AI and the HR Function: How India's People Leaders Are Automating Hiring, Engagement and Learning

When Tata Consultancy Services announced in early 2024 that it would be deploying an AI-powered platform to assess the skills of its 600,000-employee global workforce, the announcement was notable less for its ambition than for what it implied: that one of the world's largest employers had decided that the scale of talent management demanded by its business was simply too large to be addressed by human HR processes alone. AI was not a supplement to the HR function at TCS — it was becoming load-bearing infrastructure.

TCS is not alone. Across India's technology sector — from the listed IT services giants to the Series B startups of Bengaluru's Koramangala neighbourhood — the deployment of artificial intelligence in the HR function has accelerated dramatically over the past twenty-four months. The catalyst, as in so many other domains, was the release of ChatGPT in November 2022 and the subsequent rapid mainstreaming of large language model technology. But the underlying momentum came from something more specific to the HR profession: a growing recognition that the volume, velocity and complexity of people data in large organisations had outrun the capacity of human analysts to process it.

This piece examines how India's CHROs are deploying AI across the three core domains of the HR function — talent acquisition, employee engagement and learning and development — and what the evidence says about what is actually working.

AI in Talent Acquisition: Beyond Resume Screening

The first and most widespread application of AI in Indian HR has been in talent acquisition. Resume screening — the laborious process of manually reviewing thousands of applications to identify a handful of qualified candidates — was an obvious target for automation. By 2022, the majority of large Indian technology companies were already using some form of applicant tracking system with basic keyword matching. The arrival of large language models has made the screening process significantly more sophisticated.

Modern AI-powered screening tools can now assess not just keyword matches but semantic similarity between job requirements and candidate experience, identify transferable skills that a keyword match would miss, and flag potential bias patterns in job descriptions that might be deterring qualified candidates from applying. iimjobs.com, Naukri.com (owned by Info Edge India), and LinkedIn India have all built AI layers into their talent platforms that improve match quality for both candidates and employers.

The more significant development is the deployment of AI in candidate assessment. HireVue, the video interviewing platform used by several large Indian MNCs and GCCs, uses AI to analyse speech patterns, facial expressions and content coherence in recorded video interviews to generate candidate assessments. The platform has faced significant scrutiny globally regarding potential bias in its AI models, and several Indian CHROs have moved to limit its use to preliminary screening rather than final evaluation decisions.

Mettl, the Mercer-owned assessment platform with strong roots in India, has developed AI-powered cognitive and behavioural assessments that are now used by over 3,000 Indian companies for campus hiring, lateral hiring and leadership assessment. The platform's India business grew by 35% in FY2024, reflecting strong adoption particularly in the BFSI sector and among GCCs expanding their India teams.

"We processed 400,000 job applications last year for 12,000 positions. There is simply no way to give every application meaningful human attention at that volume. AI screening is not replacing our judgement — it is making our judgement possible by reducing the pile to a manageable size." — Head of Talent Acquisition at a large Indian IT services company, speaking at Nasscom's HR Summit 2024.

Interview scheduling — the logistically complex process of coordinating interviewer availability with candidate availability across time zones — is another area where AI automation has delivered immediate, measurable value. Platforms like Calendly, Paradox (Olivia), and the scheduling modules within Darwinbox and Keka are now handling the end-to-end scheduling workflow for thousands of Indian companies, reducing time-to-schedule from an average of 3.2 days to under 4 hours in documented cases.

AI in Employee Engagement: From Annual Survey to Continuous Signal

The annual employee engagement survey — that ritual of corporate life in which employees rate their managers, their work environment and their sense of purpose on a Likert scale, generating a report that arrives six months after the data was collected — is increasingly recognised as inadequate for the pace at which organisations need engagement intelligence.

The shift is toward continuous listening: shorter, more frequent pulse surveys, combined with passive signal detection from communication platforms, that generate real-time engagement data. Several Indian companies have moved to weekly or fortnightly pulse surveys using platforms like Glint (now part of LinkedIn), Culture Amp, and the Indian-built Amber from InFeedo. Amber, which uses a conversational AI interface to conduct employee check-ins via chat, has been particularly well received in the Indian market, where employees are sometimes more willing to share concerns with a perceived-neutral AI system than in a survey that feels directly connected to their manager.

InFeedo's data from over 300 Indian enterprise clients shows that AI-driven continuous listening programmes identify at-risk employees — those showing signs of disengagement or flight risk — with approximately 72% accuracy when measured against actual attrition outcomes six months later. The ability to intervene before an employee has made the decision to leave, rather than after, is where the ROI of continuous listening programmes is most visible.

Attrition prediction models represent the most commercially impactful AI application in engagement. These models, typically built on a combination of performance data, compensation benchmarking, tenure patterns, learning activity, manager quality scores and external labour market data, can identify the probability that a given employee will leave the organisation within the next 90 days. The most sophisticated versions, deployed by large IT services companies, have reduced voluntary attrition by 15–22% in pilot programmes by enabling timely, targeted retention interventions.

AI in Learning and Development: Personalisation at Scale

Learning and development is the HR domain where AI is creating perhaps the most significant structural change. Traditional corporate learning — classroom-based training, fixed curriculum, cohort scheduling — was built for an era when skills changed slowly and learning could be planned on an annual cycle. The current environment, in which the half-life of technical skills in some domains (machine learning, cloud architecture, cybersecurity) is measured in months rather than years, has made this model obsolete.

AI-powered learning platforms can now create personalised learning paths for individual employees based on their current skills profile, their role requirements, their career aspirations and their learning history. Infosys's Lex platform — which serves the company's 350,000+ global employees — uses machine learning to surface relevant content from a library of over 700,000 learning objects, in over 100 formats, based on individual learner behaviour and skills gaps identified through regular assessments.

Wipro's TalentNext initiative, launched in 2023, uses AI to map every employee's skills against a forward-looking taxonomy of capabilities required by Wipro's enterprise clients, identifying gaps and automatically assigning targeted learning content. The initiative has upskilled over 200,000 employees in its first year, with AI ensuring that the learning assignments are genuinely relevant rather than generic.

Generative AI is now being applied to content creation within L&D. Leading Indian companies are using large language models to convert internal knowledge — documented processes, expert interviews, technical documentation — into structured learning content: microlearning modules, quizzes, case studies and simulations. What previously took a learning design team six weeks to produce can now be generated in days, with the team's effort focused on quality review and contextualisation rather than content creation from scratch.

The Ethics and Governance Dimension

The rapid deployment of AI in HR has generated a set of governance questions that India's CHROs are navigating with varying degrees of sophistication. Bias in AI systems — particularly in hiring algorithms that may encode historical discrimination patterns — is the most widely discussed concern. The IEEE's guidance on ethical AI in HR, combined with India's emerging data protection framework under the Digital Personal Data Protection Act 2023, is creating a compliance environment that responsible CHROs must understand and manage.

Several Indian companies have established AI ethics committees that review HR AI deployments before they are approved for use. These committees typically include the CHRO, the Chief Data Officer or Chief AI Officer, the Chief Legal Officer, and an independent ethics advisor. The existence of such governance structures does not guarantee ethical AI deployment, but it signals a maturity of approach that is increasingly expected by investors, regulators and employees themselves.

The most thoughtful Indian CHROs are clear that AI is a decision-support tool, not a decision-making tool, in the HR function. Algorithmic outputs inform human judgements; they do not replace them. In a country where employment decisions carry profound social and economic consequences for individuals and families, maintaining human accountability at the decision point is both ethically necessary and practically wise.

Building the AI-Ready HR Function

The strategic question for India's CHROs is not whether to deploy AI in the HR function — that debate is largely settled — but how to build the organisational capabilities required to do so effectively and responsibly.

The capability requirements are significant. HR teams need data literacy — the ability to understand what AI outputs mean, what their limitations are, and when to challenge algorithmic recommendations. They need vendor evaluation skills — the ability to assess HR technology platforms critically rather than being dazzled by demonstrations. And they need change management capabilities — the ability to bring employees and managers along with AI-driven HR changes in ways that build trust rather than generating anxiety.

The CHROs who are building these capabilities systematically — investing in their HR teams' analytical skills, establishing robust AI governance frameworks, and maintaining relentless focus on the human outcomes that AI is supposed to improve — are the ones who will define what excellent HR leadership looks like in India's AI era.