Advertisement

The Artificial Intelligence Application in the Management of Contemporary Organization: Theoretical Assumptions, Current Practices and Research Review

  • Dorota Jelonek
  • Agata Mesjasz-Lech
  • Cezary Stępniak
  • Tomasz Turek
  • Leszek ZioraEmail author
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 69)

Abstract

Nowadays the artificial intelligence solutions together with data science and business analytics solutions such as Business Intelligence systems, Big data and data mining play crucial role in the management of many contemporary business organizations. The multitude of its benefits include improvement of the whole management process of business organization and especially the process of decision making, allowing for automation of tasks in many areas. The aim of the paper is to present the role of artificial intelligence solutions in the process of contemporary organization’s management, its theoretical assumptions, development and current practices. The paper also presents authors’ research carried out among the group of 12 respondents. The aim of the study was to find how the benefits and drawbacks of artificial intelligence solutions are perceived by respondents. The foreign research review includes analysis of practices in such areas and branches as production management, logistics, retail trade and financial sector.

Keywords

Artificial intelligence Neural networks Machine learning Big data Business Intelligence Data mining 

References

  1. 1.
    Henderson, H.: Artificial Intelligence: Mirrors for the Mind, p. 62. Chelsea House Publishers, New York (2007)Google Scholar
  2. 2.
    Dubitzky, W., Azuaje, F.: Artificial Intelligence Methods and Tools for Systems Biology. Springer, Heidelberg (2004)zbMATHGoogle Scholar
  3. 3.
    Jones, M.T.: Artificial Intelligence: A Systems Approach, p. 7. Infinity Science Press LLC, Hingham, New Delhi (2008)Google Scholar
  4. 4.
    Munakata, T.: Fundamentals of the New Artificial Intelligence: Neural, Evolutionary, Fuzzy and More. Springer, London (2008)Google Scholar
  5. 5.
    Rutkowski, L.: Methods and techniques of Artificial Intelligence (in Polish). PWN, Warsaw (2018)Google Scholar
  6. 6.
    Tadeusiewicz, R. (ed.): Theoretical Neurocybernetics (in Polish). WUW, Warsaw (2009)Google Scholar
  7. 7.
    Nowicki, A., Stanek, S., Ziora, L.: The applicability of hybrid systems in support of the corporate management process: a review of selected practical examples. Pol. J. Manag. Stud. 8, 269–279 (2013)Google Scholar
  8. 8.
    Flasiński, M.: Introduction to Artificial Intelligence (in Polish), p. 251. PWN, Warsaw (2018)Google Scholar
  9. 9.
    Branscombe, M.: How AI could revolutionize project management. https://www.cio.com/article/3245773/project-management/how-ai-could-revolutionize-project-management.html. Accessed 12 Jan 2018
  10. 10.
    Ng, A.: What Artificial Intelligence can and can’t do right now: harvard business review. https://hbr.org/2016/11/what-artificial-intelligence-can-and-cant-do-right-now
  11. 11.
    Ein-Dor, P. (ed.): Artificial Intelligence in Economics and Management, p. 78. Kluwer Academics Publishers, Dordrecht (1996)Google Scholar
  12. 12.
    Michie, D., Spiegelhalter, D.J., Taylor, C.C. (eds.): Machine Learning, Neural and Statistical Classification. Overseas Press, London (2009)Google Scholar
  13. 13.
    Jelonek, D., Stępniak, C., Ziora, L.: The meaning of big data in the support of managerial decisions in contemporary organizations: review of selected research. In: Proceedings of 2018 Future of Information and Communication Conference, Singapore, pp. 195–198. IEEE, New York (2018)Google Scholar
  14. 14.
    Ziora, L.: The sentiment analysis as a tool of business analytics in contemporary organizations. In: Economics Studies. Research papers of the University of Economics in Katowice, Katowice, no. 281, pp. 234–241 (2016)Google Scholar
  15. 15.
    Nowicki, A., Ziora, L.: The application of data mining models and methods in enterprises. Review of Selected foreign financial and telecommunication industry case studies. In: Economics Studies. Research papers of the University of Economics in Katowice, Katowice, no. 88, pp. 85–94 (2011)Google Scholar
  16. 16.
    Dolgui, A., Grimaud, F., Shchamialiova, K.: Supply chain management under uncertainties: lot-sizing and scheduling rules. In: Benyoucef, L., Grabot, B. (eds.) Artificial Intelligence Techniques for Networked Manufacturing Enterprises Management. Springer, London (2010)Google Scholar
  17. 17.
    Plastino, E., Purdy, M.: Game changing value from artificial intelligence: eight strategies. Strat. Leadersh. 46(1), 16–22 (2018)CrossRefGoogle Scholar
  18. 18.
    Szajt, M.: Space in Economics Studies (in Polish). Faculty of Management, Czestochowa University of Technology Publishing House (2014)Google Scholar
  19. 19.
    Kolbjørnsrud, V., Amico, R., Thomas, R.J.: The promise of artificial intelligence. Redefining management in the workforce of the future. https://www.accenture.com/us-en/insight-promise-artificial-intelligence
  20. 20.
    Shukla, P., Wilson, H.J., Alter, A., Lavieri, D.: Machine reengineering: robots and people working smarter together. Strat. Leadersh. 45(6), 50–54 (2017)CrossRefGoogle Scholar
  21. 21.
    Riikkinen, M., Saarijärvi, H., Sarlin, P., Lähteenmäki, I.: Using artificial intelligence to create value in insurance. Int. J. Bank Mark. 84 (2018)Google Scholar
  22. 22.
    Syam, N., Sharma, A.: Waiting for a sales renaissance in the fourth industrial resolution: machine learning and artificial intelligence in sales research and practice. Ind. Mark. Manag. 69, 135–146 (2018)CrossRefGoogle Scholar
  23. 23.
    Massis, B.: Artificial intelligence arrives in the library. Inf. Learn. Sci. (2018).  https://doi.org/10.1108/ILS-02-2018-0011CrossRefGoogle Scholar
  24. 24.
    Hirsch, P.B.: Tie me to the mast: artificial intelligence & reputation risk management. J. Bus. Strat. 39(1), 61–64 (2018)CrossRefGoogle Scholar
  25. 25.
    Kasie, F.M., Bright, G., Walker, A.: Decision support systems in manufacturing: a survey and future trends. J. Model. Manag. 12(3), 432–454 (2017)Google Scholar
  26. 26.
    Benyoucef, L., Grabot, B. (eds.): Artificial Intelligence Techniques for Networked Manufacturing Enterprises Management. Springer, London (2010)Google Scholar
  27. 27.
    Oztemel, E.: Intelligent manufacturing systems. In: Benyoucef, L., Grabot, B. (eds.) Artificial Intelligence Techniques for Networked Manufacturing Enterprises Management, pp. 1–3. Springer, London (2010)Google Scholar
  28. 28.
    Minsky, M.: Steps toward artificial intelligence. Proc. IRE 49(1), 8–30 (1961)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Dorota Jelonek
    • 1
  • Agata Mesjasz-Lech
    • 1
  • Cezary Stępniak
    • 1
  • Tomasz Turek
    • 1
  • Leszek Ziora
    • 1
    Email author
  1. 1.Faculty of ManagementCzestochowa University of TechnologyCzestochowaPoland

Personalised recommendations