- AI can’t exist without data – so why is the US hiring more AI specialists than data engineers?
- Less tech-mature regions are likely the worst culprits, jumping on the hype
- AI workers are being rewarded more than data engineers
More than four in five AI projects fail, according to RAND research – which is around double the rate of non-AI tech projects, and new US employment data could reveal the reason behind this.
According to DoubleTrack, the root cause isn’t AI per se, but rather the data it relies on. 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 suggests that many enterprises are yet to understand this, leading them to potential failure down the road. Three in five AI projects without AI-ready data could be abandoned by 2026, per Gartner data.
AI is failing because of poor data readiness
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 distinct positions. Sales, legal, engineering, marketing, and technology sectors all saw greater role 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 closer in balance, 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 that firms are paying more to reward workers who cannot deliver without the right foundations 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 tech-mature regions, therefore indicating 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 businesses most at risk right now aren’t the ones moving slowly on AI,” the report summarizes. “They’re the ones who’ve hired aggressively for AI roles without corresponding investment in data quality, governance, and infrastructure.”
Follow TechRadar on Google News and add us as a preferred source to get our expert news, reviews, and opinion in your feeds. Make sure to click the Follow button!
And of course you can also follow TechRadar on TikTok for news, reviews, unboxings in video form, and get regular updates from us on WhatsApp too.
