Defining the confidence-readiness paradox:<\/p>\n
AI investment is accelerating: 89% of marketers expect investment in AI-enabled marketing initiatives to increase over the next 12 to 24 months.Confidence outpaces readiness: 64% are confident they will achieve their AI-enabled marketing goals. However, only 42% rate their organization\u2019s people readiness as high, while just 36% rate their data and process readiness as high.Transparency is lacking: Fewer than half (48%) say they have enough visibility into platform-level AI to make optimization decisions with confidence.Marketers are mostly focused on efficiency: 65% of marketers measure AI success primarily through time and cost savings, but less than half use more advanced methodologies like marketing mix modeling, multi touch attribution and incrementality testing.<\/p>\n
“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, EVP, TruAudience Growth Strategy, TransUnion. “However, AI isn’t a shortcut around data challenges. It’s a force multiplier. Organizations that build strong foundations of trusted data, identity and measurement will be best positioned to close the gap between AI ambition and AI outcomes.”<\/p>\n
The Barriers to AI Readiness<\/p>\n
The study found that data fragmentation remains one of the industry’s biggest obstacles to AI readiness. The limitations inhibit marketers\u2019 ability to evaluate AI\u2019s effectiveness and achieve meaningful business outcomes.<\/p>\n
Top concerns related to data fragmentation<\/p>\n
42% reported incomplete or missing data69% said data blind spots within walled gardens limit their ability to evaluate AI effectiveness70% said cross-channel blind spots make it difficult to understand AI’s impact across the customer journey<\/p>\n
As AI becomes more deeply embedded in marketing workflows, the findings suggest that transparency, independent measurement and connected data will become increasingly important competitive advantages.<\/p>\n
“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.”<\/p>\n