# How Automated Machine Learning Can Improve Business
###### Abstract  
Automated Machine Learning (AutoML) is revolutionizing how businesses utilize data, but there seems to be a lack of clarity and a holistic view regarding all its advantages, especially concerning the benefits of AutoML for companies. In order to deeply understand how AutoML can improve businesses, a systematic review examined the bibliometric literature of this field, analyzing 74 academic and scientific documents from the Scopus database. Results showed that AutoML (i) reduces the time and resources needed to develop and deploy machine learning models, (ii) accelerates decision-making and enables quicker responses to market changes, (iii) empowers businesses to build accurate predictive models using sophisticated algorithms, (iv) optimizing model performance for reliable insights and better outcomes, and (v) enhances accessibility by reducing technical barriers and democratizing innovation. As businesses grow, AutoML scales to handle larger datasets and more complex problems without extensive manual intervention. In sum, AutoML enhances efficiency, accuracy, and scalability, becoming a crucial driver of business innovation and success.  
automated machine learning; business +
Footnote †: journal: Medical  
tuning [3]. As a result, this innovation significantly lowers the barriers to entry for businesses looking to leverage the power of machine learning. This democratization of machine learning means that organizations of various sizes and industries can now access advanced predictive analytics without needing deep expertise in the field [5]. Furthermore, the evolution of AutoML reflects a broader trend in technology: the movement toward greater automation and accessibility. Just as other automated tools have transformed industries by making complex processes more manageable, AutoML has the potential to revolutionize how businesses approach data analysis and decision-making. The technology simplifies building and deploying machine learning models, thus allowing companies to focus on using insights to drive innovation and growth. However, as presented above, the literature presents a scattered view on the topic, lacking a holistic perspective that offers a unique understanding of the potential of AutoML, especially concerning companies. Therefore, this research aims to collect and interpret data that helps consolidate the set of benefits that AutoML offers to organizations, thus allowing for a less scattered and more objective view. Thus, in order to deeply understand AutoML's advantages, this research is oriented to answer the following question: \"How can AutoML improve businesses?\" In order to answer this issue, this study employed a systematic bibliometric literature review (LRSB) to explore the role and impact of AutoML on businesses. The systematic review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, ensuring a transparent, comprehensive, and reproducible approach [6]. The Scopus database was selected as the primary source of literature due to its extensive coverage of peer-reviewed journals, conference papers, and other scholarly publications across various disciplines. The search strategy was carefully designed to capture all relevant documents related to AutoML within a business context [7; 8]. By doing so, this research aims to offer a more integrated view of AutoML, gathering the most important advantages of this tool for businesses and serving as an integrated summary for a better understanding of the topic.