Companies are hiring AI specialists rather than data engineers – and it’s a big problem
- AI cannot exist without data. So why is the United States hiring more AI specialists than data engineers?
- The least technologically mature regions are probably the worst culprits, taking advantage of the hype.
- AI workers are better rewarded than data engineers
More than four in five AI projects fail, about double the rate of non-AI technology projects, according to a RAND study, and new U.S. employment data may reveal why.
According to DoubleTrack, the root cause is not AI itself, but rather the data it relies on. The number one reason AI fails is poor, inaccessible, or ungoverned data – not weak models. In fact, nearly two in three organizations (63%) are not confident in their data management for AI.
And to date, hiring trends suggest that many companies have yet to understand this, leading them to potential failure. Three out of five AI projects without AI-ready data could be abandoned by 2026, according to Gartner data.
AI fails due to poor data preparation
DoubleTrack data revealed that U.S. employers posted 111,296 AI/ML positions, but only 76,271 data infrastructure positions, leaving a 46% difference between the two very distinct positions. Sales, legal, engineering, marketing, and technology industries have all seen greater role availability in AI and ML roles.
For example, there were 232% more AI roles than data roles in sales, which is risky given how messy CRM data is. Marketing was more balanced, but there were still 54% more AI roles.
The report also found that AI specialists earn on average $15,000 more than data engineers, meaning companies are paying more to reward workers who can’t deliver without the right foundation.
Geographically, the most AI-focused states were Mississippi (264%), Missouri (179%), Kansas (176%), and Montana (175%), which are generally perceived as less technologically mature regions, indicating they are chasing the hype.
The bottom line is that companies shouldn’t measure AI success based on speed, because that risks missing important data work.
“The companies most at risk today are not those moving slowly toward AI,” the report summarizes. “They are the ones who have been hiring aggressively for AI roles without investing accordingly in data quality, governance and infrastructure.”
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