Organisations at the forefront of AI are becoming increasingly aware of how the deep learning skills gap is becoming a pressing issue
Artificial intelligence innovator Peltarion has released a report showing that 83% of AI decision-makers believe that the skills shortage in deep learning is impacting their ability to compete at the top.
The report was produced from a survey of 350 CIOs or IT leaders from across the UK, Iceland, Sweden, Norway, Denmark, and Finland. Each of the surveyed decision-makers had direct responsibility for managing the direction of AI at companies with over 1,000 employees.
Although 99% of decision-makers are planning to include research and development budgets for deep learning over the coming years, Peltarion’s research shows that many organisations are struggling to attract the right talent in order to support ambitious artificial intelligence projects.
Deep learning has a people problem
The research confirms that new approaches are needed, due to AI decision-makers relying heavily on hiring data science talent to plug the skills gap. At present, 71% of decision-makers are recruiting to address their skills shortage, but hiring from an outlying pool of talent is causing delays to projects, with almost half (49%) of respondents confirming that fact.
44% of those surveyed believe that the need for more specialist skills, deep learning being perceived as the most conceptually complex, is ultimately blocking the path toward deep learning investment. The report shows that even though deep learning is growing in popularity, most IT specialists, data or computer scientists do not possess the skills required to lead a deep learning program.
Today’s AI workforce is stretched thin
According to 49% of those surveyed, the skills shortage is delaying projects. Efforts are becoming more and more focused on constructing increasingly specialised teams of data scientists, and less about decentralising the workload. 93% of respondents said that their data scientists were becoming overworked and that inbound talent is more selective about which jobs to take on.
The report states that “candidates who are on that market are in a position to be selective about where they take their skills and will often avoid companies without a mature deep learning program already in place.”
Businesses need deep learning preachers
The respondents agreed that deep learning programs require company-wide unification as they ultimately affect processes across the board. 96% said that businesses need an AI evangelist to instil the importance of such processes, and to educate employees, from engineers to managers, about in-house AI practices.
Key findings from the survey
- 84% said their company leaders worry about the business risks associated with failing to invest in deep learning
- 83% said that a shortfall of deep learning skills is impacting their ability to compete
- 71% of AI decision-makers are actively recruiting to plug the skills gap
- 49% said that the skills shortage is causing delays to projects
- 44% believe the need for specialist skills is a roadblock to more investment in deep learning
- 45% said they are finding it difficult to attract and hire talent because they lack a mature AI program
- 93% said their data scientists are over-worked because there is no one to share the workload with
“This report shows that companies can’t afford to wait for data science talent to come to them to progress their AI projects.”
In a press-release, Co-founder and CEO of Peltarion Luka Crnkovic-Friis 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. The current approach, which relies on hiring an isolated team of data scientists to work on deep learning projects, is delaying projects and putting a strain on the talent companies do have.”
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Crnkovic-Friis said that in order to solve the deep learning skills gap, companies must utilise transferable talent which might already exist in-house. “Deep learning will only reach its true potential if we get more people from different areas of the business using it,” he said, “taking pressure off data scientists and allowing projects to progress.”
He concluded by saying that companies must make deep learning more affordable and accessible 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.”