Artificial Intelligence
Article

Steps Enterprises Can Take To Build A Dependable AI Talent Pool

by
Forbes
February 23, 2022
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Summary

As AI establishes a strong foothold across industries, demand for AI talent is exponentially rising. However, the growing AI talent gap remains a persistent issue. In this article, Forbes lays out steps companies can take to build a dependable AI talent pool.

Steps Enterprises Can Take To Build A Dependable AI Talent Pool

As AI establishes a strong foothold across industries, demand for AI talent is exponentially rising. However, the growing AI talent gap remains a persistent issue. As evidenced in the results of a recent O’Reilly survey, the lack of skilled people and difficulty hiring remain the top two AI challenges presented by enterprises. Add to this the issue of talent drain, and enterprises have a bigger problem on their hands.

Furthermore, depending on the industries they function in, their in-house capabilities and the scope of their AI-led transformation agendas, the need and urgency for AI talent varies significantly among businesses. Fortunately, there are specific steps enterprises can take to curb the AI talent challenge issues.

Step 1: Determine the priorities and skill sets that will transform your business.

AI is a vast field and there isn’t just one area of expertise that will ensure cross-industry applications of the technology. In fact, AI expertise can span business functions and range from data analytics and engineering to research and data annotation. According to research results, companies experience skill shortages most in machine learning modeling and data science (52%), understanding business use cases (49%) and data engineering (42%).

As a result, the first step enterprises need to take to address the skill gap in AI is to determine the specific functions they’re looking to urgently automate or add efficiencies to using AI. For example, retail businesses should identify if they need to fill data scientist roles to help build better demand prediction models or if they currently lack engineers needed to build automation tools to carry out seamless customer interactions.

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