In the context of Industry 4.0, process automation and predictive maintenance play an essential role. There is a need to provide more effective and faster maintenance through the integration of industrial tools and processes, to support manufacturing operations, in the perspective of integration standards and architectures. In a typical maintenance system, registration and maintenance requests are made through maintenance orders, which consist of a standard form and usually are created and filled manually. However, predictive maintenance requires a higher level of automatization, from data acquisition to maintenance order generation in a Computerized Maintenance Management System (CMMS) / Enterprise Asset Management (EAM). The proposal is to automate the process of generating maintenance orders, providing for automated form completion. At the physical level, assets are monitored by sensors, and, based on a set of rules, the respective predictive maintenance order will be issued in CMMS/EAS. Maintenance orders may contain variable fields according to each asset, so Machine Learning (ML) and Multicriteria Decision Making (MCDM) will be applied to fill in these fields, as well as the allocation of the maintenance orders to the maintainer that best fit for the maintenance specification. This automatic process will assist the maintenance workflow, leading to a Smart Workflow concept. A serial and parallel framework are presented, the former consists in applying TOPSIS (MCDM method) to extract features for the use in ML classification, in order to automatically fill in the appropriate form fields. The latter consists in a classification using both methods (ML and MCDM), where TOPSIS performs the initial classification, and if the alternatives ranking scores are close to each other, ML is used for more accurate classification. A case study was carried out in a Brazilian company that develops a CMMS/EAM system, distributed worldwide, and the results demonstrate that the concept of Smart Workflows is valuable, simplifying and enhancing maintenance processes.