The
time
for
spring
cleaning
is
upon
us.
Closets.
Garages.
Attics.
All
are
more
than
likely
do
for
their
annual
check
up
and
clean
out.
Law
firms,
on
the
other
hand,
may
have
landed
at
an
even
more
daunting
space
on
the
chore
wheel:
they
need
to
get
their
data
AI-ready.
It’s
not
billable,
not
glamorous,
and
you
can’t
boast
about
it
at
partnership
meetings.
But
if
your
firm
doesn’t
make
progress
on
data
readiness
in
2026,
you’ll
enter
2027
increasingly
unable
to
compete
on
AI
capabilities.
And
unlike
past
technology
waves
where
you
could
wait
and
see,
this
AI
arms
race
is
moving
too
fast
for
that
strategy.
Spring
Cleaning
for
Your
DMS
Law
firms
sit
on
millions
of
documents
such
as
motions,
briefs,
agreements,
memos,
and
templates.
In
theory,
this
powers
impressive
AI
applications.
In
practice,
most
of
this
content
is
unstructured:
created
in
Word,
scanned
from
paper,
uploaded
without
systematic
organization
like
unique
filenames
or
tags.
This
misses
the
contextual
metadata
that
AI
needs.
Here’s
what
this
looks
like.
Say
we
analyze
statutory
data
from
New
York,
California,
and
Illinois.
AI
can
read
every
word
perfectly.
What
it
can’t
do
is
tell
you
which
statute
comes
from
which
state,
because
the
statute
text
doesn’t
say
“this
is
a
California
statute.”
It
just
states
the
law.
When
someone
asks,
“What
does
California
law
say
about
non-competes?”
Your
AI
genuinely
doesn’t
know
which
documents
are
California
law.
You
need
metadata
tags:
“this
document
=
California
statute,”
“this
document
=
labor
law
topic,”
“this
document
=
tech
industry.”
Multiply
this
gap
across
practice
areas
and
document
types,
and
you
see
why
firms
that
skipped
organizational
work
struggle
while
others
deploy
sophisticated
tools.
Nobody
Wants
This
Job
The
resistance
is
rational.
Data
cleanup
requires
human
effort
for
tasks
like
reviewing
documents,
applying
tags,
and
verifying
accuracy.
It
can’t
be
fully
automated.
And
frankly,
if
you’re
allocating
human
resources,
you’d
rather
apply
them
to
billable
work.
I
talk
to
research
staff
at
firms
who
completely
understand
why
this
matters.
They
see
the
connection
between
data
quality
and
AI
capability.
But
they’re
working
with
minimal
investment
because
getting
partnership
approval
for
“we
need
people
organizing
files
for
six
months”
is
genuinely
difficult.
The
firms
that
invested
early,
before
AI
became
trendy,
are
now
showing
clients
actual
AI
tools
they’ve
developed
in-house
during
pitches,
not
future
plans.
They’re
connecting
vendor
APIs
seamlessly.
They’re
winning
business
on
technical
capabilities
competitors
can’t
match.
Do
the
math.
One
million
documents?
Maybe
300,000
are
truly
critical.
Of
those,
perhaps
20,000
need
metadata
enrichment,
and
the
rest
work
fine
for
AI
based
on
text
alone.
Deliver
that
first
20,000
in
Q1.
Build
your
AI
application
on
top
of
it.
Measure
results.
Then
decide
on
phase
two.
This
creates
a
business
case
that
executives
can
understand:
targeted
investment,
quarterly
deliverable,
measurable
outcomes.
Not
an
indefinite
commitment
based
on
faith.
Lead
With
Your
Superpower
Don’t
ask
“which
data
should
we
organize?”
Ask
“which
data
supports
what
makes
us
different?”
Your
firm’s
primary
differentiator,
where
you’re
genuinely
recognized
as
experts,
that’s
your
starting
point.
Known
for
workers’
compensation
expertise?
Make
that
content
AI-ready
first.
Securities
transactions?
Start
there.
This
focused
approach
lets
you
deploy
meaningful
AI
capabilities
in
your
specialty
area
while
competitors
are
still
trying
to
organize
everything
simultaneously.
The
business
development
advantage
is
immediate
and
concrete.
Instead
of
telling
prospects,
“we’re
exploring
AI
applications,”
you
can
say
“we
built
an
AI
tool
on
our
proprietary
workers’
comp
precedent
library
and
decades
of
matter
experience.
No
other
firm
can
offer
this
capability.”
That’s
differentiation
you
can
demonstrate
in
live
demos,
not
just
promise
in
pitch
decks.
This
matters
because
clients
are
in
their
own
AI
learning
curve.
They’re
asking
firms
not
just
whether
they
use
AI,
but
how
that
AI
leverages
the
firm’s
specific
expertise.
Generic
AI
tools
are
available
to
everyone.
AI
built
on
your
firm’s
proprietary,
organized
content?
That’s
an
actual
competitive
advantage.
After
phase
one
delivers
results,
measure
carefully.
Did
you
win
pitches
based
on
the
AI
capability?
Close
matters
faster?
Increase
wallet
share
with
existing
clients
who
value
the
technology
advantage?
Those
metrics
become
your
business
case
for
expanding
to
the
next
practice
area.
And
once
infrastructure
is
built,
adding
more
data
is
dramatically
simpler
than
initial
setup.
Waiting
Isn’t
a
Strategy
Many
firms
hope
AI
will
eventually
handle
unstructured
data
well
enough
without
human
help.
That’s
not
happening,
at
least
not
in
timeframes
that
matter
for
competitive
positioning.
AI
cannot
infer
context
that
isn’t
there.
It
can’t
determine
statute
jurisdiction
if
nobody
tagged
it.
This
requires
human
knowledge.
Right
now,
clients
ask
during
pitches
how
you’re
using
AI
to
deliver
value.
Firms
with
AI-ready
data
demonstrate
tools.
Firms
without
it
discuss
pilots.
Clients
notice,
and
that
gap
widens
quarterly.
Firms
that
are
winning
treat
this
as
competitive
imperative
with
quarterly
goals,
not
someday
project.
They’re
investing
in
unglamorous
foundation
work
while
others
wait
for
easier
answers
that
aren’t
coming.
Your
90-Day
Plan
Start
with
three
questions:
What
practice
area
defines
our
advantage?
What
documents
support
that?
What
metadata
makes
those
documents
AI-useful?
Then
commit
to
90
days.
Your
research
staff
and
senior
associates
know
which
content
matters
most.
Aim
for
meaningful
progress
you
can
build
on
next
quarter,
not
perfection.
Quarterly
progress
on
data
readiness
is
a
spring
project
worth
initiating
in
2026.
Prioritize
focused
work
on
content
that
supports
what
makes
you
special.
Like
the
gym,
starting
is
the
hardest.
Unlike
the
gym,
your
competitors
are
already
there,
and
skipping
it
costs
you
pitches
and
clients.
Nicole
Stone
is
Director
of
AI
&
Agentic
Solutions
Product
Management
at
Wolters
Kluwer
Legal
&
Regulatory
U.S.,
where
she
leads
product
strategy
and
development
for
digital
legal
content
and
technology
solutions.
With
over
22
years
of
experience
in
legal
technology
and
a
background
as
a
practicing
attorney,
she
focuses
on
integrating
emerging
technologies,
including
generative
AI,
into
products
that
serve
legal
professionals.