Efficient", "Streamlined", "Accurate", – all adjectives HR tech vendors use to describe matching technology. We hear and read it will revolutionize the employers' experience, by helping in the candidate selection process through AI and machine learning. However, when both the employer and candidate use the tech, the experience isn't always as positive. It raises two questions:
Is AI/Machine Learning HR tech fit for purpose?
Do we still need the traditional Headhunter?
So, HR tech; In theory, an employer will upload a job posting and a candidate uploads a resume. The tech should be able to match those two together magically. However, it doesn’t always happen. Why? Well, here are just some of the challenges:
Matching technology requires a significant investment of time by both employer and candidate.
The employer invests time to answer multiple questions about a role so that the matching software understands what the job entails, and the qualifications required for it. The candidate should also invest time to answer each question about the role and their skills so that the AI matching element understands the candidate. But how often will both occur? One needs both for the software to work. In a job market where candidates currently have the power, they have little incentive to spend that kind of time without any guarantee of good results. There are sporadic occasions in history when the labour market is in equilibrium; however, the majority of the time the market is candidate driven. The best candidates often remain passive.
Both parties need to be well informed, accurate and articulate.
Employers complain about poorly written resumes. Candidates complain about vaguely written job postings. The reality is that they're both correct that resumes and job postings are written poorly. The matching system assumes that the information entered by both parties is accurate…but it likely won't be. If the employer doesn't articulate what the hiring manager requires from a candidate, then it increases the margin for error. The desired candidate could then go unnoticed within the matching software.
In reality, it just doesn't scale.
The scale may not affect an employer who makes a single hire one person at any one time, however more significant and growing employers, hire in volume. Meaning multiple hires for sustained periods. Employers who hire regularly and have hundreds of roles advertised requires too much staff time to go through an online questionnaire about every position manually. Rather than filtering out candidates once they’ve applied, it's more efficient and effective to engage with well-qualified candidates before the maze of a matching system.
Nearly ten years into a career in the recruitment industry, one of the frustrations is knowing that relevant, and high calibre talent may be slipping through the net. While all is becoming digitized, and daily life is fast-paced, time and accurate information are the main currencies a headhunter uses to increase the chances of success.
To summarize, is HR tech fit for purpose? Yes. However, there are levels to the game. Herewith answers the second question posed; that there is indeed still a need for traditional headhunters.
Employers usually have at least one role that requires unique or sought-after skills. When an employer realizes how critical the job is to the success of their organization, a headhunter is
engaged. The reliable headhunters who possess a trained eye and qualifying techniques can ascertain details that don’t make the job description, then do the same with the candidate.
For the other high percentage of jobs that employers need filling, they rely on job postings and the best matching technology available to them to yield volume. However, as explained above; trusting the accuracy of information from both employer and candidate is the main hindrance to solely using HR Tech for all recruitment needs.
In conclusion, the evidence supports that there is no substitute for the expertise and intuition a trained headhunter can provide when a business is hiring a critical role.