India’s AI Moment: How Policy, Platforms and People Are Shaping an Inclusive Tech Powerhouse

The article examines India’s IndiaAI Mission, its focus on inclusive growth, shared compute, localised models such as BharatGen AI, and governance measures to ensure safe, accountable AI across public services and the informal workforce.

Artificial Intelligence

-Prakash KL

India
is
trying
to
steer
the
global
Artificial
Intelligence
wave
rather
than
simply
follow
it.
With
the
IndiaAI
Mission,
large-scale
compute
access
and
a
focus
on
inclusion,
policymakers
are
treating
AI
as
a
development
tool.
The
aim
is
clear:
AI
should
improve
daily
life
for
workers,
students,
patients,
farmers
and
public
institutions
across
the
country.

India’s
broader
technology
ecosystem
is
already
reflecting
this
shift.
The
Stanford
University
2025
Global
AI
Vibrancy
Tool
places
India
third
worldwide
for
AI
competitiveness.
This
ranking
captures
growth
in
research,
startups,
talent
and
policy.
With
over
six
million
people
in
tech
and
AI-linked
roles,
and
more
than
1,800
Global
Capability
Centres,
the
ecosystem
is
expanding
quickly.

India
AI
mission
and
policy
vision

The
IndiaAI
Mission,
cleared
in
March
2024
with
₹10,371.92
crore
for
five
years,
sits
at
the
centre
of
this
push.
Guided
by
the
slogan
“Making
AI
in
India
and
Making
AI
Work
for
India”,
it
seeks
a
sovereign,
scalable
and
accountable
AI
framework.
Seven
pillars
cover
compute,
datasets,
foundation
models,
skilling,
startups,
application
development
and
safety.

Unlike
past
digital
phases
that
mainly
supported
organised
urban
sectors,
the
latest
India
AI
strategy
is
consciously
citizen-first.
Authorities
want
AI
to
narrow
long-standing
social
and
economic
gaps,
not
deepen
them.
That
approach
shapes
rules,
investment
choices
and
infrastructure,
ensuring
communities
in
smaller
cities
and
villages
can
benefit
alongside
metropolitan
hubs.

India
AI
infrastructure,
compute
and
datasets

One
of
the
toughest
barriers
in
AI
projects
worldwide
is
access
to
high-end
computing.
India
has
responded
by
growing
GPU
capacity
far
beyond
its
starting
plan.
Against
an
initial
goal
of
10,000
GPUs,
more
than
38,000
GPUs
have
already
been
onboarded,
with
subsidised
access
for
startups
and
researchers
who
usually
face
steep
hardware
costs.

This
focus
on
shared
infrastructure
signals
a
belief
that
ideas
should
not
be
blocked
by
capital.
When
small
firms
and
academic
teams
can
tap
powerful
compute
through
public
facilities,
more
experiments
become
feasible.
That,
in
turn,
helps
India
AI
developers
create
tools
tailored
to
local
markets
instead
of
depending
on
foreign
platforms
and
budgets.

Data
is
the
second
critical
building
block,
and
here
AIKosh
plays
a
key
role.
AIKosh
is
the
national
dataset
platform
that
collects
thousands
of
curated
datasets
and
AI
models
from
many
domains.
For
a
country
with
wide
linguistic,
cultural
and
economic
diversity,
such
a
resource
helps
ensure
India
AI
applications
remain
accurate
for
local
users
and
conditions.

India
AI
models,
languages
and
localisation

India
is
also
moving
towards
its
own
Large
Multimodal
Models,
reducing
dependence
on
tools
trained
mainly
on
Western
data.
BharatGen
AI,
which
supports
22
Indian
languages
and
combines
text,
audio
and
images,
is
a
notable
example.
This
localisation
supports
courts,
classrooms,
health
systems
and
welfare
departments
that
require
AI
which
understands
Indian
languages
and
contexts.

Tasks
like
translating
court
orders,
offering
voice
access
to
portals
or
screening
welfare
claims
demand
such
tailored
models.
Systems
built
for
India
AI
need
to
handle
dialects,
code-switching
and
region-specific
knowledge.
For
that
reason,
localisation
is
treated
as
essential
infrastructure
rather
than
a
luxury
feature,
especially
in
governance
and
public-facing
services.

India
AI
and
the
informal
workforce

The
biggest
test
for
this
national
project
lies
in
India’s
informal
workforce
of
nearly
490
million
people.
NITI
Aayog’s
October
2025
report,
“AI
for
Inclusive
Societal
Development”,
studies
this
question
directly.
The
report
suggests
AI
should
raise
productivity
and
support
human
effort,
instead
of
simply
replacing
workers
across
sectors
such
as
construction,
domestic
work
and
small
retail.

A
central
idea
within
the
report
is
the
proposed
Digital
ShramSetu
Mission.
This
initiative
plans
to
combine
India
AI
with
IoT,
blockchain
and
immersive
learning
tools.
Its
goal
is
to
address
persistent
challenges
for
informal
workers,
including
delayed
wages,
weak
bargaining
power,
poor
access
to
healthcare
and
limited
routes
to
skills
and
financial
products.

The
design
of
Digital
ShramSetu
focuses
on
practical
use.
Voice-first
interfaces
can
help
workers
who
may
not
read
well.
Micro-credentials
allow
short,
stackable
training
modules.
Smart
contracts
can
ensure
transparent
payment
flows.
Together,
these
features
seek
to
link
workers,
employers,
training
providers
and
service
agencies
through
a
structured
India
AI-enabled
platform.

India
AI
skills,
jobs
and
education

Concerns
about
AI-linked
job
losses
still
surface
in
many
debates,
but
available
evidence
is
more
mixed.
Demand
for
skills
in
data
science,
analytics,
model
engineering
and
data
curation
is
rising
in
India.
Almost
90
percent
of
new
startups
now
use
AI
somewhere
in
their
operations,
creating
new
profiles
rather
than
only
automating
existing
roles.

Government-supported
programmes
aim
to
prepare
a
wide
talent
pipeline
for
this
shift.
FutureSkills
PRIME
and
the
expanded
IndiaAI
Fellowship
train
students
and
working
professionals
from
diverse
disciplines,
not
just
engineering
backgrounds.
Special
focus
on
Tier
2
and
Tier
3
cities
indicates
a
desire
to
spread
India
AI
opportunities
beyond
major
tech
centres
like
Bengaluru,
Hyderabad
and
Gurugram.

India
AI
in
governance
and
public
services

AI
is
also
changing
how
public
authorities
deliver
services
and
manage
risks.
AI-assisted
weather
modelling
and
disaster
response
tools
help
forecast
cyclones,
floods
and
heatwaves
more
accurately.
Under
the
e-Courts
Project,
multilingual
digital
platforms
are
making
judicial
processes
easier
to
access.
These
efforts
show
how
India
AI
can
support
both
speed
and
fairness
in
governance.

Language
remains
a
major
barrier
in
many
schemes,
and
here
the
Bhashini
initiative
plays
an
important
role.
Bhashini
works
to
remove
language
friction
across
digital
public
services.
By
linking
translation
and
speech
technologies
to
India
AI
systems,
it
enables
residents
to
interact
with
portals,
chatbots
and
helplines
in
their
own
languages,
rather
than
shifting
to
English
or
Hindi.

India
AI
safety,
ethics
and
global
role

Such
large
ambitions
need
strong
checks
and
safeguards.
Policymakers
accept
that
India
AI
progress
must
go
together
with
clear
rules
around
privacy,
fairness
and
accountability.
The
“Safe
and
Trusted
AI” pillar
within
the
IndiaAI
Mission
focuses
on
bias
detection,
explainability
mechanisms
and
privacy-preserving
architectures.
This
reflects
a
view
that
public
trust
is
as
important
as
technical
performance.

India’s
track
record
with
large
digital
platforms
shapes
global
expectations
for
its
AI
work.
Systems
like
Aadhaar
and
UPI
have
shown
that
wide-scale,
low-cost
digital
infrastructure
is
possible
when
built
around
public
purpose.
As
India
prepares
for
the
AI
Impact
Summit
2026,
officials
are
keen
to
present
AI
as
a
tool
for
more
equal
growth
rather
than
narrow
profit.

India’s
AI
pathway
is
therefore
about
more
than
leadership
in
models
or
compute
capacity.
It
is
challenging
older
ideas
of
progress
by
tying
India
AI
ambitions
to
inclusion,
public
infrastructure
and
secure
innovation.
The
outcomes
will
be
judged
not
just
by
rankings,
but
by
how
many
lives
gain
better
services,
stronger
livelihoods
and
wider
opportunities.