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Artificial Intelligence In Business Operations: Exploring Productivity And Acceptance

Author

Listed:
  • Ioana CIOFU

    (Bucharest University of Economic Studies, Romania Doctoral School of Business Administration I)

  • Giulia KONDORT

    (: Bucharest University of Economic Studies, Romania Doctoral School of Business Administration I)

  • Stefana POP

    (Bucharest University of Economic Studies, Romania Doctoral School of Business Administration I)

  • Roxana CIOC

    (Bucharest University of Economic Studies, Romania Doctoral School of Business Administration I)

Abstract

This paper will provide information on the impact of AI in daily life and work-related activities.Today, AI functionalities could nowadays transform businesses, playing a critical role in enhancing and improving decisions. From virtual assistants to automation tools, AI covers a great amount of information, which could impact the core. In this paper, the productivity and sense of failure of AI will be paper. The productivity of AI, such as, varies by tasks and industry. AI could excel in repetitive and high-precision tasks. On the other hand, humans outperform AI in tasks requiring creativity and emotional intelligence. This qualitative study will show the perception of integrating AI into workflows and asking questions about value added. To evaluate the impact of artificial intelligence (AI) on business operations, an online survey was conducted to examine perceptions of AI's efficiency, adaptability, and fault tolerance.The analysis revealed generational differences in acceptance and trust towards AI. Younger respondents, particularly those under 25, were found to have greater tolerance for AI errors and a greater willingness to integrate AI into workflows. This is likely to reflect their familiarity with technology. In contrast, older respondents exhibited lower levels of trust and acceptance, particularly in contexts requiring precision, such as financial transactions. The results suggest that while AI is perceived as highly effective in repetitive and data-intensive tasks, its limitations in adaptability and emotional intelligence remain a concern. The findings emohasize the need for reskilling initiatives to facilitate workforce transitions and the development of ethical guidelines to address trust and reliability issues.

Suggested Citation

  • Ioana CIOFU & Giulia KONDORT & Stefana POP & Roxana CIOC, 2024. "Artificial Intelligence In Business Operations: Exploring Productivity And Acceptance," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 33(2), pages 263-274, December.
    • Handle: RePEc:ora:journl:v:2:y:2024:i:2:p:263-274
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    References listed on IDEAS

    as
    1. Olimpia Ban & Irina Maiorescu & Mihaela Bucur & Gabriel Cristian Sabou & Betty Cohen Tzedec, 2024. "AI between Threat and Benefactor for the Competences of the Human Working Force," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 26(67), pages 762-762, August.
    2. Marioni, Larissa da Silva & Rincon-Aznar, Ana & Venturini, Francesco, 2024. "Productivity performance, distance to frontier and AI innovation: Firm-level evidence from Europe," Journal of Economic Behavior & Organization, Elsevier, vol. 228(C).
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Artificial Intelligence; Productivity; Failure; Problem-Solving; Consistency; Precision;
    All these keywords.

    JEL classification:

    • M10 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - General
    • M15 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - IT Management

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