This thesis analyzes the impact of digital technologies, particularly artificial intelligence (AI) and machine learning (ML), on the psychological aspects of decision-making, with a focus on business processes related to demand planning. The aim of the research was to examine how the integration of these technologies transforms traditional decision-making patterns, reduces the influence of cognitive biases, and improves the quality of decision-making in dynamic business environments. Special attention is given to cognitive limitations, heuristics, and emotional biases, which significantly shape decisions in complex situations. A structured interview with an expert in predictive analytics provided deeper insights into the application of AI and ML in demand forecasting processes, while a survey among procurement department employees offered perspectives on employees’ perceptions of the role of technology in decision-making and managing cognitive biases. The research findings highlight the significant contribution of digital technologies in objectifying decision-making and reducing the impact of subjective factors such as overconfidence, optimism bias, and loss aversion. However, challenges were identified, including employee resistance, the complexity of implementation, and ethical dilemmas related to algorithm transparency and trust in technology. In conclusion, the thesis emphasizes the need for a strategic approach to the implementation of digital technologies, which includes employee education, changes in organizational culture, and ensuring the transparency of AI systems to maximize their contribution to business processes. The integration of AI and ML in demand planning not only improves forecasting accuracy but also serves as a cognitive support, enabling managers to make more informed and rational decisions.