On the surface, this sounds like progress. But I recently experienced an AI-related hiring assessment that made me wonder: In our pursuit of efficiency, are we slowly losing the human element from the hiring process?
One Assessment. One Attempt. One Rejection.
I recently applied for an AI-related role and spent several hours carefully reading the guidelines and instructions before taking the assessment. Unfortunately, I did not clear the assessment on my first attempt. The result was straightforward: my candidature was rejected. What struck me was not the rejection itself. Companies have every right to maintain high standards and select candidates who meet their requirements.
What I found interesting was the one-attempt approach. If a candidate spends several hours understanding the guidelines and then narrowly misses the assessment, is rejection after a single attempt really the most efficient approach?
What Happens After Rejection?
Suppose Candidate A fails the assessment. The company now needs to find Candidate B. Candidate B must go through the same recruitment process, read the same guidelines, spend time preparing, and take the assessment. But what if Candidate B also fails? The company then looks for Candidate C. The cycle continues.
From the company's perspective, this means repeated recruitment, screening, assessment, and onboarding efforts. From the candidates' perspective, it means repeated time and effort. And from the perspective of computing resources, every additional assessment consumes infrastructure and processing resources.
What If We Introduced a Learning Loop?
Instead of treating an unsuccessful first attempt as the end of the journey, why not introduce a learning-oriented approach? For example, candidates could be given up to three attempts.
After the first unsuccessful attempt, the candidate could receive appropriate feedback about the areas where improvement is required. The candidate could then study, learn, and try again.
A possible model could be:
Attempt 1 → Feedback → Learning → Attempt 2 → Feedback → Learning → Attempt 3
If the candidate clears the assessment, they proceed to the next stage. This does not mean lowering the company's standards. There is another potential benefit.
A candidate who has gone through the assessment multiple times, studied the feedback, and finally cleared it may actually be better prepared to perform the job independently. That could potentially reduce the effort required for subsequent reviews, audits, corrections, and supervision.
What If Three Attempts Are Not Enough?
There could still be a boundary. If a candidate does not clear the assessment after three attempts, the candidate could be given a 90-day cooling-off period before being allowed to apply for the same role again. Those 90 days could provide an opportunity to learn, gain experience, and improve.
If the position is still available after 90 days, the candidate could be allowed to reapply. This creates a system that is neither excessively lenient nor unnecessarily rigid.
Efficiency vs. Humanity
AI has given businesses an incredible ability to automate processes. But automation should not automatically mean elimination of human consideration.
Someone may fail because they misunderstood an instruction, overlooked a concept, struggled with the assessment format, or simply made a mistake. One unsuccessful attempt does not necessarily mean that the person lacks the ability to perform the job. It should also be about identifying people who can learn, adapt, improve, and ultimately succeed.
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