Marketers trust human reports over AI, Adverity finds

Adverity has published research suggesting marketers are far less likely to trust AI-generated reports than human-produced ones, even though AI-assisted reporting is associated with lower error rates.

The survey found that 64% of marketers are comfortable sharing insights produced without AI checks, while only 35% would share AI-generated insights without further validation. That added scrutiny slows the use of AI outputs, even though many teams adopted the technology to shorten the gap between analysis and action.

At the same time, the data points to fewer mistakes in AI-assisted reporting than in manual work. According to Adverity, 22% of manual reporting workflows experience regular errors, compared with 6% of automated processes and 2% of AI-assisted reporting.

The figures reflect a broader tension in marketing departments as businesses try to introduce AI into reporting and decision-making. While the technology can automate analysis, many teams still feel the need to review outputs before acting on them.

Context problem

The research identified missing business context as a main reason for that hesitation, with 39% of respondents citing it as a barrier. In practice, an AI system may flag a change in campaign performance or identify an anomaly, but users may not trust the result if the model lacks information about how the business defines its metrics, what commercial activity is under way, or which outcomes matter most.

The issue appears to be compounded by disagreement within organisations about the underlying data. Adverity found that 94% of marketing departments do not consistently use the same metric definitions across the business, while 82% said data disputes had delayed major decisions in the previous quarter.

These figures suggest the trust gap is not only about AI models, but also about the quality and consistency of the information fed into them. If departments work from different definitions, any system built on that data will struggle to produce outputs users accept without question.

The findings also suggest businesses are still using AI cautiously. Only 2% allow AI agents to make autonomous optimisation decisions, with most deployments limited to lower-risk internal work rather than direct operational decision-making.

Adoption gap

For marketing leaders, that creates a problem of returns as well as trust. Companies may invest in AI to reduce manual effort and generate faster insights, but those gains are weakened if staff still verify outputs by hand before sharing or acting on them.

The survey covered 300 senior marketing professionals and brand leaders across the UK, US, Germany, Austria and Switzerland. Respondents included decision-makers from sectors such as agencies, retail and financial services.

Adverity, which provides marketing data tools, argued that the issue lies less with AI itself than with the information environment around it. Inconsistent governance and weak alignment on metrics can leave AI systems producing outputs that appear plausible but still prompt users to stop and check them.

Alexander Igelsböck, Chief Executive Officer of Adverity, said the problem starts with the information the systems are given. "AI is only as useful as the information and context it has to work with," he said.

He added that marketers are wary when they cannot trace the basis for an answer. "Marketing teams have good reason to question an answer when they cannot see where it came from or understand why a model reached a particular conclusion.

The answer is not to put more checks around AI. It is to give it better foundations. A dedicated knowledge layer can connect marketing data with business rules, governance and strategic goals, giving AI the context it needs to produce insights marketers can actually trust and act on," he said.