Despite Growing Adoption of AI Platforms, More than 40% of Marketers Surveyed Face Stubbornly Slow Marketing Cycles, While Three-Quarters Say Winning Experiments Fail at Scale
, /PRNewswire/ -- GrowthLoop today released its 2026 AI and Marketing Performance Index, a survey of more than 300 marketers and data leaders across the U.S. and Canada. The study, conducted in partnership with Ascend2, found that despite rapid advancements in artificial intelligence, most marketers remain constrained by fragmented data, slow measurement cycles, and ineffective experimentation, even as they're under increasing pressure to accelerate AI adoption and prove ROI.
While the challenges are pervasive across organizations, the research reveals that companies with a fully centralized, single source of truth (SSOT) are more likely to drive improved performance outcomes. Companies with a SSOT reported significantly higher revenue growth compared to those without one (44% vs. 8%). A SSOT is also correlated with increased marketing speed, more effective data usage, and stronger returns from experimentation, driving revenue growth.
The report also highlights a fundamental mismatch between how marketers use data and what they are trying to achieve. Even as 87% of marketers implement AI in their processes, most teams still rely on patterns in historical behavior to guide decisions, optimizing for past performance rather than what actually drives outcomes. As a result, marketers struggle to determine how their actions influence customer behavior, making it difficult to scale success or drive performance.
"AI helps marketers move faster, but it doesn't necessarily compel them to move smarter," said Anthony Rotio, co-founder and co-CEO of GrowthLoop. "At the end of the day, 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, however, those tests can fall short of delivering real return on investment. The companies pulling ahead are the ones unifying data in the enterprise cloud and using AI to connect data, decisions, and outcomes, not just speed up execution."
Additional key report findings include:
- Despite diligent efforts, measurement and experimentation are ineffective: 58% of marketers spend a moderate or significant amount of time on experimentation, yet only 20% report high impact from those efforts. In fact, 77% say "winning" tests fail at scale at least sometimes.
- Causal clarity is low: Just 23% of marketers can reliably link marketing actions to business outcomes.
- Data and measurement gaps hold AI back: Even as data volumes grow, the foundation needed to make AI effective remains incomplete. Only 46% of organizations report having a fully centralized SSOT for customer data, while marketers cite difficulty measuring real impact, data latency, and fragmented systems as persistent challenges.
- Personalization is mostly NOT real-time: Despite industry narratives, only 12% of marketers use mostly real-time signals to execute campaigns, while 85% rely on historical or a mix of historical and real-time data, suggesting that real-time personalization remains aspirational for many organizations.
- Location of SSOT matters: Organizations using data clouds or lakes are less likely to struggle with challenges like measuring impact (42% vs 54%) and managing manual work (31% vs 38%) compared to those relying on marketing suites for their source of truth.
Insights from Leading Industry Experts
This year's Index reinforces what martech experts and leaders across the industry are directly observing: innovative organizations evolve how and where they activate their data. Rather than moving data between fragmented systems, leading teams bring AI closer to the source, running models directly within their cloud data infrastructure and leveraging composable AI decisioning tools to optimize marketing campaigns.
This approach allows companies to operate on complete data, reduce latency, and continuously learn from every interaction. By unifying data and applying AI within the same environment, these organizations are able to move faster, test more effectively, and make decisions that are directly tied to business outcomes rather than disconnected insights.
"The majority of marketing leaders today talk about how they plan to automate entire teams with AI agents and decisioning tools, but this report shows that most teams still struggle to execute their campaigns quickly," said Humans of Martech Founder Phil Gamache. "I've been fortunate to interview many leading minds in martech, and this idea is starting to crystallize with increased frequency: while the tools are getting smarter, the data infrastructure underneath hasn't kept pace. If teams want to move fast and stay competitive, they must figure out that data bottleneck first."
To download and read the full 2026 AI and Marketing Performance Index, visit go.GrowthLoop.com/index. To learn more about GrowthLoop, visit www.GrowthLoop.com.
About GrowthLoop
GrowthLoop is a pioneer in composable, AI-powered marketing on the data cloud, featured on G2 by its customers as a momentum leader with the best ROI for enterprise. The GrowthLoop agentic, composable CDP drives compound growth by accelerating the marketing cycle, using agentic AI powered by your enterprise cloud data. Working alongside AI agents, teams use GrowthLoop to translate customer data into precise audiences and activate those audiences across real-time customer journeys, measuring and improving performance through always-on analysis — all with zero data movement. Thousands of marketers at enterprises like Costco, Albertsons, and Ford rely on GrowthLoop to bring their AI strategy to life, grow faster with every experiment, personalize every customer touchpoint, and drive rapidly compounding results.
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