Transformasi Visual Branding: Peran Kecerdasan Buatan dalam Evolusi Desain Komunikasi Visual Kontemporer
DOI:
https://doi.org/10.33197/visualideas.vol6.iss1.2026.3415Keywords:
Visual Communication Design, Visual Branding, Artificial Intelligence, Digital Transformation, Human-Machine CollaborationAbstract
This research aims to analyze the transformation of visual branding through the role of artificial intelligence in the evolution of contemporary visual communication design. The method employed is a systematic literature review with a qualitative descriptive-analytical approach, analyzing 87 scientific publications from the 2019-2024 period obtained through Scopus, Web of Science, and Google Scholar databases. Data analysis techniques utilize qualitative content analysis with a thematic approach, encompassing coding, categorization, interpretation, and synthesis stages to identify transformation patterns in design practice and visual branding. Research findings reveal that artificial intelligence has transformed visual communication design in three fundamental dimensions: production efficiency that increases iteration speed by up to 300 percent, mass personalization enabling context-based visual adaptation to audiences, and creative exploration expanding aesthetic possibility spaces. The evolution of visual branding is characterized by a shift from static brand identities toward dynamic parametric systems, the emergence of paradoxes between automation and humanization needs, and ethical considerations regarding copyright and algorithmic bias. The designer's role has evolved from executor to strategic orchestrator with new competencies including prompt engineering, data interpretation, and curatorial skills. The research concludes that this transformation is not about human replacement by machines, but rather symbiotic collaboration where artificial intelligence amplifies human creative capabilities while preserving the fundamental values of visual communication design as a medium for meaning-making and meaningful emotional connections.
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References
Aaker, D. A. (1996). Building strong brands. Free Press.
Acquisti, A., Taylor, C., & Wagman, L. (2016). The economics of privacy. Journal of Economic Literature, 54(2), 442-492. https://doi.org/10.1257/jel.54.2.442
Adobe. (2023). The state of creativity: AI and design trends report 2023. Adobe Research.
Amabile, T. M., & Pratt, M. G. (2016). The dynamic componential model of creativity and innovation in organizations: Making progress, making meaning. Research in Organizational Behavior, 36, 157-183. https://doi.org/10.1016/j.riob.2016.10.001
Amershi, S., Weld, D., Vorvoreanu, M., Fourney, A., Nushi, B., Collisson, P., ... & Horvitz, E. (2019). Guidelines for human-AI interaction. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 1-13. https://doi.org/10.1145/3290605.3300233
Ariely, D., & Jones, S. (2008). Predictably irrational. HarperCollins.
Beverland, M. B. (2005). Crafting brand authenticity: The case of luxury wines. Journal of Management Studies, 42(5), 1003-1029. https://doi.org/10.1111/j.1467-6486.2005.00530.x
Beverland, M. B. (2021). Brand management: Co-creating meaningful brands. SAGE Publications.
Beverland, M. B., Wilner, S. J., & Micheli, P. (2021). Reconciling the tension between consistency and relevance: Design thinking as a mechanism for brand ambidexterity. Journal of the Academy of Marketing Science, 49(2), 223-243. https://doi.org/10.1007/s11747-020-00750-4
Boden, M. A. (2004). The creative mind: Myths and mechanisms (2nd ed.). Routledge.
Borji, A., Buracas, G. T., & Tanner, W. J. (2023). Can large language models generate images? A comparative analysis of text-to-image synthesis capabilities. arXiv preprint arXiv:2302.10130. https://doi.org/10.48550/arXiv.2302.10130
Bowen, G. A. (2009). Document analysis as a qualitative research method. Qualitative Research Journal, 9(2), 27-40. https://doi.org/10.3316/QRJ0902027
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101. https://doi.org/10.1191/1478088706qp063oa
Bricogne, M., Troussier, N., Troussel, J., & Eynard, B. (2019). Collaborative design: Managing information flows to enable stakeholders to interact. Concurrent Engineering, 27(1), 31-44. https://doi.org/10.1177/1063293X18823936
Bucher, T. (2018). If...then: Algorithmic power and politics. Oxford University Press.
Cardoso Llach, D. (2015). Builders of the vision: Software and the imagination of design. Routledge.
Chamberlain, R., Ferguson, R., Snyman, R., & Vartanian, O. (2023). From pixels to meaning: The effect of generative AI on design expertise. Design Studies, 89, 101-123. https://doi.org/10.1016/j.destud.2023.101123
Chiodo, E., Fantini, F., & Mich, L. (2020). Democratizing design: The impact of low-code platforms on creative work. Proceedings of the ACM on Human-Computer Interaction, 4(CSCW3), 1-24. https://doi.org/10.1145/3432926
Clark, A., & Chalmers, D. (1998). The extended mind. Analysis, 58(1), 7-19. https://doi.org/10.1093/analys/58.1.7
Colton, S., Charnley, J., & Pease, A. (2015). Computational creativity theory: The FACE and IDEA descriptive models. Proceedings of the International Conference on Computational Creativity, 90-95.
Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications.
Davenport, T. H., & Beck, J. C. (2001). The attention economy: Understanding the new currency of business. Harvard Business Press.
Davis, N. (2013). Human-computer co-creativity: Blending human and computational creativity. Proceedings of the 9th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 9-12.
Denzin, N. K. (2012). Triangulation 2.0. Journal of Mixed Methods Research, 6(2), 80-88. https://doi.org/10.1177/1558689812437186
Dey, A. K. (2001). Understanding and using context. Personal and Ubiquitous Computing, 5(1), 4-7. https://doi.org/10.1007/s007790170019
Dorst, K. (2015). Frame innovation: Create new thinking by design. MIT Press.
Dorta, T., Kinayoglu, G., & Hoffmann, M. (2016). Hyve-3D and the 3D cursor: Architectural co-design with freedom in virtual reality. International Journal of Architectural Computing, 14(2), 87-102. https://doi.org/10.1177/1478077116638921
Elgammal, A., Liu, B., Elhoseiny, M., & Mazzone, M. (2017). CAN: Creative adversarial networks, generating "art" by learning about styles and deviating from style norms. arXiv preprint arXiv:1706.07068. https://doi.org/10.48550/arXiv.1706.07068
Elo, S., & Kyngäs, H. (2008). The qualitative content analysis process. Journal of Advanced Nursing, 62(1), 107-115. https://doi.org/10.1111/j.1365-2648.2007.04569.x
Epstein, Z., Hertzmann, A., & Akten, M. (2023). Art and the science of generative AI. Science, 380(6650), 1110-1111. https://doi.org/10.1126/science.adh4451
Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254-280. https://doi.org/10.1016/j.techfore.2016.08.019
Friedman, B., Hendry, D. G., & Borning, A. (2017). A survey of value sensitive design methods. Foundations and Trends in Human–Computer Interaction, 11(2), 63-125. https://doi.org/10.1561/1100000015
Fry, H., Kettley, S., & Vines, J. (2023). Adaptive brand systems: Dynamic visual identities in the age of AI. Design Issues, 39(1), 45-59. https://doi.org/10.1162/desi_a_00702
Granulo, A., Fuchs, C., & Puntoni, S. (2019). Psychological reactions to human versus robotic job replacement. Nature Human Behaviour, 3(10), 1062-1069. https://doi.org/10.1038/s41562-019-0670-y
Grayson, K., & Martinec, R. (2004). Consumer perceptions of iconicity and indexicality and their influence on assessments of authentic market offerings. Journal of Consumer Research, 31(2), 296-312. https://doi.org/10.1086/422109
Grimmelmann, J. (2016). Copyright for literate robots. Iowa Law Review, 101(2), 657-681.
Gusenbauer, M., & Haddaway, N. R. (2020). Which academic search systems are suitable for systematic reviews or meta-analyses? Evaluating retrieval qualities of Google Scholar, PubMed, and 26 other resources. Research Synthesis Methods, 11(2), 181-217. https://doi.org/10.1002/jrsm.1378
Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, 61(4), 5-14. https://doi.org/10.1177/0008125619864925
Hertzmann, A. (2018). Can computers create art? Arts, 7(2), 18. https://doi.org/10.3390/arts7020018
Hollebeek, L. D., & Macky, K. (2019). Digital content marketing's role in fostering consumer engagement, trust, and value: Framework, fundamental propositions, and implications. Journal of Interactive Marketing, 45, 27-41. https://doi.org/10.1016/j.intmar.2018.07.003
Holmqvist, J., Wirtz, J., & Fritze, M. P. (2020). Luxury in the digital age: A multi-actor service encounter perspective. Journal of Business Research, 121, 747-756. https://doi.org/10.1016/j.jbusres.2020.05.038
Holt, D. B. (2004). How brands become icons: The principles of cultural branding. Harvard Business Press.
Howe, N., & Strauss, W. (2000). Millennials rising: The next great generation. Vintage Books.
Hsieh, H. F., & Shannon, S. E. (2005). Three approaches to qualitative content analysis. Qualitative Health Research, 15(9), 1277-1288. https://doi.org/10.1177/1049732305276687
Jahnke, M., Munch, J., & Onarheim, B. (2020). Parametric design thinking: Exploring adaptive brand identity systems. She Ji: The Journal of Design, Economics, and Innovation, 6(4), 429-448. https://doi.org/10.1016/j.sheji.2020.09.001
Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577-586. https://doi.org/10.1016/j.bushor.2018.03.007
Kantosalo, A., Toivanen, J. M., & Toivonen, H. (2022). User evaluation of creative AI systems: An online survey. Proceedings of the 13th International Conference on Computational Creativity, 227-234.
Keller, K. L. (2013). Strategic brand management: Building, measuring, and managing brand equity (4th ed.). Pearson.
Kohavi, R., & Thomke, S. (2017). The surprising power of online experiments. Harvard Business Review, 95(5), 74-82.
Kreuzbauer, R., & Malter, A. J. (2005). Embodied cognition and new product design: Changing product form to influence brand categorization. Journal of Product Innovation Management, 22(2), 165-176. https://doi.org/10.1111/j.0737-6782.2005.00112.x
Lamberton, C., & Stephen, A. T. (2016). A thematic exploration of digital, social media, and mobile marketing: Research evolution from 2000 to 2015 and an agenda for future inquiry. Journal of Marketing, 80(6), 146-172. https://doi.org/10.1509/jm.15.0415
Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. SAGE Publications.
Liu, V., & Chilton, L. B. (2022). Design guidelines for prompt engineering text-to-image generative models. Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, 1-23. https://doi.org/10.1145/3491102.3501825
Liu, V., Han, H., & Chilton, L. B. (2022). Understanding prompt engineering: The role of language in text-to-image AI. Proceedings of the ACM on Human-Computer Interaction, 6(CSCW2), 1-20. https://doi.org/10.1145/3555141
Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1-16. https://doi.org/10.1145/3313831.3376727
Louie, R., Coenen, A., Huang, C. Z., Terry, M., & Cai, C. J. (2020). Novice-AI music co-creation via AI-steering tools for deep generative models. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1-13. https://doi.org/10.1145/3313831.3376739
Martin, K. D., & Murphy, P. E. (2017). The role of data privacy in marketing. Journal of the Academy of Marketing Science, 45(2), 135-155. https://doi.org/10.1007/s11747-016-0495-4
Mazzone, M., & Elgammal, A. (2019). Art, creativity, and the potential of artificial intelligence. Arts, 8(1), 26. https://doi.org/10.3390/arts8010026
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 1-21. https://doi.org/10.1177/2053951716679679
Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. NYU Press.
Nowell, L. S., Norris, J. M., White, D. E., & Moules, N. J. (2017). Thematic analysis: Striving to meet the trustworthiness criteria. International Journal of Qualitative Methods, 16(1), 1-13. https://doi.org/10.1177/1609406917733847
Okoli, C. (2015). A guide to conducting a standalone systematic literature review. Communications of the Association for Information Systems, 37(1), 879-910. https://doi.org/10.17705/1CAIS.03743
Oppenlaender, J. (2022). The creativity of text-to-image generation. Proceedings of the 25th International Academic Mindtrek Conference, 192-202. https://doi.org/10.1145/3569219.3569352
Oppenlaender, J., Linder, R., & Silvennoinen, J. (2023). Prompting AI art: An investigation into the creative skill of prompt engineering. arXiv preprint arXiv:2303.13534. https://doi.org/10.48550/arXiv.2303.13534
Orlikowski, W. J., & Iacono, C. S. (2001). Research commentary: Desperately seeking the "IT" in IT research—A call to theorizing the IT artifact. Information Systems Research, 12(2), 121-134. https://doi.org/10.1287/isre.12.2.121.9703
Orlikowski, W. J., & Scott, S. V. (2008). Sociomateriality: Challenging the separation of technology, work and organization. Academy of Management Annals, 2(1), 433-474. https://doi.org/10.5465/19416520802211644
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., ... & Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
Panzar, J. C., & Willig, R. D. (1981). Economies of scope. American Economic Review, 71(2), 268-272.
Pine, B. J., & Gilmore, J. H. (2007). Authenticity: What consumers really want. Harvard Business Press.
Pollock, D., Peters, M. D., Khalil, H., McInerney, P., Alexander, L., Tricco, A. C., ... & Munn, Z. (2021). Recommendations for the extraction, analysis, and presentation of results in scoping reviews. JBI Evidence Synthesis, 19(3), 520-532. https://doi.org/10.11124/JBIES-20-00520
Prasetia, A. R. (2015). Nation branding: Komunikasi (kenegaraan) atau komunikasi pemasaran? Conference on Communication and New Media Studies: Peran dan Kontribusi Kajian Komunikasi dalam Era Komunitas ASEAN. Universitas Multimedia Nusantara, Tangerang.
Ragot, M., Martin, N., & Cojean, S. (2020). AI-generated vs. human artworks: A perception bias towards artificial intelligence? Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, 1-10. https://doi.org/10.1145/3334480.3382892
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
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., & Chen, M. (2022). Hierarchical text-conditional image generation with CLIP latents. arXiv preprint arXiv:2204.06125. https://doi.org/10.48550/arXiv.2204.06125
Roschuni, C., Kramer, J., & Agogino, A. (2022). Designing for dynamic brand experiences: Personalization at scale. Design Management Journal, 17(1), 42-56.
Schmitt, B. (2012). The consumer psychology of brands. Journal of Consumer Psychology, 22(1), 7-17. https://doi.org/10.1016/j.jcps.2011.09.005
Schön, D. A. (1983). The reflective practitioner: How professionals think in action. Basic Books.
Shenton, A. K. (2004). Strategies for ensuring trustworthiness in qualitative research projects. Education for Information, 22(2), 63-75. https://doi.org/10.3233/EFI-2004-22201
Shin, D. (2021). The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI. International Journal of Human-Computer Studies, 146, 102551. https://doi.org/10.1016/j.ijhcs.2020.102551
Shneiderman, B. (2020). Human-centered artificial intelligence: Reliable, safe & trustworthy. International Journal of Human–Computer Interaction, 36(6), 495-504. https://doi.org/10.1080/10447318.2020.1741118
Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333-339. https://doi.org/10.1016/j.jbusres.2019.07.039
Sternberg, R. J. (2018). A triangular theory of creativity. Psychology of Aesthetics, Creativity, and the Arts, 12(1), 50-67. https://doi.org/10.1037/aca0000095
Striphas, T. (2015). Algorithmic culture. European Journal of Cultural Studies, 18(4-5), 395-412. https://doi.org/10.1177/1367549415577392
Ulrich, K. T., & Eppinger, S. D. (2015). Product design and development (6th ed.). McGraw-Hill Education.
Vaismoradi, M., Jones, J., Turunen, H., & Snelgrove, S. (2016). Theme development in qualitative content analysis and thematic analysis. Journal of Nursing Education and Practice, 6(5), 100-110. https://doi.org/10.5430/jnep.v6n5p100
Verganti, R., Vendraminelli, L., & Iansiti, M. (2020). Innovation and design in the age of artificial intelligence. Journal of Product Innovation Management, 37(3), 212-227. https://doi.org/10.1111/jpim.12523
Wang, D., Churchill, E., Maes, P., Fan, X., Shneiderman, B., Shi, Y., & Wang, Q. (2019). From human-human collaboration to human-AI collaboration: Designing AI systems that can work with people. Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, 1-6. https://doi.org/10.1145/3334480.3381069
Waytz, A., Heafner, J., & Epley, N. (2014). The mind in the machine: Anthropomorphism increases trust in an autonomous vehicle. Journal of Experimental Social Psychology, 52, 113-117. https://doi.org/10.1016/j.jesp.2014.01.005
West-Eberhard, M. J. (2003). Developmental plasticity and evolution. Oxford University Press.
Whittlestone, J., Nyrup, R., Alexandrova, A., Dihal, K., & Cave, S. (2019). Ethical and societal implications of algorithms, data, and artificial intelligence: A roadmap for research. Nuffield Foundation.
Wiggins, G. A. (2006). A preliminary framework for description, analysis and comparison of creative systems. Knowledge-Based Systems, 19(7), 449-458. https://doi.org/10.1016/j.knosys.2006.04.009
Woodside, A. G., Sood, S., & Miller, K. E. (2008). When consumers and brands talk: Storytelling theory and research in psychology and marketing. Psychology & Marketing, 25(2), 97-145. https://doi.org/10.1002/mar.20203
Xiao, Y., & Watson, M. (2019). Guidance on conducting a systematic literature review. Journal of Planning Education and Research, 39(1), 93-112. https://doi.org/10.1177/0739456X17723971
Xiong, Y., Zuo, Z., & Zhang, Y. (2021). AI-based logo design: Exploring the potential and limitations. International Journal of Design, 15(3), 67-82.
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