<p>This study developed an approach to determine the staffing needs of administrative, professional, and technical personnel that does not rely on subjective input. Our method involves a detailed description of work processes and a time study using a web application similar to a timesheet. We determine staffing needs by assessing the workload for each task and calculating the required staffing level based on the total workload. The time study revealed an uneven distribution of workload across tasks and an unbalanced allocation based on the frequency of task performance. It also showed a positive relationship between task execution frequency and workload. Based on these findings and other data trends, we developed task workload predictors and trained a generalized regression model using time study data from various industries. Staffing needs are compared in two ways: (i) using a machine-learning model instead of expert estimates, and (ii) using a bottom-up approach that incorporates time study data and employee feedback. Results indicate that staffing levels derived from the machine-learning model are similar but more conservative than those obtained through the integrated approach, which includes time study data and employee feedback.</p>