The
integration
of
AI
into
legal
practice
has
reached
a
critical
inflection
point,
and
the
risks
of
choosing
the
wrong
solution
extend
far
beyond
simple
inefficiency.
For
legal
professionals,
the
stakes
are
uniquely
high:
accuracy
concerns,
ethical
implications,
and
professional
standards
hang
in
the
balance
with
every
AI-assisted
task.
At
the
heart
of
these
challenges
lies
a
critical
distinction
many
firms
are
only
beginning
to
understand:
the
fundamental
difference
between
consumer-grade
AI
and
professional-grade
AI.
As
the
gap
between
“using
AI”
and
“using
AI
effectively”
continues
to
widen,
legal
professionals
who
recognize
and
act
on
these
differences
will
be
positioned
to
deliver
better
outcomes,
maintain
competitive
advantage,
and
uphold
the
professional
standards
their
clients
depend
on.
Here,
we’re
sharing
some
key
distinctions,
based
on
a
recent
webinar
sponsored
by
our
friends
at
Thomson
Reuters.
(View
the
full
recording
here.
Registration
is
required,
and
CLE
credit
is
available.)
Trust
Starts
at
the
Source
There
are
many
practical
use
cases
for
consumer-grade
generative
AI,
from
streamlining
daily
communication
tasks
to
enabling
creative
experimentation,
and
these
tools
have
brought
AI
capabilities
to
millions
of
users.
“Consumer
AI
does
produce
confident
sounding
results,”
says
Thomson
Reuters’
Maddie
Pipitone.
“And
that
can
be
great
for
creative
purposes,
but
not
for
professional
purposes.”
For
professionals
who
need
to
make
confident,
defensible
decisions,
the
source
of
AI-generated
information
becomes
critical.
Drawing
from
the
general
internet,
consumer
AI
tools
introduce
uncertainty
and
may
hallucinate
data
or
fabricate
cases,
requiring
extensive
validation.
ChatGPT,
for
example,
has
often
cited
community-edited
publications
like
Reddit
and
Wikipedia
as
information
sources,
Pipitone
notes,
referring
to
recent
studies.
Certain
legal-specific
tools,
by
contrast,
will
draw
on
their
own
curated
body
of
information,
she
says,
increasing
the
reliability
of
their
large
language
models.
“When
you
have
a
tool
like
CoCounsel
Legal
from
Thomson
Reuters,
it’s
grounded
in
Westlaw
and
Practical
Law,
which
ensures
that
additional
level
of
accuracy
and
recency,”
she
says.
“The
data
is
up
to
date
and
not
a
blog
post.”
CoCounsel
will
cite
to
every
source,
allowing
you
to
validate
all
of
its
statements
instantaneously.
AI
is
Here
to
Stay
In
Thomson
Reuters’
2025
Generative
AI
for
Professional
Services
Report,
42%
of
legal
professionals
anticipate
that
GenAI
will
be
central
to
their
workflow
in
the
next
year,
and
95%
say
within
the
next
five
years.
On
if
AI
will
make
an
impact
on
workflows,
Pipitone
says:
“It’s
not
really
a
question
of
if,
at
this
point,
it’s
of
how
we
do
that
responsibly
and
how
we
incorporate
the
right
workflows
into
our
practice
to
make
sure
we’re
still
fulfilling
those
ethical
obligations
and
doing
right
by
our
clients.”
Doing
so
can
be
done
by
examining
the
capabilities
of
a
Large
Language
Model.
The
timeline
skill
in
CoCounsel,
for
example,
allows
you
to
create
a
chronology
of
events
described
in
documents.
What
would
usually
take
a
substantial
amount
of
time
to
complete
manually
can
now
be
done
in
minutes,
adding
value
to
you
and
your
clients’
time
and
making
processes
more
efficient.
Privacy
and
Privileges
Using
AI
also
creates
complexities
around
data
privacy
and
attorney-client
privilege,
and
key
differences
emerge
between
consumer
and
professional
products
in
this
space.
Some
consumer
tools
can
store
your
data
and
use
it
for
model
training,
Pipitone
notes,
and
you
have
to
affirmatively
opt
out
to
avoid
this.
Uploading
confidential
client
info
into
this
type
of
system
could
violate
confidentiality
obligations,
and
even
waive
attorney-client
privilege.
Legal-specific
tools,
by
contrast,
“are
specifically
built
for
that
confidentiality
and
security
purpose.”
These
concerns
about
data
privacy
and
privilege
are
essential
considerations
for
any
legal
professional
evaluating
AI
tools.
When
firms
select
AI
solutions
designed
specifically
for
legal
practice
with
robust
security
measures,
zero-retention
policies,
and
built-in
privilege
protections,
the
path
forward
becomes
clearer.
The
key
is
approaching
adoption
thoughtfully
rather
than
avoiding
it
entirely.
“Building
that
trust
both
with
yourself
and
with
others
in
your
firm
is
key
to
adoption,”
Pipitone
urges,
“So
starting
small,
verifying
that
output
and
then
building
from
there
to
see
where
the
AI
fits
naturally
into
your
workday.”
View
the
Webinar
For
more
on
practical
ways
to
implement
AI
and
communicating
AI
use
to
clients,
see
the
full
conversation
here.
(Registration
is
required,
and
CLE
credit
is
available.)