In today’s hyper-competitive marketplace, brands seek not only to sell products but also to cultivate lasting emotional connections with consumers. Emotional branding, a strategy that appeals to consumers’ feelings, aspirations, and identities, has long been a cornerstone of brand differentiation. Companies such as Apple, Nike, and Coca-Cola have successfully built emotional bonds that transcend functional benefits, making consumers feel inspired, empowered, or nostalgic.

However, with the rise of Artificial Intelligence (AI), the very nature of emotional branding is changing. AI-powered technologies—such as chatbots, virtual assistants, voice recognition tools, personalized recommendation engines, sentiment analysis platforms, and generative content systems—are now capable of mimicking human-like interactions and providing tailored experiences. For example, Netflix recommends shows based on user moods, Sephora’s chatbot offers beauty consultations, and Spotify curates playlists aligned with listeners’ emotions.

While these innovations make branding more personalized and efficient, they raise critical questions about authenticity and trust. Can consumers truly feel an emotional connection with a brand when the “voice” of the brand is algorithmically generated? Does personalization based on data enhance trust, or does it trigger suspicion of manipulation and surveillance?

Existing literature suggests that consumer trust is the cornerstone of successful branding. Trust ensures loyalty, word-of-mouth advocacy, and long-term relationships. Similarly, authenticity is considered a key determinant of emotional engagement, as consumers value brands that reflect genuine values, transparency, and human touch. The intrusion of AI into emotional branding complicates these dynamics: while AI can simulate empathy, it may lack the spontaneity and moral grounding of human interaction.

This research seeks to explore the intersection of emotional branding, AI, consumer trust, and authenticity. It aims to answer whether AI-enhanced emotional branding strengthens consumer-brand relationships or whether it risks alienating consumers by replacing human emotion with artificial simulations.

The study is significant for both academia and practice. From an academic perspective, it bridges two evolving domains—branding psychology and AI-driven marketing—providing fresh insights into consumer behaviour. From a managerial perspective, it guides businesses on how to ethically and effectively integrate AI into branding strategies without compromising consumer trust.

Emotional Branding

Emotional ties between businesses and customers have gained more attention recently as a powerful strategy for building advocacy, loyalty, and lasting relationships in the competitive world of modern marketing. (Aarzoo et al., 2024). AI is highly effective at using data-driven insights to predict emotional reactions. Brand engagement and loyalty can increase when marketers use technology to create highly personalized brand experiences (Chenjeri M, 2024). You can build lasting relationships with your customers and connect with them through emotional marketing. Brands can tell stories that resonate with their customers when they have a personal and emotional understanding of their target audience. Customers are more likely to return and refer a brand to others when they are genuinely satisfied with it. (Zhu et al., 2022). Pangarkar Amay et. al. (2023) believed that AI might find it difficult to properly understand human emotions and the context of certain behaviors, which could result in incorrect readings of subconscious cues and possibly the delivery of irrelevant or improper branding messages. By using data on each customer's behaviour, artificial intelligence enables businesses to create customized marketing plans that enhance the entire buying experience. To better serve their clientele, marketers must combine their technical expertise with interpersonal relationships (Sakthirama Vadivelu et.al, 2024).

Consumer Trust and Authenticity

Authenticity in branding refers to the perception that a brand is genuine, transparent, and consistent with its values (Napoli, Dickinson, Beverland, & Farrelly, 2014). Authentic brands are seen as trustworthy and emotionally engaging, fostering deeper consumer loyalty (Morhart, Malär, Guèvremont, Girardin, & Grohmann, 2015). AI complicates authenticity. While personalization enhances relevance, consumers may question whether algorithmically generated content reflects genuine values or is simply data manipulation (Berger, 2019). Research by Bolton et al. (2018) suggests that over-reliance on AI could dilute perceived authenticity if interactions lack spontaneity or moral grounding. On the other hand, scholars argue that AI can enhance authenticity when used ethically and transparently, such as disclosing AI’s role in brand interactions (Sundar, 2020). Pangarkar Amay et. al. (2023) stated that over-automation with AI can create an impression of insincerity or manipulation, undermining the brand's authenticity and eroding consumer trust.  The results of the study conducted by Jacob Larsson & Amen Chehade (2025) demonstrate that although generative AI can offer emotionally responsive responses, many users are still dubious about its sincerity and emotional complexity. When the AI was open, supportive, and able to keep the conversation flowing naturally, emotional engagement was at its highest. These findings imply that by emphasizing emotional tone, personalization, and explicit disclosure that the encounter is AI-mediated, businesses can enhance AI-based customer service.

RESEARCH GAP

Although existing literature extensively explores emotional branding and consumer trust, limited research investigates how AI specifically reshapes emotional branding in relation to trust and authenticity. Most studies either focus on AI’s technical role in personalization or on general issues of brand trust. There is a lack of empirical studies examining consumer perceptions of AI-enabled emotional branding, particularly across different demographic groups. This research seeks to fill that gap by analyzing how consumers perceive trust and authenticity when brands use AI for emotional engagement.

OBJECTIVES OF THE STUDY

1. To examine the impact of AI-driven branding on consumer trust.
2. To analyse consumer perceptions of authenticity in AI-enabled emotional branding.
3. To provide managerial insights for balancing AI and human elements in emotional branding.

RESEARCH HYPOTHESES

• H1: AI-driven emotional branding has a positive effect on consumer trust.
• H2: Consumers perceive AI-generated brand interactions as less authentic compared to human-driven interactions.
• H3: Consumer perception of authenticity mediates the relationship between AI-driven branding and overall brand trust.

Research Design

This study adopts a quantitative research design using primary data collected through a structured questionnaire. The purpose is to assess consumer perceptions of AI-enabled emotional branding with a focus on trust and authenticity. Secondary data was collected from various journals.

Sample size: 150 respondents

Sampling technique: Convenience sampling (urban consumers familiar with AI-driven brand interactions such as chatbots, recommendation engines, and AI-generated ads)

Demographics: Age groups (18–30, 31–40, 40+), gender (male, female, other), and occupation (students, professionals, homemakers, entrepreneurs).

Analysis:

• Descriptive statistics: Frequencies and percentages
• Inferential statistics: Chi-square test for demographic differences, correlation analysis between trust and authenticity

DATA ANALYSIS & INTERPRETATION:

Table 1: Demographic Profile of Respondents (n = 150)

Table 2: Awareness of AI in Branding

Interpretation: About 77% of respondents agreed or strongly agreed that they are aware of AI in branding, showing high familiarity.

Table 3: Trust in AI-Driven Branding

Interpretation: About 56% expressed trust in AI-driven branding, while 24% remained sceptical, indicating a divided perception.

Table 4: Perceived Authenticity of AI Branding

Interpretation: Only 37% of respondents perceived AI messages as authentic, while 36% disagreed, showing that authenticity remains a challenge.

INFERENTIAL ANALYSIS: Chi-Square Test: Age vs Trust in AI Branding

• H0: There is no significant relationship between age and trust in AI-driven branding.
• H1: Younger consumers show higher trust in AI-driven branding compared to older consumers.

Results: Chi-square value = 12.45, df = 4, p = 0.014 (<0.05)

Interpretation: There is a significant relationship between age and trust. Younger respondents (18–30) expressed greater trust in AI branding compared to older respondents (40+), who were more sceptical.

• Pearson correlation (r) = 0.62 (p < 0.01)

Interpretation: There is a strong positive correlation between perceived authenticity and trust. Consumers who view AI-generated brand messages as authentic are significantly more likely to trust the brand.

FINDINGS

• 77% of respondents are aware of AI-driven branding tools, indicating that AI is already integrated into consumer-brand interactions.
• 56% of respondents trust brands that use AI for personalization, while 24% remain sceptical, showing that trust is moderate but not universal.
• Only 37% of respondents perceive AI-generated brand messages as authentic, while 36% disagree, reflecting divided opinions on authenticity
• Chi-square test results indicate a significant relationship between age and trust in AI branding, with younger consumers (18–30) showing higher trust and older consumers (40+) expressing more caution.
• Correlation analysis reveals a strong positive relationship (r = 0.62, p < 0.01) between authenticity and trust, confirming that authenticity strongly influences brand trust.
• AI branding creates a paradox: it improves personalization and engagement but simultaneously raises concerns about authenticity, requiring brands to balance automation with human elements.

This study explored how emotional branding in the age of artificial intelligence influences consumer trust and perceptions of authenticity. The findings highlight that while consumers are highly aware of AI-driven brand interactions and moderately trust them, authenticity remains a critical concern. Younger consumers demonstrate greater acceptance of AI branding, whereas older consumers remain cautious, reflecting generational differences in technology adoption. Furthermore, the strong positive correlation between authenticity and trust indicates that brands cannot rely on AI alone; authenticity remains central to building sustainable consumer relationships. First, transparency is vital—brands should openly communicate when AI tools are being used in consumer engagement. Second, ethical AI practices such as safeguarding data privacy and avoiding manipulative personalization are essential to maintain trust. 

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