How Are Indian Companies Using AI to Recruit Candidates?

How are Indian companies using AI to recruit candidates?

Indian companies use AI to support candidate sourcing, evaluation, communication and repetitive recruitment work. A SHRM India survey of more than 230 HR professionals across industries and organization types reported that 65% of respondents had implemented AI in recruitment organizations.

That figure is a survey result, not a measure of every Indian employer. The sample and question matter. A Talent500 survey of about 80 Global Capability Centres, reported by The Economic Times, found candidate sourcing was the leading AI-in-HR use case: 87.9% of participants said they used AI for it. The two findings describe different groups and should not be treated as competing estimates of national adoption.

Use also depends on the role and hiring volume. A team filling many similar vacancies can use automated search and initial sorting to handle a large pool. Campus teams can use tools for screening and assessment across cohorts. Specialist hiring calls for a sharper focus on relevant skills and a recruiter’s close review of context that a search system may miss.

Campus recruitment is a distinct case. Deloitte India’s Campus Workforce Trends 2025 study drew on data from more than 200 organizations and over 500 campuses. It reported an expected 38% increase in GenAI use across campus recruitment, including resume screening, assessments and evaluations. The study also describes a shift toward skills-first early-career hiring, with behavioural, learning-agility and technical assessments.

These use cases form a chain, not a single decision. Sourcing finds possible candidates; screening narrows the pool; assessments add evidence; communication keeps people informed. A failure early in the chain can shape every later stage, so teams need to know which task the tool performs and what a human checks next.

Which parts of hiring can AI automate in India?

AI can automate or assist with repetitive recruitment tasks, including searching for candidates, sorting applications, supporting assessments, scheduling interviews and sending candidate communications. The amount of automation should match the task: handling logistics differs from judging whether someone should advance.

Survey results point to screening as a major use. A Genius HRTech survey of 1,887 professionals across industries in August 2025, covered by The Economic Times, found respondents most often saw AI benefit resume screening and shortlisting (62%). Skill-based assessments followed at 18%, interview scheduling at 14%, and candidate sourcing at 6%. These figures describe perceived benefits among respondents, not a measured ranking for every employer.

Reported efficiency gains also vary. In the Talent500 survey, among recruiters already using AI, 75% reported faster candidate screening and 59% reported improved candidate-job matching. The 2025 Genius HRTech survey found 47% reported a significant reduction in time-to-hire, while 28% reported only slight improvement. These are self-reported survey findings; they do not guarantee the same result in a different workflow.

Use this comparison to define the job before choosing a tool:

Integration is another practical test. SHRM India identifies system integration as a challenge alongside data privacy and employee resistance. Before adoption, HR teams can map where candidate records begin and end, which systems need to exchange information, and who can access the data. A tool that adds a disconnected step can create more work instead of reducing it.

Regional-language support also deserves a direct check. The supplied research does not report which Indian recruitment tools support which languages or how well they perform across them. Teams should test the language needs of their own candidate pool and ask vendors how language affects search, assessment and communication, rather than assuming English-first performance transfers.

Do Indian employers use AI to screen resumes?

Yes. Survey evidence shows Indian employers use AI for resume screening, though the reported rates apply to particular respondent groups. The Talent500 survey reported that 69.7% of respondents considered AI effective for initial resume screening and skill assessment; the Genius HRTech survey identified screening and shortlisting as the recruitment area most benefited by AI.

Resume screening is a sorting aid, not proof of a candidate’s ability. A system can search for terms or patterns, but the employer still has to decide whether those signals fit the role. If the criteria reward only familiar job titles or career paths, qualified applicants with different backgrounds can be missed.

IndiaAI, the Government of India’s AI portal, warns that systems trained on historical applicant or employee data can reproduce existing patterns of bias. It gives male-dominated IT hiring as an example. IndiaAI also notes that assessments relying on speech, facial expression or typing behaviour can disadvantage some candidates, including people with disabilities. Its article cites the 2011 Census estimate that 2.1% of India’s population, about 26 million people, had a disability.

Candidate response is not uniform either. The 2025 Genius HRTech survey found 49% had mixed reactions to AI processes and 27% disliked them for lacking a human touch. A fast process can still feel opaque. Clear updates, an accessible route to ask questions, and a person available when a decision needs explanation make the technology easier to understand.

Human oversight has broad support in that survey: 72% considered human validation essential in hiring decisions, while 15% supported fully AI-driven decisions if systems were well-calibrated. India’s AI Governance Guidelines from the Office of the Principal Scientific Adviser set out principles including fairness and equity, accountability, understandability by design, and safety. They also identify consent, purpose limitation and data minimisation under the Digital Personal Data Protection Act, 2023 as relevant safeguards for digital personal data processing.

The practical dividing line is simple: automate repeatable work, explain how candidate information is used, and keep people accountable for consequential decisions. AI can help move an application through a process. It cannot make an unclear process fair by itself.