Let me tell you about a pattern I see constantly.
Company decides to implement AI. Company gets excited about all the possibilities. Company turns on AI features. AI produces garbage results. Company blames the AI.
The actual problem, in roughly 70% of cases I see: the data was bad.
Here's what "bad data" actually looks like in practice:
Incomplete records. Your AI is trying to predict which leads will close, but 40% of your leads have blank industry fields. The AI is learning from a distorted picture.
Duplicate records. The same person exists four times in your CRM with slightly different email addresses. Your AI thinks you have four different customers with identical behavior patterns.
Outdated information. Contact records haven't been touched since 2019. Job titles are wrong. Companies have merged or gone out of business. The AI is making decisions based on reality that no longer exists.
Inconsistent formatting. Phone numbers in twelve different formats. States as "CO," "Colorado," "Colo," and "Colorodo" (yes, with the typo). The AI won't inherently recognize that these are the same thing.
None of this is exciting. Fixing data quality is tedious, unglamorous work that nobody wants to prioritize. But it's the foundation that everything else depends on.
I've started telling clients: before you spend a dollar on marketing and sales automation AI features, pull ten random contact records from your CRM. How many have complete, accurate information? If the answer is less than eight, you're not ready for AI—you're ready for data cleanup.
The companies that succeed with marketing and sales automation AI are almost always the ones that did the boring work first. They cleaned their data. They standardized their processes. They built the foundation before they tried to build the house.
It's not a sexy message. But it's an honest one.
Oh, and did I mention that HubSpot’s Data Hub makes a lot of this cleanup a breeze (no pun intended).