When AI just amplifies bad CRM data

The short answerA model trained on your historical activity will recommend what you already do, because that is the only pattern in the data. If reps only ever called enterprise accounts, the model learns enterprise accounts are what good looks like. The fix is to train on outcomes rather than activity, and to back-test against closed deals, including the ones nobody worked.

How bias gets in, and what catches it

Trained on activityLearns what reps did, not what worked. The most common single cause.Retrain on outcomes

Survivorship in the dataOnly worked accounts have history, so unworked segments look dead.Hold out a test group

Vendor defaultsWeights fitted to an industry average, not to your business.Refit on your deals

No negative examplesA model that never sees closed lost cannot learn what loses.Include both

Never re-testedFitted once, then left while the business moved under it.Quarterly, minimum

No traceabilityA score with no path back to a record cannot be audited or trusted.Require it

Where this ends up

Every point traces back to something that happened.

Kaypo sets weights by running the model backwards over won and lost deals, re-tests quarterly, and shows which signals moved a score on which record.

$99 a month, or free with the retainer

See how Kaypo works

# revenue, 7:00am

Northwind Health84 ▲

Cascadia Systems71 ▲

Meridian Labs38 ▼

Procurement joined the Northwind thread Tuesday

What this looked like in practice

re-tested quarterly

The weights were fitted on won and lost deals, including accounts nobody had worked, and re-tested each quarter against what actually closed. Without the unworked accounts the model would have learned to recommend what the team already did.

Read the engagement

Decide it with evidence instead of a demo

FreeRead-only, a written report with every gap priced, yours either way.

$2,000A month if you want it fixed, with Kaypo included.

No callYou can start from the pricing page without speaking to anyone.

Questions

Why does our AI recommend the same accounts we always work?

Because it trained on rep activity rather than on outcomes. A model fed behaviour learns behaviour, and recommends what you were already going to do.

How do we test an AI model for bias?

Back-test it against closed won and closed lost, including accounts nobody worked. If it cannot distinguish them, it is repeating your history rather than predicting.

How often should a scoring model be retrained?

Quarterly, against deals that actually closed in that period. Models fitted once at implementation decay quietly rather than failing loudly.

Want to talk it through?

Leave an email and I will reply myself, usually the same day. No sequence, no newsletter, and no calendar link unless you ask for one.

Would rather just book time? Here is the calendar.

We use cookies to run the site and to understand how it is used. You can accept all or necessary only. See our privacy notice.

Necessary, and cookieless countingRemembers this choice, keeps a guide unlocked once you have requested it, and counts page views anonymously without storing anything on your device. Always on.

Remember my visitKeeps the pages you visit joined up for this browser session so we can see the route people take. Cleared when you close the tab. Never sold or shared.

How do we avoid AI recommendations that reinforce bad data instead of surfacing new opportunities?