Many talent, recruitment and HR teams have implemented an AI recruitment solution to improve their hiring practices.
But how do you actually use AI to automate recruitment and save time?
How to use AI in Recruitment
- What is AI recruiting?
- How recruiters are using AI software
- The benefits of AI in recruitment
- Challenges of applying AI in recruitment
- What roles can you use AI recruitment software for?
- Can candidates trick an AI hiring tool?
- How will AI change the role of the recruiter?
- What’s next for AI recruitment?
- Further reading
What is AI recruiting?
AI recruiting is the practice of integrating artificial intelligence, such as machine learning or predictive algorithms, to the time-consuming or resource-intensive parts of hiring.
There are a variety of AI recruitment tools that assess a candidate’s suitability for a role. Essentially, these tools use algorithms to understand data points from responses, including:
- Analysis of facial expressions, body language, and gestures
- Voice and text sentiment analysis
- Actual ability to do the job
- Cultural fit and personality
But that’s not why recruiters are using AI. Recruiters and talent teams want to use AI to automate the hiring process to make it faster, less expensive and easier to manage high volumes of candidates.
Why AI for Recruitment
AI, or artificial intelligence, can be a powerful tool to drive efficiency in recruitment. By reducing the time spent on screening, skill testing and interviewing, HR and Talent teams can focus on ways to attract, onboard and retain employees.
As AI technology becomes more accessible, all industries are using AI to reduce or remove time-consuming activities. The world of recruitment is no different.
Talent acquisition teams are deploying AI recruitment tools to get through the most time-consuming aspects of the recruitment process. Pre-employment screening of candidates remains the biggest drain on recruiter resources. The number of candidates per open req has increased, and finding quality candidates has become more challenging.
To help you understand the landscape of AI recruitment technology, we put together this definitive guide.
The benefits of AI in recruitment
According to industry analysts, more than half of talent acquisition leaders say “the hardest part of recruitment is identifying the right candidates from a large applicant pool.” Making this harder is the fact that 56% of those surveyed reported that they expect their hiring volume to increase, yet 66% of those recruiting teams will either stay the same size or shrink. In short, companies are hiring more people with fewer human resources available.
AI recruiting tools make this feat possible.
Beyond this tool’s ability to automate low-level tasks, AI recruitment can help hiring teams improve their quality of hire, save time and money, and minimize some of the hidden human bias that prevents minority candidates from getting a fair evaluation.
|Improves recruiter performance||Enables remote hiring|
|Allows hiring to be done at scale||Predicts candidate performance|
|Offers a great candidate experience||Reduces hiring costs|
|Removes hiring bias|
Improve recruiter performance
AI and automation is a critical part of processing the influx of candidates in the hiring funnel. Traditional pre-employment screening and shortlisting takes up to 23 hours of a recruiter’s time for a single hire. An AI screening tool can process thousands of applications in an instant, and surface qualified candidates.
Another issue is that recruiters today are working in a market that favors applicants. Competition for talent is aggressive, and businesses need to do more to stand out. It’s also vital recruiters expand their hiring funnel to reach more potential new hires.
AI can also help recruiters expand their candidate pool beyond the initial application submission step. By using AI tools, recruiters can screen candidates in, rather than out based on irrelevant attributes. This increases the number of potential candidates to hire.
Enables for remote hiring
Remote work is on the rise, and, accordingly, so is remote hiring. It’s increasingly common for a workforce to be spread across the globe. Automated online screening and interview tools can help recruiters manage remote hiring. When a hiring team is limited to in-person interviews, or scheduling phone screens across time zones, their hiring pool becomes shallow. New tech lets recruiters easily assess the performance of each candidate, regardless of location and without wasting time.
Allows hiring to be done at scale
Powerful sourcing tools are channeling more candidates into the hiring funnel. While recruitment teams benefit from more candidates to evaluate, this puts additional pressure on under-resourced teams. Traditional résumé and phone screening are impossible to scale.
AI recruitment tools streamline the process. These tools allow small teams to evaluate applicants at scale, in days not weeks, without compromising quality.
Predicts candidate performance
The right screening and interview tools outperform interviews when it comes to predicting performance. Depending on the platform you choose, you can accurately predict the best fit for your role.
AI tools can assess a mock sales pitch, a customer service call, coding competency or strategy presentations. Automated interviews help understand how candidates do the tasks that matter to you most for the job you’re trying to fill.
Offer a great candidate experience
Online screening and interview tools save time for recruiters and candidates. These tools remove the friction of scheduling phone screening of interviews. It also allows candidates to apply for a role without interrupting their current employment. Additionally, improved transparency and quick communication with automated tools deliver an improved hiring experience.
Reduces hiring costs
AI is being used to help reduce the time and costs of screening candidates. AI recruitment tools assess candidates quickly, and surface candidates most likely to match the hiring criteria. In fact, early stats show that recruiters that use AI see a 30% reduction in cost-per-hire.
There are also longer-term costs that teams can mitigate by adding AI tools to the process. A 2016 CareerBuilder study found that 75% of employers participating in the survey said they hired the wrong person. The cost of making the wrong hire can be up to 2.5x salary. Where candidates routinely lie on their resume or human bias interferes during the hiring process, an AI tool can help cut the costs of hiring the wrong person.
The ROI of using AI in hiring includes some of the long-term variables which predict a candidate’s success. Consulting firm PwC points to “not only the organizational ROI in hiring, training, and remuneration, but also relating to the individual’s ability to learn on the job, develop new skills, and his or her social contributions.”
Removes hiring bias
Bias in recruiting exists for many reasons. Primarily, it’s a question of efficiency: HR teams may receive hundreds of resumes for a single open position. It’s difficult to give every candidate’s application careful consideration. As a result, many recruiters advance candidates whose backgrounds are similar to current employees. Those candidates with different backgrounds or work experience never stand a chance.
AI can make a difference in how candidates are processed through the hiring funnel. One such algorithm allows HR teams to evaluate 10,000 candidates in the same time it takes for an individual recruiter to assess one. An AI tool is able to screen candidates based on what they can actually do, rather than what their resume says they can do, evaluating their application more efficiently and more effectively than a human recruiter.
And finally, not all interviewers are as talented at providing a great candidate experience as they might believe. Interviewing is hard, and awkward interviews are more common than one might think. The reason for this? Most interviewers allow biases, not facts, to lead the discussion.
AI is a powerful way to remove bias from the equation. The results speak for themselves. When recruiters use AI to screen candidates in, rather than out, hiring becomes more democratic and merit-based, rather than about who an applicant knows. Companies who switched to one AI tool saw a 62% increase in female candidates. Nepotism and selective hiring practices get eliminated when an AI tool is correctly utilized.
Adopting and optimizing technology is a huge opportunity for recruitment. There is a broad range of AI-driven tools available to today’s recruiters to help make the hiring process more scientific, scalable and effective.
How recruiters are using AI
There are three main ways recruiters are using AI in the hiring process:
- Sourcing: finding and connecting with talent quickly
- Screening: quickly deriving the best applicants
- Interviewing: facilitate remote hiring and save time
AI-based tools for sourcing help recruiters find and connect with talent faster. Some tools focus on searching for profiles across job boards or internal databases to fill an open position. Others help maximize marketing efforts and connect with candidates in real-time. And there are AI chatbots that can interact with a candidate to determine what specific role will be the best fit and show them how to apply through the job site.
Some examples of AI sourcing tools include:
Customers use Hiretual to source across 40+ platforms and 700M+ professional profiles and hire efficiently with team collaboration, manage talent pool, customize candidate engagement, and rediscover candidates in their ATS/CRM.
Appcast uses predictive analytics, real-time data and programmatic bidding to maximize recruitment results.
Not limited to candidate sourcing, Shapr is sort of a Tinder for professional relationships. The machine-learning algorithm suggests 15 relevant people to meet each day, and communicate when interest to connect is mutual.
As shown, screening is the most time-consuming aspect of the hiring process. AI screening tools aim to quickly derive information from applications to speed up this step.
AI screening tools range in approach, from resume parsing to behavioral and skill assessments. Predictive performance based on skill testing tends to be a better way to match candidates with open positions, as a skill test indicates current knowledge and ability, versus the historic nature of resumes. Either way, AI tools streamline the process and help screen candidates in, not out.
Some examples of AI screening tools include:
Has resume parsing functionality within their applicant tracking system, as well as flexible application forms to help screen candidates.
Ideal’s candidate screening software provides automated resume screening to shortlist candidates.
AI-based skills assessments that let you evaluate at scale, and spend more time with high performing candidates. Instantly auto-grades and ranks candidates according to job-related skills.
AI tools are used in interviews in two key scenarios. In the first instance, companies like Unilever, Google, and Facebook have begun to use AI to assess video interviews, using voice and facial expression analysis to assess personality traits. While this can help cut down on the time and attention required of recruiters to review each candidate’s recorded response, there are some big red flags with using facial recognition to select candidates – more on that in a minute.
The second way to use an AI tool with a pre-recorded video interview is to analyze the content of the answer. For example, a skills test may ask a candidate to write a sample blog post on a topic relevant to the company’s industry. Then, in a pre-recorded video interview, the tool asks why the candidate selected the topic they chose to write about. The video portion can be used to give more context to the candidate’s skills test and help a candidate stand out beyond their initial response.
Some examples of AI candidate interviews are:
Digital interview platform. HireVue uses AI in their interviews to analyze work style and cognitive ability.
Uses chat and a phone calling bot to phone screen candidates, then invites them to video interviews with facial recognition.
Uses a chatbot to conduct text-based interviews with candidates.
These uses of AI in recruiting are just the tip of the iceberg.
Recruiting is now the biggest AI market in HR, with AI-based sourcing, assessment, screening, interviewing, and candidate experience management now available.Josh Bersin HR Technology 2020: Disruption Ahead
Challenges of applying AI in recruitment
1. It can take time to gather data for AI
In general, AI needs plenty of data to learn from. Machine learning algorithms rely on getting thousands or millions of data points to accurately mimic human intelligence.
Depending on your vendor, this means there might be a lengthy implementation process. Algorithms that rely on existing internal data, like testing your high performers, are costly and time-consuming.
This can also help you decide at which stage of the funnel you should use your AI tool. Because more data is required, it’s best to use AI recruitment tools at the top of your funnel. Not only will you cut screening time, you’ll ensure a bias-free and accurate result.
2. The right data needs to be used to predict outcomes
The most important question in using AI tools is: am I giving the algorithm the right data, and does that data accurately predict the outcomes I want to achieve?
An example of questionable data choice lies in facial analysis. Recently, there have been complaints about this technology, with experts worried the systems could unfairly penalize candidates and hide biases in how they assess ideal candidates for recruitment.
Modeling based on top performers also stifles innovation and growth. Instead of assessing ability, a tool that seeks to hire people based on high performers reduces diversity by promoting “look-alikes” of existing employees.
Ensure you understand what data your vendor uses for the learning algorithm. Importantly, candidate age, gender and race should never be taken into account in AI recruitment.
3. AI can potentially replicate human bias
The biggest issue that arises is when some AI tools are used to interview candidates is in multiplying or replicating human bias. The notorious example of Amazon scrapping their AI recruiting tool after it was discovered to have been replicating the biases found in the human recruiting processes is just one example. The flaw in this tool wasn’t the technology; it was the data being used to feed the algorithm.
Ensure your vendor understands the risks of bias issues and has a process to identify and mitigate patterns of bias.
What roles can you use AI recruitment for?
AI recruitment tools can be used for any industry. However, some positions are harder to assess than others. According to one expert, professional industries with fewer candidates, such as IT, nursing, and senior-level positions are more complicated to hire for – and therefore may not be the best places to use AI interviews.
The key to using automated interviewing tools is to understand what success looks like in an open position. For example, if you’re hiring a call center rep, the AI tool should be assessing voice cues and qualities like patience. For programmers, an AI tool can prompt coding challenges in a certain language or skill set. Identify what competencies are important for on-the-job performance before using your AI tool to ensure you surface the best candidate.
AI can help evaluate these skills, in a range of formats.
Can candidates trick an AI recruitment tool?
It’s difficult to trick an AI tool, especially when used in a skills test, as the purpose of AI ranking is mostly to screen candidates in, rather than out. However, some machine learning tools have been set up exclusively to prevent cheating on one-way interviews, for example. One automated interview tool detects if the candidate is regularly looking away from the screen (and possibly relying on cue cards) to answer the question. Or, the tool can pick up if there’s another voice on the recording coaching the candidate to answer questions.
Regardless of the tactics that some candidates may try, inevitably the AI tool is part of a larger recruitment process. If a candidate “cheats” or tries to outsmart an AI tool, a recruiter should be able to triangulate a submission with other facets of their application. It’s all part of creating a formula that helps the right candidate stand out from the crowd.
How will AI change the role of the recruiter?
Recruiters won’t be replaced by robots. In fact, AI will help recruiters spend more time on high-value tasks, like enhancing the candidate value proposition, engaging passive candidates, and improving the onboarding experience.
By using AI to remove the repetitive tasks involved in screening candidates, recruiters can:
- Spend more time creating great job marketing campaigns
- Have deeper conversations with hiring managers to understand roles
- Focus on interviewing top candidates, engage them and get to know them
- Concentrate on creating a great onboarding experience, enhancing EVP
The best AI solutions will augment human decision making, continuously learn from humans in the environment, and allow recruiters to spend more time with candidates to ensure a great placement.
What’s next for AI recruitment?
Will hiring soon be delegated exclusively to robot representatives? Probably not in the near future. But, according to one AI trendwatcher, it may not be a ridiculous proposition.
In some ways, recruitment tools have already ushered in the possibility of constructing a candidate-side AI tool. A recent article by TheVerge outlines how prospective job applicants have already been reduced to a “series of data points.” Between a candidate’s social media profiles, LinkedIn, and internet history, an employer has a rough outline of a person’s ability before the interview process. It’s not far off to suggest that a candidate may attempt to utilize AI to construct their own digital representation, consolidating their internet presence in a way that best represents them to a recruitment algorithm.
Editor’s note: This article was originally published in July 2019, and has been revamped and updated for accuracy and comprehensiveness.