AI automation cannot fix a CRM that the team does not agree on. Duplicate contacts, inconsistent stages, missing owners, and free-text fields make routing and summaries unreliable. A short cleanup pass creates a safer foundation and often improves everyday sales work even before AI is added.
Agree on the meaning of each field
Write plain-language definitions for lead, opportunity, customer, source, stage, owner, next action, and consent status. Remove fields that nobody uses and make required information visible at the right step.
- Choose one source of truth for each key field.
- Standardise values and date formats.
- Define which fields are user-entered and which are calculated.
Find duplicates and stale records
Use deterministic matching where possible, then review uncertain matches. Do not merge records automatically without a rollback or audit trail. Separate inactive records from records that still require action.
- Create a duplicate review queue.
- Keep original identifiers and merge notes.
- Assign a data owner for ongoing quality.
Only then automate
Digital Allo’s workflow automation can connect clean intake, summaries, and routing to a clearer CRM process. Start with one workflow and log every update so the team can see what the automation did.
- Test missing, duplicate, and unusual inputs.
- Keep approval for important status changes.
- Review data quality as a recurring operational task.
A two-week cleanup plan
| Days | Focus |
|---|---|
| 1 to 2 | Agree field definitions and lead stages with sales and operations |
| 3 to 4 | Export the CRM and measure duplicates, blanks and invalid values |
| 5 to 7 | Merge clear duplicates using rules; queue uncertain ones for review |
| 8 to 9 | Fill or archive records with no owner or next step |
| 10 | Lock key fields to dropdowns and make essentials required |
| 11 to 14 | Pilot one automation on the cleaned records and review every change |
Before and after: one field
Before: the “Source” field contains 47 different free-text values, including “google”, “Google Ads”, “GAds”, “website?” and blanks.
After: a dropdown with eight agreed values, such as Google search, Google Ads, Referral, Website form, Phone and Event, plus a rule that the field is required before a lead can move to the next stage. Reporting by source now works, and an AI summary can be routed reliably.
Data quality measures to track monthly
- Percentage of open leads with an owner and next action
- Duplicate rate in new records
- Records missing consent status
- Records not updated in more than 90 days
A practical starting point
- Define the data model before the prompts.
- Clean duplicates with an audit trail.
- Pilot one write action at a time.
Frequently asked questions
Should we clean every record first?
Prioritise active customers, open opportunities, and the records used by the pilot. Create a plan for the rest.
Can AI detect duplicates?
It can suggest matches, but deterministic rules and human review should control merges.
Should we delete old records?
Archive or delete records you no longer need for a clear purpose, in line with your retention policy and privacy obligations. Keeping everything forever increases risk without adding value.
Further reading
- Office of the Privacy Commissioner of Canada: Privacy for businesses
- Statistics Canada: AI use by Canadian businesses
How Digital Allo can help
Digital Allo helps teams clean up their CRM before connecting workflow automation, so the first automation runs on data people trust. Ask us for a CRM data review.
