Product management isn’t just evolving—it’s getting completely rewritten. In this episode, Hannah sits down with Dr. Maryam Ashoori, VP of Product and Engineering at IBM Watsonx, to talk about how AI is reshaping not only the tools product managers use, but the very nature of the role itself.
Maryam brings two decades of experience in AI across design, research, and engineering, and shares what she's seeing from the front lines of one of the most powerful Gen AI platforms on the planet. They explore how AI is shifting expectations, blurring team roles, and opening new paths for productivity and creativity. If you’ve ever felt like the PM playbook just got tossed out the window, you’re not alone—and this conversation is a thoughtful look at how to navigate what comes next.
Resources from this episode:
- Subscribe to The Product Manager newsletter
- Connect with Maryam on LinkedIn
- Check out IBM Watsonx
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Hannah Clark (00:01):
As technology evolves, so do our behaviors.
In the past, those changeswould feel pretty incremental.
Like instead of handingin a resume in person,
now we apply online.
But in a time of drastictechnology changes, product
people are in the middleof a total reinvention.
Not only are we adapting to newbehaviors as users ourselves,
but we're also adapting tocompletely new standards
(00:22):
in the ways that we work.
And let's be honest, thatshift sometimes feels
like we're building theparachute after we've already
jumped out of the plane.
So how do you build a decentproverbial parachute when
you're already disorientedand there's no formal
manual to instruct you?
Well, a good start is tolook around at the skydivers
who have managed to takethe same materials you have
and found a way to fly.
(00:43):
My guest today is Dr. MaryamAshoori, VP of Product and
Engineering at IBM Watsonx.
Dr. Ashoori, who assuredme I could simply call her
Maryam, has worked in theAI space for over 20 years.
And while there are very fewpeople who have sat closer
to the cutting edge of thistechnology than she has,
it's only been recently thatshe's seen a monumental shift
in the behaviors of highperforming product people.
(01:04):
So if you'd like to borrowthe playbook of the product
team closest to some of themost significant contributions
to AI in history, thisepisode might interest you.
Let's jump in.
Oh, by the way, wehold conversations
like this every week.
So if this sounds interestingto you, why not subscribe?
Okay, now let's jump in.
Welcome back to theProduct Manager podcast.
(01:26):
I'm here today withDr. Maryam Ashoori.
She is the VP of Product andEngineering at IBM Watsonx.
Maryam, I'm so excitedto talk to you.
How are you doing today?
Maryam Ashoori (01:34):
I'm good.
How are you?
Hannah Clark (01:36):
I'm doing great.
I'm so excited to get into thisconversation and we'll start it
off the way we always start off.
Can you tell us a little bitabout your background and how
you got to where you are today?
Maryam Ashoori (01:44):
Absolutely.
I've been working in AI forover 20 years now, across
multiple disciplines.
Throughout my careers,I've worked as a designer
building AI driven systemswith a user in mind.
I've watched as an engineerbringing that data and
optimization to thesystems that we built.
I've watched as an AI researcherpushing the boundaries
(02:06):
of what AI could do, andultimately I found my home
in product bringing all ofthese perspectives together
to solve the right problems.
And more recently.
To be precise.
Over the past two years withgenerative AI, I've been at
the forefront designing andbuilding for what's the next
study, AI, which is the coreIBM Gen AI enterprise platform.
(02:28):
From the ground up, that workbrought together everything
that I love from definingstrategy in fast-paced
market, the evolving marketto navigating legal and ethics
stuff, and new field like GenAI in this case, with all the
limitations associated to that.
To build something usefulthat solves a real problem.
(02:49):
And throughout that, Irealized, and I've come to
see responsible AI as thecrucial element that we have to
closely work on and design for.
And that led me to my currentrole, which is leading
product and engineeringfor AI governance.
Hannah Clark (03:05):
This is such an honor for us on the show
because AI has been such a hugeconversation for us, and so it's
so fascinating to be able totalk to someone who's got the
context of being in this fieldfor much longer than it's been
in the public conversation inthe way that it is right now.
But today we're gonna befocusing specifically on the
role of product managementand how it's evolving with
the emergence of AI-poweredtools and workflows.
(03:28):
This is something that'sreally close to your field.
So I'd love if you could kick usoff by telling us a little bit
about some of the shifts thatyou've been observing at IBM
Watsonx and what those changeshave been signaling about
the future of the profession.
Maryam Ashoori (03:39):
Well, there are two things
that I would highlight.
One is on the pro professionitself, and the second one
is on the products thatthis product managers built.
Let's start with the first one.
There's no doubt that thereis much acceleration into
the productivity aspects ofGen AI bringing to everyday
life of a product manager.
(04:00):
If I just look at my own productmanagers, they are white coding.
They were not white coding ayear ago, so in a year ago,
if I wanted them to create aPRD and go work with designers
to put together the mockupsand then go to engineers to
put together a prototype andcome back, pitch the idea.
Now they just build it in lessthan 24 hours and they show
(04:23):
me something fully fledged,fully functional, and I'm
like, what am I looking at?
Is it the actual productor is the mockup that
you are building?
It shows how the profusionis changing using AI
and at every layer, thiswas just acceleration
to test out an idea.
But you can also think aboutPRD generation using AI to help
(04:44):
you brainstorm and evaluatemultiple options that you have.
So that's on the productivityside of the house, on the
product side of the house,these product managers are
assigned to build a productthat solves the problem.
That product, there is avery good chance that can
potentially benefit fromthe acceleration of Gen AI.
So it's essential for theseproduct managers to have a
(05:06):
deep expertise of how thistechnology can help my own
product and my own work andbring those acceleration
to what they are building.
Hannah Clark (05:14):
Yeah, and I think this is like a
competency that is, it's a veryinteresting space right now
'cause it's rapidly evolving.
There's not really a wholelot of formal education
that's able to kind of keepup with the pace of change.
From what you've seen in yourown team and in the field right
now, what are some of the waysthat product managers that are
wanting to stay competitivein this field and really keep
up with that productivitygap, stay competitive.
(05:35):
How can we buildup our skillset?
Maryam Ashoori (05:37):
That weakness that you mentioned
represents an opportunity.
All this acceleration, atsome point in the future
is gonna become a norm.
The metaphor that I keepthinking about is calculator.
Many years ago, people weredoing manual calculations,
and even I remember atthe school calculator was
banned because they wantedus to do it manually.
(05:58):
But then we crossed that chasmand we got to a point that
you just move on to solvingdifferent problems rather
than doing manual calculation.
It's the same metaphorand the same thinking.
The product managers onthe future are expected to
effectively use AI in theirjob, but there is a period
of transition that whoevertakes advantage of that
(06:22):
can potentially create anopportunity for themselves
to stay ahead of the game.
So this weakness is actuallyrepresenting a good opportunity.
And I remember recently,actually we ran a study with
thousand people just focused on.
Figuring out thatproductivity acceleration
that we get from AI.
And we asked themone simple question.
(06:42):
These were thousand AIapplication developers focused
on enterprise building in US.
And then we asked them, Hey,do you use AI assisted coding?
38%? They said frequentlythey use that and we
are like, wonderful.
How much time saving areyou getting out of that?
And 41% they said oneto two hours per day.
(07:05):
So one to two hours oftime saving per day.
4% they said morethan four hours.
What does it tell me?
And it's independentfrom the road.
If you know how to effectivelyuse AI to get AI to work
for you, you can potentiallybe unlock so many new
ways of thinking, even inproduct management that
(07:27):
we haven't explored there.
And I think that's the trueopportunity that this technology
represent for product managers.
Hannah Clark (07:33):
I would agree.
I think that Vibe coding inparticular really combines a
number of different skill setsthat are kind of unique to an
experienced product managerand able to kind of create
something that can be a startingpoint that's, like you said,
worlds ahead of just the PRDsthat you'd be kind of starting
at early on and you know, ayear ago or two years ago.
So now that we think about thetools that are involved in AI,
(07:54):
speaking of vibe coding, you'venoted in a past conversation
that building an AI applicationusually requires a lot of tools,
about six to 15 different tools.
And so if we're working withlimited AI expertise, how do
you recommend approaching thechallenge of understanding
and orchestrating complextechnology stacks, especially
now that tools have become morecomplex and more sophisticated?
Maryam Ashoori (08:14):
I would say two things.
Think about the technologypart of this question and the
people part of this question.
If you just purely lookat the technology, the
field is evolving rapidly.
As a product manager, probablyyou have less than two hours
to go and explore a new thingthat comes to the market to
make a decision if this issomething useful that you
wanna spend more time and bringin to your product, or it's
(08:36):
just basically treat it as anoise and leave it out there.
So limited time evolvingmarket, limited expertise in AI.
As a product manager, youprobably don't have a deep
expertise in AI the same wayand level that an AI researcher
has spending years on this.
So really.
Figuring out how toevaluate the situation and
(08:59):
the technology out there.
If you just rely on the tools,I feel like that's, you are
missing out on the peoplepart that can come and help
you because of the limitedknowledge that you have.
On the people side, when youlook into how the market is
evolving and how differentroles are evolving in the
market, probably the best helpthat you can get is from your
(09:22):
partners in engineering andyour partners in research, or
in some companies they call themscientists, research scientist.
Because they understand how thefield is evolving and what is
the value of that technologyto what you are applying.
If you look into the field asa whole, the line between these
roles and responsibilitiesare dissolving to, in
(09:45):
particular, between engineersand product managers.
Historically, we were sayingthat, hey, for every one product
managers, you probably wannahave five to 10 engineers.
A month ago, we saw AndrewInc on a stage and he said,
Hey, my team proposed tohave one product majors for
(10:05):
every 0.5 engineer, so youhave more product majors,
and this is exploration.
Not saying that's the right.
Ratio, but what it showsis the line between roles
and responsibilities arechanging, so does the
expertise that is neededto build the right product.
My advice for product managersis to focus on building the
(10:28):
right product because onceyou name that definition,
you can pass it to your humanpartners or the AI partners to.
Make them work for you,but the essence and the
crucial part is to get itright and define it right.
Hannah Clark (10:42):
That is the challenge that we're all kind of
trying to figure out is what'sthe balance between how much of
the tools we want to lean on andhow much of our own, how do we
kind of inject our own expertiseand the expertise of those
that we're collaborating with.
So when we think aboutevaluating AI technologies
themselves for a productroadmap, for example, what are
some of the criteria that you'duse to distinguish between tools
that are worth investing inversus those that might be here
(11:04):
and gone again in six months?
Maryam Ashoori (11:05):
Yeah, I've seen people that chase solutions
versus solved problem.
A very good example of thatis someone that came to me
and said, Hey, by the endof the year I wanna bring
in two agents to my product.
And I'm like, hold on,like two agents to do what?
Exactly.
So this to me is chasingthe technology to figure out
(11:27):
how I can bring it versus.
Having a really goodunderstanding of what is the
problem that you're tryingto solve, and if this new AI
tool, it's a distraction orit's a benefit, true benefit
to your product, I would startthere because then you are
gonna have a framework andlens at which you can evaluate
(11:48):
the value of any piece oftechnology that comes your way.
Figure out if you wanna spendmore resources or more of
your time on evaluating that.
Hannah Clark (11:59):
Yeah.
You know, this is echoinga conversation that we had
recently on a panel event.
Thomas Stokes, who's aco-principal at Drill Bit Labs.
So he's heavily in UX researchand he was echoing some of
those same sentiments in whichwe're kind of seeing this
sort of a emergence of this.
Solution first thinkingof how do we use AI
rather than thinkingabout the problem first.
And I think that this is areally good moment in time
(12:20):
that a lot of us kind of haveto go back to basics and think
about, you know, like thetechnology is very exciting.
It's, there's obviously a lotof use cases for it, but we
still need to be thinking aboutthings from this problem first
mindset in order to ensure thatwe're using it in a way that's
accurate and actually solves theproblem of the heart of things.
So how do you, at IBM, how doyou strike a balance between
(12:41):
building AI capabilitiesin-house and adopting
third party solutions?
I know you guys are pioneersin this space, so I'm
very interested to see howyou guys are approaching
this kind of an issue.
Maryam Ashoori (12:49):
I wouldn't say it's an issue.
It's an opportunity to amplifyyour effort, especially
when this comes to Gen AI.
Just look at what's going onoutside in even open source.
The innovation is comingfrom academia, is coming from
industry, it's coming fromdevelopers just doing it on
their own as a passion project.
It is coming from.
(13:10):
Every resource.
So if you limit yourselfto, Hey, I just wanna build
in-house myself, I feel likeyou are just restricting the
access to innovation thatyou could possibly have.
So for every single areathat we focus on, usually
we are looking to what isthe value of leveraging
our in-house capabilities.
(13:31):
For example, for usspecifically, we have access
to 2,500 people in IBM researchthat are living and breeding
the state of the art technology.
So I have very frequentconversation with them
to figure out where theirthinking is and what can
potentially benefit the product.
But at the same time, I'mhaving the same conversation
(13:53):
with my partners.
In the market or even thecommunity, like selected people
in the community that areshaping the community to see,
hey, where do you think it'sgoing in the next three months?
How can we support thecommunity building activities?
And at the same time, I'mmaking my time and I know
that's the hard part,but I am dedicating time.
(14:15):
In my calendar just to goand look for random news that
are out there because that'swhere you find the passionate
developers building something,putting out there, which is
not in the spotlight of bigcompanies announcing major.
Features.
So throughout these channels youget a visibility and exposure
(14:36):
to broad spectrum of technologythat is coming to the market.
And then back to the pointof what problem I'm trying to
solve, then I can look aroundand see what is the cost and
benefit of getting this pieceof technology from here in.
Versus community versusthose developers.
Maybe I can hire them or Ican do something about it
(14:57):
to bring it and then makea, an informed decision.
Hannah Clark (15:00):
Oh that's a really holistic way of
looking at that kind offramework of decision making.
Speaking of kind of decisionmaking, I wanna kinda
shift gears into more of aleadership perspective here.
So, you know, as someonewho's leading a team, what
are some of the new skillsand competencies that you're
really looking for when you'rein hiring mode for new product
managers to join the team?
Even compared to a year agowhen the technology was newer?
Maryam Ashoori (15:21):
Again, I would look into the problem
that I'm trying to solve.
With that hire, I typicallylook into the skillset of
the people on the team andidentify the gap because we
usually hire for that gap.
Then for that gap, I would lookout the bar for me is, would I
(15:41):
one day report to this person?
So if I wanna hire someone,you wanna hire someone that
knows more, is filling thatgap and is the expert in
that very specific thing thatyou are hiring them for, and
then get out of their way.
Give them the autonomy todeliver that because they
should be truly the best.
(16:02):
So that's the mindset that I'madopting, really understanding
what are the gaps and for gaps.
I'm not looking into, oh, I havea product manager, so I have
a designer, no, specifically,like this person is very good
at, for example, go to marketand customer conversation.
The other person is very goodat growing the SaaS space.
The third person is at what?
(16:22):
So basically you lookinginto skills versus titles.
Try to feel that skill andfind the best person that
I can possibly bring onthe team to cover that.
Hannah Clark (16:34):
This is a very interesting thing because I feel
like this is also a shift inthinking in terms of, you know,
what are some of the tertiaryexperiences that this person or.
More scenarios or connectionsor other aspects of this
person who stand outversus just qualifications.
There's a real departure froma different kind of hiring
mentality In the past when we'rereally focused on very specific
(16:56):
hard skills and certificationsand qualifications, it seems now
that the breadth of someone'sexperience and other things
that they bring to the tablethat are unique to them as
an individual matter more.
Would you say that's accurate?
Maryam Ashoori (17:09):
That's accurate.
And I think what's common inmost of the hires that I've seen
over the past two years, we, atthe age of Gen AI, is basically
having full characteristics.
One is be curious, show thatyour curiosity about where
the field is going, jump onit, explore, build things.
(17:31):
And the second one is.
Be technical, reallyunderstand what's going on.
I've seen some of thePMs that they categorize
themselves as a go tomarket, PM versus technical
pm and I'm like, hold on.
We are talkingabout the age of AI.
You need to have a very goodunderstanding of how it can
(17:51):
help your product and yourself.
And if you can't answerthat, maybe you're in
the wrong position.
Hannah Clark (17:57):
Yeah, I agree.
What you'd said also about whatI wanna report to this person
someday really reminds me.
I spoke to an Italian aboutKova at Atlassian a, a year
and a half ago about a similarkind of thing about things that
she looks for in a candidate.
And one thing that I thoughtreally stuck out was.
If I talk to this person aboutwho they admire, have they
ever talked about someonethat was more junior to them?
(18:17):
And I thought that was areally interesting thing,
like looking for qualitiesabout, you know, what is this
person's ability to collaborateor be respectful to all the
competencies in the chain?
And I feel like now especially,is this a really good
opportunity to be looking for,you know, how is this person
collaborate with others,given that the technology is
so collaborative in nature,I find this very fascinating.
Moving on, you mentioned beforethat two key areas where AI
(18:40):
impacts product management was,you know, enhancing the existing
products as well as improvingthe personal productivity.
I wanna talk a little bitnow about how we enhance our
existing products with AIsince we've, you know, we kind
of touched on this briefly.
What are some of the reallybrilliant use cases that you've
seen in both of these areas?
I'm sure that you'refull of examples.
Maryam Ashoori (18:59):
So there are different ways that we can
bring AI to our products.
One is you can be the supplierof that technology to the world.
For example, what's the next AI?
The platform that I buildis packaging LLMs and
tools and everything thatyou need, and offer it to
other roles to build up on.
So that's one way of thinkingabout it, bringing them
(19:21):
as a platform, supplyingthem to the market to help
application build applications.
The second one is tobuild applications that
are enriched with AI.
So no matter what productyou have, you can think
of, oh, how can I bringAI to enrich some of these
features that I have and takeadvantage of the acceleration.
(19:42):
It can be either one of thedirect use cases that gen AI
unlocks, like classificationinformation, extraction
question, and answering likecustomer care, chatbots,
basically content generation,code generation, one of them.
Or it can be automation.
It's like just,you know, agents.
You can connect all of theseuse cases that I mentioned
(20:03):
and through function, callingto every single business
workflow that you have.
So how can I bring thatefficiency to my product
to have a better product?
So that's the second category.
The third category is howcan I come up with a new
category of product thatis powered up by LLMs?
One example is all the codeassisted technologies out there.
(20:25):
LLM, behind the scene,and now you're using it
to help the developers todo vibe coding, basically.
So that's a verygood example of that.
New opportunity areas.
And the fourth oneis, I would categorize
that through services.
The challenge thatyou mentioned for.
Product managers on educationis not unique to this role.
(20:45):
It's unique to the whole market.
Enterprise market,consumer market.
Everyone needs to educatethemselves to make better AI
decisions and it representsan opportunity for services.
So if you know the field,you can go in and help
them pick the right modelfor the right use case.
Make a better decision.
So it represents four differentareas of opportunities for
(21:07):
product managers, and dependingon which area you're targeting,
the advice for how to approachthat is completely different.
Hannah Clark (21:15):
I'm really interested in this fourth
category of the servicesbecause I have also noticed
this kind of a trend towardsa popularization of even
product managers moving intoa services or education space.
I've seen just an explosionof former PMs offering Maven
courses and it seems like thereis a huge push towards feeding
this opportunity now thatespecially vibe coding tools
(21:36):
have made coding and developmentavailable to just about anyone.
I can really see that trendtowards, well, how do we kind of
offer other services to empowerpeople to develop the solutions
that are really bespoke to them?
So it's really interesting.
That's a whole other shiftthat's kind of occurring
kind of concurrently withall the developments that
are happening in product, Ithink is really interesting.
So looking ahead then, whatwould you say are some of
(22:00):
the developments of trends inAI that you think will have
a really significant impacton the next 12 to 18 months?
Just given kind of what you'reseeing starting out right now?
Maryam Ashoori (22:08):
You know, in our world, every three months
is a generation, so 12, 18months is five generation ahead.
A little bit too far out wherewe are gonna go is too far.
But if I just look intomaybe three generations,
six to nine months fromnow, I would say that.
I'm gonna see a lot moreon automation, and we
see that today, but thetechnology is not there yet.
(22:32):
It's more like exploringnew use cases, figuring out
collectively with the communityhow to effectively use this.
It represents an opportunityfor product managers, and when
I say automation is literallyautomating every single
thing from email generationto talk to your customers,
(22:54):
to optimizing your roadmaps,depending on the dev effort
needed to deliver something.
At the mo.
What timeline to generatingcontent for your PRD.
To exploring new optionsfor your customers to help
them make better decisions,like literally every single
(23:16):
decision that you're making.
I argue that it canpotentially be enhanced with
AI if you figure out howto make AI work for you.
On the other side though,what I would add is all
these beautiful accelerationthat we get with AI.
There is a cost associatedto that, and that cost is
the risks you can make orbreak your career depending
(23:40):
on how you're using this.
And it's essential for productmanagers not only to have a
very good understanding ofthe benefits, but also risks.
Because risk canimpact their product.
Week time things can gowrong, risk can impact
their or personal identityand branding as well.
So I would say that like alwaysthink about the cost benefit
(24:02):
balance of what you're doing.
Hannah Clark (24:04):
Yeah.
And well, and I'm sure thatthis is an issue that's
so close to your heart.
Given that ethical useof AI is a cornerstone of
your career, how do we kindof navigate those risks?
I feel that oftentimes mistakescan be made by really well
intending people who arereally just kind of in this
play and exploration mode.
So do you have any kind offramework or kind of guardrails
(24:26):
that you like to think aboutwhen exploring with AI or
anything that you would kindof impart on folks who are
kind of looking to incorporaterisk management into their
development and their skillset?
Maryam Ashoori (24:35):
I'll give you an example.
Let's say that you wanna usean agent that sends email
on your behalf or summarizesyour emails and it has access
to your calendar to schedulesomething on your calendar.
It's amazing the benefitsthat you get from them.
On the other side, youhave to give access to this
agent to your email and yourcalendar schedule, knowing
(24:59):
that potentially a bad actorcan misuse the information
of when you are gonna be atwhat time and talking to who.
So every permission thatyou give to these agents
potentially creates avulnerability point for you,
and that's not just for you.
It's the same for yourproduct when you bring
those to your product.
(25:20):
So I would start with back tothe drawing board of use case.
It's essential to understandwhat is the problem that
you're trying to solve for thecommunity, for your product.
What is this thing?
And then when it comes to AI,have a very good understanding
of what is the benefit to that.
But also what are thevulnerability points that I'm
(25:43):
introducing to this product?
Am I confident to have amitigation plan to resolve
those, or I'm just blindlyfollowing and bringing
that AI to apply to myproduct and see what happen.
Hannah Clark (25:58):
Yeah, I'm really glad that you said that
because I think that is kindof the less sexy part of AI
development and exploration.
This is time of such excitementwhen risk management, I think,
has been a little bit of anafterthought, if a thought
at all in some circumstanceswhere, you know, we're sort of
entering this wild west phase ofdevelopment and exploration and
almost like a second generationof pioneers with regards to
(26:19):
vibe coding and using AI forcompletely new use cases.
So I think that is kind of animportant thing to call out.
I did kind of want to end onsort of a, you know, next steps
for those who are interested indeveloping their own skillset.
In this current generationthat we're in right now, what
have you found to be some ofthe most useful resources,
either for yourself orfor folks on your team who
are upskilling naturally?
(26:40):
Like just playing out withtechnology is one way, but
are there any other kind ofresources that you'd recommend?
Maryam Ashoori (26:45):
Yeah.
In addition to all of thosegoodies that are out there,
I would also say findyour way to filter out.
What do you wanna be exposed to?
Because there are so many thingshappening in the market, and
I've seen product majors dealingwith this fatigue of technology
fatigue, maybe we call it.
So where do you get your data?
(27:06):
And I think the most effective.
What I've been encouragingmy teams to do too is to find
your trusted voice in thecommunity and follow them.
If it's on LinkedIn, if it'ssomewhere else, just follow
them because if they have thepoint of view that is consistent
(27:27):
with what you are thinking.
There's a good chance thata new piece of technology
comes out there and you wannahear the perspective from
them versus going to thecommunity and figure that out.
And it's like a shortcut tohear their opinion, the opinion
from an expert on a technology.
And that's very helpful.
But also like even given thatyou know, for your product
(27:50):
managers cost and benefit, yougotta make sure that the person
that you follow is a reallyworse following kind of person.
Because otherwise youare limiting your view
to the world to that.
Window, and if that's notaccurate, then you have a
clouded view of the board.
So be very selective, butfind people that are sharing
(28:13):
the same point of view andare expert in what you care
about and just follow them.
Hannah Clark (28:18):
I love that advice and I think that it's
very important right nowas we're kind of entering a
period of rapid saturation.
The advice of being selective,I think applies to many things
in AI and technology and Ithink we'll find also in life.
Also to your point abouttrusting folks and following
them, how can folks followyour work after this episode?
Maryam Ashoori (28:37):
I'm on LinkedIn and I try to write
as much as possible, so Iwould love to stay in touch
with them on LinkedIn.
Hannah Clark (28:46):
Wonderful.
Well, thank you so muchfor joining us, Maryam.
We'll make sure to addthe link to your LinkedIn
in our show notes today.
Thank you so much.
I really appreciate your time.
I know you're sucha busy person.
Maryam Ashoori (28:54):
Wonderful.
Thanks for having me.
Hannah Clark (28:58):
Thanks for listening in.
For more great insights,how-to guides 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.
As technology evolves, so do our behaviors.
In the past, those changeswould feel pretty incremental.
Like instead of handingin a resume in person,
now we apply online.
But in a time of drastictechnology changes, product
people are in the middleof a total reinvention.
Not only are we adapting to newbehaviors as users ourselves,
but we're also adapting tocompletely new standards
(00:22):
in the ways that we work.
And let's be honest, thatshift sometimes feels
like we're building theparachute after we've already
jumped out of the plane.
So how do you build a decentproverbial parachute when
you're already disorientedand there's no formal
manual to instruct you?
Well, a good start is tolook around at the skydivers
who have managed to takethe same materials you have
and found a way to fly.
(00:43):
My guest today is Dr. MaryamAshoori, VP of Product and
Engineering at IBM Watsonx.
Dr. Ashoori, who assuredme I could simply call her
Maryam, has worked in theAI space for over 20 years.
And while there are very fewpeople who have sat closer
to the cutting edge of thistechnology than she has,
it's only been recently thatshe's seen a monumental shift
in the behaviors of highperforming product people.
(01:04):
So if you'd like to borrowthe playbook of the product
team closest to some of themost significant contributions
to AI in history, thisepisode might interest you.
Let's jump in.
Oh, by the way, wehold conversations
like this every week.
So if this sounds interestingto you, why not subscribe?
Okay, now let's jump in.
Welcome back to theProduct Manager podcast.
(01:26):
I'm here today withDr. Maryam Ashoori.
She is the VP of Product andEngineering at IBM Watsonx.
Maryam, I'm so excitedto talk to you.
How are you doing today?
Maryam Ashoori (01:34):
I'm good.
How are you?
Hannah Clark (01:36):
I'm doing great.
I'm so excited to get into thisconversation and we'll start it
off the way we always start off.
Can you tell us a little bitabout your background and how
you got to where you are today?
Maryam Ashoori (01:44):
Absolutely.
I've been working in AI forover 20 years now, across
multiple disciplines.
Throughout my careers,I've worked as a designer
building AI driven systemswith a user in mind.
I've watched as an engineerbringing that data and
optimization to thesystems that we built.
I've watched as an AI researcherpushing the boundaries
(02:06):
of what AI could do, andultimately I found my home
in product bringing all ofthese perspectives together
to solve the right problems.
And more recently.
To be precise.
Over the past two years withgenerative AI, I've been at
the forefront designing andbuilding for what's the next
study, AI, which is the coreIBM Gen AI enterprise platform.
(02:28):
From the ground up, that workbrought together everything
that I love from definingstrategy in fast-paced
market, the evolving marketto navigating legal and ethics
stuff, and new field like GenAI in this case, with all the
limitations associated to that.
To build something usefulthat solves a real problem.
(02:49):
And throughout that, Irealized, and I've come to
see responsible AI as thecrucial element that we have to
closely work on and design for.
And that led me to my currentrole, which is leading
product and engineeringfor AI governance.
Hannah Clark (03:05):
This is such an honor for us on the show
because AI has been such a hugeconversation for us, and so it's
so fascinating to be able totalk to someone who's got the
context of being in this fieldfor much longer than it's been
in the public conversation inthe way that it is right now.
But today we're gonna befocusing specifically on the
role of product managementand how it's evolving with
the emergence of AI-poweredtools and workflows.
(03:28):
This is something that'sreally close to your field.
So I'd love if you could kick usoff by telling us a little bit
about some of the shifts thatyou've been observing at IBM
Watsonx and what those changeshave been signaling about
the future of the profession.
Maryam Ashoori (03:39):
Well, there are two things
that I would highlight.
One is on the pro professionitself, and the second one
is on the products thatthis product managers built.
Let's start with the first one.
There's no doubt that thereis much acceleration into
the productivity aspects ofGen AI bringing to everyday
life of a product manager.
(04:00):
If I just look at my own productmanagers, they are white coding.
They were not white coding ayear ago, so in a year ago,
if I wanted them to create aPRD and go work with designers
to put together the mockupsand then go to engineers to
put together a prototype andcome back, pitch the idea.
Now they just build it in lessthan 24 hours and they show
(04:23):
me something fully fledged,fully functional, and I'm
like, what am I looking at?
Is it the actual productor is the mockup that
you are building?
It shows how the profusionis changing using AI
and at every layer, thiswas just acceleration
to test out an idea.
But you can also think aboutPRD generation using AI to help
(04:44):
you brainstorm and evaluatemultiple options that you have.
So that's on the productivityside of the house, on the
product side of the house,these product managers are
assigned to build a productthat solves the problem.
That product, there is avery good chance that can
potentially benefit fromthe acceleration of Gen AI.
So it's essential for theseproduct managers to have a
(05:06):
deep expertise of how thistechnology can help my own
product and my own work andbring those acceleration
to what they are building.
Hannah Clark (05:14):
Yeah, and I think this is like a
competency that is, it's a veryinteresting space right now
'cause it's rapidly evolving.
There's not really a wholelot of formal education
that's able to kind of keepup with the pace of change.
From what you've seen in yourown team and in the field right
now, what are some of the waysthat product managers that are
wanting to stay competitivein this field and really keep
up with that productivitygap, stay competitive.
(05:35):
How can we buildup our skillset?
Maryam Ashoori (05:37):
That weakness that you mentioned
represents an opportunity.
All this acceleration, atsome point in the future
is gonna become a norm.
The metaphor that I keepthinking about is calculator.
Many years ago, people weredoing manual calculations,
and even I remember atthe school calculator was
banned because they wantedus to do it manually.
(05:58):
But then we crossed that chasmand we got to a point that
you just move on to solvingdifferent problems rather
than doing manual calculation.
It's the same metaphorand the same thinking.
The product managers onthe future are expected to
effectively use AI in theirjob, but there is a period
of transition that whoevertakes advantage of that
(06:22):
can potentially create anopportunity for themselves
to stay ahead of the game.
So this weakness is actuallyrepresenting a good opportunity.
And I remember recently,actually we ran a study with
thousand people just focused on.
Figuring out thatproductivity acceleration
that we get from AI.
And we asked themone simple question.
(06:42):
These were thousand AIapplication developers focused
on enterprise building in US.
And then we asked them, Hey,do you use AI assisted coding?
38%? They said frequentlythey use that and we
are like, wonderful.
How much time saving areyou getting out of that?
And 41% they said oneto two hours per day.
(07:05):
So one to two hours oftime saving per day.
4% they said morethan four hours.
What does it tell me?
And it's independentfrom the road.
If you know how to effectivelyuse AI to get AI to work
for you, you can potentiallybe unlock so many new
ways of thinking, even inproduct management that
(07:27):
we haven't explored there.
And I think that's the trueopportunity that this technology
represent for product managers.
Hannah Clark (07:33):
I would agree.
I think that Vibe coding inparticular really combines a
number of different skill setsthat are kind of unique to an
experienced product managerand able to kind of create
something that can be a startingpoint that's, like you said,
worlds ahead of just the PRDsthat you'd be kind of starting
at early on and you know, ayear ago or two years ago.
So now that we think about thetools that are involved in AI,
(07:54):
speaking of vibe coding, you'venoted in a past conversation
that building an AI applicationusually requires a lot of tools,
about six to 15 different tools.
And so if we're working withlimited AI expertise, how do
you recommend approaching thechallenge of understanding
and orchestrating complextechnology stacks, especially
now that tools have become morecomplex and more sophisticated?
Maryam Ashoori (08:14):
I would say two things.
Think about the technologypart of this question and the
people part of this question.
If you just purely lookat the technology, the
field is evolving rapidly.
As a product manager, probablyyou have less than two hours
to go and explore a new thingthat comes to the market to
make a decision if this issomething useful that you
wanna spend more time and bringin to your product, or it's
(08:36):
just basically treat it as anoise and leave it out there.
So limited time evolvingmarket, limited expertise in AI.
As a product manager, youprobably don't have a deep
expertise in AI the same wayand level that an AI researcher
has spending years on this.
So really.
Figuring out how toevaluate the situation and
(08:59):
the technology out there.
If you just rely on the tools,I feel like that's, you are
missing out on the peoplepart that can come and help
you because of the limitedknowledge that you have.
On the people side, when youlook into how the market is
evolving and how differentroles are evolving in the
market, probably the best helpthat you can get is from your
(09:22):
partners in engineering andyour partners in research, or
in some companies they call themscientists, research scientist.
Because they understand how thefield is evolving and what is
the value of that technologyto what you are applying.
If you look into the field asa whole, the line between these
roles and responsibilitiesare dissolving to, in
(09:45):
particular, between engineersand product managers.
Historically, we were sayingthat, hey, for every one product
managers, you probably wannahave five to 10 engineers.
A month ago, we saw AndrewInc on a stage and he said,
Hey, my team proposed tohave one product majors for
(10:05):
every 0.5 engineer, so youhave more product majors,
and this is exploration.
Not saying that's the right.
Ratio, but what it showsis the line between roles
and responsibilities arechanging, so does the
expertise that is neededto build the right product.
My advice for product managersis to focus on building the
(10:28):
right product because onceyou name that definition,
you can pass it to your humanpartners or the AI partners to.
Make them work for you,but the essence and the
crucial part is to get itright and define it right.
Hannah Clark (10:42):
That is the challenge that we're all kind of
trying to figure out is what'sthe balance between how much of
the tools we want to lean on andhow much of our own, how do we
kind of inject our own expertiseand the expertise of those
that we're collaborating with.
So when we think aboutevaluating AI technologies
themselves for a productroadmap, for example, what are
some of the criteria that you'duse to distinguish between tools
that are worth investing inversus those that might be here
(11:04):
and gone again in six months?
Maryam Ashoori (11:05):
Yeah, I've seen people that chase solutions
versus solved problem.
A very good example of thatis someone that came to me
and said, Hey, by the endof the year I wanna bring
in two agents to my product.
And I'm like, hold on,like two agents to do what?
Exactly.
So this to me is chasingthe technology to figure out
(11:27):
how I can bring it versus.
Having a really goodunderstanding of what is the
problem that you're tryingto solve, and if this new AI
tool, it's a distraction orit's a benefit, true benefit
to your product, I would startthere because then you are
gonna have a framework andlens at which you can evaluate
(11:48):
the value of any piece oftechnology that comes your way.
Figure out if you wanna spendmore resources or more of
your time on evaluating that.
Hannah Clark (11:59):
Yeah.
You know, this is echoinga conversation that we had
recently on a panel event.
Thomas Stokes, who's aco-principal at Drill Bit Labs.
So he's heavily in UX researchand he was echoing some of
those same sentiments in whichwe're kind of seeing this
sort of a emergence of this.
Solution first thinkingof how do we use AI
rather than thinkingabout the problem first.
And I think that this is areally good moment in time
(12:20):
that a lot of us kind of haveto go back to basics and think
about, you know, like thetechnology is very exciting.
It's, there's obviously a lotof use cases for it, but we
still need to be thinking aboutthings from this problem first
mindset in order to ensure thatwe're using it in a way that's
accurate and actually solves theproblem of the heart of things.
So how do you, at IBM, how doyou strike a balance between
(12:41):
building AI capabilitiesin-house and adopting
third party solutions?
I know you guys are pioneersin this space, so I'm
very interested to see howyou guys are approaching
this kind of an issue.
Maryam Ashoori (12:49):
I wouldn't say it's an issue.
It's an opportunity to amplifyyour effort, especially
when this comes to Gen AI.
Just look at what's going onoutside in even open source.
The innovation is comingfrom academia, is coming from
industry, it's coming fromdevelopers just doing it on
their own as a passion project.
It is coming from.
(13:10):
Every resource.
So if you limit yourselfto, Hey, I just wanna build
in-house myself, I feel likeyou are just restricting the
access to innovation thatyou could possibly have.
So for every single areathat we focus on, usually
we are looking to what isthe value of leveraging
our in-house capabilities.
(13:31):
For example, for usspecifically, we have access
to 2,500 people in IBM researchthat are living and breeding
the state of the art technology.
So I have very frequentconversation with them
to figure out where theirthinking is and what can
potentially benefit the product.
But at the same time, I'mhaving the same conversation
(13:53):
with my partners.
In the market or even thecommunity, like selected people
in the community that areshaping the community to see,
hey, where do you think it'sgoing in the next three months?
How can we support thecommunity building activities?
And at the same time, I'mmaking my time and I know
that's the hard part,but I am dedicating time.
(14:15):
In my calendar just to goand look for random news that
are out there because that'swhere you find the passionate
developers building something,putting out there, which is
not in the spotlight of bigcompanies announcing major.
Features.
So throughout these channels youget a visibility and exposure
(14:36):
to broad spectrum of technologythat is coming to the market.
And then back to the pointof what problem I'm trying to
solve, then I can look aroundand see what is the cost and
benefit of getting this pieceof technology from here in.
Versus community versusthose developers.
Maybe I can hire them or Ican do something about it
(14:57):
to bring it and then makea, an informed decision.
Hannah Clark (15:00):
Oh that's a really holistic way of
looking at that kind offramework of decision making.
Speaking of kind of decisionmaking, I wanna kinda
shift gears into more of aleadership perspective here.
So, you know, as someonewho's leading a team, what
are some of the new skillsand competencies that you're
really looking for when you'rein hiring mode for new product
managers to join the team?
Even compared to a year agowhen the technology was newer?
Maryam Ashoori (15:21):
Again, I would look into the problem
that I'm trying to solve.
With that hire, I typicallylook into the skillset of
the people on the team andidentify the gap because we
usually hire for that gap.
Then for that gap, I would lookout the bar for me is, would I
(15:41):
one day report to this person?
So if I wanna hire someone,you wanna hire someone that
knows more, is filling thatgap and is the expert in
that very specific thing thatyou are hiring them for, and
then get out of their way.
Give them the autonomy todeliver that because they
should be truly the best.
(16:02):
So that's the mindset that I'madopting, really understanding
what are the gaps and for gaps.
I'm not looking into, oh, I havea product manager, so I have
a designer, no, specifically,like this person is very good
at, for example, go to marketand customer conversation.
The other person is very goodat growing the SaaS space.
The third person is at what?
(16:22):
So basically you lookinginto skills versus titles.
Try to feel that skill andfind the best person that
I can possibly bring onthe team to cover that.
Hannah Clark (16:34):
This is a very interesting thing because I feel
like this is also a shift inthinking in terms of, you know,
what are some of the tertiaryexperiences that this person or.
More scenarios or connectionsor other aspects of this
person who stand outversus just qualifications.
There's a real departure froma different kind of hiring
mentality In the past when we'rereally focused on very specific
(16:56):
hard skills and certificationsand qualifications, it seems now
that the breadth of someone'sexperience and other things
that they bring to the tablethat are unique to them as
an individual matter more.
Would you say that's accurate?
Maryam Ashoori (17:09):
That's accurate.
And I think what's common inmost of the hires that I've seen
over the past two years, we, atthe age of Gen AI, is basically
having full characteristics.
One is be curious, show thatyour curiosity about where
the field is going, jump onit, explore, build things.
(17:31):
And the second one is.
Be technical, reallyunderstand what's going on.
I've seen some of thePMs that they categorize
themselves as a go tomarket, PM versus technical
pm and I'm like, hold on.
We are talkingabout the age of AI.
You need to have a very goodunderstanding of how it can
(17:51):
help your product and yourself.
And if you can't answerthat, maybe you're in
the wrong position.
Hannah Clark (17:57):
Yeah, I agree.
What you'd said also about whatI wanna report to this person
someday really reminds me.
I spoke to an Italian aboutKova at Atlassian a, a year
and a half ago about a similarkind of thing about things that
she looks for in a candidate.
And one thing that I thoughtreally stuck out was.
If I talk to this person aboutwho they admire, have they
ever talked about someonethat was more junior to them?
(18:17):
And I thought that was areally interesting thing,
like looking for qualitiesabout, you know, what is this
person's ability to collaborateor be respectful to all the
competencies in the chain?
And I feel like now especially,is this a really good
opportunity to be looking for,you know, how is this person
collaborate with others,given that the technology is
so collaborative in nature,I find this very fascinating.
Moving on, you mentioned beforethat two key areas where AI
(18:40):
impacts product management was,you know, enhancing the existing
products as well as improvingthe personal productivity.
I wanna talk a little bitnow about how we enhance our
existing products with AIsince we've, you know, we kind
of touched on this briefly.
What are some of the reallybrilliant use cases that you've
seen in both of these areas?
I'm sure that you'refull of examples.
Maryam Ashoori (18:59):
So there are different ways that we can
bring AI to our products.
One is you can be the supplierof that technology to the world.
For example, what's the next AI?
The platform that I buildis packaging LLMs and
tools and everything thatyou need, and offer it to
other roles to build up on.
So that's one way of thinkingabout it, bringing them
(19:21):
as a platform, supplyingthem to the market to help
application build applications.
The second one is tobuild applications that
are enriched with AI.
So no matter what productyou have, you can think
of, oh, how can I bringAI to enrich some of these
features that I have and takeadvantage of the acceleration.
(19:42):
It can be either one of thedirect use cases that gen AI
unlocks, like classificationinformation, extraction
question, and answering likecustomer care, chatbots,
basically content generation,code generation, one of them.
Or it can be automation.
It's like just,you know, agents.
You can connect all of theseuse cases that I mentioned
(20:03):
and through function, callingto every single business
workflow that you have.
So how can I bring thatefficiency to my product
to have a better product?
So that's the second category.
The third category is howcan I come up with a new
category of product thatis powered up by LLMs?
One example is all the codeassisted technologies out there.
(20:25):
LLM, behind the scene,and now you're using it
to help the developers todo vibe coding, basically.
So that's a verygood example of that.
New opportunity areas.
And the fourth oneis, I would categorize
that through services.
The challenge thatyou mentioned for.
Product managers on educationis not unique to this role.
(20:45):
It's unique to the whole market.
Enterprise market,consumer market.
Everyone needs to educatethemselves to make better AI
decisions and it representsan opportunity for services.
So if you know the field,you can go in and help
them pick the right modelfor the right use case.
Make a better decision.
So it represents four differentareas of opportunities for
(21:07):
product managers, and dependingon which area you're targeting,
the advice for how to approachthat is completely different.
Hannah Clark (21:15):
I'm really interested in this fourth
category of the servicesbecause I have also noticed
this kind of a trend towardsa popularization of even
product managers moving intoa services or education space.
I've seen just an explosionof former PMs offering Maven
courses and it seems like thereis a huge push towards feeding
this opportunity now thatespecially vibe coding tools
(21:36):
have made coding and developmentavailable to just about anyone.
I can really see that trendtowards, well, how do we kind of
offer other services to empowerpeople to develop the solutions
that are really bespoke to them?
So it's really interesting.
That's a whole other shiftthat's kind of occurring
kind of concurrently withall the developments that
are happening in product, Ithink is really interesting.
So looking ahead then, whatwould you say are some of
(22:00):
the developments of trends inAI that you think will have
a really significant impacton the next 12 to 18 months?
Just given kind of what you'reseeing starting out right now?
Maryam Ashoori (22:08):
You know, in our world, every three months
is a generation, so 12, 18months is five generation ahead.
A little bit too far out wherewe are gonna go is too far.
But if I just look intomaybe three generations,
six to nine months fromnow, I would say that.
I'm gonna see a lot moreon automation, and we
see that today, but thetechnology is not there yet.
(22:32):
It's more like exploringnew use cases, figuring out
collectively with the communityhow to effectively use this.
It represents an opportunityfor product managers, and when
I say automation is literallyautomating every single
thing from email generationto talk to your customers,
(22:54):
to optimizing your roadmaps,depending on the dev effort
needed to deliver something.
At the mo.
What timeline to generatingcontent for your PRD.
To exploring new optionsfor your customers to help
them make better decisions,like literally every single
(23:16):
decision that you're making.
I argue that it canpotentially be enhanced with
AI if you figure out howto make AI work for you.
On the other side though,what I would add is all
these beautiful accelerationthat we get with AI.
There is a cost associatedto that, and that cost is
the risks you can make orbreak your career depending
(23:40):
on how you're using this.
And it's essential for productmanagers not only to have a
very good understanding ofthe benefits, but also risks.
Because risk canimpact their product.
Week time things can gowrong, risk can impact
their or personal identityand branding as well.
So I would say that like alwaysthink about the cost benefit
(24:02):
balance of what you're doing.
Hannah Clark (24:04):
Yeah.
And well, and I'm sure thatthis is an issue that's
so close to your heart.
Given that ethical useof AI is a cornerstone of
your career, how do we kindof navigate those risks?
I feel that oftentimes mistakescan be made by really well
intending people who arereally just kind of in this
play and exploration mode.
So do you have any kind offramework or kind of guardrails
(24:26):
that you like to think aboutwhen exploring with AI or
anything that you would kindof impart on folks who are
kind of looking to incorporaterisk management into their
development and their skillset?
Maryam Ashoori (24:35):
I'll give you an example.
Let's say that you wanna usean agent that sends email
on your behalf or summarizesyour emails and it has access
to your calendar to schedulesomething on your calendar.
It's amazing the benefitsthat you get from them.
On the other side, youhave to give access to this
agent to your email and yourcalendar schedule, knowing
(24:59):
that potentially a bad actorcan misuse the information
of when you are gonna be atwhat time and talking to who.
So every permission thatyou give to these agents
potentially creates avulnerability point for you,
and that's not just for you.
It's the same for yourproduct when you bring
those to your product.
(25:20):
So I would start with back tothe drawing board of use case.
It's essential to understandwhat is the problem that
you're trying to solve for thecommunity, for your product.
What is this thing?
And then when it comes to AI,have a very good understanding
of what is the benefit to that.
But also what are thevulnerability points that I'm
(25:43):
introducing to this product?
Am I confident to have amitigation plan to resolve
those, or I'm just blindlyfollowing and bringing
that AI to apply to myproduct and see what happen.
Hannah Clark (25:58):
Yeah, I'm really glad that you said that
because I think that is kindof the less sexy part of AI
development and exploration.
This is time of such excitementwhen risk management, I think,
has been a little bit of anafterthought, if a thought
at all in some circumstanceswhere, you know, we're sort of
entering this wild west phase ofdevelopment and exploration and
almost like a second generationof pioneers with regards to
(26:19):
vibe coding and using AI forcompletely new use cases.
So I think that is kind of animportant thing to call out.
I did kind of want to end onsort of a, you know, next steps
for those who are interested indeveloping their own skillset.
In this current generationthat we're in right now, what
have you found to be some ofthe most useful resources,
either for yourself orfor folks on your team who
are upskilling naturally?
(26:40):
Like just playing out withtechnology is one way, but
are there any other kind ofresources that you'd recommend?
Maryam Ashoori (26:45):
Yeah.
In addition to all of thosegoodies that are out there,
I would also say findyour way to filter out.
What do you wanna be exposed to?
Because there are so many thingshappening in the market, and
I've seen product majors dealingwith this fatigue of technology
fatigue, maybe we call it.
So where do you get your data?
(27:06):
And I think the most effective.
What I've been encouragingmy teams to do too is to find
your trusted voice in thecommunity and follow them.
If it's on LinkedIn, if it'ssomewhere else, just follow
them because if they have thepoint of view that is consistent
(27:27):
with what you are thinking.
There's a good chance thata new piece of technology
comes out there and you wannahear the perspective from
them versus going to thecommunity and figure that out.
And it's like a shortcut tohear their opinion, the opinion
from an expert on a technology.
And that's very helpful.
But also like even given thatyou know, for your product
(27:50):
managers cost and benefit, yougotta make sure that the person
that you follow is a reallyworse following kind of person.
Because otherwise youare limiting your view
to the world to that.
Window, and if that's notaccurate, then you have a
clouded view of the board.
So be very selective, butfind people that are sharing
(28:13):
the same point of view andare expert in what you care
about and just follow them.
Hannah Clark (28:18):
I love that advice and I think that it's
very important right nowas we're kind of entering a
period of rapid saturation.
The advice of being selective,I think applies to many things
in AI and technology and Ithink we'll find also in life.
Also to your point abouttrusting folks and following
them, how can folks followyour work after this episode?
Maryam Ashoori (28:37):
I'm on LinkedIn and I try to write
as much as possible, so Iwould love to stay in touch
with them on LinkedIn.
Hannah Clark (28:46):
Wonderful.
Well, thank you so muchfor joining us, Maryam.
We'll make sure to addthe link to your LinkedIn
in our show notes today.
Thank you so much.
I really appreciate your time.
I know you're sucha busy person.
Maryam Ashoori (28:54):
Wonderful.
Thanks for having me.
Hannah Clark (28:58):
Thanks for listening in.
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