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AI – powered Business Services in the Hyperautomation Era

Author

Listed:
  • Anica-Popa Liana-Elena

    (1 Bucharest University of Economic Studies, Bucharest, Romania)

  • Vrîncianu Marinela

    (2 Bucharest University of Economic Studies, Bucharest, Romania)

  • Petrică Papuc Iuliana-Mădălina

    (3 Bucharest University of Economic Studies, Bucharest, Romania)

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.

Suggested Citation

  • Anica-Popa Liana-Elena & Vrîncianu Marinela & Petrică Papuc Iuliana-Mădălina, 2023. "AI – powered Business Services in the Hyperautomation Era," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 17(1), pages 1036-1050, July.
    • Handle: RePEc:vrs:poicbe:v:17:y:2023:i:1:p:1036-1050:n:28
    DOI: 10.2478/picbe-2023-0094
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    References listed on IDEAS

    as
    1. Erik Brynjolfsson, 2022. "The Turing Trap: The Promise & Peril of Human-Like Artificial Intelligence," Papers 2201.04200, arXiv.org.
    2. Abang Zainoren Abang Abdurahman & Wan Fairos Wan Yaacob & Syerina Azlin Md Nasir & Serah Jaya & Suhaili Mokhtar, 2022. "Using Machine Learning to Predict Visitors to Totally Protected Areas in Sarawak, Malaysia," Sustainability, MDPI, vol. 14(5), pages 1-16, February.
    3. Ahmed, Shamima & Alshater, Muneer M. & Ammari, Anis El & Hammami, Helmi, 2022. "Artificial intelligence and machine learning in finance: A bibliometric review," Research in International Business and Finance, Elsevier, vol. 61(C).
    4. Dekimpe, Marnik G., 2020. "Retailing and retailing research in the age of big data analytics," International Journal of Research in Marketing, Elsevier, vol. 37(1), pages 3-14.
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