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 | February 2026Percentage of respondents who use gen AI for work, outside of work, and overall. All figures are weighted by age and gender.
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 over N=1,500 Indians from an online and on ground sample, reweighted by age and gender. We plan to collect data every 3 months to track trends over time.
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 and gender to align with India's population distribution.
The reported numbers should be interpreted as upper-bound estimates of Generative AI adoption in India. Despite reweighting for age and gender, 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.
In addition to the survey data, this project includes actual ChatGPT usage data collected by asking N=600 participants to export their complete ChatGPT conversation histories. The conversation topics were classified using the same methodology as described in "How People Use ChatGPT" by Chatterji et al. from OpenAI. This allows us to compare actual usage patterns in India with global ChatGPT usage patterns (more analysis of this data coming soon).
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.