The industry has bought the engine, but no one has bought the fuel.
At 52% of financial institutions, generative AI is operational or better in marketing, and 44% are increasing their spending on it in 2026. Not one respondent describes their customer data as real-time and AI-ready.
Institutions have invested in producing more marketing, not in the capabilities that determine who should receive it, when it should arrive, or whether it worked. Eighty-three percent are at segment-based personalization or below, and 1% describe themselves as hyper-personalized. Two-thirds of what the industry calls personalization is simply choosing which audience gets the message, which is list selection with better tools and far more data behind it.
The funding pattern says the same thing, with a dollar figure attached. Fragmented data is the number one barrier named in this research, while first-party data and CDP infrastructure sit near the bottom of the increase list at 20%, with 60% holding flat. Fifty-five percent are not using predictive AI at all, the capability that would aim everything the content engine now produces.
Nothing in the current dashboard would raise an alarm. Fifty-two percent do not measure incremental lift from personalization against a control group, and 43% do not measure customer lifetime value. Personalization is being funded, staffed, and reported across this industry without a control group.
The most consequential findings appear near the back of the report. When asked what will change over the next 12 to 18 months, the most common answer across nearly every function was no change. Compliance and content governance show 80% no change among institutions under $10 billion, even as generative content production is scaling. These gaps are not closing on their own. They are scheduled to remain.
There is real encouragement here as well. Fragmented data and a lack of internal talent rank well ahead of insufficient budget and executive sponsorship, so marketers describe an execution problem rather than a permission problem. Execution problems are solvable. Scale is not the dividing line either. Personalization maturity, the technology stack, and the zero look nearly identical above and below $10 billion, so the balance sheet cannot explain the position.
This report, with comparisons to our previous reports, breakouts by asset size, and recommendations for future actions, is the place to go to find out where your own gap sits, defend the sequence in your next budget conversation, and move from intention to execution.