What it’s going to take for advanced AI to reshape the enterprise landscape
According
to
Infosys
research,
data
and
artificial
intelligence
(AI)
could
generate
$467
billion
in
incremental
profits
worldwide
and
become
the
cornerstone
of
enterprises
gaining
a
competitive
edge.
According
to
Infosys
research,
data
and
artificial
intelligence
(AI)
could
generate
$467
billion
in
incremental
profits
worldwide
and
become
the
cornerstone
of
enterprises
gaining
a
competitive
edge.
But
while
opportunities
to
use
AI
are
very
real
–
and
ChatGPT’s
democratisation
is
accelerating
generative
AI
test-and-learn
faster
than
QR
code
adoption
during
the
Covid
pandemic
–
the
utopia
of
substantial
business
wins
through
autonomous
AI
is
a
fair
way
off.
Getting
there
requires
process
and
operational
transformation,
new
levels
of
data
governance
and
accountability,
business
and
IT
collaboration,
and
customer
and
stakeholder
trust.
The
reality
is
many
organisations
still
struggle
with
the
data
and
analytics
foundations
required
to
progress
down
an
advanced
AI
path.
Infosys
research
found
63
per
cent
of
AI
models
function
at
basic
capability
only,
are
driven
by
humans,
and
often
fall
short
on
data
verification,
data
practices
and
data
strategies.
It’s
not
surprising
only
one
in
four
practitioners
are
highly
satisfied
with
their
data
and
AI
tools
so
far.
This
status
quo
can
be
partly
explained
by
the
fact
eight
in
10
only
began
their
AI
journey
in
the
last
four
years.
Just
15
per
cent
of
organisations
have
achieved
what’s
described
as
an
‘evolved’
AI
state,
where
systems
can
find
causes,
act
on
recommendations
and
refine
their
own
performances
without
oversight.
Then
there
are
the
trust
and
accuracy
considerations
around
AI
utilisation
to
contend
with.
Gartner
predictions
forecast
85
per
cent
of
all
AI
projects
by
2022to
wind
up
with
an
erroneous
outcome
through
mistakes,
errors,
bias
and
things
that
go
wrong.
One
in
three
companies,
according
to
Infosys,
are
using
data
processes
that
increase
the
risk
of
bias
in
AI
right
now.
Ethical
use
of
AI
is
therefore
an
increasingly
important
movement
being
led
by
government,
industry
groups
and
thought
leaders
as
this
disruptive
technology
advances.
It’s
for
these
important
reasons
the
Australian
Government
deployed
the
AI
Ethics
Principles
framework,
which
followed
an
AI
ethics
trial
in
2019
supported
by
brands
such
as
National
Australia
Bank
and
Telstra.
Yet
even
with
all
these
potential
inhibitors,
it’s
clear
the
appetite
for
AI
is
growing
and
spend
is
increasing
with
it.
So
what
can
IT
leaders
and
their
teams
do
now
to
take
AI
out
of
the
data
science
realm,
and
into
practical
business
applications
and
innovation
pipelines?
What
data
governance,
operational
and
ethical
considerations
must
we
factor
in?
And
what
human
oversight
is
required?
It’s
these
questions
technology
and
transformation
leaders
from
finance,
education
and
retail
sectors
explored
during
a
panel
session
at
the
Infosys
APAC
Confluence
event.
Here’s
what
we
discovered.
Operational
efficiency
is
the
no-brainer
use
case
for
AI
While
panellists
agreed
use
cases
for
AI
could
well
be
endless
and
societally
positive,
the
ones
gaining
most
favour
right
now
orient
to
operational
efficiency.
“We
are
seeing
AI
drive
a
lot
deeper
into
the
organisation
around
how
we
can
revolutionise
our
business
processes,
change
how
we
run
our
organisation,
and
all
add
that
secret
sauce
from
a
data
and
analytics
perspective
to
improve
customer
outcomes,”
said
ANZ
Bank
CIO
for
Institutional
Banking
and
Markets,
Peter
Barrass.
An
example
is
meeting
legislative
requirements
to
monitor
communications
traders
generate
in
23
countries.
AI
is
successfully
used
to
analyse,
interpret
and
monitor
for
fraudulent
activity
at
a
global
scale.
Crunching
and
digitisation
of
documents,
and
chatbots
are
other
examples.
Across
retail
and
logistics
sectors,
nearly
three
in
10
retailers
are
actively
adopting
AI
with
strong
business
impact,
said
Infosys
APAC
regional
head
for
Consumer,
Retail
and
Logistics,
Andal
Alwan.
While
personalisation
is
often
a
headline
item,
AI
is
also
increasing
operational
efficiencies
and
frictionless
experiences
across
the
end-to-end
supply
chain.
Cyber
security
is
another
favoured
case
for
AI
across
multiple
sectors,
once
again
tying
to
risk
mitigation
and
governance
imperatives.
Advancing
AI
can’t
be
done
without
a
policy
and
process
rethink
But
realising
advanced
AI
isn’t
only
a
technical
or
data
actionability
feat.
It
requires
transformation
at
a
systematic,
operational
and
cultural
level.
Just
take
the
explosion
of
accessible
AI
to
students
from
a
learning
perspective.
With
mass
adoption
comes
the
need
for
education
institutions
such
as
the
Melbourne
Archdiocese
Catholic
Schools
(MACS)
to
actively
build
policies
and
positions
around
AI
use.
One
consideration
is
how
open
accessibility
of
such
tools
can
influence
students.
Another
is
protecting
academic
integrity.
Then
it’s
making
sure
leadership
is
very
clear
from
an
education
system
perspective
to
gain
consistency
across
MACS’
300
schools
for
how
to
utilise
AI
in
learning.
“We
need
to
educate
our
teachers
to
be
able
to
think
about
how
their
students
will
use
AI
and
how
they
can
maximise
the
learning
for
individual
students,
taking
on-board
some
of
these
types
of
tools
available,”
MACS
chief
technology
and
transformation
officer,
Vicki
Russell,
said.
Elevating
data
governance
and
sharing
is
critical
Simultaneously,
data
governance
and
practices
need
refinement.
Alwan
outlined
two
dimensions
to
the
data
strategy
debate:
Intra-organisation;
and
inter-organisation.
“Intra-organisation
is
about
how
I
govern
the
data:
What
data
I
collect,
why
I’m
collecting
it
and
how
am
I
protecting
and
using
it,”
she
explained.
“Then
there’s
inter-organisation,
or
between
retailers,
producers
and
logistic
firms,
for
instance.
Collaboration
and
sharing
of
data
is
very
important.
Unless
there
is
visibility
end-to-end
of
the
supply
chain,
a
retailer
isn’t
going
to
know
what’s
available
and
when
it’s
going
to
be
arriving.
All
of
this
requires
huge
amounts
of
data,
which
means
we’re
going
to
need
AI
for
scaling
and
to
predict
trends
too.”
A
further
area
of
data
collaboration
is
between
retailers
and
consumers,
which
Alwan
referred
to
as
“autonomous
supply
chains”.
“It’s
about
understanding
demand
signals
from
the
point
of
consumption,
be
it
online
or
physical,
then
translating
that
in
real
time
to
organisation
systems
to
get
more
security
of
planning
and
supply
chain.
That’s
another
area
of
AI
maturity
we’re
seeing
evolving.”
Infosys
Knowledge
Institute’s
Data
+
AI
Radar
found
organisations
wanting
to
realise
business
outcomes
from
advanced
AI
must
develop
data
practices
that
encourage
sharing
and
position
data
as
currency.
But
even
as
the
financial
sector
works
to
pursue
data
sharing
through
the
Open
Banking
regime,
Barrass
reflected
on
the
need
to
protect
the
information
and
privacy
of
customers
and
be
deliberate
about
the
value
data
has
to
both
organisation
and
customer.
“In
the
world
of
data,
you
have
to
remember
you
have
multiple
stakeholders,”
he
commented.
“The
customer
and
person
who
owns
the
data
and
who
the
data
is
associated
with
is
really
the
curator
of
that
information,
and
should
have
right
to
where
it’s
shared
and
how
it’s
shared.
Corporates
like
banks
have
a
responsibility
to
customers
to
enable
that.
That
needs
to
be
wrapped
up
in
your
data
strategy.”
Internally,
utilising
the
wealth
of
education
learning
and
data
points
MACS
has
been
capturing
is
a
critical
foundation
to
using
AI
successfully.
“The
data
and
knowledge
a
business
has
about
itself
before
it
enters
into
an
AI
space
is
really
important
in
that
maturity
curve,”
Russell
said.
“Having
great
data
and
knowing
what
you
have
to
a
certain
extent
before
you
jump
into
your
AI
body
of
work
or
activities
is
important.
But
I
also
think
AI
can
help
us
leapfrog.
There’s
knowing
enough
but
also
being
open
to
what
you
might
discover
along
that
journey.”
Building
trust
with
customers
around
AI
still
needs
human
oversight
What’s
clear
is
the
onus
is
on
organisations
to
structurally
address
trust
and
bias
issues,
especially
as
they
lean
towards
allowing
AI
to
generate
outcomes
for
customers
autonomously.
Ethical
use
of
data
and
trust
in
what
and
how
information
is
used
must
come
into
play.
As
a
result,
parallel
human
oversight
of
what
the
machine
is
doing
to
ensure
outcomes
are
accurate
and
ethical
remains
critical.
“Trust
in
the
source
of
information
and
really
clear
ownership
of
that
information
is
really
important,
so
there’s
clear
accountability
in
the
organisational
structure
for
who
is
responsible
for
maintaining
a
piece
of
information
driving
customer
decision
outcomes,”
said
Barrass.
“Then
over
time,
as
this
matures,
we
potentially
could
have
two
sets
of
AI
tools
looking
at
the
same
problem
sets
and
validating
each
other’s
outcomes
based
on
different
data
sets.
So
you
at
least
get
some
validation
of
one
set
of
information
drivers.”
Transparency
of
AI
outcomes
is
another
critical
element
with
customers
if
trust
in
AI
is
to
evolve
over
time.
This
again
comes
back
to
stronger
collaboration
with
data
owners
and
stakeholders,
an
ability
to
detail
the
data
points
driving
an
AI-based
outcome,
and
explaining
why
a
customer
got
the
result
they
did.
“It’s
very
important
to
be
conscious
of
the
bias
and
how
you
balance
and
provide
vast
sets
of
data
that
constantly
work
against
the
bias
and
correct
it,”
Alwan
added.
“That’s
going
to
be
key
for
the
success
of
AI
in
the
business
world.”
We
all
need
to
work
with
ChatGPT,
not
against
it
Even
as
we
strive
for
responsible
AI
use,
ChatGPT
is
accelerating
generative
AI
adoption
at
an
unprecedented
rate.
Test
cases
are
being
seen
in
everything
from
architectural
design
to
writing
poetry,
creating
law
statement
of
claims
and
developing
software
code.
Panellists
agreed
we’re
only
scratching
the
surface
of
use
cases
this
generative
AI
can
tackle.
In
banking,
it’s
about
experimenting
in
a
controlled
way
and
understanding
the
‘why’
so
generative
AI
is
applied
to
achieve
solid
business
outcome,
Barrass
said.
In
the
world
of
retail
and
consumer-facing
industries,
conversational
commerce
is
already
front
and
centre
and
ChatGPT
is
set
to
accelerate
this
further,
Alwan
said.
For
Russell,
the
most
important
thing
is
ensuring
the
future
generation
learns
how
to
harness
openly
accessible
AI
tools
and
can
prompt
it
appropriately
to
gather
great
information
out
of
it,
then
reference
it.
In
other
words,
education
evolves
and
works
with
it.
It’s
a
good
lesson
for
us
all.
About Author
