Affiliation:
1. 1 Bucharest University of Economic Studies , Bucharest , Romania
2. 2 Bucharest University of Economic Studies , Bucharest , Romania
3. 3 Bucharest University of Economic Studies , Bucharest , Romania
Abstract
Abstract
Hyperautomation is a business-driven approach, conceptualized in 2019 by Gartner Inc., that combines various technologies such as Artificial Intelligence (AI), Robotic Process Automation (RPA) and integrated platforms as a service (iPaas) with the aim of making business processes more efficient by substituting human intervention. Among these, implementations of AI within business services use technologies like Natural Language Processing, Voice and Image Recognition, Virtual Agents, Machine Learning or Deep Learning platforms. Acknowledging this reality, we are interested in developing answers to the following research questions: (1) What are the main categories of business services which integrate specific AI tools? (2) What are the transformed business processes and their operations provided by AI tools? (3) What are the benefits related to AI integrated tools? For this triadic purpose, a systematic literature review on the implementation of Artificial Intelligence in the field of business services was carried out. Only works indexed in the Web of Science database, published in the last 5 years, were selected. Moreover, the websites of the main developers and client companies were investigated. Our findings include a selection of identified AI solutions, structured by main business services categories; we have also outlined the performed tasks and the resulting benefits of each listed AI tool. The synopsis of AI-powered tools presented in the paper could serve professionals, managers and researchers in designing future policies, operational procedures and research approaches to cope with new challenges of disruptive technologies from the AI spectrum.
Subject
General Earth and Planetary Sciences,General Environmental Science
Reference69 articles.
1. Abdurahman, A.Z.A., Yaacob, W.F.W., Nasir, S.A.M., Jaya, S. & Mokhtar, S. (2022). Using Machine Learning to Predict Visitors to Totally Protected Areas in Sarawak, Malaysia. Sustainability, 14(5), 2735. https://doi.org/10.3390/su14052735
2. Accern Corporation (2021). Deploy Enterprise AI More Efficiently Than Ever. Retrieved December 22, 2022 from https://meet.accern.com/artificial-intelligence-tool?utm_campaign=Capterra%20%20AI%20Tool&utm_source=ppc&utm_medium=AI%20Tool&utm_term=Artificial%20Intelligence% (5), 2735.
3. Ahmed, S., Alshater, M.M., El Ammari, A. & Hammami, H. (2022). Artificial intelligence and machine learning in finance: A bibliometric review. Research in International Business and Finance, 61. DOI: 10.1016/j.ribaf.2022.101646
4. Ammen, N., Sharma, G.D., Tarba, S., Rao, A. & Chopra, R. (2022). Toward advancing theory on creativity in marketing and artificial intelligence. Psychology & Marketing, 39(9), 1802-1825. https://doi.org/10.1002/mar.21699
5. Anica-Popa, I., Anica-Popa, L., Rădulescu, C. & Vrîncianu, M. (2021). The Integration of Artificial Intelligence in Retail: Benefits, Challenges and a Dedicated Conceptual Framework. Amfiteatru Economic, 23(56), 120-136. DOI: 10.24818/EA/2021/56/120