Tracking the adoption and impact of generative AI among working-age adults in India. Understanding how AI is transforming work and daily life across the nation.
Survey Data from India | April 2026Percentage of respondents who use gen AI for work, outside of work, and overall. All figures are weighted by age, gender, and urbanicity.
How does GenAI adoption in India compare to the US? Data from GenAI Adoption Tracker (November 2025)
This project tracks the adoption and impact of Generative AI among working-age adults in India. It is run by Kiran Garimella at Rutgers University School of Communication and Information.
The survey uses the instrument developed by the Real-Time Population Survey (RPS), a national online labor market survey of working-age adults aged 18-64 that has run in the United States since 2020. This project aims to provide similar insights into GenAI adoption for India, comparable to the US GenAI Adoption Tracker.
The project collects data from N=1,795 Indians drawn from both an online panel and an on-ground rural face-to-face top-up, reweighted by age, gender, and urbanicity to match India's population. We plan to collect data every 3 months to track trends over time. For the full methodology and results, see the working paper.
The survey data was collected from multiple sources to capture a diverse cross-section of the Indian population. One source is an online crowd-worker platform, Clickworker (similar to Prolific or Amazon Mechanical Turk), which provides access to a broad pool of internet-connected respondents. In addition, approximately 600 respondents were sampled offline from rural areas to ensure representation of populations that are less likely to be reached through online-only recruitment. All responses were reweighted by age, gender, and urbanicity to align with India's population distribution.
Every wave included in the current pooled estimate and the tracker-over-time series. Online tracker waves contribute to the time-series headline; the rural Uttar Pradesh face-to-face supplement is a one-time top-up that corrects the online panel's urban and educational skew and is not plotted as a tracker point.
| Wave ID | Label | Date | Mode | N (after filter) | Series |
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The reported numbers should be interpreted as upper-bound estimates of Generative AI adoption in India. Despite reweighting for age, gender, and urbanicity, the sample likely over-represents online participants who are inherently more likely to use Generative AI tools. There is no straightforward way to correct for the biases introduced by self-reporting and self-selection in survey participation. Readers should keep these limitations in mind when interpreting the results.
For raw data access or more insights, please contact:
kg766@rutgers.edu
If you are interested in funding this project and helping expand it to other Global South countries, please reach out to Kiran.