The rapid proliferation of artificial intelligence (AI) in marketing has fundamentally transformed the dynamics of brand communication, consumer engagement, and organizational strategy. Among the most disruptive innovations is the rise of AI-enabled virtual influencers, which blur the boundaries between authenticity, automation, and persuasion. Simultaneously, concerns surrounding AI alignment, transparency, corporate digital responsibility, and sociotechnical safety have intensified. This study develops a comprehensive, integrative research article grounded exclusively in established academic literature to examine the intersection between AI influencers and sociotechnical alignment. Drawing upon systematic review methodologies, theoretical marketing frameworks, and sociotechnical systems theory, this paper synthesizes findings from prior empirical and conceptual studies to construct a multidimensional explanatory framework. The research explores how credibility, authenticity, disclosure practices, and corporate digital responsibility shape consumer trust and engagement, particularly among Generation Z. It further evaluates organizational adoption drivers, ethical constraints, and psychosocial implications for workers within AI-mediated marketing systems. The findings reveal that virtual influencer effectiveness is contingent upon perceived authenticity calibration, transparent disclosure, and alignment between technological capabilities and human values. Moreover, AI adoption in marketing is shaped by structural, cultural, and institutional factors, reinforcing the need for responsible governance mechanisms. This study contributes theoretically by integrating AI alignment theory with influencer marketing literature, methodologically by demonstrating rigorous systematic synthesis, and managerially by offering evidence-informed strategic implications. It concludes that AI influencers represent not merely a technological tool but a sociotechnical phenomenon requiring interdisciplinary governance to ensure ethical and sustainable digital transformation.

Artificial Intelligence Influencers, Sociotechnical Alignment, Consumer Trust, Digital Marketing Ethics, AI Adoption, Corporate Digital Responsibility

Dahlgren Lindström, A., Methnani, L., Krause, L., et al. (2025). Helpful, harmless, honest? Sociotechnical limits of AI alignment and safety through Reinforcement Learning from Human Feedback. Ethics and Information Technology, 27, 28.

Jayasingh, S., Sivakumar, A., & Vanathaiyan, A. A. (2025). Artificial intelligence influencers: Credibility effect on consumer engagement and purchase intention. Journal of Theoretical and Applied Electronic Commerce Research, 20(1), 17.

Kanwal, N., bin Isha, A. S. N., & Ali, K. (2024). New ways of working: A comparative empirical analysis appertaining to health and well-being and psychosocial hazards. Technology Analysis and Strategic Management, 36, 1–14.

Khalfallah, D., & Keller, V. (2025). Authenticity, ethics, and transparency in virtual influencer marketing: A cross-cultural analysis of consumer trust and engagement: A systematic literature review. Acta Psychologica.

Kitchenham, B., & Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. Software Engineering Group, School of Computer Science and Mathematics, Keele University.

Kraus, S., Breier, M., & Dasí-Rodríguez, S. (2020). The art of crafting a systematic literature review in entrepreneurship research. International Entrepreneurship and Management Journal, 16(3), 1023–1042.

Li, P., Bastone, A., Mohamad, T. A., & Schiavone, F. (2023). How does artificial intelligence impact human resources performance. Evidence from a healthcare institution in the United Arab Emirates. Journal of Innovation and Knowledge, 8(2), 100340.

Lobschat, L., Mueller, B., Eggers, F., Brandimarte, L., Diefenbach, S., Kroschke, M., & Wirtz, J. (2021). Corporate digital responsibility. Journal of Business Research, 122, 875–888.

Looi, J., & Kahlor, L. A. (2024). Artificial intelligence in influencer marketing: A mixed-method comparison of human and virtual influencers on Instagram. Journal of Interactive Advertising, 24(2), 107–126.

McElheran, K., Li, J. F., Brynjolfsson, E., Kroff, Z., Dinlersoz, E., Foster, L., & Zolas, N. (2024). AI adoption in America: Who, what, and where. Journal of Economics and Management Strategy, 33, 375–415.

Nanayakkara. (2020). Application of artificial intelligence in marketing mix: A conceptual review. International Conference on Business and Information (ICBI), 530–542.

Neuman, W. L. (2014). Social research methods: Qualitative and quantitative approaches (7th ed.). Pearson.

Paschen, J., Kietzmann, J., & Kietzmann, T. C. (2019). Artificial intelligence (AI) and its implications for market knowledge in B2B marketing. Journal of Business and Industrial Marketing, 34(7), 1410–1419.

Peyravi, B., Nekrošienė, J., & Lobanova, L. (2020). Revolutionized technologies for marketing: Theoretical review with focus on artificial intelligence. Business: Theory and Practice, 21(2), 827–834.

Polisetty, A., Chakraborty, D., G, S., Kar, A. K., & Pahari, S. (2023). What determines AI adoption in companies? Mixed-method evidence. Journal of Computer Information Systems, 64(1), 1–18.

Shrestha, R. (2025). The AI disclosure effect: Gen Z's perception of virtual influencers. Master’s Thesis, Bob Schieffer College of Communication, Texas Christian University.

Siau, K. L. (2017). Impact of artificial intelligence, robotics, and machine learning on sales and marketing. Association for Information Systems AIS Electronic Library.

Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339.

Sorosrungruang, T., Ameen, N., & Hackley, C. (2024). How real is real enough? Unveiling the diverse power of generative AI-enabled virtual influencers and the dynamics of human responses. Psychology and Marketing.

Talwar, S., Talwar, M., Kaur, P., & Dhir, A. (2020). Consumers’ resistance to digital innovations: A systematic review and framework development. Australasian Marketing Journal, 28(4), 286–299.

Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence-informed management knowledge by means of systematic review. British Journal of Management, 14(3), 207–222.

Verma, S., Sharma, R., Deb, S., & Maitra, D. (2021). Artificial intelligence in marketing: Systematic review and future research direction. International Journal of Information Management Data, 1(1), 1–8.

Vlacic, B., Corbo, L., Costa, E., Silva, S., & Dabic, M. (2021). The evolving role of artificial intelligence in marketing: A review and research agenda. Journal of Business Research, 128, 187–203.

Wirth, N. (2018). Hello marketing, what can artificial intelligence help you with? International Journal of Market Research, 60(5), 435–438.