Future of Generative AI: Predictions and Possibilities

Generative
AI
has
emerged
as
one
of
the
prominent
highlights
of

artificial
intelligence.
Many
industries
have
embraced
the
diverse
use
cases
of
generative
AI
with
the
help
of
tools
such
as

ChatGPT,
Stable
Diffusion
and

DALL-E.
However,
it
is
important
to
recognize
that
the
future
of
generative
AI
is
still
far
away.

Innovative
developments
in
generative
AI
can
help
in
introducing
substantial
changes
in
business
and
the
functioning
of
society.
With
the
adoption
of

AI
tools,
businesses
could
accomplish
tasks
within
days
that
required
weeks
to
complete.
At
the
same
time,
some
reports
have
suggested
that
AI
takes
care
of
almost
44%
of
customer
service
requests. 

On
the
other
hand,
more
people
are
concerned
about
data
privacy,
bias,
and
ethics
in
generative
AI.
As
a
matter
of
fact,
around
81%
of
users
want
a
human
in
the
loop
to
review
and
verify
generative
AI
outputs. 

With
only
37%
of
users
trusting
AI
outputs,
the
future
prospects
of
generative
AI
might
appear
pessimistic.
However,
brands
have
been
turning
to
generative
AI
to
improve
customer
engagement
with
better
efficiency.
IDC
had
predicted
that
the
global
AI
spending
would
increase
by
almost
27%
in
2023.
Let
us
unravel
some
of
the
prominent
predictions
for
generative
AI
to
determine
its
potential.

Top
Predictions
for
the
Future
of
Generative
AI 

The
year
2023
would
be
one
of
the
most
noticeable
milestones
in
the
history
of
generative
AI
as
it
gained
mainstream
popularity.
As
the
domain
of
generative
AI
moves
forward
in
2024,
it
is
expected
to
bring
a
slew
of
changes
and
trends
that
will
transform
generative
AI
and
applications. 

The
responses
to
queries
like
“Does
generative
AI
have
a
future?”
draw
attention
towards
research-based
predictions.
For
example,
McKinsey
research
states
that
generative
AI
can
add
almost
$4.4
trillion
to
the
global
economy
every
year.
Will
generative
AI
stay
strong
in
the
long
term?
Here
are
some
of
the
important
predictions
about
generative
AI
that
you
should
watch
out
for
in
the
future.

• 
  Generative
  AI
  Becomes
  More
  Mainstream

The
most
important
prediction
about
the
future
of
AI
revolves
around
the
reduction
in
hype
for
generative
AI.
Following
the
launch
of
ChatGPT
by
OpenAI,
the
domain
of
generative
AI
expanded
continuously
with
the
introduction
of
new
models.
It
is
important
to
note
that
the
advanced

machine
learning
algorithms
underlying
generative
AI
solutions
have
been
under
development
for
decades.
However,
no
one
paid
attention
to
them
until
the
arrival
of
ChatGPT.

Since
late
2022,
the
world
has
witnessed
multiple
iterations
of
generative
AI
technology.
As
a
matter
of
fact,
new
generative
AI
models
have
been
introduced
multiple
times
in
a
month.
In
March
2023,
the
domain
of
generative
AI
witnessed
promising
improvements
with
the
arrival
of
solutions
for
financial
services
support
and
customer
relationship
management
solutions. 

Moving
forward
in
2024,
generative
AI
will
no
longer
be
a
technological
miracle.
On
the
contrary,
it
would
evolve
at
a
rapid
pace
and
come
closer
to
users
without
any
barriers.

Identify
new
ways
to
leverage
the
full
potential
of
generative
AI
in
business
use
cases
and
become
an
expert
in
generative
AI
technologies
with Generative
AI
Skill
Path

• 
  Faster
  Progress
  towards
  Productivity 

Artificial
intelligence
has
been
designed
to
replicate
human
intelligence.
Therefore,
it
is
reasonable
to
assume
that
generative
AI
predictions
would
shed
light
on
the
possibilities
of
achieving
human-like
performance.
McKinsey
research
suggests
that
generative
AI
may
achieve
a
median
level
of
human
performance
by
2030.

In
addition,
the
predictions
indicate
that
the
performance
of
generative
AI
would
be
at
par
with
the
top
25%
of
people
completing
specific
tasks
by
2040.
Therefore,
generative
AI
can
help
technology
achieve
human-like
performance
in
some
tasks
sooner
than
expected.

• 
  Automation
  of
  Work
  in
  Knowledge 

The
predictions
about
generative
AI
also
point
towards
possibilities
for
automation
in
the
field
of
knowledge.
Better
technical
capabilities
of
generative
AI
can
have
a
formidable
influence
on
the
activities
of
educators,
creatives
and
professionals.
The
future
of
generative
AI
can
mirror
the
past
instances
in
which
automation
technology
affected
different
physical
activities.

Generative
AI
is
likely
to
have
a
massive
influence
on
the
field
of
knowledge,
particularly
in
the
domains
of
collaboration
and
decision-making.
Professionals
working
in
the
areas
of
education,
the
arts,
law,
and
technology
can
expect
faster
automation
in
different
aspects
of
their
jobs.
Such
types
of
developments
would
be
driven
by
the
ability
of
generative
AI
to
predict
patterns
in
natural
language.
On
top
of
that,
generative
AI
could
also
ensure
the
dynamic
use
of
patterns
in
natural
language
for
better
results.

Want
to
develop
the
skill
in
ChatGPT
to
familiarize
yourself
with
the
AI
language
model?
Enroll
now
in
the

ChatGPT
Fundamentals
Course

• 
  Multimodal
  Generative
  AI 

One
of
the
most
formidable
predictions
about
the
future
of
AI
focuses
on
multimodal
generative
AI.
You
can
find
different
types
of
generative
AI
tools
with
distinctive
functionalities.
For
example,
some

generative
AI
tools
can
write,
while
others
can
create,
hear,
see
and
read.
As
generative
AI
expands
further,
it
can
address
all
these
tasks
at
once,
such
as
creating
text
and
images
simultaneously.
For
example,
DALL-E
3
can
generate
high-quality
text
incorporated
in
its
images.

As
a
result,
it
would
be
a
better
and
more
powerful
alternative
to
the
competing
image-generation
tools.
Therefore,
predictions
about
generative
AI
indicate
that
multimodal
generative
AI
would
become
the
norm.
It
would
help
generative
AI
create
in
different
ways
in
real-time,
just
like
a
human
would.

• 
  Interactive
  AI

The
discussions
around
questions
such
as
“Does
generative
AI
have
a
future?”
also
invite
attention
towards
interactive
AI.
With
the
arrival
of
interactive
AI,
generative
AI
bots
would
not
only
interact
with
users
but
also
carry
out
tasks.
Interactive
AI
bots
can
delegate
important
tasks
to
other
software
as
well
as
people
to
complete
them
with
desired
results.

You
can
refer
to
the
example
of
software
development
to
understand
the
implications
of
interactive
AI.
Generative
AI
has
proved
to
be
useful
in
generating
and
testing
code.
The
introduction
of
interactive
capabilities
in
generative
AI
can
help
it
complete
an
app
development
project
without
human
intervention. 

• 
  Generative
  AI
  Tools
  for
  Broader
  Use
  Cases

Generative
AI
tools
have
proved
effective
in
creating
different
types
of
written,
audio,
video
and
image
content.
As
a
matter
of
fact,
software
developers
can
trust
generative
AI
tools
to
create
code
for
their
new
projects.
Businesses
have
been
actively
developing
generative
AI
apps
for
leveraging
the
capabilities
of
generative
AI
in
these
areas.
Interestingly,
generative
AI
predictions
for
the
future
suggest
that
generative
AI
must
move
beyond
walled
gardens.
In
the
future,
you
can
expect
generative
AI
apps
and
tools
to
target
specific
industries,
use
cases
and
functions.
On
top
of
that,
the
new
apps
and
tools
are
likely
to
provide
more
value
than
the
general
generative
AI
tools.

Excited
to
learn
the
fundamentals
of
AI
applications
in
business?
Enroll
now
in
the AI
For
Business
Course

• 
  Certain
  Industries
  Would
  Reap
  More
  Benefits 

Generative
AI
has
caught
the
attention
of
business
leaders
in
almost
every
major
industry.
However,
it
is
important
to
measure
the
impact
of
generative
AI
on
business
functions
across
different
industries.
According
to
the
generative
AI
forecast
by
McKinsey,
some
industries
can
achieve
more
benefits
than
others.

The
impact
of
generative
AI
on
an
industry
would
depend
on
different
factors,
including
the
significance
of
business
functions,
the
scale
of
industry
revenue
and
the
mix
of
business
operations.
Almost
all
industries
would
notice
crucial
improvements
in
marketing
and
sales
functions
with
the
use
of
generative
AI.
On
the
other
hand,
banking
and
high
tech
would
reap
more
benefits
from
generative
AI
for
the
acceleration
of
software
development.

• 
  Emphasis
  on
  Bridging
  the
  Skill
  Gap

As
businesses
across
different
industries
establish
goals
for
generative
AI,
it
is
important
to
understand
the
necessity
of
skill
development.
How
can
organizations
embrace
AI
without
the
right
talent?
Therefore,
the
future
of
generative
AI
would
depend
on
how
organizations
identify
workers
with
generative
AI
skills.

Generative
AI
tools
can
offer
enhanced
value
to
early
adopters
only
if
they
can
cross
the
skill
gap.
Therefore,
businesses
would
compete
against
each
other
to
stay
on
top
of
the
generative
AI
talent
market.
Organizations
would
have
to
work
on
improving
their
talent
management
capabilities
alongside
offering
rewarding
working
experiences
to
generative
AI
experts. 

• 
  Impact
  of
  Generative
  AI
  on
  Global
  GDP

Another
important
addition
to
the
generative
AI
forecast
for
the
future
revolves
around
the
contribution
of
generative
AI
to
global
GDP.
According
to
McKinsey,
generative
AI
can
have
a
substantial
impact
on
the
enhancement
of
labour
productivity
across
different
sectors.
Workers
can
capitalize
on
the
value
of
such
productivity
boosts
by
shifting
to
other
work
activities
that
help
them
achieve
better
productivity.
Support
for
training
workers
to
help
them
learn
new
skills
and
change
their
roles
could
strengthen
GDP
growth
while
ensuring
inclusiveness
and
sustainability.

Challenges
Expected
in
the
Domain
of
Generative
AI 

The
benefits
of
generative
AI
showcase
that
it
can
emerge
as
a
powerful
force
of
change
for
business
and
the
everyday
lives
of
people.
On
the
other
hand,
it
is
also
important
to
reflect
on
the
challenges
that
you
should
expect
with
generative
AI.
One
of
the
most
prominent
challenges
in
the
use
of
generative
AI
points
to
the
use
of
deepfakes. 

The
detrimental
consequences
of
deepfake
technology
have
been
amplified
due
to
the
lack
of
resources
to
differentiate
deepfakes
from
authentic
content.
Deepfakes
present
a
major
deterrent
to
the
future
of
AI
and

machine
learning
by
creating
negative
sentiments
among
the
public.
Unethical
use
of
deepfake
technology
has
immediate
and
long-term
consequences.
As
a
matter
of
fact,
it
has
far-reaching
implications
for
the
society.

Another
notable
challenge
for
generative
AI
emerges
in
the
form
of
regulations
and
oversight.
Generative
AI
can
be
used
as
a
tool
for
spreading
misinformation
or
creating
autonomous
and
biological
weapons.
Therefore,
it
is
important
to
establish
regulations
that
protect
the
world
and
ensure
the
use
of
generative
AI
for
the
positive
transformation
of
society. 

On
top
of
that,
industry
experts
must
also
pay
attention
to
the
enhancement
of
transparency
when
working
on
generative
AI
models.
At
the
same
time,
it
is
important
to
advocate
for
the
ethical
use
of
AI
with
effective
frameworks,
policies,
and
guidelines.

Enroll
now
in
the Ethics
Of
Artificial
Intelligence
(AI)
Course and
familiarize
yourself
with
the
important
considerations
and
future
directions
for
policy
and
regulations
regarding
ethical
AI.

Final
Words

The
insights
on
the
future
of
generative
AI
reveal
its
potential
to
transform
different
industries
and
society.

Generative
AI
would
become
more
mainstream
and
evolve
beyond

ChatGPT
and
other
general
tools.
Businesses
across
different
industries
can
tap
into
the
potential
of
generative
AI
to
create
advanced
generative
AI
apps
and
tools
for
specific
use
cases.

On
the
other
hand,
some
industries
are
likely
to
reap
better
returns
from
generative
AI
in
specific
business
functions.
At
the
same
time,
it
is
also
important
to
reflect
on
challenges
for
generative
AI,
such
as
the
use
of
deepfake
technology,
lack
of
regulations
and
limited
transparency
in
the
working
mechanisms
of
generative
AI
models.
Find
more
insights
into
the
world
of
generative
AI
to
discover
how
it
will
shape
up
in
the
future.