- AI cannot exist without data – so why is the US hiring more AI specialists than data engineers?
- Less technologically mature regions are probably the worst culprits for jumping on the hype
- AI workers are rewarded more than data engineers
More than four out of five AI projects fail, according to RAND research — which is about double the rate of non-AI technology projects, and new US employment data could reveal the reason behind this.
According to DoubleTrack, the main reason is not AI itself, but rather the data it relies on. The main reason AI fails is thanks to bad, unavailable or uncontrolled 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 have yet to understand this, leading them to potential failure down the road. Three out of five AI projects without AI-ready data may be abandoned by 2026, according to Gartner data.
AI fails due to poor data availability
DoubleTrack data showed that US employers posted 111,296 AI/ML roles but only 76,271 data infrastructure roles, leaving a 46% gap between the two widely separated positions. The sales, legal, engineering, marketing and technology sectors all saw greater availability across 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 can be. Marketing was more closely balanced, but there were still 54% more AI roles.
The report also found that AI specialists earn an average of $15,000 more than data engineers, meaning companies pay more to reward workers who can’t deliver without the right foundation in place.
In terms of geography, the highest AI-first states were Mississippi (264%), Missouri (179%), Kansas (176%) and Montana (175%), which are generally perceived as less technologically mature regions, indicating that they may be chasing the hype.
The bottom line is that companies should not measure AI success on speed because this risks skipping important data work.
“The companies most at risk right now are not the slow movers on AI,” the report sums up. “They are the ones who have been hiring aggressively for AI roles without corresponding investments in data quality, governance and infrastructure.”
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