ADOPTING AI-DRIVEN DATA CULTURE IN ORGANIZATION: CHALLENGES AND OPPORTUNITIES FROM EMPLOYEES’ PERSPECTIVES
DOI:
https://doi.org/10.29121/shodhkosh.v5.i6.2024.3591Keywords:
Artificial Intelligence, Data Culture, Organizational Growth, Challenges and Opportunities from Employees’ PerspectivesAbstract [English]
Data functions as an organization's growth engine fuel. In the age of digitalization, it is among the most precious resources. Organizations now use data-driven decision-making processes instead of product-centered ones. This is due to the fact that it projects more accurate, impartial, and objective predictions. IT companies have faced challenges over the years relating to big data, including security, accessibility, reuse, automation, and decision-making. So, a lot of businesses have concentrated on implementing AI-driven data culture. The purpose of this paper is to investigate the possibilities for establishing an AI-driven data culture in Pune's IT/ITES companies. Additionally, it attempts to pinpoint the opportunities and difficulties associated with implementing an AI-driven data culture from the viewpoint of the workforce. Regression analysis was used to examine data from a survey of 200 participants from various organizational levels. The results show a substantial gender gap in perception, emphasizing the necessity for gender-specific adoption tactics for AI-driven data culture. Lack of advanced internal expertise, recurrent data use, inadequate infrastructure, restricted or nonexistent finance, and inadequate data management (quality, sources, validity, accessibility, approvals, execution, alignment with organisation goals, etc.) are a few of the main obstacles. Opportunities include standardization, automation, quality report generating, integration, and simplification. From the standpoint of process improvement, performance improvement, compliance, audits, trend analysis, and competitive analysis, the current study is noteworthy. It may result in long-term sustainable growth and the best possible use of available resources.
References
Agrawal, K. (2023). Organizational Sustainability of Generative AI-Driven Optimization Intelligence. Journal of Computer Information Systems, 1-15. https://www.tandfonline.com/doi/abs/10.1080/08874417.2023.2286540 DOI: https://doi.org/10.1080/08874417.2023.2286540
Aldoseri, A., Al-Khalifa, K. N., & Hamouda, A. M. (2024). Methodological Approach to Assessing the Current State of Organizations for AI-Based Digital Transformation. Applied System Innovation, 7(1), 14. https://www.mdpi.com/2571-5577/7/1/14 DOI: https://doi.org/10.3390/asi7010014
Allioui, H., & Mourdi, Y. (2023). Unleashing the potential of AI: Investigating cutting-edge technologies that are transforming businesses. International Journal of Computer Engineering and Data Science (IJCEDS), 3(2), 1-12. https://ijceds.com/ijceds/article/view/59
Anton, E., Oesterreich, T. D., Aptyka, M., & Teuteberg, F. (2023). Beyond digital data and information technology: conceptualizing data-driven culture. Pacific Asia Journal of the Association for Information Systems, 15(3), 1. https://aisel.aisnet.org/pajais/vol15/iss3/1/ DOI: https://doi.org/10.17705/1pais.15301
Campos Zabala, F. J. (2023). The Barriers for Implementing AI. In Grow Your Business with AI: A First Principles Approach for Scaling Artificial Intelligence in the Enterprise (pp. 85-110). Berkeley, CA: Apress. https://link.springer.com/chapter/10.1007/978-1-4842-9669-1_4 DOI: https://doi.org/10.1007/978-1-4842-9669-1_4
Chowdhury, S., Dey, P., Joel-Edgar, S., Bhattacharya, S., Rodriguez-Espindola, O., Abadie, A., & Truong, L. (2023). Unlocking the value of artificial intelligence in human resource management through AI capability framework. Human Resource Management Review, 33(1), 100899. https://www.sciencedirect.com/science/article/pii/S1053482222000079 DOI: https://doi.org/10.1016/j.hrmr.2022.100899
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... & Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://www.sciencedirect.com/science/article/pii/S026840121930917X DOI: https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Fredriksen, S. F. B., & Skjærvik, M. E. (2021). Understanding the role of organizational culture on Artificial Intelligence capabilities and organizational performance (Master's thesis, University of Agder). https://uia.brage.unit.no/uia-xmlui/handle/11250/2825428
Jöhnk, J., Weißert, M., & Wyrtki, K. (2021). Ready or not, AI comes—an interview study of organizational AI readiness factors. Business & Information Systems Engineering, 63, 5-20. https://link.springer.com/article/10.1007/s12599-020-00676-7 DOI: https://doi.org/10.1007/s12599-020-00676-7
Kar, A. K., & Kushwaha, A. K. (2023). Facilitators and barriers of artificial intelligence adoption in business–insights from opinions using big data analytics. Information Systems Frontiers, 25(4), 1351-1374. https://link.springer.com/article/10.1007/s10796-021-10219-4 DOI: https://doi.org/10.1007/s10796-021-10219-4
Ledro, C., Nosella, A., & Dalla Pozza, I. (2023). Integration of AI in CRM: Challenges and guidelines. Journal of Open Innovation: Technology, Market, and Complexity, 9(4), 100151. https://www.sciencedirect.com/science/article/pii/S2199853123002536 DOI: https://doi.org/10.1016/j.joitmc.2023.100151
Strann, P. (2022). Adopting artificial intelligence to support knowledge work: drivers of trust according to experts. https://www.tandfonline.com/doi/abs/10.1080/13662716.2023.2242285
Taherizadeh, A., & Beaudry, C. (2023). An emergent grounded theory of AI-driven digital transformation: Canadian SMEs’ perspectives. Industry and Innovation, 30(9), 1244-1273. https://www.tandfonline.com/doi/abs/10.1080/13662716.2023.2242285 DOI: https://doi.org/10.1080/13662716.2023.2242285
Weber, M., Engert, M., Schaffer, N., Weking, J., & Krcmar, H. (2023). Organizational capabilities for ai implementation—coping with inscrutability and data dependency in ai. Information systems frontiers, 25(4), 1549-1569. https://link.springer.com/article/10.1007/s10796-022-10297-y DOI: https://doi.org/10.1007/s10796-022-10297-y
Yilmaz, A. A. (2024). AI-Driven Data Analytics in Information Sciences and Organizational Management. In AI and Data Analytics Applications in Organizational Management (pp. 19-35). IGI Global. https://www.igi-global.com/chapter/ai-driven-data-analytics-in-information-sciences-and-organizational-management/338504 DOI: https://doi.org/10.4018/979-8-3693-1058-8.ch002
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