Launching
an
AI
product
without
a
rigorous
in-house
review
is
like
sending
a
driverless
car
onto
the
highway
without
checking
the
brakes.
It
might
work
perfectly.
Until
it
doesn’t.
The
most
successful
AI
launches
I’ve
seen
share
a
common
thread:
in-house
counsel
who
know
exactly
what
to
ask
before
anyone
hits
“go.”
These
questions
don’t
just
uncover
compliance
gaps.
They
often
help
shape
the
product
into
something
more
ethical,
more
defensible,
and
more
competitive.
Here
is
the
conversation
every
in-house
lawyer
should
have
with
an
AI
product
before
launch
day.
1.
What
Exactly
Are
You?
Before
you
can
manage
launch
risks,
you
need
a
plain-language
description
of
the
AI
system
itself.
Is
it
generating
new
content
or
making
predictions?
What
decisions
or
outputs
will
it
influence?
What
business
need
is
it
addressing?
Too
often,
in-house
teams
hear
about
“the
AI”
in
vague,
hype-filled
terms.
Without
clarity
on
the
model’s
type
and
scope,
your
launch
strategy
is
shooting
in
the
dark.
2.
Where
Did
You
Learn
This?
Every
AI
has
a
training
history.
You
need
to
know
whether
that
data
came
from
licensed
sources,
open
datasets,
internal
archives,
or
less
reliable
sources
like
mass
web
scraping.
For
an
in-house
launch
review,
data
provenance
is
not
just
a
nice-to-have.
It
is
often
the
hinge
point
in
IP
disputes,
privacy
claims,
and
regulatory
investigations.
3.
Which
Rules
Apply
To
You?
No
AI
product
launches
into
a
legal
vacuum.
It
enters
a
patchwork
of
global
and
sector-specific
laws.
Map
out
every
jurisdiction
where
the
product
will
operate
and
what
each
requires.
Some
frameworks,
like
the
EU
AI
Act,
focus
on
risk
classification.
Others,
like
financial
or
health
care
regulations,
demand
strict
explainability
and
audit
trails.
Knowing
this
before
launch
prevents
costly
redesigns
after
the
fact.
4.
Can
You
Prove
You’re
Fair
And
Accurate?
Before
launching,
confirm
how
the
AI
performs
across
different
demographic
groups
and
scenarios.
If
one
group
consistently
receives
worse
outcomes,
that
is
not
a
mere
technical
issue.
It
is
a
legal
and
reputational
liability.
Your
prelaunch
testing
should
be
designed
to
uncover
problems,
not
to
validate
optimistic
assumptions.
5.
Can
You
Explain
Yourself?
If
you
can’t
explain
how
an
AI
reached
its
decision,
be
prepared
for
skepticism
from
regulators,
courts,
and
your
own
executives.
Black-box
models
might
be
fine
for
recommending
playlists,
but
they
won’t
survive
scrutiny
in
hiring,
lending,
or
health
care
contexts.
In-house
counsel
should
ensure
transparency
plans
are
in
place
before
launch,
including
technical
documentation
for
auditors,
plain-language
summaries
for
users,
and
thorough
internal
records.
6.
Who
Owns
The
Output
And
The
Data
Trail?
Ownership
and
governance
questions
should
never
be
left
until
after
launch.
Who
controls
the
AI’s
outputs?
Can
they
be
reused,
sold,
or
licensed?
How
is
input
data
stored,
and
for
how
long?
If
external
vendors
are
involved
in
the
launch
process,
confirm
their
contractual
obligations
align
with
your
company’s
risk
tolerance
and
compliance
requirements.
7.
What’s
The
Plan
When
Something
Goes
Wrong?
Even
the
most
carefully
prepared
launch
will
encounter
surprises.
The
question
is
not
whether
your
AI
will
make
an
error,
but
how
your
in-house
team
will
respond.
A
solid
launch
plan
includes
escalation
protocols,
predrafted
regulatory
responses,
designated
decision-makers,
and
clear
user
communication
strategies.
These
should
be
tested
before
they
are
needed.
Final
Check
Before
You
Launch
If
your
in-house
team
can
confidently
answer
all
seven
questions,
your
AI
product
is
far
more
likely
to
launch
smoothly
and
stay
out
of
trouble.
If
not,
the
smartest
move
may
be
to
pause
and
fix
the
gaps
before
they
become
public
or
legal
crises.
For
in-house
counsel,
these
questions
are
not
about
slowing
innovation.
They
are
about
launching
responsibly,
building
trust,
and
ensuring
your
AI
can
survive
legal
scrutiny,
market
pressure,
and
the
unpredictable
nature
of
machine
learning.
When
in-house
lawyers
lead
with
the
right
questions,
the
launch
conversation
shifts
from
“Can
we
do
this?”
to
“How
do
we
do
this
well?”
Olga
V.
Mack (Opens
in
a
new
window) is
the
CEO
of TermScout (Opens
in
a
new
window),
an
AI-powered
contract
certification
platform
that
accelerates
revenue
and
eliminates
friction
by
certifying
contracts
as
fair,
balanced,
and
market-ready.
A
serial
CEO
and
legal
tech
executive,
she
previously
led
a
company
through
a
successful
acquisition
by
LexisNexis.
Olga
is
also
a Fellow
at
CodeX,
The
Stanford
Center
for
Legal
Informatics (Opens
in
a
new
window),
and
the
Generative
AI
Editor
at
law.MIT.
She
is
a
visionary
executive
reshaping
how
we
law—how
legal
systems
are
built,
experienced,
and
trusted.
Olga teaches
at
Berkeley
Law (Opens
in
a
new
window),
lectures
widely,
and
advises
companies
of
all
sizes,
as
well
as
boards
and
institutions.
An
award-winning
general
counsel
turned
builder,
she
also
leads
early-stage
ventures
including Virtual
Gabby
(Better
Parenting
Plan) (Opens
in
a
new
window), Product
Law
Hub (Opens
in
a
new
window), ESI
Flow (Opens
in
a
new
window),
and Notes
to
My
(Legal)
Self (Opens
in
a
new
window),
each
rethinking
the
practice
and
business
of
law
through
technology,
data,
and
human-centered
design.
She
has
authored The
Rise
of
Product
Lawyers (Opens
in
a
new
window), Legal
Operations
in
the
Age
of
AI
and
Data (Opens
in
a
new
window), Blockchain
Value (Opens
in
a
new
window),
and Get
on
Board (Opens
in
a
new
window),
with Visual
IQ
for
Lawyers (ABA)
forthcoming.
Olga
is
a
6x
TEDx
speaker
and
has
been
recognized
as
a
Silicon
Valley
Woman
of
Influence
and
an
ABA
Woman
in
Legal
Tech.
Her
work
reimagines
people’s
relationship
with
law—making
it
more
accessible,
inclusive,
data-driven,
and
aligned
with
how
the
world
actually
works.
She
is
also
the
host
of
the
Notes
to
My
(Legal)
Self
podcast
(streaming
on Spotify (Opens
in
a
new
window), Apple
Podcasts (Opens
in
a
new
window),
and YouTube (Opens
in
a
new
window)),
and
her
insights
regularly
appear
in
Forbes,
Bloomberg
Law,
Newsweek,
VentureBeat,
ACC
Docket,
and
Above
the
Law.
She
earned
her
B.A.
and
J.D.
from
UC
Berkeley.
Follow
her
on LinkedIn (Opens
in
a
new
window) and
X
@olgavmack.