Written by

Bernard Marr

Bernard Marr is a world-renowned futurist, influencer and thought leader in the fields of business and technology, with a passion for using technology for the good of humanity. He is a best-selling author of 20 books, writes a regular column for Forbes and advises and coaches many of the world’s best-known organisations. He has over 2 million social media followers, 1 million newsletter subscribers and was ranked by LinkedIn as one of the top 5 business influencers in the world and the No 1 influencer in the UK.

Bernard’s latest book is ‘Business Trends in Practice: The 25+ Trends That Are Redefining Organisations’

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The AI Skills Crisis And How To Close The Gap

2 July 2021

Synopsis:

Now that the potential of artificial intelligence (AI) to solve business problems is accepted more than it’s denied, companies are excited to begin implementation. The biggest impediment to AI implementation is finding skilled AI talent. We look at why there’s an AI skills crisis and how we can close the gap.

Now that nearly every company is considering how artificial intelligence (AI) applications can positively impact their businesses, they are on the hunt for professionals to help them make their vision a reality. According to research done by Glassdoor, data scientists have the No. 1 job in the United States. The survey looked at salary, job satisfaction and the number of job openings. If you have recent experience looking for AI specialists to join your team, it’s quite clear that we’re facing an AI skills crisis. In order to move AI projects from ideation into implementation, companies will need to determine how to close the AI skills gap so they have experts on their team to get the job done.

Factors that contribute to the AI talent shortage

One report suggested there about 300,000 AI professionals worldwide, but millions of roles available. While these are speculative figures, the competitive salaries and benefits packages and the aggressive recruiting tactics rolled out by firms to recruit AI talent would suggest the supply of AI talent is nowhere near matching up to the demand.

As the democratization of AI and deep learning applications expands—possible not just for tech giants but now viable for small- and medium-sized businesses—the demand for AI professionals to do the work has ballooned as well. The C-suite and corporate management’s excitement for AI’s various applications is building and then once they have bought into the concept (which is happening much more rapidly), they want to make it real right away.

The 2018 “How Companies Are Putting AI to Work Through Deep Learning” survey from O’Reilly reveals the AI skills gap is the largest barrier to AI adoption, although data challenges, company culture, hardware and other company resources are also impediments. These results parallel a recent Ernst & Young poll that confirmed 56% of senior AI professionals believed the lack of qualified AI professionals was the single biggest barrier to AI implementation across business operations.

Another reason for the AI skills crisis is that our academic and training programs just can’t keep up with the pace of innovation and new discoveries with AI. Not only do AI professionals need official training, they need on-the-job experience. Therefore, there aren’t enough experienced AI professionals to step into the leadership roles required by organizations who are just beginning to adopt AI strategies into their operations.

How to close the AI skills gap

One of the ways to address the AI skills gap is to increase resources for digital, math and technical education such as the UK Government announced in its 2017 Industrial Strategy whitepaper. While skills acquisition for younger generations will help in the future, a singular focus on just driving more students into computer science will not solve the issue—the number of computer science graduates in the UK would need to increase 10X to meet the demand.

Many of the tech giants such as Google and Amazon are investing internationally to expand their talent pool. Google Brain Toronto is a facility dedicated to AI and Amazon has an AI-focused lab near the University of Cambridge in England and plans to build a similar facility in Barcelona.

Until there are new graduates to step into AI roles, many companies can focus now on retraining and upskilling current employees. According to a Forbes survey, 63% of companies are now providing in-house data analytics training. The future of work will require employees to be agile and change their skills over time.

It’s challenging to find a person who has the appropriate hard skills and technical know-how as well as the soft skills such as being “highly collaborative” that would make them successful in a high-level AI position that can take their tech skills and apply them to business problems. Recent grads typically don’t have the business acumen and leadership experience that is required to manage AI implementation throughout an organization. As AI matures in organizations, it will be imperative that diverse personnel and thinking is integrated.

Some AI positions could be filled by those with mathematics or physics degree who get trained on AI specialties. Deeplearning.ai, launched in collaboration with Coursera, is hoping to bring AI to a mass audience and help people build their careers in AI.

Could the solution for the AI skills gap be solved by AI? Possibly.  Google is developing AutoML, AI that can actually create other AI systems. If this initiative is successful it could be very powerful for mid-sized businesses that are unable to compete with tech giants for top-tier talent.

Technology has a long history of embracing collaboration across borders and gaining advantage from insights allowed due to using open-source software. Another way to improve the AI skills crisis is to tap into this decentralized network of data professionals.

There’s clearly not one way to solve the AI skills crisis. It will require a multipronged approach over a period of time and the approach will evolve over time. While there is no easy solution, all companies who plan to use AI today or in the future must consider how they will attack the AI talent gap.


Data Strategy Book | Bernard Marr

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