New research has revealed that nearly half (49 per cent) of artificial intelligence (AI) projects are being held back by a lack of deep learning skills.

Research among 350 chief information officers and senior IT makers from the UK and the Nordic region, commissioned by operational AI firm Peltarion, found that 83 per cent of AI decision-makers thought the lack of skills in areas such as data science, machine learning and AI was impacting their business’ ability to compete in the market; with 49 per cent saying it is causing projects to be delayed.

The survey found that 99 per cent of respondents are planning to include deep learning in their research and development (R&D) budgets over the next three years, but many are struggling to find the right talent.

Less than half (48 per cent) of respondents said they currently employ data scientists who can create deep learning models, compared to 94 per cent that have data scientists who can create other machine learning models.

A substantial majority (93 per cent) of AI decision-makers said their data scientists are over-worked to some extent because they believe there is no one else who could share the workload, placing added strain on existing teams.

As a result, 71 per cent of AI decision-makers are actively recruiting for staff with deep learning skills, while a focus on hiring data scientists alone slowing progress for deep learning projects. However, almost half (45 per cent) said they are struggling to hire because they don’t have a mature AI program already in place.

Meanwhile, 44 per cent believe the need for specialist skills is a major barrier to further deep learning investment, creating a vicious cycle.

Luka Crnkovic-Friis, co-founder and chief executive at Peltarion, said: “This report shows that companies can’t afford to wait for data science talent to come to them to progress their AI projects – the fact is, many organisations are already starting to lose their competitive edge by waiting for specialised data scientists.

“In order to solve the deep learning skills gap, we need to make use of transferrable talent that can be found right under companies’ noses,” he added. “Deep learning will only reach its true potential if we get more people from different areas of the business using it, taking pressure off data scientists and allowing projects to progress.”

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