Supermetrics released its AI Readiness Gap report today, based on a survey of 435 marketing leaders across the U.S., U.K., Germany, Australia and Singapore. The findings show that leadership pressure to adopt AI is accelerating, but most organizations have not built the data infrastructure, system integrations or ownership models required to make AI-driven marketing accountable.
Eighty-five percent of organizations have no formal AI strategy or lack clear ownership. Just 15% reported a defined roadmap and measurable success metrics. Supermetrics calls this disconnect the "AI readiness gap" - the distance between the pressure to adopt AI and the actual capacity to deploy it reliably.
The ownership problem behind AI readiness
The report argues that AI readiness is not primarily a technology problem. It's an ownership and execution problem. Marketing teams are being asked to move faster with AI while often lacking control over the data strategy and activation workflows that determine whether AI produces dependable business results.
"AI alone isn't the answer. AI needs high-quality data, transparency and trust, and it needs to be integrated in a way that marketers understand what it does and have control over it," said Anssi Rusi, CEO of Supermetrics. For marketing leaders under pressure to build a formal AI strategy with measurable success metrics, an AI Learning Path for CMOs addresses the strategic gap that the report identifies.
Data quality and integration bottlenecks
Only 11% of organizations describe their marketing data as extremely high quality, accurate and accessible across systems. When insights do emerge, turning them into action often stalls. Forty percent of SMB marketing teams and 34% of enterprise teams cite a lack of system integration between analytics tools and activation platforms as the biggest blocker. Another 20% of SMB teams and 27% of enterprise teams point to time-consuming manual data handoffs.
Without connected data, AI for Marketing efforts risk amplifying broken workflows instead of improving decision-making. The report underscores that AI needs a foundation of accessible, reliable data to move beyond experimentation.
Speed of data access falls short
Speed is another core barrier. Just 7% of marketing teams say data requests are answered in real time. Half wait one to three business days for ad hoc data questions, and 42% say that wait time doesn't meet their needs. These delays erode the responsiveness that AI-powered marketing promises.
Why this matters for marketing professionals
Marketing leaders can't outsource AI readiness to a vendor or a single tool. The report makes clear that reliable AI output starts with data ownership, system integration and governance that marketing teams help shape. Pushing for connected analytics and activation platforms, clear data-quality standards and faster data access will determine whether AI accelerates performance or simply adds speed to broken processes. Read the full AI Readiness Gap report here.
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