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Can AI ever feel truly authentic? Behavioural science explores the biggest creative question of our time.
The Customer Experience Lab
26 minutes
3 weeks ago
Can AI ever feel truly authentic? Behavioural science explores the biggest creative question of our time.
So why do we trust imperfect human stories more than flawless AI creations?In this episode, we explore the psychology behind why people prefer real, messy, and human-made work over machine-made perfection. And why that emotional connection might be the ultimate competitive advantage in the age of AI.
We consider:
Why authenticity beats perfection every time
The science behind the Pratfall Effect, how small mistakes make us more likeable
Why we trust content that shows a human fingerprint
How AI’s 'uncanny perfection' triggers discomfort
What studies say about our bias for human-created stories, voices, and visuals
Why trust, empathy and accountability can’t be automated
Taking insights from psychology, behavioural science, and academic studies to uncover how audiences perceive AI vs. human creativity. And most crucially, what this means for marketers, creators, and brands in 2025 and beyond.
Studies & further reading:
Zhang & Gosline (2023) Human favoritism, not AI aversion: People’s perceptions (and bias) toward generative AI, human experts, and human–GAI collaboration in persuasive content generation
Kirk & Givi (2024) The AI-authorship effect: Understanding authenticity, moral disgust, and consumer responses to AI-generated marketing communications
Piercy et al. (2025) Credibility and organizational reputation perceptions of news releases produced by artificial intelligence
Velásquez-Salamanca et al. (2025) Interpretation of AI-Generated vs. Human-Made Images
Omeish et al. (2025) Between human and AI influencers: parasocial relationships, credibility, and social capital formation in a collectivist market: a study of TikTok users in the Middle East
Aronson, E. (1966) The Pratfall Effect: The Effect of a Perceived Blunder on Interpersonal Attractiveness
Schilke & Reimann (2025) The Transparency Dilemma: How Disclosing AI Use Impacts Trust
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