UK businesses in chronic need of deep learning skills

AI skills shortage fast becoming a crisis, new research argues

British businesses are in dire need of more deep learning talent and risk falling behind other countries if the skills gap is not bridged, according to new research.

AI firm Peltarion surveyed UK and Nordic firms about the impact of the AI skills shortage on their businesses. 83 percent of the AI decision-makers surveyed said a deep learning skills shortage is hampering business productivity and competitiveness. 49 percent said AI projects had been delayed due to the gap, while 44 percent said the shortage was preventing further investment in the technology.

Organisations around the world are racing to take advantage of deep learning to solve complex data-rich business problems and predict customer behaviours.

The UK government has underlined the importance of AI innovation to the future of the economy. In 2018, the government secured £1 billion investment into AI with the help of US tech giants, EU telecom firms and Japanese venture capital.

The UK is currently third in the world for raising investment in AI after hitting an all-time high in 2018. As it stands, however, UK businesses are at risk of missing out on AI’s benefits and falling way behind global leaders US and China.

To help address the gap, 71 percent of UK businesses are actively recruiting machine learning engineers and data scientists, Peltarion said. However, it added that this could simply be a sign that the skills draught is prolonging the recruitment process.

Exacerbating the problem is a lack of mature projects with which to attract top talent. 45 percent of businesses claim this is the reason they are struggling to hire.

“This report shows that companies can’t afford to wait for data science talent to come to them to progress their AI projects,” explained Luka Crnkovic-Friis, Co-Founder and CEO at Peltarion. “The current approach, which relies on hiring an isolated team of data scientists to work on deep learning projects, is delaying projects and putting strain on the talent companies do have.”

Crnkovic-Friis advocates internal training to discover existing deep learning talent within the business, allowing other members of staff to contribute to AI projects.

“This will reduce the strain on data scientists and lower deep learning’s barrier to entry. We need to make deep learning more affordable and accessible to all by reducing its complexity,” he noted.