Blessing or Backlash? The Organizational Shift Toward In-House Generative AI Advertising Production in Indonesia
Main Article Content
Abstract
This study examines the organizational shift toward in-house generative AI advertising production in Indonesia. As generative AI enables brands to produce visual assets, synthetic models, advertising copy, and campaign variations internally, traditional production arrangements involving external agencies, production houses, photographers, and model partners are being reconfigured. Guided by the Resource-Based View, Dynamic Capabilities Theory, Transaction Cost Economics, and the Automation–Augmentation Paradox, this study investigates whether this shift represents a strategic blessing or creates organizational and ecosystem backlash. A qualitative multiple-case study design was used, with data collected through semi-structured interviews with 14 informants representing in-house production teams, brand representatives, and external agency or production partners. The data were analyzed using thematic analysis. The findings show that in-house generative AI advertising production provides strategic benefits in terms of speed, cost efficiency, content scalability, personalization, campaign optimization, and creative workflow acceleration. However, the findings also reveal risks related to authenticity fatigue, emotionally weak content, copyright concerns, cultural misalignment, skill gaps, and pressure on external model agencies and production partners. The study concludes that this shift is best understood as selective internalization rather than full replacement. It becomes a blessing when AI augments human creativity and strengthens strategic control, but it becomes a backlash when AI is used merely as a cost-cutting substitute that weakens authenticity, displaces creative ecosystems, or undermines trust.
Downloads
Article Details

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
https://creativecommons.org/licenses/by-sa/4.0/
References
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3 (2),77–101. https://doi.org/10.1191/1478088706qp063oa
Danesi, M. (2024). AI in marketing and advertising. In M. Danesi, AI-generated popular culture. Palgrave Macmillan. https://doi.org/10.1007/978-3-031-54752-2_7
Davenport, T. H., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24–42. https://doi.org/10.1007/s11747-019-00696-0
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., … Wright, R. (2023). Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
Eisenhardt, K. M. (1989). Building theories from case study research. Academy of Management Review, 14(4), 532–550. https://doi.org/10.5465/amr.1989.4308385
Eisenhardt, K. M., & Martin, J. A. (2000). Dynamic capabilities: What are they? Strategic Management Journal, 21(10–11), 1105–1121. https://doi.org/10.1002/1097-0266(200010/11)21:10/11%3C1105::AID-SMJ133%3E3.0.CO;2-E
Gujarathi, S., Darne, H. S., & Borse, A. R. (2023). Impact of artificial intelligence in online advertising. Journal of Research in Technology and Digital Development, 6(1). https://doi.org/10.53555/jrtdd.v6i1.2556
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
Kolo, C., Ranner, T., Herfort, M., & Kleine, D. (2025). The bright and dark sides of value co-creation using generative AI: A survey of decision-makers in German advertising, communication and media agencies. Journal of Brand Strategy. https://doi.org/10.69554/ovwe7043
Mikalef, P., & Gupta, M. (2021). Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Information & Management, 58(3), 103434. https://doi.org/10.1016/j.im.2021.103434
Patil, D. (2025). Generative artificial intelligence in marketing and advertising: Advancing personalization and optimizing consumer engagement strategies. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5057404
Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210. https://doi.org/10.5465/amr.2018.0072
Ramachandran, R., Pillai, S., & Dahal, R. K. (2025). The transformative role of AI in advertising and marketing. IDJINA: An Interdisciplinary Journal of Innovation in Nepalese Academia. https://doi.org/10.32674/2q7y1a35
Sands, S., Campbell, C. L., Plangger, K., & Ferraro, C. (2022). Unreal influence: Leveraging AI in influencer marketing. European Journal of Marketing, 56(6), 1721–1747. https://doi.org/10.1108/EJM-12-2019-0949
Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533. https://doi.org/10.1002/(SICI)1097-0266(199708)18:7%3C509::AID-SMJ882%3E3.0.CO;2-Z
Williamson, O. E. (1981). The economics of organization: The transaction cost approach. American Journal of Sociology, 87(3), 548–577. https://doi.org/10.1086/227496
Yin, R. K. (2018). Case study research and applications: Design and methods (6th ed.). SAGE Publications.