"For a long time, marketers have gotten away with flying blind. Campaigns have been run on a patchwork of incomplete inputs across various platforms, missing key fields and rarely aligned goals. Brands didn't question performance. As long as the top line and bottom line were fine, who cares how clean the data was? The consequence? Multimillion-dollar decisions were made based on the wrong data. Siloed teams were operating on different definitions of success. Media and measurement stacks didn't speak the same language."
"But that approach is no longer sustainable. The industry has entered what I've termed the "AI-volution," an era where campaigns are designed, distributed and optimized by machines. AI is now embedded across every step of the media value chain. While this unlocks new potential, it also comes with a critical dependency: AI is only as good as the data it's fed."
Marketers historically operated with incomplete, siloed inputs and loosely aligned goals, accepting messy data as long as top-line and bottom-line results appeared satisfactory. This caused multimillion-dollar decisions based on incorrect information, conflicting definitions of success across teams, and incompatible media and measurement stacks. AI now underpins campaign design, distribution and optimization, creating a critical dependency on input data quality. Messy, misaligned or incorrect data is not corrected by AI; instead it amplifies errors and produces bad decisions at scale. Attribution often functions as storytelling rather than measurement, with platforms claiming overlapping credit and inflating impact. Correlation is frequently mistaken for precision.
Read at AdExchanger
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