- Without data, artificial intelligence cannot exist. So why does the US hire more AI specialists than computer engineers?
- The less technologically advanced regions are probably the biggest culprits of this upheaval.
- AI workers are paid more than computer engineers
This is why more than four out of five AI projects fail RAND research – That’s about twice as many as non-AI technology projects. New US employment data may reveal why.
That’s why double trackThe reason is not the AI itself. The data on which it is based. The main reason AI fails is due to poor quality, unavailable or uncontrolled data, not weak models. In fact. Nearly two in three organizations (63%) do not trust AI to manage their data.
And hiring trends to date indicate that many companies still don’t understand this. Leading to potential failure. According to data from Gartner. Three out of five AI projects without AI-ready data could be abandoned by 2026.
AI fails due to insufficient data input
DoubleTrack data showed that US employers advertised 111. 296 AI/ML positions. Only 76. 271 positions in data infrastructure. Leaving a 46% gap between the two very different positions. The sales. Legal. Engineering. Marketing.
Technology industries have seen increased availability of AI. ML roles.
For example. In sales. AI roles outnumbered data roles by 232%. Risky given the complexity of CRM data. is is by which. More balanced is was by Marketing. Still 54% more AI features.
The report also shows that AI specialists earn an average of $15 is were by there. 000 more than data engineers. Meaning companies pay more to reward employees who don’t meet their expectations without a good reason.
Geographically.
The most AI-focused states were Mississippi (264%). Missouri (179%). Kansas (176%). Montana (175%). Generally considered less tech-savvy regions is are by which. Suggesting they are chasing expectations.
Ultimately. Companies should not measure AI success based on speed. As this risks losing important data.
“The companies most at risk today are not those moving slowly towards artificial intelligence. ” the report concludes. “They are the ones aggressively recruiting for AI-related positions without investing enough in data quality, governance and infrastructure.”
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