Beyond the perfect CV: How AI is changing the search for talent

A candidate with the right skills may have spent three years at a small company, studied at a college outside the major hiring circuits or moved between industries before finding the right role. On paper, their experience may look less straightforward than that of someone with a familiar degree and a recognised employer on their CV. An automated screening system must decide whether that difference matters.

Indian recruiters are dealing with a crowded application market. A February 2026 report by The Economic Times, citing LinkedIn data, found that seven in ten recruiters struggled to find qualified candidates. Applicants per open role in India had more than doubled since 2022. AI tools are increasingly being used to process that volume and identify relevant skills.

There is evidence that the technology can broaden the search. Among Indian recruiters already using AI, 71% said it had helped them uncover candidates with skills they might previously have missed, according to the LinkedIn survey. The potential is significant for employers trying to move beyond conventional credentials and find people with transferable skills.


Yet the quality of the shortlist depends on what the system has been asked to recognise. If screening criteria lean heavily on particular qualifications, job titles or previous employers, candidates with less conventional backgrounds may struggle to get through. A person who acquired a skill through practical experience could be overlooked if the system is configured to search for a familiar credential.

When a pattern becomes a barrier

The concern is not limited to hypothetical scenarios. The consequence of automated screening extends beyond who makes the shortlist. A survey of 1,066 US job seekers conducted by Enhancv in April 2026 found that 50.5% had received at least one rejection in the past year without any human contact.

Also read: What happens to the apprenticeship model when AI does the easy work?

Among this group, 63.8% believed an AI system had made the decision. Only 9.7% of all respondents said employers had clearly disclosed AI’s involvement in evaluating their applications. The findings point to a problem of transparency: candidates may be rejected without knowing how their applications were assessed or whether a human eye even reviewed them.

India's geography adds another dimension. According to the India AI Workforce Report 2026 by Scaler, nearly one in five AI learners came from Tier-2 cities, including Lucknow, Patna, Jaipur, Indore and Coimbatore. The finding concerns AI learners specifically, rather than the entire hiring market, but it illustrates how relevant talent increasingly exists beyond the largest metropolitan centres.

A screening system that relies on familiar career signals could fail to recognise that wider pool. For employers, the cost may be a narrower shortlist and missed candidates. For applicants, the decision may be difficult to contest, particularly when they have little visibility into how the initial rejection occurred.

Also Read: Who owns the bug when AI writes the code?

What should a fair shortlist measure?

Recruiters need to examine what their systems reward. Skills assessments, structured interviews and human review of borderline applications can help test whether a candidate has the ability to do the job, even when their CV does not follow a conventional path. Regular audits of rejection patterns can also reveal whether particular groups or backgrounds are being screened out disproportionately.

Speed remains a legitimate hiring objective. The greater test is whether automation improves the quality of the decision alongside the time it takes to reach one. A shortlist that arrives faster has limited value if it repeatedly returns the same familiar profiles.

At the Future of Knowledge Work Summit 2026, taking place on 19 November 2026 in Mumbai, the discussion around AI and organisational decision-making has a direct bearing on hiring. As employers use technology to search a larger talent pool, they will have to decide whether their systems are identifying the people best suited to a role or simply those who most closely resemble the candidates they have hired before.

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