Has the Indian Public Been Able to Adopt AI?
-by Jaya Pathak
Artificial Intelligence is spreading quickly in the Indian cities, especially urban areas. People are adopting AI in their everyday chores. But the problematic picture is that even after a massive adoption of AI, the reach is unequal among various demographics and geographies. The net picture is of a country leading regional consumer usage while still working to close gaps in connectivity, affordability, and trust that shape nationwide outcomes.
Here, in this blog, we will discuss whether the Indian public has been able to adopt AI.
Adoption landscape:
In 2025, India ranked first in the Asia–Pacific region for consumer generative AI uptake, with 56% of city adults using such tools, up from 44% a year earlier, signalling a broadening base of habitual users.
Awareness is similarly elevated, with a majority of surveyed adults reporting that they “understand AI well,” a precursor to sustained mainstream usage when paired with simple, low-friction experiences.
Policy and infrastructure:
The Cabinet-approved IndiaAI Mission has allocated ₹10,371.92 crore over the next five years to strengthen the country’s AI infrastructure. The initiative focuses on enhancing computing capacity, creating open public datasets, developing advanced skill programs, and supporting AI-driven startups to ensure that policy efforts translate into impactful, user-facing AI services. India’s computing ecosystem is rapidly expanding, with tens of thousands of GPUs already operational and the demand expected to touch nearly 100,000 units as new deployments increase.
Urban lead, uneven base:
The spread of digital technology, especially artificial intelligence is happening mainly in big cities, but plenty of people in India are still not part of the digital economy because of issues like cost, lack of device access and not enough content in local languages. In order to include everyone, technology must be made affordable and available in Indian languages so that it can be made easy to use option.
In order to bridge this digital divide, a continued investment is required for the expansion of internet and device access, specially to design simple tools such as voice based interaction, which can help people to fulfil their language needs. Another possible key advantage can be to make the process of inclusion broader and fairer. It is possible only if more and more people will join this digital wave,especially from rural and underserved areas.
Everyday touchpoints:
Most people first notice this shift in everyday tools—chat windows, voice assistants, and study or productivity apps that shorten the wait for answers and tidy routine work. You see it even more at business touchpoints: banks, retail platforms, and service providers now weave the same approach into support, personalization, and risk checks, so the better option quietly becomes the default.
Across companies, well over eight in ten are testing agentic or autonomous setups, which means users will increasingly rely on multi‑step automated flows running in the background.
Talent and ecosystem:
India’s developer and data talent base underpins product availability, with national initiatives highlighting skilling, datasets, and compute as complementary pillars of an innovation pipeline.
As infrastructure costs decline and model access improves, this talent supply accelerates the conversion of prototypes into reliable consumer-facing utilities.
Economic stakes:
Artificial intelligence has the potential to add almost 500-600 billion dollars to the GDP of India by 2035, chiefly by improving work productivityand services better for consumers. Sectors, which are used highly by public such as financial services, healthcare, retails and education can turn these business benefits into faster and useful services for regular people.
Barriers that still matter:
There are several barriers such as trust, safety and data governance. Henceforth, it is very important to have a clear set of rules and regulations about the privacy of data to promote safety among users. For a sustained adoption of AI, transparent disclosures areessentially required. Plethora of people are still struggling with issues including affordability and device access. Therefore, making technology affordable and available will serve as the common good of all.
What will determine the next phase:
Three levers are most likely to govern diffusion beyond early adopters: accessible compute (including shared GPU clusters), credible local-language experiences, and enterprise–public sector partnerships that ship reliable, everyday tools.
If these levers continue to align, adoption should transition from city-led enthusiasm to durable, nationwide usage embedded in routine transactions and services.
Conclusion:
Yes, the Indian public is adopting AI, with metros setting the pace and a clear policy–infrastructure pipeline accelerating access, but the journey to universal use depends on inclusion, trust, and daily relevance.Given rising compute capacity, proactive policy, and strong enterprise interest, the conditions exist to convert today’s momentum into broad-based participation over the next few years.
