IDEAS home Printed from https://ideas.repec.org/a/vrs/poicbe/v18y2024i1p1819-1830n1016.html
   My bibliography  Save this article

Improving the Performance of Corporate Employees through the Use of Artificial Intelligence: The Case of Copilot Application

Author

Listed:
  • Vasilescu Cristina

    (Microsoft Company, Bucharest, Romania)

  • Gheorghe Militaru

    (National University of Science Technology POLITEHNICA Bucharest, Romania)

Abstract

In today's dynamic business landscape, the use of artificial intelligence becomes an important factor for companies to succeed. This research aims to see how Copilot application can help employees in their work environment. Microsoft Copilot application being an artificial intelligent tool which aims to be a teammate for each employee. There is a lot of research on how artificial intelligence can help users succeed in their work environment but there was still more to learn about how Copilot could help more specifically. Therefore, in order to find out more, we have used a survey and an interview in this scope. The survey was spread to 30 IT professionals and the interview was held with a support engineer to see how well Copilot worked for him. In the survey we looked at how Copilot could help people work faster, make better decisions, be more productive, and be happier at work. Whereas in the interview we looked at how useful Microsoft Copilot was in day to day activities in the workplace. For those interested to learn more about how Microsoft Copilot is changing the way we work, and how the Microsoft Copilot tool could help companies be more efficient, then the insights from this research provide an excellent foundation.

Suggested Citation

  • Vasilescu Cristina & Gheorghe Militaru, 2024. "Improving the Performance of Corporate Employees through the Use of Artificial Intelligence: The Case of Copilot Application," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 18(1), pages 1819-1830.
    • Handle: RePEc:vrs:poicbe:v:18:y:2024:i:1:p:1819-1830:n:1016
    DOI: 10.2478/picbe-2024-0153
    as

    Download full text from publisher

    File URL: https://doi.org/10.2478/picbe-2024-0153
    Download Restriction: no
    File URL: https://libkey.io/10.2478/picbe-2024-0153?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Giacomo Damioli & Vincent Van Roy & Daniel Vertesy, 2021. "The impact of artificial intelligence on labor productivity," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 11(1), pages 1-25, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tao Chen & Shuwen Pi & Qing Sophie Wang, 2025. "Artificial Intelligence and Corporate Investment Efficiency: Evidence from Chinese Listed Companies," Working Papers in Economics 25/05, University of Canterbury, Department of Economics and Finance.
    2. Czarnitzki, Dirk & Fernández, Gastón P. & Rammer, Christian, 2023. "Artificial intelligence and firm-level productivity," Journal of Economic Behavior & Organization, Elsevier, vol. 211(C), pages 188-205.
    3. Saima Javed & Yu Rong & Babar Nawaz Abbasi, 2024. "Convergence analysis of artificial intelligence research capacity: Are the less developed catching up with the developed ones?," Journal of International Development, John Wiley & Sons, Ltd., vol. 36(4), pages 2172-2192, May.
    4. Garcia-Macia, Daniel & Korosteleva, Julia, 2025. "Tracing productivity growth channels in the UK," Research Policy, Elsevier, vol. 54(1).
    5. Hajkowicz, Stefan & Sanderson, Conrad & Karimi, Sarvnaz & Bratanova, Alexandra & Naughtin, Claire, 2023. "Artificial intelligence adoption in the physical sciences, natural sciences, life sciences, social sciences and the arts and humanities: A bibliometric analysis of research publications from 1960-2021," Technology in Society, Elsevier, vol. 74(C).
    6. Damioli, G. & Van Roy, V. & Vertesy, D. & Vivarelli, M., 2021. "May AI revolution be labour-friendly? Some micro evidence from the supply side," GLO Discussion Paper Series 823, Global Labor Organization (GLO).
    7. Mammadov Huseyn & Africa Ruiz-Gandara & Luis Gonzalez-Abril & Isidoro Romero, 2024. "Adoption of Artificial Intelligence in Small and Medium-Sized Enterprises in Spain: The Role of Competences and Skills," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 26(67), pages 848-848, August.
    8. Yang, Siying & Liu, Fengshuo, 2024. "Impact of industrial intelligence on green total factor productivity: The indispensability of the environmental system," Ecological Economics, Elsevier, vol. 216(C).
    9. Vivarelli, Marco & Piva, Mariacristina & Tani, Massimiliano, 2025. "The role of business visits in fostering R&D investment," GLO Discussion Paper Series 1586, Global Labor Organization (GLO).
    10. Fontanelli, Luca & Guerini, Mattia & Miniaci, Raffaele & Secchi, Angelo, 2025. "Predictive AI and productivity growth dynamics: evidence from French firms," FEEM Working Papers 355806, Fondazione Eni Enrico Mattei (FEEM).
    11. Parteka, Aleksandra & Kordalska, Aleksandra, 2023. "Artificial intelligence and productivity: global evidence from AI patent and bibliometric data," Technovation, Elsevier, vol. 125(C).
    12. Wang, Linhui & Chen, Qi & Dong, Zhiqing & Cheng, Lu, 2024. "The role of industrial intelligence in peaking carbon emissions in China," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
    13. Davide Lanfranchi & Laura Grassi, 2021. "Translating technological innovation into efficiency: the case of US public P&C insurance companies," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 11(4), pages 565-585, December.
    14. Zhao, Yantong & Said, Rusmawati & Ismail, Normaz Wana & Hamzah, Hanny Zurina, 2024. "Impact of industrial robot on labour productivity: Empirical study based on industry panel data," Innovation and Green Development, Elsevier, vol. 3(2).
    15. Jacques Mairesse & Pierre Mohnen & Ad Notten, 2025. "Innovation and productivity: the recent empirical literature and the state of the art," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 15(1), pages 1-27, March.
    16. Flavio Calvino & Luca Fontanelli, 2025. "Decoding AI: Nine facts about how firms use artificial intelligence in France," LEM Papers Series 2025/13, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    17. Madanaguli, Arun & Sjödin, David & Parida, Vinit & Mikalef, Patrick, 2024. "Artificial intelligence capabilities for circular business models: Research synthesis and future agenda," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    18. Li, Chao & Zhang, Yuhan & Li, Xiang & Hao, Yanwei, 2024. "Artificial intelligence, household financial fragility and energy resources consumption: Impacts of digital disruption from a demand-based perspective," Resources Policy, Elsevier, vol. 88(C).
    19. Wang, Linhui & Cao, Zhanglu & Dong, Zhiqing, 2023. "Are artificial intelligence dividends evenly distributed between profits and wages? Evidence from the private enterprise survey data in China," Structural Change and Economic Dynamics, Elsevier, vol. 66(C), pages 342-356.
    20. Veronica Lupi & Valentina Morretta & Lorenzo Zirulia, 2024. "Earth Observation data, innovation and economic performance: a study of the downstream sector in Italy," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 14(1), pages 103-136, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:vrs:poicbe:v:18:y:2024:i:1:p:1819-1830:n:1016. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.sciendo.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.