March 4, 2025 • 40 mins
Generative AI is everywhere—transforming industries, dominating conversations, and yet, for many, it’s still just a tool for note-taking and meal planning. If you’re a project professional feeling stuck in a GenAI rut, this episode is for you.
Host Galen Low sits down with AI expert Kathleen Walch to explore how project managers can move beyond basic chatbot use, rethink their AI approach, and unlock new career opportunities. Tune in to discover what GenAI truly has to offer beyond prompt engineering.
Resources from this episode:
- Join DPM Membership
- Subscribe to the newsletter to get our latest articles and podcasts
- Connect with Kathleen on LinkedIn
- Check out The Project Management Institute and AI Today Podcast
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Galen Low (00:00):
You're about 11 months into your journey with
generative AI, and you'vearrived at three conclusions.
Number one, GenAI's capabilitiestruly are unprecedented.
Your uncle Rastin genuinelythinks it's sorcery.
Number two, Generative AIis a global phenomenon.
People aren't going to stoptalking about it anytime
soon — not at your dinnerparties, and definitely
(00:21):
not in your LinkedIn feed.
And number three, evenamidst all the awe and fervor
around generative AI, sofar, all you're using it
for is note taking and alittle bit of meal planning.
If you're a project personwho feels like you're stuck in
a rut with your AI chatbots,or know someone who is,
this episode is for you.
We're going to be divinginto how to shape your
(00:41):
mindset and reflexes aroundgenerative AI to avoid "doing
GenAI for GenAI's sake".
We're going to be talking aboutwhat the future of AI holds
beyond prompt engineering.
And we're going to be exploringhow expanding your understanding
of AI and how AI projects workmight actually open up a whole
new branch of your careerthat you never knew existed.
Ready to dive in?
(01:05):
Hey folks, thanks for tuning in.
My name is Galen Low withThe Digital Project Manager.
We are a community of digitalprofessionals on a mission
to help each other getskilled, get confident, and
get connected so that we canamplify the value of project
management in a digital world.
If you want to hear moreabout that, head on over
to thedpm.com/membership.
Okay, today we are talkingabout, surprise, generative AI,
(01:28):
but also prompt engineering, andwhether professionals like us
project managers may be lookingat Bruce Lee's proverbial finger
while it's pointing at the moon.
And I've brought in the bigguns to tackle questions about
what GenAI has on offer forthe craft of project management
beyond just chat-basedinterfaces and prompting.
(01:49):
So with me today is KathleenWalch, Director of AI Engagement
and Learning at the ProjectManagement Institute and proven
AI thought leader and educator.
Kathleen, thanks forjoining me here today.
Kathleen Walch (01:59):
Yeah, thanks so much for having me.
I'm really looking forwardto this discussion.
Galen Low (02:03):
I am so happy you came back on the show.
So Kathleen's been on theshow before with Ron Schmelzer
and talking about AI.
Gosh, probably two yearsago, maybe two years ago?
Kathleen Walch (02:12):
Maybe.
I know, time flies, right?
Galen Low (02:14):
It's moving so fast.
And I was thinkingabout that conversation.
I was like, so much has changed.
So much has changed.
It was almost fringe back then.
It was like AI andproject management.
Yeah.
Okay.
Now it's just it's mainstream.
It's baked in.
Everyone is.
Just immersing themselves in it.
You can't get away from it.
And I'm excited to have yourexpertise on the show today.
For folks who don't know, you'vebeen steeping in the AI world
(02:37):
for a while now, and you'veseen our current understanding
of AI in the professionalcontext evolve over like the
past seven or eight years.
And so as fast as some ofthis change has seemed to
us newcomers to AI, I'm justwondering, do you feel that
most professionals are maybegetting stuck on everyday
prompting without seeing thebigger picture of what the
technology is capable of?
Kathleen Walch (02:59):
Yeah, that's a great question
because you're right, Ihave been in this space.
I say I've been inAI since before GenAI
made it cool, right?
I know.
And I always call it theoldest, newest technology
because the term wasofficially coined in 1956.
So it's 70 plus yearsold, yet it feels so new.
And why is that?
We've been into two previousAI winners, which is a period
(03:21):
of decline in investment,decline in popularity.
Big reasons for that is we overpromise and under deliver on
what the technology can do.
So we really need tounderstand AI as a tool.
And that it's not good ateverything, but it is good
at certain things, andso make sure that you're
using it in that way.
And now with generative AI,what's made it so exciting
is it's really put it inthe hands of everybody.
(03:42):
Seven, eight years ago,we still were using AI.
On a pretty regular basis,it just didn't feel like it.
Where we would have predictivetext with our emails or we
would have spam filters, right?
And that would be using AI orwith GPS and driving around,
Waze or Google Maps that wouldhelp us with route optimization.
But it didn't really feellike AI because it just
(04:04):
was in an application thatwe were already using.
And then generative AI,ChatGPT in particular, right?
Because it wasthe first one out.
Put it in thehands of everybody.
And so now I was able,it's what we call augmented
intelligence, where it'snot replacing the human, but
helping you do your job better.
And you could feel it every day.
So it would help you writea better email or help you
brainstorm, or it couldhelp you with translation.
It could help youcreate images for your
(04:26):
PowerPoint presentation.
And it really felt thatcollaboration and you saw
the direct benefit from it.
That's where, we've seen thewhole world go, and it's been
really wonderful, but then atthe same time, people still
need to understand that it'sa tool, and they still need
to understand when you shouldand when you shouldn't use AI.
Galen Low (04:45):
I love that sort of AI in the background, and now
AI is like a person, that youlike interact with every day
that everyone has access to.
That is the new popular kid.
I like what you said aboutGenAI made AI popular, but
I love that whole notion ofsome of the winters, right?
The AI winter is because we overpromised and under delivered
and granted technology wasnot at a certain point that
(05:06):
it may have needed to be todeliver on those promises.
Now, maybe it is.
We're talking about yes,ChatGPT, and yes, all of
the sort of big LLMs thatare running the show right
now, but we're also talkingabout, here in February 2025,
we're talking about DeepSeek.
We're talking about, technologythat is mind blowing that
maybe doesn't need all thetechnology necessarily that
(05:27):
it's been touted as needing.
I'm getting a little off topichere, but I like the sort of
prospect of what it brings.
I like that peopleare talking about it.
They're embracing it.
But, that can be acomplicated thing where
everybody is in there.
Everybody has an opinion.
Everybody thinks they knowwhat it does and everybody
thinks it's for everything.
And maybe it's not.
(05:47):
If I was to put a projectmanagement lens on it,
like what are some of yourfavorite use cases for
GenAI chat based interfacesfor project management?
Kathleen Walch (05:55):
Yeah, that's a great question.
And, at Project ManagementInstitute we have a lot of
learning courses that arefree for members or, pretty
cheap for non members.
And we go over a bunchof different use cases.
And what I like to say is Ialways break it down and I
say, put down a list of allof the different pain points
that you have or areas thatyou need help or areas that
(06:17):
you can see improvement.
And then figure out which oneof those can be easily done
with a generative AI system.
So when we think about projectmanagers, we always think
about meeting minutes, right?
It's like the stereotypicalexample that we've been given.
So if that's a pain point foryou, then how do you fix that?
How do you get help with AI?
(06:38):
And there's a lot of tools outthere that can do that already.
But then what I alwayssay too is, yes, it's
great to have somethingthat can help you once.
So we talk about gettinghelp with a project charter.
Okay, that's wonderful.
But how many times do youdo that during a project?
Probably once, right?
I mean, you're not revisitinga project charter every week.
What's something that's goingto help you on a regular basis?
(06:58):
A daily, a weekly basis.
Maybe that's stakeholderengagement or better
communication, right?
How do I craft emailsor documentation for
different levels?
Sometimes I need to have ahigh level executive summary
or I need to tailor it towardsstakeholders, or I need to
tailor it towards internalversus external customers.
Figure out what Those painpoints are and work to address
(07:19):
them because that's whereyou're going to see that
real incremental improvement.
And then also I say,just practice, right?
Because you only getbetter with time.
There really is very lowfailure when it comes
to prompt engineering,because just redo it.
But then this alsobrings in what PMI calls
power skills, right?
Which are soft skills.
And critical thinking,collaboration, communication,
(07:41):
I always say, how do youuse GenAI to help you with
your power skills and how doyour power skills help you
be better with prompting?
For example, with communicationit can help you be a
better communicator, right?
Because I said it can helpmaybe write emails or put it
in different tones or shortenthings, summarize stuff.
But then how does yourcommunication skills help
(08:01):
you be a better prompter?
Maybe you need to tweak yourprompt, or maybe you need
to change the length of theprompt, edit it over time.
So I really like to see thekind of two sides of that
coin, how it can help withyour power skills and how
power skills can help you bebetter at prompt engineering.
Galen Low (08:17):
I wanna come back to that later on.
But first I wondered ifmaybe we could zoom out a bit
because I have the context,not all of our listeners do.
But during your time asa Managing Partner at
Cognilytica, you co-developedthe CPMAI certification,
which is a project managementframework for specialists.
And I've written this and tellme if I'm right or wrong, but
my interpretation was it wasbuilt for specialists like
(08:39):
data scientists and analystsworking on AI projects.
And then more recently,Cognolitica joined forces
with the Project ManagementInstitute, making CPMAI
like an official part ofthe PMI's rather prestigious
portfolio of projectmanagement certifications.
And just cause we were talkingabout courses and PMI and
learning, I was wondering couldyou just talk to me a bit about
(09:00):
who the certification is fornowadays and how is it different
than say, taking just like theprompt engineering courses, the,
the free one on PMI for members.
Or something on Udemy, whatmakes the CPMAI certification
important and different?
Kathleen Walch (09:13):
Yeah, that's a great question.
And the CPMAI certification, Ialways like to talk about this
as two sides of the coin, right?
95 percent of the conversationis focused on how can I
use AI tools to help medo my job better, right?
So we talk about, there's a tonof different tools out there.
People always ask me,what's the best tool?
And I go, it depends on whatyou're trying to do, right?
I mean, new tools come outliterally every single day.
(09:36):
So whatever it is that you'retrying to do, I'm sure that
there's a tool out there and youneed to understand how to use
it and, how to get better at it.
But that's about where95 percent of the
conversations are.
And that's where a lotof the e learning that
PMI offers comes in.
So we have an overviewof generative AI,
data landscape for AI.
We have our promptengineering course.
(09:56):
We have a applicationsof AI course as well.
So it talks about how toput all of these different
tools and applications intouse as a project manager
and a project professional.
And that's wonderful, but thathelps you do your job better.
Then we have to say as aproject manager or a project
professional, or these projectadjacent that you talked about a
data scientist, a data engineer,an AIML engineer, you think
(10:19):
about whatever that title is,you're being tasked with running
and managing an AI project.
And we have to understand that.
AI projects are data projects,and so you have to use data
centric methodologies, and youcan't, run it in a traditional
software application developmentstyle, or you're going to
quickly realize that's not theright approach and your project
has a higher rate of failure.
(10:40):
A number of years ago now,that's when we developed the
CPMAI methodology, becauseorganizations were coming to
us and saying, and this was,again, seven or eight years ago,
well before generative AI wasaround, we needed to build these
systems from scratch, figure outwhat algorithm we needed, have
all of our data requirements.
A lot of it is still thesame, even if we are using
(11:00):
one of these, but theysaid, where do we begin?
And so we looked out there,and there really was no
step by step approach.
So we created CPMAI methodologywith a large bank and a large
government institution to havethat step by step approach.
And now back in September of2024, we officially joined
PMI, they acquired Cognilytica.
So now CPMAI is an officialPMI certification and it's
(11:23):
so incredibly wonderful.
It always has been for projectmanagers, project professionals,
product managers as well,but then going beyond that,
those project adjacent folks.
I always give this example,my husband's a software
engineer, has been forabout two decades now.
He sometimes needs to runand manage projects, and
(11:44):
if he's being tasked withrunning and managing an AI
project, He doesn't identifyas a project manager, nor
would he ever identify asa project manager, right?
Because being a softwareengineer is how he identifies,
but he's put in that position.
And more and more, we'reseeing that, especially with
AI projects, people who don'ttraditionally identify as a
project manager are being putin that project management role.
(12:07):
And so this certification reallyis for everybody who fits in
those different categories.
Galen Low (12:12):
I think it's really important what you said.
I didn't know that you startedthis out with a government
institution and a bank.
And when you're talking aboutthe difference between a
software project and a dataproject, and you think about
data that a bank might haveor the government, serious
business, and it actually itbrings it into sharp relief
for me, the idea that, yeah,you can't necessarily run
it like a regular project.
There's things to take intoaccount and consideration.
(12:34):
And I agree with you now thatlike more and more, you want
the whole team to have thatsensibility because we've
seen it gone wrong ethically.
We've seen it gone wrong interms of just printing too
fast towards a goal thatwe don't really know with a
technology that's moving veryfast and culturally is shifting
in terms of our, people'sadoptions and their feelings
and their anxiety around it.
I really like that notion thatit's an understanding of how
(12:56):
to make a data project thatinvolves AI or machine learning
go well, because sometimes theregular approach, the software
development approach mightnot yield the right result.
You might hit that dead end.
You might hit these roadblocksthat you could probably
avoid if you were thinkingabout it, ahead of time.
Kathleen Walch (13:12):
Yeah, and I always like to frame
it to where with softwaredevelopment, code is the
most important part, right?
Like you would never giveaway your code because that
is your most important part.
But with an AI project, datais your most important part.
So you would nevergive that away.
And code is a really smallpart of it and not a super
critical part of it, right?
It's your data.
It's the data that's unique.
It's the data that'sgoing to make or break
(13:34):
this project, right?
We say garbage inis garbage out.
And I think thathelps frame it too.
Especially if you have somebodywho's project adjacent, not
a traditional project managerwhere they need to understand,
okay, that's how I need to shiftin my mindset, or it's not about
the code, it's about the data.
And that's why I need adata centric methodology.
Galen Low (13:53):
There's so many places I want to go
with that, but I'll tryand stay on top of it.
But I think that's a wonderfulslash mind blowing point that.
Like data is the important bitcoming out of a data project.
Code is almost secondary.
It's off to the side, it's aweird idea, at least for me.
Coming from like a digitalspace, but I really like it.
I thought maybe I'd shiftit back around to career
(14:14):
and certifications becauseI know a lot of folks in my
community, yes, the PMP is agreat credential and it has a
lot of weight in the industry.
And I think a lot of people lookto it after they've been doing a
job a while as a project manageror even being project adjacent
and just accruing a bunchof project management hours.
PMP is like the pinnacle thingto get to tell people on your CV
and in your, profile and in jobinterviews that, you're serious.
(14:36):
Like you play at this level,you're in demand and you
should probably get paid more.
So now that CPMAI isin the mix as well.
Like I'm wondering ifemployers in the AI-enabled
software space, or actually,I guess, maybe anywhere
are they looking for theCPMAI certification already?
Or in your mind, is thismore about sort of like
practical on the job skillsfor somebody who's already
working on AI and data projects?
(14:57):
Is it something that can makea project manager stand out?
Is it for that?
Kathleen Walch (15:02):
Yes.
Oh, absolutely.
It is.
And we see that, you're right.
The PMP is the pinnacle, right?
It is our gold standardcertification at PMI.
And so we're working onmaking other certifications
gold standards as well.
And so what doesthat mean, right?
And it's, incredibly robust.
It goes through a lotof different steps.
There's a lot of differentrequirements to make
it a gold standard.
And so we are working to getCPMAI to become a gold standard
(15:25):
as well, and it does letemployers know that you know how
to run and manage AI projects.
You've been levelset on terminology.
You've been level set onwhat AI can and cannot do.
You've been level set onthe, six phases of CPMAI,
and you know how to runyour projects like a data
project and following thisstep by step approach.
Because far too often, we'veseen organizations Not have a
(15:46):
plan, and that's for a number ofdifferent reasons, this industry
really is moving fast, peoplefeel FOMO fear of missing out,
they are like, our competitionis doing it, we need to do it,
we're just gonna move forward,and it's okay, we can move
forward, but let's have a plan.
And so that's whatCPMAI does, right?
And it's this stepby step approach.
So we start with phase one,business understanding.
What problem arewe trying to solve?
(16:07):
And I know this soundsso simple, but so many
projects skip that step.
And so it's, what problemare we trying to solve?
Is AI the right solution?
And then we have an AI go no gothat we need to go through, that
walks through data feasibility,business feasibility, and
implementation feasibility.
And we think about theselike traffic lights.
So if they're all green,your chances of project
success are pretty high.
If they're yellow or red,Your chances of failure are
(16:31):
increasing, and doesn't meanyou can't move forward, but
we say proceed with caution,and know that there might
be some stumbling blocks, orknow that your project might
not succeed as you think.
One of them is what is theROI, the return on investment
of your project, and a lot oforganizations don't necessarily
think about that up front,nor maybe measure it at all,
and so it doesn't need to bea financial return, but you
(16:53):
have to have some return,and if you're not measuring
that, then the project itselfcould be a success, right?
Technically it did whatit was supposed to, but at
a super high cost, that'sactually negative, right?
You had a negativereturn on investment.
And if you're not measuringthat, you have no idea.
And so you go, Oh, thiswas successful, but why are
(17:13):
we suddenly, in the red?
And it's because your returnon investment was not a
positive return on investment.
And that's what we'veseen far too often.
And so CPMAI helps with that.
And it does signal toemployers that you have the
skill set to run and managean AI project, that you have
gone through this very robusttraining and certification,
and you're certifiedwith a PMI certification.
Galen Low (17:36):
It's funny because I'm like, for folks listening,
I keep Moving my mouse,looking for the like clap
reaction, like in Zoom orGoogle Meet, and I'm like yes.
And by the way, if you'resomebody who is interested in
the CPMAI and wanted to leveragelike the CPMAI credential in
a job interview to someonewho didn't know what the CPMAI
certification was, just takethat clip of Kathleen just now,
(17:57):
and then just transcribe it.
Use it in your interview,because honestly, that was such
a crisp and clear explanationof like why this matters and
why it is a bit different.
And to your point, some ofthe things are like, yeah,
this seems like maybe, quoteunquote common sense or, other
frameworks, do consider this.
But when you're talkingabout like data, the speed
(18:18):
of technology and thaturge for businesses to
just right now, everyone'sjust running at it yet.
Like you said, like trying tostay competitive, they might not
even know why they need AI to bea part of their product or their
organization or their solutions.
And I just liked that there is abit of a framework around that.
I'm curious becauseproject managers, we
(18:40):
love our frameworks.
We love our methodologies.
We love our certifications.
And then we get inbig fights about them.
You know what I mean?
Like we're guilty sometimesof creating camps and factions
and whether or not they wereintended to be rivals, we still
put them against one another.
And then you go into anorganization, they're like,
we run projects this way.
And you're like, I'dlike to run it this way.
And then there'sa big fist fight.
(19:02):
Do you find that to be thecase with folks who are
going through this program?
They go out, they're like, Hey,now I know how to run a sort of
data ML, AI-oriented project.
But my team doesn'twant to do these things.
I'm trying to like preachto them and they're like,
no, we do it this way.
And this is howwe're going to do it.
Do you find it's an uphillbattle getting people and
teams and organizations likeon side with the phases and
(19:24):
the steps within the framework?
Kathleen Walch (19:25):
So folks that take it obviously
see the light, right?
And they go, wow,this is wonderful.
And then they do need to bringthat back to their organization
where this is different.
I get it, right?
It's predictive, waterfall,hybrid, agile all
these different terms.
How do you run a project?
Usually it ends upbeing hybrid, agile is a
real term for a reason.
(19:46):
I know.
When you run your project, yourAI project, like a software
application project, you'llrealize that it doesn't work
and you have to bring in adifferent set of skills and a
different step by step approach.
And you can run itin an agile way.
So it should be smalliterative sprints.
We shouldn't be running it inthat, predictive waterfall way.
(20:08):
Folks realize they look tothis because they've done it
and it's been failing and theyneed to do something else.
So a lot of people who findthis methodology Have been
running into those problems andunderstand that they need to get
on board with something becauseit's not working the other
ways that they're doing it.
So that's what we've seen.
(20:28):
Also, some folks are, theyget ahead of it and they go, I
want to move into this space.
I need to understandthis and we give a really
comprehensive training.
So it's for everybody, right?
You do not need to havehad a project under your
belt to get started.
You currently don't haveto have any requirements.
I know PMP has some prettyrobust requirements and we're
working towards that with ourgold standard certification.
(20:49):
But for now, you don't needrequirements, which is nice
because anybody can take it.
And it really walksyou through everything.
So it first levelsets on terminology.
And folks think that theyknow terms and they're
like, Oh, I don't need this.
And then they get into it andthey're like, Wow, I didn't
realize what I didn't know.
And this really wasa great level set.
And that's what we sayfor teams too, right?
You want the teamto get on board.
(21:11):
Which is the same reasonthat you take a PMP, and
that you hire PMP, becauseit's that terminology
that you want, right?
It's everybody'son the same page.
Everybody uses thesame terminology.
Everybody has the same basiclevel of understanding.
When it comes to CPMAI,it's no different.
Galen Low (21:25):
I really like that.
And actually, I love yourframing on it because I realized
that I probably made it seemlike it's a methodology,
but if I'm understanding itcorrectly, it just layers on
in terms of, you may have acertain way of working, these
are considerations and stepsthat you can weave into that.
Kathleen Walch (21:40):
Yeah, we call it a methodology, but then people
get tripped up on that term.
And so we always say,look, don't get tripped
up on terminology.
You just need to followsomething for success.
And so you can callit a framework.
You can call it astep by step approach.
You can call it amethodology, but understand
that it is six phases.
It's iterative.
You can go back a phase ortwo or three if you need.
(22:02):
So we start withbusiness understanding.
Then we go to dataunderstanding, so what data
do we need, the sources ofthat data, is it internal,
is it external, do wehave access to that data?
Then we know we need toclean our data, so data
cleaning is the next step.
Unfortunately, data isnever clean and nice,
and it's always messy,especially unstructured data.
Then we get to ourmodel development.
(22:23):
So now we're actually,doing the fun stuff.
I air quote that, whichis where people usually
start and skip those supercritical beginning steps.
Then we have to test it, makesure that it's performing
as expected, which also isa really critical step and
sometimes people skip andthen things go wonky and they
wonder why it's hallucinatingand giving terrible results
(22:43):
or not performing as expected.
And then weoperationalize it, right?
We put it into the real world.
And so we should be doingthese in small iterative
phases and steps, eachiteration of CPMAI should
take about two weeks, right?
Like a sprint.
It should not be taking sevenmonths or twelve months.
That's more of that waterfall,predictive approach, and
(23:05):
think about how much theworld changes in seven months.
But for a number ofreasons, this is what
happens, and usually becausepeople get tripped up at
the data phase, right?
Data understanding, where theydon't have access to that data,
and so it takes weeks and monthsto get access to that data.
And we say, okay think big,start small, and iterate often.
So start smaller, pick asmaller data set, or, really
(23:26):
control that scope and pare itdown a lot and say, what's the
smallest thing that I can do?
That will provide anincremental value and
provide that positive ROI.
Show wins.
And then we can continueto move forward, right?
And just iterate more in thenext phase, the next step.
Because far too often wesee that it's taking way too
long and then people give upon the project in phase two
(23:46):
because they just couldn'tget access to the data.
Galen Low (23:48):
It's like the Project Winter.
Kathleen Walch (23:50):
Yeah.
A lot of good things.
Galen Low (23:52):
Just get disillusioned with it and
then just leave it alone.
I was going to ask you actuallywhat Wonky looks like, but I
think you framed it really wellin terms of like hallucinations
and when you start thinkingabout, even small data sets
are pretty big data sets,so you can get really good
at yourself and then, yoursolution is not performing
the way you want it to.
And you're wondering why, andyou have to unwind this entire
(24:13):
ball of yarn to find whatwas somewhere in the middle
there at the beginning that.
Set this astray, probablydata cleanliness, right?
Kathleen Walch (24:22):
Probably, access to it, how clean it is, and then
how much of it you have as well.
Because some people thinkmore data, the better, right?
And that's not alwaysthe case because we can
be training on noise.
Data is not free, right?
There's a cost to cleaning it.
There's a cost to processing it.
So sometimes more is not better.
Galen Low (24:43):
That's interesting.
It's funny.
It's like I started thisout, we will answer the
mail on this, but I startedthis out with what's beyond
prompt engineering, but whatI find fascinating about
this conversation is that, Ithink there's a whole world
that many project managershaven't seen themselves in,
they might be software andlike in the software space or
the digital space or the techsector, IT, they might not
be in any of those sectors.
(25:03):
And they're like, is projectmanagement still going to exist?
We'll get into itin a little bit.
But there's this whole worldof like data projects, the
ML projects that, there'sa lot of projects going
on at this very minute.
And yeah, sometimes they'rebeing led by someone who's
project adjacent, right?
Someone who's not necessarilyself identifying as a
project manager, but thereis this opportunity, like
there's a lot going on therethat at least folks in my
(25:25):
community, I don't know thatwe all know a lot about it.
It's actually inspiring.
Kathleen Walch (25:29):
Yeah, we say, education is cheap, right?
I mean, the costof failure is high.
And to learn the CPMAImethodology, to get
CPMAI certified is fairlycheap in comparison.
We always encourage people, wesay this is a yes and, right?
Have your PMP and your CPMAI.
Follow and learn differentmethodologies and learn CPMAI.
(25:53):
This is not an eitheror, and organizations
always do this, right?
They learn something and thenthey adapt it for themselves.
And as long as you're, followingthat high level, step by step
approach, then adapt it for yourown organization, but learn it.
Galen Low (26:07):
I love that.
We opened on thetopic of prompting.
I was thinking inmy head, right?
Those use cases that involvechat based interfaces,
like ChatGPT, yourGemini, your Copilot, your
Claude, your Perplexity,DeepSeek, and others.
But there's a bigworld beyond that.
What's next for AI inour professional lives
as project managers?
And how can someone like,a project manager like
(26:28):
me, how can we keep up?
Kathleen Walch (26:30):
Yeah, that's a great question because, there's
been that saying out there thatAI is not going to replace your
job, but someone that knows AIis going to replace your job.
And so it reallyis here to stay.
We hope that we'vecrossed the chasm.
We're not going to be fallinginto another AI winter.
But how do you use it?
I always like to thinkabout that idea of
augmented intelligence.
So how do I use it tonot replace me, but do
(26:53):
whatever it is better?
So you can replace a taskor a, certain role, but not
replace me as the human.
And I know a lot ofproject professionals
are concerned about that.
But if a lot of projects aremoving towards AI projects, then
we're just going to continue toneed project managers, right?
And folks with these skillsets.
(27:13):
So how do you learn?
I think it goes backto what I said before.
Put a list togetherof everything that
is annoying to you.
Because that'sdifferent for everybody.
And so to make sure that youdon't replace things that you
enjoy, and then keep all thethings that you hate, because
then you're pretty unsatisfied.
So think about all the thingsthat you enjoy, and put them
maybe in one column, and allthe things that you don't enjoy,
(27:35):
and put them in another column.
And then looktowards communities.
PMI has a lot of, learningresources that you can go to.
Or maybe internally, andI always say, advocate
for internal groups where,especially if you have
prompts, they should notbe proprietary, right?
We should be sharing prompts,we should be learning, we
should be collaborating,so have a prompt library
(27:57):
internally at your organization.
Document which platform you'veused it on and when, because
we know that your promptsdo need to be changed and
iterated over time to continueto get those results, right?
That's part of our step by stepapproach, where you need to
be testing it, and sometimesit's not going to perform as
expected, so sometimes justtweaking one word really
makes a big difference.
(28:17):
And if you have that communityand you can go back and forth
with, it really does help.
I know that some organizations,they're leaning into AI
and being AI first, right?
And they're really AI driven.
But if your organization isn't,then figure out how you can
start with those iterative,step by step approaches.
Like I said, if you'rea PMI member, reach
out to the community.
We have a lot ofchapters as well.
(28:38):
Internally, you can haveresources, and so whether
that's your group, or maybeeven look externally outside
of your group, so that you canbe collaborative, because if
you're not learning, you'renot growing, and then you're
going to just fall fartherbehind, because you're going
to be afraid to use thesetools, or you don't know how
to use these tools, and soreally, don't be afraid to
reach out and ask for help.
(28:58):
That's the best way to learn.
Galen Low (29:00):
I love the sort of prompt repository, and sharing.
Sharing is the thingthat moves us forward.
The technology is goingto move fast, people
need to move fast too.
The best way to do thatis to share knowledge.
Kathleen Wal (29:10):
Sharing is caring.
Galen Low (29:12):
There you go, yes! I have two questions
to wrap this up.
Some of the things that we'retalking about, we're like, make
that list of annoying things.
And, you can chat with yourGenAI tool to augment your
sort of personal professionallife, like your own
individual professional life.
On the one hand, somefolks, me, are you know
(29:34):
what, this is great.
It's a fantastic technology.
It is fundamentallythe way I use it.
The tab based interfacesfor me, it's, it's natural
language processing, tothe nth degree from where
it was, a few decades ago.
And then I'm like, isn't itjust a fancy parlor trick
where it's languagifying stuffand giving it back to us and
we're like, Oh my goodness,this is like a living,
breathing, sentient creature.
(29:55):
Whereas actually it'slike remixing language.
Are we maybe even, I startedthis with, are we like
selling ourselves shortby not using enough AI?
On the opposite side, isthere a risk of us thinking
that AI is doing like reallymagical things and can do
anything and not understandingthat actually it is good
at doing a certain thing?
And is that dangerousor is it inspiring?
Kathleen Walch (30:15):
It's dangerous because it's over promising
and under delivering.
So a number of years ago now,about in 2019, because AI is
such an umbrella term, andbecause now with generative
AI and, these large languagemodels that are out there,
people think it can doeverything, and are asking
it to do everything, and thenmaybe not getting the results
(30:35):
that they want, they go whycan't it do this math problem?
Why can't it do this?
A human can do this, andit's because we need to
understand what it canand what it cannot do.
So we said, why don't we breakit down one level deeper.
And say, what are wetrying to accomplish?
And then let's see if AI isthe right solution to that.
So we looked at hundreds,if not thousands of use
cases and broke it down intothe seven patterns of AI.
(30:56):
So that's hyperpersonalization, right?
Treating each individualas an individual.
So we think about this asa marketer's dream, but
also it can be with hyperpersonalized education or
healthcare or finance, right?
And this is a really hottopic with education,
especially lifelong learning.
How do I, beyond that Kto 12, tailor education
(31:18):
to fit different people's,the way that they learn.
Then we have recognition.
So this is making senseof unstructured data.
And we think about imagerecognition here, right?
But also audio recognitionor hand gesture recognition.
And then we have our predictiveanalytics and decision support,
so this is taking past ourcurrent data and helping
humans make better decisions.
(31:38):
We have our predictiveanalytics, our
patterns and anomalies.
So that's looking at largeamounts of data and being
able to spot trends in thatdata, but then also some of
the outliers, so we thinkabout fraud detection.
We have goal drivensystems, which is really
around reinforcementlearning and optimization.
We have the autonomouspattern, so that's, the
goal of that is to removethe human from the loop.
(31:58):
So whenever you're tryingto remove the human, that's
obviously pretty hard.
So we say that is the mostdifficult pattern of AI.
But what do we have now?
We can have hardwareor software.
So we can have autonomousvehicles, for example, or
autonomous delivery bots.
But then we can also haveautonomous business processes.
And so how do we have systemsthat can Autonomously navigate
(32:18):
within our workflows, and wedon't need humans in there.
So it's different thanautomation, right?
Because automationis not intelligence.
We're just repeating somethingover and over, which is
absolutely incredibly useful,but just not intelligence.
So if there's exceptions,you need a human to go in.
Can't handle that, dothat exception handling.
If fields change, the humanneeds to go in, and say, okay,
(32:40):
these fields have changed.
We think about robotic processautomation there, but you know,
what's agentic AI going tolook like, especially, in this
year, but then in the comingyears, and how can we have more
of the, autonomous agents outthere working with other agents.
So when we think about theseven patterns, it really helps
us break down what AI is goodat and what AI is not good
at and when we should use itand what we shouldn't use it.
(33:02):
And we go throughthat in phase one.
Of business understanding ofCPMAI, and I think that really
helps too, so if people aretrying to say where should
I apply AI, and how can Iapply AI, if we have our
conversational pattern of AIthat's where we have humans
and machines talking to eachother in the language of humans,
that's where large languagemodels fall, and AI enabled
(33:24):
chatbots, but that's just onepattern of AI, and maybe that
we shouldn't have our LLMstry and do, hyper personalized
offerings or we can't usean LLM to drive a vehicle.
Galen Low (33:36):
I love that.
And honestly, thank youfor that crash course.
It actually brings thingsa lot into perspective.
And guess what?
Perfect segue into my lastquestion, which is just,
we're talking about thingsthat AI coming in, helping
you do your day to day.
Kathleen Walch (33:50):
Oh, augmented intelligence.
Galen Low (33:51):
Augmented intelligence.
Thank you.
But then, you had mentionedautonomous, you had mentioned
agentic, everyone's talkingabout it right now, a world
where, yeah, it's justoften doing its own thing.
Maybe you're bidding, butnot in the same way where
you're like having a chatand we're like, Hey, could
you do this thing for me?
Do you have an opinion onwhere that goes in terms of
the project management worldor just the world in general?
Are people's anxietiesfounded around that?
(34:13):
Or is it also one ofthose things where maybe
people think it's like,Oh, that's basically AGI.
I guess we can skipto the apocalypse.
Whereas actually it's just,within these patterns?
Kathleen Walch (34:22):
If we understand what AI can and
cannot do, and we understandwe're still in narrow AI.
So we're applying one or moreof these seven patterns, but
we aren't in AGI where we havea system that's human right?
And can do everything.
Cause we still don'thave machine reasoning.
So I like to talk aboutthis DIKUW pyramid.
So data is at the bottom, thefoundation, but data on its own,
(34:46):
you can't do much with, right?
We need to applysomething to it.
So we get our informationlevel, and this is
where we have dashboardsand, things like that.
And then we go to D I K, right?
So we're at the knowledge,and that's where machine
learning comes into play.
Then we have the U, theunderstanding, and this
is machine reasoning,we still aren't there.
And then at thetop we have wisdom.
So we're far ways offfrom AGI, it's important
(35:08):
to understand that.
I know that there's differentthings in the media about
how far off we actually are.
Some people think we'remaybe a decade away.
Some people thinkwe're a year away.
Some people thinkwe're 100 years away.
Some people think we're nevergoing to get there, right?
I mean, experts in theindustry cannot agree on this.
But it is important tounderstand the limitations that
we have right now, and again,apply it where it makes sense.
(35:31):
Agentic AI is thehot topic of 2025.
It's going to continue tobe the hot topic of 2025.
At PMI, we have Infinity, whichis our, tool, our AI tool.
And we are going to haveagentic AI capabilities there.
But it is important tounderstand, too, I think,
because we don't have a commonlyaccepted definition of AI, then
(35:51):
that means What is agentic AI?
We don't really have a commonlyaccepted definition of agentic
AI in the industry either.
So is it actually autonomous?
Is it augmented?
Is it automated?
We're still moving towardsthat, but it is exciting.
I mean, things are changingso incredibly fast.
So if people are listeningto this in February of 2025,
what's it going to looklike in February of 2026?
(36:13):
What's it going to looklike in June of 2025?
Things change.
And you're just that one realbreakthrough away or that one
different platform, right?
Earlier you brought up DeepSeekthat wasn't around a month ago.
How do things continueto evolve and how are
project professionals?
Adopting that agenticAI and how do they bring
it into their workflow?
(36:34):
These are larger conversations,your organization, how
are they bringing this in?
How are they adopting thesetools and technologies?
We need to make sure that we'redoing it in a trustworthy,
ethical and responsible way.
I know there's a lot ofconcerns around that.
Data privacy, right?
Governance issues.
the trustworthy aspects.
Do we trust these systems?
Do we have them internally?
Can we use external ones?
(36:55):
So it's a really exciting time.
And these tools arecontinuing to get better.
New ones are coming outevery single day, like I had
said earlier on the podcast.
And so that canget overwhelming.
So it's where do you even start?
And you had said, how doyou even keep up with it?
And I think Learningand doing every day.
You have to practice, right?
It has to startbeing that reflex.
How do you make itpart of your every day?
(37:18):
I say never start with ablank page anymore, right?
If you're starting witha blank page, you're
doing something wrong.
Have it help you with thatbrainstorming, or have it write
a first draft, and then youcan edit it, or whatever it
is that you're trying to do.
But it is that reflex that youneed to get used to, right?
And how do you do that?
Just continue to practice.
Galen Low (37:35):
I love that.
The mindset and the reflex.
Kathleen, this was a reallyinspiring conversation.
I don't think I've takenso many notes in an
episode, probably ever.
And I mean this in agood way, as in capturing
knowledge for myself, notlike what to edit in post.
I think PMI islucky to have you.
I'm excited about what comesnext, and maybe we should
have you back February2026, if not in between to
(37:56):
do a comparison on, wherewe landed after 365 days.
Kathleen Walch (37:59):
Yeah.
I know it's changing so fast.
Sometimes people go where arewe going to be, a year from
now, five years from now?
And I'm like, where are wegoing to be a month from now?
Galen Low (38:09):
Oh, I love it.
I love it.
I love it.
Where can people find moreabout the CPMAI certification?
Kathleen Walch (38:14):
Yeah, so go to PMI.org.
You can find it there.
Also, I have a podcast,AI Today, where we
talk about CPMAI.
We're in the middle of ause case series right now,
so that is now an officialPMI podcast, and I'm
really excited about that.
I know we're transitioningeverything over to the PMI
system, as anybody withacquisitions know, it does
take a little bit of time,but we're getting there, and
(38:35):
I'm really excited about that.
You can also find meon LinkedIn, Kathleen
Walch, or PMI Cognilyticais on LinkedIn as well.
Galen Low (38:45):
Amazing.
I will also include all thoselinks in the show notes.
Kathleen, thank you somuch for joining me today.
It has been so much fun.
Kathleen Walch (38:51):
Yeah, thanks.
I always love totalk to you, Galen.
Galen Low (38:54):
All right folks, there you have it.
As always, if you'd like tojoin the conversation with
over a thousand like-mindedproject management champions,
come join our collective.
Head on over tothedpm.com/membership
to learn more.
And if you like what youheard today, please subscribe
and stay in touch onthedigitalprojectmanager.com.
Until next time,thanks for listening.
You're about 11 months into your journey with
generative AI, and you'vearrived at three conclusions.
Number one, GenAI's capabilitiestruly are unprecedented.
Your uncle Rastin genuinelythinks it's sorcery.
Number two, Generative AIis a global phenomenon.
People aren't going to stoptalking about it anytime
soon — not at your dinnerparties, and definitely
(00:21):
not in your LinkedIn feed.
And number three, evenamidst all the awe and fervor
around generative AI, sofar, all you're using it
for is note taking and alittle bit of meal planning.
If you're a project personwho feels like you're stuck in
a rut with your AI chatbots,or know someone who is,
this episode is for you.
We're going to be divinginto how to shape your
(00:41):
mindset and reflexes aroundgenerative AI to avoid "doing
GenAI for GenAI's sake".
We're going to be talking aboutwhat the future of AI holds
beyond prompt engineering.
And we're going to be exploringhow expanding your understanding
of AI and how AI projects workmight actually open up a whole
new branch of your careerthat you never knew existed.
Ready to dive in?
(01:05):
Hey folks, thanks for tuning in.
My name is Galen Low withThe Digital Project Manager.
We are a community of digitalprofessionals on a mission
to help each other getskilled, get confident, and
get connected so that we canamplify the value of project
management in a digital world.
If you want to hear moreabout that, head on over
to thedpm.com/membership.
Okay, today we are talkingabout, surprise, generative AI,
(01:28):
but also prompt engineering, andwhether professionals like us
project managers may be lookingat Bruce Lee's proverbial finger
while it's pointing at the moon.
And I've brought in the bigguns to tackle questions about
what GenAI has on offer forthe craft of project management
beyond just chat-basedinterfaces and prompting.
(01:49):
So with me today is KathleenWalch, Director of AI Engagement
and Learning at the ProjectManagement Institute and proven
AI thought leader and educator.
Kathleen, thanks forjoining me here today.
Kathleen Walch (01:59):
Yeah, thanks so much for having me.
I'm really looking forwardto this discussion.
Galen Low (02:03):
I am so happy you came back on the show.
So Kathleen's been on theshow before with Ron Schmelzer
and talking about AI.
Gosh, probably two yearsago, maybe two years ago?
Kathleen Walch (02:12):
Maybe.
I know, time flies, right?
Galen Low (02:14):
It's moving so fast.
And I was thinkingabout that conversation.
I was like, so much has changed.
So much has changed.
It was almost fringe back then.
It was like AI andproject management.
Yeah.
Okay.
Now it's just it's mainstream.
It's baked in.
Everyone is.
Just immersing themselves in it.
You can't get away from it.
And I'm excited to have yourexpertise on the show today.
For folks who don't know, you'vebeen steeping in the AI world
(02:37):
for a while now, and you'veseen our current understanding
of AI in the professionalcontext evolve over like the
past seven or eight years.
And so as fast as some ofthis change has seemed to
us newcomers to AI, I'm justwondering, do you feel that
most professionals are maybegetting stuck on everyday
prompting without seeing thebigger picture of what the
technology is capable of?
Kathleen Walch (02:59):
Yeah, that's a great question
because you're right, Ihave been in this space.
I say I've been inAI since before GenAI
made it cool, right?
I know.
And I always call it theoldest, newest technology
because the term wasofficially coined in 1956.
So it's 70 plus yearsold, yet it feels so new.
And why is that?
We've been into two previousAI winners, which is a period
(03:21):
of decline in investment,decline in popularity.
Big reasons for that is we overpromise and under deliver on
what the technology can do.
So we really need tounderstand AI as a tool.
And that it's not good ateverything, but it is good
at certain things, andso make sure that you're
using it in that way.
And now with generative AI,what's made it so exciting
is it's really put it inthe hands of everybody.
(03:42):
Seven, eight years ago,we still were using AI.
On a pretty regular basis,it just didn't feel like it.
Where we would have predictivetext with our emails or we
would have spam filters, right?
And that would be using AI orwith GPS and driving around,
Waze or Google Maps that wouldhelp us with route optimization.
But it didn't really feellike AI because it just
(04:04):
was in an application thatwe were already using.
And then generative AI,ChatGPT in particular, right?
Because it wasthe first one out.
Put it in thehands of everybody.
And so now I was able,it's what we call augmented
intelligence, where it'snot replacing the human, but
helping you do your job better.
And you could feel it every day.
So it would help you writea better email or help you
brainstorm, or it couldhelp you with translation.
It could help youcreate images for your
(04:26):
PowerPoint presentation.
And it really felt thatcollaboration and you saw
the direct benefit from it.
That's where, we've seen thewhole world go, and it's been
really wonderful, but then atthe same time, people still
need to understand that it'sa tool, and they still need
to understand when you shouldand when you shouldn't use AI.
Galen Low (04:45):
I love that sort of AI in the background, and now
AI is like a person, that youlike interact with every day
that everyone has access to.
That is the new popular kid.
I like what you said aboutGenAI made AI popular, but
I love that whole notion ofsome of the winters, right?
The AI winter is because we overpromised and under delivered
and granted technology wasnot at a certain point that
(05:06):
it may have needed to be todeliver on those promises.
Now, maybe it is.
We're talking about yes,ChatGPT, and yes, all of
the sort of big LLMs thatare running the show right
now, but we're also talkingabout, here in February 2025,
we're talking about DeepSeek.
We're talking about, technologythat is mind blowing that
maybe doesn't need all thetechnology necessarily that
(05:27):
it's been touted as needing.
I'm getting a little off topichere, but I like the sort of
prospect of what it brings.
I like that peopleare talking about it.
They're embracing it.
But, that can be acomplicated thing where
everybody is in there.
Everybody has an opinion.
Everybody thinks they knowwhat it does and everybody
thinks it's for everything.
And maybe it's not.
(05:47):
If I was to put a projectmanagement lens on it,
like what are some of yourfavorite use cases for
GenAI chat based interfacesfor project management?
Kathleen Walch (05:55):
Yeah, that's a great question.
And, at Project ManagementInstitute we have a lot of
learning courses that arefree for members or, pretty
cheap for non members.
And we go over a bunchof different use cases.
And what I like to say is Ialways break it down and I
say, put down a list of allof the different pain points
that you have or areas thatyou need help or areas that
(06:17):
you can see improvement.
And then figure out which oneof those can be easily done
with a generative AI system.
So when we think about projectmanagers, we always think
about meeting minutes, right?
It's like the stereotypicalexample that we've been given.
So if that's a pain point foryou, then how do you fix that?
How do you get help with AI?
(06:38):
And there's a lot of tools outthere that can do that already.
But then what I alwayssay too is, yes, it's
great to have somethingthat can help you once.
So we talk about gettinghelp with a project charter.
Okay, that's wonderful.
But how many times do youdo that during a project?
Probably once, right?
I mean, you're not revisitinga project charter every week.
What's something that's goingto help you on a regular basis?
(06:58):
A daily, a weekly basis.
Maybe that's stakeholderengagement or better
communication, right?
How do I craft emailsor documentation for
different levels?
Sometimes I need to have ahigh level executive summary
or I need to tailor it towardsstakeholders, or I need to
tailor it towards internalversus external customers.
Figure out what Those painpoints are and work to address
(07:19):
them because that's whereyou're going to see that
real incremental improvement.
And then also I say,just practice, right?
Because you only getbetter with time.
There really is very lowfailure when it comes
to prompt engineering,because just redo it.
But then this alsobrings in what PMI calls
power skills, right?
Which are soft skills.
And critical thinking,collaboration, communication,
(07:41):
I always say, how do youuse GenAI to help you with
your power skills and how doyour power skills help you
be better with prompting?
For example, with communicationit can help you be a
better communicator, right?
Because I said it can helpmaybe write emails or put it
in different tones or shortenthings, summarize stuff.
But then how does yourcommunication skills help
(08:01):
you be a better prompter?
Maybe you need to tweak yourprompt, or maybe you need
to change the length of theprompt, edit it over time.
So I really like to see thekind of two sides of that
coin, how it can help withyour power skills and how
power skills can help you bebetter at prompt engineering.
Galen Low (08:17):
I wanna come back to that later on.
But first I wondered ifmaybe we could zoom out a bit
because I have the context,not all of our listeners do.
But during your time asa Managing Partner at
Cognilytica, you co-developedthe CPMAI certification,
which is a project managementframework for specialists.
And I've written this and tellme if I'm right or wrong, but
my interpretation was it wasbuilt for specialists like
(08:39):
data scientists and analystsworking on AI projects.
And then more recently,Cognolitica joined forces
with the Project ManagementInstitute, making CPMAI
like an official part ofthe PMI's rather prestigious
portfolio of projectmanagement certifications.
And just cause we were talkingabout courses and PMI and
learning, I was wondering couldyou just talk to me a bit about
(09:00):
who the certification is fornowadays and how is it different
than say, taking just like theprompt engineering courses, the,
the free one on PMI for members.
Or something on Udemy, whatmakes the CPMAI certification
important and different?
Kathleen Walch (09:13):
Yeah, that's a great question.
And the CPMAI certification, Ialways like to talk about this
as two sides of the coin, right?
95 percent of the conversationis focused on how can I
use AI tools to help medo my job better, right?
So we talk about, there's a tonof different tools out there.
People always ask me,what's the best tool?
And I go, it depends on whatyou're trying to do, right?
I mean, new tools come outliterally every single day.
(09:36):
So whatever it is that you'retrying to do, I'm sure that
there's a tool out there and youneed to understand how to use
it and, how to get better at it.
But that's about where95 percent of the
conversations are.
And that's where a lotof the e learning that
PMI offers comes in.
So we have an overviewof generative AI,
data landscape for AI.
We have our promptengineering course.
(09:56):
We have a applicationsof AI course as well.
So it talks about how toput all of these different
tools and applications intouse as a project manager
and a project professional.
And that's wonderful, but thathelps you do your job better.
Then we have to say as aproject manager or a project
professional, or these projectadjacent that you talked about a
data scientist, a data engineer,an AIML engineer, you think
(10:19):
about whatever that title is,you're being tasked with running
and managing an AI project.
And we have to understand that.
AI projects are data projects,and so you have to use data
centric methodologies, and youcan't, run it in a traditional
software application developmentstyle, or you're going to
quickly realize that's not theright approach and your project
has a higher rate of failure.
(10:40):
A number of years ago now,that's when we developed the
CPMAI methodology, becauseorganizations were coming to
us and saying, and this was,again, seven or eight years ago,
well before generative AI wasaround, we needed to build these
systems from scratch, figure outwhat algorithm we needed, have
all of our data requirements.
A lot of it is still thesame, even if we are using
(11:00):
one of these, but theysaid, where do we begin?
And so we looked out there,and there really was no
step by step approach.
So we created CPMAI methodologywith a large bank and a large
government institution to havethat step by step approach.
And now back in September of2024, we officially joined
PMI, they acquired Cognilytica.
So now CPMAI is an officialPMI certification and it's
(11:23):
so incredibly wonderful.
It always has been for projectmanagers, project professionals,
product managers as well,but then going beyond that,
those project adjacent folks.
I always give this example,my husband's a software
engineer, has been forabout two decades now.
He sometimes needs to runand manage projects, and
(11:44):
if he's being tasked withrunning and managing an AI
project, He doesn't identifyas a project manager, nor
would he ever identify asa project manager, right?
Because being a softwareengineer is how he identifies,
but he's put in that position.
And more and more, we'reseeing that, especially with
AI projects, people who don'ttraditionally identify as a
project manager are being putin that project management role.
(12:07):
And so this certification reallyis for everybody who fits in
those different categories.
Galen Low (12:12):
I think it's really important what you said.
I didn't know that you startedthis out with a government
institution and a bank.
And when you're talking aboutthe difference between a
software project and a dataproject, and you think about
data that a bank might haveor the government, serious
business, and it actually itbrings it into sharp relief
for me, the idea that, yeah,you can't necessarily run
it like a regular project.
There's things to take intoaccount and consideration.
(12:34):
And I agree with you now thatlike more and more, you want
the whole team to have thatsensibility because we've
seen it gone wrong ethically.
We've seen it gone wrong interms of just printing too
fast towards a goal thatwe don't really know with a
technology that's moving veryfast and culturally is shifting
in terms of our, people'sadoptions and their feelings
and their anxiety around it.
I really like that notion thatit's an understanding of how
(12:56):
to make a data project thatinvolves AI or machine learning
go well, because sometimes theregular approach, the software
development approach mightnot yield the right result.
You might hit that dead end.
You might hit these roadblocksthat you could probably
avoid if you were thinkingabout it, ahead of time.
Kathleen Walch (13:12):
Yeah, and I always like to frame
it to where with softwaredevelopment, code is the
most important part, right?
Like you would never giveaway your code because that
is your most important part.
But with an AI project, datais your most important part.
So you would nevergive that away.
And code is a really smallpart of it and not a super
critical part of it, right?
It's your data.
It's the data that's unique.
It's the data that'sgoing to make or break
(13:34):
this project, right?
We say garbage inis garbage out.
And I think thathelps frame it too.
Especially if you have somebodywho's project adjacent, not
a traditional project managerwhere they need to understand,
okay, that's how I need to shiftin my mindset, or it's not about
the code, it's about the data.
And that's why I need adata centric methodology.
Galen Low (13:53):
There's so many places I want to go
with that, but I'll tryand stay on top of it.
But I think that's a wonderfulslash mind blowing point that.
Like data is the important bitcoming out of a data project.
Code is almost secondary.
It's off to the side, it's aweird idea, at least for me.
Coming from like a digitalspace, but I really like it.
I thought maybe I'd shiftit back around to career
(14:14):
and certifications becauseI know a lot of folks in my
community, yes, the PMP is agreat credential and it has a
lot of weight in the industry.
And I think a lot of people lookto it after they've been doing a
job a while as a project manageror even being project adjacent
and just accruing a bunchof project management hours.
PMP is like the pinnacle thingto get to tell people on your CV
and in your, profile and in jobinterviews that, you're serious.
(14:36):
Like you play at this level,you're in demand and you
should probably get paid more.
So now that CPMAI isin the mix as well.
Like I'm wondering ifemployers in the AI-enabled
software space, or actually,I guess, maybe anywhere
are they looking for theCPMAI certification already?
Or in your mind, is thismore about sort of like
practical on the job skillsfor somebody who's already
working on AI and data projects?
(14:57):
Is it something that can makea project manager stand out?
Is it for that?
Kathleen Walch (15:02):
Yes.
Oh, absolutely.
It is.
And we see that, you're right.
The PMP is the pinnacle, right?
It is our gold standardcertification at PMI.
And so we're working onmaking other certifications
gold standards as well.
And so what doesthat mean, right?
And it's, incredibly robust.
It goes through a lotof different steps.
There's a lot of differentrequirements to make
it a gold standard.
And so we are working to getCPMAI to become a gold standard
(15:25):
as well, and it does letemployers know that you know how
to run and manage AI projects.
You've been levelset on terminology.
You've been level set onwhat AI can and cannot do.
You've been level set onthe, six phases of CPMAI,
and you know how to runyour projects like a data
project and following thisstep by step approach.
Because far too often, we'veseen organizations Not have a
(15:46):
plan, and that's for a number ofdifferent reasons, this industry
really is moving fast, peoplefeel FOMO fear of missing out,
they are like, our competitionis doing it, we need to do it,
we're just gonna move forward,and it's okay, we can move
forward, but let's have a plan.
And so that's whatCPMAI does, right?
And it's this stepby step approach.
So we start with phase one,business understanding.
What problem arewe trying to solve?
(16:07):
And I know this soundsso simple, but so many
projects skip that step.
And so it's, what problemare we trying to solve?
Is AI the right solution?
And then we have an AI go no gothat we need to go through, that
walks through data feasibility,business feasibility, and
implementation feasibility.
And we think about theselike traffic lights.
So if they're all green,your chances of project
success are pretty high.
If they're yellow or red,Your chances of failure are
(16:31):
increasing, and doesn't meanyou can't move forward, but
we say proceed with caution,and know that there might
be some stumbling blocks, orknow that your project might
not succeed as you think.
One of them is what is theROI, the return on investment
of your project, and a lot oforganizations don't necessarily
think about that up front,nor maybe measure it at all,
and so it doesn't need to bea financial return, but you
(16:53):
have to have some return,and if you're not measuring
that, then the project itselfcould be a success, right?
Technically it did whatit was supposed to, but at
a super high cost, that'sactually negative, right?
You had a negativereturn on investment.
And if you're not measuringthat, you have no idea.
And so you go, Oh, thiswas successful, but why are
(17:13):
we suddenly, in the red?
And it's because your returnon investment was not a
positive return on investment.
And that's what we'veseen far too often.
And so CPMAI helps with that.
And it does signal toemployers that you have the
skill set to run and managean AI project, that you have
gone through this very robusttraining and certification,
and you're certifiedwith a PMI certification.
Galen Low (17:36):
It's funny because I'm like, for folks listening,
I keep Moving my mouse,looking for the like clap
reaction, like in Zoom orGoogle Meet, and I'm like yes.
And by the way, if you'resomebody who is interested in
the CPMAI and wanted to leveragelike the CPMAI credential in
a job interview to someonewho didn't know what the CPMAI
certification was, just takethat clip of Kathleen just now,
(17:57):
and then just transcribe it.
Use it in your interview,because honestly, that was such
a crisp and clear explanationof like why this matters and
why it is a bit different.
And to your point, some ofthe things are like, yeah,
this seems like maybe, quoteunquote common sense or, other
frameworks, do consider this.
But when you're talkingabout like data, the speed
(18:18):
of technology and thaturge for businesses to
just right now, everyone'sjust running at it yet.
Like you said, like trying tostay competitive, they might not
even know why they need AI to bea part of their product or their
organization or their solutions.
And I just liked that there is abit of a framework around that.
I'm curious becauseproject managers, we
(18:40):
love our frameworks.
We love our methodologies.
We love our certifications.
And then we get inbig fights about them.
You know what I mean?
Like we're guilty sometimesof creating camps and factions
and whether or not they wereintended to be rivals, we still
put them against one another.
And then you go into anorganization, they're like,
we run projects this way.
And you're like, I'dlike to run it this way.
And then there'sa big fist fight.
(19:02):
Do you find that to be thecase with folks who are
going through this program?
They go out, they're like, Hey,now I know how to run a sort of
data ML, AI-oriented project.
But my team doesn'twant to do these things.
I'm trying to like preachto them and they're like,
no, we do it this way.
And this is howwe're going to do it.
Do you find it's an uphillbattle getting people and
teams and organizations likeon side with the phases and
(19:24):
the steps within the framework?
Kathleen Walch (19:25):
So folks that take it obviously
see the light, right?
And they go, wow,this is wonderful.
And then they do need to bringthat back to their organization
where this is different.
I get it, right?
It's predictive, waterfall,hybrid, agile all
these different terms.
How do you run a project?
Usually it ends upbeing hybrid, agile is a
real term for a reason.
(19:46):
I know.
When you run your project, yourAI project, like a software
application project, you'llrealize that it doesn't work
and you have to bring in adifferent set of skills and a
different step by step approach.
And you can run itin an agile way.
So it should be smalliterative sprints.
We shouldn't be running it inthat, predictive waterfall way.
(20:08):
Folks realize they look tothis because they've done it
and it's been failing and theyneed to do something else.
So a lot of people who findthis methodology Have been
running into those problems andunderstand that they need to get
on board with something becauseit's not working the other
ways that they're doing it.
So that's what we've seen.
(20:28):
Also, some folks are, theyget ahead of it and they go, I
want to move into this space.
I need to understandthis and we give a really
comprehensive training.
So it's for everybody, right?
You do not need to havehad a project under your
belt to get started.
You currently don't haveto have any requirements.
I know PMP has some prettyrobust requirements and we're
working towards that with ourgold standard certification.
(20:49):
But for now, you don't needrequirements, which is nice
because anybody can take it.
And it really walksyou through everything.
So it first levelsets on terminology.
And folks think that theyknow terms and they're
like, Oh, I don't need this.
And then they get into it andthey're like, Wow, I didn't
realize what I didn't know.
And this really wasa great level set.
And that's what we sayfor teams too, right?
You want the teamto get on board.
(21:11):
Which is the same reasonthat you take a PMP, and
that you hire PMP, becauseit's that terminology
that you want, right?
It's everybody'son the same page.
Everybody uses thesame terminology.
Everybody has the same basiclevel of understanding.
When it comes to CPMAI,it's no different.
Galen Low (21:25):
I really like that.
And actually, I love yourframing on it because I realized
that I probably made it seemlike it's a methodology,
but if I'm understanding itcorrectly, it just layers on
in terms of, you may have acertain way of working, these
are considerations and stepsthat you can weave into that.
Kathleen Walch (21:40):
Yeah, we call it a methodology, but then people
get tripped up on that term.
And so we always say,look, don't get tripped
up on terminology.
You just need to followsomething for success.
And so you can callit a framework.
You can call it astep by step approach.
You can call it amethodology, but understand
that it is six phases.
It's iterative.
You can go back a phase ortwo or three if you need.
(22:02):
So we start withbusiness understanding.
Then we go to dataunderstanding, so what data
do we need, the sources ofthat data, is it internal,
is it external, do wehave access to that data?
Then we know we need toclean our data, so data
cleaning is the next step.
Unfortunately, data isnever clean and nice,
and it's always messy,especially unstructured data.
Then we get to ourmodel development.
(22:23):
So now we're actually,doing the fun stuff.
I air quote that, whichis where people usually
start and skip those supercritical beginning steps.
Then we have to test it, makesure that it's performing
as expected, which also isa really critical step and
sometimes people skip andthen things go wonky and they
wonder why it's hallucinatingand giving terrible results
(22:43):
or not performing as expected.
And then weoperationalize it, right?
We put it into the real world.
And so we should be doingthese in small iterative
phases and steps, eachiteration of CPMAI should
take about two weeks, right?
Like a sprint.
It should not be taking sevenmonths or twelve months.
That's more of that waterfall,predictive approach, and
(23:05):
think about how much theworld changes in seven months.
But for a number ofreasons, this is what
happens, and usually becausepeople get tripped up at
the data phase, right?
Data understanding, where theydon't have access to that data,
and so it takes weeks and monthsto get access to that data.
And we say, okay think big,start small, and iterate often.
So start smaller, pick asmaller data set, or, really
(23:26):
control that scope and pare itdown a lot and say, what's the
smallest thing that I can do?
That will provide anincremental value and
provide that positive ROI.
Show wins.
And then we can continueto move forward, right?
And just iterate more in thenext phase, the next step.
Because far too often wesee that it's taking way too
long and then people give upon the project in phase two
(23:46):
because they just couldn'tget access to the data.
Galen Low (23:48):
It's like the Project Winter.
Kathleen Walch (23:50):
Yeah.
A lot of good things.
Galen Low (23:52):
Just get disillusioned with it and
then just leave it alone.
I was going to ask you actuallywhat Wonky looks like, but I
think you framed it really wellin terms of like hallucinations
and when you start thinkingabout, even small data sets
are pretty big data sets,so you can get really good
at yourself and then, yoursolution is not performing
the way you want it to.
And you're wondering why, andyou have to unwind this entire
(24:13):
ball of yarn to find whatwas somewhere in the middle
there at the beginning that.
Set this astray, probablydata cleanliness, right?
Kathleen Walch (24:22):
Probably, access to it, how clean it is, and then
how much of it you have as well.
Because some people thinkmore data, the better, right?
And that's not alwaysthe case because we can
be training on noise.
Data is not free, right?
There's a cost to cleaning it.
There's a cost to processing it.
So sometimes more is not better.
Galen Low (24:43):
That's interesting.
It's funny.
It's like I started thisout, we will answer the
mail on this, but I startedthis out with what's beyond
prompt engineering, but whatI find fascinating about
this conversation is that, Ithink there's a whole world
that many project managershaven't seen themselves in,
they might be software andlike in the software space or
the digital space or the techsector, IT, they might not
be in any of those sectors.
(25:03):
And they're like, is projectmanagement still going to exist?
We'll get into itin a little bit.
But there's this whole worldof like data projects, the
ML projects that, there'sa lot of projects going
on at this very minute.
And yeah, sometimes they'rebeing led by someone who's
project adjacent, right?
Someone who's not necessarilyself identifying as a
project manager, but thereis this opportunity, like
there's a lot going on therethat at least folks in my
(25:25):
community, I don't know thatwe all know a lot about it.
It's actually inspiring.
Kathleen Walch (25:29):
Yeah, we say, education is cheap, right?
I mean, the costof failure is high.
And to learn the CPMAImethodology, to get
CPMAI certified is fairlycheap in comparison.
We always encourage people, wesay this is a yes and, right?
Have your PMP and your CPMAI.
Follow and learn differentmethodologies and learn CPMAI.
(25:53):
This is not an eitheror, and organizations
always do this, right?
They learn something and thenthey adapt it for themselves.
And as long as you're, followingthat high level, step by step
approach, then adapt it for yourown organization, but learn it.
Galen Low (26:07):
I love that.
We opened on thetopic of prompting.
I was thinking inmy head, right?
Those use cases that involvechat based interfaces,
like ChatGPT, yourGemini, your Copilot, your
Claude, your Perplexity,DeepSeek, and others.
But there's a bigworld beyond that.
What's next for AI inour professional lives
as project managers?
And how can someone like,a project manager like
(26:28):
me, how can we keep up?
Kathleen Walch (26:30):
Yeah, that's a great question because, there's
been that saying out there thatAI is not going to replace your
job, but someone that knows AIis going to replace your job.
And so it reallyis here to stay.
We hope that we'vecrossed the chasm.
We're not going to be fallinginto another AI winter.
But how do you use it?
I always like to thinkabout that idea of
augmented intelligence.
So how do I use it tonot replace me, but do
(26:53):
whatever it is better?
So you can replace a taskor a, certain role, but not
replace me as the human.
And I know a lot ofproject professionals
are concerned about that.
But if a lot of projects aremoving towards AI projects, then
we're just going to continue toneed project managers, right?
And folks with these skillsets.
(27:13):
So how do you learn?
I think it goes backto what I said before.
Put a list togetherof everything that
is annoying to you.
Because that'sdifferent for everybody.
And so to make sure that youdon't replace things that you
enjoy, and then keep all thethings that you hate, because
then you're pretty unsatisfied.
So think about all the thingsthat you enjoy, and put them
maybe in one column, and allthe things that you don't enjoy,
(27:35):
and put them in another column.
And then looktowards communities.
PMI has a lot of, learningresources that you can go to.
Or maybe internally, andI always say, advocate
for internal groups where,especially if you have
prompts, they should notbe proprietary, right?
We should be sharing prompts,we should be learning, we
should be collaborating,so have a prompt library
(27:57):
internally at your organization.
Document which platform you'veused it on and when, because
we know that your promptsdo need to be changed and
iterated over time to continueto get those results, right?
That's part of our step by stepapproach, where you need to
be testing it, and sometimesit's not going to perform as
expected, so sometimes justtweaking one word really
makes a big difference.
(28:17):
And if you have that communityand you can go back and forth
with, it really does help.
I know that some organizations,they're leaning into AI
and being AI first, right?
And they're really AI driven.
But if your organization isn't,then figure out how you can
start with those iterative,step by step approaches.
Like I said, if you'rea PMI member, reach
out to the community.
We have a lot ofchapters as well.
(28:38):
Internally, you can haveresources, and so whether
that's your group, or maybeeven look externally outside
of your group, so that you canbe collaborative, because if
you're not learning, you'renot growing, and then you're
going to just fall fartherbehind, because you're going
to be afraid to use thesetools, or you don't know how
to use these tools, and soreally, don't be afraid to
reach out and ask for help.
(28:58):
That's the best way to learn.
Galen Low (29:00):
I love the sort of prompt repository, and sharing.
Sharing is the thingthat moves us forward.
The technology is goingto move fast, people
need to move fast too.
The best way to do thatis to share knowledge.
Kathleen Wal (29:10):
Sharing is caring.
Galen Low (29:12):
There you go, yes! I have two questions
to wrap this up.
Some of the things that we'retalking about, we're like, make
that list of annoying things.
And, you can chat with yourGenAI tool to augment your
sort of personal professionallife, like your own
individual professional life.
On the one hand, somefolks, me, are you know
(29:34):
what, this is great.
It's a fantastic technology.
It is fundamentallythe way I use it.
The tab based interfacesfor me, it's, it's natural
language processing, tothe nth degree from where
it was, a few decades ago.
And then I'm like, isn't itjust a fancy parlor trick
where it's languagifying stuffand giving it back to us and
we're like, Oh my goodness,this is like a living,
breathing, sentient creature.
(29:55):
Whereas actually it'slike remixing language.
Are we maybe even, I startedthis with, are we like
selling ourselves shortby not using enough AI?
On the opposite side, isthere a risk of us thinking
that AI is doing like reallymagical things and can do
anything and not understandingthat actually it is good
at doing a certain thing?
And is that dangerousor is it inspiring?
Kathleen Walch (30:15):
It's dangerous because it's over promising
and under delivering.
So a number of years ago now,about in 2019, because AI is
such an umbrella term, andbecause now with generative
AI and, these large languagemodels that are out there,
people think it can doeverything, and are asking
it to do everything, and thenmaybe not getting the results
(30:35):
that they want, they go whycan't it do this math problem?
Why can't it do this?
A human can do this, andit's because we need to
understand what it canand what it cannot do.
So we said, why don't we breakit down one level deeper.
And say, what are wetrying to accomplish?
And then let's see if AI isthe right solution to that.
So we looked at hundreds,if not thousands of use
cases and broke it down intothe seven patterns of AI.
(30:56):
So that's hyperpersonalization, right?
Treating each individualas an individual.
So we think about this asa marketer's dream, but
also it can be with hyperpersonalized education or
healthcare or finance, right?
And this is a really hottopic with education,
especially lifelong learning.
How do I, beyond that Kto 12, tailor education
(31:18):
to fit different people's,the way that they learn.
Then we have recognition.
So this is making senseof unstructured data.
And we think about imagerecognition here, right?
But also audio recognitionor hand gesture recognition.
And then we have our predictiveanalytics and decision support,
so this is taking past ourcurrent data and helping
humans make better decisions.
(31:38):
We have our predictiveanalytics, our
patterns and anomalies.
So that's looking at largeamounts of data and being
able to spot trends in thatdata, but then also some of
the outliers, so we thinkabout fraud detection.
We have goal drivensystems, which is really
around reinforcementlearning and optimization.
We have the autonomouspattern, so that's, the
goal of that is to removethe human from the loop.
(31:58):
So whenever you're tryingto remove the human, that's
obviously pretty hard.
So we say that is the mostdifficult pattern of AI.
But what do we have now?
We can have hardwareor software.
So we can have autonomousvehicles, for example, or
autonomous delivery bots.
But then we can also haveautonomous business processes.
And so how do we have systemsthat can Autonomously navigate
(32:18):
within our workflows, and wedon't need humans in there.
So it's different thanautomation, right?
Because automationis not intelligence.
We're just repeating somethingover and over, which is
absolutely incredibly useful,but just not intelligence.
So if there's exceptions,you need a human to go in.
Can't handle that, dothat exception handling.
If fields change, the humanneeds to go in, and say, okay,
(32:40):
these fields have changed.
We think about robotic processautomation there, but you know,
what's agentic AI going tolook like, especially, in this
year, but then in the comingyears, and how can we have more
of the, autonomous agents outthere working with other agents.
So when we think about theseven patterns, it really helps
us break down what AI is goodat and what AI is not good
at and when we should use itand what we shouldn't use it.
(33:02):
And we go throughthat in phase one.
Of business understanding ofCPMAI, and I think that really
helps too, so if people aretrying to say where should
I apply AI, and how can Iapply AI, if we have our
conversational pattern of AIthat's where we have humans
and machines talking to eachother in the language of humans,
that's where large languagemodels fall, and AI enabled
(33:24):
chatbots, but that's just onepattern of AI, and maybe that
we shouldn't have our LLMstry and do, hyper personalized
offerings or we can't usean LLM to drive a vehicle.
Galen Low (33:36):
I love that.
And honestly, thank youfor that crash course.
It actually brings thingsa lot into perspective.
And guess what?
Perfect segue into my lastquestion, which is just,
we're talking about thingsthat AI coming in, helping
you do your day to day.
Kathleen Walch (33:50):
Oh, augmented intelligence.
Galen Low (33:51):
Augmented intelligence.
Thank you.
But then, you had mentionedautonomous, you had mentioned
agentic, everyone's talkingabout it right now, a world
where, yeah, it's justoften doing its own thing.
Maybe you're bidding, butnot in the same way where
you're like having a chatand we're like, Hey, could
you do this thing for me?
Do you have an opinion onwhere that goes in terms of
the project management worldor just the world in general?
Are people's anxietiesfounded around that?
(34:13):
Or is it also one ofthose things where maybe
people think it's like,Oh, that's basically AGI.
I guess we can skipto the apocalypse.
Whereas actually it's just,within these patterns?
Kathleen Walch (34:22):
If we understand what AI can and
cannot do, and we understandwe're still in narrow AI.
So we're applying one or moreof these seven patterns, but
we aren't in AGI where we havea system that's human right?
And can do everything.
Cause we still don'thave machine reasoning.
So I like to talk aboutthis DIKUW pyramid.
So data is at the bottom, thefoundation, but data on its own,
(34:46):
you can't do much with, right?
We need to applysomething to it.
So we get our informationlevel, and this is
where we have dashboardsand, things like that.
And then we go to D I K, right?
So we're at the knowledge,and that's where machine
learning comes into play.
Then we have the U, theunderstanding, and this
is machine reasoning,we still aren't there.
And then at thetop we have wisdom.
So we're far ways offfrom AGI, it's important
(35:08):
to understand that.
I know that there's differentthings in the media about
how far off we actually are.
Some people think we'remaybe a decade away.
Some people thinkwe're a year away.
Some people thinkwe're 100 years away.
Some people think we're nevergoing to get there, right?
I mean, experts in theindustry cannot agree on this.
But it is important tounderstand the limitations that
we have right now, and again,apply it where it makes sense.
(35:31):
Agentic AI is thehot topic of 2025.
It's going to continue tobe the hot topic of 2025.
At PMI, we have Infinity, whichis our, tool, our AI tool.
And we are going to haveagentic AI capabilities there.
But it is important tounderstand, too, I think,
because we don't have a commonlyaccepted definition of AI, then
(35:51):
that means What is agentic AI?
We don't really have a commonlyaccepted definition of agentic
AI in the industry either.
So is it actually autonomous?
Is it augmented?
Is it automated?
We're still moving towardsthat, but it is exciting.
I mean, things are changingso incredibly fast.
So if people are listeningto this in February of 2025,
what's it going to looklike in February of 2026?
(36:13):
What's it going to looklike in June of 2025?
Things change.
And you're just that one realbreakthrough away or that one
different platform, right?
Earlier you brought up DeepSeekthat wasn't around a month ago.
How do things continueto evolve and how are
project professionals?
Adopting that agenticAI and how do they bring
it into their workflow?
(36:34):
These are larger conversations,your organization, how
are they bringing this in?
How are they adopting thesetools and technologies?
We need to make sure that we'redoing it in a trustworthy,
ethical and responsible way.
I know there's a lot ofconcerns around that.
Data privacy, right?
Governance issues.
the trustworthy aspects.
Do we trust these systems?
Do we have them internally?
Can we use external ones?
(36:55):
So it's a really exciting time.
And these tools arecontinuing to get better.
New ones are coming outevery single day, like I had
said earlier on the podcast.
And so that canget overwhelming.
So it's where do you even start?
And you had said, how doyou even keep up with it?
And I think Learningand doing every day.
You have to practice, right?
It has to startbeing that reflex.
How do you make itpart of your every day?
(37:18):
I say never start with ablank page anymore, right?
If you're starting witha blank page, you're
doing something wrong.
Have it help you with thatbrainstorming, or have it write
a first draft, and then youcan edit it, or whatever it
is that you're trying to do.
But it is that reflex that youneed to get used to, right?
And how do you do that?
Just continue to practice.
Galen Low (37:35):
I love that.
The mindset and the reflex.
Kathleen, this was a reallyinspiring conversation.
I don't think I've takenso many notes in an
episode, probably ever.
And I mean this in agood way, as in capturing
knowledge for myself, notlike what to edit in post.
I think PMI islucky to have you.
I'm excited about what comesnext, and maybe we should
have you back February2026, if not in between to
(37:56):
do a comparison on, wherewe landed after 365 days.
Kathleen Walch (37:59):
Yeah.
I know it's changing so fast.
Sometimes people go where arewe going to be, a year from
now, five years from now?
And I'm like, where are wegoing to be a month from now?
Galen Low (38:09):
Oh, I love it.
I love it.
I love it.
Where can people find moreabout the CPMAI certification?
Kathleen Walch (38:14):
Yeah, so go to PMI.org.
You can find it there.
Also, I have a podcast,AI Today, where we
talk about CPMAI.
We're in the middle of ause case series right now,
so that is now an officialPMI podcast, and I'm
really excited about that.
I know we're transitioningeverything over to the PMI
system, as anybody withacquisitions know, it does
take a little bit of time,but we're getting there, and
(38:35):
I'm really excited about that.
You can also find meon LinkedIn, Kathleen
Walch, or PMI Cognilyticais on LinkedIn as well.
Galen Low (38:45):
Amazing.
I will also include all thoselinks in the show notes.
Kathleen, thank you somuch for joining me today.
It has been so much fun.
Kathleen Walch (38:51):
Yeah, thanks.
I always love totalk to you, Galen.
Galen Low (38:54):
All right folks, there you have it.
As always, if you'd like tojoin the conversation with
over a thousand like-mindedproject management champions,
come join our collective.
Head on over tothedpm.com/membership
to learn more.
And if you like what youheard today, please subscribe
and stay in touch onthedigitalprojectmanager.com.
Until next time,thanks for listening.
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