Technologies that through Synergic Development can support the Intelligent Automation of Business Processes

Vasile Mazilescu and Adrian Micu
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Vasile Mazilescu: Dunarea de Jos University of Galati, Romania
Adrian Micu: Dunarea de Jos University of Galati, Romania

Economics and Applied Informatics, 2019, issue 2, 91-100

Abstract: Intelligent Automation (InA) activities using large and heterogeneous data and knowledge is an old concern in IT&C and computer science, being the next generation of Robotic Process Automation (RPA) at the enterprise level. Software systems and better solutions are developed to integrate business logic rules at the application level and to provide efficient work tools for accurate results. By designing such systems, the aim is to obtain better decisions as soon as possible in real time and make more efficient the process through which they can be made. InA is a holistic approach based on the variety of existing technologies based on Digital Transformation (DT) and automation of manual activities, using for this purpose digital workers, Artificial Intelligence (AI), cloud computing, Big Data. In this world of consumption, much of the technology we introduce into our daily lives is based on AI models and methods. Cognitive Technologies (CTs) are being developed using more and more specialized platforms. This is how different automation and intelligent capabilities are developed, which in turn adds a semantic connectivity between people and information systems [41]. The present paper analyzes the main important technologies that can effectively support the development, automation and robotization of business processes (BPA/R), identifying in a rational way how they can be applied for a synergic balance of tasks between people and computing systems, with a corresponding increase of the value added of business processes.

Keywords: DT; Business Process Automation/Robotization (BPA/R); AI; InA; CTs; Web4.0 (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:ddj:fseeai:y:2019:i:2:p:91-100

DOI: 10.35219/eai1584040937

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