Here’s Our In-Depth Guide to AI in Recruitment (Pros, Cons & How to Get Started)

AI is rapidly taking root in recruitment. 

According to our Hiring Humans report, 79.4% of business leaders see AI as a top priority for talent acquisition.

These numbers underscore AI’s growing prominence in recruiting. But if not implemented properly, AI can degrade your hiring process, making it more complicated, biased, and inefficient.

In this Willo article, we’ll explore the current state of AI in recruiting, its pros and cons, and how to safely integrate it into your recruitment strategy.

Current State of AI in Recruitment 

HR spending on AI recruiting tools skyrocketed in 2020, the year OpenAI released GPT-3 and kicked off the current AI craze. Since then, it has been climbing steadily and is set to grow from $617.5 million in 2024 to over $1 billion by 2030.

Source: AI Recruitment Market Research Report

At the heart of this growth is the mass adoption of AI by recruiters. Recent studies have found that essentially every Fortune 500 company uses AI in their hiring process… though not always in the most ethical ways (more on this later).

So, where does AI fit into the recruitment process? Almost anywhere in the recruiting cycle, from candidate sourcing to the final offer and onboarding. The potential is limitless, but here’s a quick rundown of areas where AI is typically applied:

AI application

Example

Data-intensive tasks

AI can scan and analyze thousands of interview responses in minutes to find the best fit.

Pattern recognition and predictive analysis

You can use AI to evaluate work history, skills, and other details to help match candidates to roles where they’ll excel (even if they hadn’t applied for that role initially).

Automating repetitive tasks

AI can handle scheduling interviews, sending follow-up emails, and managing interview feedback.

Candidate communication

AI chatbots can interact with candidates, answer questions, provide feedback and updates, and conduct preliminary interviews.

On the flip side, candidates are using AI, too. According to an Arctic Shores report, 72% of students and job candidates use AI regularly, and 47% believe companies should allow them to use AI in their job applications. More than 30% wouldn’t consider working for a company that bans generative AI (GenAI).

Advantages of AI in Recruitment 

Reduced time to fill

AI can drastically reduce the time spent on recruiting tasks. 

According to a LinkedIn study: 

• 57% of recruiters find it much quicker and easier to write job descriptions with AI 
• 35% say it speeds up communication with candidates
• 42% report that it takes mundane tasks off their plates

These are just a few ways AI can help you save time and resources for recruitment. 

Minimized human bias

Unconscious bias is a big concern in recruitment. While AI isn't completely free from bias, it can often reduce the impact of human biases by providing more consistent, data-driven assessments.

Unlike humans, AI doesn't have emotional responses or personal preferences—it does what it’s trained to do. When properly trained and implemented, AI can help ensure that candidates are evaluated based on fair, inclusive criteria rather than subjective factors.

Enhanced candidate experience

AI can also improve your candidate experience. For example, AI tools like chatbots and personalized job recommendation engines make the process smoother by providing quick responses and relevant opportunities. 

Plus, by using AI to automate routine, low-level tasks, you free up your recruiters to focus on what humans do best—connecting with candidates on an empathetic, emotional level and thinking through complex problems. 

The result is a better experience for all involved.

Challenges of AI in Recruitment 

Potential for bias

AI systems are great at reducing human bias, but they also have biases of their own that need to be managed. AI (in its current state) uses patterns in data to make decisions. If a data set has a biased pattern, the AI will replicate that bias in its decisions.

Governments worldwide are recognizing and attempting to address this issue. For example, New York City recently introduced legislation around automated employment decision tools (AEDT) like AI. If you want to use an AEDT in NYC, you need to:

• Conduct or source a bias audit for the tool
• Post a summary of the results

Data privacy concerns

Using AI in recruiting often involves collecting and analyzing personal data. This can raise data privacy concerns and the risk of misusing sensitive information. To protect candidate data, you need to ensure compliance with data protection regulations (such as GDPR in Europe or the CCPA in California) and implement strong security measures. 

Over-reliance on automation

While AI can certainly improve efficiency and reduce manual labor, chasing these productivity increases can lead to an overreliance on automation. This is especially risky in a human-centric process like recruiting.

For example, some companies have begun using AI to analyze interviews and make hiring decisions, but this isn’t always the best or most ethical approach; AI is bad at assessing cultural fit, soft skills, and personality, so you might miss out on some great candidates.

Instead, AI should enhance, not replace, human involvement and decision making. 

Take interviews, for example. Rather than using AI to automate this process, look for solutions that are human and efficient. Recruits can use async interview tools like Willo to screen candidates at scale while using AI to handle: 

• Low-level candidate questions
• Scheduling live interviews
• Pre-screening resumes

How to Ethically Implement AI in Recruitment

For a more detailed overview of the implementation process, we recommend that recruiters interested in ethical AI practices check out our Hiring Humans ebook. Read on for a summary of key steps to consider when implementing AI in recruitment.

1. Conduct a needs assessment 

Before implementing AI, you need to review your recruitment processes to see where AI could really make a difference. Here’s a straightforward way to go about it:

• List recruitment steps: Write down every step in your hiring process, from sourcing to onboarding.
• Spot the pain points: Identify the challenges and inefficiencies you face. These could range from slow response times to difficulty finding qualified candidates or high attrition rates.

2. Find AI opportunities 

Next, look at the pain points you’ve identified and see where AI can make a difference (or not). Think about it in three categories:

• AI can handle tasks independently, like scheduling, data analysis, and text analysis.
• Areas where combining AI with human input is essential, such as decision-making and structured candidate interviews.
• Tasks where AI falls short or has minimal impact, like showing empathy and building relationships with candidates.

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Source: Hiring Humans

Generally, AI is good at analytical “thinking” and non-emotional tasks. It’s not good at understanding or analyzing emotions, displaying empathy, negotiating, working through nuanced problems, and understanding deeper differences between people or cultures.

3. Prepare to integrate 

Once you’ve picked the processes to automate with AI, it’s time to make sure your organization is ready for the transition. There are a few key things to consider:

Cultural acceptance of new technologies 

One in five recruiters worries that AI might take over their jobs. Additionally, our Hiring Humans report found that most business leaders (45.2%) think it should be used cautiously in recruitment to limit its risks.

Source: Hiring Humans

The bottom line? Expect some pushback when integrating AI. To ensure buy-in, just reassure your team that AI is here to support and enhance their work, not replace them. Also, make sure to set up training for your recruiting team on using the new AI systems.

Data infrastructure 

To avoid bias in your recruitment, think carefully about your data sources. If you're setting up your own AI infrastructure, ensure your data is unbiased by incorporating multiple sources and conducting tests like:

• Chi-square test: Checks for associations between demographic variables (e.g., gender, race) and hiring decisions.
• Correlation analysis: Examines correlations between protected characteristics and hiring outcomes.

If you're using third-party tools, ask about their data sources and put necessary guardrails in place to mitigate bias. Also, pay close attention to data regulations and set up security measures to protect candidate information.

AI Usage Policies

Last but not least, think about how your AI systems will be used and establish clear policies that:

• Define what AI will do (and what it will never do). For example, will it make decisions on its own or just provide recommendations?
• Outline who is responsible for the actions and decisions made by AI. You can’t hold an algorithm accountable, so there needs to be a human to own each AI process.
• Set requirements for the AI tools you bring on board. For example, how transparent are they with how they make decisions and handle data?

4. Prioritize your list of opportunities

AI implementation shouldn’t be rushed. That means you need to prioritize the AI opportunities you identified earlier so that you can focus on the most valuable ones first.

Focus on the categories defined in step two. Prioritize the opportunities where AI is able to handle the entire process from start to finish—that’s where you’ll get the most value. Then, look at the opportunities that require a bit of human oversight.

To help with this prioritization, think about the following factors for each opportunity:

• Time savings: How much time will this AI implementation save?
• Cost savings: Will this AI implementation result in cost savings for your business?
• Candidate satisfaction: Will implementing AI improve candidate satisfaction?
• Team sentiment: How much do your team members actually want this solution?

You can use a prioritization matrix to balance all of these considerations. The example below demonstrates how this might look, but the actual scores will differ from business to business.

Sample prioritization matrix

For example, you might find that responding to candidate queries eats up a ton of recruiter time. In this case, you’d assign a higher score to time savings.

5. Research solutions and pilot-test

This is the fun part—picking the right tools for your needs. Ideally, look for AI tools that are accurate, transparent, and have good bias-mitigation features.

Also, consider tools that:

• Fit smoothly with your current workflow
• Offer analytics to track your progress
• Include the features you need (not a bunch of extras)
• Allow for some level of customization

Once you've chosen, start with a pilot test to spot any issues before rolling it out to your candidates. If you have a good internal mobility system, you might begin with internal candidates or set up a beta test for interested external candidates. You can confidently move on to full-scale implementation if the pilot goes well.

Leveraging AI for Efficient Recruitment

AI is transforming the recruitment space, promising efficiency, accuracy, and a positive candidate experience. However, it also comes with major challenges like bias, overreliance, and data privacy concerns. 

At Willo, we’re big believers in the power of AI. However, we also feel strongly that recruitment is a human process. Our async interviews let you balance the efficiency and standardization of AI with the empathy and personal touch of human interaction.

Want to see Willo in action? Sign up for a free trial today. 

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