MARKETING EVOLUTION
When you actually dig into readiness and look at the conditions required to successfully deploy AI or an agentic workforce – things like data structure, cleanliness, integration, accessibility and governance – that assumption breaks down very quickly.
We conducted research to better understand that gap. We surveyed senior marketing leaders – CMOs, VPs, Directors and Senior Managers – and found that 71 % believe they are AI – ready.
But when we evaluated whether the foundational data conditions required for AI readiness were actually in place, only 37 % met the standard.
That’ s a massive gap. And, even more striking, only 3 % reported seeing consistent AI performance gains.
The reality is that many organisations hit a ceiling with AI very quickly because the underlying data foundation isn’ t there. If AI is constantly trying to reconcile fragmented systems, disconnected sources and inconsistent structures, the complexity of the problem expands dramatically. That leads to weaker outputs, incorrect conclusions and more frequent hallucinations.
Organisations that meet the foundational data conditions for AI readiness are seeing roughly two times stronger AI performance outcomes than organisations that don’ t. It’ s critical to get the data layer right in order to extract real value from AI – especially as AI becomes more expensive and organisations lose the luxury of experimenting indefinitely at low cost.
Q. HOW DOES A MARKETING PERFORMANCE SUBSTRATE SOLVE DATA UNIFICATION FOR AI?
» A Marketing Performance Substrate— or a system of record for marketing performance— solves this problem because the layer itself is intelligent. It’ s able to recover missing data, synthetically filling observational gaps and reconstruct consumer – level journeys that simply wouldn’ t be possible if the data were just sitting in a static warehouse or data lake somewhere. In that sense, it’ s extracting meaning from the data before it ever enters more complex downstream models.
92 July 2026