Traditional recruitment methods are showing clear signs of aging. Today, more than 70% of the workforce is not actively looking for jobs, so employers can’t just post jobs on the job board and hope that people find them.
This problem is particularly pronounced in headhunting, which requires even more accuracy in candidate matching and a proactive and personalized touch to attract high-level candidates who may not be actively looking for a new role.
For this reason and others, many recruiters and headhunters are adopting artificial intelligence and machine learning to help them make better, more informed decisions.
Of course, changes rarely happen without a bit of skepticism. It’s the same with introducing AI in recruitment and headhunting. So, let’s think of AI as just another employee joining you and find out where its potential can best be used to improve your team.
What are the differences between headhunting and traditional recruiting?
Say you have a job position you need to fill out. How would you do that?
You’re probably thinking of posting a job description on some job boards, collecting a few (dozen or hundred) resumes, conducting a few rounds of interviews, and finally, finding the (hopefully) right candidate at the end of the process.
You wouldn’t be wrong. But here’s the problem. This only works if you’re looking to hire someone for low—and mid-tier positions.
But if you want to fill a more senior or high-level position within the company, traditional recruiting won’t quite cut it; instead, you must go headhunting, aka executive search.
First reason lies in the fact that the talent pool for high-level positions is much smaller than for entry and mid-level candidates, and most of them are passive candidates. This means you need to approach exceptional talent and not wait for them to come to you.
Second, companies typically don’t want to publicize that they are looking to hire new executives, meaning that headhunters need to work “under the radar” and ensure the whole process is discreet and confidential until a decision is made.
Finally, there are different expectations for high-level positions than for low—and mid-level positions. When looking for executives, C-level people, or higher managers, you’re not just looking for someone with suitable qualifications and skills that match the job description. You’re looking for someone who brings more to the table, like leadership, strategic thinking, cultural fit, vision, industry experience, networking connections, etc.
Recruiting and headhunting have similar goals (finding the best candidate for your company), but some nuances distinguish these two recruiting strategies.
| Aspect | Recruiting | Headhunting |
| Target positions | Low to mid-tier positions (customer service representatives, retail staff, administrative roles, etc.) | High-tier positions (C-suite, senior managers, highly specialized roles, high-profile specialists, etc.) |
| Method for finding candidates | Posting job ads, collecting resumes/CVs, screening and interviewing | Seeking candidates more actively |
| Candidate pool | Wide range of candidates, depending on the job | A narrower range of candidates, usually only top-tier professionals |
| Approach | Reactive, based on advertising job openings | Proactive, with personal outreach |
| Candidate engagement | Candidates are encouraged to apply voluntarily | Candidates are approached even if they haven’t shown prior interest |
| Confidentiality | Less confidentiality. Roles are publicly advertised | Higher level of confidentiality |
| Cost | Lower cost, although it is related to volume | Higher since it requires a more specialized search |
Understanding the role of AI in headhunting
AI can be seamlessly integrated into the headhunting process by automating and speeding up manual tasks such as resume screening, interview scheduling, and others.
Of course, that doesn’t mean these should now be entirely in the hands of AI.
If allowed to work with human recruiters, AI technologies can significantly help by streamlining communication and using large volumes of data, providing better accuracy in finding suitable candidates.
This leads to better hiring outcomes and a more effective headhunting process.
When it comes to some specific AI tools used in identifying and evaluating potential employees, the list is growing, but some examples include:
- Applicant tracking systems (ATS) can analyze resumes faster than humans, rank candidates based on qualification, and manage the application process.
- Chatbots – can answer candidates’ initial questions, schedule interviews, and improve engagement while allowing recruiters to focus on more strategic tasks.
- Candidate sourcing—Another use of AI in executive search is searching databases and social media for potential candidates matching the job requirements. This is especially useful in headhunting, where potential candidates are often not actively looking for new opportunities.
What are the benefits of AI in headhunting?
AI potentially brings several benefits to recruiting and headhunting, including:
1. Accelerating the headhunting process
The average time to fill a position for most business functions is 20 to 30 days. This process can be even longer for executive searches, lasting 6 to 12 weeks.
For example, in the banking and financial sector, the time-to-fil process for a Chief Financial Officer (CFO) in 2023 was 123 days, and for Chief Executive Officer (CEO) and President roles, 149 days. Compared to the previous year, this increased 13% for CFOs and 8% for CEOs and Presidents.
Leveraging AI tools, companies like Alpha Apex, which provides executive search for the financial, fintech, and banking industries, report a 60% faster-than-national average time-to-fill for full-time hires.
2. Reducing bias
According to a Pew Research Center survey, 79% of Americans say that bias in hiring is a significant (37%) or minor (42%) problem. However, the same survey also shows that 53% of participants believe using AI could eliminate or minimize unfair recruitment bias. The World Economic Forum’s (WEF) Global Gender Gap Report 2023, found that women in North America make only 12.4% and 17.8% of C-suite and VPs, but 29.4% of entry-level workers in STEM (science, technology, engineering and mathematics) occupations.
One study revealed that white people receive 50% more interview calls than black people. The experiment consisted of authors sending fictitious resumes under either very white-sounding names or black-sounding names, and it found that those with white-sounding names had a much better chance of getting an interview.
Humans often suffer from unconscious bias based on previous experiences, traditions, or personal opinions, while AI is free of this. Many companies now use “blind resumes, ” which don’t include personal details like ethnicity, gender, or age. The idea is to eliminate any factors that can impose a bias on a recruiter and focus on candidate skills and qualifications.
3. Data-driven insights
AI doesn’t do anything based on “gut instinct.”. Instead, recruiters can leverage AI’s insights and analytics to make data-driven decisions and optimize their recruiting strategies.
This is especially important when hiring for leadership roles, and companies increasingly leverage a range of predictive analytics and assessment AI tools to streamline their process.
Daniel Baker, project manager at ECA Partners, says:
“AII will impact search in at least two ways: making executive search professionals more efficient through time-saving advances in software, and making search more accurate through AI-driven data techniques.”
4. Improved candidate matching
Finding the candidate that matches the position you want to fill can be difficult, and the results are not always optimal.
Candidate matching software has become a (not so) secret weapon for HR and recruiters who need to hire for hard-to-fill and high-demand roles as it helps them find ideal candidates.
For instance, AI can use natural language processing (NLP) to understand the context or meaning behind words and phrases in a resume or CV, recognize synonyms, correlate historical data with successful hires, use behavioral analysis or 360-degree candidate views, and more to better match candidates with job roles.
One example of an AI tool that matches executive candidates with roles is Fetcher.ai. This tool provides personalized executive candidate matching, tailoring candidate matches based on specific job requirements and company culture.
5. Better candidate experience
Over 65% of candidates abandon the recruitment process due to a poor candidate experience. For some, it could be because the process took too long; for others, it could be because their time was disrespected (e.g., a recruiter being late or canceling a scheduled interview at the last minute), or other reasons.
Candidates will often have additional questions after the interview and may not always consider that the recruiter has the time to respond to them when candidates want. Chatbots, however, can free up recruiters by answering at least the more common candidate questions in real-time, improving their experience.
Another way to improve candidate experience is through feedback. One 6-month study, which involved the company providing personalized feedback for candidates through its AI system following an interview, found that 99% of candidates were satisfied with their experience, and 70% said they would recommend the company as a place to work.
Challenges and risks of AI in headhunting
However, there is another side of the coin regarding AI in recruitment and headhunting. While there are numerous benefits, we can’t ignore the challenges that come with it.
These include:
1. Data privacy and security
AI often has to collect and analyze immense amounts of sensitive data belonging to candidates, including their personally identifiable information (PII), demographic, health and financial information, criminal history, education history, employment history verification, biometric data, and more.
Not surprisingly, one of the biggest concerns people have about AI in recruitment is whether their sensitive data will be adequately protected.
As we can see from the 2018 PageUp data breach, this concern is not without reason. In 2018, the Australian talent acquisition software company, which includes clients such as Hong Kong Airlines and Kansas State University, notified the public that it was a victim of unauthorized access by an unknown party.
The breach potentially exposed the PII and financial data of over 2 million users in 190 countries, including their names, physical addresses, telephone numbers, email addresses, employment information, bank details, and login information (usernames and hashed passwords).
Fortunately, there was no evidence of malicious data use in this case, but this still serves as a cautionary tale regarding AI and third-party HR and talent acquisition software.
2. Increasing bias
Just like AI in headhunting and recruiting can decrease bias, it can also do the opposite and increase it.
This is because the algorithm essentially learns from humans and often relies on the data we feed it. So, if a particular bias is already deeply entrenched, AI will likely consider it “normal” and continue with the biased practice.
For instance, if 90% of people hired for a specific position tend to be white males between 30 and 45, AI will likely recommend those candidates over candidates of other races, genders, and age groups.
Unfortunately, not even large companies are immune to this. In 2017, Amazon had to scrap its AI-based recruiting engine after it was found that the algorithm penalized women more than men.
This was because the system was trained to observe resume patterns over a 10-year period. Since most jobs in the tech industry are taken by men (women represent only 26% of the tech workforce), Amazon’s AI was heavily biased toward men.
Amazon could have avoided the fiasco by incorporating the human element more in the process and “teaching” AI to pay more attention to DEIB principles.
3. Lack of human touch
While AI is great at predicting patterns, it’s still no substitute for human judgment, especially when screening applicants.
This is where AI’s overreliance on data can be a double-edged sword. For example, if the AI was taught to look for specific keywords in the resume, a smart applicant could “game the system” by cleverly placing those keywords throughout their resume and get the position that way instead based on the merit of their actual skills and experience.
On the other hand, the opposite is also possible. If the candidate uses a different word or phrase to describe something, the AI might not recognize it and penalize them for it.
Humans must review AI’s insight and not unquestioningly accept it. Artificial intelligence is like a child that “learns” by observing what its parents are doing. It doesn’t have a subjective concept of good or bad. That’s where we humans come in to “teach” it.
4. Bad data
AI’s accuracy relies on the quality and correctness of the data it has been fed. AI cannot deliver accurate data if it is inaccurate or insufficient.
For instance, one automated hiring software used by hospitals was programmed to only accept candidates with “computer programming” in their CVs, automatically rejecting hundreds of qualified candidates.
5. Impersonal candidate experience
While AI can improve candidate experience, too much of it can also have a negative effect.
Using chatbots to answer some common questions candidates might have or employ automated feedback is one thing. Still, with a personal touch, these can improve candidates’ experience during recruitment.
For instance, if the automated feedback says nothing other than “Unfortunately, we have decided not to move forward with your application. Have a nice day,” without specific details, the candidate’s experience will not be the best.
How will AI applications change the role of human recruiters?
As you can see, AI can positively and negatively impact candidates and recruiters.
AI can help recruiters by automating manual and repetitive tasks such as resume screening and interview scheduling, freeing recruiters to focus more on building candidate relationships.
Additionally, AI can leverage data analytics and provide insights into candidate performance and hiring trends, allowing recruiters to better match candidates for specific roles.
That being said, AI will not replace human recruiters (at least not anytime soon), as it can still suffer from inadequate data and entrenched bias. Therefore, it needs to be reviewed by an experienced recruiter who better understands cultural differences, for example.
Tips on how to use AI in headhunting
Incorporating AI technology into your recruitment and headhunting process can pay dividends, but only if done strategically and smartly.
Here are some tips and best practices that will make its adoption and implementation smooth for your organization:
1. Ensure transparency and consent for AI usage
The jury is still out on using AI in recruitment, with 71% of American adults saying they wouldn’t apply for a job that uses AI to make hiring decisions. That’s why you have to be open and communicate the use of AI in this process, what data is being collected, and how it is used.
2. Maintain legal compliance
When incorporating any technology that deals with users’ data, it’s vital to ensure compliance with all relevant data protection regulations, such as the EU’s GDPR, and meet their standards to avoid fines and penalties.
Additionally, you must consider relevant employment laws. In this regard, it’s vital to work closely with legal experts to ensure your organization’s compliance with these regulations.
3. Audit AI against bias
An AI algorithm learns from human data, so it can also discover our biases. Many companies (Amazon being one) have learned this the hard way when AI amplified the existing bias toward a particular group because it was present in its training data.
We must periodically audit AI to avoid such scenarios, ensure it doesn’t repeat our mistakes, and discriminate against specific candidates.
4. Don’t neglect the human touch
Lastly, avoid using AI as a crutch in headhunting and recruitment. It’s a handy tool, but a tool nevertheless, and a human should make the final decision.
Conclusion
The landscape has changed significantly thanks to using artificial intelligence in recruitment. AI has become a significant boon for the HR department in their recruiting and headhunting strategies, as it helps them screen prospects and conduct interviews faster and with a better chance of success (i.e., finding the right candidate).
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