When artificial intelligence took a giant leap forward in 2022, industry leaders worried this new innovation would eliminate people from the people business. Now, leaders see the potential for AI to relieve the unyielding recruiting workload and enhance companies’ ability to hire top talent — with caution.

Staffing firms need to achieve a balancing act. According to a Capterra survey of 3,000 job seekers from 12 countries, 38% of respondents will turn down a job offer if they feel AI is used too much. See? The human touch still matters.

With that in mind, here are a few AI use cases that can help you streamline the hiring lifecycle without alienating your talent pool.

Sourcing and Screening

Automation has empowered recruiters to comb job boards and social media for exceptional talent to import to their ATS, but AI sourcing and screening tools are taking this innovation even further.

AI empowers your hiring team to get a sense of the top contenders faster. Natural language processing features can:

• Summarize key selling points from resumes and LinkedIn
• Cross-compare candidates to visualize their differences
• Rank candidates on different parameters

Ranking candidates must be done cautiously to minimize biases and ensure that critical qualifications aren’t oversimplified or overlooked. AI tools help to improve search parameters and offer recruitment analytics that show how you to refine your search criteria. However, you should pick an AI solutions provider that is transparent about their process  and methodology so you understand and limit the risk of potential biases.

Candidate Engagement

Though recruiters see the best results by building personal connections with candidates, some aspects of that process can and should be automated. AI can automate parts of the candidate engagement process, such as:

• Initial outreach
• Follow-up reminders
• Information sharing

Automation frees up resources, allowing recruiters to focus on building personal connections in later stages, where human interaction is most valuable.

Some AI integrations pull information from your ATS to add specific details to engagement campaigns, making communications feel tailored. Prompting AI to play with voice or address certain generational demographics crafts effective messages faster than any human could.

From there, candidate engagement is a matter of calibration. However, a human touch is still crucial, as candidates have a low tolerance for the poor implementation of AI (e.g., generic messages or total communication automation).

Interviewing

While candidates might appreciate a dialogue deeper into the interview process, the majority is not opposed to AI for screening. The Capterra survey found 61% of respondents were comfortable with algorithms conducting screening interviews. Giving candidates an option between AI and human screenings helps optimize your interviewing process.

AI-powered interviewing tools also accelerate the hiring decision. Generative AI creates incisive interview questions to reveal more details about specific skillsets, closing gaps in your questions and enhancing the depth of your interviews. Some AI tools transcribe and analyze the conversation, emphasizing the most relevant talking points. Instead of taking notes, hiring managers stay engaged in the conversation and extract more valuable insights from the candidates.

Onboarding

Once you’ve decided to hire candidates, AI can streamline your onboarding communication. AI embedded in your HR platform can send offer messages, initiate credentialing and employment checks, schedule start dates and handle other automated steps. 

Organizations could use generative AI to produce onboarding documents tailored to specific roles, departments and divisions. They can design chatbots to answer employee questions about responsibilities, procedures, performance metrics, benefits and more, providing quick way for people to get answers when their managers aren’t available.

Though AI enhances the recruiting process, your organization shouldn’t leave technology to its own devices. AI is only as good as its training data, which could contain unintentional biases.

You need to evaluate and determine how AI analyzes data and comes to conclusions. That way, your team learns to identify situations where AI makes the wrong assumptions and override the tool’s recommendation.

Keeping people as the final deciders in the process helps make sure your organization gets the best out of AI and your workforce. When AI is done right today, you can create a more ethical and efficient tomorrow.