Organizations can’t wait for AI talent to come knocking

A shortage of specialists hindering progress. Businesses must rethink their approach.
7 February 2020 | 14 Shares

Deep learning skills are lacking in the business. Source: Shutterstock

When we talk about the movement towards artificial intelligence, we tend to assume organizations’ eventual adoption will be led by a fresh team of newly-appointed experts — our co-workers of tomorrow will be Python developers and data scientists. 

Indeed, the swelling pressure to invest in AI means there’s now greater demand than ever for those types of skillsets. But there’s not an endless supply of candidates, and that’s getting some organizations worried. 

Based on a survey of 350 IT leaders actively involved in organizations’ AI initiatives, a new study by Peltarion revealed that 83 percent are concerned that a lack of deep learning skills is impacting their ability to compete. 

These companies are exclusively focusing on recruiting data scientists — 71 percent are actively scouting for this role, in particular, but a lacking in-flow of experienced candidates is causing delays to initiatives. 

The need for specialist skills is presenting a “major barrier” for AI projects among nearly half (49 percent). 

Combating this problem requires a rethink, said Peltarion Co-founder and CEO, Luka Crnkovic-Friis, and gunning for competitive talent might not be the most effective or sustainable approach for ongoing progress.

“This report shows that companies can’t afford to wait for data science talent to come to them to progress their AI projects,” said Crnkovic-Friis. “The fact is, many organizations are already starting to lose their competitive edge by waiting for specialized 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,” he explained. 

Instead, companies should look at transferable talent under their noses: Organizations won’t advance business-wide AI initiatives if they’re relying on attracting external specialists to sit and lead projects in siloed teams. 

By fostering AI talent on the inside, businesses can promote familiarity with the technology across relevant departments which, the report states, could have a “serious impact” on the success of AI strategies. That could also help to alleviate ever-growing workloads from the pureplay specialists themselves.

“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,” said Crnkovic-Friis.  

Making AI “affordable and accessible” is key to its uptake, and that’s less about investing in technology and new talent, and more about investing in the training and awareness initiatives in-house. 

“By operationalizing deep learning to make it more scalable, affordable and understandable, organizations can put themselves on the fast track and use deep learning to optimize processes, create new products and add direct value to the business,” Crnkovic-Friis concluded.