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TDWI Upside - Where Data Means Business

Recruiting AI Talent

Finding the right candidates with AI skills can be tough. These tips can help you find and land the right people.

The increasing use of artificial intelligence in business has led to a growing skills shortage. Practitioners are snapped up from academia and move constantly between firms. A December 2017 Ernst & Young poll found that over half (56 percent) of senior AI professionals believe a lack of talent is the greatest barrier to AI implementation in business.

For Further Reading:

Hiring Trends in Artificial Intelligence

Working AI into Your Enterprise Initiatives

When You're Ready for AI But Your Company Isn't

AI recruiting can be complicated. Demand for these skills, particularly at the advanced level, remains diverse. Different firms require different types of skills as they implement AI, advanced automation, and analytics.

The skills market is constantly changing, and you should develop a feel for what is happening on the ground as you lay recruitment plans. To sort out some of these issues we spoke with Chris Oakes, Metro Market Manager for corporate recruiter Robert Half Technology. Oakes has been focused on high tech industries for the past four years in a territory that includes California.

Filling the Skills Gap

"We are seeing a strong demand for data scientists with a focus on statistics, machine learning engineers, AI engineers, and big data/analytics engineers," says Oakes. "Some additional areas of related focus are computer vision, used for facial recognition; natural language processing (NLP), which monitors text and voice; and deep learning. All of these involve big data analytics looking for pattern recognition, trends, and regression. These skills are highly sought after and companies are trying hard to hold tight to their top talent."

In many respects, the current AI skills gap is like previous shortages that have occurred whenever new technologies arose in Silicon Valley. Keeping up to date with these trends is important in creating effective recruitment strategy.

"This is always a push/pull scenario," says Oakes. "DevOps, for example, when it was very new four or five years ago, saw a shortage of candidates capable of doing both sides. Now these roles are easier to fill because the candidates have been learning these skills as more companies adopt this model. AI is still very new, so there is a shortage of candidates. Some organizations are seeking Ph.D.s and experienced data scientists with AI experience, making the search especially difficult."

A New Set of Skills

The skill set requirements for AI are not stable or well known because AI has only recently become a common business tool. Issues such as the most appropriate skill level, training, and background are not yet consistent. There are also wide differences in requirements between companies, depending on business size, industry, and potential business advantage.

AI is a complex and developing field with many platforms, and applications suitable for lower-level skills are only beginning to emerge. It still requires highly knowledgeable and skilled people at the top to create strategies and implement them, and this frequently demands Ph.D.-level applicants or those heavily skilled from years of direct industry participation.

For recruiters, skilled candidates are definitely in the driver's seat. "The most difficult part in finding talent for open roles is matching the open roles with what the candidates want," says Oakes. "Talented professionals with highly sought-after skills can pretty much write their own ticket. In addition to salary requirements, each candidate is looking for specific elements in the roles they choose, including industry, team environment, and all other components contributing to the company's culture."

With AI developing across numerous industries, the choices for skilled candidates are vast. Positioning, benefits, and salary can all be competitive vectors. Above all, candidates need to know that their new employer will support their skills and provide opportunities for them to progress within their field. Companies that are unable to provide a solid career path will be at a disadvantage. This tends to give larger firms with established data analysis teams a definite edge in recruitment.

AI for AI Recruiting

One area of interest is the use of AI in recruitment itself. The complex nature of candidate selection and fitting seems an ideal area for an AI intervention. Recruitment has grown to embrace automatic applicant tracking systems capable of handling the thousands of resumes companies receive daily. Screening is increasingly automated -- many evaluations take place without a human's touch.

Recruitment is a microcosm of the business problems currently driving AI and machine learning across industries as data, technology, and global connections create a surge in processing and evaluation demands. In a complex environment with millions of potential applicants, companies need to be able to find ideal candidates who can fully meet objectives, fit into corporate culture, and thrive.

"AI is everywhere now for recruitment -- in the tools used to find candidates with the skills the organizations are looking for," says Oakes. "AI will likely continue to impact industry, especially as it relates to getting more talent into organizations looking to boost AI initiatives."

About the Author

Brian J. Dooley is an author, analyst, and journalist with more than 30 years' experience in analyzing and writing about trends in IT. He has written six books, numerous user manuals, hundreds of reports, and more than 1,000 magazine features. You can contact the author at [email protected].

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