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Despite some claims that Artificial Intelligence (AI) is over-hyped, companies continue to invest in Deep Learning to improve their own products and services and make employees’ jobs easier. In order to truly reap the benefits of all AI has to offer, organizations need highly skilled engineers and developers to make sense of it all. But, according to new research findings, talent is becoming a lot harder to find.
To gain a better understanding of where enterprise AI really stands, a survey was commissioned to find out more about industry usage, adoption and barriers to Deep Learning for businesses. While the results suggest that the democratization of AI and Deep Learning applications will continue, as development tools and libraries improve, the shortage of AI-trained professionals will persist. For example, while 54 percent of respondents indicated AI will play a big role (35%) or essential role (19%) in their organization’s future projects, lack of skilled people was the number one bottleneck reported among respondents.
AI talent is scarce, and the increase in AI projects means the pool of available talent will continue to get smaller before the problem gets better. While some organizations may be able to get past the skills gap by hiring developers with strong software capabilities and providing on-the-job training in deep learning and AI, most will find themselves desperate for talent. With that in mind, here are some ways the tech industry can overcome the main barriers to enterprise AI adoption.
Make AI More Accessible
According to the Global AI Talent Report 2018, there are currently 22,000 PhD-educated AI researchers. Another report from Tencent claims that there are over 200,000 active developers in the industry, along with another 100,000 students and academic researchers. While this sounds like a bright outlook, for deep learning to succeed in the enterprise, there’s a need for developers who aren’t PhDs – and we need millions of them, not thousands.
Making Deep Learning accessible to developers and domain experts in other disciplines – who aren’t doctors – is essential for progress. And it’s not all bad news, although a large majority of survey respondents reported that lack of skilled people was a bottleneck, tools for using Deep Learning have become easier to use and the underlying math used in its applications is within reach of most developers. Additionally, training is becoming a bigger focus for organizations, whether it’s provided in-house or led by a third-party company. Looking for solutions beyond the PhD pool is vital to filling the AI skills gap at the rate of technological change.
Prioritize AI Training
Companies like Google provide in-house training programs for software developers interested in incorporating Deep Learning into existing products. Additionally, multi-week training programs within organizations are coming to fruition all over the globe. While it’s clear that organizations are addressing training to a degree, how serious are they about implementing it?
According to the survey results, 75 percent of respondents indicated that their company is using some form of in-house or external training. Nearly half of the respondents said that their company offered “in-house on-the-job training.” While this sounds promising, “on-the-job training” could encompass anything from being paired with an expert to being expected to learn by asking questions on StackOverflow and other public forums. Only 21 percent of respondents said their company offered “formal, in-house” training options, while 35 percent indicated their company went a step further and used either formal training from a third party or from individual training consultants or contractors. In order to ready developers and other professionals for the AI-centric future, training can’t be a second thought, and organizations should make sure it’s a priority in their onboarding process.
Determine Clear AI Hiring Needs
The good news is that, although a majority of the survey respondents reported a lack of skilled people as a bottleneck, the gap between PhDs and the AI-trained developers needed should be a much bigger bottleneck than it is. The bad news is, this is likely a product of where most organizations are in the hiring process.
When asked if they’ve hired specifically for Deep Learning applications, only 11 percent responded affirmatively. If only 28 percent of respondents are currently using Deep Learning, but 54 percent believe it will play a large or essential role in the future, it’s likely that many organizations simply haven’t started hiring for these positions yet. In turn, if most companies are thinking about AI projects but haven’t yet started, the talent shortage will get much worse before it gets better. With talent scarce, for most, it doesn’t make sense to invest in hiring an AI-specific role right away. It makes more sense to hire developers with good software skills and expect them to learn on the job through constant training.
A large majority of organizations are bracing for the seismic AI shift already taking hold – only 8 percent of respondents believe that deep learning will not play a role in their future projects- but the path towards real and practical applications of Deep Learning still remains unclear for many. Organizations who want to truly leverage AI’s great potential must put a focus on finding and nurturing AI talent – the future of business depends on it.