National Strategy for Artificial Intelligence (#AIFORALL): What It Says and What It Became

A clear explanation of India's National Strategy for Artificial Intelligence, released by NITI Aayog in June 2018 under the tagline #AIFORALL: the five priority sectors, the CORE and ICTAI research structure, the barriers it flagged, and how the strategy evolved into the Responsible AI papers and the IndiaAI Mission. Includes what it actually means for students, professionals and institutions today.

The short answer

The National Strategy for Artificial Intelligence is a discussion paper released by NITI Aayog in June 2018, and #AIFORALL was its tagline. It argued that India should not chase leadership in building the biggest AI models, but should aim to be the place where AI is applied to problems that affect large populations, then export those solutions to other developing countries. The paper called this the AI Garage for 40 percent of the world.

It picked five sectors to focus on, laid out a two-tier research structure, listed the things blocking adoption, and asked for a national compute and data backbone. It was never a law and never had a budget attached. It was a direction-setting document, and most of what India has done on AI since 2018 traces back to it in some form.

Priority sector	Why the strategy chose it
Healthcare	Doctor and specialist shortage, weak rural access, high diagnostic load
Agriculture	Small landholdings, yield gaps, price and weather uncertainty
Education	Teacher shortages, uneven quality, high dropout at scale
Smart cities and infrastructure	Rapid urbanisation with strained civic systems
Smart mobility and transportation	Congestion, road safety, and logistics inefficiency

What was actually inside the paper

The research architecture was the most concrete proposal. NITI Aayog wanted Centres of Research Excellence (COREs) doing fundamental AI research inside academic institutions, and International Centres for Transformational AI (ICTAIs) doing applied work with industry, funded through public-private partnership. Above both sat a proposed national cloud and compute platform, AIRAWAT, so that researchers and startups without money for hardware could still train models.

The barriers section aged better than anything else in the document. It was blunt about why India was not going to get AI adoption by wishing for it.

• Very small pool of people with real AI research and deployment experience
• No usable data ecosystem: data existed, but scattered, unlabelled and legally unclear
• High cost of compute and low awareness among the businesses that would benefit most
• No clear rules on privacy, security and ethics, which stalled anything involving personal data
• Little collaboration between research, industry and government, so pilots stayed pilots

What happened after 2018

The strategy did not stay a PDF. NITI Aayog followed it with the Responsible AI for All papers, the first on principles in February 2021 and the second on operationalising them later that year. India then chaired the Global Partnership on AI and hosted the GPAI Summit in New Delhi in December 2023.

The real successor arrived in March 2024, when the Union Cabinet approved the IndiaAI Mission with an outlay of roughly ₹10,371 crore over five years. That is where the 2018 wishlist got funding: shared GPU compute, a national datasets platform, an innovation centre for foundation models, startup financing, a safe and trusted AI pillar, and a skilling pillar called IndiaAI FutureSkills. If you are reading the 2018 strategy today, read it as the diagnosis and the IndiaAI Mission as the prescription.

Year	Milestone
2018	NITI Aayog releases National Strategy for AI, #AIFORALL
2020	MeitY and Intel launch the free AI For All digital literacy course
2021	Responsible AI for All: principles, then operationalisation
2023	India chairs GPAI and hosts the summit in New Delhi; DPDP Act passed
2024	Cabinet approves IndiaAI Mission, roughly ₹10,371 crore over five years
2026	India hosts the AI Impact Summit in New Delhi

A common mix-up: two different things are called AI For All

If you searched this phrase and landed on a four-hour free online course, that is not the NITI Aayog strategy. MeitY and Intel launched a separate initiative also named AI For All, a short self-paced awareness programme for the general public, with a certificate at the end. It is genuinely useful for a parent, a teacher or a school student who wants to know what AI is.

It will not make you employable. That is not what it was built for, and pretending otherwise is how people waste six months collecting certificates that no hiring manager reads.

What this means if you are a student, a professional or an institution

Policy documents matter to individuals only when they change where money and jobs go. Here the link is direct. Compute is being subsidised, datasets are being centralised, and the government has explicitly said the bottleneck is people, not machines. Every one of the five priority sectors from 2018 now has funded application work attached to it, and almost all of it needs people who can build and deploy, not people who can define AI.

So the practical read is this. If you are a student or early-career professional, the skill that pays is shipping something in one of those sectors: a working agriculture advisory bot, a document workflow for a hospital's front desk, an automation that saves an SME six hours a week. One deployed project beats a stack of completion certificates in an interview, every time. That is the gap the AI Creator Fellowship is built for, eight weeks of building real AI projects and automations with mentors, a cohort and placement support, rather than watching lectures alone.

For colleges and schools, the version of #AIFORALL that matters is capacity on campus. The NEP and CBSE already push AI into the curriculum, but a subject on paper without a lab, a teacher who has built something, and a certification path produces students who can spell AI and nothing else. IndiaFutureAI's AI workshops and certification programmes exist for exactly that gap, and if you run an institution and want to know what a working AI lab needs, tell our team what you already have and we will map the rest.

The 2018 strategy asked a question India has still not fully answered: who is going to build all this. If you want to be in that answer, start building now.

FAQs

1. Is the National Strategy for AI a law or a policy India must follow?

Neither. It is a discussion paper published by NITI Aayog in June 2018 with no statutory force and no budget of its own. Its recommendations were later picked up through separate decisions, most notably the IndiaAI Mission approved in March 2024.

2. Where can I download the NITI Aayog AI strategy PDF?

It is hosted free on the NITI Aayog website under publications, titled National Strategy for Artificial Intelligence #AIFORALL. It runs to about 115 pages, and the barriers and sector chapters are the parts worth reading closely.

3. What are COREs and ICTAIs?

Centres of Research Excellence (COREs) were proposed for fundamental AI research inside academic institutions, while International Centres for Transformational AI (ICTAIs) were meant for applied, commercially oriented research run through public-private partnerships. The two-tier split was the core institutional idea of the 2018 strategy.

4. How is the IndiaAI Mission different from the 2018 strategy?

The 2018 strategy diagnosed the problems and recommended fixes without funding. The IndiaAI Mission, approved in March 2024 with an outlay of around ₹10,371 crore over five years, actually funds compute, datasets, startups, safe AI research and skilling across seven pillars.

5. Is the free AI For All course by MeitY and Intel worth doing?

It is a good four-hour awareness programme if you want to understand what AI is and get a certificate, and it costs nothing. It is not a job-ready course, so treat it as a starting point rather than training.

6. Which sectors did India's AI strategy prioritise?

Five: healthcare, agriculture, education, smart cities and infrastructure, and smart mobility and transportation. They were chosen for social impact at scale rather than for commercial return.

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