AI Investment Is Moving Faster Than Marketing Readiness

By Becky Johnson, Contributor and Host of Advertising Week’s Modern Marketing + Measurement Podcast

Marketers are putting more money into AI, but investment alone does not create results. As Matt Spiegel, EVP, TruAudience Growth Strategy at TransUnion, explains, many organizations are pursuing the technology before building the data, processes, and internal understanding needed to use it effectively.

The problem is not a lack of information. Companies already have customer signals spread across CRM systems, ecommerce platforms, physical stores, media partners, and other sources. The problem is that those signals are often disconnected. Without a common way to link them, marketers cannot develop a complete view of the customer or confidently measure what is working.

AI can help teams work faster, run more models, and examine more information, but it cannot correct an incomplete foundation on its own. If the underlying data remains divided among separate systems, the resulting insights will still be limited. Matt’s advice is straightforward: get your data house in order before expecting AI to deliver meaningful differentiation.

Measurement also needs to work at more than one level. Marketing mix modeling can provide a broader view of performance, attribution can offer more detail, and controlled testing can help determine whether an investment created an incremental result. The answer is not to choose one method for every situation, but to understand what each one can show and connect them into a consistent story.

That story matters because the C-suite does not always trust the results marketing presents. Reports from individual platforms and vendors may be accurate within their own boundaries, but they do not necessarily account for overlap or reflect the company’s actual growth. Marketers need a shared set of top-level business measures that leadership understands, supported by more detailed metrics for the teams making day-to-day decisions.

Getting there does not require rebuilding everything at once. A company can begin with one clear problem, such as improving segmentation, understanding incrementality, or creating more relevant customer experiences. The right starting point will vary, but the goal remains the same: connect the information used to understand customers, reach them, and measure the results.

As marketers prepare their 2027 budgets, the strongest AI strategy may begin well before choosing another tool. It begins with knowing what problem needs to be solved, connecting the data required to solve it, and making sure the results can be clearly explained across the organization.