Marketers' AI confidence is outrunning their readiness
New TransUnion research shows a wide gap between how confident marketers feel about AI and how prepared their data actually is.
Mariano De Vitto · August 2026
Marketers have never sounded more optimistic about AI, but a new study suggests that optimism is running well ahead of the systems needed to back it up. Research from TransUnion, cited by eMarketer, finds that 64% of marketers are confident they will hit their goals using AI, while only 36% feel their data and processes are actually ready to support that work. That 28 point gap is not just an academic curiosity, the report says it is already dragging down measurable returns on AI investment.
The confidence gap
The numbers tell a clear story. Nearly two thirds of marketers believe AI will deliver on their objectives, whether that means sharper targeting, better personalization or more efficient campaign optimization. But barely a third of that same group trusts that the underlying data and internal processes can actually carry the weight of those ambitions. When confidence so dramatically outpaces readiness, the difference tends to show up later, in underperforming campaigns, wasted spend and results that fall short of what the tools were supposed to deliver.
TransUnion's findings point to a familiar pattern inside marketing organizations. Teams are eager to adopt AI for personalization, targeting and campaign optimization, but the infrastructure behind those use cases, from data hygiene to governance workflows, has not caught up. This mismatch matters because AI models are only as effective as the data feeding them. When marketers rely on fragmented customer records or inconsistent tagging across platforms, even the most advanced generative or predictive tools produce weaker results than promised, no matter how sophisticated the model itself is.
Adoption outpaces infrastructure
The research, published in TransUnion's newsroom and amplified by eMarketer, lands at a moment when more brands are pushing AI deeper into media buying, creative production and customer journey mapping. TransUnion built its business on identity resolution and data connectivity, which makes this particular warning notable, it is coming from a company whose core product depends on the kind of clean, unified customer data that many marketers apparently still lack. Vendors and platforms across the industry are moving fast to ship new AI features, but TransUnion's data suggests the operational groundwork inside marketing departments, things like unified data taxonomies, clean identity resolution and clear process ownership, is lagging well behind the pace of adoption.
That lag is easy to overlook in the middle of a hype cycle. New AI capabilities get announced constantly, and there is real pressure on marketing teams to show they are keeping up. But adopting a tool is not the same as being ready to use it well. Without solid data foundations, AI investments risk becoming another layer of complexity rather than a genuine driver of performance.
Closing the gap
Why it matters: confidence without readiness is a costly combination. Marketing leaders should treat this gap as a signal to audit data quality and internal workflows before scaling AI use cases further. That means taking a hard look at how customer data is collected, tagged and reconciled across platforms, and deciding who actually owns each step of that process. It is unglamorous work compared with rolling out a new AI feature, but it is the work that determines whether AI actually moves the needle.
Investing in that groundwork, cleaning data pipelines, clarifying who owns what process, and testing AI outputs against real business outcomes, will do more for ROI than adding another tool to the stack. Teams that close this readiness gap first will be positioned to capture the returns that others are currently overestimating, turning today's confidence into results that actually hold up under scrutiny.
The Signal Brief · Mariano De Vitto — Head of Marketing, Barcelona