How do you prepare CRM data for AI?

Q: How do you prepare CRM data for AI?

Make the data mean one thing, fill the fields the model will read, and mark outcomes honestly: clear stage definitions, enrichment for the attributes you score on, duplicates merged, and every closed deal labeled won or lost with a reason. You do not need perfect data to start, but you do need to know which fields can be trusted.

Why AI makes data problems visible

A person reading a CRM fills gaps with memory. A model cannot. If industry is blank on half the accounts, the model learns that industry does not matter. If lost deals are left open forever, it never learns what losing looks like. AI does not create data problems. It stops you working around them.

The four things to fix first

Stages: written entry and exit criteria, so the same stage means the same thing for every rep. Outcomes: every old deal closed as won or lost, with a reason from a short list. Identity: duplicates merged, contacts tied to the right account. Coverage: the fields you intend to score on enriched automatically, not typed by reps.

What you can leave alone

Free-text notes, old activity logs and fields nobody reports on. Cleaning everything is how data projects fail. Choose the use case, list the fields it depends on, and fix those. The rest can wait until something needs it.

How to keep it clean

By automation rather than reminders. Enrich on creation. Catch duplicates on entry. Require a field only at the stage where it matters, so reps are not asked for information they do not have yet. Flag stale deals to their owner. Hygiene that depends on discipline lasts about a quarter.

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