February 19, 2025 • 31 mins
The times are changing, and AI is driving a major shift in how we approach product and leadership. It’s exciting, unpredictable, and inevitable—but how do we navigate this change?
Hannah Clark sits down with Greg Petroff, a design thought leader and seasoned executive, to discuss the tools, organizational shifts, and strategies that leaders need to adopt in this fast-paced era. Tune in to hear Greg’s insights on adapting to change and staying ahead in the AI-driven world.
Resources from this episode:
- Subscribe to The Product Manager newsletter
- Connect with Greg on LinkedIn
- Check out Greg’s Substack: Improbable Futures
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Hannah Clark (00:01):
In the immortal words of Bob Dylan, ‘the
times they are a’changin,’and change on this scale is...
well, it's a lot of things.
It's scary.
It's exciting.
It's unpredictable.
And it's inevitable.
Regardless of how you feelabout AI, it has triggered
a paradigm shift in how weapproach just about every
aspect of product, especiallyat the leadership level.
(00:24):
Which of these new toolsdo we deploy and how?
How will this impactthe structure and
functions of our teams?
And as our organizationschange, how do we need
to change with them?
My guest today is the wonderfulGreg Petroff, a professor,
design thought leader, andseasoned executive whose
resume includes companieslike SAP, GE Digital, Google,
ServiceNow, Compass, and Cisco.
(00:44):
And as Greg and I werediscussing these changing times
before recording the show, hemade the point that we've been
here before, with the Dot-Comera, but the difference is the
pace of change and in turn, therequired pace of adaptation.
You're about to hear Gregtalk through the tools,
organizational changes, andstrategies that executives
need to be thinking aboutright now—because, Bob Dylan
says, and warns us very well,‘you better start swimmin’
(01:06):
or you'll sink like a stone.
Let's jump in.
Welcome back to TheProduct Manager podcast.
I'm here todaywith Greg Petroff.
Thank you so much forjoining us today, Greg.
How are you doing?
Greg Petroff (01:17):
I'm well, thanks Hannah.
Thanks for having me.
Hannah Clark (01:19):
Yeah.
Cheers.
So we'll start off theway we always start off.
I'd love if you could tellus a little bit about your
background and how you gotto where you are today.
Greg Petroff (01:26):
Yeah, I am an architect
originally in my career.
I started in architecturea long time ago.
And then I've had roles insort of enterprise software
space from product managementto design leadership
over the last 25 years.
Places like SAP, GE,Google, ServiceNow.
Compass most recently I was atCisco and now at this point,
(01:48):
I'm doing some independentconsulting, a little bit of
fractional design leadership andcoaching, and I'm teaching at
California college for the arts.
Hannah Clark (01:56):
Busy schedule.
So today we're going to bechatting about some big topics,
mindset shifts that leaders needto adopt in order to adapt to
this new AI era that we're allin and trying to figure out.
So to kick us off, in the pastwe've drawn some interesting
parallels between our currentAI moment that we're in and
the Dot-Com era, which wasalso a big shift in mindset.
(02:16):
So, what are some of thekey lessons from that
earlier period of techdisruption that are currently
relevant in this shift?
Greg Petroff (02:22):
Yeah, I think there's a couple of
things that are going on.
I think one expertiseisn't necessarily as
valuable as it used to be.
I think that we have a kindof a prediction framework
of how we see the world, andit's based on our experience.
And then when we start tolook at work, we use that
framework to help us navigateand do the next set of things.
And that's useful when therules are consistent, but in
moments of change, the rules maychange and you may not see them.
(02:46):
And so curiosity is veryimportant behavior to
cultivate right now.
And I think in the Dot-Comera, there were lots of
companies that sat on thesidelines and didn't really
understand what it was.
And then there were abunch of people that didn't
really understand what itwas, but they were curious
and just jumped into it.
And started making things andI think we're in a very similar
moment where it's moving so fastthat the way that you have to
(03:09):
find your way into understandinghow you might use AI is to be
using it as much as possible.
Hannah Clark (03:14):
Yeah, with everything shifting, that
also makes me think about howtraditional roles traditional
as we see them now, obviouslytraditional to us now is
not really that traditional.
It's all changed so rapidlyover the past several decades.
But can you elaborate on howyou've seen some of the changes
in role boundaries and productdevelopment play out so far?
Greg Petroff (03:34):
Obviously, it's situational.
So every company has itsown culture and strengths,
like Google's very strongengineering culture and,
the other disciplines areimportant, but the engineering
kind of drives the car.
Other organizations designsmore focus in the center and
others are product management.
It's more focused.
So, culturallyorganizations have a bad.
Also, I think some organizationsare very orthodox about the role
(03:57):
definition and they say, thisis what you do and you don't
do things outside of that and.
This is what your crossfunctional peer does.
And I think thatworked for a while.
I've never been really orthodox.
I've been one of those people inblended boundaries in my career.
And sometimes I've been accusedof being a designer who does
product management or a productmanager who does design.
And my perspective is it'sabout product culture.
(04:19):
And I think one of thethings that's happening
right now is that the toolingthat we're using is so
different than it used to be.
It's so much faster andit's more accessible
for a wider audience.
And so those of us who mighthave been in a silo can start to
do some of the work that mighthave been a peer's contribution.
(04:39):
And the question is, wheredo those boundaries map?
And, so the Venndiagram is collapsing
a little bit on itself.
And I think, the future is goingto be teams where, there's this
term T shaped people, right?
So you have, you're very deepin one area and you can navigate
horizontally and I'm almostfeeling like it's going to be
(05:00):
a double T, we're going to getto a point where you're going
to find these sort of hybrids,someone who can design and
write some code or someonewho's a product leader who
came from design or an engineerwho loves product management.
And so I think the moreimportant thing is that whatever
organization you find yourselfin, that there's an open
conversation around the toolsand what they enable and who's
(05:23):
on first, basically, who's goingto tackle certain aspects of the
product development life cycle.
And it gives us an opportunityto shift from like an
engineering led culture orproduct led culture, or a
design led culture to a productoutcome culture and where
everyone is contributing,based on the skills that
they can bring to the table.
And so I think we'll see hybridorganizations that may look
(05:46):
very different along the way,in the project management
space, I think, the definitionof what to build is going to
become much more importantbecause it's becoming easier
and easier to build things.
And the incrementalist mindsetof just adding a feature or
adding a piece of something youcan do that, but for the same
amount of effort, you coulddo something that's much more
(06:07):
valuable for end users andcustomers, and you can imagine
things that might have beennot possible before and so.
That's the thing I'm interestedin is like, how do we create the
space for that cross functionalpartnership to be bolder in
the things that they're tryingto do, because it's actually
more straightforward toactually accomplish that stuff.
Hannah Clark (06:27):
Oh, okay.
You just opened threeforks in the road here.
We have tooling, we've gotthe changing of roles and how
those are evolving and justorganizational leadership.
So there's three pathswe could go down.
I'm going to choose toolingfirst, and then we'll double
back on the other one.
Let's talk a little bit about AItools and product development,
because you hinted at thetools are changing, tooling is
(06:47):
changing, and this kind of goesdown to discernment, because we
have a lot of tools availableto us, and we're really at an
interesting juncture where wereally have to exercise good
judgment about which toolsdo we decide to use and how.
So how do you help teams developthat good judgment or that,
that kind of gut feeling aboutwhen and how to use AI tools?
Greg Petroff (07:06):
And also, I think there's an important
corollary on discernment.
When you are using an AItool, do you believe the
answer that it's giving you?
Or do you have the ability toco-creator, work together with
the AI in a way where it'scollectively moving towards the
outcome is trying to get to.
I think part of it isthe experimentation.
I mean, the space is movingreally fast, but I think
there are some immediate areasthat make a lot of sense.
(07:28):
I think in product discovery.
There's a whole bunch of toolsnow that you can do, you can
build custom rag models onyour research insights and, for
organizations that have multipleyears of research, you can
take all the transcripts fromyour research and build models
that you can ask questions of.
It's almost like havinga virtual persona.
(07:50):
I know that's controversial.
And I wouldn't callit a virtual persona.
I would just call it makingthe knowledge base that you
already have more accessible andeasier for people to traverse.
And specifically when youonboard new members of the
team, helping them upscaletheir domain knowledge
because they can have thiscorpus of information that's
really useful to them.
I mean, it's not easy to do,but it's one that once done,
(08:13):
and I've been experimentingwith that with some friends.
And, the fact that it comes upwith Insights and elements of
knowledge that may be commonlyunderstood, but maybe not as
clearly described is reallyinteresting, certainly in the
design side of the tooling side,you're looking at, there's a
bunch of things coming in theFigma space, Figma itself is
(08:35):
a new tool, it wasn't herereally 5 years ago, and it's
changed the way that we work.
I think teams are workingstraight high fidelity, and
I'm not 100 percent sure that'sthe best idea because sometimes
the low fidelity allows youto see things that you might
not see once you're lookingat something that feels like
it's a completed product,but it's so much faster.
And so teams can iterate andmake very quickly so that,
(08:58):
when the design team can makefaster than you can think,
then it's often useful forproduct teams to make to
think versus thinking to make.
And what I mean by that isthe conversation you have with
mistakes that you make in theartifact that you're creating,
inform you about the outcomeand the customer problem in
ways that just talking aboutit sometimes are insufficient.
(09:20):
And, pictures like,a thousand words.
What we're finding is thatdesign teams that work with
their product teams veryearly and just iterate, and
they're not really making thefinal product, they're just
iterating and making stuff,can basically replace the PRD
with a prototype and vet theprototype and actually have
something that they know thatwould be of value in a way
(09:41):
that was expensive to do in thepast and took too much time.
And so, so that's an example.
And then certainly on theengineering side, I mean,
People are experimenting withthings like Copilot and tools
that allow them to move faster.
I think we're still earlydays there because I think
engineering folks are onthe fence about its utility.
But the fact that largeparts of code bases could
(10:03):
be partner written with you.
Also, it frees up theengineering teams to work
on maybe harder problems.
So all these things are there.
And I think one of the thingsI think we have to have a
mindset of collectively ishow do we disrupt our work,
because most of us are tryingto place these tools into
our products and we wouldunderstand better the outcomes
that they could provide by alsousing them and, our day to day
(10:27):
product development processes.
Hannah Clark (10:28):
I'm right there with you.
I think that there's,right now we're looking at
developing this muscle ofusing AI tools as an extension
of our expertise ratherthan a replacement for the
institutional knowledge that isstill critical to have, because
we spoke about this before.
We're in the position to overseeAI tools as if they're carrying
out some of the tasks that wewould have in our purview and
(10:49):
that we're still accountable to.
So that kind of plays well intoour conversation about the value
of institutional knowledge,particularly in research
teams, I think that researchis an area where the use of
AI is a little controversial.
So, how do you see thatrelationship between human
expertise and AI poweredknowledge management evolving?
Greg Petroff (11:08):
Yeah, I mean, I alluded to it earlier.
Most research teams havea corpus of content, and
they've been using Yearsago, it was really hard
to share that content.
And you do a researchproject, and you present
your findings, and then noone would ever benefit from
that content again, becauseit would go into an archive.
And, also research is temporal,it may have efficacy for
(11:29):
a period of time and thenbehavior patterns and your
customer base shift or have newtechnologies arrive, et cetera.
However, in large organizations,there's usually a lot of
content available and not manypeople actually access it.
And the research teamwould like more people to
access it because they feellike it will drive better
insights and those insightswill drive better outcomes.
(11:49):
So I think one part that'sinteresting is building
custom knowledge graphsof your research corpus.
And then enabling anyonein the organization to
ask questions of it.
And that's notreplacing research.
All that's doing is allowing youto see the patterns of insights
that collectively you have.
(12:11):
And it shares that knowledgeover a wider audience
in the organization.
So it's not just owned bythe researcher anymore.
The researcher has done thework and it democratizes
access to that knowledge.
In a way, it helpseveryone understand who
are we serving and why?
And then it allows the researchteam to keep feeding that.
Their job is then, at somelevel, to continue doing
(12:31):
the work that they do.
And I'm a big believer inqualitative research and
even quant methods where,you're instrumenting et
cetera, and not giving thatup to an AI to do for you.
The ability to synthesize thatknowledge and share it more
broadly, I think, is goingto be something that's going
to be really interesting.
And I've always looked atdemocratizing research.
I know people feelcontroversy around that idea.
(12:53):
Whenever I've seen access tosomething that's a value in an
organization become more openand more available, the demand
for expertise actually goes up.
And what I mean by that is thatif more people see insights,
the more they want them.
And the more they recognizethat they need professional
insight gatherers on their teamto do the work that, they know
(13:15):
how to do well and do it ina methodologically solid way.
And so I think that's thearea that I'm interested in
is how do we make sure thateverybody is deeply seeped in
the knowledge of who they'reserving and taking that
information to make betterproducts, I think is things that
actually make better products.
Hannah Clark (13:35):
So we've covered this topic a little bit as well,
because, yeah, this, I thinkthis is a really interesting
area of innovation for AI.
We had a really popular episodeon product discovery that was
enabled by Gen AI with CraigWatson, who's the founder
of Arro, some time ago, thatwas, that really dove into
how these tools are really, Ilike what you said about using
a persona, because, like yousaid, we're not really trying
(13:57):
to just, Replace the personadevelopment that we need.
It's more about makingthat data interactive like
what we have developedand what we have learned.
And similarly, we did acouple of episodes on the
product analytics toolsthat are Gen AI enabled
that are really fascinating.
We did one with Mo Hallaba,who's the CEO of Datawisp
and also one with MarioCiabarra at Quantum Metric.
The way they put it is thedemocratizing of data and
(14:20):
making it more available andmore interactive to people who
don't have that data scientistcompetency or that skill set.
And I agree, I think that once,once data is more accessible
and we can really see, nomatter what kind of stakeholder
we are in the organization.
We can really understand howthose insights benefit us and
interact with them in a waythat makes sense for our role.
Then it really does open up thedoor for, okay what does a data
(14:42):
informed culture, what can itreally look like if we're really
using those tools effectively?
So I think it's a veryexciting moment in product.
Greg Petroff (14:49):
Yeah, we'll see what happens.
I mean, I think domainexpertise is tightly held.
It's not something that'suniversal in organizations.
It's also somethingthat is hierarchical.
So in some organizations,the, those who have the most
domain expertise have the mostinfluence, I'm not going to
say power, but most influencein the organization, what
happens in an organization whereeveryone has access to a deeper
(15:12):
understanding of who you'reserving and why I think that
part will be interesting to seehow that unfolds and if it helps
or if it breaks some conventionsaround, the structures
of how we see ourself,that part's interesting.
The last thing is, I get introuble sometimes for saying
this, but I think the roleof research is to de-risk
(15:33):
product decision making.
And what I mean by that is, yes,we are trying to understand and
empathize with our end users andunderstand deeply the problems
we're trying to solve and whatoutcomes are important to them.
Product leadership makesassumptions about what features
and functionality will benefitend users and also make a
great product that peopleare willing to pay money for.
(15:54):
And oftentimes some of thoseassumptions are loosely held,
but are expensive to implementand they're not vetted in a way
where you have some degree ofcertainty that if you do that,
people will actually value it.
And so I think this is 1 of thethings that researchers can do
is if they want to move up thestack in terms of importance.
It's to be able to obviouslygive the general understanding
(16:18):
of who you're serving, but alsofind the parts of the product
development process that thereare lots of assumptions about
that haven't really deeplybeen validated and use their
strength to provide more signalso that when leadership makes
decisions on what they wantto execute on, they have more
certainty that if they do thatwork, the expected outcome
(16:39):
will be the expected outcome.
Hannah Clark (16:41):
Yeah, I'm actually surprised that you
get in trouble for sayingthat, but I won't tell on you.
As a show, we are really onthe side of the UX researchers.
And we think that they are,they present an immense
amount of value to me.
I think it's quite surprisingthat we, if we're about to
invest a great deal of moneyinto producing a product
that we really want to beabsolutely sure that it's
going to be, resonant withour target audience kind of
(17:01):
alludes me why we would not.
Greg Petroff (17:03):
The other thing is interesting too.
Like we now have accessto do full circle, right.
Which we hadn't before.
So, we have these tools likeAmplitude that allow us to
get if they're instrumentedwell, get really great
telemetry on behavior.
We have product success metrics.
We've got a featureutilization for insight.
We've got, in SaaS businesses,we have renewal rates and
(17:26):
then we can do cohort analysisover which customers are
renewing with the leastamount of effort on the sales
organization, et cetera, andyou can take all those signals
and historically, they've beeninformation that has been owned
by customer success or ownedby the team and owned by this
corporate strategy team andowned by the research team.
(17:47):
And I think there's anopportunity for research to
see all of that in a big 360.
And so that you can get a healthcheck of the experience that
you're creating, the businessthat you have, the behavior
of your customers, et cetera.
That was just almostimpossible to do before.
And it's not easy to do.
I mean, there's only a fewpeople I know in the research
(18:08):
community have done that.
I think that's where AIcan also help is to start
melding different data setson customer success and user
success together to get abetter picture of the health
of your product and then alsowhat needs to happen next.
Hannah Clark (18:23):
Yeah, I think that's one of the biggest
benefits that we have of someof these conversational models
is being able to train themwith, all of these kind of
siloed outputs and be able tounify them into some, something
that was a little bit moreusable that we can really
ask more pointed questionsof, I mean, like you said,
we'll see how it shakes out.
I'm sure that there's, thatthere'll be a lot of kinks.
Greg Petroff (18:41):
I'm going to say one last piece on that too,
which is then once you getthe answer, another reason why
you need a research team isyou should have discernment
and you should go doublecheck because it may just be,
your data isn't that great.
And you're getting an insightthat is really not real.
Hannah Clark (18:55):
Yeah.
I kind of wonder yeah, as westart to rely more on these
models, how data hygiene isgoing to become more part of
the conversation about, isthe data that we're feeding
into it of high quality?
Greg Petroff (19:05):
I can't imagine the moment where you say, our AI
thinks that you behave this way.
Is this true or not?
No, I'm kidding.
Hannah Clark (19:12):
You touched on some kind of organizational
and adaptation themes here.
So I want to dig alittle bit into that.
So we're talking a littlebit about the uncertainty
of role evolution, people'sroles changing, things
melding and shifting.
And then there's also this coreexpertise that kind of when we
have many roles as an individualcontributor or a leader,
(19:34):
I think that there's somequestions about what is the core
expertise that you're bringinginto the table and how do you
defend that when you're in asituation where other people
have overlap with what you'redoing, what you're contributing?
There's also AI tools thatcontribute some of that.
So how do we help teamsnavigate that uncertainty of
role evolution and maintainingtheir core expertise and value
and kind of protecting likethe area of expertise that
(19:55):
they're bringing to the team?
Greg Petroff (19:57):
Yeah, I almost questioned the last statement
protecting your turf.
I'm not sure that's whatyou meant by it, but I have
probably changed my careerlike, 11 times in the last 25
years as things have evolved.
I started as an architectand it was 3 graphics
and then I was in.
Broadcast media and thenit was in Dot-Com and I was
information architect and Iwas an interaction designer.
(20:18):
Then I was a product leaderand I was a researcher.
Then I was back tointeraction design.
Then I was a UX leader.
You and I think 1 of the thingsinteresting about this moment is
we've actually had stability inthe roles for the last 10 years.
And so a lot of peoplehave grown up in that era.
And have a solid definitionof their value, and a
solid definition of whothey are, and a solid
(20:40):
definition of what they do.
Haven't experienced having therag pulled out from underneath
them because there's somenew way of doing things or
a new piece of technology.
Some of us have been aroundlong enough, it's happened
to us so many times thatwe just see it and say, oh.
Okay, the rules justchanged, okay, now we
got to be more adaptable.
And I say, expertise isreally important, right?
You know what I mean?
So I think you shouldknow what you're great at.
(21:02):
And I think the way I sortof coach people is lean into
your strengths and value them.
But don't get caught up inyour role definition, we're
moving into a moment whereit's more about the outcome
and the problem definition.
And then the execution,there are a lot of
different ways to get there.
And if we hold on to theorthodoxy of our role or
(21:25):
position, we might justbe fighting the currents
that are around us.
And so, stay curious, havea mindset of evolving and
constantly, learning, and at thesame time, don't shift away from
what you love and what you'regood at, because the best teams
are made up of people who areexceptionally good at something.
(21:47):
I'll give you an example.
When I was at SAP, this wasalmost like 15 years ago.
I was part of a team.
We weren't a design team.
We weren't a product team.
We weren't an engineering team.
We were an innovation team.
And it was made up of, we hadanthropologists on the team, we
had engineers, we had productmanagers, we had designers,
and we would be tasked with aproject, and we would assemble
a team, and the team was alwaysan eclectic group of people,
(22:11):
and we would just look at theproblem and figure out how
we were going to work on ittogether, and I anticipate
there's going to be more of thatkind of behavior in the future.
I think in large organizations,you have to organize in a
certain way, because It helpsyour product development
process, but in smallerorganizations, I think that
we're going to be lookingat an environment where,
we're just gonna be morenimble about the tools and
(22:33):
the things that we have.
And if we stay orthodoxabout what we do, it'll
actually get in the way ofactually doing the work.
Hannah Clark (22:39):
Okay.
So I'm glad that youbrought up a departure from
orthodoxy, I want to talkabout leadership styles.
So you've observed that there'sa bit of a generational shift
right now that's happeningin product leadership styles.
So we're moving away from thisvery command and control top
down approach to somethingthat's a lot more collaborative.
Now that we're on thisanecdote train, would you mind
(23:00):
sharing a specific exampleof how a shift like this has
impacted product developmentoutcomes that you've observed?
Greg Petroff (23:05):
I don't know which strain of product development
will be a last or if they willboth be there in the future, but
there are certainly two models.
There's one model of the reallystrong product leader who is
not 100 percent transparentwith the organization and it's
making moves along the wayto move the project forward.
(23:27):
And it's a very much kindof a command and control
kind of environment.
And there are many organizationsthat operate that way.
And historically, there's beensome incredibly successful
people in the product managementspace who behave that way.
So many earlier careerproduct leaders will look
at those people and say.
I want to be like them and I'mgoing to do the same thing the
(23:48):
challenge I've seen, especiallyin large organizations, is that
there's a behavior that canhappen where if resources are
constrained and the incentivesare set up for you to deliver
product leaders will competewith each other for resources.
Because if they over deliver,they get the next job promotion.
If there's no one at the topsort of saying, these are the
things that we're not goingto do incentivizes people
(24:11):
to try to get more done inthe time that they have.
And what they don't recognize inthat moment is they place strain
on the system because there'sonly a finite set of resources.
And so they're actuallyusing those resources less
efficiently because they'veput too much into the system.
And therefore, you end upget a bit of crazy town, and
then people move into kindof command and control to try
(24:33):
to whack a bullet into place.
I've also seen the other side ofit, where you're seeing people
who are like, hey, I'm enteringthis project, and I don't
know what we're going to do.
I don't have to be theperson with all the answers.
But I'm going to assemblea team, and collectively,
we're going to learn togetheras quickly as possible, and
we're going to build a setof experiments along the
(24:54):
way that allow us to learnas quickly as possible, and
we're going to collaborateand share that information.
And the project leaders whodo that, I find, get more
out of their teams, they havemore clarity, there's more
visibility on the work, peoplefeel like they have autonomy in
that, they can find their rolein the work and the effort.
And it may be an indirectobservation, but it feels to
me that generationally, it'smore earlier career or younger
(25:18):
PMs who behave this way.
And I think it's partiallybecause they're digital
natives to begin with.
Some of the more senior people,aren't really if you look at
demographically and have grownup with great software and
want to create a space forvalue for the design team and
for the research team and forthe engineering team and the
(25:38):
product team to collectivelysolve a problem together.
And, I was at the Canadianfriends conference this
fall, and it was just reallyfascinating for me to hear the
conversations around how thatcommunity of product leaders
thought about how they workedwith a cross functional peers.
And it was really heartwarmingfor me because it was a
very open conversation.
(26:00):
It was that it was a 1where people were willing
to be vulnerable about that.
They didn't have the answers,but they were organizing their
team to go find the answers and.
Yeah, that's a little bitdifferent from feeling
like you have to have theanswers and your team's
counting on you to have them.
And so you fake it tillyou make it and pivot a
(26:20):
lot until you get there.
Right.
And so I think there'ssort of two strands that
are happening that productdevelopment community that I
would be curious to see if,better outcomes happened,
one law or the other.
I don't have great examplesfor you, but I just, these
are observations of behavior.
Hannah Clark (26:35):
I tend to agree with you though, because I've
observed that work culturein general has become a
lot less hierarchical anda lot more collaborative.
And I really like that shift.
I think that it's, we're goingin a really positive direction
in which vulnerability,attention to psychological
safety with your teammates,these kinds of the concepts
of emotional intelligencethat are making their way
into the leadership sphere.
(26:56):
And are really starting tohave this nominal effect on
how people lead teams and workwith people cross functionally
is it's really hearteningto see how that's really
enabling a lot of people todo some of their best work.
But I'm gonna put you onthe spot a little bit since
we're talking about this,you're a leader, you're a
seasoned executive, and you'vemanaged a great deal of teams.
So what strategies haveyou found to be very
(27:17):
effective for fostering,as you mentioned before,
curiosity, experimentation,while maintaining this
high standards of quality?
Greg Petroff (27:23):
Well, clarity is the biggest
gift you can give a team.
You can't be transparent abouteverything in leadership,
and it doesn't actually helppeople because, there are a
lot of things that happen inthe life of the company that
sometimes are transactionalor are a little bit hard to
understand and communicate.
(27:44):
And then if it, if everybody'stalking about it, then no
one's focused on work, butclarity is really important,
helping people have ownershipof the, of that clarity, right?
So it cascades down sopeople can see their role
and how it adds up to it.
It's hard for organizationsto say no to things, but I
think the organizations thatdeclare what they're not going
(28:05):
to do are, very, it's usefulbecause it stops eager beaver
behavior around Hey, we coulddo this, or I have this pet
project that I want to do.
And if you say, no, we'renot doing that doesn't mean
it's not, we're not evergoing to do it just means
we're not going to do it now.
So that people, again,that's a part of that sort
of kind of clarity aspect.
I think giving feedback ina courteous way, but also
(28:27):
holding people accountable.
I think the notion of creatingenvironments where people
feel safe makes sense.
And it's incredibly important.
But you also have, youcan't shirk away from giving
constructive feedback.
And when I was at Cisco thatone of the tenants that we
had that I really loved, andit came from Duo, which was a
(28:47):
startup that Cisco had purchasedabout five years ago, and
they had this tenant that theycall be kinder than necessary.
And what that meant was ifyou had spinach on your front
teeth and you're walking into ameeting, you'd want someone to
tell you that you have spinach.
It's not about you.
It's just that this is somethingthat you should know about.
And so I think it's aboutconstantly giving feedback
(29:09):
to each other and holdingeach other accountable to a
high standard and making itabout the work, not about you.
And that creates the conditionswhere people learn to
listen to that feedback anddon't take it personally.
They take it like, Oh, okay,that's an opportunity for
me to improve my game, orthat's an opportunity for
the project work to, to bein a different direction.
So I think those are thetwo things that for me.
(29:32):
I'm not saying I'm great atit, but I materially try to
practice is try to be as clearas possible and try to give
as much feedback as possible.
Hannah Clark (29:39):
I really appreciate those insights.
I agree with him.
I'm sure you'rejust being humble.
Thanks for joiningus today, Greg.
This has been agreat conversation.
Yeah, I've talked about a lot ofdifferent topics that are really
highly relevant right now.
So I'm hoping that we getsome interesting feedback
from folks who are listening.
And speaking of feedback, wherecan people follow your work
or connect with you online?
Greg Petroff (29:58):
Yeah, I'm on LinkedIn.
So you can find me there.
I have a profile there.
I have a Substack calledImprobable Futures.
So I do a little bit of writingon the idea around possibility.
And so you can find mein those two places.
Hannah Clark (30:13):
Awesome.
Well, thank you so muchfor joining us here.
Greg Petroff (30:15):
All right, thank you.
Hannah Clark (30:18):
Thanks for listening in.
For more great insights, how-toguides, and tool reviews,
subscribe to our newsletter attheproductmanager.com/subscribe.
You can hear more conversationslike this by subscribing to
The Product Manager, whereveryou get your podcasts.
In the immortal words of Bob Dylan, ‘the
times they are a’changin,’and change on this scale is...
well, it's a lot of things.
It's scary.
It's exciting.
It's unpredictable.
And it's inevitable.
Regardless of how you feelabout AI, it has triggered
a paradigm shift in how weapproach just about every
aspect of product, especiallyat the leadership level.
(00:24):
Which of these new toolsdo we deploy and how?
How will this impactthe structure and
functions of our teams?
And as our organizationschange, how do we need
to change with them?
My guest today is the wonderfulGreg Petroff, a professor,
design thought leader, andseasoned executive whose
resume includes companieslike SAP, GE Digital, Google,
ServiceNow, Compass, and Cisco.
(00:44):
And as Greg and I werediscussing these changing times
before recording the show, hemade the point that we've been
here before, with the Dot-Comera, but the difference is the
pace of change and in turn, therequired pace of adaptation.
You're about to hear Gregtalk through the tools,
organizational changes, andstrategies that executives
need to be thinking aboutright now—because, Bob Dylan
says, and warns us very well,‘you better start swimmin’
(01:06):
or you'll sink like a stone.
Let's jump in.
Welcome back to TheProduct Manager podcast.
I'm here todaywith Greg Petroff.
Thank you so much forjoining us today, Greg.
How are you doing?
Greg Petroff (01:17):
I'm well, thanks Hannah.
Thanks for having me.
Hannah Clark (01:19):
Yeah.
Cheers.
So we'll start off theway we always start off.
I'd love if you could tellus a little bit about your
background and how you gotto where you are today.
Greg Petroff (01:26):
Yeah, I am an architect
originally in my career.
I started in architecturea long time ago.
And then I've had roles insort of enterprise software
space from product managementto design leadership
over the last 25 years.
Places like SAP, GE,Google, ServiceNow.
Compass most recently I was atCisco and now at this point,
(01:48):
I'm doing some independentconsulting, a little bit of
fractional design leadership andcoaching, and I'm teaching at
California college for the arts.
Hannah Clark (01:56):
Busy schedule.
So today we're going to bechatting about some big topics,
mindset shifts that leaders needto adopt in order to adapt to
this new AI era that we're allin and trying to figure out.
So to kick us off, in the pastwe've drawn some interesting
parallels between our currentAI moment that we're in and
the Dot-Com era, which wasalso a big shift in mindset.
(02:16):
So, what are some of thekey lessons from that
earlier period of techdisruption that are currently
relevant in this shift?
Greg Petroff (02:22):
Yeah, I think there's a couple of
things that are going on.
I think one expertiseisn't necessarily as
valuable as it used to be.
I think that we have a kindof a prediction framework
of how we see the world, andit's based on our experience.
And then when we start tolook at work, we use that
framework to help us navigateand do the next set of things.
And that's useful when therules are consistent, but in
moments of change, the rules maychange and you may not see them.
(02:46):
And so curiosity is veryimportant behavior to
cultivate right now.
And I think in the Dot-Comera, there were lots of
companies that sat on thesidelines and didn't really
understand what it was.
And then there were abunch of people that didn't
really understand what itwas, but they were curious
and just jumped into it.
And started making things andI think we're in a very similar
moment where it's moving so fastthat the way that you have to
(03:09):
find your way into understandinghow you might use AI is to be
using it as much as possible.
Hannah Clark (03:14):
Yeah, with everything shifting, that
also makes me think about howtraditional roles traditional
as we see them now, obviouslytraditional to us now is
not really that traditional.
It's all changed so rapidlyover the past several decades.
But can you elaborate on howyou've seen some of the changes
in role boundaries and productdevelopment play out so far?
Greg Petroff (03:34):
Obviously, it's situational.
So every company has itsown culture and strengths,
like Google's very strongengineering culture and,
the other disciplines areimportant, but the engineering
kind of drives the car.
Other organizations designsmore focus in the center and
others are product management.
It's more focused.
So, culturallyorganizations have a bad.
Also, I think some organizationsare very orthodox about the role
(03:57):
definition and they say, thisis what you do and you don't
do things outside of that and.
This is what your crossfunctional peer does.
And I think thatworked for a while.
I've never been really orthodox.
I've been one of those people inblended boundaries in my career.
And sometimes I've been accusedof being a designer who does
product management or a productmanager who does design.
And my perspective is it'sabout product culture.
(04:19):
And I think one of thethings that's happening
right now is that the toolingthat we're using is so
different than it used to be.
It's so much faster andit's more accessible
for a wider audience.
And so those of us who mighthave been in a silo can start to
do some of the work that mighthave been a peer's contribution.
(04:39):
And the question is, wheredo those boundaries map?
And, so the Venndiagram is collapsing
a little bit on itself.
And I think, the future is goingto be teams where, there's this
term T shaped people, right?
So you have, you're very deepin one area and you can navigate
horizontally and I'm almostfeeling like it's going to be
(05:00):
a double T, we're going to getto a point where you're going
to find these sort of hybrids,someone who can design and
write some code or someonewho's a product leader who
came from design or an engineerwho loves product management.
And so I think the moreimportant thing is that whatever
organization you find yourselfin, that there's an open
conversation around the toolsand what they enable and who's
(05:23):
on first, basically, who's goingto tackle certain aspects of the
product development life cycle.
And it gives us an opportunityto shift from like an
engineering led culture orproduct led culture, or a
design led culture to a productoutcome culture and where
everyone is contributing,based on the skills that
they can bring to the table.
And so I think we'll see hybridorganizations that may look
(05:46):
very different along the way,in the project management
space, I think, the definitionof what to build is going to
become much more importantbecause it's becoming easier
and easier to build things.
And the incrementalist mindsetof just adding a feature or
adding a piece of something youcan do that, but for the same
amount of effort, you coulddo something that's much more
(06:07):
valuable for end users andcustomers, and you can imagine
things that might have beennot possible before and so.
That's the thing I'm interestedin is like, how do we create the
space for that cross functionalpartnership to be bolder in
the things that they're tryingto do, because it's actually
more straightforward toactually accomplish that stuff.
Hannah Clark (06:27):
Oh, okay.
You just opened threeforks in the road here.
We have tooling, we've gotthe changing of roles and how
those are evolving and justorganizational leadership.
So there's three pathswe could go down.
I'm going to choose toolingfirst, and then we'll double
back on the other one.
Let's talk a little bit about AItools and product development,
because you hinted at thetools are changing, tooling is
(06:47):
changing, and this kind of goesdown to discernment, because we
have a lot of tools availableto us, and we're really at an
interesting juncture where wereally have to exercise good
judgment about which toolsdo we decide to use and how.
So how do you help teams developthat good judgment or that,
that kind of gut feeling aboutwhen and how to use AI tools?
Greg Petroff (07:06):
And also, I think there's an important
corollary on discernment.
When you are using an AItool, do you believe the
answer that it's giving you?
Or do you have the ability toco-creator, work together with
the AI in a way where it'scollectively moving towards the
outcome is trying to get to.
I think part of it isthe experimentation.
I mean, the space is movingreally fast, but I think
there are some immediate areasthat make a lot of sense.
(07:28):
I think in product discovery.
There's a whole bunch of toolsnow that you can do, you can
build custom rag models onyour research insights and, for
organizations that have multipleyears of research, you can
take all the transcripts fromyour research and build models
that you can ask questions of.
It's almost like havinga virtual persona.
(07:50):
I know that's controversial.
And I wouldn't callit a virtual persona.
I would just call it makingthe knowledge base that you
already have more accessible andeasier for people to traverse.
And specifically when youonboard new members of the
team, helping them upscaletheir domain knowledge
because they can have thiscorpus of information that's
really useful to them.
I mean, it's not easy to do,but it's one that once done,
(08:13):
and I've been experimentingwith that with some friends.
And, the fact that it comes upwith Insights and elements of
knowledge that may be commonlyunderstood, but maybe not as
clearly described is reallyinteresting, certainly in the
design side of the tooling side,you're looking at, there's a
bunch of things coming in theFigma space, Figma itself is
(08:35):
a new tool, it wasn't herereally 5 years ago, and it's
changed the way that we work.
I think teams are workingstraight high fidelity, and
I'm not 100 percent sure that'sthe best idea because sometimes
the low fidelity allows youto see things that you might
not see once you're lookingat something that feels like
it's a completed product,but it's so much faster.
And so teams can iterate andmake very quickly so that,
(08:58):
when the design team can makefaster than you can think,
then it's often useful forproduct teams to make to
think versus thinking to make.
And what I mean by that isthe conversation you have with
mistakes that you make in theartifact that you're creating,
inform you about the outcomeand the customer problem in
ways that just talking aboutit sometimes are insufficient.
(09:20):
And, pictures like,a thousand words.
What we're finding is thatdesign teams that work with
their product teams veryearly and just iterate, and
they're not really making thefinal product, they're just
iterating and making stuff,can basically replace the PRD
with a prototype and vet theprototype and actually have
something that they know thatwould be of value in a way
(09:41):
that was expensive to do in thepast and took too much time.
And so, so that's an example.
And then certainly on theengineering side, I mean,
People are experimenting withthings like Copilot and tools
that allow them to move faster.
I think we're still earlydays there because I think
engineering folks are onthe fence about its utility.
But the fact that largeparts of code bases could
(10:03):
be partner written with you.
Also, it frees up theengineering teams to work
on maybe harder problems.
So all these things are there.
And I think one of the thingsI think we have to have a
mindset of collectively ishow do we disrupt our work,
because most of us are tryingto place these tools into
our products and we wouldunderstand better the outcomes
that they could provide by alsousing them and, our day to day
(10:27):
product development processes.
Hannah Clark (10:28):
I'm right there with you.
I think that there's,right now we're looking at
developing this muscle ofusing AI tools as an extension
of our expertise ratherthan a replacement for the
institutional knowledge that isstill critical to have, because
we spoke about this before.
We're in the position to overseeAI tools as if they're carrying
out some of the tasks that wewould have in our purview and
(10:49):
that we're still accountable to.
So that kind of plays well intoour conversation about the value
of institutional knowledge,particularly in research
teams, I think that researchis an area where the use of
AI is a little controversial.
So, how do you see thatrelationship between human
expertise and AI poweredknowledge management evolving?
Greg Petroff (11:08):
Yeah, I mean, I alluded to it earlier.
Most research teams havea corpus of content, and
they've been using Yearsago, it was really hard
to share that content.
And you do a researchproject, and you present
your findings, and then noone would ever benefit from
that content again, becauseit would go into an archive.
And, also research is temporal,it may have efficacy for
(11:29):
a period of time and thenbehavior patterns and your
customer base shift or have newtechnologies arrive, et cetera.
However, in large organizations,there's usually a lot of
content available and not manypeople actually access it.
And the research teamwould like more people to
access it because they feellike it will drive better
insights and those insightswill drive better outcomes.
(11:49):
So I think one part that'sinteresting is building
custom knowledge graphsof your research corpus.
And then enabling anyonein the organization to
ask questions of it.
And that's notreplacing research.
All that's doing is allowing youto see the patterns of insights
that collectively you have.
(12:11):
And it shares that knowledgeover a wider audience
in the organization.
So it's not just owned bythe researcher anymore.
The researcher has done thework and it democratizes
access to that knowledge.
In a way, it helpseveryone understand who
are we serving and why?
And then it allows the researchteam to keep feeding that.
Their job is then, at somelevel, to continue doing
(12:31):
the work that they do.
And I'm a big believer inqualitative research and
even quant methods where,you're instrumenting et
cetera, and not giving thatup to an AI to do for you.
The ability to synthesize thatknowledge and share it more
broadly, I think, is goingto be something that's going
to be really interesting.
And I've always looked atdemocratizing research.
I know people feelcontroversy around that idea.
(12:53):
Whenever I've seen access tosomething that's a value in an
organization become more openand more available, the demand
for expertise actually goes up.
And what I mean by that is thatif more people see insights,
the more they want them.
And the more they recognizethat they need professional
insight gatherers on their teamto do the work that, they know
(13:15):
how to do well and do it ina methodologically solid way.
And so I think that's thearea that I'm interested in
is how do we make sure thateverybody is deeply seeped in
the knowledge of who they'reserving and taking that
information to make betterproducts, I think is things that
actually make better products.
Hannah Clark (13:35):
So we've covered this topic a little bit as well,
because, yeah, this, I thinkthis is a really interesting
area of innovation for AI.
We had a really popular episodeon product discovery that was
enabled by Gen AI with CraigWatson, who's the founder
of Arro, some time ago, thatwas, that really dove into
how these tools are really, Ilike what you said about using
a persona, because, like yousaid, we're not really trying
(13:57):
to just, Replace the personadevelopment that we need.
It's more about makingthat data interactive like
what we have developedand what we have learned.
And similarly, we did acouple of episodes on the
product analytics toolsthat are Gen AI enabled
that are really fascinating.
We did one with Mo Hallaba,who's the CEO of Datawisp
and also one with MarioCiabarra at Quantum Metric.
The way they put it is thedemocratizing of data and
(14:20):
making it more available andmore interactive to people who
don't have that data scientistcompetency or that skill set.
And I agree, I think that once,once data is more accessible
and we can really see, nomatter what kind of stakeholder
we are in the organization.
We can really understand howthose insights benefit us and
interact with them in a waythat makes sense for our role.
Then it really does open up thedoor for, okay what does a data
(14:42):
informed culture, what can itreally look like if we're really
using those tools effectively?
So I think it's a veryexciting moment in product.
Greg Petroff (14:49):
Yeah, we'll see what happens.
I mean, I think domainexpertise is tightly held.
It's not something that'suniversal in organizations.
It's also somethingthat is hierarchical.
So in some organizations,the, those who have the most
domain expertise have the mostinfluence, I'm not going to
say power, but most influencein the organization, what
happens in an organization whereeveryone has access to a deeper
(15:12):
understanding of who you'reserving and why I think that
part will be interesting to seehow that unfolds and if it helps
or if it breaks some conventionsaround, the structures
of how we see ourself,that part's interesting.
The last thing is, I get introuble sometimes for saying
this, but I think the roleof research is to de-risk
(15:33):
product decision making.
And what I mean by that is, yes,we are trying to understand and
empathize with our end users andunderstand deeply the problems
we're trying to solve and whatoutcomes are important to them.
Product leadership makesassumptions about what features
and functionality will benefitend users and also make a
great product that peopleare willing to pay money for.
(15:54):
And oftentimes some of thoseassumptions are loosely held,
but are expensive to implementand they're not vetted in a way
where you have some degree ofcertainty that if you do that,
people will actually value it.
And so I think this is 1 of thethings that researchers can do
is if they want to move up thestack in terms of importance.
It's to be able to obviouslygive the general understanding
(16:18):
of who you're serving, but alsofind the parts of the product
development process that thereare lots of assumptions about
that haven't really deeplybeen validated and use their
strength to provide more signalso that when leadership makes
decisions on what they wantto execute on, they have more
certainty that if they do thatwork, the expected outcome
(16:39):
will be the expected outcome.
Hannah Clark (16:41):
Yeah, I'm actually surprised that you
get in trouble for sayingthat, but I won't tell on you.
As a show, we are really onthe side of the UX researchers.
And we think that they are,they present an immense
amount of value to me.
I think it's quite surprisingthat we, if we're about to
invest a great deal of moneyinto producing a product
that we really want to beabsolutely sure that it's
going to be, resonant withour target audience kind of
(17:01):
alludes me why we would not.
Greg Petroff (17:03):
The other thing is interesting too.
Like we now have accessto do full circle, right.
Which we hadn't before.
So, we have these tools likeAmplitude that allow us to
get if they're instrumentedwell, get really great
telemetry on behavior.
We have product success metrics.
We've got a featureutilization for insight.
We've got, in SaaS businesses,we have renewal rates and
(17:26):
then we can do cohort analysisover which customers are
renewing with the leastamount of effort on the sales
organization, et cetera, andyou can take all those signals
and historically, they've beeninformation that has been owned
by customer success or ownedby the team and owned by this
corporate strategy team andowned by the research team.
(17:47):
And I think there's anopportunity for research to
see all of that in a big 360.
And so that you can get a healthcheck of the experience that
you're creating, the businessthat you have, the behavior
of your customers, et cetera.
That was just almostimpossible to do before.
And it's not easy to do.
I mean, there's only a fewpeople I know in the research
(18:08):
community have done that.
I think that's where AIcan also help is to start
melding different data setson customer success and user
success together to get abetter picture of the health
of your product and then alsowhat needs to happen next.
Hannah Clark (18:23):
Yeah, I think that's one of the biggest
benefits that we have of someof these conversational models
is being able to train themwith, all of these kind of
siloed outputs and be able tounify them into some, something
that was a little bit moreusable that we can really
ask more pointed questionsof, I mean, like you said,
we'll see how it shakes out.
I'm sure that there's, thatthere'll be a lot of kinks.
Greg Petroff (18:41):
I'm going to say one last piece on that too,
which is then once you getthe answer, another reason why
you need a research team isyou should have discernment
and you should go doublecheck because it may just be,
your data isn't that great.
And you're getting an insightthat is really not real.
Hannah Clark (18:55):
Yeah.
I kind of wonder yeah, as westart to rely more on these
models, how data hygiene isgoing to become more part of
the conversation about, isthe data that we're feeding
into it of high quality?
Greg Petroff (19:05):
I can't imagine the moment where you say, our AI
thinks that you behave this way.
Is this true or not?
No, I'm kidding.
Hannah Clark (19:12):
You touched on some kind of organizational
and adaptation themes here.
So I want to dig alittle bit into that.
So we're talking a littlebit about the uncertainty
of role evolution, people'sroles changing, things
melding and shifting.
And then there's also this coreexpertise that kind of when we
have many roles as an individualcontributor or a leader,
(19:34):
I think that there's somequestions about what is the core
expertise that you're bringinginto the table and how do you
defend that when you're in asituation where other people
have overlap with what you'redoing, what you're contributing?
There's also AI tools thatcontribute some of that.
So how do we help teamsnavigate that uncertainty of
role evolution and maintainingtheir core expertise and value
and kind of protecting likethe area of expertise that
(19:55):
they're bringing to the team?
Greg Petroff (19:57):
Yeah, I almost questioned the last statement
protecting your turf.
I'm not sure that's whatyou meant by it, but I have
probably changed my careerlike, 11 times in the last 25
years as things have evolved.
I started as an architectand it was 3 graphics
and then I was in.
Broadcast media and thenit was in Dot-Com and I was
information architect and Iwas an interaction designer.
(20:18):
Then I was a product leaderand I was a researcher.
Then I was back tointeraction design.
Then I was a UX leader.
You and I think 1 of the thingsinteresting about this moment is
we've actually had stability inthe roles for the last 10 years.
And so a lot of peoplehave grown up in that era.
And have a solid definitionof their value, and a
solid definition of whothey are, and a solid
(20:40):
definition of what they do.
Haven't experienced having therag pulled out from underneath
them because there's somenew way of doing things or
a new piece of technology.
Some of us have been aroundlong enough, it's happened
to us so many times thatwe just see it and say, oh.
Okay, the rules justchanged, okay, now we
got to be more adaptable.
And I say, expertise isreally important, right?
You know what I mean?
So I think you shouldknow what you're great at.
(21:02):
And I think the way I sortof coach people is lean into
your strengths and value them.
But don't get caught up inyour role definition, we're
moving into a moment whereit's more about the outcome
and the problem definition.
And then the execution,there are a lot of
different ways to get there.
And if we hold on to theorthodoxy of our role or
(21:25):
position, we might justbe fighting the currents
that are around us.
And so, stay curious, havea mindset of evolving and
constantly, learning, and at thesame time, don't shift away from
what you love and what you'regood at, because the best teams
are made up of people who areexceptionally good at something.
(21:47):
I'll give you an example.
When I was at SAP, this wasalmost like 15 years ago.
I was part of a team.
We weren't a design team.
We weren't a product team.
We weren't an engineering team.
We were an innovation team.
And it was made up of, we hadanthropologists on the team, we
had engineers, we had productmanagers, we had designers,
and we would be tasked with aproject, and we would assemble
a team, and the team was alwaysan eclectic group of people,
(22:11):
and we would just look at theproblem and figure out how
we were going to work on ittogether, and I anticipate
there's going to be more of thatkind of behavior in the future.
I think in large organizations,you have to organize in a
certain way, because It helpsyour product development
process, but in smallerorganizations, I think that
we're going to be lookingat an environment where,
we're just gonna be morenimble about the tools and
(22:33):
the things that we have.
And if we stay orthodoxabout what we do, it'll
actually get in the way ofactually doing the work.
Hannah Clark (22:39):
Okay.
So I'm glad that youbrought up a departure from
orthodoxy, I want to talkabout leadership styles.
So you've observed that there'sa bit of a generational shift
right now that's happeningin product leadership styles.
So we're moving away from thisvery command and control top
down approach to somethingthat's a lot more collaborative.
Now that we're on thisanecdote train, would you mind
(23:00):
sharing a specific exampleof how a shift like this has
impacted product developmentoutcomes that you've observed?
Greg Petroff (23:05):
I don't know which strain of product development
will be a last or if they willboth be there in the future, but
there are certainly two models.
There's one model of the reallystrong product leader who is
not 100 percent transparentwith the organization and it's
making moves along the wayto move the project forward.
(23:27):
And it's a very much kindof a command and control
kind of environment.
And there are many organizationsthat operate that way.
And historically, there's beensome incredibly successful
people in the product managementspace who behave that way.
So many earlier careerproduct leaders will look
at those people and say.
I want to be like them and I'mgoing to do the same thing the
(23:48):
challenge I've seen, especiallyin large organizations, is that
there's a behavior that canhappen where if resources are
constrained and the incentivesare set up for you to deliver
product leaders will competewith each other for resources.
Because if they over deliver,they get the next job promotion.
If there's no one at the topsort of saying, these are the
things that we're not goingto do incentivizes people
(24:11):
to try to get more done inthe time that they have.
And what they don't recognize inthat moment is they place strain
on the system because there'sonly a finite set of resources.
And so they're actuallyusing those resources less
efficiently because they'veput too much into the system.
And therefore, you end upget a bit of crazy town, and
then people move into kindof command and control to try
(24:33):
to whack a bullet into place.
I've also seen the other side ofit, where you're seeing people
who are like, hey, I'm enteringthis project, and I don't
know what we're going to do.
I don't have to be theperson with all the answers.
But I'm going to assemblea team, and collectively,
we're going to learn togetheras quickly as possible, and
we're going to build a setof experiments along the
(24:54):
way that allow us to learnas quickly as possible, and
we're going to collaborateand share that information.
And the project leaders whodo that, I find, get more
out of their teams, they havemore clarity, there's more
visibility on the work, peoplefeel like they have autonomy in
that, they can find their rolein the work and the effort.
And it may be an indirectobservation, but it feels to
me that generationally, it'smore earlier career or younger
(25:18):
PMs who behave this way.
And I think it's partiallybecause they're digital
natives to begin with.
Some of the more senior people,aren't really if you look at
demographically and have grownup with great software and
want to create a space forvalue for the design team and
for the research team and forthe engineering team and the
(25:38):
product team to collectivelysolve a problem together.
And, I was at the Canadianfriends conference this
fall, and it was just reallyfascinating for me to hear the
conversations around how thatcommunity of product leaders
thought about how they workedwith a cross functional peers.
And it was really heartwarmingfor me because it was a
very open conversation.
(26:00):
It was that it was a 1where people were willing
to be vulnerable about that.
They didn't have the answers,but they were organizing their
team to go find the answers and.
Yeah, that's a little bitdifferent from feeling
like you have to have theanswers and your team's
counting on you to have them.
And so you fake it tillyou make it and pivot a
(26:20):
lot until you get there.
Right.
And so I think there'ssort of two strands that
are happening that productdevelopment community that I
would be curious to see if,better outcomes happened,
one law or the other.
I don't have great examplesfor you, but I just, these
are observations of behavior.
Hannah Clark (26:35):
I tend to agree with you though, because I've
observed that work culturein general has become a
lot less hierarchical anda lot more collaborative.
And I really like that shift.
I think that it's, we're goingin a really positive direction
in which vulnerability,attention to psychological
safety with your teammates,these kinds of the concepts
of emotional intelligencethat are making their way
into the leadership sphere.
(26:56):
And are really starting tohave this nominal effect on
how people lead teams and workwith people cross functionally
is it's really hearteningto see how that's really
enabling a lot of people todo some of their best work.
But I'm gonna put you onthe spot a little bit since
we're talking about this,you're a leader, you're a
seasoned executive, and you'vemanaged a great deal of teams.
So what strategies haveyou found to be very
(27:17):
effective for fostering,as you mentioned before,
curiosity, experimentation,while maintaining this
high standards of quality?
Greg Petroff (27:23):
Well, clarity is the biggest
gift you can give a team.
You can't be transparent abouteverything in leadership,
and it doesn't actually helppeople because, there are a
lot of things that happen inthe life of the company that
sometimes are transactionalor are a little bit hard to
understand and communicate.
(27:44):
And then if it, if everybody'stalking about it, then no
one's focused on work, butclarity is really important,
helping people have ownershipof the, of that clarity, right?
So it cascades down sopeople can see their role
and how it adds up to it.
It's hard for organizationsto say no to things, but I
think the organizations thatdeclare what they're not going
(28:05):
to do are, very, it's usefulbecause it stops eager beaver
behavior around Hey, we coulddo this, or I have this pet
project that I want to do.
And if you say, no, we'renot doing that doesn't mean
it's not, we're not evergoing to do it just means
we're not going to do it now.
So that people, again,that's a part of that sort
of kind of clarity aspect.
I think giving feedback ina courteous way, but also
(28:27):
holding people accountable.
I think the notion of creatingenvironments where people
feel safe makes sense.
And it's incredibly important.
But you also have, youcan't shirk away from giving
constructive feedback.
And when I was at Cisco thatone of the tenants that we
had that I really loved, andit came from Duo, which was a
(28:47):
startup that Cisco had purchasedabout five years ago, and
they had this tenant that theycall be kinder than necessary.
And what that meant was ifyou had spinach on your front
teeth and you're walking into ameeting, you'd want someone to
tell you that you have spinach.
It's not about you.
It's just that this is somethingthat you should know about.
And so I think it's aboutconstantly giving feedback
(29:09):
to each other and holdingeach other accountable to a
high standard and making itabout the work, not about you.
And that creates the conditionswhere people learn to
listen to that feedback anddon't take it personally.
They take it like, Oh, okay,that's an opportunity for
me to improve my game, orthat's an opportunity for
the project work to, to bein a different direction.
So I think those are thetwo things that for me.
(29:32):
I'm not saying I'm great atit, but I materially try to
practice is try to be as clearas possible and try to give
as much feedback as possible.
Hannah Clark (29:39):
I really appreciate those insights.
I agree with him.
I'm sure you'rejust being humble.
Thanks for joiningus today, Greg.
This has been agreat conversation.
Yeah, I've talked about a lot ofdifferent topics that are really
highly relevant right now.
So I'm hoping that we getsome interesting feedback
from folks who are listening.
And speaking of feedback, wherecan people follow your work
or connect with you online?
Greg Petroff (29:58):
Yeah, I'm on LinkedIn.
So you can find me there.
I have a profile there.
I have a Substack calledImprobable Futures.
So I do a little bit of writingon the idea around possibility.
And so you can find mein those two places.
Hannah Clark (30:13):
Awesome.
Well, thank you so muchfor joining us here.
Greg Petroff (30:15):
All right, thank you.
Hannah Clark (30:18):
Thanks for listening in.
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