Abstract
In the industry 4.0 era, there exists a pressing need for intelligent data management solutions to enhance the operations of small businesses. This study introduces a pioneering methodology that harnesses the power of AI-driven analysis of internal voice communications, an often-overlooked source of valuable insights within the small business environment. The research centers on an advanced platform that utilizes the Regularized Bayesian Approach, meticulously tailored for the processing of unstructured and semi-structured data, with a specific focus on internal voice messages. This methodology enables the generation of in-depth insights into employees’ emotional, psychological, and motivational states. Furthermore, the integration of data with a psychometric system enables the production of comprehensive personality evaluations, providing digital portraits for every employee. These portraits offer valuable insights into employee well-being and motivations, particularly beneficial for small businesses with limited HR resources. The potential benefits for small businesses are multifaceted and research-driven, including enhanced employee safety, improved efficiency, advanced risk management, and streamlined HR processes. Additionally, this research underscores the growing relevance and potential of this approach in the Emotion AI market. Through the analysis of voice messages, entities, intent, and relationships between utterances can be discerned, offering a comprehensive view of employee sentiment, loyalty, and satisfaction. This study serves as the foundation for fostering a positive work environment, enhancing productivity, and providing a roadmap for mental health improvement and reduced attrition in small businesses. It contributes to the evolving field of intelligent data management and its applications in enhancing small business operations. Keywords: voice recognition, small business enhancement, emotion AI, artificial intelligence, Bayesian approach
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