AI and Recruitment Transformation: Why “Clean Data” Is Rarely What People Think It Is

When AI initiatives stall in recruitment businesses, the explanation usually sounds familiar.

“The tech didn’t work.” “The outputs weren’t reliable.” “The system wasn’t as clever as promised.”

In practice, when we dig a little deeper, the issue is almost always the same.

Data.

And more specifically, a misunderstanding of what good data actually looks like in a recruitment context.

“We’ve done a data cleanse”This is one of the most common phrases I hear ahead of a technology migration or AI implementation.

When I ask what that cleanse involved, the answer is usually:

• Removing duplicate candidates
• Deleting obviously old records
  
  

That’s it.

De-duplication is important, but it is only one small part of what makes data usable for modern CRMs and AI tools. On its own, it doesn’t fix the underlying problem.

Which is that many recruitment databases were never designed with AI, insight, or decision-support in mind. They were built to get people paid, fill roles quickly, and move on.

AI asks much more of the data underneath.

What “good data” actually means in practice

When people talk about “getting data into shape”, they often mean tidying it up.

What they should be doing is defining what good looks like, based on how the business wants to operate going forward.

At a practical level, good data has a few core characteristics.