Behavioral AI Nexus: A Cognitive-Emotional Framework for Adaptive and Human-Centric Organizations

Shrikarnag Bangalore Prahallada; Ranjitha Sridhar Rao Nag; Lohith Dayananda Ram; Shrivatsa Bangalore Prahallada; Deepashree Abhaya; Rudri Jani

Shrikarnag Bangalore Prahallada Tymeline Inc 651 N Broad St Suite 201 Middletown DE 19709, USA
Ranjitha Sridhar Rao Nag Tymeline Inc 651 N Broad St Suite 201 Middletown DE 19709, USA
Lohith Dayananda Ram Tymeline Inc 651 N Broad St Suite 201 Middletown DE 19709, USA
Shrivatsa Bangalore Prahallada Tymeline Inc 651 N Broad St Suite 201 Middletown DE 19709, USA
Deepashree Abhaya Tymeline Inc 651 N Broad St Suite 201 Middletown DE 19709, USA
Rudri Jani Tymeline Inc 651 N Broad St Suite 201 Middletown DE 19709, USA

Keywords: Behavioral AI, Neuroscience-Inspired AI, Emotional Intelligence Mapping, Team Dynamics, Real-Time Sentiment Analysis, Predictive Analytics, Team Collaboration, Adaptive Workflows, Employee Engagement, Cultural DNA Mapping, Organizational Psychology, Workforce Optimization, Team Chemistry Analysis, Burnout Prediction, Intelligent People Analytics, Cognitive Science, Temporal Behavior Modeling

Abstract

By combining behavioral analytics, neuroscience-inspired AI, and real-time interaction models, the Behavioral AI Nexus (BAN) transforms organizational intelligence by mapping, forecasting, and influencing team dynamics. This state-of-the-art module functions as an AI-powered managerial psychologist, monitoring emotional states, predicting behavioral changes, and coordinating team actions with organizational objectives and culture. BAN enables executives to take action on disengagement, enhance collaboration, and cultivate a vibrant, high-performance culture by converting communication habits and work practices into actionable insights. With the increasing complexity of today's workplaces, BAN provides a game-changing solution that fosters psychological safety, increases emotional intelligence at volume, and boosts productivity through behavioral improvement.

References

Chiao, C., Chen, Y., & Lin, S. (2022). The interplay between culture, behavior, and brain: Emerging perspectives. Frontiers in Psychology, 13, 1–15. https://www.frontiersin.org/articles/10.3389/fpsyg.2022.884929
Gaurav, R., Singh, A., & Sharma, P. (2020). Adaptive task allocation in human-machine teams with trust models. ResearchGate. https://www.researchgate.net/publication/347629197
Gündüz, S., Yıldırım, E., & Kaya, M. (2022). AI-based prediction of burnout using multimodal behavioral data. Frontiers in Public Health, 10, 1–10. https://pmc.ncbi.nlm.nih.gov/articles/PMC9120306/
Hofstede, M., & Minkov, T. (2020). Cultural mapping and organizational alignment. Journal of International Business Studies, 51(2), 195–212. https://www.sciencedirect.com/science/article/abs/pii/S0148296320305191
Huang, S., Zhang, J., Wang, L., Liu, Y., & Chen, X. (2024). Emotion recognition using deep learning techniques: A review. Applied Sciences, 14(15). https://www.mdpi.com/2076-3417/14/15/6631
Kim, Y., Lee, H., Park, J., & Choi, S. (2022). Aspect-based emotion analysis on speech for predicting performance in collaborative learning. ResearchGate. https://www.researchgate.net/publication/355424756
Kumar, A., & van der Aalst, W. M. P. (2012). Dynamic work distribution in workflow systems. Data & Knowledge Engineering, 72(1), 1–18. https://www.researchgate.net/publication/220591295
Wang, L., Zhao, Q., & Sun, H. (2023). Human-centered AI for personalized workload management. ResearchGate. https://www.researchgate.net/publication/383764838