Marketing teams are investing heavily in AI, but a new survey reveals that poor data quality is undermining those efforts. GrowthLoop's 2026 AI and Marketing Performance Index, conducted with Ascend2, surveyed over 300 marketers and data leaders across the U.S. and Canada. The core finding: despite 87% of marketers implementing AI in their processes, most are not seeing the expected returns because their underlying data is fragmented, stale, or incomplete.
The Data Bottleneck
More than 40% of respondents report stubbornly slow marketing cycles. The bigger problem: 77% say that tests which initially appear successful fail when scaled. Only 20% of marketers report high impact from their experimentation efforts, even though 58% spend a moderate or significant amount of time on testing. The disconnect is clear — teams are running experiments, but the results don't hold up because the data foundation is weak.
Causal Clarity Is Rare
Just 23% of marketers can reliably link their marketing actions to business outcomes. Most teams still rely on historical behavior patterns to guide decisions, optimizing for past performance rather than what actually drives results. This means AI tools are being fed data that reflects what happened, not what caused it. The report calls this a lack of "causal clarity" — a fundamental mismatch between how data is used and what marketers need to achieve.
The Single Source of Truth Advantage
Companies with a fully centralized single source of truth (SSOT) for customer data reported significantly better outcomes. Those with a SSOT saw 44% revenue growth compared to 8% for those without. A SSOT also correlated with faster marketing cycles, more effective data usage, and stronger returns from experimentation. However, only 46% of organizations report having such a centralized system.
Real-Time Personalization Remains Aspirational
Despite industry hype, only 12% of marketers use mostly real-time signals to execute campaigns. The vast majority — 85% — rely on historical data or a mix of historical and real-time data. This suggests that real-time personalization, a key promise of AI-driven marketing, remains out of reach for most organizations due to data latency and fragmented systems.
Where the Data Lives Matters
The location of the SSOT also affects performance. Organizations using data clouds or lakes for their source of truth were less likely to struggle with measuring impact (42% vs. 54%) and managing manual work (31% vs. 38%) compared to those relying on marketing suites. The report recommends bringing AI closer to the data source — running models directly within cloud data infrastructure — rather than moving data between fragmented systems.
Bottom Line
The 2026 AI and Marketing Performance Index makes a practical point: AI adoption alone doesn't solve marketing problems. Without clean, centralized, and causally linked data, even the best AI tools will produce unreliable results. Teams should prioritize data infrastructure — specifically a single source of truth in a data cloud or lake — before expecting AI to deliver on its personalization and experimentation promises.