Marketing teams handed AI agents more control this year, and the data feeding those agents did not catch up. The Validity State of CRM Data Management report, based on a survey of 500 B2B and B2C marketing professionals across the US, UK, Brazil, Australia, and New Zealand, found that 66 percent of organizations increased the number of marketing decisions delegated to autonomous AI agents over the past year. Only 21 percent of respondents said their CRM data was very well prepared for that kind of AI use. The gap between how much authority agents got and how ready the underlying records were is the real story here, not the agents themselves.
That gap already has a cost. The same Validity survey found 78 percent of C-suite respondents and 92 percent of SVP and VP respondents said someone at their organization had acted on an AI recommendation that later turned out to be based on incorrect data; 41 percent of individual contributors reported the same. Sixty-two percent of organizations said they lost revenue directly because of poor CRM data quality, 67 percent linked it to delayed or abandoned campaigns, and 63 percent said it contributed to compliance risk. Teams are not just running imperfect campaigns. They are making automated calls on records nobody has checked.
The data is not holding still long enough to fix. The Demand Gen Report 2026 Database Strategies and Contact Acquisition Benchmark Survey found that 42 percent of marketing databases are decaying faster than teams can replenish them, while 61 percent of organizations still clean their data by hand. Sixty-eight percent of marketers named contact data quality as the single biggest barrier to revenue growth, ahead of budget or headcount. Manual cleansing cannot keep pace with a database that is actively rotting, which means every automated workflow built on top of it inherits the rot.
This is not a one-year problem. Adverity surveyed 200 CMOs at B2C brands and agencies in 2025 and found 45 percent of the marketing data used for business decisions was incomplete, inaccurate, or outdated, even though 85 percent of the same CMOs said they trusted their data. An estimate from Gartner, cited in that research, puts the average annual cost of poor data quality at 12.9 million dollars per organization. Three different survey firms, three different years, the same number showing up in different clothes: somewhere between two fifths and half of the data driving marketing decisions does not hold up.
Contact-level accuracy is where a lot of this starts, and it is worth naming a specific example. EmailAddress.ai, a company related to mGrowTech, focuses specifically on the catch-all and decayed-contact problem that shows up in these surveys: it says it resolves 85 to 95 percent of catch-all domains that standard verification tools mark as risky, using SMTP-level signal analysis rather than a simple ping. Whether or not a team uses that specific tool, the surveys above point to the same fix: someone has to own contact accuracy as its own job, not a side effect of the CRM.
None of this argues against AI agents in marketing. It argues against trusting them with records nobody has audited this quarter. The organizations in the Validity survey that reported the most confidence in their AI-driven decisions were not the ones with the newest agents. They were the 39 percent who named continuous, automated monitoring, not a one-time cleanup, as the thing most likely to make their CRM data trustworthy.
Sources referenced in reporting:
Validity: State of CRM Data Management in 2026
Demand Gen Report: 2026 Database Strategies and Contact Acquisition Benchmark Survey
Adverity marketing data quality research, via PPC Land