ARTIFICIAL INTELLIGENCE IN CREATIVE WORKFORCE ANALYTICS: MANAGING TALENT AND PERFORMANCE IN VISUAL ARTS ORGANIZATIONS

Abstract

The visual arts organizations are moving toward a more complicated, project-driven ecosystem in which it is challenging to quantify creativity, collaboration, and performance through more traditional management strategies. The paper explores how artificial intelligence may be used as a tool to analyze the creative workforce, in this case, how talent management and performance optimization in visual arts institutions could be boosted through the use of data-driven approaches. The suggested model combines diverse information sources such as digital portfolios, project history, peer ratings, and audience engagement indicators to form multidimensional creative professional profiles. Clustering, predictive modeling, and sentiment analysis are some of the machine learning methods used to facilitate the segmentation of talent, alignment of roles, and prediction of the creative performance outcomes. AI-aided recruitment processes can improve portfolio analysis because it helps to detect hidden competencies, stylistic coherence and innovation potential that cannot be evaluated through subjective human judgment. Parallel to it, performance analytics models integrate quantitative metrics with qualitative feedback in order to determine creative productivity, collaborative and emotional reactions to critique. The evidence provided by the experiment, which is based on simulated institutional datasets, shows that the efficiency of talent utilization, the quality of project outcomes and transparency of the decision made significantly improve in comparison with the conventional workforce management techniques. The results emphasize the ability of AI to strike a balance between creative subjectivity and analytical rigor that allows to make an evidence-based decision and retain the autonomy of the creativity.