The research focuses on developing an algorithm for managerial decision-making using big data and artificial intelligence (AI). It relies on such scientific research methods as structural–functional, critical, and comparative analysis and schematic visualization of organizational-economic processes as a sub-method of qualitative modeling of dynamic economic systems. The scientific novelty of the proposed algorithm lies in its two levels of decision-making (AI and manager), unlike the existing algorithm. Management is limited to the manager, with all stages seamlessly transitioning from one to another and being intrinsically linked. The theoretical significance of the developed algorithm is that it addresses the shortcomings of the existing algorithm and adapts the decision-making process to contemporary challenges. The practical significance of the author’s algorithm is that automating decision-making using big data and AI offers the following advantages for management: faster decision-making by eliminating intermediaries in the form of lower-level managers, comprehensive identification of relevant business opportunities and problems, reduced managerial workload and a smaller managerial apparatus, and the possibility of remote employment for managers. This collectively supports the humanization of managerial labor. Automated big data collection (through managerial communications, problem identification, alternative selection, optimal decision-making, and implementation) ensures improved corporate monitoring and control.