Artificial intelligence (AI) is increasingly transforming recruitment by helping organizations streamline candidate screening, assess skills, and improve hiring efficiency. As businesses compete for skilled talent and face growing pressure to fill vacancies faster, AI-powered tools are challenging traditional recruitment methods by analyzing larger candidate pools and supporting more informed hiring decisions.
However, adopting AI in recruitment involves more than automating individual processes. Organizations must understand how the technology contributes to measurable business outcomes, including cost per hire, time to hire, and quality of hire. Maintaining human oversight, addressing potential biases, and preparing recruitment teams to work alongside AI systems are also becoming important considerations.
In the latest episode of the Analytics Insight Podcast, host Priya Dialani interviews Kartik Narayan, CEO of Apna.co, about the changing recruitment landscape and the growing role of artificial intelligence in hiring. Kartik shares insights into AI-powered candidate screening, conversational assessments, recruitment metrics, human oversight, organizational readiness, and the future of workforce intelligence in India. Here are the excerpts:
AI is having a meaningful impact on recruitment by helping organizations address persistent challenges in traditional hiring, particularly time to hire, cost per hire, and quality of hire.
Kartik explains that recruiters often struggle to handle large volumes of applications. For instance, when 100 candidates apply for a position, recruiter fatigue and time constraints can prevent hiring teams from evaluating everyone. Recruiters may end up considering only the first 50 applicants, potentially overlooking suitable candidates.
AI can help address this screening challenge by interacting with candidates through conversational questions and identifying individuals who match the recruiter's requirements.
For example, candidates applying for sales positions may be asked whether they own a two-wheeler, are comfortable with a salary of Rs. 25,000 plus incentives, or understand the benefits associated with a formal job, such as provident fund contributions.
AI-powered systems can also allow candidates to ask questions about medical insurance and other employment-related details.
Sharing figures from Apna's platform, Kartik says the company records between 5,500 and 12,000 interviews daily. Approximately 10 lakh users have participated in these interviews, accounting for more than 50 lakh minutes of interview time. Candidates from over 3,000 towns have also accessed these opportunities.
He believes AI is making recruitment more accessible by allowing candidates to participate in the hiring process without being limited by geographical barriers or traditional screening methods.
Kartik distinguishes between two important aspects of AI-powered recruitment: screening and assessment. Screening primarily involves identifying candidates who meet a job's basic requirements. AI can help recruiters evaluate large applicant pools through simple, conversational interactions and shortlist candidates based on predefined criteria.
Assessment, however, involves evaluating a candidate's actual capabilities and suitability for a particular role. He highlights business process outsourcing (BPO) companies that use assessments to evaluate candidates' communication abilities, articulation, and language proficiency, including mother tongue influence.
Some AI-powered conversational systems can now interact with candidates while assessing their communication skills against established frameworks such as the Common European Framework of Reference for Languages (CEFR). These developments can help organizations move beyond traditional resumes and interviews to understand candidates' capabilities better.
This is particularly relevant for entry-level recruitment, where candidates may have limited professional experience and may not possess extensive resumes to demonstrate their suitability for a position.
Despite AI's growing capabilities, Kartik believes human involvement will remain an important part of recruitment for the foreseeable future.
Kartik explains that screening is a relatively routine activity where AI can function as an assistant to recruiters. However, assessment involves deeper evaluation and requires greater attention to context and individual circumstances.
Kartik emphasizes that AI should enhance a recruiter's ability to evaluate candidates, not replace human decision-making. He also highlights the importance of monitoring AI systems to prevent unintended bias and ensure that their recommendations are accurate.
Sharing an example from Apna's experience, he explains how an AI agent initially struggled with a seemingly straightforward screening question about whether a candidate owned a two-wheeler.
A candidate might respond that they did not currently own one but could borrow their brother-in-law's vehicle before joining the job. While a human recruiter could understand the candidate's intention, an AI system might initially classify the response as a partial or negative match.
Such situations require carefully designed prompts and guardrails to help AI systems interpret conversational responses more accurately. Kartik also stresses that organizations should continuously monitor AI recommendations, retain human reviews for important hiring decisions, and transparently inform candidates when AI is being used during recruitment.
He believes the bigger opportunity lies in expanding access to employment by ensuring candidates have a chance to be heard, even when their initial interaction is with an AI agent.
Organizations should keep measuring recruitment performance with established business metrics, even after introducing AI into their hiring processes.
He identifies three important parameters: cost per hire, time to hire, and quality of hire. Cost per hire helps organizations determine whether AI reduces recruitment expenditure or adds another operational expense.
Companies must evaluate whether spending on AI tools, platforms, and tokens is reducing overall hiring costs. Time to hire is another important metric. For instance, if an organization previously took 21 days to fill a vacancy, AI-enabled recruitment should ideally help reduce that period to 14 days by reaching and evaluating more candidates quickly.
The third and perhaps most important metric is quality of hire. Kartik explains that organizations must assess whether shortlisted candidates perform well after joining and remain with the company for a reasonable period.
He particularly highlights early attrition, commonly referred to in the HR industry as employees leaving within the first 30 days. If AI-powered screening improves recruitment quality, organizations should ideally see fewer candidates leave during this initial period.
He believes these three metrics should remain central to recruitment strategies, regardless of the technology being deployed.
Successfully integrating AI into recruitment requires a cultural shift. He explains that some recruiters initially viewed AI agents and bots as potential competitors rather than tools that could support their work. This created skepticism, especially when AI systems misidentified suitable candidates.
Kartik believes organizations must clearly communicate AI's benefits and position it as a workforce capability rather than just another HR technology project. Recruiters need to understand how to work with AI, interpret its recommendations and identify situations where human intervention is necessary.
He also highlights the importance of building AI literacy across different business functions rather than limiting it to HR teams. Kartik points out that AI-related job postings, previously led by sales and business development at about 20%, are increasingly appearing across marketing, brand management, digital marketing, teaching and training.
He compares the growing importance of AI familiarity to the expectation that professionals should know tools such as PowerPoint and Excel. Organizations must also account for changing skill requirements within existing roles and consider the costs associated with deploying AI models.
One development that has surprised Kartik is the adoption of AI-powered recruitment tools among small and medium enterprises (SMEs).
While large enterprises are adopting AI to improve operational efficiency, several smaller businesses are also embracing the technology to simplify recruitment.
Kartik explains that many SMEs lack dedicated HR teams or professional recruiters. AI-powered screening tools can therefore help business owners identify suitable candidates without managing the entire recruitment process manually.
He points out that India has approximately 10 crore small and medium enterprise businesses, while the government’s Udyam portal has around 5.3 crore registrations, according to the figures he shared.
For smaller businesses, AI can make recruitment more manageable by helping them identify and connect with relevant candidates. He believes adoption is gathering momentum in India, which he identifies as the second-largest market for several AI tools.
Workforce intelligence could become a key differentiator for Indian enterprises as organizations increasingly use employee and candidate data to make business decisions.
Kartik explains that companies already possess considerable information about candidates and employees. However, this information is often spread across different databases, making it difficult to connect individual data points and generate meaningful insights.
He believes the real advantage will come from integrating workforce information and using it to respond to changing business requirements.
For example, organizations could identify employees whose skills match emerging business requirements or who may be willing to work from a different geographical location. This would allow companies to make better use of their existing workforce instead of relying entirely on external hiring.
Kartik also observes that AI skill adoption is growing among candidates, while employer demand is expanding beyond traditional technology requirements. He believes connecting workforce data with evolving business needs will become increasingly important for organizations seeking to improve productivity and remain competitive.
The future of recruitment will depend on how effectively organizations combine AI capabilities with human judgment and workforce intelligence.
AI can help companies reach larger candidate pools, automate repetitive screening activities and improve the assessment process. However, its effectiveness will depend on clear business objectives, appropriate guardrails, continuous monitoring, and recruitment teams' ability to interpret its recommendations.
Kartik believes organizations must treat AI as a force multiplier that enhances human capabilities, not as a replacement for recruiters.
For Indian enterprises, the opportunity extends beyond faster hiring. By connecting workforce data, identifying emerging skills and understanding changing employee capabilities, businesses can make more informed decisions about their talent requirements.
Ultimately, AI's success in recruitment will depend on whether it delivers measurable improvements in hiring costs, recruitment timelines, and the quality of talent organizations bring on board.
The full discussion can be heard on the Analytics Insight Podcast.