The Trust Deficit Behind the AI Content Boom
Generative AI has made brand content faster and cheaper to produce. Research suggests it has also made that content harder for audiences to trust.
Apeksha Mehta
3/15/20252 min read
The cost of producing marketing content has fallen sharply due to generative AI, yet audience trust in that content appears to be moving in the opposite direction. Jan Kelley's 2026 marketing trends analysis notes that even as AI becomes mainstream in content production, trust remains fragile, with audiences increasingly questioning the authenticity, sourcing and curation behind what they consume.
This finding complicates a straightforward productivity narrative around generative AI adoption in marketing. NetSuite's 2026 marketing trends research observes that while generative AI tools assist marketers in brainstorming, drafting and scaling content across channels, brands that use AI to produce content without maintaining a distinct brand voice risk diluting their identity in the eyes of their audience. The same research identifies a resulting shift in priority: in an environment of near-constant content output, a strong and recognisable brand identity has become more valuable, not less.
Jan Kelley's analysis offers a related caution specific to creative quality, noting that AI-generated content can impress on a technical level without reliably producing the emotional resonance that drives consumer behaviour. This distinction between technical polish and persuasive effectiveness appears increasingly central to how marketing performance is being evaluated.
Industry guidance emerging from this research converges on a few recurring recommendations. Generative AI is best positioned as a tool for accelerating production processes, such as ideation, drafting and localisation of existing assets, rather than as a substitute for the specific, verifiable detail that underpins audience trust. NetSuite's research specifically highlights asset reinvention, the regeneration, localisation and recontextualisation of existing brand assets at scale, as a lower-risk application of AI than fully novel content generation.
A further point of consensus concerns accuracy. As AI-assisted content production scales, verification of factual claims, statistics and sourced information becomes a more significant editorial responsibility rather than a diminishing one, given that fluency in AI-generated text does not guarantee factual reliability.
The broader implication for marketing organisations is that content velocity and content credibility are not automatically correlated, and in some cases may be inversely related as production scales. As audiences become more attuned to the presence of AI-generated material, the research suggests that specificity, verifiable detail and consistent brand voice are likely to become more valuable differentiators than sheer volume of output.
