A TransUnion survey released August 5, 2026 shows 89% of senior US marketing leaders plan to raise AI spending over the next two years, yet only 36% consider their data and processes ready to support that expansion. The gap between budget commitments and operational readiness explains why just 53% of those teams report meaningful return on investment.
The research covered 100 directors and above at US enterprises with annual marketing budgets of at least $50 million. Respondents already used AI in core marketing workflows and excluded those relying solely on basic productivity tools. Even among this experienced group, scaling remains rare. Only 13% said their organizations have fully deployed AI across the enterprise.
Adoption outpaces execution
Productivity applications lead adoption at 92 percent, followed by basic content generation at 89 percent and creative optimization at 83 percent. Beyond tactical execution, usage drops sharply. Teams exploring structured approaches to AI for Marketing often find that productivity applications lead adoption at 92 percent. More than half of teams apply AI to data quality or identity work, while fewer than half use it for marketing measurement or customer insights. Reported outcomes follow the same pattern. Seventy-five percent noted reduced manual effort, but revenue impact metrics trail significantly. Improving conversion rates reached 35 percent, and expanding audience reach at equal spend hit 30 percent.
Data fragmentation blocks scale
Sixty-seven percent of respondents cited siloed or fragmented data systems as the primary obstacle. Forty-two percent reported incomplete data, and 31 percent struggled with latency. AI models inherit the quality of their inputs, which means outputs built on unreliable data compound errors in measurement and optimization rather than content creation. Readiness scores reflect this constraint. Only 36 percent rated their data readiness as highly prepared, while 42 percent felt similarly confident about people and skills, and process governance sat at 36 percent.
"Marketers are increasingly confident in AI's ability to drive business results, but many are still working to build the foundations needed to scale it effectively," said Matt Spiegel, executive vice president of TruAudience Growth Strategy at TransUnion. "AI isn't a shortcut around data challenges. It's a force multiplier."
Sixty-five percent of marketing leaders track AI impact through estimated time or cost savings. Formal experimentation lags behind. Marketing mix modelling reached 42 percent, randomized tests hit 35 percent, and multi-touch attribution stood at 31 percent. Twenty-two percent rely on stakeholder perception, and 14 percent do not measure AI impact at all. Structural limits compound the problem. Seventy percent of leaders reported cross-channel blind spots, and 69 percent cited walled garden reporting restrictions that prevent accurate evaluation. Teams deploy AI, measure efficiency gains, then allocate more budget without a complete view of performance.
"What this research makes clear is that AI success is no longer defined by access to the technology itself," said Michael Burke, principal at UTA Advisory. "The real differentiator is whether organizations can connect their data, measure outcomes and operationalize AI at scale."
Why this matters for marketers
Budget increases driven by confidence figures do not guarantee returns when measurement relies on efficiency metrics alone. Finance teams will evaluate AI investments against cost savings rather than revenue evidence if causal measurement remains a minority practice. Organizations should treat data integration and governance as prerequisites before scaling AI beyond content generation and campaign execution. Leaders reviewing frameworks for AI for CMOs typically encounter the same pattern: sixty-five percent of marketing leaders track AI impact through estimated time or cost savings.
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