AI can suggest that two records refer to the same customer or help classify inconsistent descriptions. It should not be given unrestricted permission to rewrite the company database. Start by separating formatting problems, missing information and genuinely uncertain matches: each needs a different treatment.
Identify the problem before choosing a tool
A date stored in two formats may need a conversion rule. A blank mandatory field needs an owner who can supply the missing information. Two similar customer names may need investigation, not automatic merging.
Make a sample of the problems and ask the team to label the correct outcome. Include records that look similar but must remain separate. A shared address, surname or phone number does not always identify one person or organisation.
Use explicit rules where the answer is known
Trimming extra spaces, standardising an agreed date format and checking allowed status values do not usually require a language model. Rules can be tested and explained, which makes them useful for repeatable corrections.
Even simple changes need context. Removing leading zeros from every identifier can damage valid codes. A missing value is not automatically zero, and a blank consent field must not be interpreted as permission. Agree the meaning before applying a transformation to the entire dataset.
Use AI to propose uncertain matches
For ambiguous descriptions, AI can create a shortlist for review. Show the original values, the suggested match and the fields that support it. Treat any confidence score as a signal to evaluate, not as proof.
Reviewers need a “not enough information” option. If the system forces a choice, uncertainty may be converted into a confident-looking error. Do not invent missing addresses, identifiers or transaction details just to make a record appear complete.
Protect the original records
Test changes on a copy first. Keep a change log connecting old values, new values, the reason and the approving person or rule. Before a bulk update, verify that the recovery process actually works.
Merging can affect linked orders, permissions and reporting totals. A record that appears redundant may still carry references needed elsewhere. Plan how those references are handled rather than deleting one row and hoping nothing depends on it.
Decide whether the cleaning worked
Check both wrong merges and duplicates left unresolved. A process that removes many duplicates but joins unrelated customers is not necessarily an improvement. Use a reviewed sample that was not used to tune the matching rules.
Ask how much work reviewers save and how often corrections must be reversed. Report uncertain cases separately instead of hiding them inside an overall “accuracy” number.
Prevent the same problem returning
Improve validation at entry, agree ownership of shared fields and define what integrations may change. Otherwise, the next import may recreate the same duplicates you just cleaned.
Our AI readiness service includes preparing usable data before an AI implementation. Bring a small authorised sample and examples of correct decisions; those are more valuable than a promise that AI will automatically fix everything.