Deep crisis for deep learning?

Peltarion has published the findings of a survey of AI decision-makers examining what they see as the impact of the skills shortage, and suggestions on how to overcome it. The research, ‘AI Decision-Makers Report: The human factor behind deep learning’, presents the findings of a survey of 350 IT leaders in the UK and Nordics with direct responsibility for shepherding AI at companies with more than 1,000 employees.

The report finds that many AI decision-makers are concerned about the business impact of the deep learning skills shortage. 84% of respondents said their company leaders worry about the business risks of not investing in deep learning, with 83% saying that a lack of deep learning skills is already impacting their ability to compete in the market. These companies are exclusively focusing on recruiting data scientists (71% of AI decision-makers are actively recruiting to plug the deep learning skills gap), and this is already impacting their ability to progress with AI projects:

• Almost half (49%) say the skills shortage is causing delays to projects
• 44% believe the need for specialist skills is a major barrier to further investment in deep learning
• However, almost half (45%) say they are struggling to hire because they don’t have a mature AI program already in place
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“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. 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,” explains Luka Crnkovic-Friis, Co-Founder and CEO at Peltarion. “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. 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.” 

Less than half (48%) of respondents said they currently employ data scientists who can create deep learning models, compared to 94% that have data scientists who can create other machine learning models. This shortage is having a direct impact on teams: 93% of AI decision-makers say their data scientists are over-worked to some extent because they believe there is no one else who can share the workload. However, with the right tools, others can make a serious impact on AI projects.

“Organisations need to move projects forward by bringing on existing domain experts and investing in tools that will help them input into AI projects. This will reduce the strain on data scientists and lower deep learning’s barrier to entry,” concludes Crnkovic-Friis. “We need to make deep learning more affordable and accessible to all by reducing its complexity. By operationalising deep learning to make it more scalable, affordable and understandable, organisations can put themselves on the fast track and use deep learning to optimise processes, create new products and add direct value to the business.”