Over the past fifteen years, artificial intelligence has developed at remarkable speed (Giattino and Samborska, 2025). In 2025, approximately 378.8 million people worldwide were using AI tools, representing 3.9% of the global population. As shown in Figure 3，by 2030，the number of active AI tool users is expected to grow by 92.4%, reaching 729.1 million (Naik, 2025).
Figure 1. Number of AI Users Worldwide. Source: Naik（2025)
Grand View Research further notes that AI continues to transform multiple industries, with the market projected to grow at a compound annual rate of 36.6% from 2023 to 2030. This rapid rise highlights AI’s expanding influence (
Haan, 2024). As a result, many brands are accelerating the adoption of AI in visual content production—to increase efficiency, reduce costs, and enhance customer experience (BoF Team & McKinsey & Company, 2023). Forbes Advisor also reports that 64% of business owners believe AI has the potential to improve customer relationships (Haan, 2024).
Visuals play a critical role in shaping brand experience. Visual identity affects consumers’ perceptions of brand quality, personality, satisfaction, loyalty, favourability, purchasing intention, and even broader social attitudes (Yu & Zainal Abidin, 2024; Kiapour & Piramuthu, 2018). When brands delegate visual creation to AI, the quality of AI-generated output directly influences the brand–consumer relationship.
From my perspective as a project partner at an AI visual content platform, I observe the other side of this technological acceleration: while AI is indeed high-efficiency, its outputs frequently show creative deviations—unstable colours, stylistic drift, inconsistent atmospheres, or distorted details. Even with repeated parameter tuning and human review, these issues often cannot be fully resolved.
Industry cases show similar risks. BBC News reports that a Guess advertisement in Vogue featuring AI-generated visuals triggered public backlash over transparency, unrealistic beauty standards and concerns about its impact on diversity and creative labour (Rufo, 2025). This demonstrates that when AI visuals misalign with a brand’s emotional aesthetic or social values, brand trust may be damaged rather than strengthened.
Consumer attitudes reflect this shift. Acceptance of brand use of AI dropped from 57% in 2023 to 46% in 2024 (Ross, 2025). Users are increasingly wary of misleading or inauthentic AI-produced experiences. In today’s fast-paced digital environment, customer loyalty is more crucial than ever.
Therefore, a critical research gap emerges:
How does AI intervention reshape brand visual consistency, emotional expression, and user relationship experience?
This study addresses this real-world tension and explores the dynamic relationship between AI adoption, brand visuals, and consumer perception.
Reflection: Critical Thinking
While AI promises unprecedented speed and scale in visual production, my observations—both as a researcher and as a practitioner in an AI visual platform—highlight a deeper contradiction beneath this technological progress. The industry often assumes that increased efficiency naturally translates to enhanced brand experience. However, the evidence suggests otherwise: efficiency gains do not automatically produce emotional resonance, nor do they guarantee authenticity or trust.
The decline in consumer acceptance—from 57% to 46%—indicates a widening gap between technological capability and social readiness. This suggests that the ethical, psychological, and cultural implications of AI-generated visuals are still insufficiently addressed. As designers and researchers, we must ask: Who holds creative agency when AI becomes a co-designer? And what forms of accountability should accompany this shift?
This project therefore positions AI not merely as a tool, but as a disruptive force that challenges existing notions of creativity, authorship, aesthetic consistency, and brand–consumer relationships. Recognising these tensions is essential—not to reject AI, but to reimagine how it can be integrated responsibly, sensitively, and meaningfully.
References
BoF Team & McKinsey & Company (2023) ‘The Year Ahead: How Gen AI Is Reshaping Fashion’s Creativity’, The Business of Fashion, 18 December. Available at: https://www.businessoffashion.com/articles/technology/the-state-of-fashion-2024-report-generative-ai-artificial-intelligence-technology-creativity/ (Accessed: 24 July 2025).
Giattino, C., Samborska, V. (2025) Since 2010, the training computation of notable AI systems has doubled every six months. Our World in Data. Available at: https://ourworldindata.org/data-insights/since-2010-the-training-computation-of-notable-ai-systems-has-doubled-every-six-months (Accessed: 21 July 2025).
Grand View Research (2024) Artificial Intelligence Market (2025–2033). Grand View Research. Available at: https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-ai-market (Accessed: 20 July 2025).
Haan, K. (2024) 22 Top AI Statistics and Trends. Forbes. Available at: https://www.forbes.com/advisor/business/ai-statistics/ (Accessed: 29 July 2025).
Naik, S. (2025) AI Statistics (2025): Total Users, Funding, Usage, and More. ResourceRa. Available at: https://resourcera.com/data/artificial-intelligence/ai-statistics/ (Accessed: 27 July 2025).
Ross, C. (2025) Artificial intelligence (AI) use in marketing – statistics & facts. Statista. Available at: https://www.statista.com/topics/5017/ai-use-in-marketing/ (Accessed: 25 July 2025).
Rufo, Y. (2025) ‘Does this look like a real woman? AI model in Vogue raises concerns about beauty standards’, BBC News, 27 July. Available at:
https://www.bbc.co.uk/news/articles/cgeqe084nn4o (Accessed: 22 November 2025).
Yu, M., Zainal Abidin, S. and Shaari, N. (2024) Effects of Brand Visual Identity on Consumer Attitude: A Systematic Literature Review. Preprints.org, 16 May. Available at:
https://www.preprints.org/manuscript/202405.1109.v1 (Accessed: 27 July 2025).
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