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Upcoming Digital Transformation And Artificial Intelligence Trends In The Public Sector

Author

Listed:
  • Milja ORLANDIC

    (PhD student; University Business Academy in Novi Sad, Faculty of Applied Management, Economics and Finance, Belgrade, Serbia)

  • Tijana ÐUKIC

    (Assistant Professor; University Business Academy in Novi Sad, Faculty of Applied Management, Economics and Finance, Belgrade, Serbia)

  • Marija MLADENOVIC

    (PhD student; University Business Academy in Novi Sad, Faculty of Applied Management, Economics and Finance, Belgrade, Serbia)

Abstract

Robotic Process Automation (RPA) continues to be recognized in the field of digital transformation due to its capacity to boost productivity, enhance quality, and elevate employee satisfaction. Combining RPA with artificial intelligence and machine learning enhances operational efficiency, reduces costs, and enhances performance. This article employs the Piprecia method to determine the crucial factors for successfully building a Robotic Process Automation (RPA) platform. The analysis focuses on matters such as technological framework, compatibility, employee learning, adaptability, and ability to expand. Findings provide strategic recommendations for the effective, practical, and customized implementation of RPA platforms in the public sector.

Suggested Citation

  • Milja ORLANDIC & Tijana ÐUKIC & Marija MLADENOVIC, 2024. "Upcoming Digital Transformation And Artificial Intelligence Trends In The Public Sector," REVISTA ADMINISTRATIE SI MANAGEMENT PUBLIC, Faculty of Administration and Public Management, Academy of Economic Studies, Bucharest, Romania, vol. 2024(42), pages 45-59.
  • Handle: RePEc:rom:rampas:v:2024:y:2024:i:42:p:45-59
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    JEL classification:

    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory

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