How AI Is Creating The Recruiter Skill Set Of The Future

As AI continues to automate the administrative burden of recruiting, there’s a growing consensus that the skills recruiters have today won’t be the same ones needed tomorrow.

The majority of HR leaders predict AI will be a regular part of their workflow within the next five years. This large scale adoption of AI and automation will require recruiter re-skilling to adapt to the new workplace.

In the age of AI, the new recruiter skill set includes skills focused on both people and data.

1. People-focused social skills

When it comes to hiring, we still want to talk to another human and that desire isn’t going to go away anytime soon.

A recent survey by Randstad found that while 91% of job seekers believe technology has made the job search process significantly more effective, 87% also believe it’s made the job search process more impersonal.

As the job search becomes more efficient and automated through AI, recruiters will be relied on more than ever to add the missing piece: a human touch.

Accenture predicts people-focused social skills such as creativity, critical thinking, and empathy will become even more valuable. This prediction is based on the argument that social skills are hard to automate.

Research is showing this is already true. A recent study found that nearly all job growth since 1980 has been in occupations that rely heavily on social skills. Workplace tasks that require social skill tasks grew by 24% from 1980 to 2012, compared to only 11% for math-based tasks.

This people-focused skill set will be required to augment recruiting with a talent advisory function.

Talent advisor

Rob McIntosh calls the talent acquisition leader of the future a talent advisor, which he defines as:

“a trusted recruiting partner to the business who consistently delivers the best candidates in support of the business mission while continually improving the hiring process and candidate experience.”

Among the critical skills of a talent advisor are solving recruiting problems through creativity, using business acumen to get you better outcomes, and influencing hiring managers and candidates.

A major benefit of AI and automation is freeing up recruiters’ time.

Talent advisors will be able to better spend their time on initiatives such as reducing bias in recruiting, analyzing the ROI of their recruiting software tools, and planning strategies for proactive hiring based on future growth and revenue rather than reactive backfilling.

2. Data-focused analytical skills

The demand for data-focused recruiters exists today.

A Visier survey found that 70% of hiring managers believe recruiting departments need to become more data-driven to improve long-term business impact.

how AI is changing the recruiter skillset

This greater alignment means which recruiting metrics an organization considers crucial will depend on what the desired business outcomes are.

The skill set of the data-focused recruiter includes a scientific mindset for collecting and testing data, domain knowledge to properly interpret results, and business acumen to get buy-in to implement their recommendations.

The data-focused recruiter of the present and future will need to become a convincing data storyteller.

Data storyteller

Although recruiting has always been a data-heavy function, this data has mainly been used to create descriptive reports on metrics such as cost of hire.

RJ Milnor, Head of Talent Analytics at Chevron, states today’s analytics experts have progressed from reporting on metrics to advising on analytics.

It’s no longer enough to explain what happened, you need to explain why something happened along with a recommended solution based on your analysis.

For example, the “what” in new-hire attrition is the data on attrition rates and the financial costs of attrition, the “why” is a proposed cause such as new hires leaving because the job isn’t what they expected, and the recommended solution is redesigning the recruitment process to provide a more realistic preview of the job’s responsibilities and the company culture.

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Ji-A Min

Ji-A Min

Head Data Scientist at Ideal
Ji-A Min is the Head Data Scientist at Ideal. With a Master’s in Industrial-Organizational Psychology, Ji-A promotes best practices in data-based recruitment. She writes about research and trends in talent acquisition, recruitment tech, and people analytics.
Ji-A Min

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