<p>Generative artificial intelligence (GenAI) is a subdivision of artificial intelligence that utilizes generative models to produce data such as images and videos. In recent years, GenAI has been integrated into workplaces such as corporate offices, financial services, and information technology services, to boost its performance by increasing productivity and creativity. In this study, a Walrus optimized spiking neural network (WoSNN) to evaluate the impact of GenAI in improving organizational performances in workplaces. The proposed strategy commences with the collection of a dataset containing the usage of GenAI gathered from workplaces. The gathered database was preprocessed to improve its quality and consistency for further analysis. Consequently, a novel feature selector was developed based on a fire hawk optimizer, which selects the most relevant and informative features from the preprocessed dataset. This optimized feature selection helps in improving the model’s evaluation performance and enhances computational efficiency. Finally, the organizational performances are assessed using the developed WoSNN in which SNN learns long-term dependencies in the data and evaluates the organizational performance improvement achieved through the integration of GenAI in the workplace. On the other hand, the walrus optimizer fine-tunes the parameters of SNN and makes the model adaptable to changing workplace constraints. The designed framework was implemented in Python and the experimental outcomes depicted that it achieved minimal mean absolute error of 0.04714, lower root-mean-square error of 0.05383, high R-squared value of 0.96317, and computational time of 0.01&#xa0;s. Furthermore, the comparative assessment with existing models validated the robustness of the presented approach in workplace performance assessment.</p>