Many revenue AI disappointments are data problems that were visible before the contract was signed. A short readiness check saves months.
Five questions per use case
- What data does it need? Fields, history length, activity data, external data.
- Do we have it, and is it good enough? Completeness and freshness of those fields.
- Can the tool reach it? Integrations, permissions, data residency.
- Who owns fixing it? A named RevOps or data owner.
- How will we know it works? Baseline metric and comparison group.
Typical data needs
| Use case | Minimum data |
|---|---|
| Lead scoring | 12+ months of leads with outcomes, consistent lead sources |
| AI forecasting | Several quarters of opportunities with stage history and consistent close dates |
| Deal intelligence | Activity capture, contact roles, call data |
| AI SDR agent | Clean contacts with consent status, suppression lists, ICP definition, routing rules |
| Churn prediction | Usage, support and billing data joined to accounts, with past churn outcomes |
These are practical rules of thumb, not standards — ask each vendor what their model needs and test on your data.
Test on your records
Ask vendors to run on a sample of your real data during evaluation. Look at the outputs that will reach sellers or customers. Many problems — duplicate contacts, wrong account hierarchy, missing renewal dates — show up in the first hour.
Why this matters now
Gartner predicts that by 2028 sales leaders who overhaul data, automation and user experience will be five times more likely to gain ROI from AI than those choosing quick fixes. The quick fix is buying a tool; the durable fix is the data under it.