derived from digital interactions, decision-making processes, collaborative tasks, and task-based simulations offer unprecedented insight into entrepreneurial tendencies [9]. These insights extend beyond observable traits to latent patterns of adaptability, innovation orientation, and problem-solving capacity [10]. Predictive models leverage such data to forecast entrepreneurial potential, offering a higher degree of accuracy and objectivity than conventional methods [11]. The integration of AI into entrepreneurship assessment enables continuous evaluation and feedback, facilitating timely interventions that guide skill enhancement, cognitive flexibility, and entrepreneurial mindset development [12].
Behavioral analytics is a central component of AI-driven entrepreneurship assessment, capturing nuanced features such as emotional regulation, decision latency, and social interaction patterns [13]. Mapping these behaviors allows the identification of traits that are strongly correlated with entrepreneurial success, including resilience, leadership, and creativity [14]. The combination of behavioral modeling with skill profiling enhances the interpretability of prediction models, enabling stakeholders to understand the underlying drivers of potential performance [15]. This multidimensional approach supports adaptive learning pathways and personalized development programs tailored to individual strengths and weaknesses [16]. By providing actionable insights, AI-based behavioral analytics bridges the gap between theoretical understanding of entrepreneurship and practical applications in education, incubation, and professional development ecosystems [17].
