# Artificial Intelligence-Based Methods for Business Processes: A Systematic Literature Review
## 3 Methodology  
We applied a systematic literature review to determine which AI-based methods for BPs were available in the literature for automatizing business processes and supporting the decision-making processes of companies. We identified AI-based methods from large volumes of articles by performing an SLR [28]. As a result, developers and decision-makers will be able to visualize AI-based methods to improve their business processes in a systematic manner. Based on [29; 30; 31], we divided our methodology into three stages: (i) planning, (ii) execution, and (iii) reporting and dissemination. First, we chose the academic databases, and studied how to select the relevant material. After, we determined the applicable search keywords based on our RQ and the best combinations of these keywords in the databases search. In the execution phase, we collected the articles and applied multiple criteria to select the most appropriate ones. In the third phase, we defined categories for the selected articles and summarized their main points, methods, techniques used, and problems tackled. Our search space was based on the following RQ: which AI-based methods exist in the literature, to automatize business processes and support the decision-making processes of companies? Scopus and Web of Science, two of the top eight academic databases [32], were chosen for our research. We developed four exclusion criteria to refine the selection of papers, as shown in Table 1. After this definition, we selected the keywords below to retrieve relevant papers based on our RQ:  
* **Method:** a suitable study should, as one of its main outputs, have a method, its creation, or analysis. * **Artificial intelligence:** a relevant study must include some level of automation and less human action, which could be identified as computational or artificial intelligence technique. * **Business process:** an adequate study needs to focus on the processes in a business context. The term \"Method\" was part of our string search to find works that proposed a sequence of well-defined steps to achieve a specific goal, as in the [PERSON] and [PERSON] definition [24]. In addition, with the term \"Artificial Intelligence\", we targeted works using intelligent techniques covered by the artificial intelligence umbrella. This umbrella included sub-fields, such as machine learning, neural networks, evolutionary computation, expert systems, etc. Therefore, we relied on the authors and indexed keywords from the two used repositories. Finally, the AI-based methods were addressed to \"Business Process\", our last keyword. Though the terms above are the most related keywords for our RQ, we included synonyms to comprehend all possible works. Therefore, we included: referential model, computational intelligence, organizational, administrative, and workflow, respectively. Our academic database search was structured as follows: (method OR \"reference model\") AND (\"computational intelligence\" OR \"Artificial Intelligence\") AND ((business OR organizational OR administrative) w/2 (process OR workflow)). The expression \"w/2\" denoted a two-word distance between the two terms in question, regardless of order. Figure 2: Definition of method and model. Source: adapted from [PERSON] [24], [PERSON] et al. [25] and Merriam-Webster [27]. Using these keywords, we derived a search string submitted in the chosen academic databases. The retrieved papers had the three terms included in their titles, keywords, or abstracts, to include as much information as possible from the database on this subject. The search included papers from 2000 to May 2021. The authors chose these years to have a reasonable representative subset.