AI is taking on a bigger role in marketing decisions. But there is a problem: many organizations are giving AI more authority before fixing the data it relies on.
In a recent MarTech article, Constantine von Hoffman, Senior Editor at MarTech, highlights findings from Validity’s State of CRM Data Report 2026.
The gap is hard to ignore:
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91% of marketers say data readiness is critical for AI adoption.
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Only 21% believe their CRM data is very well prepared for AI.
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45% are already using agentic AI capable of acting without human review.
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62% believe poor CRM data has probably or definitely cost their organization revenue.
For Marketing Operations teams, this changes the conversation around data quality.
Bad data can now trigger bad decisions
Poor CRM data used to create inaccurate reports, bad segmentation and manual cleanup.
With AI, the consequences can move much faster.
An AI agent working from inaccurate or incomplete data could:
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Score the wrong lead
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Route an opportunity incorrectly
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Personalize a campaign using outdated information
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Recommend the wrong next action
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Reallocate effort or budget based on unreliable performance data
In other words, bad data is no longer just a reporting problem. It can become an automated decision.
AI readiness starts with the foundation
Before connecting AI to CRM and marketing systems, organizations need to understand what the AI will actually see.
Are customer and account records accurate?
Are lifecycle stages consistently maintained?
Are campaign, lead source and revenue fields trustworthy?
Are integrations creating duplicates or conflicting values?
Is there clear ownership of data quality and governance?
If the answer to these questions is unclear, adding AI does not solve the problem. It can amplify it.
Data quality needs to be continuous
One of the most telling findings in the MarTech article is that 39% of respondents said continuous automated monitoring that catches and fixes data issues in real time would most increase their confidence in CRM data.
That matters because CRM data is constantly changing.
New records enter through forms, events, enrichment platforms, sales activity, integrations and third party sources. A one time database cleanup cannot keep pace with that.
As AI becomes more embedded in Marketing Operations, data quality needs to move from a periodic project to an ongoing operating discipline.
Build the foundation before giving AI more authority
AI can make Marketing Operations faster, more responsive and increasingly autonomous.
But autonomy requires trust.
Before giving AI more control over campaigns, leads, reporting or revenue workflows, organizations need reliable data, clear governance and appropriate controls around what AI can access and change.
Otherwise, AI may simply help the organization make the wrong decision faster.
Is your Marketing Operations stack ready for AI?
RightWave’s AI Readiness Audit helps you assess the data, systems, processes and governance foundations that AI will depend on before you scale adoption.
Identify the gaps before your AI does.
Get your AI Readiness Assessment →
Source: This article draws on insights from “Marketers know AI is using bad data to make decisions,” by Constantine von Hoffman, Senior Editor at MarTech, published by MarTech on August 28, 2026, based on findings from Validity’s State of CRM Data Report 2026.
