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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/
Chirag Mehta: Me being a Chief Product
Officer, I used to say that Chief Product
Officers are chief backlog officers.
They sit on a backlog.
That's what used to happen in the past.
And because you could
not deliver it enough.
Yeah.
And there were always more things to do.
And I think now you're introducing
this concept, which is you cannot get
something right without experimentation.
And you need to have skills
to, to be able to experiment.
You just don't have to build features
and outcomes and whatnot, but you
really have to understand what is
working and what is not working.
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, welcome back to Hard Calls,
the podcast that highlights the best
product leaders from across the globe.
If you're new to the show, I'd
like to invite you to hit follow
or subscribe so you can stay up to
date with all the latest episodes.
Today's episode is a conversation
I had with Chirag Mehta
from Constellation Research.
We discussed the growing shift from
being feature factories to become
outcome driven product leaders.
And here's why I wanted you to hear this.
We're at this inflection point where AI is
compressing product life cycles in ways I
have never seen before from years to days.
In many cases.
You can literally ship something
the same day you start building it,
but that also means you can ship
the wrong thing faster than ever.
In this conversation, we talk about
what it really means to run your
product team like a research lab.
Why your North Star metrics matter
more than your feature count.
And how the ratio of product managers to
engineering is fundamentally changing.
We also get into something, I don't think
enough people are talking about how to
make sure your good decisions actually
get rewarded and not buried in backlogs.
If you've ever felt like a Chief Backlog
Officer instead of a Chief Product
Officer, or if you're trying to figure
out how to experiment without burning
through your budget, this one's for you.
Chirag Mehta: So we're gonna
have a fun talking about.
Anything and everything about
product and experience lifecycle.
I have been a Chief Product Officer in
the past and now I serve Chief Product
Officer as buyers and as end users.
So this is gonna be a fun conversation.
So let me share, let me start
with something what I'm seeing,
and there's been a lot of chatter
around features versus outcomes,
what we have been hearing.
That features are great, but
features won't take you anywhere.
Anyone can build a feature
in a short period of time.
You can ship a product in a day if you
want to, but it might not do anything.
So you might end up building and
shipping something, but that might
not help achieve a business outcome.
So we are seeing this transition.
From this feature driven
software development to outcome
driven product development.
Do you see the same thing?
Maybe?
I mean, that's a good starting point.
Trisha Price: Yeah, it's
a great starting point.
Chirag, and this is gonna be so
fun, both of us serving product
people, but being product people.
So I'm really looking
forward to the discussion.
I do, I, I think that even
prior to aI momentum that
we've seen over the last year.
There really has been a shift in
focus from feature factory to outcomes
because we can spend a lot of money
and celebrate that we shift a new
product or a new feature, but so what?
If people don't use it?
If it doesn't drive ARR, if it
doesn't reduce support costs, who
cares that we shipped it because
we shipped the wrong thing?
Well, fast forward to today, and
I know we'll spend more time on
this throughout, AI has come along.
The ability to use incredible
prototyping tools and even get to
real product is easier than ever
and quicker than ever, right?
And you've got tools like Lovable
and Bolt and Cursor and all these
tools are incredible and they get
product out there even quicker.
But it can also mean we just build
more bad product or more features
that still don't drive the outcomes
that we're trying to drive.
And it's really fun watching this sort of
evolution in product that product people
are truly strategy and business people.
And accountability is to the same
things that the rest of the company
has in terms of business metrics.
Chirag Mehta: Yeah, so I, I like what
you said, strategy and business people.
And if you look at the sort of product
manager, typical product manager and
I'm an engineer by background, and as
part of my team I have product manager,
so we're engineers and people who
are doing engineering experiences.
So we have seen this combination
where anyone and everyone
could be a product manager.
You have to be passionate about
technology domain and all that.
But you make a point that
things are changing now.
There's this idea being you
have to be technical enough.
To figure out what is going on.
Because once again, you're, you know,
it's, it's all about technology.
You are building products, but we,
we are talking about outcomes and
outcome is about business and strategy.
You have to translate your company's
goals into what is happening outside
with respect to your market competition
and with respect to your customers.
So what does this blend of
technology and business looks like?
Trisha Price: I like to think of it as
a combination of inside out thinking
and outside in thinking, and I think
engineers, maybe I'm biased because I
am one, can make great product people.
They really can because they nail the
inside out thinking the ability to play
with technology and think of the art of
possible to innovate something different.
And especially in the era of AI, to be
able to break a problem set down, right?
To understand here's the outcome
I'm trying to drive, here's the
problem, and then what are the pieces?
What are the chunks to start shipping and
get velocity and get things out there.
And that inside out thinking is very
natural for engineers, but that can
lead you to ship a lot of bad product.
And so to me, it doesn't
matter, engineer or not.
Outside in thinking.
Also, some people might call it
product sense, it is all about
knowing your market and the
outcome you're trying to drive.
And to have very strong product sense.
You have to know those jobs to be done.
You have to know your users inside
and out, and what problems are truly
painkillers, not vitamins for them.
But that still has to layer up
to your own company's outcomes.
So like the outside in thinking
part kind of, and the business
strategy comes in two flavors, right?
The first is.
Knowing your users, the value
you're trying to drive for them,
the problems you're trying to solve.
And then the second is, what am
I trying to do for my company?
Right?
Am I trying to get new ARR?
Am I trying to create a new product for
the same customer, but solve a different
problem and then really get your product
North Star metrics aligned to that.
Chirag Mehta: It's interesting
you said that you could end
up shipping a wrong product.
I think what we are seeing with
AI that it really compresses the
lifecycle and as I said just early
on, is you could build something, ship
something the same day, and guess what?
You ship the wrong thing.
So do you see that as, as part of this
strategy, past part of this validation
and what, what we, what people
generally refer to as a product sense
as, as you know, rightfully described,
which is really know your end users,
but now it is giving us this ability
to accelerate these feedback loops?
Trisha Price: Yeah,
Chirag Mehta: We used to wait for hours,
days, weeks, sometimes to get feedback and
like, "Hey man, we, we shipped the wrong
thing." We have to go actually go fix it.
But now those feedback
loops are much faster.
What is, what is your sense, how
does, do you see that feedback loops?
Feedback loops are faster?
And if so, you know, how
does product management as a
discipline evolving with that?
Trisha Price: Yeah, well, I think
it's magical that feedback loops are
shortening and cycles are shortening.
Because if you are strategic, if you
do know the business outcome, if you do
have product sense and know your users,
these quick feedback cycles allow us to
do true experimentation at scale, right?
And so if we know we're laser focused on
our north star metric and whatever that
might be, could be a time to value metric.
Could be a conversion metric,
could be retention, whatever it
is that you're trying to drive.
If you understand it now all of a sudden
you can try three different things
or a hundred different experiences
to different subsegments of your
population, get feedback and really
nail what that product experience is.
What the features are that you need to
build even before you engage engineering.
Right?
Chirag Mehta: Even before you engage.
Interesting.
Trisha Price: Yeah.
I mean, obviously you should be engaging
your partners and having conversations
all along, but you don't need hands-on
keyboard for engineering to get prototypes
out and do experiments and get feedback.
Not with today's tools that are out there.
And what you need engineering, I always
think engineering is my gold resource.
It's scarce and I have
to use it really wisely.
Now, by the time I'm asking engineering
to build something at scale that
users love, my confidence on the right
feature, driving the right outcome
is super high because I've done those
experiments and feedback loops with
prototypes I can build in product.
Chirag Mehta: Yeah, I think.
I think I, I would, I I love this thing.
I think what you're saying is the, the
high, the signal is of very high quality.
One of the challenges what people have
seen, and I've been on both sides.
I've been on the product
side and engineering side.
Engineers would tell you their
product managers don't know anything.
Product managers would go and say,
well, engineers are way too demanding.
They just don't listen to us.
And they're both right.
Yeah.
Because what has happened in the
past is that we just never had.
Such a high quality signal
from customers from market.
So you end up wasting time building
something or modifying something
that actually no one wants, and
doesn't actually solve any problem.
So now given the accelerated prototyping
going out there just going out there,
putting it in front of customers,
doesn't, they don't have to be super
working, engineered, you know, highly
scalable, you know, kind of prototypes.
But you actually get that, that
strong signal that it is actually
solving the problem or not.
So given, given that and, and given,
you know, where this, where this, this
dynamics are going, I think we get the
outcome part, which is you can get to the
outcome much faster and you can experiment
a lot, but it's still about cost.
I think the cost is, it's still the cost
and companies have been looking at and
the companies, the clients that we advise,
one of the questions is like, look, I
don't want to do endless experimentation.
And because it's still expensive,
even if you have to, even with the
help of ai, if you're doing that.
So what is your recommendation?
What are you seeing on the cost side?
How do you make sure that.
You stay within the budget
and you still experiment, but
still deliver a great outcome?
It's like having a cake and eat it
too, but I hope with AI we can do that.
Trisha Price: Yeah, I think we can.
You know, look, I think it is very
easy to give yourself a false sense
of, oh, I did a great job and I stayed
in budget and got the project done.
When you don't focus on outcomes.
Hey, yeah, I can get the project
done now is what I built amazing?
And I think you can go the
flip side, which is you can
do endless experimentation
and also not drive outcomes.
So to me, I don't think it's about, if
you're focused on ROI, which everybody
should be, you know, engineering,
technical products, especially in
the world of AI, are expensive to
build and expensive to maintain.
And so holding yourself and your
team as a business person accountable
to ROI is incredibly important.
So if you're doing that.
And you say, this is the amount
of money that we're gonna spend
right now, and this is the amount
of time and you've time boxed
something to get something out there.
If you're focused on outcomes, you're
not focused on, did it take me 10
experiments or a hundred or two?
You're focused on, I delivered a
product that delivers outcomes.
And so I think that's how you
hold the team accountable.
And I think that your likelihood of
getting an outcome is much higher if
you are experimenting than if you just
do a couple of surveys, do a few zooms,
talk to a few important customers,
and have a false sense of security.
Because it's not about, I mean,
it is about building a product that
people will buy or use, it's often
about the details when we talk about
product sense and then product taste.
It's about getting the details
right that make people wanna come
back and do something over and
over again that make the value.
Something they can't live without.
And you will never get to that
level of detail without some
pretty serious experimentation.
And so I think that I don't think
experimentation gets in the way of
ROI, I think it's what delivers ROI.
But either way, you're not gonna get it
if you don't know your North Star metric
and you're not relentlessly measuring.
Chirag Mehta: So experimentation.
Sounds like a research lab.
Hope it's not a physical research
lab, but it sounds like...
Trisha Price: yeah.
Chirag Mehta: ...you know, we
are experimenting a lot more
than we could have in the past.
And I, I generally joke and, and sort
of me being a Chief Product Officer,
I used to say that Chief Product
Officers, our chief backlog officers,
they, they, they sit on a backlog.
That's what, that's what
used to happen in the past.
And because you could not deliver enough.
And there were always more things to
do, and I think now you're introducing
this, this concept, which is you
cannot get something right without
experimentation and you need to have
skills to, to be able to experiment.
You just don't have to build
features and outcomes and whatnot.
But you really have to understand what
is working and what is not working.
So how do you, how do you think about
this concept of research lab or sort
of research driven or experiment
driven product management sort of
as part of the larger discipline?
Trisha Price: Yeah, I think that one
comes back to what I said before, which
is you have to have extreme understanding
of the problems you're trying to
solve and empathy and understanding of
your users and their jobs to be done.
But I think equally you have
to understand how to measure
and what success looks like.
And it's not, you know, when I spend
time and, and I know you get to too
with product leaders across the world.
They know they're trying
to drive ARR, right?
They know their new product
needs 5 million in ARR, 10
million in ARR, whatever it's.
Chirag Mehta: 10 million today and
hundred million at the end of the year.
Yes.
Trisha Price: Yeah.
They know that.
That's not a question, but that's
not a North star metric in product.
That's a lagging indicator,
not a leading indicator.
Chirag Mehta: That's a lagging indicator.
Okay.
Yeah.
Interesting.
Trisha Price: That takes time, right?
I can't tell when I shipped a
feature or a new product this
week if that's driving ARR.
That takes so much time.
So then you have to start to think,
well, this is what's hard in an,
in a research lab, is what is
the thing that I should measure?
Right?
What is the North star metric that matters
that eventually is going to drive ARR?
And so for example, it could be
eventually just, are people logging in?
Okay, they're logging in.
Are they doing the job to be done
and are they getting it done quicker
with my product than they were
before they bought the product?
Then it could be, well, okay,
what's the real they, they may
be getting a workflow done, but.
Maybe that's or, or they may be completing
transactions, but maybe the ultimate
value, especially in this era of AI,
is they don't have to do anything.
So now you start getting really cute and
tricky when you think about analytics
and metrics and outcomes, because it
might be that the true value is that the
person doesn't have to do anything at all.
But it just gets done.
It gets done by your agents
that you're building.
Chirag Mehta: Yeah.
I say that, you know, the best
user interface is no interface.
Trisha Price: Yeah.
Really.
I don't wanna do anything in your product.
I just want it to work.
Right.
So I think that having that mindset that
you just shared, right, the best user
interface is me doing nothing at all.
And you just building my confidence
that it was done and done correctly.
Like that's how we have to think.
And when we're running this research lab,
we're gonna try three different things.
Which one built trust?
Which one was most efficient, right?
Which one was highest quality?
And then how do I put each of those
things together to nail this experience?
Chirag Mehta: Trust, efficiency
and quality interesting.
And you could prioritize
any which way you like.
Sometimes you don't wanna be efficient
if your job is to just get trust, then
you just focus on that one metric.
So I think this leads to the
product life cycle because it the,
you are never working in a sort
of, in one part of the life cycle.
Life cycle, you're, you're always as
a product manager, as product leaders.
You are building something new.
You are maintaining something that's
going on and you are putting out fires.
There's an escalation
and you're on the call.
That's, that's so, that's a life, you
know, that's a life all product leaders
and all product managers actually live.
But the thing is the discovery to
delivery, like as part of Pendo and
part of, you know, Pendo platform
and products, you are in this like
a smack middle of this lifecycle.
What is changing?
I know AI is impacting
all parts of lifecycle.
We have seen some in a good way.
Some not so much in a good way.
What are you, what are you seeing?
Trisha Price: Yeah, I definitely think
it is changing the product development
lifecycle, where even though it was,
it's been iterative for a long time.
You know, there was, "Hey, let's define
the business outcome." Let's plan it.
Let's do designs.
Let's get feedback, let's build, let's
watch, like, you know, let's ship,
let's enable the customers in the
field, especially in B2B software.
And then let's, let's measure, learn.
And sort of start back over.
It's now that from business outcome
definition, design, build, get feedback
is sort of all meshed together because
we can use these prototyping tools to
build something that feels really real
and it is real and get feedback from it.
That whole sort of double diamond process
and portion of the lifecycle sort of gets
all smooshed into one and it's like, Hey,
I have an outcome I'm trying to drive.
Here's a whole bunch of
different experiments to run.
And a lot of that's
happening before build.
So I think what we used to think
of as build is like, let's get this
minimum set of features out there
and get feedback on it and iterate.
Now if build is like true engineering
build that happens after we've done
a ton of prototyping and have a much
stronger sense of our product that we're
trying to build and what's gonna work.
So it really just sort of
really accelerates that
first part of the lifecycle.
Chirag Mehta: I was chatting with
one of the buyers and large financial
company and what they shared with
me they use traditional sprints.
This two weeks this concept of sprint
zero, which is you get your, you know,
designers and PMs and engineers together.
You put a plan and all that.
And what he told me that the end of the
sprint zero, we actually have a product.
It's not a working product.
It's not something that
we would actually ship.
But sprint zero is not,
not about planning.
To your point, there is no double diamond,
Trisha Price: right?
Chirag Mehta: The end of sprint zero.
You are delivering some, you
are delivering a version of
what you're actually gonna ship.
Then the build begins, to your point,
people are getting in and now like,
okay, how do I bring this thing to life?
How do I make sure that this thing scales
and this thing has the right compliance?
And once again, this is a bank.
Trisha Price: Yeah.
Chirag Mehta: Has all these rules.
And I think what we are also seeing
that we are hearing this chatter
from non-product leaders as well.
And this is coming from CFOs, which
is finally, like, I now have a tool.
And common set of common set
of set of matrix that I can
measure the product teams on.
'cause in the past, as you
said, people would go and say,
well, I'm gonna go ship some.
They're gonna not gonna find out
until the end of the year whether
we increase our ARR or not.
So you can't, you can't really
hold teams accountable to that
because that's just too complicated.
So what do we do?
So I think given this outcomes
and given this ability to
actually get early validation.
We are seeing that more
non-product leaders are getting
engaged in, in the lifecycle.
Do you see that with your customers?
Is it, is it going beyond product now?
Trisha Price: For sure.
I mean, I think first is we can
certainly hold ourselves accountable
and our peers can hold us accountable
as product leaders to our people
using the things that we built.
And are they delivering value?
And I see product scorecards
having to show up in C-Suite.
You know, those being
rocks that people plan.
And so those are business
level conversations.
Like it's not an infinite budget,
it's not an infinite set of your
cool next idea, but CFOs, CEOs, and,
and really the whole C-suite holding
product and engineering accountable.
To are people using what you shipped?
And that's where Pendo is a huge help.
Um, and then in turn, not
just did they use it, but are
they getting value from it?
And then that turns into to
revenue for the company, right?
And so I do see that being a
broad C-suite conversation and
certainly one that a great.
Finance, CFO leader is gonna care a
lot about, but even the CRO, right?
The CROs, you know, if you've
got a leaky bucket, right?
Chirag Mehta: Yeah.
Trisha Price: This is very applicable
in any software company you're selling
a product and if people don't renew.
That's a problem, right?
And the CROs spending all this
work to acquire new customers.
And if we're losing customers,
that's a big problem.
And product has
accountability there for sure.
And so, again, like that's a great
place where you see CROs logging in
and saying, Hey, are people using
the product that we sold them?
And then using that data to drive risk
conversations of, is this a healthy
customer, red, yellow, or green?
Well.
I mean, A CRO cares deeply about
that in terms of making their number.
Chirag Mehta: Yeah.
I was chatting with one of the,
this is a software company,
very lost software company.
And they are changing the way the product
teams are funded and the, the retention.
There's a very specific bucket
dedicated for just product retention,
and they had a version of that.
Usually it was funded by go to market
teams, but it was very one off in the
past saying, "Hey, I have this renewal
with this customer coming up. Some
strange thing is going, can you go
work on these two features that's not
working anymore." So now what they have
done is there is a retention budget.
It is directly tied to whether
we can renew someone or not.
Because one of the things which I
explain to enterprise software or people
who are not in enterprise software,
the way it is different than consumer
software is an enterprise software.
Your buyers are not your end users.
Trisha Price: Yeah.
Chirag Mehta: People who are writing
checks to you are not the ones
who are using your software and
people who are using your software
have little to no influence on,
you know, who is buying software.
It happens, especially in large companies.
Versus consumer software is
you're paying, you know, with your
money, paying with your wallet.
And so you, you have a say in what
you actually use, what you don't use.
Trisha Price: Yeah.
Chirag Mehta: And I think it's good to
see that this, this rapid acceleration
AI compressing lifecycle is actually
making it is actually favoring end users
because now that retention end users
are logging in, whether they're doing
something or not, it actually matters.
Trisha Price: Yeah.
Chirag Mehta: So let's, let's
switch to AI a little bit, right?
For a few minutes.
Now there's AI obviously that is impacting
the lifecycle and the way you do things
differently because you are using AI to
do X, Y, and Z. But also many companies
now, they're just building AI products
and that software looks very different.
That software does not look anything
like that we have seen before.
It probably uses some of
the components that we know.
It still runs in the cloud and it
still has maybe containers and it
uses APIs and it tests certain SDKs.
But the nature of sort of how you build
software, as in you train models to,
you build agents to, you deploy this
agents to you deploy this models you,
the way you work with your customer data
to the way you work with your own data.
This is, this is very complicated.
Trisha Price: Yeah.
?
Chirag Mehta: So how are you helping.
People build what we call
sort of AI native products?
Trisha Price: Well, first, you know,
if you go back to Pendo roots of
analytics, we helped you understand
if features got used, what people's
workflow or click paths were to getting
a job done and helped you really
understand that and optimize that.
Well, the same thing needs to happen
in Ag Agentic experiences, except
there's not really clicks, right?
We're typing in questions,
we're having conversations, and
there's a lot of similarities,
yet there's a lot of differences.
Similarities mean I
still need to get value.
I came in to ask the software question.
Did I click and go run a report
or look at a dashboard, or
did I ask an agent a question?
Either way, I need the correct response.
Now, here's what's wildly different
for engineering and product teams.
If you go back to our Qlik traditional
world of SaaS, things being
correct was pretty binary, right?
We built unit test cases.
Even if it was an API call it
came back, yes, no, it worked.
We clicked it.
You know, the, the answer came back.
But when you're talking
about having a conversation.
It's not binary.
There's not one correct response, right?
This is an agent on the other side
that's been trained, and multiple
answers could be the right answer.
And so understanding quality and getting
evals right is critical for all of us
building ag agentic experiences and
also as product managers being on to
able to understand the conversations
your agents that you're building are
having and the workflows that they're
completing and what is your user
base trying to accomplish, right?
You kind of knew what they were
trying to accomplish when they went
in and clicked X, Y, and Z. Now
you've got to inspect conversations.
To have that same level of knowledge.
And for us at Pendo, that's been a big
focus for us over the last year, is
building what we call agent analytics
to help product managers really
understand those conversations and what
their users are trying to accomplish.
Chirag Mehta: Interesting.
Now one of the things which actually
none of us and none of the customers,
you know, we talked to have a clear
answer on so far at least, is.
How do we structure teams and how
do we, there's a golden ratio in the
past between product managers and
designers and engineers and Microsoft
had one way of doing things and large
company Google did a different way.
And the industry kinda adopted a sort
of version of that, and we were fine.
I think we understood what that good
ratio kind of looks like for your company.
Whatever you are doing.
That's falling apart.
I don't think that ratio is true anymore.
We are also hearing and this is sort of
unfortunate, where, hey, I can replace
a junior engineer, you know, with AI.
I don't need to hire anyone, you
know, from, from college anymore.
It's obviously not true.
I think it's, you know, he colleagues,
one of the most bizarre ideas that
he has ever heard, that you can
replace entry-level jobs with ai.
There, there's no such thing.
We, we have known this thing for a while.
But the thinking you know, people
are still thinking about it.
It's kind of, you know, odd that way.
How do you see that?
Do you, do you think ratio is changing?
Do you think it's the same thing?
Do you think team composition is
changing as you build AI products?
Trisha Price: I think it does depend
on the kind of product that you're
building and the team you're in.
I mean, even AI first products
typically have some sort of.
Backend special sauce, right?
Where the work is happening, where
the data's stored, where the analytics
might be processed, and those teams
might look like they looked before.
But when you talk about in general.
AI is giving engineering
scale and velocity.
Can it make great architectural decisions?
I don't think so yet.
So can it build software on a shaky
foundation and create a shaky experience?
It can.
Can it augment great engineers who
have built a great architecture?
And a great foundation and
give it scale and velocity.
I absolutely have seen that
happening and I think that's
only going to continue to expand.
So if you multiply that by the
efficiency you get by product
managers using prototyping tools.
And not wasting engineering time, sort of
in the experimentation phase and in the
discovery phase, and being more confident
in the outcomes that they're gonna deliver
by the time they get to engineering.
I am definitely seeing areas where
the ratio of product to engineering is
changing and where you might have needed
a product manager, you know, in seven,
eight engineers to build a great product.
You might get away with a product manager
in three or four engineers, sometimes
two, to build the same great product.
But that comes back to the
product manager knowing.
The outcomes, having a very strong
product sense going through and curating
experiences through their research lab.
And then it depends on engineering
being top-notch architects, you
know, distinguished engineers in
terms of their foundation building
and then utilizing AI to scale them.
But I'm definitely seeing that happen.
Chirag Mehta: It's interesting
you say this and I. I've described
this, that people have misunderstood
the the biggest hidden part of ai.
The AI initially was seen as sort of
the efficiency, engineering efficiency
that you can drive, you can write
code, you can ship code, you can
write test cases is great, but I don't
think it's the biggest accelerant.
I think it's a low hanging fruit,
which everyone should use it.
If you can, if AI can write code,
you should ask AI to write code.
You do.
You don't have to do that, but I don't
believe that's the biggest unlock.
The biggest unlock is if you
make a right decision upfront.
It'll give you the scale and the
velocity that you're referring to.
So in the past, even if people made
bad decisions or not so good decisions
it didn't really mean much because
you wasted some effort anyways.
And it took year, year and a half, and by
the time you have no idea what happened.
So a lot of good product managers
that, that I have worked with and that
I advise they were very frustrated
by this fact that their good
decisions were not being rewarded.
Even if you had great product sense
you became a chief backlog officer
because like, what am I gonna do now?
It's a great decision.
I know for sure that we need this.
I have customers demanding this.
I know we can get revenue.
I know we can get to the outcome, but
you could not actually go deliver that.
So how do you drive, how do you use ai?
To make informed decisions and judgments
upfront so that you can actually use
the scale and velocity at the tail
end to actually shift much faster.
So that, that's fascinating.
This is great.
I would like to ask you one last question.
If you want product leaders
to have one key takeaway.
If they remember one thing out of
this conversation, what would that be?
Trisha Price: I think the number one
would have to be, have a really strong
product scorecard and understand
the outcomes you're trying to drive.
And I'm not just talking about revenue
at that level, but the specific product
metrics that are leading indicators that
are going to drive those outcomes and then
create that research lab and experiment
relentlessly to achieve your outcome.
Chirag Mehta: Awesome.
Thank you so much, Trisha.
This was a fantastic conversation.
I really enjoyed it.
Thank you.
Trisha Price: Yeah, thanks Shaad.
Thanks for having me here.
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