Intelligent Virtual Assistants for Enterprise Automation

  • Authors

    • Priya Natarajan Associate Professor, University of Madras, India Author

    DOI:

    https://doi.org/10.67228/30715636/IJETMR-2019PI6Q4F

    Published 03-05-2019

  • Intelligent Virtual Assistants, Enterprise Automation, Artificial Intelligence, Conversational AI, Natural Language Processing, Robotic Process Automation, Digital Transformation, Machine Learning, Knowledge Management

    Issue

    Vol. 2 No. 1 (2019)

    Section

    Articles

    How to Cite

    [1]
    P. Natarajan, “Intelligent Virtual Assistants for Enterprise Automation”, IJETMR, vol. 2, no. 1, pp. 01–17, Mar. 2019, doi: 10.67228/30715636/IJETMR-2019PI6Q4F.

  • Abstract

    Intelligent Virtual Assistants (IVAs) have become a revolutionary technology that allows the automation of the enterprises by combining artificial intelligence, natural language processing, machine learning, and robot process automation. Companies in various sectors including banking, healthcare, manufacturing, logistics and customer care are turning to IVAs to increase their efficiency in operations, decrease human manpower, and enhance decision making as well as providing better customer experiences. In this paper, IVAs are studied in detail and related to their architecture, approach, deployment models, difficulties, and further extent. An extensive literature review is performed to examine the current IVA models and approaches to enterprise automation. The proposed approach is based on the combination of conversational AI, intelligent workflow orchestration, and adaptive learning schemes to develop a scalable and safe automation framework. The results of performance evaluation include enhanced response time, accuracy of tasks, cost-effectiveness, and user satisfaction. The paper comes to the conclusion that IVAs can be discussed as one of the foundations of digital transformation and smart enterprise systems that help organizations become more productive, resilient and competitive in the more data-driven economy of the future

  • References

    [1] Weizenbaum, J. (1966). ELIZA—A Computer Program for the Study of Natural Language Communication Between Man and Machine. Communications of the ACM, 9(1), 36–45.

    [2] Wallace, R. S. (2009). The Anatomy of A.L.I.C.E. In Parsing the Turing Test (pp. 181–210). Springer.

    [3] McTear, M., Callejas, Z., & Griol, D. (2016). The Conversational Interface: Talking to Smart Devices. Springer.

    [4] Radziwill, N. M., & Benton, M. C. (2017). Evaluating Quality of Chatbots and Intelligent Conversational Agents. Journal of Quality Technology, 49(4), 1–13.

    [5] van der Aalst, W. (2016). Process Mining: Data Science in Action. Springer.

    [6] Willcocks, L., Lacity, M., & Craig, A. (2017). Robotic Process Automation: Strategic Transformation Lever for Global Business Services? Journal of Information Technology Teaching Cases.

    [7] Davenport, T. H., & Ronanki, R. (2018). Artificial Intelligence for the Real World. Harvard Business Review.

    [8] Devlin, J., Chang, M., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL-HLT.

    [9] Paulheim, H. (2017). Knowledge Graph Refinement: A Survey of Approaches and Evaluation Methods. Semantic Web Journal.

    [10] European Union (2018). General Data Protection Regulation (GDPR). Official Journal of the European Union.

  • Downloads