The Authenticity Crisis: A Systematic Review of Trust Erosion in Human vs. AI Influencers

influencer marketing social media influencers human influencer AI influencer virtual influencer social media marketing consumer behavior digital marketing

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August 14, 2026

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The rapid integration of Artificial Intelligence into social media has disrupted the digital marketing environment, creating an authenticity crisis that affects both human and AI influencers as audiences struggle to distinguish genuine engagement from manufactured content. This study aims to synthesize the drivers of trust erosion and compare the perception of authenticity between Human and AI influencers, ultimately proposing a framework for their coexistence within contemporary marketing practice. Utilizing a Systematic Literature Review following the PRISMA 2020 guidelines, this research analyzed a final dataset of 31 articles sourced from the Scopus database and published between 2020 and 2026. The findings reveal a dual trust challenge: human influencers lose credibility through hyper-commercialization and deceptive engagement metrics, while AI influencers face scepticism due to the uncanny valley effect and the hidden motives of their creators. Consequently, the concept of authenticity is bifurcating; human influencers are expected to demonstrate emotional authenticity through real and unscripted moments, whereas AI influencers are valued for functional authenticity, characterized by consistency, reliability, and rationality in the information they provide. These findings suggest that authenticity should no longer be treated as a single, universal standard but rather as a construct that varies according to influencer type. Marketers must therefore adapt to this new environment by ensuring full transparency regarding sponsorships and AI usage, and by strategically deploying each influencer type for distinct purposes: human influencers to build deep emotional connections with audiences, and AI influencers to achieve broad reach and disseminate product information efficiently and consistently at scale.