The research aims to create a neural network model. Using neural networks, the authors modeled criteria for top management motivation and strategic potential of the region. Material motivation refers to the salary of a senior civil servant. Non-material motivation refers to career growth. In this case, the target function is the coefficient of natural increase in the region’s population. Its positive value is evaluated positively (1—Success), and its negative value is evaluated negatively (0—Failure). As a result, the problem of binary classification in the trained neural network is solved. In the previous model, the total verification error for the functions of non-material and material motivation and strategic potential amounted to 39%. In our case, this error amounted to only 12%. This suggests that neural networks can achieve much greater prediction accuracy. The research results are used to develop a constructive motivation system for top managers.