# Artificial Intelligence-Based Methods for Business Processes: A Systematic Literature Review
## 1 Introduction  
Business process management (BPM) is a discipline that involves concepts, methods, and techniques, to design, enact, measure, and configure business processes [1]. [PERSON], [PERSON], and [PERSON] were essential precursors of today's configuration of BPM, by respectively showing the advantages of the division of labor, science management, and production lines in the industry. The beginning of the computational era, around 1950, marked another profound impact in BPM. It changed company structures, since they began to rely on information systems. It became imperative to model cross-organizational processes that could document procedures and generate insights [2]. In recent decades, we-the world-has become a digital society; data are collected everywhere. Data come from mobile phones, personal computers, and smart home appliances. As data are \"constantly growing\", organizations face challenges surrounding the exploration of such data, in regard to adding value to their operations [3]. Business Intelligence (BI) tools and process-aware information systems (PAISs) may help to extract knowledge from data via computer tools and decision-makers. However, these tools may be limited when dealing with large volumes of data, since the output has to be analyzed further by specialists in an environment where time is critical. Artificial intelligence (AI) is used in learning algorithms, i.e., to make decisions in milliseconds within a \"Big Data\" context. AI machine learning and deep learning subfields, for instance, have shown great success in complex tasks, such as natural language processing, speech recognition, computer vision, medical diagnosis, recommendation systems, and many others [4]. It has been successfully applied in several disciplines, including biology, experimental psychology, communication theory, game theory, mathematics, statistics, logic, and philosophy [5]. Swarm intelligence is another AI branch primarily employed for optimization tasks. This field contains diverse bio-inspired metaheuristics capable of finding relevant solutions in high-dimensional search spaces. Swarm intelligence algorithms have good results in a wide range of tasks, including telecommunications, industries, social sciences, and military operations [6]. AI techniques have changed the corporate world by providing quicker and less error-prone techniques. Business processes (BPs) are becoming increasingly powerful, with the incorporation of techniques from the digital era. Due to these changes, organizations can concentrate on decision-making and business strategies other than manual and repetitive operations. This new context also leads to more mature and predictable processes, highly scalable operations, and an overall improvement in an organization's performance [7]. Artificial intelligence is critical because of the complexities of the changes required to integrate a new organization into a larger one [8]. Investors and business leaders are unanimous in their belief that AI and machine learning are transforming their organizations, by lowering costs, streamlining operations, managing risks, accelerating growth, and boosting innovation [9]. However, when a business problem or opportunity is identified as having the potential to be transformed and optimized by AI, it is not always clear on how to execute and develop the solution. Here, we consider optimization from a business perspective. Therefore, we are interested in improvements in a company's business processes that directly leads to better decision processes, to achieve the company's goals. For example, we can cite AI solutions that automatize manual and time-consuming procedures, provide insights to decision-makers, or align process goals with a company's business goals. Thus, the question from this paper emerges: what AI-based methods, in the literature, are used to automatize business processes and support the decision-making processes of companies? This paper answers this question through a systematic literature review (SLR).