New Delhi, Dec. 3 -- Real-time digital hiring signals, analysed through AI, offer India a powerful new way to anticipate and close emerging skill gaps
India's debate on skills often concentrate on curriculum reforms, institutional capacity, and the widening gap between education and employer needs. However, a more fundamental challenge concerns our ability to interpret the labour market almost in real time to assess skill shortages (a shortfall in the number of workers required to meet a particular vacancy) and skill gaps.
For years, India has relied on surveys, administrative data, and employer consultations to identify skill shortages. Yet these methods are now insufficient in a labour market transformed by Artificial Intelligence (AI), cloud computing, e-commerce, platform-based work, electric-vehicle manufacturing, digitised services, and rapid technological developments.
The nature of work is evolving swiftly, with new roles emerging and demand fluctuating monthly, making traditional tracking methods inadequate. Fortunately, India's digital platforms generate continuous, high-frequency indicators that reveal how skills are changing. When these signals are responsibly analysed using AI and machine learning (ML), they allow skilling systems to become more adaptable, precise, and forward-looking.
Key signals come from major job portals such as Naukri, LinkedIn Jobs, and similar sites. These platforms list millions of job postings with detailed descriptions of the skills employers seek. They offer insights beyond simple vacancies, indicating which technical tools are gaining popularity, which job categories are expanding, how salary ranges are evolving, where companies are flexible on experience, and which cities are becoming hiring hubs.
Advanced natural-language processing (NLP) and ML models can analyse these large datasets to identify emerging roles, regional hiring trends, and early indications of skill shortages. These AI tools can categorise job descriptions, group similar roles, analyse skill overlaps, and track the spread of new technologies across sectors.
Professional networking platforms like LinkedIn offer another essential layer of workforce insight. Signals such as recruiter posts, urgent job openings, referral requests, emerging certification trends, changes in candidate search locations, and surges in skill endorsements collectively form a behavioural map of labour-market dynamics. Using AI-based pattern recognition, these indicators reveal how long job openings remain unfilled, where mid-career shortages are intensifying, and how occupational shifts occur in real time. These insights originate from genuine professional activity and serve as the digital fingerprints of the labour market.
India's platform economy broadens this landscape beyond white-collar jobs, providing insights into emerging economic hotspots across other sectors. Platforms such as Urban Company, Swiggy, Zomato, Ola, Uber, and Upwork offer ongoing indicators of demand for technicians, drivers, delivery workers, freelancers, electricians, and repair experts. AI and ML tools can analyse these behavioural data to identify shifts in consumption, logistical issues, and technological adoption - whether it's the growth of rooftop solar, the rise of home automation, or the expansion of EV charging stations.
Image-recognition models can also analyse geotagged visual data from public sources such as construction activity, warehouse occupancy, EV infrastructure growth, or retail footfall, to gauge local economic activity and forecast future workforce demand. Additionally, ed-tech platforms with millions of monthly learners reveal which skills young Indians view as future-ready. ML models can identify growing interest in areas such as cloud security, product management, data science, and digital marketing in Tier-II and Tier-III cities, revealing emerging talent pools before they appear in corporate datasets.
AI/ML-based methods of assessing and forecasting demand for workers may not apply to more traditional sectors or to sectors where firms hire through networks rather than public job postings. Nor do they fully address skill gaps, i.e. cases in which workers have the appropriate educational background but lack the requisite skills.
However, alternative data need not substitute traditional surveys and employer consultations; instead, it complements and reinforces existing trends. For example, AI models may detect rising demand for embedded-systems engineers in a specific area. This could prompt targeted surveys or industry discussions to understand the underlying causes.
Persistent recruitment difficulties for mid-career data architects identified on LinkedIn may encourage Sector Skill Councils to redesign upskilling initiatives. An increase in cybersecurity course enrolments might indicate regional ambitions for digital roles, prompting institutions to expand relevant offerings. These indicators provide evidence-based guidance for decision-making rather than relying solely on intuition.
India's research community has already begun adopting this integrated approach. The MSDE-National Council of Applied Economic Research (NCAER), in its national skill-gap study across high-growth sectors, used alternative data sources alongside surveys and firm interviews. This mixed approach-enhanced by AI-driven trend analysis-recognises that India's labour market is too fluid for traditional tools alone. It demonstrates how integrating conventional data with digital signals offers a more comprehensive, current, and precise picture of industry shifts.
For example, using Naukri.com date for nine cities in 2024, we found that most advertised jobs were in data and cloud roles, with over 70 per cent in the BFSI sector. Different cities exhibit different patterns. Bengaluru sees strong demand for software development, while Delhi shows high demand for both data-and-cloud roles and software development. Both cities, however, show the greatest need for workers with 2-5 years of experience or more than five years.
Instead of waiting for labour-market trends to materialise, India can now forecast them in advance. Through AI-based analytics, skill-adjacency modelling, and predictive labour-market forecasting, universities can update their curricula more rapidly; Sector Skill Councils can revise occupational standards more regularly; states can identify emerging talent hotspots early; and employers can build workforce pipelines before shortages become critical.
Young Indians also stand to gain, as real-time insights into growing occupations, wage patterns, and regional demand trends can help them make better- informed decisions about skills and career pathways.
The skilling ecosystem can be transformed by responsibly utilising digital signals from hiring platforms, labour-market activity, and workforce behaviour, all while adhering to strict privacy standards under the DPDP Act. With world-class digital infrastructure, abundant talent, and increasing momentum, India can build a skilling system that is far more adaptive and insight-driven. By harnessing digital hiring data, platform behaviour, AI-enabled analytics, ML-based trend recognition, and image-driven economic indicators, India can create a dynamic, future-oriented workforce strategy aligned with its rapidly evolving economy. Such an approach embodies the vision of 'Viksit Bharat', where opportunity and capabilities rise together, and every region has the skills foundation to participate fully in India's growth journey.
No Techcircle journalist was involved in the creation/production of this content.
Published by HT Digital Content Services with permission from TechCircle.