AI features in Salesforce, Einstein scoring, generative replies, agent assistance, are only as good as the records they read. Teams switch them on expecting insight and get generic or plainly wrong output. The model is rarely the problem. The data underneath it is.
Start with the fields the feature actually uses
Every AI feature draws on a defined set of fields. Before enabling anything, map which objects and fields feed it. A lead-scoring model reading Industry, Annual Revenue, and Lead Source will score poorly if those fields are half empty. Audit completeness on exactly those fields rather than boiling the ocean across the whole org. A short report grouped by field-fill rate tells you in minutes where the gaps are.
Fix consistency, not just blanks
Empty fields hurt, but inconsistent values hurt more quietly. Ten spellings of "United States," free-text job titles, and picklists that grew organically over years all blur the patterns a model tries to find. Standardize the high-signal fields first: convert free text to restricted picklists, add validation rules to stop new drift, and run a cleanup pass on historical records before the AI ever sees them.
Give the model outcomes to learn from
Predictive features need results to correlate against. If your closed-won records don't reliably capture close date, amount, or a real close reason, there is nothing for the model to learn. Enforce the fields that represent outcomes going forward, and backfill where the history is worth recovering. Pay particular attention to how losses are recorded: a model that only sees wins, or that treats every stalled deal as a loss, learns a distorted picture of what good looks like. This is unglamorous work, but it is what separates a model that predicts from one that guesses.
Keep a human in the loop at first
Treat the first few weeks as a supervised trial. Route AI output to a review step, compare it against what an experienced rep would have decided, and tune from there. This protects adoption more than any feature toggle: one bad automated email to a strategic account erodes trust faster than months of good suggestions can rebuild it.
The takeaway
The highest-leverage AI investment most teams can make is not a bigger model or more licenses. It is a disciplined pass at the data those features depend on. Clean, consistent,
outcome-rich inputs are the whole game. Get those right and the intelligence tends to follow; skip them and no amount of tuning will save the rollout.