Is AI making hiring better or just faster?
AI is becoming part of almost every stage of hiring. It can reduce repetitive work, but I think the more important question is whether it is improving the quality of the process for both the company and the candidate.
AI can already help write job descriptions, organise applications, screen for clear requirements, schedule interviews and summarise information. I see real value in that.
But speed is not the same as quality. We still see roles stay open for a long time, disappear, and then return with almost the same job description. That tells me that many hiring problems are not simply screening problems. They can start with an unclear role, unrealistic requirements, slow decisions or a process that was not well designed in the first place.
AI can reduce the noise
Anyone who has worked in recruitment knows what a busy application pipeline can look like. Some applications are clearly not relevant to the role. A person may not have a technical skill that is genuinely essential, the required work authorisation, a language that the job truly depends on, or another clear requirement.
This is where AI can be useful. It can reduce the first layer of noise and turn a very large pipeline into something a recruiter can work with.
But the criteria matter. They should be limited, clear and genuinely necessary for the job.
A degree level, a specific number of years of experience or a familiar job title should not automatically become a rejection rule just because they appeared in an old job description. A recruiter still needs to ask what is really essential, what is only preferred and what the market can realistically provide.
If the criteria are poor, AI may simply apply poor criteria faster and at a larger scale.
The recruiter should not become a system operator
I do not think the future value of a recruiter is in reading hundreds of CVs one by one. It is in understanding the market, challenging unclear requirements, helping the hiring manager define what matters and making better decisions about talent.
If AI removes repetitive work, that should create more time for the parts of recruitment where judgment adds value.
That also means recruiters need to question the process itself. Are we asking for the right things? Are the requirements realistic? Could we be excluding good people for the wrong reasons?
What happens to the candidates we never see?
Imagine 1,000 people apply and a system shows the recruiter the 30 strongest matches. The recruiter can review those 30 carefully. But how do we know there were not another five strong candidates among the people who never reached that stage?
Maybe their job title was different. Maybe their career path was less traditional. Maybe they had transferable skills described in a different way. Or maybe they simply did not know how to write a CV that the system would recognise.
Candidates are already being told to rewrite their CVs around keywords so that applicant-tracking systems and AI tools will notice them. I find that uncomfortable. If AI is really making recruitment smarter, it should eventually help us move away from the keyword game, not make people better at playing it.
If the company cannot explain a rejection, should it trust it?
This is one of the questions I keep coming back to.
If an automated system has a meaningful role in screening or rejecting someone, the company should at least be able to understand why. The explanation does not need to be long or technical. It might be that a required work authorisation was missing, an essential language level was not met, or a genuinely critical technical skill could not be found.
If the company itself cannot understand why the system rejected someone, I would question how much it should trust that rejection.
For me, this is not only about candidate communication. It is also about the quality of the company’s own decision. If a decision cannot be explained internally, it becomes difficult to check whether it was reasonable.
Candidate experience should matter too
Companies often talk about AI as a way to make recruitment more efficient. But efficient for whom?
As a candidate, I have experienced processes where I uploaded a CV and then had to enter the same work history, education and other information again by hand. If AI is meant to make hiring more efficient, I think that efficiency should also be visible to the person applying.
A smarter process should reduce duplication, communicate clearly and help candidates understand where they stand. It should not simply move more administrative work onto them.
I have been thinking about this problem for a while
This is also a question I explored through Veenro, an early-stage AI-supported job-discovery platform we built but never fully launched. The product was coded and reached an early working stage, but we did not continue developing it into a full service.
The idea was to let people describe their skills, interests, language needs and preferences more naturally, instead of repeatedly rewriting themselves for each application. I was interested in a system that could have a more useful conversation with the person, understand more than a job title, and then surface relevant opportunities.
I still think that direction is interesting. A system could show where a role matches someone, where the gaps are and what the person should know before applying. But I would rather let the person make the final decision about whether to apply than have the system decide, “This job is not for you.”
Where I currently draw the line
I am not arguing that AI should stay out of recruitment. I think it can remove a lot of unnecessary work.
But today, I would use three principles:
- The criteria used for screening should be clear and genuinely related to the job.
- Important rejection decisions should still have human oversight.
- The process should create value for the candidate as well as the company.
And I would add one more:
If a company cannot explain why its system rejected someone, it should question how much it trusts that decision.
AI can make hiring faster. The more interesting challenge is whether we can use it to make hiring clearer, more useful and better for everyone involved.