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Machine learning skills are lacking, CIOs lament

Like it or not, it appears that the continuing skills gap that continues to plague many sections of the software world, including development, testing and more, has found a new victim: digital transformation through the use of machine learning.

A survey conducted by ServiceNow looked at the eagerness of organizations to incorporate machine learning as part of their digital transformation. Mainly, senior executives want to buy into machine learning in order to support faster and more accurate decision making. But the survey polled some interesting numbers that point to what appears to be a significant lack of machine learning skills needed to manage intelligent machines within organizations.

The report shows that 72% of CIOs surveyed said they are leading their company’s digitalization efforts, and just over half agree that machine learning plays a critical role in that. Nearly half (49%) say their companies are using machine learning and 40% said that they plan to adopt.

However, as ambitious as these CIOs are, a serious machine learning skills gap is occurring. Only 27% of those surveyed report having hired employees with skill sets related to intelligent machines, and just 40% of respondents have redefined job descriptions to highlight work with intelligent machines. Furthermore, 41% say they lack the skills to manage intelligent machines, and 47% of CIOs surveyed said they lack the budget for new skills development.

While a good portion of these companies appear to at least be making an effort to find the skills they need to make the most of machine learning, it’s striking that just over half of those CIOs who strongly believe machine learning is essential have managed to acquire the skills they need to make it happen within their organization.

But it’s no surprise that CIOs say they lack the budget for machine learning skills: According to Glassdoor.com, the national average salary in the U.S. for machine learning engineers is $128,000 a year. That’s a serious chunk of change, and it’s probably not enough just to hire one. You also have to consider the upfront cost of machine learning when you add in the money you inevitably will have to spend on software, hardware or various other services your engineers will demand to use.

Again, it seems that a bright sector of the software world suffers again from the ongoing skills gap in the market, one that continues to drive up the cost of engineers and leave those who lack large budgets stuck behind the goliaths. And yet again, it shows that it’s high time to push for better computer science curriculums in schools and also considering the value software development and management talent coming out of developing countries, two things that may help both level the playing field and open up job opportunities for millions of people.

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