- AI cannot exist without data. So why is the United States hiring more AI specialists than data engineers?
- Less technologically mature regions are probably the worst culprits, taking advantage of the hype
- AI Workers Are Being Rewarded More Than Data Engineers
More than four in five AI projects fail, roughly double the rate of non-AI technology projects, according to RAND research, and new US employment data could reveal the reason behind this.
According to DoubleTrack, the root cause is not the AI itself, but the data on which it is based. The main reason AI fails is thanks to poor, inaccessible or ungoverned data, not weak models. In fact, nearly two in three (63%) organizations lack confidence in their data management for AI.
And to date, hiring trends suggest that many companies still don’t understand this, leading to potential failure in the future. Three in five AI projects without AI-ready data could be abandoned by 2026, according to Gartner data.
AI is failing due to poor data preparation
DoubleTrack data found that US employers posted 111,296 AI/ML roles, but only 76,271 data infrastructure roles, leaving a 46% difference between the two very different positions. Sales, legal, engineering, marketing, and technology sectors experienced increased role availability in AI and ML functions.
For example, there were 232% more AI roles than data roles in sales, which is risky given how confusing CRM data can be. 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 highest AI states were Mississippi (264%), Missouri (179%), Kansas (176%) and Montana (175%), which are generally perceived as less mature regions in technology, indicating that they may be chasing expectations.
The bottom line is that companies should not measure AI success based on speed because this risks skipping important data jobs.
“The companies most at risk right now are not those moving slowly toward AI,” the report summarizes. “They are the ones who have aggressively hired for AI roles without corresponding investment in data quality, governance and infrastructure.”
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