4 AI Recruiting Strategies for a Better Candidate Journey

As talent acquisition pros turn to AI recruiting to attract candidates and save time and money, it’s going to become harder to craft job postings and candidate communications that grab the attention of top talent.

Kat Kibben, the CEO of job post writing company Three Ears Media, explained why in a presentation during the most recent BambooHR Virtual Summit: “Using AI can create a better first draft than some of us do … starting with a blank document. The challenge here is that everyone’s using the same data set — the internet. And inevitably a lot of people are using the same AI.”

“Standing out by doing the same thing as everyone else means you’re not actually going to stand out,” she said.

Other factors that can disrupt AI recruiting efforts:

• Research indicates that attention spans are getting shorter. “The average person is only reading 250 words before you lose their attention. … Most recruiting messages are a lot longer than 250 words,” Kibben said.
• Because the average person sees over 3,000 messages a day, emails that follow a generic AI-generated template could resemble other unimportant messages in a candidates’ inbox and end up being deleted, and
• Achievers Workforce Institute survey research indicates that “emotional salary” factors, such as lack of work recognition and dissatisfaction with company culture and values, are influencing people to change jobs. “You can’t just ask the AI to tell someone about [your] team,” she said. “Your company is unique and that means you have to be really good at telling the [company] story. … We can’t just use these generically good templates.”

The key is knowing when to step in as the human editor for the AI recruiting tools you use. Your team may need training to become proficient AI editors. Good places to start are the Premier Learning Solutions on-demand workshop “AI in Hiring: Navigating Risks & Complying with EEOC” and the HRMorning guide “How AI Technology Transforms Recruiting & Hiring.”

AI Recruiting: Searching Across Platforms, Emailing

Kibben encouraged using more than one AI tool for compiling candidate contact information and creating a draft of your candidate sourcing list. She offered this AI recruiting prompt for talent sourcing: “Give me 15 communities where software engineers spend time.”

When it’s time to email candidates you’re interested in, human input makes a big difference in the candidate experience. “Too many recruiters take advantage of the [AI-generated email] template and do zero editing. … What you say has to be better than what the machine tells you to send,” she said.

Essential elements of your emails to candidates — whether it’s for sourcing, first response, interview scheduling, check-ins or making an offer — include:

• A direct call to action
• Details on what you need them to do next and a timeline to do it, and
• A transactional call to action, such as “If you have any questions, text me at ____.” Offering that personal contact option is, in Kibben’s words, “the ultimate move when you’re using automation to make it human.”

AI Recruiting and Interviews

Kibben advised not putting AI in charge of interviewing, but instead utilizing it to write interview questions. It helps train hiring managers to ask better questions, she said.

A prompt to try out: “Write me 15 interview questions based on this job posting. Questions should help me assess their technical skills.” Then copy and paste the text of the job posting. This is particularly helpful for quickly coming up with questions about a candidate’s specific technical skills.

Bonus tip: Creating a landing page in the careers section of your company’s website with some sample job interview questions builds trust with candidates.

Avoiding Bias When AI Recruiting

“The thing we need to be thinking about when we invest in AI for screening tools is the data we’re going to use to screen people in and out,” Kibben said.

When screening candidates, Kibben’s AI recruiting best practices include asking questions that require mostly yes/no answers and ensuring screening questions address the most common reasons why people in low-retention roles quit. Some examples: “Can you pass a drug test?” and “Are you available to work nights or weekends?”

And if you’re subject to anti-bias state employment laws that apply to screening questions, make sure your questions are in compliance.

Speaking of bias when AI recruiting, unless absolutely necessary for the position, you should try to steer clear of questions about:

• years of experience (potential age bias)
• education, and
• skill keywords (“‘Expert user’ might mean something different to you than it means to me,” she said).

For hiring managers that insist on experience and degree requirements, Kibben suggested asking them contrast questions, such as:

• “What does someone with 10 years’ experience know that someone with seven might not?”, or
• “Is college the only place these skills can be learned?”

Job Postings and AI

Writing job descriptions may seem like a no-brainer task to totally turn over to AI. But remember its learning starting point is the descriptions that are already online, and as Kibben put it, “Most job postings suck.”

Until AI tools fully learn about your company brand, culture and employment needs, fine-tuning your job postings while AI recruiting is going to require discernment on when to consult a human (the hiring manager) and when you can just ask AI. Some examples:

• “What are alternative job titles for ‘data specialist that specializes in machine learning?'” — ask AI.
• “What must a new hire know what to do on day one?” — ask a hiring manager.