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Every product leader has to make them: the high-stakes decisions that define outcomes, shape careers, and don't come with easy answers.
The Hard Calls podcast, hosted by Trisha Price, features candid conversations with product and tech leaders about the pivotal decisions that drive great products and the pressure that comes with it. From conflicting priorities and unclear success metrics to aligning teams and navigating executive expectations, you will hear compelling stories and best practices that drive business outcomes and help you make the Hard Calls.
Real decisions. Real stakes. Real leadership.
Presented by Pendo
Learn more at pendo.io/
Follow Trisha Price on LinkedIn: https://www.linkedin.com/in/trisha-price-3063081/
Trisha Price: Hi everyone, I have an
exclusive discount for Hard Call's
listeners to Pendomonium - Pendo's Product
Festival happening March 24th through
26th, 2026 in Raleigh, North Carolina.
Listen in at the halfway point today
to get this special discount to the
product festival, bringing today's top
leaders in product, AI, and software.
Naomi Lariviere: I oversee our
client facing AI program at ADP.
It's called ADP Assist.
And we've been working on Agentic AI and
you know, in it, in the space of payroll.
The hard call for us is as we started
to talk to our clients and we were
showing them early concepts of what
we wanted to do, what we realized is
that our innovation was colliding with
the trust that our clients had in us.
We service over 1.1 million clients.
We pay one in six people
in the United States.
That's a lot of people.
When you say you can't get things wrong.
Imagine getting a
paycheck that's not right?
So you know what we learned is yes,
speed matters, but trust compounds.
Trisha Price: If you build
software or lead people who do,
then you're in the right place.
This is Hard Calls, real decisions,
real leaders, real outcomes.
Hi everyone, I am Trisha Price, and
welcome back to Hard Calls, the podcast
where we bring on the best product
leaders from across the globe to talk
about those moments, the decisions
that mattered, the hard calls.
Today on Hard Calls, we have Naomi
Lariviere, Chief Product Owner and
VP of Product Management at ADP.
The company many of us rely
on to get paid, including me.
Naomi and I first connected when
she was a keynote speaker at Pendo's
user conference a few years back.
We had the joy of backstage jitters
together, and that comradery was
maintained as we continue to get together.
To share strategies, challenges,
and just network CPO to CPO.
What I love about Naomi is how
she thinks about innovation,
especially in an industry where
there's little to no room for error.
In today's episode, we're digging in
to how Naomi balances innovation with
trust, builds psychological safety
on our teams and makes the hard calls
that define great product leaders.
Welcome to Hard Calls, Naomi.
Naomi Lariviere: Thank you
my friend, for having me.
That was such a nice introduction.
Thank you.
Trisha Price: Well it's easy to do a
great introduction when you mean it and
when you know someone and admire someone.
So I'm really looking forward
to our conversation today.
Naomi Lariviere: It's very mutual.
Thanks for having me.
Trisha Price: So before we jump into
hard calls, could you share a little
bit with our listeners about your
journey into product leadership and
what your role at ADP looks like today?
Naomi Lariviere: Yeah,
that's a great question.
I'm somebody who kind of
fell into product management.
I did not go to school for computer
science or kind of any of the other
paths that tend to end up here.
I have a business and
management background.
My first job outta college, I was in an
early career program, which I think a lot
of people coming outta university are - I
got into business analysts and over
time that role with the companies that
I worked for kind of merged into product
management and really understanding
both the business needs, the client
needs, as well as being able to talk
to the development team about like what
we were actually trying to accomplish.
And maybe 15-years ago, I got put into
a leadership role and it's just kind of
snowballed ever since with additional
responsibilities and bigger portfolios.
And I think when you love
what you do, your career just
kind of keeps feeding you.
And that's what I've gotta say about ADP.
I joined here, I had a very small
team and ended up overseeing our large
enterprise portfolio, and now I oversee
one of the largest portfolios at ADP,
which really services all of our business
units in a shared product fashion.
Which really for us means we build
products that we can build once and
deploy multiple times across our product
ecosystem, which is, which helps us be
scalable and helps us grow as we need to.
So, it's a fun job.
Love working at ADP and love to bring
what I know about product back to my team.
Trisha Price: Great, well I look
forward to digging into that a bit
more as we go through the podcast, but
this show is Hard Calls and we like
to start every episode with a hard
call that our guest has had to make.
So tell us, looking back over your career.
Recent or a while back, tell us
about a hard call you've had to make.
What made it challenging?
You know, what considerations or
process or data led you to make the
decision and what'd you learn from it?
Naomi Lariviere: Yeah, no, I think
in our job, in our profession, we
make hard calls kind of routinely.
It's part of the job and you're
prioritizing things all the time
in terms of what you wanna do.
But maybe I'll talk about something
that's a little bit more recent.
So I oversee our client facing AI
program at ADP, it's called ADP Assist.
And again, this is another initiative
where it's build once, deploy
it across our entire ecosystem.
We've been working on agentic AI and
you know, in it, in the space of payroll
and sometimes in the space of payroll.
You know, and with this technology we
wanna go as fast as humanly possible.
And there's so many wonderful
things you can do with it from just
task automation to like actually
doing the entire process for you.
And that's actually this
initiative had that goal.
We are just gonna do everything for you.
You don't need to worry.
And the hard call for us is as we started
to talk to our clients and we were showing
them early concepts of what we wanted to
do, and you know, we were iterating on it.
What we realized is that our
innovation was colliding with the
trust that our clients had in us.
We service over 1.1 million clients.
We pay one in six people
in the United States.
That's a lot of people.
When you say you can't get things wrong.
Imagine getting a paycheck
that's not right, right?
Like that is not a good scenario for us.
It's not a good scenario for our clients.
So you know, when you are doing things
that may erode the trust of your user.
You might wanna reconsider
what you're doing.
So we actually, as we talked to the
clients, we're like, okay, well if
we wanted to get to that, they're
like, well, that's a really great
thing in the farer in the future.
Like, how can we get you on
that trust building journey?
So what we learned is yes, speed
matters, but trust compounds.
So, to a certain extent, we've had to
delay kind of what our overall goal is
to really ensure that we have long-term
credibility with our users and that we're
bringing them along on this innovation
journey as we continue to progress
where we're going with Agentic AI.
Trisha Price: I love that
you shared that story.
I feel like.
So many of us building AI and agentic
experiences are going through, I
mean, yours is magnified even more
so when you talk about something
so critical as people's paychecks.
But it's true for all experiences.
It's like our customers want AI,
they want the ease of use of it.
They want the value it provides
the automation it provides.
And if you're not doing
those things, you're falling
behind in their expectations.
But at the same time, the very few
are ready to jump from here to here.
And maybe if it's something really
not critical to their business and
it's like a playful tool, that's fine.
But I think in anything, that's
critical to people's business.
This like crawl, walk, run strategy
of building trust and sort of being in
more co-pilot mode than full agentic
automation mode is something we have
to get people comfortable with on the
journey, even though the real value unlock
comes when we can get to automation.
Naomi Lariviere: Yeah.
And you know, like we view what
we're doing as does like our
job is to design for people.
This is the world of work.
We are changing how people experience
their day-to-day lives and.
You know, just as much as you or
I walk would walk into our C-suite
with you know, "Hey, we're gonna do
this." They want the data behind it.
They want us to be able to explain it.
They want it to be very transparent.
And that's what we are definitely
weaving throughout what we're doing as
we reimagine how work is actually done.
So explainability, transparency, like
those are kind of like, especially with
AI, those are quintessential elements that
all of us have to be paying attention to.
Trisha Price: For sure.
Well, I know ever since I met you,
one thing you and I have always had
in common and believe in is everything
that we do has to deliver client value.
And the great product management
starts with client value.
And you know, your example here is,
is clear that that matters to you.
How do you help your teams stay
focused on delivering client value,
and delivering outcomes not just
for your clients but for ADP.
Naomi Lariviere: Yeah.
it's funny 'cause I was just talking
with some folks just this morning,
earlier today, a about like, what's
the prioritization rubric like?
What are we looking for when we sit
down and go, is this an idea that
has merit, that we should progress?
So there's really three kind of
things that we're looking at is
one, is it good for our users?
So is it good for the client?
Like is it going to help them have a
better day or experience our product in
a way that makes them happy or delighted?
Right?
So that's question number one, yes or no.
Very simple.
second one is, does it help ADP?
So will it help us grow our revenue?
Does it help us acquire new logos?
You know, will it help
us deflect service calls?
Like what that's kind of
like, does it help ADP?
And then third is, how does it
help us with either our market
position or competitive position?
So we're not necessarily the organization
that's trying to do everything the
same as what our competitors are doing.
We are really trying to service the needs
of our clients and really think about.
How work does evolve, so we don't
have to be the same as everybody
else, but there are things that if
you have a sales prospect come to you,
like there are certain table stakes.
And so in, in those instances
it's either a yes or a no on
whether it helps us with that.
Now, this is where I love Pendo.
We leverage Pendo a lot in every single
aspect of that decision making process.
You know, in terms of how our users
are using our system, where they
might be running into problems,
kind of what the journey they're
taking through our systems are.
And you know, that data is so
critical for us in terms of how
we answer those questions and
how we then make the ultimate
decision about what we're gonna do.
Trisha Price: I love to hear that.
You know, that warms my heart to hear
that Pendo is driving decision making
and an important part of how you measure
value for ADP and for your users.
Naomi, do you guys have scorecards,
goals, KPIs like that you think about
for the product team to make sure
that these outcomes are achieved?
Naomi Lariviere: Yeah, we use OKRs.
So everything is outcome based driven.
So we have our vision, our mission the
outcome that we're trying to drive.
And then as we decompose the idea
into a roadmap, then we actually
are going, okay, in Q1, we're gonna
achieve this part of the outcome.
And we keep tracking towards it.
outcomes for us always are
metric bound, so it could be that
we're reducing service calls.
It could be that we're
helping those new logo sales.
Whatever that element is, and then
we're just tracking that as we go along.
I mean, what I love about ADP, I mean
automatic data processing, but if
you think about data, data, it's our
middle name and everything we do, we
are probably one of the most metric'd
organizations that you have, and I love
that about my job is we, we can pretty
much tell you anything about what it is
that we're doing and how we got to an
outcome that we were trying to drive.
So, yeah, just lots of, you know.
Really monitoring it because just because
you made a decision to actually invest in
something doesn't mean that you actually
have to continue to invest in something.
We've had things where
as we were building it.
And maybe we put it in pilot, we
just weren't getting the adoption
or we weren't getting pilot
clients to sign up for the idea.
And given some time and some, some more
analysis as to what might be happening.
We kill ideas all the time.
We kill projects.
There's probably a lot of stuff that goes
on here that never sees the light of day.
And that's okay.
And that's how data can really
help influence your decision.
And not just at the inception of an idea,
but as you are continuing to go along.
And the SDLC,
Trisha Price: I love that that is - as
you said when we started off, you
and I, this role, we make hard calls
every day, and killing a product or
pausing something is probably one of
the hardest calls that we have to make
because sometimes it's easy to say like
the outcome's right around the corner.
We're just not there yet because
we fall in love with our ideas and
we're trying to innovate and we're
trying to do things different.
and that I think is just one of the
hardest calls that we have to make.
'cause you just want it to be,
you knew it was a good idea.
and it's like, oh, but we just have to
do this one more feature and the outcome
will come, but sometimes it doesn't.
Naomi Lariviere: Exactly.
I think I really believe in, in
the phrase, and you've probably
heard it before as well, is
like, you're not the user.
I'm not the user and so I never
actually get too caught up in
whether my idea is actually gonna
make it into production or not.
For me it's all about that person at the
end of the computer screen or the mobile
device that is actually experiencing them.
I believe what they tell me, and
that is how you make your decisions.
Because if they don't see value
in it, then why are we doing it?
Right?
Trisha Price: Yeah.
Then they're not gonna pay for it.
They're not gonna appreciate you.
Naomi Lariviere: Exactly.
Trisha Price: We to listen.
We have to listen.
Naomi Lariviere: Exactly.
Trisha Price: Well, as you
mentioned, and we all know, ADP
is a highly regulated space.
and your role is fascinating to me
around bringing AI to your users,
bringing AI in a scalable way to ADP.
and you have to do this in a place
where precision matters, right?
Mm-hmm.
Even a small mistake
has major consequences.
As you said, none of us want our
paycheck to be wrong unless, unless.
It's in the positive direction,
but then there's probably still
somebody there who's not happy.
so tell us like, how do you
approach bringing AI in?
How do you balance innovation with the
need for almost perfect reliability?
Naomi Lariviere: Yeah.
Very carefully.
So I think, And I'm gonna apply
what I'm about to say as like before
AI, so BC so before AI happened,
generally most organizations,
they would build their products.
Test it and then hand it over to your
security, your legal, your compliance
team, and they would look at it and
do a checklist of yes, yes, yes.
And then it would actually go
and become generally available
or be released clients to use.
That we have completely shifted left.
So as we come up with our ideas
for AI, what we realize, because we
do have a lot of data, we have the
largest HCM data set in the industry.
We service organizations in 150 or
140 different countries, so there's
lots of laws, regulations, especially
like in Europe where there's been
a a new legislation around that,
even here in the US, California.
So what we, we did realize is we
need to shift that entire process.
Left.
And now any idea that comes in for AI it
goes through, we call it the CDO process.
It's governed by our Chief
Data Officer and that team.
And basically it's looking at the
security elements of the idea.
It's looking at the data, how we
wanna use it, are we using it in a
way that complies with privacy laws?
We look at it in terms of how does
it watch or observe compliance
laws around you know, the different
statutes across the world.
And then last but not least, like legal.
So are we thinking about bias?
Are we thinking about
the ethical use of it?
All of that is kind of like
our shift left philosophy.
Now, it doesn't just happen
at the first time that you.
Come up with the idea as we are going
from A-A-P-O-C to a pilot to generally
available, that analysis or that
work that our CDO office has deployed
gets progressively more difficult.
So there's harder questions
as you go through.
So by the time that actually
is in our products, it's.
We been thoroughly vetted based
off of our understanding of the way
the world is right at this second.
I mean, laws are changing
every single day.
So our process does adapt
as we go through it.
But that is generally what we do
now, overall, our principles And
how we have been thinking about AI.
We started an AI and
Ethics Council in 2019.
This is made up of subject matter
experts in the field of artificial
intelligence and ethics from some
of the major universities out there.
And they work alongside us to help us lay
out our plan in terms of the things that
we should be watching for in this space,
because it's not just about your payroll.
You know, being your
paycheck being correct.
It's also about how you're
recruited into an organization.
It's about your performance review.
It's about hiring and firing decisions.
All of that is as we
want to apply AI to it.
We just have to be super
thoughtful about what it is.
Now you can pass all of these checks that
we're doing internally, but again, it goes
back to does the user need this solution?
And is it good for a DP and does
it help us with the competitive?
And so like this, all of this process
goes in tandem with how we actually are
making decisions about what we bring.
Trisha Price: So fascinating when you
think about all of the aspects that
you're bringing AI all the way from
first touch of candidates to hiring, to
onboarding, to performance management.
I mean, that's just critical,
critical to how so many all
companies run their business.
I mean, our number one
asset is our people.
Naomi Lariviere: Yep.
Trisha Price: And so it is
fascinating to think about the
legal implications of everything
you're doing across that life cycle.
Naomi, can you give us a concrete example
of a new AI feature or product that
you've launched into your products?
And the impact it's had.
Naomi Lariviere: Yeah.
We've done a actually quite a lot and
we actually, so I mainly important
to note, we don't talk about like,
ideas that we have that we're just
kind of like thinking about today.
We only talk about things once
it's actually in our product.
It's.
Being used by either pilot clients
or by or it's generally available,
but we actually have quite a lot
that we've delivered across our
six major platforms that we've got.
And I'd say like probably the one
I'm most excited about, it's been
in pilot now for several months.
And I say pilot, it's like we're
rolling pieces of it out to
generally available as we go.
So it's not fully GA right now, but like
clients do have pieces that are using.
and it's called payroll anomalies.
So the bread and butter of what
a DP does is a while we are a HCM
provider, what people mostly use
us for is the payroll process.
Payroll is a very complex process.
on average, a payroll practitioner
or the HR department, they do about a
hundred different activities to make
sure that you get a correct paycheck.
That process is done over the
course of generally two days.
Most organizations, usually
Monday and Tuesday are kind of big
days for, for the HR department.
They're running all of their checks.
So this is like.
All the new hires that came in, are we,
are they accounted for people who left?
Are they accounted for anybody
going on leave of absence?
Do we have our benefits
data, our 401k information?
All of that information's
coming into the system.
And then payroll basically checks all of
that data to make sure that it's correct.
And we call that.
Anomalies.
So what we're looking for is
anything that is out of the norm.
So maybe you're an hourly worker, but all
of a sudden you have like 80 hours on your
weekly pay stub, and that's kind of odd.
So did we overpay you?
So it's flagging that those kinds
of decisions back to the user to go,
Hey, you, you wanna look at this?
And what we've what?
Used to happen is our payroll
practitioners is, they would, we would
flag all this information, put it in
a PDF, they would have to print it off
and then go through it line by line.
Some of these reports can be
like 200 pages long, right?
And we're like, there's
gotta be a better way.
On average it takes 'em about
90 minutes to do this process.
90 minutes is a lot of time.
And you know, I always talk about it
like, we want you to get in, get on and
get on with the rest of your day because
you shouldn't live in our systems.
Our systems are used to facilitate
work and we really felt that problem.
It was important because out of all
the payrolls that we run, we can see
that at least 70% of payrolls have at
least one anomaly that will show up.
So it, it is a critical step in the
process that people need to look at.
It's high impact it's had.
But it also had high feasibility in
terms of can we apply agentic AI to it.
So what we've created is the ability to
detect, make the user aware, and then
actually resolve the issue for them.
Now, this is where kind of
the trust factor came in.
So when we first started, we were
like, yeah, we just wanna, you know.
Everything's all solved.
Like the world is beautiful.
That 90 minutes it's maybe a five minute
process where you just kind of check it.
That's where clients are like, "no,
no, no, no, no, no, no, no, no, no.
Show me the math.
How did you get here?
Show me why you, you did it." And really
what we've went back to the the tinkering
board to, to go and actually look at
okay, how do we make it more explainable?
How do we make it transparent?
How do we actually show them our homework?
Right?
And also, how do we let
them make the final choice?
What we understand about our users
is because this is such a it's
one of the most audited processes.
In a organization we wanted to make
sure that they felt comfortable
and they could check off.
So while we have automatic detection
awareness and resolution capabilities,
they are the final human in the loop to
actually go, yes, I accept this work.
Yes, this is the right thing to do.
And then it moves on.
But the other part that we then wove
into our process from an audit tracking
perspective, while we have audit logs.
For every single process in our,
in our ecosystem, we actually
brought in an agent control center.
So this tells them all of the
things that the agent is doing
versus what the human did.
So that way if they ever were audited
or you know, God forbid they were
sued or something like that, they have
that information at their fingertips.
They can produce it and
they are good to go.
So our clients, and now let's
talk about impact because that is
something that we track, right?
You know, what we can actually see is
how many, like literally, and this is
where Pendo helped us, is to be able
to tell when they see the anomaly how
they click on it, and then actually
how many go and do the action to say,
yes, I'm okay with how you solve this.
And that we can see like they
prioritize some of the anomalies
that they look at it, you know.
Basically varies by user.
And then last but not least, it
generally it's taking 90 minutes
in the process that they use, if
they're going with the PDF, it now is
reducing up to an hour's worth of time.
From that process.
So like, it's significantly,
significantly improved.
Kind of like their happiness
with that part of the process.
And you know, clients are just you.
I think we have a quote on our website,
like the client was just like, this
means so much to me because it's.
It's easy, It's smart.
It's doing the things that help me with
my job and hopefully we, we'd say maybe
that makes them the situation a little
bit more human for them in that process.
Trisha Price: Registrations for
Pendomonium 2026 are now open.
We are bringing together the most
inspiring minds in product and leadership
who will challenge your thinking on
everything from product-led growth.
To the future of product to gaining
value from your AI investments,
it is likely you'll even run into
some of our guests from hard calls.
The product festival is designed to spark
curiosity, create conversation, and build
community while spotlighting the newest
tech for software experience leaders.
I would like to invite you to join
me in Raleigh, North Carolina from
March 24th to 26th with an exclusive
30% discount when you use the code
HardCalls30 That's Hard Calls, all
lowercase and the numbers three zero.
Get your discounted ticket
at pendo.io/pendomonium.
See you there.
I mean, that is real value
and you know, we hear so much.
Around AI and everybody's building AI
features, but in a lot of cases, for
a lot of people, AI has yet to give
an ROI, and this is a real example
of your customers getting actual
time back from your AI investment.
and that's incredibly impressive.
All of us have had to pivot and
learn new skills in this era of AI.
Whether it's the engineers or
product managers, designers, in
terms of how to build trustworthy
interfaces and interactions with
agentic interfaces for our customers.
How did you do that as
a leader with your team?
Did you have to go out and hire
people that had experience?
I mean, it's kind of new for everyone,
so how do you find that experience
or how did you upskill your team
so that they were able to have the
kind of success you've had so far?
Naomi Lariviere: Yeah.
I'd probably say a little
bit of gorilla tactics.
So I think you just said something
that's really important that
everybody should understand.
This is new technology,
we're all learning together.
Right?
You know, we weren't sitting as PhD
students at Stanford like learning
this as part of our coursework.
So we're all learning it
together, including our users.
They're learning it together for us,
we have a really great leader who was
like, "Hey, you know," Maria Black,
she's our president, and CEO, she
basically said, "listen, I think this
could really be the wave of the future,
especially in our industry, and we can
really think about how we design the
work for people and reimagine work." And
she was just like, "I need everybody."
To jump on board.
So, especially within the product and
technology organization we have had
coursework that we've all gone through.
We do a lot of webinars where
they're more like a lunch and learn.
So here's a team that was doing
early experimentation, what they've
learned, what they understand.
And then when I say gorilla tactics
we've also leveraged content from
the big LLM companies, they all have
free learning available to them.
Coursera has a ton of learning as well.
The universities are making
education available for free
if you want to do some of that.
Like Duke University is a
good one in your home state.
And they all have coursework.
But I think what I love about where
we've been on in this journey over
the last two and a half years is.
The collaboration that
the organization has.
I don't think you, you can just
go, I wanna be innovative one day.
It really takes a lot of bold
thinking and you have to, to
drive kind of that disruption.
You can't just be satisfied with,
well, this is how we've always done
it and this is the way our clients
always wanted in order to make.
You know, work reimagined,
you have to think differently.
And this technology gives
you that opportunity.
So we have we I talked about we have
outcome-based teams, but we also have
fleets of teams that are working on AI.
And they work on problems
across the, what I would've said
might be a traditional silo.
They are working across it to go like,
"Oh, the payroll team did this, well,
maybe we can use that same concept
over in benefits or retirement or
recruiting." And so they're learning
off of each other and I think most
of the ways that maybe you or I kind
of grew up in the corporate world is.
You learn on the job.
And so it's been a great opportunity
to see those teams really push the
boundaries of we're like bold, you
gotta be bold and they from having
sandboxes where they have the freedom to
experiment with all the different tools
to try and determine which one's the
best one for the problem that they're
trying to solve, to just kind of saying.
We can think differently.
We can do things differently.
It doesn't have to be the same.
And they have that permission
and autonomy to do that.
And I think you know, given our
predictable approach around how
we bring AI to market, that allows
us to really kind of celebrate the
learnings that we're getting as we're
going along it, and just not pushing
features that maybe clients don't want.
So it's been a great time.
Trisha Price: I love that.
I mean, I don't think it's common and
easy or typical for companies of your
size and scale to be able to do a pivot
the way you have to this experimentation,
continuous learning mindset of AI.
And that's clearly showing up
in your ability to deliver it to
your client and the experiences.
I think easier sometimes in small
companies that are just getting started
to have this experimentation and learning
mindset, but I think is often harder for
companies who have probably gotten into a
pretty predictable delivery methodology,
SDLC, we could probably, you and I
have been doing this for a long time.
We can probably, with reasonable
confidence most of the time, understand
when a new product or feature is gonna
come to market, what the risks are.
But this is a whole different ball game,
and you're working through it in a really
interesting way that seems to be working.
Naomi Lariviere: Yeah, and I mean, it
starts from the very first use case
that we actually brought to production
it that went from the idea and the
data part of like why it was a good
idea to actually execute on that
went from you know like day one to in
production with clients in 13 weeks.
So I don't think anything
at a DP has gone that fast.
Yeah.
But.
I would say that the process that we've
now applied our like when I talk about
our shift left on our compliance and our,
our AI, responsible AI program that has
really actually enabled us to move faster
than we probably would have traditionally
on a regular capability or feature.
And I'd say it's refreshing to see
the pace of what we've been able
to deliver over, like we, it was
like that from that one use case.
Within six months we had 10 and
then 20 and like we just keep.
Like it's become kind of this like, not
necessarily a conveyor belt, but like
it we're, it's faster, more predictable.
We're learning, as the technology
is changing we're having to
go, oh wait, that idea wasn't
necessarily so great to do that way.
Now we have a new toolkit in
our bag, let's go and use that.
And so we're trying to be very nimble
and not locked into any kind of form
or fashion in terms of how we do this.
Trisha Price: It's interesting.
We had the same experience at Pendo Naomi.
We were building agents and we built
one agent into our listen product.
The one that looks at support tickets
and call transcripts and looks at like
portals And any kind of survey data
and helps product managers know what
our customers users are asking for.
And we built an agent on top of that.
So you could ask a questions like, what
are the top 10 enhancements that my
enterprise customers are looking for?
And then we went and we built
an agent for our guides, right?
So you can say, Hey, I wanna put a new
onboarding guide into my product that
does X, Y, Z, and you can ask it and
it starts to create the guide for you.
And then we built one for our analytics.
So you can ask questions like, how is
this particular feature performing?
Tell me what's working
well and who's using it.
And we learned from e different teams
built each one of those because we
were trying to go fast and we were
trying to experiment and learn.
And then we realized what we really
needed to build was an MCP server.
And we wanted to do that because you
might be building your own agent for
your product management team, and you
might wanna be able to ask it questions
about any of those things, of your Pendo
data without coming to your Pendo agent.
And so it's like, okay, well now
I gotta scrap all of these and
move to this new architecture.
And I think like, but that's okay, right?
It's not wasted time because we
actually learned lessons from
each one of those experiences.
And I think that's just part of
the world we're living in because
the technology's moving so fast.
Naomi Lariviere: Yeah.
And I think the I talked about
collaboration and what I think is good
and it sounds like at Pendo, you guys
are practicing this, is that you need
to give teams psychological safety
in terms of how they're coming to.
How they're showing up and how they're
actually advocating for what we're doing.
I think it's really important
that we give them the space to,
and the power to speak the truth.
I don't want people to just feed me
a line and go, oh yeah, we're gonna
make that date when really it's.
Failing miserably, right?
I really want them to tell me what
risks that we might have early,
what challenges we might run into.
Because this technology is new, it's
unpredictable in certain elements.
And you really have to you know,
maybe slow down to go fast.
So we put a lot of accountability on
our teams in terms of what they're
delivering and how they're delivering it
and how they speak up because I think a
lot of organizations can fall into the
trap of like, well, my exec wants it
by this date, and then they don't speak
up even though they know that it's not
doing the thing that you wanted it to.
Yeah.
i talked a little bit
about experimentation.
We spend a lot of time experimenting
in lower environments that are not in
production, really looking at all 360
degrees of the issue because we really
wanna make sure that we're doing the right
thing for our clients and for their data.
And so experimentation
happens in lower environments.
Never in our production environment.
and with that experimentation
principle, it's you can fail fast in
those lower environments, but then
you're succeeding very deliberately
in our production environment.
So we're really trying to balance
creativity, keep that alive, but also your
quality and what we bring to our clients.
That's uncompromised.
Trisha Price: I'm continually
impressed with your leadership
style, with your ability to drive
outcomes, with your ability to
innovate in a complex environment.
But I also respect your leadership
sort of on a different angle, which
is something I know both you and I
have been passionate about for a long.
Long, long portion of our careers
which is our belief that diverse teams
are better teams and we both have put
significant energy into lifting other
women up especially in technology where
that's been a challenge for both of
our many parts, of both of our careers.
Can you talk a little bit about
that and why that's a passion of
yours and why you think that's
led to your team's success too?
Naomi Lariviere: Yeah.
Well, I, when you think about like,
at the end of the day, you and I
are in the business of building
products that people buy, right?
And not everybody looks like me.
You know, and I really do believe
that diversity drives better products.
That different perspectives are gonna
catch different edge cases that might
happen that I might not think about.
And then as it relates to like the teams
that I cultivate and as I look when
I. When I got into this business not,
not a DP, but into the tech business I
would look around, I was oftentimes the
only woman who was the business analyst
or the only woman who was on the team.
And it's kind of like, well,
sometimes you get afraid to speak
up and you, and you don't do that.
And I think over the course of my career
and maybe like the ambitions that I've
had for myself, I really want to bring
other females or underrepresented groups
along that journey with me because I do
believe that everybody has a seat at the
table, that we all have a voice and that
we all can contribute to the growth and
development of our own organizations.
And I've benefited from both
male and female mentors that
have helped me, grow my career.
And I really do believe that it's our
responsibility to be able to give that
back to those who are falling behind us.
Because having an example of a strong
female leader who's getting things done.
That inspires other women to do that.
In our profession, 35% of women in tech
are leaving the career by the midpoint.
And we can save those women if we give
them the examples within the profession
that they can aspire to because someone
just has to tell them it is possible.
Trisha Price: I love that.
And it is possible.
And while I think you and I both have
seen change in a positive direction
and we see more diversity and more
women in leadership positions,
there's still a long way to go.
and I'm with you.
I'm passionate about this, not just
because I think it's the right thing
to do, which I do think it's the right
thing to do, but I actually believe and
have seen it produce better business
results when you have different.
Perspectives at the table willing to
challenge each other that come from
different backgrounds or look different.
and you're right, our buyers and our users
don't all look like each other like us.
So It's helpful to keep that in mind when
we're driving business results for sure.
Yep.
Yeah.
Well, Naomi, thank you much so
much for sharing your story.
Your passion for people, your approach
to leadership but most importantly,
for hard calls in our hard calls
audience, how you have successfully.
Delivered AI features that are
driving value for a DP driving
value for your clients in a complex.
Make no mistakes, almost,
space that you're in.
So I know that our listeners are
gonna learn a lot from today's
conversation and enjoy hearing from you.
So thank you so much
for joining Hard Calls.
Thank you for having me.
Thank you for listening to Hard
Calls, the product podcast, where
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