Published on: July 22, 2026
LONDON, 21 July 2026 — More than half (53%) of marketers are failing to use data insights to consistently drive campaign action, according to new research from Adverity, the marketing data intelligence company.
With the study exposing a stark gap between insight and action for marketers, data shows that 91% of respondents take at least several days to respond to clearly underperforming campaigns, with one in three saying they often wait weeks before acting.
The report revealed that long reporting times remain the biggest barrier to turning insights into action for marketing professionals (33%), leaving marketing teams paralysed while underperforming campaigns continue unchecked. This is followed by data being spread across too many platforms (21%).
These findings are particularly striking given how widely AI has been adopted. 92% of marketers are now experimenting with AI across their reporting workflows, yet reporting delays remain the industry’s biggest operational challenge.
The research points to a clear reason for this scepticism. Rather than doubting the technical capabilities of the models, marketers note that AI tools operate in a vacuum, completely separated from real-world business realities. The two largest issues cited by marketing professionals are:
A lack of business context (39%): Tools do not understand specific campaign objectives, business KPIs, or the wider decision context.
A lack of governance context (24%): AI tools fail to comprehend or abide by internal data governance and quality rules.
“Everyone is excited about what AI can do, but we are looking at a classic case of running before we can walk,” said Alexander Igelsböck, CEO of Adverity. “Right now, the industry is trying to build advanced AI strategies on top of foundations that are fundamentally broken. Even with clean, connected data, AI remains limited without the business and governance context to act effectively. A marketing intelligence and knowledge layer can help to close this gap, enabling faster decisions to be made accurately and operational waste to be reduced.”
