Key Takeaways
- Sequence your investments the right way, data integrity first, then workflows, measurement and AI, because building on a shaky foundation just moves bad data faster.
- Teams with stronger AI-human integration are nearly three times more likely to report measurable ROI, so prove trust on one small use case before you scale.
Sequence your investments correctly, data integrity first, then diagnose bottlenecks, prove trust on a small use case and scale from there. The Clean Data: The Engine Behind Every AI Motion report found organizations with stronger AI-human integration are nearly three times more likely to report measurable ROI.
Most teams get the order backward. They redesign workflows, buy measurement tools and enable AI, all on a shaky data foundation. Then they wonder why the numbers nobody believes keep piling up.
The Clean Data report, sponsored by Convertr, lays out the sequence that actually turns AI speed into pipeline.
Why Data Integrity Has to Come First
“Data integrity first, without exception,” says Scott Houchin of eClerx. “On a weak data foundation, redesigned workflows move bad data faster, measurement produces numbers nobody believes, and AI confidently automates errors.”
Workflow redesign, measurement and AI enablement all matter. But they compound on top of strong data pipelines. Build the foundation, then stack the rest.
The payoff is measurable. The CMO Council found that organizations with stronger AI-human integration are nearly three times more likely to report measurable ROI from AI initiatives.
How to Diagnose Before You Fix
You can’t unblock data flows you can’t see. Houchin’s first step is diagnosis: “Map where data stagnates: which insights exist, who can access them and how long they take to reach a decision.”
Then assign ownership. “Activation fails most often not from missing technology but from nobody owning the flow,” Houchin said. Buy all the tools you want, but without an owner insight dies in a dashboard nobody checks.
Don’t roll AI across your whole motion on day one. “To avoid random acts of AI, you have to start with the low-hanging fruit, account-level research, and prove the data is trustworthy before you scale,” advises Stephanie McArthur, senior ABM strategist at Demandbase.
The discipline matters. Only 22% of teams rely on proven, data-driven marketing strategies. A full 44% admit their efforts are tactical, reactive or ad hoc.
Ready to sharpen your pipeline? Read the full report.
