Why Bad Data Kills AI Investments: 30% C-Suite Decay in 12 Months

Three quarters of chief revenue officers say data quality is their biggest challenge. That number matters because it points to a specific problem: AI tools are only as good as the data they run on, and that data is decaying faster than most companies realize.

Roughly 30% of C-suite data becomes inaccurate within 12 months, according to recent research. Within 19 to 22 months, more than half of CMO and CRO records are wrong. CEO and CFO data takes a little longer – 25 to 32 months – before 50% of it goes out of date.

A company refreshing its database every 6 to 12 months is not just inefficient. It is commercially risky, especially as go-to-market AI tools increasingly reach out to those same decision makers. If the underlying contact data is outdated, the AI model cannot win business.

The consolidation shift

About 20% of buying decisions now relate to AI, automation, and data-driven technologies, according to intent-data analysis. One in five topics companies explore falls into those categories. The focus is on productivity and intelligence – solutions that increase output and improve decision making.

Companies are not adding more tools. They are consolidating. The trend is toward building a data infrastructure first, then picking tools that can pull information from that existing data lake. Data strength is replacing tool count as the source of advantage.

That shift is a correction. Many companies are playing catch-up because legacy tech investments left their data sources fragmented across departmental siloes. Untangling those disconnected systems is the first step.