The Biggest Digital Disruption is an AI Skills Shortage

Esther Shein

Kids: listen up. If you want to secure your future, become a data scientist.

As if enterprises don’t have enough headaches as they scramble to deploy digital initiatives, they also must deal with more demand than supply of data scientists with artificial intelligence and analytics skills, which are among the core disruptive technologies driving innovation.

Consider that by 2030, 45 percent of total economic gains will come from AI-powered product enhancements, stimulating consumer demand, with increased personalization, attractiveness and affordability, according to the recent PwC report “Sizing the Prize.”

IBM has projected that by 2020, there will be 364,000 open positions for data and analytics professionals in the U.S. Additionally, job openings for data scientists and similar advanced analytics positions will climb to 61,799 in 2020, the firm says.

“If your business is operating in one of the sectors or economies that is gearing up for fast adoption of AI, you’ll have to move quickly if you want to capitalize on the openings and ensure your business doesn’t lose out to faster-moving and more cost-efficient competitors,” the PwC report warns.

“The big question is how to secure the talent, technology and access to data to make the most of this opportunity.”

Every sector and business will be impacted by AI, the report stresses. “The big question is how to secure the talent, technology and access to data to make the most of this opportunity.”

Demand for data scientists and robotics engineers, among other tech specialists is especially acute, industry observers say. As more enterprises look to deploy AI and machine learning systems, there are certain skills that machines simply won’t be able to replicate, the PwC report notes. These include creativity, leadership and emotional intelligence.

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“It’s important to prepare for a hybrid workforce in which AI and human beings work side-by-side. The challenge for your business isn’t just ensuring you have the right systems in place but judging what role your people will play in this new model,’’ the PwC report states. “People will need to be responsible for determining the strategic application of AI and providing challenge and oversight to decisions.”

Trip planning company Kayak is among the many feeling the pain. “You can apply AI to many problems,’’ said Giorgos Zacharia, chief technology officer, speaking at the recent MIT CIO Consortium. “What’s unpleasant is the talent challenge.”

Gartner has weighed in on the AI talent shortage, too, noting that “Skills are the greatest challenge in AI deployment” for 54 percent of respondents to a recent Gartner Research Circle survey. The firm maintains that management skills must evolve before starting to manage AI-based systems and services. “It's only recently that more managers have come to understand and rely upon advanced statistical techniques that extract ‘signals from noise’ to improve decision making,” according to Gartner Predicts 2018: Artificial Intelligence report. Technical skills — especially for deep learning — remain limited and are still evolving.

Although universities are stepping up and producing graduates with valuable deep-learning skills, Gartner notes that “few of these graduates have the intuition that delivers great foundations for a successful [deep neural network] model.”

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The IBM report takes a more hardline stance, stating that “academia must strive to make data literacy an option, if not a requirement, for every student in any field of study.”

Indianapolis-based venture capital firm High Alpha is forming partnerships with area universities to help deal with the staffing shortage, says Mark Clerkin, a data scientist at the firm. High Alpha officials also talk about what AI is with internal staff and how it can be used, to take away some of the mysteries of machine learning, he says.

With that approach, the firm has started to find SysOps and quality assurance employees and those with math skills who are interested in working on certain components of AI, such as extracting data from files, he says. That way, they can develop talent inhouse.

“So if you think of it as team sport, we can find lot of those players inhouse already,’’ Clerkin says.

They also do speaking engagements, utilize social media and publish some of the work they are doing. “We’re trying to cast a really broad net with the thinking that really smart engineers and data scientists will read it … and think we’re doing cool stuff and want to come work with us.”

 “We’ve hit the tipping point in computational abilities and in the amount of data required to perform these techniques.”

Clerkin believes the AI talent shortage is due to the fact that “we’ve hit the tipping point in computational abilities and in the amount of data required to perform these techniques.” Although a lot of algorithms were developed dating back to the 1960s, he says, “they have just reached the point where they’ve become viable from a business perspective,” due to increasing computational processing power and the amount of data required for initiatives in business and government, he says.

“The ability to do AI and the barrier to entry have gotten so low,” Clerkin adds, “that essentially, it happened so quickly that there weren’t enough data scientists in waiting to do those jobs.”