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The
clock
is
ticking
for
organizations
to
create
significant
and
sustained
value
through
their
generative
AI
initiatives,
according
to
the
latest
State
of
Generative
AI
in
the
Enterprise research
from
Deloitte.
The
report
identified
key
ways
that
companies
can
move
from
potential
to
performance
including:
Also:
Businesses
can
reach
decision
dominance
using
AI.
Here's
how
Building
success
on
initial
success: Improved
efficiency,
productivity,
and
cost
reduction
are
still
the
top
benefits
sought
by
organizations.
Those
are
also
cited
by
42%
of
respondents
--
2,770
enterprise
leaders
--
as
their
most
important
benefits
achieved
to
date.
And
58%
reported
realizing
a
more
diverse
range
of
important
benefits,
such
as
increased
innovation,
improved
products
and
services,
or
enhanced
customer
relationships.
Strive
to
scale: Two
of
three
surveyed
organizations
said
they
are
increasing
their
investments
in
generative
AI
because
they
have
seen
strong
early
value.
Yet
nearly
70%
of
respondents
said
their
organization
has
moved
30%
or
fewer
of
their
generative
AI
experiments
into
production
Modernize
data
foundations: Three-quarters
of
respondents
said
their
organizations
have
increased
investment
around
data
life
cycle
management
to
enable
their
generative
AI
strategy.
Top
actions
include
enhancing
data
security
(54%)
and
improving
data
quality
(48%).
However,
data
issues
still
negatively
impact
progress
--
55%
of
organizations
reported
avoiding
certain
generative
AI
use
cases
because
of
data-related
issues.
Mitigating
risks
and
preparing
for
regulation: Organizations
feel
far
less
ready
for
the
challenges
that
generative
AI
brings
to
risk
management
and
governance
--
only
23%
rated
their
organization
as
highly
prepared.
In
fact,
three
of
the
top
four
factors
holding
organizations
back
from
developing
and
deploying
generative
AI
tools
and
applications
are
risk,
regulation
(such
as
the
European
Union's
AI
Act),
and
governance
issues.
Maintaining
momentum
by
measuring: More
than
40%
of
respondents
said
their
companies
are
struggling
to
define
and
measure
the
exact
impacts
of
their
generative
AI
initiatives.
Here
are
10
key
takeaways
of
Deloitte's
report:
Most
businesses
are
increasing
their
investments
in
generative
AI: Given
the
strong
value
seen
to
date,
67%
of
organizations
said
they
are
increasing
investments
in
generative
AI.
Most
are
citing
benefits
beyond
productivity,
efficiency
and
cost
reductions
--
58%
include
benefits
such
as
increased
innovation
(12%),
improved
products
and
services
(10%),
and
enhanced
customer
relationships
(9%).
Business
leaders
care deeply about
AI:
Survey
respondents
said
that
interest
in
generative
AI
remains
"high"
or
"very
high"
among
most
senior
executives
(63%)
and
boards
(53%).
Scaling
AI
adoption
in
the
enterprise
must
be
a
priority: However,
many
generative
AI
efforts
are
still
at
the
pilot
or
proof-of-concept
stage,
with
a
large
majority
of
respondents
(68%)
saying
their
organization
has
moved
30%
or
fewer
of
their
generative
AI
experiments
fully
into
production.
A
large
majority
of
organizations
have
deployed
less
than
a
third
of
their
generative
AI
experiments
into
production
The
essential
elements
for
scaling
generative
AI
initiatives
from
pilot
to
production
include
(I've
bolded
the
elements
that
I
believe
matter
most):
-
Clear,
high-impact
use
case
portfolio
-
Ambitious
strategy
and
value
management
focus
-
Strong
ecosystem
collaboration
-
Robust
governance
-
Agile
operating
model
and
delivery
methods
-
Integrated
risk
management
-
Transparency
to
build
trust
in
secure
AI
-
Transformed
roles,
activities,
and
culture
-
Acquiring
external
and
developing
internal
talent
-
Modular
architecture
and
common
platforms
-
Modern
data
foundation
-
Provisioning
the
right
AI
infrastructure
-
Effective
model
management
and
operations
The
obstacles
for
generative
AI
adoption
and
scaling
is
legacy
technology:
Technology
infrastructure
(45%)
and
data
management
(41%)
fared
the
best,
followed
by
strategy
(37%),
risk
and
governance
(23%),
and
talent
(20%).
Do
organizations
think
they
are
ready
for
generative
AI?
No.
Readiness
by
category
--
technology
infrastructure
(45%),
data
management
(41%),
strategy
(31%),
risk
and
governance
(23%),
and
talent
(20%).
All
AI
projects
start
and
end
as
data
projects
so
these
readiness
numbers
are
alarming.
Businesses
are
investing
more
in
data
life
cycle
management:
5%
of
organizations
have
increased
their
technology
investments
around
data
life
cycle
management
due
to
Generative
AI.
Levels
of
concern
in
data
management
are
high:
Using
sensitive
data
in
models
(57%),
managing
data
privacy-related
issues
(58%),
managing
data
security-related
issues
(57%),
complying
with
data,
governance
(49%),
using
company
proprietary
data
in
models
(38%).
A
data
trust
layer
is
key
to
the
successful
deployment
of
generative
AI
solutions.
Data-related
issues
have
caused
55%
of
the
organizations
we
surveyed
to
avoid
certain
generative
AI
use
cases.
The
top
three
barriers
to
successful
development
and
deployment
of
generative
AI
tools
and
apps
are
risk-related:
Worries
about
regulatory
compliance
(36%),
difficulty
managing
risk
(30%),
and
lack
of
governance
models
(29%).
Only
23%
rated
their
organization
as
highly
prepared
to
manage
risks.
Measuring
value
in
AI
investments
is
difficult
but
doable:
According
to
Deloitte's
survey
results,
41%
of
organizations
have
struggled
to
define
and
measure
the
exact
impacts
of
their
generative
AI
efforts.
Some
enterprises
reported
employing
formal
approaches
to
measure
and
communicate
generative
AI
value
creation,
including
using
specific
KPIs
for
evaluating
generative
AI
performance
(48%)
and
building
a
framework
for
evaluating
generative
AI
investments
(38%).
It
is
worth
noting
that
although
a
majority
(54%)
of
organizations
are
seeking
efficiency
and
productivity
improvements,
only
38%
reported
they
are
tracking
changes
in
employee
productivity.
And
only
35%
track
return
on
AI
investments.
Also:
1
in
3
workers
are
using
AI
multiple
times
a
week
-
and
they're
shouting
about
it
The
research
found
that
only
16%
of
organizations
reported
they
produce
regular
reports
for
the
CFO
about
the
value
being
created
with
generative
AI.
Smart
technology
leaders
know
this:
There
are
no
IT
projects,
there
are
only
business
projects.
Investment,
deployment,
and
adoption
of
AI
must
be
measured
based
on
business
outcomes
--
and
it
should
go
beyond
productivity
and
cost-cutting
objectives.
The
best
use
of
technology
is
to
improve
the
quality
of
life
and
work
--
for
your
employees,
customers,
business
partners,
and
communities
that
you
serve.
To
learn
more
about
Deloitte's
State
of
Generative
AI
in
the
Enterprise
report,
you
can
visit
here.
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