Attracting and retaining high performance talent is difficult no matter what the discipline. However, for positions that require extensive technical training such as those related to data science and artificial intelligence (AI) development, many hiring managers report that the competition is especially stiff. In fact:

• a 2019 Deloitte survey found that 21% of companies surveyed (globally) reported having a “major” or “extreme” shortage in AI talent
• A 2020 survey of about 1,000 executives by RELX found 39% said they were not using AI because of “a lack of technical expertise.

However, things are not always as they seem. An extensive research report released by the Center for Security and Emerging Technology found that demand for AI, data analytics and computer science positions from 2015-2019 did not outpace the job market as a whole. In addition, the evidence showed “that existing talent development pipelines are working to meet demand.” In fact, there might actually be a surplus of some technical skill sets such as software developers and data scientists 

That being said, they did list a number of caveats: 

• Demand for such talent is expected to grow in 2021 and beyond so fears surrounding talent shortages are not unfounded
• The AI/data workforce is geographically concentrated around major U.S. cities 

Identifying, recruiting and delighting data scientists and engineers is no doubt challenging. However, there are a number of things companies can and should do differently in order to ensure they have the best-fit technical talent in place to thrive in the digital economy. While flexible hours and remote work options are great options for some skilled workers, inspiring them to do their best work and engaging them for the long-haul is another story. 

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You Got to Pay to Play

Salaries for data and AI-related positions can range anywhere from the mid-double digits to the low seven figures, depending on the company and experience level of the employee. Afterall, these people are highly trained experts who often possess advanced degrees. 

With that in mind, it is no surprise that the number one reason skilled data and AI workers leave is salary. If you’re not paying a competitive wage, they can fairly easily find another company who will, especially in larger cities.  

Empower Innovation & Data Literacy

All too often, data and AI experts are confronted with unrealistic expectations about what data can and cannot do. Compounding this is that many enterprise data groups are sequestered off into their own siloes or CoEs and are not properly integrated with the rest of the business. As a result, aligning data analytics strategies with business priorities is almost impossible.

Not only do major data science projects such as advanced analytics require extensive data infrastructure and tools, they also require a certain level of data literacy amongst the user in order to succeed. In other words, if the internal customer does understand how to action data-driven insights, it will all be for naught. 

To combat these issues, many companies, such as Netflix, embed data scientists into the business units. This allows them to keep pace with business needs as well as promote data literacy on the ground-level.  

In addition, companies, such as DNB Bank, AirBnB and Cisco have launched formal data literacy training programs that seek to build “citizen analysts” throughout the entire organization. Not only does this approach help ensure AI-powered insights are effectively used, it also help organizations identify the next wave of data talent. 

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