April 22, 2025 • 46 mins
AI projects aren’t just another trend—they’re already reshaping workplaces in ways big and small. From HR to finance, employees are integrating AI into daily tasks, often without formal initiatives. Yet many organizations are still struggling to align teams and create a clear strategy for AI adoption. So, how can companies move from scattered, anxiety-driven adoption to a cohesive, strategic approach?
In this episode, host Galen Low sits down with Ron Schmelzer, Global Head of AI Partnerships at PMI, to explore how organizations can better support AI-driven projects. They discuss what it takes to get teams on the same page, manage AI’s impact on workflows, and create a mindset shift that ensures businesses thrive in this new wave of digital transformation.
Resources from this episode:
- Join DPM Membership
- Subscribe to the newsletter to get our latest articles and podcasts
- Connect with Ron on LinkedIn
- Check out Project Management Institute
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Galen Low (00:00):
“AI projects.
Pfffft.” — your colleaguescoffs at the idea over lunch.
“A passing trend at best.”adds your program director.
“They’re no differentthan other projects.”
But you start looking aroundthe cafeteria, and you can't
help but feel that these peopleyou respect enough to break
bread with might be buryingtheir heads in the sand.
At the next table over,Germaine from Finance is
(00:22):
explaining to Lateicia fromHR how Gemini is fixing her
formulas in Google Sheets.
At the coffee station, Paulois pitching an idea for
using AI and machine learningto convert gigabytes of
unstructured customer feedbackinto actionable insights.
By the elevators, Sonayais having a futurist
conversation with theirdepartment chief about nimble
AI-supported processes thatself-document and self-improve
(00:44):
through user feedback.
Heck, even Andréa is there inthe corner using Perplexity to
plan their own retirement party.
AI projects.
They're everywhere.
And there's a lot more toconsider than whether AI will
come up with a good enoughsuggestion for a party theme.
Wondering if your organizationhas the chops to take on
projects that involve AI?
Keep listening.
We're gonna be diving intowhat organizations need to be
(01:06):
doing to support people beingimpacted by AI and how they
can get their people on thesame page when it comes to AI,
so that they can all surviveand thrive in this next wave
of digital transformation.
Hey folks, thanks for tuning in.
My name is Galen Low withthe Digital Project Manager.
We are a community of digitalprofessionals on a mission
(01:28):
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.
And if you're intofuture-forward conversations
and practical insights arounddigital project leadership,
consider subscribing to theshow for weekly episodes.
Alright, today we are talkingabout the next wave of digital
(01:50):
transformation that's beingcreated by generative AI and
how a shared team mindset aroundAI can take an organization
from spotty, haphazard,anxiety-ridden adoption to a
more cohesive and deliberatebenefit that lifts all boats.
With me today is Ron Schmelzer,co-founder of Cognilytica
and now the Global Head ofAI Partnerships and Outreach
(02:13):
for PMI Cognilytica at theProject Management Institute.
Ron, thanks for beinghere with me today.
Ron Schmel (02:19):
Thrilled to be here.
Always love chatting with you.
Galen Low (02:21):
Yeah, I love having you back on the show and I've
been like quietly observingyour journey Fanboying a little
bit for the past several yearsas we've been talking about,
Cognilytica, what you'redoing there and now, this
sort of merger into the PMIecosphere many exciting things
that I wanted to dig into.
I thought maybe I would juststart with one hot question.
(02:43):
The big question, and Ithink this is tying back
to something you and Iwere jamming on earlier.
Which is that the currentadoption of generative AI
solutions has been likened rightby you, by me, has been likened
to the advent of the computer orword processing software or the
telephone, or practically likeany big shift that quickly made
(03:04):
many roles within the workingworld, more or less redundant.
So the big question is this, howcan organizations support people
whose job will be affectedby AI in the imminent future?
Ron Schmelzer (03:15):
I think part of the reason why AI feels
like that is because ofits transformative nature.
We talk about it changesthings and that's what
transformative is.
It transforms, it changesone thing into another thing.
Maybe I just definetransformation for, in this
case it's like people see thata lot of the tools, especially
for knowledge workers, wetalked about this earlier, but.
We have a knowledge recordwhose primary job is dealing
(03:37):
with information, whetherit's MO putting information
into an information systemor taking information out
of an information system orcondensing it or summarizing
it or describing it or puttingit into reports or doing things
like filing compliance reports.
All of that stuff now isso relatively trivial for
machines to do where itwasn't trivial for them
(03:57):
to do a couple years ago.
So organizations noware rethinking this.
The non transformational wayof doing this would be somebody
who's in that role usingthose tools to do the existing
things that they do, and theprocess doesn't change and
things really don't change.
Maybe they save a little cost,they speed some things up,
they improve their accuracy,whatever those measures are.
But the processremains the same.
(04:18):
But the organizations thatare really transforming or
kind of really rethinking theprocesses from the get go.
As you mentioned, we had thisexperience so many times.
Before the internet, theshopping experience was
you go to a store and theyhave what they have in
stock or they don't, youpurchase and you box it out.
And then it took rethinking.
E-commerce wasn't justthe same experience.
You walk into a store, but it'sonline and things on a shelf.
(04:39):
I think people tried todo that, like we actually
looked like a store andthere were things on shelves.
But then it took peopleto realize, wait a second.
We don't need to have the sameideas of inventory and we can
do all this sorts of stuff.
Same thing's gonnahappen with AI.
So I think the scary partis that when you're in an
organization that hasn't madethat process transformation,
then AI is a little bitlike a sledgehammer, right?
(05:01):
It's coming in and it'sjust causing a lot of
destruction and chaos, right?
It may be getting thingsdone, but it's doing things.
At the expense of theexisting processes because
the processes haven't changed.
All we're doing is we'rejust bringing in these tools
and that means that peopleare getting squeezed out of
the process, if you will.
Galen Low (05:20):
That's so interesting.
You know what I found reallyinteresting about that is that
a lot of the conversationsthat I've been having and
that I've been seeing onlinearound AI is don't worry.
It's gonna be here tohelp you do the same
things you always do.
And what you just said therewas very interesting because.
You're saying that from anorganizational standpoint,
one of the better thingsthat an organization can
do is cast a transformativevision for the future.
(05:41):
That like we know thingswon't be the same.
We don't want you to writephone numbers into your Rolodex
and then scan them and thenput them into your phone.
That's not what we want.
We want to reimagine whatwork looks like and you have
a place in that and we'regonna help you get there.
It's not tomorrow.
It's like we're gonna get there,but we have to start thinking
about it not as the AI versionof what we were doing before,
(06:05):
but like a new thing thatwe're doing with AI involved.
Ron Schmelzer (06:09):
And I would argue that's actually
harder to do those kind oftransformations than it is
to implement technology.
'cause you could say theprocess works like this.
Part of the process are thesesteps, I'm gonna replace those
steps of the machine that'sactually cognitively, it's
easier to think that thanto say, wait a second, if
I had to rethink this wholething, how would it work?
That's a lot harder to do.
It's riskier in some waysbecause you don't know
(06:29):
if the new process willwork out, but it would be
so much more impactful.
So I think that's the hard part.
The hard part, we actually dida little keynote at the end
of the last PM expo sessionand we said, the thing that
differentiates, we were doing alot of observation of the people
who were trying to make AI work.
A lot of people wereusing it for themselves.
It wasn't like the organizationmandated the use of it, in
some cases even allowed it,but people were just using
(06:51):
it on their own anyways.
And what we foundwas that there's four
rough groups of people.
There were people whoweren't using it at all.
They're like we call sideliners or just observers.
You're just lookingto see what it is.
They're either skepticalor fearful or whatever, or
doubtful, and they'd rathernot do anything with AI.
Then you have this othergroup, which are like taskers.
They're like, okay,I'm doing my job.
Oh, once in a while I'mgonna use AI to write
(07:13):
these emails or to do this.
Spreadsheet and nowwhatever it is, right?
But I'm still doing my thing.
It's just you know I, it'slike they call them taskers.
It's I'm gonna use AI forthis task when that task.
But fundamentally, nothingelse has really changed.
Then you have a third group,which they love using technology
for, technology's sake,early adopters, enthusiasts,
whatever you wanna call them.
AI is the latest tool.
(07:33):
It'll be one model today, thenanother model the next day, some
new tool they discovered online.
It's more like they enjoythe process of discovery.
And I would say they'reprobably just as marginally
useful as the taskersbecause it's not like they're
changing their job either.
There's a fourth groupthat we observed, but we
call the Leapers, which arebasically, they're the ones
who are being strategic andthey're like, you know what?
(07:55):
I'm not gonna just use someAI for my task, but I'm
gonna change something in.
I'm gonna just redo the wholeconcept of my task, the job
or the thing that I'm tryingto do, and rethink it from
that AI first perspective.
We would call them the leapers.
Those are the ones that wewanna keep an eye out of for,
because ironically, they'reguaranteeing their job security.
(08:16):
It's weird becausethey're saying No.
Now I'm capable of usingthis technology in a way
that can drive positivetransformation value.
You need me here, right?
All the others, we couldthink about that later.
Galen Low (08:27):
There's so much in there.
And thank you forthat breakdown.
I love your sort of groupingsand I certainly know which
one I fall into, but we'llkeep mum about that for now.
What's interesting isthis combination of like
organizational leadershiparound AI is required, but it's
not necessarily your currentleadership structure that
is going to have that visionof AI and what it can do.
(08:47):
In fact, there's gonna be a lotof apprehension, which is why
a lot of organizations haven'tfully baked it into like their
policies and their proceduresand their processes and why it
happens on an individual basis.
But then you have this rampantsort of everyone, free for
all trying out tools, whetherthey're allowed to or not
within their organization.
To help them do the same job maybetter, but if I'm understanding
what you're putting down here.
(09:09):
If organizational leaders canlook out for those leapers
and give them a bit of anecosystem to like almost r and
d it for the organization witha bit more blue sky thinking.
Not that sort of, I'm gonna messthis term up, but I think we use
the term brownfield sometimesin consulting where it's yeah,
you have what you have whatyou have versus a blue sky kind
of thing, or like greenfield,where it's just like.
We can do anything we want.
(09:29):
Let's have these, like whatever.
I don't know, hackathon'sprobably an outdated concept
at this point, but like, howcan we create these communities
of practice where we haveleapers that could literally
lead the charge of transformingthe business because
they're in that category.
They're not the taskers,they're not the tester
early adopter folks.
They are folks who have a visionthat we can probably support.
(09:50):
I know that the, like criticin my head is steal, but
that's not what I mean.
What I mean is the peoplein your organization can
help you build this sortof collective mindset.
I'm borrowing your word fromlater in the conversation,
but this collective mindsetaround AI, it's not up to
individual, whatever executivesaround the world to just
be visionary AI people.
You gotta lean onthe folks who get it.
Ron Schmelzer (10:12):
Yeah.
I'm gonna use a left fieldanalogy here, but like
I'm sorting exactly whatyou're talking about.
Left field, just talkingabout Brownfield.
So I don't know what.
Galen Low (10:19):
There's a lot of fields in this conversation.
Ron Schmelzer (10:20):
No, it's exactly.
I think about the changingnature of warfare.
So if you think about what'shappening in Ukraine right now,
I. When the war started, we hadour, there, they had, I should
say, very traditional the latestmindset in fighting warfare
with armored divisions and largetroops and frontal assaults.
And even things like theidea of trying to gain air
(10:42):
dominance first, like youcome in with that first wave.
None of that workedfor a couple reasons.
One, they didn't expectresistance, which is
a whole other story.
They were expectingthe government to just
flee and, that happen,which is intriguing in.
The second thing is that thenature warfare has changed.
Of course, we'retalking drone warfare.
We're talking the use ofsmall disposable little units
(11:05):
that have actually made thewhole idea of an armed attack
with large armed piecesuseless and it's so strange.
So they just like, wheredid that come from?
I remember at the verybeginning of the world,
we had these people likeexperimenting we don't know.
We never tried this drone thing.
Let's see if we can dropgrenades from a drone.
And at the beginning itwas like, oh, look at
these interesting people.
(11:25):
They're trying somethingweird in left field.
Then someone's let's do thisFPV drone thing where we can fly
the drone, put something on it.
At first it wasinteresting and comical.
Now it is the way, it's likethe funny thing, it went
from a something that wason the fringes to now the
core, and I think it's calledinto question, even the
largest armies, like whether.
(11:45):
Any of this matters anymore.
Whether the $60 million tankis worth anything anymore, they
have to put cages around them.
Now it does remind me of allthis AI experience going on the
edge and someone's wait, there'ssomething more fundamentally
transformative here if we justconsider this to be fringe.
We're missing out onthe big picture here.
Galen Low (12:02):
I like that analogy because I think there are a
couple of traditional forcingfunctions for human innovation.
For better or for I would saypersonally, unfortunately.
Combat and survival is oneof the fastest drivers of
innovation, thank you, DARPANET.
Thank you, CD-ROM.
Thank you.
All sort of military technologythat was necessary to be
(12:23):
either prepared for waror to be effective in war.
But even you and I in thegreen room, we were talking
about the pandemic, right?
We're talking aboutreturn to office.
We're talking about someof these like kind of
forcing functions that likemake us have to innovate.
And then I think you're right.
I think there's like.
Frankly, even right now withthe wars that are happening,
it's a lot of, sidelinergoing let's see what happens
(12:45):
because this is the firsttime we've been able to like
test and see how this likeactually can work in practice.
And I think AI islike that as well.
And coming back to the sortof world of knowledge work.
I think there is thatsame thing happening.
They're like, okay,this is interesting.
Let's like let them all fight itout and see where this nets out.
It leaves us in thisvery uncomfortable space
(13:07):
where everyone's we feelinsecure, actually we
feel unsafe in our jobs.
This is getting hashed out andno one's telling us anything.
No one's saying, don't worry,this discomfort will end.
No one saying.
Don't worry about this,like this is trivial.
We're saying this isserious and we don't know
where it's gonna go, butwe have to let it play out.
And there's this justpsychological lack of safety and
lack of security in everyone'slike professional lives now.
Ron Schmelzer (13:30):
I think there's a lot of that
same lack of psychologicalsafety 'cause nobody really
knows where this is going.
Maybe all things are acalled into question.
So bringing it back to AI here Ithink one of the biggest things
that organizations can do now.
Is to broaden theexperiential and learning
parts of the organization.
It shouldn't be like siloedinto this will be the AI
(13:51):
experimentation group, ormaybe we'll do something within
product, or we'll do somethingwithin it that doesn't make any
sense because of how impactfulAI is just across the board.
Maybe you can rethinkthe HR department.
Maybe you can rethink finance.
Maybe you can rethink supplychain management, the whole
thing, logistics, and getback to the core of what
it is you're trying to do.
Because every organizationhas a mission as an objective.
(14:13):
And the mission isn't theprocesses, the mission
isn't the technology.
Those are means to an end.
Right?
And so part of it isnow the thing that we're
adding to the picture.
As we did with computers,as we did with the phone, as
we did with the internet, iswe're adding AI to the picture.
Now, AI becomes a resourceor a tool that we can use
to facilitate the mission.
(14:36):
This is why it's a lot of peopleare, it does stir things up.
As I said, transformation, ashas both positive and negative
change associated with it.
Galen Low (14:43):
It's interesting you describe it as a tool.
You see a lot of whateverTerminator references nowadays.
Is this Skynet?
Will it become self-awareand destroy us?
And yes, those are big questionsin the AI world, top to bottom.
Like they're serious questions,but like right now and like
the way, like I think it'sa refreshing reframe of
the fact that, yeah, it'slike not the super villain
in the story right now.
It's just like partof the picture.
(15:04):
It might even be abackground actor that's just.
Influencing the way the storygoes and we might need to decide
how we use that character,but it's not necessarily going
to be the one that's gonnaredefine everything for us.
And maybe it, disruptor, maybenot Antagonizer, but a bit of
a disruptor to force our maincharacters to do something to
center back to their mission,to continue on their journey.
Ron Schmelzer (15:25):
It's kinda interesting, one of the
things you'd always spendtoo much time talking about.
It's been in like our CPMAItraining for a long time.
Even we glossed it overwhat should he be afraid of?
AI losing my job.
Super intelligence, badpeople doing bad things.
Those are things that wetalk about all the time.
There's a little sub note,which is that people have a
fear of a lot of power beingconcentrated into a small
number of people's hands.
(15:45):
We always glossed it over, but Inow, especially what's happening
here in the US and with politicsand somebody who actually knows
how to wield this technologyon government employees.
It's like maybe that islike the bigger fear than
the super intelligent.
We're afraid of machines beingsmart enough to out smart
people, but I'm like, maybe weshould be more afraid of just a
small number of people who haveaccess to all the data, all the
(16:07):
models, all the resources in theworld to do whatever they want.
That should be, I think, muchmore of a existential, immediate
threat than the future, whichwe haven't even achieved yet.
Part of that is like part ofme is feeling that one of the
great antidotes to that, oneof the things that can make
people feel a little lessfearful because the fear is out
there, a little less uncertain.
'cause the uncertaintyis certainly out there
(16:29):
is personal empowerment.
I'm starting to choose,feel that what we need more
is personal empowerment.
People doing thingsfor themselves.
Whether it's part of theirwork or whether it's not,
with basically using AI asa means to augment their own
personal abilities and wantto give them the resilience.
(16:52):
That they need.
And two, I think as acounterweight, because it's
very hard to put your faith nowin companies, in governments,
it's hard to put your faith inalmost anything at this point.
And so if you have to putyour faith in something, it's
probably best right now tobe putting it in yourself.
Galen Low (17:07):
No, I think that sound advice and actually a
really good response to theoriginal question, which is
like, how can organizationssupport folks whose job
is gonna be impacted?
Yeah, arm themwith the knowledge.
Don't make it so thatyou can lock them in.
Don't pay for the MBA and makethem stay for five years, but
just arm them because they'reon their own personal mission
and you probably will benefitas an organization, but you
(17:28):
also don't own that brain.
And you don't wanna yeah.
The loyalty game, the sortof getting the watch or the
gold pen after 50 years ofservice is not a thing anymore.
Ron Schmelzer (17:37):
One of the things I'm gonna be talking about,
I have a talk coming up atBarcelona for the Global Summit
that PMI does and is I wantedto like really think about this
idea of resilience, actuallymentioned in a little bit and
what organizational resiliencemeans in the face of AI.
And the irony of that isif you want an organization
that's resilient is able tothrive and survive in the face
(17:58):
of constant change, right?
That's really whatresilience is all about.
And the irony of resilienceis that it does require you
to be much more flexible andadaptable, especially in things
like processes and you'rethe way you're doing things.
It's if you are overlycommitted to the way you're
doing things, that actuallymakes you less resilient.
(18:19):
If you just simply focus onautomating the things you do
and removing the people fromthe picture, you're actually
doubling down in the way you'redoing things now, and you
actually have decreased yourorganizational resilience.
'cause now you have fewerpeople in the organization
who can deal with the change,and now you have an entire
dependence on the systems.
Maybe you can ask the AIhow to improve itself.
That's possible.
(18:40):
Like organizational resiliencereally is all about speeding
up the velocity of learningand increasing the speed to
respond to change, which meansreally empowering people to be
forces of change and drivingthat force of change, not from
the top, but from the bottom,and enabling people to do that.
(19:02):
I think there's a change.
I think there's a mindshift change in the,
of what organizationalresilience really means.
Galen Low (19:07):
I really like that notion of
organizational resilience.
In other words, like areframing on resilience,
'cause a lot of folks wouldbe like, cool, we have like
airtight processes and we'vebeen doing this for years and
that's how we're gonna survivethis and weather the storm
and, but maybe not anymore.
I wondered if, actually Ican use that as a bit of
a pivot point because Iwanted to zoom out a bit.
You mentioned the CPMAIcertification and things I
(19:29):
know about you, you were aco-founder of Cognilytica,
which is an AI and projectfocused organization, which
was, in my world, way ahead ofthe curve of when it launched.
The first, and I don'tknow, maybe only, I should
probably have fact checkthat before, but I think the
first certification for teamsdelivering AI driven solutions.
Ron Schmelzer (19:47):
It's the only, there's a few other certs for
using AI and project managementand different aspects of
AI project management, butlike for whatever reason, no
one's really created a vendorneutral methodology on how
to run and manage a project.
So that's whatCPMAI was all about.
We're like, let's do, yeah.
Galen Low (20:01):
It's a big ask, right?
A, it's moving at the speedof light or sometimes, has
these moments of gosh, theword you use, not a nice
age, but this moment ofyeah, we give up on AI.
Oh the winters.
The winters, yeah.
Like we've gone throughmultiple AI winters, over the
past like several decades.
And it's a hard thing toplace a bet on, right?
To be like, yeah, here'show to do an AI project.
The funny thing is that likeyou were on the show a couple
(20:23):
years ago, you and Kathleen,and at the time I thought of
AI projects as like the kindsof projects where, you've got
a team of like data scientistsand analysts and developers,
and they are creating an AI.
But one thing you pointedout to me in the, and you
alluded to now, is that.
Many projects thesedays will involve AI
somewhere in the solution.
In fact, all of thisorganizational resilience
(20:44):
that we're talking about andprocesses and how can we use
AI to rethink some of thesethings are all projects.
You know what I mean?
And the thing you also saidwhich is like part of the
certification is understanding.
Having ways to manage fearand apprehension around AI.
The ethical side of thequestion, this sort of
mindset and these steps togo through to really create
(21:06):
a considered AI solution thatis resilient in and of itself.
Also isn't that thing thatwill become self-aware
and take over the worldwith that in mind, right?
Like I'm thinking oforganizations, what
type of organizations isthe CPMAI certification
relevant for today?
And what's the differencebetween building a project
(21:28):
team that just has experienceusing gen AI tools versus
having a team that like hasthe CPMAI certification?
Ron Schmelzer (21:34):
We'll start first by talking
about why it was created.
Where did CPMAI come from?
Because we thought.
We were talking to a largegovernment organization and a
large bank, and they were bothlooking at putting AI into use
for some, one of their coreprocesses we're like, like we
just wanna just tell us theapproach that we should use
so that we know that at theend of the day when we deliver
and deploy this solution, wecan rely on this AI to do the
(21:56):
things that we had previouslyrequired people to do.
And we had to be like,okay, certainly there's
gotta be some approach.
You can't just take.
Traditional project developmentmethodology itself, it
works when you're buildingsoftware, a website 'cause
you have a deliverable.
Then you could be agile,you could be like, maybe
you're discovering thefunctionality as you go along.
Maybe you have a connectionwith the user and
(22:16):
what they're gonna do.
You could definefunctionality points.
You still can't make the AIdo what you want it to do.
That's the irony of it.
It's how do wemake this AI thing?
Do we what we wanna do?
It's data driven.
And what we realized oh, okay,we need a data driven, we need
a data focused methodologywhere the functionality is
not driven by the programming.
The functionality isdriven by the data.
It would be like as ifwe're building some sort of
(22:38):
spreadsheet analysis thing waslike what's the methodology
you would use to build acomplicated spreadsheet?
It's not.
Project development.
What is it exactly?
It's there was a methodologycalled CRISP DM that was out
there for a while and it wasreally good, but it's not
iterative, it wasn't agileand it wasn't AI focused.
So we're like, what we'regonna do is we're gonna
marry these worlds together.
Let's use a data-centricmethodology, crisp dm, but
(22:58):
let's enhance it and make itAI relevant so that way we can
deal with the fact that AI doesnot give guaranteed results.
It.
You're building a projectwhere you're depending
on a very importantcomponent of that project.
Perhaps an intern that youhired a week ago who knows
something, but not everything,but you can't fire them
because you're dependent onthem, but you can't also just
(23:21):
delegate everything to them.
So how would you work a projectwhere you're like, this intern
is delivering half the value.
They're like,they're looking at.
Medical images and tellingyou if there's a possible
aneurysm in them or something.
So that's what CPMAI is likethis approach is this approach
to running and managingprojects where you can increase
your rate of success giventhe fact that AI is this
constantly moving things.
The irony of is that ccp,MI is methodology has been
(23:42):
fairly stable even with allof these AI changes because
the method that you use shouldbasically stay the same.
The technology that you'reusing can change and
adapt as your free grant.
New ways to do it.
Interestingly enough, thathas meant that over time we've
required fewer and fewer datascientists in our AI projects.
Now, I would say the vastmajority of AI projects are
(24:04):
being run and managed by peoplewho are not data scientists
and machine learning engineers.
'cause we're using off the shelfmodels and things like that.
Now with the Gentech AI,it comes down to this idea
of, now I have to stitchtogether these multiple pieces
and each of these pieceshave to be run and managed
properly to make it work.
And so CPMAI hasbeen really helpful.
Now we have sort of two usercommunities, if you wanna
think about it, that adopt it.
(24:25):
We have sort of organization,organizational user
communities that is, that areimplementing it for themselves.
They're trying to speed uptheir AI initiatives and they're
trying to do it in a sort ofstandard way, whether they're.
Banks or insurancecompanies, pharmaceutical,
construction, manufacturing,government agencies, it
goes, it's across the board.
And then we have consultancieswho are basically out there
(24:45):
implementing AI projectson behalf of others and
want to show that they havethe process where they can
give a guaranteed result,which is what you wanna do.
And there's a lot of CPMAII is about, we have the six
phases, and some of it'sabout documenting decisions
and some of it's about doingthings in the right order and
discovering things that youdon't have before you get there.
Also coming up with plansfor you to evaluate and test
(25:08):
your AI solutions before youdeploy them and manage them
and monitor them as they'reconstantly in progress.
So it's a weird kindahybrid between maybe product
management methodology andproject management methodology,
and it's got a little bitof everything in there.
Galen Low (25:23):
What I found really interesting about it from where
I stand in my knowledge set,is that thing you said about
doing things in the right order.
Because my project managerbrain went, okay, when we don't
know what we're building andwe can't guarantee that we're
gonna be able to build it,we'll just iterate through it.
And in my digital worldit's code and yeah, some
data structure, right?
There's still likearchitecture that's important.
(25:43):
These are more or lessknown quantities that are
architects can play with.
Like it's malleable versusthe data side where your
entire solution is relyingon like the fuel for the
actual machine is clean data.
And there is a sort offoundational first step of
making sure you have goodclean data and how to police
whether or not you have goodclean data coming in before
you know if your AI solutionactually is working or not.
(26:06):
Because it could justbe, for lack of a
better word, sucky data.
Which is funny because the thingyou were saying earlier about
you can't isolate power and likethese similar groups, we have
to have a bottom up approach.
I think our like dress rehearsalfor AI has been data where,
I still see organizations,and don't get me wrong, it's
a very complex challenge,but I've seen a lot of
organizations right up to.
(26:27):
Struggling with datagovernance and who building
the cross-functional teams tomake data do something useful.
They're, we're greatat collecting it.
We have every tool inthe world to collect it.
But the committee, the groupof people, the conversation
almost becomes too largebecause HR and marketing and
it, and people from everypart of an organization.
Trying to be at this table andthe table isn't necessarily
(26:49):
big enough for us to beproductive about how to get us
good clean data to begin with.
Ron Schmelzer (26:53):
Yeah, I'm running into that problem
as we speak, unfortunately.
Unfortunately, it's likesometimes like just getting
access to the data is an issueand like the timeliness of
it and it's trapped insideof one system and you don't
have access to that system.
It's like it kills the whole AIproject if you're depending on
that for your AI project, andyou can't even get to your data.
(27:14):
You're not gonna get anywhere.
So it's a good idea is todiscover that first before
you're like, well down thepath of buying and implementing
tools, then realize you don'teven have the data you need.
Buying and implementingtools is like step four
in the CPM methodology.
Step one is, what problemare you trying to solve?
Doesn't need AI and whatpattern of AI and with
how the AI go, no go.
Can you even proceedwith this project?
(27:35):
And then step two isthe data discovery data.
Understanding whatdata do I need?
Do I have access to it?
What formats it in?
Where is it?
Is it, does it secure?
Does it need to be anonymized?
Do I need to protect it?
Blah, blah, blah.
Yeah.
Answer those questions firstbecause you might be surprised
to realize that, oh, we haveeverything you need, or maybe
we need to open some doors hereto make the AI system work.
And opening some ofthose doors is great.
(27:56):
Or maybe there's.
Regulatory privacy, dataissues, security issues,
you'll, that may makeyour project impossible.
Galen Low (28:03):
I think that's really interesting that the
first bit is like readinessand that there is this avenue
to be like, Nope, you'renot ready for your project.
We always think of likedelivery frameworks.
We're like, we knowwhat to do, we just need
to know how to do it.
This is we don't knowif we know what to do.
Let's figure that out.
And there might be an off rampthat says, actually AI is not
the solution here, which Ithink is really interesting.
Actually, it brings me toanother question because again,
(28:24):
I've got my project managerhat on, but the more I dig
into this, it has projectmanagement in the title, but
it's not necessarily for projectmanagers because what you just
described to me are like highlevel organizational questions.
Our questions for data folksand technology folks, our
questions for the business.
I think it actually.
(28:44):
Spreads it out a little more.
I came into this going Ron,tell me what organizations
should ask the project managersto get the certification.
But I think it'sbigger than that.
Is it meant for project managersor like what roles should
be getting this training?
And maybe actually the flip sideof that, what roles probably
just don't need this training.
Ron Schmelzer (28:59):
It's funny.
Because I did also strugglewith that whole idea of like
project professionals like you,is it really for project people?
I would say that CPMAI isa methodology for running
and managing the AIproject, whatever it is.
It could be a very shortterm one, it could be a
longer one that you dividein multiple projects.
The idea is that it is aproject from the definition
of a project, which is likewhat a temporary initiative
(29:20):
meant to deliver value.
Galen Low (29:22):
I forget the exact, yeah.
Something unique thatwe don't know if it's
gonna be possible or not.
Ron Schmelzer (29:27):
And so the role of the project manager
is to facilitate that process.
And so it's always beentroubling because on the
one hand you have peoplewho develop strategy.
The C levels and thestrategists they're defining
the mission, the direction.
They're defining theimperatives, they're defining
the objectives, they'redefining the requirements.
They understand whatneeds to happen.
Maybe the external factorslike regulatory and legal, the
(29:49):
internal factors, the resourcesare available and they're
basically setting the groundrules and the ground conditions,
and they're planting the flagand say, we need to be here.
On the other side, you havethese individual contributors
that can do things like,they're builders or they're
finance people, right?
They're there toexecute on the vision.
So you have the visionand you have the
executors of the vision.
But what I see is the roleof the project professionals,
(30:11):
that middle, that messymiddle, which is translating
the organizationalimperatives, the strategy,
the goals and directions.
And pulling in theright resources to
execute on that vision.
So that's not just amadhouse of people all
trying to execute on thesame vision at the same time.
I've seen that before too,where it's like everybody's
just executing, like the visionis this, let's all execute.
(30:32):
Let's do it a second.
We got 20 different versions.
The same conflicting.
It's like some work, some don't.
This happens a lot when you havea highly global organization
with a lot of regions andthe regions all execute that
vision in different ways.
And they're done using maybeeven conflicting things.
It happens, and it canhappen even in smaller
organizations too.
So like the professionals,the project professionals, I
(30:54):
see 'em, there's facilitators.
If I could rebrand projectmanagement to transformation
facilitation, call me.
Maybe that's what it'll be.
But it's basically it's reallyabout facilitating between
the requirements and visionof the people who own the
end results, whether that'sat the C level or whatever.
And the resources that arethere to execute and create
(31:17):
people money, inventory,and technology and AI.
So I actually do say, I'm notsaying this is because I'm
part of PMI, but we obviouslycreated CPMAI even before we
even realized honestly about theproject management profession.
Kathleen and I are notproject management people.
We don't come outta theproject management world,
but ironically, we developedsomething that was in the
(31:39):
heart of PM and I thinkbecause we saw that.
It's this role in that ittakes an organization still
is comprised of peoplewho are trying to create
value for other people.
We haven't yet built anorganization that is completely
autonomous and has nopeople or maybe one person.
(31:59):
People are workingon that by the way.
Until that point we aretrying to, so it almost
feels I wish, like my visionfor what I believe the
ideal state of the futureorganization is much longer
and separate conversation.
I almost feel like the waythat movie studios build
movies is like an idealstate, because movies are
temporary endeavors too.
(32:20):
They build products andyou could say a movie is
not something that deliversconstant value and you
have to provide support.
So I agree there's differencesthere, but when movie studios
need to pull together people fora production we pull together
the individual contributors,you have the producers and
that sort of stuff, and thensomeone is managing this.
Process.
Process to get to a verywell defined end goal on
(32:40):
budget, on time with theresource you have dealing
with the complexity situation.
And without those people, themovie would not been thinking
about it organizational.
Galen Low (32:51):
I like that.
I like that a lot actually.
And you know what, like it'sI was talking earlier about,
how it's been interestingto watch your journey.
It's also been interesting towatch PMI's Journey and you
were just mentioning PM Expo.
And without going too far offscript, like my understandings
is Pierre announced ordescribed the MORE framework.
I don't remember what it standsfor, but the idea is that PMI
is also has been recognizing.
(33:13):
Project management is biggerthan a project manager.
Title is bigger than the projectmanagement profession or world.
It's about translating avision into execution and that
individual or those individualswho are responsible for that.
You don't have to identify asproject manager is, but the
whole getting it done thingand making it deliver value
(33:34):
is valuable and important.
And I know you'vealways believed that.
So it's been interestingto watch those streams.
Join philosophically, but alsonow you're a part of PMI and
I'm like, wow, what a great fit.
Like I think that's a reallyinteresting evolution.
I wanted to come backto something you were
saying, like with themovie studio thing, right?
Earlier we were talking aboutLeapers and then we're talking
about folks who understandthe steps to translate
(33:58):
a vision reliably into amore predictable outcome.
When should an organization behiring someone who might have
the CPMAI designation versussay, a whole bunch of leapers.
What stages should theybe looking at that?
What are the rightroles for them?
Ron Schmelzer (34:13):
Obviously, from my selfish perspectives,
I believe everybodyshould get CP master.
So if you're a leaper andyou wanna truly leap and
get ahead, it's not just amatter of mastering the latest
tools because that will bea bit of a constant chase.
The latest tools keep changing,but you should have, again,
for yourself, this frameworkand how do you run and manage
these things so that youyourself can be successful.
And now as being part of PMIactually the cost driven way
(34:37):
somewhere.
$2,800 and $3,800.
Now I'm talking about,that's very expensive, right?
Now, it's now that CPM is onthe PMI platform, it took a
little while to do it, butnow you can actually get
it directly from pmi.org.
It's 699.
Oh, wow.
Like price.
Yeah, and and it's this zonepricing too, so it's like even
cheaper in, in different zones.
Price should not be the barrier.
(34:57):
So we tell everybody, it's like.
This is something that'sgonna provide long-term value
for you and your career.
One, because it'll be acredential that other people
can say, oh, this personknows what they're doing.
They'll hire you for it, butalso because it'll make you
personally more effective.
But in terms of augmentingthe organization, I
think it requires someform of learning, right?
Whether that's gonna beexperiential learning,
(35:18):
which is important too.
These things are not atodds, so you could say.
You call them hackathons,whatever it is, we need
like opportunities.
We need to give people thefreedom and the psychological
safety to be able toexperiment and use tools
that may or may not be.
Useful in the immediateshort term, but the process
(35:39):
of learning them and gettingexperience, that process by
itself is highly valuable andwill give the organization both
the knowledge and the resilienceit needs to say, oh, we have
people in the organization.
We figured this out.
We know what works.
We know what doesn't work.
The latest tools great.
It's all good.
Learning of someform is required.
In this environment of change.
So it's either experientiallearning, we call formal
(36:01):
forms of learning.
CP A would be a formalform of learning.
I think the one thingI would like to tell
organizations is don't makeup your own methodology.
If you're gonna get make amodel methodology, let's just
get one that already exists.
You can learn it and then fromthat creates something else.
But it's better to start froma position of knowledge than
not to reinvent the wheel.
I hate when that happens.
The second thing I would liketo say is that if you're hiring
(36:22):
other people to help, if you'rebringing in consultants, you're
bringing in contractors, theydo need to have some creds.
It's way too easy now,especially now with like how
easy it's to use AI tools to belike, oh yeah, we're AI experts.
How we've all been likecranking on prompts.
It's big deal.
It's so is my mom.
It doesn't makeher an a AI expert.
So I would think that for us,some sort of certification,
(36:44):
CPMAI, for those who areimplementing parts, should be a
requirement for those doing it.
Galen Low (36:48):
I love that.
One point there is now thatyou're part of PMI, there's
that accountability thing.
I had one client, the onesort of notable time when
having a PMP certificationwas useful in my career.
As a government client,they're like, we want
them to have their PMP.
The project manager needsto have their PMP Why?
So that we can complainabout them to PMI if they
don't do their job likewe can lodge a complaint.
Is that now part of it?
(37:09):
Could someone be like,Hey, listen, I'm working
with some someone who hasa CPMAI certification,
but turns out they have noidea what they're doing.
Can they like lodge a complaintand report them at PMI?
Ron Schmelzer (37:19):
You can't guarantee that people know
what they're necessarily canknow what they're doing when
they, even if you give themthe toolkit to, to do it.
I think, yeah,the answer is yes.
The throat to choke, there'sa certification registry,
there's a community beingpart of it, it's standards.
I think it's really important.
Also, I think justhaving some sort of.
Critical mass is in andof itself, there's some
(37:39):
safety in numbers, right?
Let's say there's anothermethodology somebody created.
It may be great, but it'sonly a few people know it.
Then the problem is thatif you have a problem with
that person, you can't quitego to other people and say
are they doing it right?
Because they're, nobodyelse knows what that is, but
it's that's why I think alot of these methodologies
have worked over time, eventhough some of them may
(38:00):
be of questionable value.
Lean Six Sigma, it's gotsome value, but not in
every industry for a while.
Some of these things havebeen fads the OKRs, that's not
even a project methodology,a management methodology.
Yeah.
It's come into favor,come out of favor, but
what you get is you get.
One, an approach.
So an approach isbetter than no approach.
And two, you get the sortof community of people who
(38:21):
have built things around it.
That's the biggest thing thatwe are actually really hopeful
for and expecting that partof being a PMI is that we're
not the only ones who needto create this ecosystem now.
Now we actually would loveother people to come in here.
Build on top of CPI createmore materials, create more
learning, create more training,create more opportunities.
We don't wanna be in themonopolistic position of
(38:41):
being the only people whosell training around this.
So far that's the casebecause we haven't built the
training partner program.
But in the future,that won't be the case.
We want people to adopt thisand say, maybe there's a CPI
thing for pharmaceutical.
That's done in a very particularway to deal with those things.
I would say there's opportunity.
I mean there's lots ofopportunity here, to
really get things right.
And as I mentioned, we reallyvalue project professionals
(39:03):
are the doers, the peoplewho are really trying to
take vision and help makethat execution a reliability.
Galen Low (39:09):
It's funny because as you were talking the
note I took just as foodsafe, I was like, is this
just for project managers?
And your positionwas actually like.
In a perfect world,like selfishly,
everyone would have it.
But then when I stopped andthought about it, I was like,
that's actually right becauselet's say you're in the food
business, you're a restaurant,or food preparation, whatever,
food safe is that thing that'sgonna underline everything to
(39:32):
make sure that everyone is doingthings in a sort of safe way.
And normally in a projectmanagement, I'd be like,
it's not really safety, butwhen it comes to AI, for
some reason it is, becauseit's a lot of responsibility.
There's a lot ofcriticality to it, right?
Like it's not justfrom my world, right?
Building a website, weknow how to control that.
We know what it's gonna do.
This is actually like, how arewe collectively making good
(39:52):
decisions and following a sharedapproach based on other people's
experience, our community, ourglobal community experience with
AI to make sure we don't either.
Trigger the end of the worldor even just build something
that's actually reallycrap, so I really like that.
You mentioned somethingelse though that I thought
was really interesting.
You said experientialalso is valuable.
(40:12):
And I know that, there'san academic lens, to some
of these certifications.
What is a good potentpairing, right?
So you're gonna do CPMAI,it's not necessarily gonna
be like, Hey, let's do aproject together where you
deliver, an AI driven solution.
But it might be goodto take that learning.
Maybe put it intopractice somehow.
What are some of theopportunities to do safe
practice like HandsOn?
Ron Schmelzer (40:32):
Yeah, that's so powerful.
For right now we have what'scalled a workbook that
we tell people as they'regoing through CPMAI to
work through the workbook.
And the workbook ismeant to be experiential.
So it's take a problem thatyou're trying to solve.
That was actually one of ourlittle final little notes
at the end of our leap orkeynote was that like, what
are you gonna do today?
What's one thingyou can do today?
What's one process thatyou can change today?
It might be small and tiny,but maybe it's something
(40:53):
that you do all the time.
You can just like change thatone thing and AI enable it
and make your life happier.
And then of course, reallythe biggest thing is to
gain that experience, right?
So the workbook, wedo the same thing.
It's go pick something, dosomething you're gonna do
and let's run it through thisCPMAI process and go through
it from beginning to an end.
And then you'll seehow valuable it is.
Then you'll want to do it again.
So we use the workbook for that.
But I would say this is a longertopic, but I think the way that
(41:16):
the training is done and theway that people do workbooks is
still a holdover from the past.
I'd love to do is to AIenable the learning itself.
Yes.
So that you're learningthe whole idea of
learning and doing.
'cause you're talkingabout there's the doing
the experience part, andthen there's the learning.
And the problem isdoing them either in
isolation is problematic.
Learning without doing isconceptuals theoretical.
(41:39):
It's like when you go to collegeand you send the lecture and
you're like, that's nice, butwhat am I gonna do with this?
And then you havethe, just the doing.
But then the doingwithout learning feels
almost aimless sometimes.
You're like.
I will do some prompts.
I'll use this tool, butwhat am I other than
just playing with it?
Or maybe someone told me to useit, like what am I gonna use it?
And maybe if I did, someonedid tell me to use it.
I used it for that one thingand then a couple weeks later,
(42:01):
I'm not doing it anymore.
So it hasn't reallyfundamentally changed.
So what we wanna do is combinethe learning and doing together.
That would be, Ithink, phenomenal.
That's one of the things we'retrying to really work out,
which is that, okay, pull upCMPAI as your sidekick while
you're doing your project.
And work with it, workwith CPMAI to say, Hey,
I'm doing this project,what should I do next?
(42:22):
Or What should I do first?
And then CPMAI say what problemare you trying to solve?
I gotta build my keynoteslides, but I don't
know where to start it.
Would you like me tohelp ideate that for you?
Or something like that.
And if this is an I AI project,oh, this is a good AI thing.
Now the next thing is, thedata part would ask you
like, where's the data from?
Can I get access to it?
The next thing would belike let's prep this.
Is this clean?
The next thing?
(42:42):
Be like, okay, let'sgo ahead and build this
now let's go test this.
Now let's go deploy it.
Then you could seehow that works.
I'm starting to see someorganizations out there
that are starting to do thisblended learning, doing.
Model and I'm keeping aneye on them for ideas to
borrow to see how they do it.
But I think this would be great.
I think every organizationthat has a learning component
(43:06):
will need to think abouthow to integrate the
learning and doing together.
Galen Low (43:09):
No, I love that.
And it's funny because likeeven in my world, from a
training standpoint, ourtraining actually is in
order for it to be practiceoriented and scenario based,
it requires people, right?
It becomes like a service.
Which, without goingtoo into the details is
difficult to scale, right?
Yeah.
Versus if we are training amodel that can be that tough
client or stakeholder andwork and coach you through
(43:29):
a conversation that like isa very practical but like
directional learning, I'dlove to see where that goes.
And honestly, I think you'd bea great person to be working
at that with BMI, and likein the AI world overall.
Ron Schmelzer (43:44):
See, now we're talking about
transformation, right?
This goes back full circleto where we started, which is
that we're not talking aboutjust implementing a tool in
the way we're doing things now.
It's just rethinking theway things are doing.
'cause it might actually bemore powerful for everybody.
See, we tied all thesubject topics together.
Galen Low (43:59):
Ah, see that's how the pros do it.
Ladies and gentlemen,Ron Schmelzer.
Honestly, Ron, thankyou so much for spending
the time with me today.
Love having you on the show.
Love what you're doing.
And we're gonna haveto have you back there.
Were at least threeother podcasts in there.
Ron Schmelzer (44:12):
Oh, for sure.
Always thrill to be talkingto you and to sharing all
this with your audience.
Being out there and beinghelpful, I think we're all
on this journey together.
Galen Low (44:19):
Absolutely.
Alright 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 to
thedpm.com/membershipto learn more.
And if you like what youheard today, please subscribe
and stay in touch onthedigitalprojectmanager.com.
Until next time,thanks for listening.
“AI projects.
Pfffft.” — your colleaguescoffs at the idea over lunch.
“A passing trend at best.”adds your program director.
“They’re no differentthan other projects.”
But you start looking aroundthe cafeteria, and you can't
help but feel that these peopleyou respect enough to break
bread with might be buryingtheir heads in the sand.
At the next table over,Germaine from Finance is
(00:22):
explaining to Lateicia fromHR how Gemini is fixing her
formulas in Google Sheets.
At the coffee station, Paulois pitching an idea for
using AI and machine learningto convert gigabytes of
unstructured customer feedbackinto actionable insights.
By the elevators, Sonayais having a futurist
conversation with theirdepartment chief about nimble
AI-supported processes thatself-document and self-improve
(00:44):
through user feedback.
Heck, even Andréa is there inthe corner using Perplexity to
plan their own retirement party.
AI projects.
They're everywhere.
And there's a lot more toconsider than whether AI will
come up with a good enoughsuggestion for a party theme.
Wondering if your organizationhas the chops to take on
projects that involve AI?
Keep listening.
We're gonna be diving intowhat organizations need to be
(01:06):
doing to support people beingimpacted by AI and how they
can get their people on thesame page when it comes to AI,
so that they can all surviveand thrive in this next wave
of digital transformation.
Hey folks, thanks for tuning in.
My name is Galen Low withthe Digital Project Manager.
We are a community of digitalprofessionals on a mission
(01:28):
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.
And if you're intofuture-forward conversations
and practical insights arounddigital project leadership,
consider subscribing to theshow for weekly episodes.
Alright, today we are talkingabout the next wave of digital
(01:50):
transformation that's beingcreated by generative AI and
how a shared team mindset aroundAI can take an organization
from spotty, haphazard,anxiety-ridden adoption to a
more cohesive and deliberatebenefit that lifts all boats.
With me today is Ron Schmelzer,co-founder of Cognilytica
and now the Global Head ofAI Partnerships and Outreach
(02:13):
for PMI Cognilytica at theProject Management Institute.
Ron, thanks for beinghere with me today.
Ron Schmel (02:19):
Thrilled to be here.
Always love chatting with you.
Galen Low (02:21):
Yeah, I love having you back on the show and I've
been like quietly observingyour journey Fanboying a little
bit for the past several yearsas we've been talking about,
Cognilytica, what you'redoing there and now, this
sort of merger into the PMIecosphere many exciting things
that I wanted to dig into.
I thought maybe I would juststart with one hot question.
(02:43):
The big question, and Ithink this is tying back
to something you and Iwere jamming on earlier.
Which is that the currentadoption of generative AI
solutions has been likened rightby you, by me, has been likened
to the advent of the computer orword processing software or the
telephone, or practically likeany big shift that quickly made
(03:04):
many roles within the workingworld, more or less redundant.
So the big question is this, howcan organizations support people
whose job will be affectedby AI in the imminent future?
Ron Schmelzer (03:15):
I think part of the reason why AI feels
like that is because ofits transformative nature.
We talk about it changesthings and that's what
transformative is.
It transforms, it changesone thing into another thing.
Maybe I just definetransformation for, in this
case it's like people see thata lot of the tools, especially
for knowledge workers, wetalked about this earlier, but.
We have a knowledge recordwhose primary job is dealing
(03:37):
with information, whetherit's MO putting information
into an information systemor taking information out
of an information system orcondensing it or summarizing
it or describing it or puttingit into reports or doing things
like filing compliance reports.
All of that stuff now isso relatively trivial for
machines to do where itwasn't trivial for them
(03:57):
to do a couple years ago.
So organizations noware rethinking this.
The non transformational wayof doing this would be somebody
who's in that role usingthose tools to do the existing
things that they do, and theprocess doesn't change and
things really don't change.
Maybe they save a little cost,they speed some things up,
they improve their accuracy,whatever those measures are.
But the processremains the same.
(04:18):
But the organizations thatare really transforming or
kind of really rethinking theprocesses from the get go.
As you mentioned, we had thisexperience so many times.
Before the internet, theshopping experience was
you go to a store and theyhave what they have in
stock or they don't, youpurchase and you box it out.
And then it took rethinking.
E-commerce wasn't justthe same experience.
You walk into a store, but it'sonline and things on a shelf.
(04:39):
I think people tried todo that, like we actually
looked like a store andthere were things on shelves.
But then it took peopleto realize, wait a second.
We don't need to have the sameideas of inventory and we can
do all this sorts of stuff.
Same thing's gonnahappen with AI.
So I think the scary partis that when you're in an
organization that hasn't madethat process transformation,
then AI is a little bitlike a sledgehammer, right?
(05:01):
It's coming in and it'sjust causing a lot of
destruction and chaos, right?
It may be getting thingsdone, but it's doing things.
At the expense of theexisting processes because
the processes haven't changed.
All we're doing is we'rejust bringing in these tools
and that means that peopleare getting squeezed out of
the process, if you will.
Galen Low (05:20):
That's so interesting.
You know what I found reallyinteresting about that is that
a lot of the conversationsthat I've been having and
that I've been seeing onlinearound AI is don't worry.
It's gonna be here tohelp you do the same
things you always do.
And what you just said therewas very interesting because.
You're saying that from anorganizational standpoint,
one of the better thingsthat an organization can
do is cast a transformativevision for the future.
(05:41):
That like we know thingswon't be the same.
We don't want you to writephone numbers into your Rolodex
and then scan them and thenput them into your phone.
That's not what we want.
We want to reimagine whatwork looks like and you have
a place in that and we'regonna help you get there.
It's not tomorrow.
It's like we're gonna get there,but we have to start thinking
about it not as the AI versionof what we were doing before,
(06:05):
but like a new thing thatwe're doing with AI involved.
Ron Schmelzer (06:09):
And I would argue that's actually
harder to do those kind oftransformations than it is
to implement technology.
'cause you could say theprocess works like this.
Part of the process are thesesteps, I'm gonna replace those
steps of the machine that'sactually cognitively, it's
easier to think that thanto say, wait a second, if
I had to rethink this wholething, how would it work?
That's a lot harder to do.
It's riskier in some waysbecause you don't know
(06:29):
if the new process willwork out, but it would be
so much more impactful.
So I think that's the hard part.
The hard part, we actually dida little keynote at the end
of the last PM expo sessionand we said, the thing that
differentiates, we were doing alot of observation of the people
who were trying to make AI work.
A lot of people wereusing it for themselves.
It wasn't like the organizationmandated the use of it, in
some cases even allowed it,but people were just using
(06:51):
it on their own anyways.
And what we foundwas that there's four
rough groups of people.
There were people whoweren't using it at all.
They're like we call sideliners or just observers.
You're just lookingto see what it is.
They're either skepticalor fearful or whatever, or
doubtful, and they'd rathernot do anything with AI.
Then you have this othergroup, which are like taskers.
They're like, okay,I'm doing my job.
Oh, once in a while I'mgonna use AI to write
(07:13):
these emails or to do this.
Spreadsheet and nowwhatever it is, right?
But I'm still doing my thing.
It's just you know I, it'slike they call them taskers.
It's I'm gonna use AI forthis task when that task.
But fundamentally, nothingelse has really changed.
Then you have a third group,which they love using technology
for, technology's sake,early adopters, enthusiasts,
whatever you wanna call them.
AI is the latest tool.
(07:33):
It'll be one model today, thenanother model the next day, some
new tool they discovered online.
It's more like they enjoythe process of discovery.
And I would say they'reprobably just as marginally
useful as the taskersbecause it's not like they're
changing their job either.
There's a fourth groupthat we observed, but we
call the Leapers, which arebasically, they're the ones
who are being strategic andthey're like, you know what?
(07:55):
I'm not gonna just use someAI for my task, but I'm
gonna change something in.
I'm gonna just redo the wholeconcept of my task, the job
or the thing that I'm tryingto do, and rethink it from
that AI first perspective.
We would call them the leapers.
Those are the ones that wewanna keep an eye out of for,
because ironically, they'reguaranteeing their job security.
(08:16):
It's weird becausethey're saying No.
Now I'm capable of usingthis technology in a way
that can drive positivetransformation value.
You need me here, right?
All the others, we couldthink about that later.
Galen Low (08:27):
There's so much in there.
And thank you forthat breakdown.
I love your sort of groupingsand I certainly know which
one I fall into, but we'llkeep mum about that for now.
What's interesting isthis combination of like
organizational leadershiparound AI is required, but it's
not necessarily your currentleadership structure that
is going to have that visionof AI and what it can do.
(08:47):
In fact, there's gonna be a lotof apprehension, which is why
a lot of organizations haven'tfully baked it into like their
policies and their proceduresand their processes and why it
happens on an individual basis.
But then you have this rampantsort of everyone, free for
all trying out tools, whetherthey're allowed to or not
within their organization.
To help them do the same job maybetter, but if I'm understanding
what you're putting down here.
(09:09):
If organizational leaders canlook out for those leapers
and give them a bit of anecosystem to like almost r and
d it for the organization witha bit more blue sky thinking.
Not that sort of, I'm gonna messthis term up, but I think we use
the term brownfield sometimesin consulting where it's yeah,
you have what you have whatyou have versus a blue sky kind
of thing, or like greenfield,where it's just like.
We can do anything we want.
(09:29):
Let's have these, like whatever.
I don't know, hackathon'sprobably an outdated concept
at this point, but like, howcan we create these communities
of practice where we haveleapers that could literally
lead the charge of transformingthe business because
they're in that category.
They're not the taskers,they're not the tester
early adopter folks.
They are folks who have a visionthat we can probably support.
(09:50):
I know that the, like criticin my head is steal, but
that's not what I mean.
What I mean is the peoplein your organization can
help you build this sortof collective mindset.
I'm borrowing your word fromlater in the conversation,
but this collective mindsetaround AI, it's not up to
individual, whatever executivesaround the world to just
be visionary AI people.
You gotta lean onthe folks who get it.
Ron Schmelzer (10:12):
Yeah.
I'm gonna use a left fieldanalogy here, but like
I'm sorting exactly whatyou're talking about.
Left field, just talkingabout Brownfield.
So I don't know what.
Galen Low (10:19):
There's a lot of fields in this conversation.
Ron Schmelzer (10:20):
No, it's exactly.
I think about the changingnature of warfare.
So if you think about what'shappening in Ukraine right now,
I. When the war started, we hadour, there, they had, I should
say, very traditional the latestmindset in fighting warfare
with armored divisions and largetroops and frontal assaults.
And even things like theidea of trying to gain air
(10:42):
dominance first, like youcome in with that first wave.
None of that workedfor a couple reasons.
One, they didn't expectresistance, which is
a whole other story.
They were expectingthe government to just
flee and, that happen,which is intriguing in.
The second thing is that thenature warfare has changed.
Of course, we'retalking drone warfare.
We're talking the use ofsmall disposable little units
(11:05):
that have actually made thewhole idea of an armed attack
with large armed piecesuseless and it's so strange.
So they just like, wheredid that come from?
I remember at the verybeginning of the world,
we had these people likeexperimenting we don't know.
We never tried this drone thing.
Let's see if we can dropgrenades from a drone.
And at the beginning itwas like, oh, look at
these interesting people.
(11:25):
They're trying somethingweird in left field.
Then someone's let's do thisFPV drone thing where we can fly
the drone, put something on it.
At first it wasinteresting and comical.
Now it is the way, it's likethe funny thing, it went
from a something that wason the fringes to now the
core, and I think it's calledinto question, even the
largest armies, like whether.
(11:45):
Any of this matters anymore.
Whether the $60 million tankis worth anything anymore, they
have to put cages around them.
Now it does remind me of allthis AI experience going on the
edge and someone's wait, there'ssomething more fundamentally
transformative here if we justconsider this to be fringe.
We're missing out onthe big picture here.
Galen Low (12:02):
I like that analogy because I think there are a
couple of traditional forcingfunctions for human innovation.
For better or for I would saypersonally, unfortunately.
Combat and survival is oneof the fastest drivers of
innovation, thank you, DARPANET.
Thank you, CD-ROM.
Thank you.
All sort of military technologythat was necessary to be
(12:23):
either prepared for waror to be effective in war.
But even you and I in thegreen room, we were talking
about the pandemic, right?
We're talking aboutreturn to office.
We're talking about someof these like kind of
forcing functions that likemake us have to innovate.
And then I think you're right.
I think there's like.
Frankly, even right now withthe wars that are happening,
it's a lot of, sidelinergoing let's see what happens
(12:45):
because this is the firsttime we've been able to like
test and see how this likeactually can work in practice.
And I think AI islike that as well.
And coming back to the sortof world of knowledge work.
I think there is thatsame thing happening.
They're like, okay,this is interesting.
Let's like let them all fight itout and see where this nets out.
It leaves us in thisvery uncomfortable space
(13:07):
where everyone's we feelinsecure, actually we
feel unsafe in our jobs.
This is getting hashed out andno one's telling us anything.
No one's saying, don't worry,this discomfort will end.
No one saying.
Don't worry about this,like this is trivial.
We're saying this isserious and we don't know
where it's gonna go, butwe have to let it play out.
And there's this justpsychological lack of safety and
lack of security in everyone'slike professional lives now.
Ron Schmelzer (13:30):
I think there's a lot of that
same lack of psychologicalsafety 'cause nobody really
knows where this is going.
Maybe all things are acalled into question.
So bringing it back to AI here Ithink one of the biggest things
that organizations can do now.
Is to broaden theexperiential and learning
parts of the organization.
It shouldn't be like siloedinto this will be the AI
(13:51):
experimentation group, ormaybe we'll do something within
product, or we'll do somethingwithin it that doesn't make any
sense because of how impactfulAI is just across the board.
Maybe you can rethinkthe HR department.
Maybe you can rethink finance.
Maybe you can rethink supplychain management, the whole
thing, logistics, and getback to the core of what
it is you're trying to do.
Because every organizationhas a mission as an objective.
(14:13):
And the mission isn't theprocesses, the mission
isn't the technology.
Those are means to an end.
Right?
And so part of it isnow the thing that we're
adding to the picture.
As we did with computers,as we did with the phone, as
we did with the internet, iswe're adding AI to the picture.
Now, AI becomes a resourceor a tool that we can use
to facilitate the mission.
(14:36):
This is why it's a lot of peopleare, it does stir things up.
As I said, transformation, ashas both positive and negative
change associated with it.
Galen Low (14:43):
It's interesting you describe it as a tool.
You see a lot of whateverTerminator references nowadays.
Is this Skynet?
Will it become self-awareand destroy us?
And yes, those are big questionsin the AI world, top to bottom.
Like they're serious questions,but like right now and like
the way, like I think it'sa refreshing reframe of
the fact that, yeah, it'slike not the super villain
in the story right now.
It's just like partof the picture.
(15:04):
It might even be abackground actor that's just.
Influencing the way the storygoes and we might need to decide
how we use that character,but it's not necessarily going
to be the one that's gonnaredefine everything for us.
And maybe it, disruptor, maybenot Antagonizer, but a bit of
a disruptor to force our maincharacters to do something to
center back to their mission,to continue on their journey.
Ron Schmelzer (15:25):
It's kinda interesting, one of the
things you'd always spendtoo much time talking about.
It's been in like our CPMAItraining for a long time.
Even we glossed it overwhat should he be afraid of?
AI losing my job.
Super intelligence, badpeople doing bad things.
Those are things that wetalk about all the time.
There's a little sub note,which is that people have a
fear of a lot of power beingconcentrated into a small
number of people's hands.
(15:45):
We always glossed it over, but Inow, especially what's happening
here in the US and with politicsand somebody who actually knows
how to wield this technologyon government employees.
It's like maybe that islike the bigger fear than
the super intelligent.
We're afraid of machines beingsmart enough to out smart
people, but I'm like, maybe weshould be more afraid of just a
small number of people who haveaccess to all the data, all the
(16:07):
models, all the resources in theworld to do whatever they want.
That should be, I think, muchmore of a existential, immediate
threat than the future, whichwe haven't even achieved yet.
Part of that is like part ofme is feeling that one of the
great antidotes to that, oneof the things that can make
people feel a little lessfearful because the fear is out
there, a little less uncertain.
'cause the uncertaintyis certainly out there
(16:29):
is personal empowerment.
I'm starting to choose,feel that what we need more
is personal empowerment.
People doing thingsfor themselves.
Whether it's part of theirwork or whether it's not,
with basically using AI asa means to augment their own
personal abilities and wantto give them the resilience.
(16:52):
That they need.
And two, I think as acounterweight, because it's
very hard to put your faith nowin companies, in governments,
it's hard to put your faith inalmost anything at this point.
And so if you have to putyour faith in something, it's
probably best right now tobe putting it in yourself.
Galen Low (17:07):
No, I think that sound advice and actually a
really good response to theoriginal question, which is
like, how can organizationssupport folks whose job
is gonna be impacted?
Yeah, arm themwith the knowledge.
Don't make it so thatyou can lock them in.
Don't pay for the MBA and makethem stay for five years, but
just arm them because they'reon their own personal mission
and you probably will benefitas an organization, but you
(17:28):
also don't own that brain.
And you don't wanna yeah.
The loyalty game, the sortof getting the watch or the
gold pen after 50 years ofservice is not a thing anymore.
Ron Schmelzer (17:37):
One of the things I'm gonna be talking about,
I have a talk coming up atBarcelona for the Global Summit
that PMI does and is I wantedto like really think about this
idea of resilience, actuallymentioned in a little bit and
what organizational resiliencemeans in the face of AI.
And the irony of that isif you want an organization
that's resilient is able tothrive and survive in the face
(17:58):
of constant change, right?
That's really whatresilience is all about.
And the irony of resilienceis that it does require you
to be much more flexible andadaptable, especially in things
like processes and you'rethe way you're doing things.
It's if you are overlycommitted to the way you're
doing things, that actuallymakes you less resilient.
(18:19):
If you just simply focus onautomating the things you do
and removing the people fromthe picture, you're actually
doubling down in the way you'redoing things now, and you
actually have decreased yourorganizational resilience.
'cause now you have fewerpeople in the organization
who can deal with the change,and now you have an entire
dependence on the systems.
Maybe you can ask the AIhow to improve itself.
That's possible.
(18:40):
Like organizational resiliencereally is all about speeding
up the velocity of learningand increasing the speed to
respond to change, which meansreally empowering people to be
forces of change and drivingthat force of change, not from
the top, but from the bottom,and enabling people to do that.
(19:02):
I think there's a change.
I think there's a mindshift change in the,
of what organizationalresilience really means.
Galen Low (19:07):
I really like that notion of
organizational resilience.
In other words, like areframing on resilience,
'cause a lot of folks wouldbe like, cool, we have like
airtight processes and we'vebeen doing this for years and
that's how we're gonna survivethis and weather the storm
and, but maybe not anymore.
I wondered if, actually Ican use that as a bit of
a pivot point because Iwanted to zoom out a bit.
You mentioned the CPMAIcertification and things I
(19:29):
know about you, you were aco-founder of Cognilytica,
which is an AI and projectfocused organization, which
was, in my world, way ahead ofthe curve of when it launched.
The first, and I don'tknow, maybe only, I should
probably have fact checkthat before, but I think the
first certification for teamsdelivering AI driven solutions.
Ron Schmelzer (19:47):
It's the only, there's a few other certs for
using AI and project managementand different aspects of
AI project management, butlike for whatever reason, no
one's really created a vendorneutral methodology on how
to run and manage a project.
So that's whatCPMAI was all about.
We're like, let's do, yeah.
Galen Low (20:01):
It's a big ask, right?
A, it's moving at the speedof light or sometimes, has
these moments of gosh, theword you use, not a nice
age, but this moment ofyeah, we give up on AI.
Oh the winters.
The winters, yeah.
Like we've gone throughmultiple AI winters, over the
past like several decades.
And it's a hard thing toplace a bet on, right?
To be like, yeah, here'show to do an AI project.
The funny thing is that likeyou were on the show a couple
(20:23):
years ago, you and Kathleen,and at the time I thought of
AI projects as like the kindsof projects where, you've got
a team of like data scientistsand analysts and developers,
and they are creating an AI.
But one thing you pointedout to me in the, and you
alluded to now, is that.
Many projects thesedays will involve AI
somewhere in the solution.
In fact, all of thisorganizational resilience
(20:44):
that we're talking about andprocesses and how can we use
AI to rethink some of thesethings are all projects.
You know what I mean?
And the thing you also saidwhich is like part of the
certification is understanding.
Having ways to manage fearand apprehension around AI.
The ethical side of thequestion, this sort of
mindset and these steps togo through to really create
(21:06):
a considered AI solution thatis resilient in and of itself.
Also isn't that thing thatwill become self-aware
and take over the worldwith that in mind, right?
Like I'm thinking oforganizations, what
type of organizations isthe CPMAI certification
relevant for today?
And what's the differencebetween building a project
(21:28):
team that just has experienceusing gen AI tools versus
having a team that like hasthe CPMAI certification?
Ron Schmelzer (21:34):
We'll start first by talking
about why it was created.
Where did CPMAI come from?
Because we thought.
We were talking to a largegovernment organization and a
large bank, and they were bothlooking at putting AI into use
for some, one of their coreprocesses we're like, like we
just wanna just tell us theapproach that we should use
so that we know that at theend of the day when we deliver
and deploy this solution, wecan rely on this AI to do the
(21:56):
things that we had previouslyrequired people to do.
And we had to be like,okay, certainly there's
gotta be some approach.
You can't just take.
Traditional project developmentmethodology itself, it
works when you're buildingsoftware, a website 'cause
you have a deliverable.
Then you could be agile,you could be like, maybe
you're discovering thefunctionality as you go along.
Maybe you have a connectionwith the user and
(22:16):
what they're gonna do.
You could definefunctionality points.
You still can't make the AIdo what you want it to do.
That's the irony of it.
It's how do wemake this AI thing?
Do we what we wanna do?
It's data driven.
And what we realized oh, okay,we need a data driven, we need
a data focused methodologywhere the functionality is
not driven by the programming.
The functionality isdriven by the data.
It would be like as ifwe're building some sort of
(22:38):
spreadsheet analysis thing waslike what's the methodology
you would use to build acomplicated spreadsheet?
It's not.
Project development.
What is it exactly?
It's there was a methodologycalled CRISP DM that was out
there for a while and it wasreally good, but it's not
iterative, it wasn't agileand it wasn't AI focused.
So we're like, what we'regonna do is we're gonna
marry these worlds together.
Let's use a data-centricmethodology, crisp dm, but
(22:58):
let's enhance it and make itAI relevant so that way we can
deal with the fact that AI doesnot give guaranteed results.
It.
You're building a projectwhere you're depending
on a very importantcomponent of that project.
Perhaps an intern that youhired a week ago who knows
something, but not everything,but you can't fire them
because you're dependent onthem, but you can't also just
(23:21):
delegate everything to them.
So how would you work a projectwhere you're like, this intern
is delivering half the value.
They're like,they're looking at.
Medical images and tellingyou if there's a possible
aneurysm in them or something.
So that's what CPMAI is likethis approach is this approach
to running and managingprojects where you can increase
your rate of success giventhe fact that AI is this
constantly moving things.
The irony of is that ccp,MI is methodology has been
(23:42):
fairly stable even with allof these AI changes because
the method that you use shouldbasically stay the same.
The technology that you'reusing can change and
adapt as your free grant.
New ways to do it.
Interestingly enough, thathas meant that over time we've
required fewer and fewer datascientists in our AI projects.
Now, I would say the vastmajority of AI projects are
(24:04):
being run and managed by peoplewho are not data scientists
and machine learning engineers.
'cause we're using off the shelfmodels and things like that.
Now with the Gentech AI,it comes down to this idea
of, now I have to stitchtogether these multiple pieces
and each of these pieceshave to be run and managed
properly to make it work.
And so CPMAI hasbeen really helpful.
Now we have sort of two usercommunities, if you wanna
think about it, that adopt it.
(24:25):
We have sort of organization,organizational user
communities that is, that areimplementing it for themselves.
They're trying to speed uptheir AI initiatives and they're
trying to do it in a sort ofstandard way, whether they're.
Banks or insurancecompanies, pharmaceutical,
construction, manufacturing,government agencies, it
goes, it's across the board.
And then we have consultancieswho are basically out there
(24:45):
implementing AI projectson behalf of others and
want to show that they havethe process where they can
give a guaranteed result,which is what you wanna do.
And there's a lot of CPMAII is about, we have the six
phases, and some of it'sabout documenting decisions
and some of it's about doingthings in the right order and
discovering things that youdon't have before you get there.
Also coming up with plansfor you to evaluate and test
(25:08):
your AI solutions before youdeploy them and manage them
and monitor them as they'reconstantly in progress.
So it's a weird kindahybrid between maybe product
management methodology andproject management methodology,
and it's got a little bitof everything in there.
Galen Low (25:23):
What I found really interesting about it from where
I stand in my knowledge set,is that thing you said about
doing things in the right order.
Because my project managerbrain went, okay, when we don't
know what we're building andwe can't guarantee that we're
gonna be able to build it,we'll just iterate through it.
And in my digital worldit's code and yeah, some
data structure, right?
There's still likearchitecture that's important.
(25:43):
These are more or lessknown quantities that are
architects can play with.
Like it's malleable versusthe data side where your
entire solution is relyingon like the fuel for the
actual machine is clean data.
And there is a sort offoundational first step of
making sure you have goodclean data and how to police
whether or not you have goodclean data coming in before
you know if your AI solutionactually is working or not.
(26:06):
Because it could justbe, for lack of a
better word, sucky data.
Which is funny because the thingyou were saying earlier about
you can't isolate power and likethese similar groups, we have
to have a bottom up approach.
I think our like dress rehearsalfor AI has been data where,
I still see organizations,and don't get me wrong, it's
a very complex challenge,but I've seen a lot of
organizations right up to.
(26:27):
Struggling with datagovernance and who building
the cross-functional teams tomake data do something useful.
They're, we're greatat collecting it.
We have every tool inthe world to collect it.
But the committee, the groupof people, the conversation
almost becomes too largebecause HR and marketing and
it, and people from everypart of an organization.
Trying to be at this table andthe table isn't necessarily
(26:49):
big enough for us to beproductive about how to get us
good clean data to begin with.
Ron Schmelzer (26:53):
Yeah, I'm running into that problem
as we speak, unfortunately.
Unfortunately, it's likesometimes like just getting
access to the data is an issueand like the timeliness of
it and it's trapped insideof one system and you don't
have access to that system.
It's like it kills the whole AIproject if you're depending on
that for your AI project, andyou can't even get to your data.
(27:14):
You're not gonna get anywhere.
So it's a good idea is todiscover that first before
you're like, well down thepath of buying and implementing
tools, then realize you don'teven have the data you need.
Buying and implementingtools is like step four
in the CPM methodology.
Step one is, what problemare you trying to solve?
Doesn't need AI and whatpattern of AI and with
how the AI go, no go.
Can you even proceedwith this project?
(27:35):
And then step two isthe data discovery data.
Understanding whatdata do I need?
Do I have access to it?
What formats it in?
Where is it?
Is it, does it secure?
Does it need to be anonymized?
Do I need to protect it?
Blah, blah, blah.
Yeah.
Answer those questions firstbecause you might be surprised
to realize that, oh, we haveeverything you need, or maybe
we need to open some doors hereto make the AI system work.
And opening some ofthose doors is great.
(27:56):
Or maybe there's.
Regulatory privacy, dataissues, security issues,
you'll, that may makeyour project impossible.
Galen Low (28:03):
I think that's really interesting that the
first bit is like readinessand that there is this avenue
to be like, Nope, you'renot ready for your project.
We always think of likedelivery frameworks.
We're like, we knowwhat to do, we just need
to know how to do it.
This is we don't knowif we know what to do.
Let's figure that out.
And there might be an off rampthat says, actually AI is not
the solution here, which Ithink is really interesting.
Actually, it brings me toanother question because again,
(28:24):
I've got my project managerhat on, but the more I dig
into this, it has projectmanagement in the title, but
it's not necessarily for projectmanagers because what you just
described to me are like highlevel organizational questions.
Our questions for data folksand technology folks, our
questions for the business.
I think it actually.
(28:44):
Spreads it out a little more.
I came into this going Ron,tell me what organizations
should ask the project managersto get the certification.
But I think it'sbigger than that.
Is it meant for project managersor like what roles should
be getting this training?
And maybe actually the flip sideof that, what roles probably
just don't need this training.
Ron Schmelzer (28:59):
It's funny.
Because I did also strugglewith that whole idea of like
project professionals like you,is it really for project people?
I would say that CPMAI isa methodology for running
and managing the AIproject, whatever it is.
It could be a very shortterm one, it could be a
longer one that you dividein multiple projects.
The idea is that it is aproject from the definition
of a project, which is likewhat a temporary initiative
(29:20):
meant to deliver value.
Galen Low (29:22):
I forget the exact, yeah.
Something unique thatwe don't know if it's
gonna be possible or not.
Ron Schmelzer (29:27):
And so the role of the project manager
is to facilitate that process.
And so it's always beentroubling because on the
one hand you have peoplewho develop strategy.
The C levels and thestrategists they're defining
the mission, the direction.
They're defining theimperatives, they're defining
the objectives, they'redefining the requirements.
They understand whatneeds to happen.
Maybe the external factorslike regulatory and legal, the
(29:49):
internal factors, the resourcesare available and they're
basically setting the groundrules and the ground conditions,
and they're planting the flagand say, we need to be here.
On the other side, you havethese individual contributors
that can do things like,they're builders or they're
finance people, right?
They're there toexecute on the vision.
So you have the visionand you have the
executors of the vision.
But what I see is the roleof the project professionals,
(30:11):
that middle, that messymiddle, which is translating
the organizationalimperatives, the strategy,
the goals and directions.
And pulling in theright resources to
execute on that vision.
So that's not just amadhouse of people all
trying to execute on thesame vision at the same time.
I've seen that before too,where it's like everybody's
just executing, like the visionis this, let's all execute.
(30:32):
Let's do it a second.
We got 20 different versions.
The same conflicting.
It's like some work, some don't.
This happens a lot when you havea highly global organization
with a lot of regions andthe regions all execute that
vision in different ways.
And they're done using maybeeven conflicting things.
It happens, and it canhappen even in smaller
organizations too.
So like the professionals,the project professionals, I
(30:54):
see 'em, there's facilitators.
If I could rebrand projectmanagement to transformation
facilitation, call me.
Maybe that's what it'll be.
But it's basically it's reallyabout facilitating between
the requirements and visionof the people who own the
end results, whether that'sat the C level or whatever.
And the resources that arethere to execute and create
(31:17):
people money, inventory,and technology and AI.
So I actually do say, I'm notsaying this is because I'm
part of PMI, but we obviouslycreated CPMAI even before we
even realized honestly about theproject management profession.
Kathleen and I are notproject management people.
We don't come outta theproject management world,
but ironically, we developedsomething that was in the
(31:39):
heart of PM and I thinkbecause we saw that.
It's this role in that ittakes an organization still
is comprised of peoplewho are trying to create
value for other people.
We haven't yet built anorganization that is completely
autonomous and has nopeople or maybe one person.
(31:59):
People are workingon that by the way.
Until that point we aretrying to, so it almost
feels I wish, like my visionfor what I believe the
ideal state of the futureorganization is much longer
and separate conversation.
I almost feel like the waythat movie studios build
movies is like an idealstate, because movies are
temporary endeavors too.
(32:20):
They build products andyou could say a movie is
not something that deliversconstant value and you
have to provide support.
So I agree there's differencesthere, but when movie studios
need to pull together people fora production we pull together
the individual contributors,you have the producers and
that sort of stuff, and thensomeone is managing this.
Process.
Process to get to a verywell defined end goal on
(32:40):
budget, on time with theresource you have dealing
with the complexity situation.
And without those people, themovie would not been thinking
about it organizational.
Galen Low (32:51):
I like that.
I like that a lot actually.
And you know what, like it'sI was talking earlier about,
how it's been interestingto watch your journey.
It's also been interesting towatch PMI's Journey and you
were just mentioning PM Expo.
And without going too far offscript, like my understandings
is Pierre announced ordescribed the MORE framework.
I don't remember what it standsfor, but the idea is that PMI
is also has been recognizing.
(33:13):
Project management is biggerthan a project manager.
Title is bigger than the projectmanagement profession or world.
It's about translating avision into execution and that
individual or those individualswho are responsible for that.
You don't have to identify asproject manager is, but the
whole getting it done thingand making it deliver value
(33:34):
is valuable and important.
And I know you'vealways believed that.
So it's been interestingto watch those streams.
Join philosophically, but alsonow you're a part of PMI and
I'm like, wow, what a great fit.
Like I think that's a reallyinteresting evolution.
I wanted to come backto something you were
saying, like with themovie studio thing, right?
Earlier we were talking aboutLeapers and then we're talking
about folks who understandthe steps to translate
(33:58):
a vision reliably into amore predictable outcome.
When should an organization behiring someone who might have
the CPMAI designation versussay, a whole bunch of leapers.
What stages should theybe looking at that?
What are the rightroles for them?
Ron Schmelzer (34:13):
Obviously, from my selfish perspectives,
I believe everybodyshould get CP master.
So if you're a leaper andyou wanna truly leap and
get ahead, it's not just amatter of mastering the latest
tools because that will bea bit of a constant chase.
The latest tools keep changing,but you should have, again,
for yourself, this frameworkand how do you run and manage
these things so that youyourself can be successful.
And now as being part of PMIactually the cost driven way
(34:37):
somewhere.
$2,800 and $3,800.
Now I'm talking about,that's very expensive, right?
Now, it's now that CPM is onthe PMI platform, it took a
little while to do it, butnow you can actually get
it directly from pmi.org.
It's 699.
Oh, wow.
Like price.
Yeah, and and it's this zonepricing too, so it's like even
cheaper in, in different zones.
Price should not be the barrier.
(34:57):
So we tell everybody, it's like.
This is something that'sgonna provide long-term value
for you and your career.
One, because it'll be acredential that other people
can say, oh, this personknows what they're doing.
They'll hire you for it, butalso because it'll make you
personally more effective.
But in terms of augmentingthe organization, I
think it requires someform of learning, right?
Whether that's gonna beexperiential learning,
(35:18):
which is important too.
These things are not atodds, so you could say.
You call them hackathons,whatever it is, we need
like opportunities.
We need to give people thefreedom and the psychological
safety to be able toexperiment and use tools
that may or may not be.
Useful in the immediateshort term, but the process
(35:39):
of learning them and gettingexperience, that process by
itself is highly valuable andwill give the organization both
the knowledge and the resilienceit needs to say, oh, we have
people in the organization.
We figured this out.
We know what works.
We know what doesn't work.
The latest tools great.
It's all good.
Learning of someform is required.
In this environment of change.
So it's either experientiallearning, we call formal
(36:01):
forms of learning.
CP A would be a formalform of learning.
I think the one thingI would like to tell
organizations is don't makeup your own methodology.
If you're gonna get make amodel methodology, let's just
get one that already exists.
You can learn it and then fromthat creates something else.
But it's better to start froma position of knowledge than
not to reinvent the wheel.
I hate when that happens.
The second thing I would liketo say is that if you're hiring
(36:22):
other people to help, if you'rebringing in consultants, you're
bringing in contractors, theydo need to have some creds.
It's way too easy now,especially now with like how
easy it's to use AI tools to belike, oh yeah, we're AI experts.
How we've all been likecranking on prompts.
It's big deal.
It's so is my mom.
It doesn't makeher an a AI expert.
So I would think that for us,some sort of certification,
(36:44):
CPMAI, for those who areimplementing parts, should be a
requirement for those doing it.
Galen Low (36:48):
I love that.
One point there is now thatyou're part of PMI, there's
that accountability thing.
I had one client, the onesort of notable time when
having a PMP certificationwas useful in my career.
As a government client,they're like, we want
them to have their PMP.
The project manager needsto have their PMP Why?
So that we can complainabout them to PMI if they
don't do their job likewe can lodge a complaint.
Is that now part of it?
(37:09):
Could someone be like,Hey, listen, I'm working
with some someone who hasa CPMAI certification,
but turns out they have noidea what they're doing.
Can they like lodge a complaintand report them at PMI?
Ron Schmelzer (37:19):
You can't guarantee that people know
what they're necessarily canknow what they're doing when
they, even if you give themthe toolkit to, to do it.
I think, yeah,the answer is yes.
The throat to choke, there'sa certification registry,
there's a community beingpart of it, it's standards.
I think it's really important.
Also, I think justhaving some sort of.
Critical mass is in andof itself, there's some
(37:39):
safety in numbers, right?
Let's say there's anothermethodology somebody created.
It may be great, but it'sonly a few people know it.
Then the problem is thatif you have a problem with
that person, you can't quitego to other people and say
are they doing it right?
Because they're, nobodyelse knows what that is, but
it's that's why I think alot of these methodologies
have worked over time, eventhough some of them may
(38:00):
be of questionable value.
Lean Six Sigma, it's gotsome value, but not in
every industry for a while.
Some of these things havebeen fads the OKRs, that's not
even a project methodology,a management methodology.
Yeah.
It's come into favor,come out of favor, but
what you get is you get.
One, an approach.
So an approach isbetter than no approach.
And two, you get the sortof community of people who
(38:21):
have built things around it.
That's the biggest thing thatwe are actually really hopeful
for and expecting that partof being a PMI is that we're
not the only ones who needto create this ecosystem now.
Now we actually would loveother people to come in here.
Build on top of CPI createmore materials, create more
learning, create more training,create more opportunities.
We don't wanna be in themonopolistic position of
(38:41):
being the only people whosell training around this.
So far that's the casebecause we haven't built the
training partner program.
But in the future,that won't be the case.
We want people to adopt thisand say, maybe there's a CPI
thing for pharmaceutical.
That's done in a very particularway to deal with those things.
I would say there's opportunity.
I mean there's lots ofopportunity here, to
really get things right.
And as I mentioned, we reallyvalue project professionals
(39:03):
are the doers, the peoplewho are really trying to
take vision and help makethat execution a reliability.
Galen Low (39:09):
It's funny because as you were talking the
note I took just as foodsafe, I was like, is this
just for project managers?
And your positionwas actually like.
In a perfect world,like selfishly,
everyone would have it.
But then when I stopped andthought about it, I was like,
that's actually right becauselet's say you're in the food
business, you're a restaurant,or food preparation, whatever,
food safe is that thing that'sgonna underline everything to
(39:32):
make sure that everyone is doingthings in a sort of safe way.
And normally in a projectmanagement, I'd be like,
it's not really safety, butwhen it comes to AI, for
some reason it is, becauseit's a lot of responsibility.
There's a lot ofcriticality to it, right?
Like it's not justfrom my world, right?
Building a website, weknow how to control that.
We know what it's gonna do.
This is actually like, how arewe collectively making good
(39:52):
decisions and following a sharedapproach based on other people's
experience, our community, ourglobal community experience with
AI to make sure we don't either.
Trigger the end of the worldor even just build something
that's actually reallycrap, so I really like that.
You mentioned somethingelse though that I thought
was really interesting.
You said experientialalso is valuable.
(40:12):
And I know that, there'san academic lens, to some
of these certifications.
What is a good potentpairing, right?
So you're gonna do CPMAI,it's not necessarily gonna
be like, Hey, let's do aproject together where you
deliver, an AI driven solution.
But it might be goodto take that learning.
Maybe put it intopractice somehow.
What are some of theopportunities to do safe
practice like HandsOn?
Ron Schmelzer (40:32):
Yeah, that's so powerful.
For right now we have what'scalled a workbook that
we tell people as they'regoing through CPMAI to
work through the workbook.
And the workbook ismeant to be experiential.
So it's take a problem thatyou're trying to solve.
That was actually one of ourlittle final little notes
at the end of our leap orkeynote was that like, what
are you gonna do today?
What's one thingyou can do today?
What's one process thatyou can change today?
It might be small and tiny,but maybe it's something
(40:53):
that you do all the time.
You can just like change thatone thing and AI enable it
and make your life happier.
And then of course, reallythe biggest thing is to
gain that experience, right?
So the workbook, wedo the same thing.
It's go pick something, dosomething you're gonna do
and let's run it through thisCPMAI process and go through
it from beginning to an end.
And then you'll seehow valuable it is.
Then you'll want to do it again.
So we use the workbook for that.
But I would say this is a longertopic, but I think the way that
(41:16):
the training is done and theway that people do workbooks is
still a holdover from the past.
I'd love to do is to AIenable the learning itself.
Yes.
So that you're learningthe whole idea of
learning and doing.
'cause you're talkingabout there's the doing
the experience part, andthen there's the learning.
And the problem isdoing them either in
isolation is problematic.
Learning without doing isconceptuals theoretical.
(41:39):
It's like when you go to collegeand you send the lecture and
you're like, that's nice, butwhat am I gonna do with this?
And then you havethe, just the doing.
But then the doingwithout learning feels
almost aimless sometimes.
You're like.
I will do some prompts.
I'll use this tool, butwhat am I other than
just playing with it?
Or maybe someone told me to useit, like what am I gonna use it?
And maybe if I did, someonedid tell me to use it.
I used it for that one thingand then a couple weeks later,
(42:01):
I'm not doing it anymore.
So it hasn't reallyfundamentally changed.
So what we wanna do is combinethe learning and doing together.
That would be, Ithink, phenomenal.
That's one of the things we'retrying to really work out,
which is that, okay, pull upCMPAI as your sidekick while
you're doing your project.
And work with it, workwith CPMAI to say, Hey,
I'm doing this project,what should I do next?
(42:22):
Or What should I do first?
And then CPMAI say what problemare you trying to solve?
I gotta build my keynoteslides, but I don't
know where to start it.
Would you like me tohelp ideate that for you?
Or something like that.
And if this is an I AI project,oh, this is a good AI thing.
Now the next thing is, thedata part would ask you
like, where's the data from?
Can I get access to it?
The next thing would belike let's prep this.
Is this clean?
The next thing?
(42:42):
Be like, okay, let'sgo ahead and build this
now let's go test this.
Now let's go deploy it.
Then you could seehow that works.
I'm starting to see someorganizations out there
that are starting to do thisblended learning, doing.
Model and I'm keeping aneye on them for ideas to
borrow to see how they do it.
But I think this would be great.
I think every organizationthat has a learning component
(43:06):
will need to think abouthow to integrate the
learning and doing together.
Galen Low (43:09):
No, I love that.
And it's funny because likeeven in my world, from a
training standpoint, ourtraining actually is in
order for it to be practiceoriented and scenario based,
it requires people, right?
It becomes like a service.
Which, without goingtoo into the details is
difficult to scale, right?
Yeah.
Versus if we are training amodel that can be that tough
client or stakeholder andwork and coach you through
(43:29):
a conversation that like isa very practical but like
directional learning, I'dlove to see where that goes.
And honestly, I think you'd bea great person to be working
at that with BMI, and likein the AI world overall.
Ron Schmelzer (43:44):
See, now we're talking about
transformation, right?
This goes back full circleto where we started, which is
that we're not talking aboutjust implementing a tool in
the way we're doing things now.
It's just rethinking theway things are doing.
'cause it might actually bemore powerful for everybody.
See, we tied all thesubject topics together.
Galen Low (43:59):
Ah, see that's how the pros do it.
Ladies and gentlemen,Ron Schmelzer.
Honestly, Ron, thankyou so much for spending
the time with me today.
Love having you on the show.
Love what you're doing.
And we're gonna haveto have you back there.
Were at least threeother podcasts in there.
Ron Schmelzer (44:12):
Oh, for sure.
Always thrill to be talkingto you and to sharing all
this with your audience.
Being out there and beinghelpful, I think we're all
on this journey together.
Galen Low (44:19):
Absolutely.
Alright 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 to
thedpm.com/membershipto 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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