Marketers are moving faster with AI. The data suggests they are not moving smarter — and the gap between the two is where growth is being lost.
Despite the rapid and widespread adoption of artificial intelligence, most marketing teams remain constrained by fragmented data, slow measurement cycles, and experiments that fail to translate into scalable results. That is the central finding of the 2026 AI and Marketing Performance Index, a survey of more than 300 marketers and data leaders across the United States and Canada, released by GrowthLoop in partnership with research firm Ascend2.
The report arrives at a moment of apparent contradiction. Eighty-seven percent of marketers say they have implemented AI in their processes. Yet the structural problems that have historically limited marketing effectiveness — siloed data, lagging measurement, and an inability to connect actions to outcomes — remain largely unresolved.
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The Experimentation Paradox
The report’s most striking finding concerns the state of marketing experimentation. Fifty-eight percent of marketers say they spend a moderate or significant amount of time running tests. Only 20 percent report high impact from those efforts. More tellingly, 77 percent say that winning experiments fail at scale at least some of the time — a finding that points not to a failure of effort, but of foundation.
The report argues that the underlying cause is reliance on historical behavioral data to guide decisions. Most teams are optimizing for past performance rather than building a causal understanding of what actually drives outcomes. Just 23 percent of marketers surveyed say they can reliably link marketing actions to business results.
“AI helps marketers move faster, but it doesn’t necessarily compel them to move smarter,” said Anthony Rotio, co-founder and co-chief executive of GrowthLoop. “Many marketing teams assume they’re data-driven because they’re running tests. Without a foundation of causal data to show what’s actually driving outcomes, those tests can fall short of delivering real return on investment.”
The Data Infrastructure Gap
The report identifies fragmented data infrastructure as the root cause of most of the performance gaps it documents. Only 46 percent of organizations report having a fully centralized, single source of truth for customer data. Among those that do, the performance differential is significant: companies with a unified data foundation reported revenue growth of 44 percent, compared with 8 percent for those without one. A centralized data foundation is also associated with faster marketing speed, more effective data use, and stronger returns from experimentation.
The location of that data foundation matters as well. Organizations using data clouds or lakes are less likely to struggle with measuring real impact — 42 percent versus 54 percent — and managing manual work — 31 percent versus 38 percent — compared with those relying on marketing suites as their primary source of truth.
Personalization: Still Mostly Aspirational
Despite the volume of industry conversation around real-time personalization, the data suggests the reality is considerably more modest. Only 12 percent of marketers say they use primarily real-time signals to execute campaigns. Eighty-five percent rely on historical data, or a mix of historical and real-time data — indicating that truly dynamic, signal-driven personalization remains an aspiration for the overwhelming majority of marketing organizations.
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The Path Forward
The report points to a consistent pattern among the organizations outperforming their peers: rather than moving data between fragmented systems, leading teams are bringing AI closer to the source — running models directly within their cloud data infrastructure and using composable AI decisioning tools to optimize campaigns in the same environment where the data lives.
“While the tools are getting smarter, the data infrastructure underneath hasn’t kept pace,” said Phil Gamache, founder of Humans of Martech. “If teams want to move fast and stay competitive, they must figure out that data bottleneck first.”
The report’s conclusion is direct: AI adoption without data consolidation is acceleration without direction. The companies pulling ahead are not simply using more AI. They are using it on better foundations.