Navigating the AI Revolution: Tools and Skills Transforming Marketing Practices
Abstract
This study explores the specific AI tools used by marketing professionals, their applications, and the subsequent impact on the skills needed in the AI era. The aim is to understand how these tools are integrated into marketing tasks and the evolving set of skills required for professionals in this field. The study uses a qualitative exploratory method with semi-structured interviews of twelve marketing professionals, selected for their varying expertise in AI tools. Interpretive content analysis was conducted to identify patterns and themes in AI tool usage and required skills.
The study reveals that AI tools boost efficiency and creativity in marketing. Entry-level professionals focus on content creation and data analysis, while senior professionals leverage AI for strategic planning and data-driven decisions. The rapid evolution of AI demands for continuous learning and adaptation. Additionally, concerns about job displacement underscore the importance of cultivating human skills that complement AI capabilities. A limitation of this study is its cross-sectional design, which captures current practices but does not address the long-term evolution of AI's impact. Future research could use a longitudinal approach to explore these changes over time.
Marketing professionals should focus on integrating AI tools in a way that enhances their unique human capabilities, such as creativity, empathy, and critical thinking. Continuous education and upskilling are essential to remain competitive in the AI-enhanced marketing landscape. This study provides valuable insights into the practical application of AI tools in marketing and highlights the evolving skill set required for professionals. It contributes to the literature by emphasizing a balanced approach that values both technology and human creativity.
DOI: https://doi.org/10.54663/2182-9306.2024.SpecialIssueMBP.55-74
Keywords
Full Text:
PDFReferences
Ahuvia, A. (2001). Traditional, interpretive, and reception-based content analyses: Improving the ability of content analysis to address issues of pragmatic and theoretical concern. Social Indicators Research, 54(2), 139-172. https://doi.org/10.1023/A:1011087813505
Baidoo, I., & Ansah, R. (2023). Generative AI tools and their impact on human creativity. Journal of Artificial Intelligence Research, 67(4), 213-229.
Bengio, Y., LeCun, Y., & Hinton, G. (2017). Deep learning for AI. Communications of the ACM, 60(6), 84-90. https://doi.org/10.1145/3448250
De Bruyn, A., Viswanathan, V., Beh, Y., Brock, J. K., & von Wangenheim, F. (2020). Artificial intelligence and marketing: Pitfalls and opportunities. Journal of Interactive Marketing, 51, 91-105. https://doi.org/10.1016/j.intmar.2020.04.007
Fontana, A., & Frey, J. H. (2005). The interview: From neutral stance to political involvement. In N. K. Denzin & Y. S. Lincoln (Eds.), The Sage Handbook of Qualitative Research (3rd ed., pp. 695-728). Sage Publications.
Guest, G., Bunce, A., & Johnson, L. (2006). How many interviews are enough? An experiment with data saturation and variability. Field Methods, 18 (1), 59-82. https://doi.org/10.1177/1525822X05279903
Haleem, A., Javaid, M., Khan, I. H., & Suman, R. (2022). Understanding the role of artificial intelligence and machine learning in healthcare and marketing. Healthcare Management Review, 47 (2), 117-128.
Helm, R., & Holzner, P. (2020). The impact of artificial intelligence on customer interaction in marketing. Journal of Business Research, 121, 141-153.
Huang, M. H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30-50. https://doi.org/10.1007/s11747-020-00749-9
Kissinger, H. A., Schmidt, E., & Huttenlocher, D. (2021). The Age of AI: And Our Human Future. Little, Brown and Company.
Kotler, P., Kartajaya, H., & Setiawan, I. (2017). Marketing 4.0: Moving from Traditional to Digital. Wiley.
Kotler, P., Kartajaya, H., & Setiawan, I. (2021). Marketing 5.0: Technology for Humanity. Wiley.
Kshetri, N., Sharma, S., & Rana, N. P. (2023). The role of artificial intelligence in reshaping marketing. Journal of Business Research, 152, 62-78.
Kumar, V., & Smith, A. (2020). Leveraging AI for marketing in the 21st century. Journal of Marketing, 84(1), 41-45.
Malhotra, N. K. (2019). Marketing Research: An Applied Orientation (7th ed.). Pearson Education.
Rubin, H. J., & Rubin, I. S. (2011). Qualitative Interviewing: The Art of Hearing Data (3rd ed.). Sage Publications.
Saunders, M., Lewis, P., & Thornhill, A. (2009). Research Methods for Business Students (5th ed.). Pearson Education.
Shneiderman, B. (2020). Human-centred artificial intelligence: Reliable, safe & trustworthy. International Journal of Human-Computer Interaction, 36 (6), 495-504. https://doi.org/10.1080/10447318.2020.1741118
Susarla, A., Oh, J.-H., & Tan, Y. (2023). Generative AI and its impact on innovation. MIS Quarterly, 47 (2), 329-358.
Tewari, R., & Pant, R. (2020). The impact of artificial intelligence on human resources practices. Journal of Human Resources Management and Services, 29(3), 45-58. https://ir.iba.edu.pk/sbsic/2024/program/23
Vears, D. F., & Gillam, L. (2022). Qualitative research in personalized medicine: Challenges and opportunities. Personalized Medicine, 19(2), 107-118.
Refbacks
- There are currently no refbacks.
Copyright (c) 2025 Mafalda Nogueira
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
International Journal of Marketing, Communication and New Media
ISSN: 2182-9306
DOI: 10.54663/2182-9306
Qualis Periódicos - CAPES: B2
REBIB: Q2
Indexing:
Web of Science - Emerging Sources Citation Index - Clarivate Analytics
Journal Citation Reports (JCR) 2021, 2022, 2023
QUALIS CAPES - GOOGLE SCHOLAR - LATINDEX - REDIB - RCAAP - OAJI - DRJI - MIAR - LIVRE - ERIH PLUS - INDEX COPERNICUS -
