Sådan kan en fælles AI-tankegang effektivisere organisatorisk transformation i mødet med forstyrrelser

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Galen Low: “AI projects. Pfffft.” — your colleague scoffs at the idea over lunch.

“A passing trend at best.” adds your program director. “They’re no different than other projects.”

But you start looking around the cafeteria, and you can't help but feel that these people you respect enough to break bread with might be burying their heads in the sand.

At the next table over, Germaine from Finance is explaining to Lateicia from HR how Gemini is fixing her formulas in Google Sheets.

At the coffee station, Paulo is pitching an idea for using AI and machine learning to convert gigabytes of unstructured customer feedback into actionable insights.

By the elevators, Sonaya is having a futurist conversation with their department chief about nimble AI-supported processes that self-document and self-improve through user feedback.

Heck, even Andréa is there in the corner using Perplexity to plan their own retirement party.

AI projects. They're everywhere. And there's a lot more to consider than whether AI will come up with a good enough suggestion for a party theme.

Wondering if your organization has the chops to take on projects that involve AI? Keep listening. We're gonna be diving into what organizations need to be doing to support people being impacted by AI and how they can get their people on the same page when it comes to AI, so that they can all survive and thrive in this next wave of digital transformation.

Hey folks, thanks for tuning in. My name is Galen Low with the Digital Project Manager. We are a community of digital professionals on a mission to help each other get skilled, get confident, and get connected so that we can amplify the value of project management in a digital world. If you want to hear more about that, head on over to thedpm.com/membership.

And if you're into future-forward conversations and practical insights around digital project leadership, consider subscribing to the show for weekly episodes.

Alright, today we are talking about the next wave of digital transformation that's being created by generative AI and how a shared team mindset around AI can take an organization from spotty, haphazard, anxiety-ridden adoption to a more cohesive and deliberate benefit that lifts all boats.

With me today is Ron Schmelzer, co-founder of Cognilytica and now the Global Head of AI Partnerships and Outreach for PMI Cognilytica at the Project Management Institute.

Ron, thanks for being here with me today.

Ron Schmelzer: Thrilled to be here. Always love chatting with you.

Galen Low: Yeah, I love having you back on the show and I've been like quietly observing your journey Fanboying a little bit for the past several years as we've been talking about, Cognilytica, what you're doing there and now, this sort of merger into the PMI ecosphere many exciting things that I wanted to dig into.

I thought maybe I would just start with one hot question. The big question, and I think this is tying back to something you and I were jamming on earlier. Which is that the current adoption of generative AI solutions has been likened right by you, by me, has been likened to the advent of the computer or word processing software or the telephone, or practically like any big shift that quickly made many roles within the working world, more or less redundant.

So the big question is this, how can organizations support people whose job will be affected by AI in the imminent future?

Ron Schmelzer: I think part of the reason why AI feels like that is because of its transformative nature. We talk about it changes things and that's what transformative is. It transforms, it changes one thing into another thing.

Maybe I just define transformation for, in this case it's like people see that a lot of the tools, especially for knowledge workers, we talked about this earlier, but. We have a knowledge record whose primary job is dealing with information, whether it's MO putting information into an information system or taking information out of an information system or condensing it or summarizing it or describing it or putting it into reports or doing things like filing compliance reports.

All of that stuff now is so relatively trivial for machines to do where it wasn't trivial for them to do a couple years ago. So organizations now are rethinking this. The non transformational way of doing this would be somebody who's in that role using those tools to do the existing things that they do, and the process 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 process remains the same. But the organizations that are really transforming or kind of really rethinking the processes from the get go. As you mentioned, we had this experience so many times.

Before the internet, the shopping experience was you go to a store and they have what they have in stock or they don't, you purchase and you box it out. And then it took rethinking. E-commerce wasn't just the same experience. You walk into a store, but it's online and things on a shelf. I think people tried to do that, like we actually looked like a store and there were things on shelves.

But then it took people to realize, wait a second. We don't need to have the same ideas of inventory and we can do all this sorts of stuff. Same thing's gonna happen with AI. So I think the scary part is that when you're in an organization that hasn't made that process transformation, then AI is a little bit like a sledgehammer, right?

It's coming in and it's just causing a lot of destruction and chaos, right? It may be getting things done, but it's doing things. At the expense of the existing processes because the processes haven't changed. All we're doing is we're just bringing in these tools and that means that people are getting squeezed out of the process, if you will.

Galen Low: That's so interesting. You know what I found really interesting about that is that a lot of the conversations that I've been having and that I've been seeing online around AI is don't worry. It's gonna be here to help you do the same things you always do. And what you just said there was very interesting because.

You're saying that from an organizational standpoint, one of the better things that an organization can do is cast a transformative vision for the future. That like we know things won't be the same. We don't want you to write phone numbers into your Rolodex and then scan them and then put them into your phone.

That's not what we want. We want to reimagine what work looks like and you have a place in that and we're gonna 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 version of what we were doing before, but like a new thing that we're doing with AI involved.

Ron Schmelzer: And I would argue that's actually harder to do those kind of transformations than it is to implement technology. 'cause you could say the process works like this. Part of the process are these steps, I'm gonna replace those steps of the machine that's actually cognitively, it's easier to think that than to say, wait a second, if I had to rethink this whole thing, how would it work?

That's a lot harder to do. It's riskier in some ways because you don't know if the new process will work out, but it would be so much more impactful. So I think that's the hard part. The hard part, we actually did a little keynote at the end of the last PM expo session and we said, the thing that differentiates, we were doing a lot of observation of the people who were trying to make AI work.

A lot of people were using it for themselves. It wasn't like the organization mandated the use of it, in some cases even allowed it, but people were just using it on their own anyways. And what we found was that there's four rough groups of people. There were people who weren't using it at all.

They're like we call side liners or just observers. You're just looking to see what it is. They're either skeptical or fearful or whatever, or doubtful, and they'd rather not do anything with AI. Then you have this other group, which are like taskers. They're like, okay, I'm doing my job.

Oh, once in a while I'm gonna use AI to write these emails or to do this. Spreadsheet and now whatever it is, right? But I'm still doing my thing. It's just you know I, it's like they call them taskers. It's I'm gonna use AI for this task when that task. But fundamentally, nothing else 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. It'll be one model today, then another model the next day, some new tool they discovered online. It's more like they enjoy the process of discovery.

And I would say they're probably just as marginally useful as the taskers because it's not like they're changing their job either. There's a fourth group that we observed, but we call the Leapers, which are basically, they're the ones who are being strategic and they're like, you know what?

I'm not gonna just use some AI for my task, but I'm gonna change something in. I'm gonna just redo the whole concept of my task, the job or the thing that I'm trying to do, and rethink it from that AI first perspective. We would call them the leapers. Those are the ones that we wanna keep an eye out of for, because ironically, they're guaranteeing their job security.

It's weird because they're saying No. Now I'm capable of using this technology in a way that can drive positive transformation value. You need me here, right? All the others, we could think about that later.

Galen Low: There's so much in there. And thank you for that breakdown. I love your sort of groupings and I certainly know which one I fall into, but we'll keep mum about that for now.

What's interesting is this combination of like organizational leadership around AI is required, but it's not necessarily your current leadership structure that is going to have that vision of AI and what it can do. In fact, there's gonna be a lot of apprehension, which is why a lot of organizations haven't fully baked it into like their policies and their procedures and their processes and why it happens on an individual basis.

But then you have this rampant sort of everyone, free for all trying out tools, whether they're allowed to or not within their organization. To help them do the same job may better, but if I'm understanding what you're putting down here. If organizational leaders can look out for those leapers and give them a bit of an ecosystem to like almost r and d it for the organization with a bit more blue sky thinking.

Not that sort of, I'm gonna mess this term up, but I think we use the term brownfield sometimes in consulting where it's yeah, you have what you have what you have versus a blue sky kind of thing, or like greenfield, where it's just like. We can do anything we want. Let's have these, like whatever.

I don't know, hackathon's probably an outdated concept at this point, but like, how can we create these communities of practice where we have leapers that could literally lead the charge of transforming the 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 vision that we can probably support. I know that the, like critic in my head is steal, but that's not what I mean. What I mean is the people in your organization can help you build this sort of collective mindset. I'm borrowing your word from later in the conversation, but this collective mindset around AI, it's not up to individual, whatever executives around the world to just be visionary AI people.

You gotta lean on the folks who get it.

Ron Schmelzer: Yeah. I'm gonna use a left field analogy here, but like I'm sorting exactly what you're talking about. Left field, just talking about Brownfield. So I don't know what.

Galen Low: There's a lot of fields in this conversation.

Ron Schmelzer: No, it's exactly. I think about the changing nature of warfare.

So if you think about what's happening in Ukraine right now, I. When the war started, we had our, there, they had, I should say, very traditional the latest mindset in fighting warfare with armored divisions and large troops and frontal assaults. And even things like the idea of trying to gain air dominance first, like you come in with that first wave.

None of that worked for a couple reasons. One, they didn't expect resistance, which is a whole other story. They were expecting the government to just flee and, that happen, which is intriguing in. The second thing is that the nature warfare has changed. Of course, we're talking drone warfare.

We're talking the use of small disposable little units that have actually made the whole idea of an armed attack with large armed pieces useless and it's so strange. So they just like, where did that come from? I remember at the very beginning of the world, we had these people like experimenting we don't know.

We never tried this drone thing. Let's see if we can drop grenades from a drone. And at the beginning it was like, oh, look at these interesting people. They're trying something weird in left field. Then someone's let's do this FPV drone thing where we can fly the drone, put something on it. At first it was interesting and comical.

Now it is the way, it's like the funny thing, it went from a something that was on the fringes to now the core, and I think it's called into question, even the largest armies, like whether. Any of this matters anymore. Whether the $60 million tank is worth anything anymore, they have to put cages around them.

Now it does remind me of all this AI experience going on the edge and someone's wait, there's something more fundamentally transformative here if we just consider this to be fringe. We're missing out on the big picture here.

Galen Low: I like that analogy because I think there are a couple of traditional forcing functions for human innovation.

For better or for I would say personally, unfortunately. Combat and survival is one of the fastest drivers of innovation, thank you, DARPANET. Thank you, CD-ROM. Thank you. All sort of military technology that was necessary to be either prepared for war or to be effective in war.

But even you and I in the green room, we were talking about the pandemic, right? We're talking about return to office. We're talking about some of these like kind of forcing functions that like make us have to innovate. And then I think you're right. I think there's like. Frankly, even right now with the wars that are happening, it's a lot of, sideliner going let's see what happens because this is the first time we've been able to like test and see how this like actually can work in practice.

And I think AI is like that as well. And coming back to the sort of world of knowledge work. I think there is that same thing happening. They're like, okay, this is interesting. Let's like let them all fight it out and see where this nets out. It leaves us in this very uncomfortable space where everyone's we feel insecure, actually we feel unsafe in our jobs.

This is getting hashed out and no 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 is serious and we don't know where it's gonna go, but we have to let it play out. And there's this just psychological lack of safety and lack of security in everyone's like professional lives now.

Ron Schmelzer: I think there's a lot of that same lack of psychological safety 'cause nobody really knows where this is going. Maybe all things are a called into question. So bringing it back to AI here I think one of the biggest things that organizations can do now. Is to broaden the experiential and learning parts of the organization.

It shouldn't be like siloed into this will be the AI experimentation group, or maybe we'll do something within product, or we'll do something within it that doesn't make any sense because of how impactful AI is just across the board. Maybe you can rethink the HR department. Maybe you can rethink finance.

Maybe you can rethink supply chain management, the whole thing, logistics, and get back to the core of what it is you're trying to do. Because every organization has a mission as an objective. And the mission isn't the processes, the mission isn't the technology. Those are means to an end. Right?

And so part of it is now 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, is we're adding AI to the picture. Now, AI becomes a resource or a tool that we can use to facilitate the mission. This is why it's a lot of people are, it does stir things up.

As I said, transformation, as has both positive and negative change associated with it.

Galen Low: It's interesting you describe it as a tool. You see a lot of whatever Terminator references nowadays. Is this Skynet? Will it become self-aware and destroy us? And yes, those are big questions in the AI world, top to bottom.

Like they're serious questions, but like right now and like the way, like I think it's a refreshing reframe of the fact that, yeah, it's like not the super villain in the story right now. It's just like part of the picture. It might even be a background actor that's just. Influencing the way the story goes and we might need to decide how we use that character, but it's not necessarily going to be the one that's gonna redefine everything for us.

And maybe it, disruptor, maybe not Antagonizer, but a bit of a disruptor to force our main characters to do something to center back to their mission, to continue on their journey.

Ron Schmelzer: It's kinda interesting, one of the things you'd always spend too much time talking about. It's been in like our CPMAI training for a long time.

Even we glossed it over what should he be afraid of? AI losing my job. Super intelligence, bad people doing bad things. Those are things that we talk about all the time. There's a little sub note, which is that people have a fear of a lot of power being concentrated into a small number of people's hands.

We always glossed it over, but I now, especially what's happening here in the US and with politics and somebody who actually knows how to wield this technology on government employees. It's like maybe that is like the bigger fear than the super intelligent. We're afraid of machines being smart enough to out smart people, but I'm like, maybe we should be more afraid of just a small number of people who have access to all the data, all the models, all the resources in the world to do whatever they want.

That should be, I think, much more of a existential, immediate threat than the future, which we haven't even achieved yet. Part of that is like part of me is feeling that one of the great antidotes to that, one of the things that can make people feel a little less fearful because the fear is out there, a little less uncertain.

'cause the uncertainty is certainly out there is personal empowerment. I'm starting to choose, feel that what we need more is personal empowerment. People doing things for themselves. Whether it's part of their work or whether it's not, with basically using AI as a means to augment their own personal abilities and want to give them the resilience.

That they need. And two, I think as a counterweight, because it's very hard to put your faith now in companies, in governments, it's hard to put your faith in almost anything at this point. And so if you have to put your faith in something, it's probably best right now to be putting it in yourself.

Galen Low: No, I think that sound advice and actually a really good response to the original question, which is like, how can organizations support folks whose job is gonna be impacted? Yeah, arm them with the knowledge. Don't make it so that you can lock them in. Don't pay for the MBA and make them stay for five years, but just arm them because they're on their own personal mission and you probably will benefit as an organization, but you also don't own that brain.

And you don't wanna yeah. The loyalty game, the sort of getting the watch or the gold pen after 50 years of service is not a thing anymore.

Ron Schmelzer: One of the things I'm gonna be talking about, I have a talk coming up at Barcelona for the Global Summit that PMI does and is I wanted to like really think about this idea of resilience, actually mentioned in a little bit and what organizational resilience means in the face of AI.

And the irony of that is if you want an organization that's resilient is able to thrive and survive in the face of constant change, right? That's really what resilience is all about. And the irony of resilience is that it does require you to be much more flexible and adaptable, especially in things like processes and you're the way you're doing things.

It's if you are overly committed to the way you're doing things, that actually makes you less resilient. If you just simply focus on automating the things you do and removing the people from the picture, you're actually doubling down in the way you're doing things now, and you actually have decreased your organizational resilience.

'cause now you have fewer people in the organization who can deal with the change, and now you have an entire dependence on the systems. Maybe you can ask the AI how to improve itself. That's possible. Like organizational resilience really is all about speeding up the velocity of learning and increasing the speed to respond to change, which means really empowering people to be forces of change and driving that force of change, not from the top, but from the bottom, and enabling people to do that.

I think there's a change. I think there's a mind shift change in the, of what organizational resilience really means.

Galen Low: I really like that notion of organizational resilience. In other words, like a reframing on resilience, 'cause a lot of folks would be like, cool, we have like airtight processes and we've been doing this for years and that's how we're gonna survive this and weather the storm and, but maybe not anymore.

I wondered if, actually I can use that as a bit of a pivot point because I wanted to zoom out a bit. You mentioned the CPMAI certification and things I know about you, you were a co-founder of Cognilytica, which is an AI and project focused organization, which was, in my world, way ahead of the curve of when it launched.

The first, and I don't know, maybe only, I should probably have fact check that before, but I think the first certification for teams delivering AI driven solutions.

Ron Schmelzer: It's the only, there's a few other certs for using AI and project management and different aspects of AI project management, but like for whatever reason, no one's really created a vendor neutral methodology on how to run and manage a project.

So that's what CPMAI was all about. We're like, let's do, yeah.

Galen Low: It's a big ask, right? A, it's moving at the speed of light or sometimes, has these moments of gosh, the word you use, not a nice age, but this moment of yeah, we give up on AI. Oh the winters. The winters, yeah. Like we've gone through multiple AI winters, over the past like several decades.

And it's a hard thing to place a bet on, right? To be like, yeah, here's how to do an AI project. The funny thing is that like you were on the show a couple years ago, you and Kathleen, and at the time I thought of AI projects as like the kinds of projects where, you've got a team of like data scientists and analysts and developers, and they are creating an AI.

But one thing you pointed out to me in the, and you alluded to now, is that. Many projects these days will involve AI somewhere in the solution. In fact, all of this organizational resilience that we're talking about and processes and how can we use AI to rethink some of these things are all projects.

You know what I mean? And the thing you also said which is like part of the certification is understanding. Having ways to manage fear and apprehension around AI. The ethical side of the question, this sort of mindset and these steps to go through to really create a considered AI solution that is resilient in and of itself.

Also isn't that thing that will become self-aware and take over the world with that in mind, right? Like I'm thinking of organizations, what type of organizations is the CPMAI certification relevant for today? And what's the difference between building a project team that just has experience using gen AI tools versus having a team that like has the CPMAI certification?

Ron Schmelzer: We'll start first by talking about why it was created. Where did CPMAI come from? Because we thought. We were talking to a large government organization and a large bank, and they were both looking at putting AI into use for some, one of their core processes we're like, like we just wanna just tell us the approach that we should use so that we know that at the end of the day when we deliver and deploy this solution, we can rely on this AI to do the things that we had previously required people to do.

And we had to be like, okay, certainly there's gotta be some approach. You can't just take. Traditional project development methodology itself, it works when you're building software, a website 'cause you have a deliverable. Then you could be agile, you could be like, maybe you're discovering the functionality as you go along.

Maybe you have a connection with the user and what they're gonna do. You could define functionality points. You still can't make the AI do what you want it to do. That's the irony of it. It's how do we make 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 methodology where the functionality is not driven by the programming.

The functionality is driven by the data. It would be like as if we're building some sort of spreadsheet analysis thing was like what's the methodology you would use to build a complicated spreadsheet? It's not. Project development. What is it exactly? It's there was a methodology called CRISP DM that was out there for a while and it was really good, but it's not iterative, it wasn't agile and it wasn't AI focused.

So we're like, what we're gonna do is we're gonna marry these worlds together. Let's use a data-centric methodology, crisp dm, but let's enhance it and make it AI relevant so that way we can deal with the fact that AI does not give guaranteed results. It. You're building a project where you're depending on a very important component of that project.

Perhaps an intern that you hired a week ago who knows something, but not everything, but you can't fire them because you're dependent on them, but you can't also just delegate everything to them. So how would you work a project where you're like, this intern is delivering half the value. They're like, they're looking at.

Medical images and telling you if there's a possible aneurysm in them or something. So that's what CPMAI is like this approach is this approach to running and managing projects where you can increase your rate of success given the fact that AI is this constantly moving things. The irony of is that ccp, MI is methodology has been fairly stable even with all of these AI changes because the method that you use should basically stay the same.

The technology that you're using can change and adapt as your free grant. New ways to do it. Interestingly enough, that has meant that over time we've required fewer and fewer data scientists in our AI projects. Now, I would say the vast majority of AI projects are being run and managed by people who are not data scientists and machine learning engineers.

'cause we're using off the shelf models and things like that. Now with the Gentech AI, it comes down to this idea of, now I have to stitch together these multiple pieces and each of these pieces have to be run and managed properly to make it work. And so CPMAI has been really helpful. Now we have sort of two user communities, if you wanna think about it, that adopt it.

We have sort of organization, organizational user communities that is, that are implementing it for themselves. They're trying to speed up their AI initiatives and they're trying to do it in a sort of standard way, whether they're. Banks or insurance companies, pharmaceutical, construction, manufacturing, government agencies, it goes, it's across the board.

And then we have consultancies who are basically out there implementing AI projects on behalf of others and want to show that they have the process where they can give a guaranteed result, which is what you wanna do. And there's a lot of CPMAI I is about, we have the six phases, and some of it's about documenting decisions and some of it's about doing things in the right order and discovering things that you don't have before you get there.

Also coming up with plans for you to evaluate and test your AI solutions before you deploy them and manage them and monitor them as they're constantly in progress. So it's a weird kinda hybrid between maybe product management methodology and project management methodology, and it's got a little bit of everything in there.

Galen Low: 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 manager brain went, okay, when we don't know what we're building and we can't guarantee that we're gonna be able to build it, we'll just iterate through it.

And in my digital world it's code and yeah, some data structure, right? There's still like architecture that's important. These are more or less known quantities that are architects can play with. Like it's malleable versus the data side where your entire solution is relying on like the fuel for the actual machine is clean data.

And there is a sort of foundational first step of making sure you have good clean data and how to police whether or not you have good clean data coming in before you know if your AI solution actually is working or not. Because it could just be, for lack of a better word, sucky data. Which is funny because the thing you were saying earlier about you can't isolate power and like these similar groups, we have to have a bottom up approach.

I think our like dress rehearsal for 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. Struggling with data governance and who building the cross-functional teams to make data do something useful.

They're, we're great at collecting it. We have every tool in the world to collect it. But the committee, the group of people, the conversation almost becomes too large because HR and marketing and it, and people from every part of an organization. Trying to be at this table and the table isn't necessarily big enough for us to be productive about how to get us good clean data to begin with.

Ron Schmelzer: Yeah, I'm running into that problem as we speak, unfortunately. Unfortunately, it's like sometimes like just getting access to the data is an issue and like the timeliness of it and it's trapped inside of one system and you don't have access to that system. It's like it kills the whole AI project if you're depending on that for your AI project, and you can't even get to your data.

You're not gonna get anywhere. So it's a good idea is to discover that first before you're like, well down the path of buying and implementing tools, then realize you don't even have the data you need. Buying and implementing tools is like step four in the CPM methodology. Step one is, what problem are you trying to solve?

Doesn't need AI and what pattern of AI and with how the AI go, no go. Can you even proceed with this project? And then step two is the data discovery data. Understanding what data 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 first because you might be surprised to realize that, oh, we have everything you need, or maybe we need to open some doors here to make the AI system work. And opening some of those doors is great. Or maybe there's. Regulatory privacy, data issues, security issues, you'll, that may make your project impossible.

Galen Low: I think that's really interesting that the first bit is like readiness and that there is this avenue to be like, Nope, you're not ready for your project. We always think of like delivery frameworks. We're like, we know what to do, we just need to know how to do it. This is we don't know if we know what to do. Let's figure that out. And there might be an off ramp that says, actually AI is not the solution here, which I think is really interesting.

Actually, it brings me to another question because again, I've got my project manager hat on, but the more I dig into this, it has project management in the title, but it's not necessarily for project managers because what you just described to me are like high level organizational questions.

Our questions for data folks and technology folks, our questions for the business. I think it actually. Spreads it out a little more. I came into this going Ron, tell me what organizations should ask the project managers to get the certification. But I think it's bigger than that. Is it meant for project managers or like what roles should be getting this training?

And maybe actually the flip side of that, what roles probably just don't need this training.

Ron Schmelzer: It's funny. Because I did also struggle with that whole idea of like project professionals like you, is it really for project people? I would say that CPMAI is a methodology for running and managing the AI project, whatever it is.

It could be a very short term one, it could be a longer one that you divide in multiple projects. The idea is that it is a project from the definition of a project, which is like what a temporary initiative meant to deliver value.

Galen Low: I forget the exact, yeah. Something unique that we don't know if it's gonna be possible or not.

Ron Schmelzer: And so the role of the project manager is to facilitate that process. And so it's always been troubling because on the one hand you have people who develop strategy. The C levels and the strategists they're defining the mission, the direction. They're defining the imperatives, they're defining the objectives, they're defining the requirements.

They understand what needs to happen. Maybe the external factors like regulatory and legal, the internal factors, the resources are available and they're basically setting the ground rules and the ground conditions, and they're planting the flag and say, we need to be here. On the other side, you have these individual contributors that can do things like, they're builders or they're finance people, right?

They're there to execute on the vision. So you have the vision and you have the executors of the vision. But what I see is the role of the project professionals, that middle, that messy middle, which is translating the organizational imperatives, the strategy, the goals and directions. And pulling in the right resources to execute on that vision.

So that's not just a madhouse of people all trying to execute on the same vision at the same time. I've seen that before too, where it's like everybody's just executing, like the vision is this, let's all execute. 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 have a highly global organization with a lot of regions and the regions all execute that vision in different ways. And they're done using maybe even conflicting things. It happens, and it can happen even in smaller organizations too. So like the professionals, the project professionals, I see 'em, there's facilitators.

If I could rebrand project management to transformation facilitation, call me. Maybe that's what it'll be. But it's basically it's really about facilitating between the requirements and vision of the people who own the end results, whether that's at the C level or whatever. And the resources that are there to execute and create people money, inventory, and technology and AI.

So I actually do say, I'm not saying this is because I'm part of PMI, but we obviously created CPMAI even before we even realized honestly about the project management profession. Kathleen and I are not project management people. We don't come outta the project management world, but ironically, we developed something that was in the heart of PM and I think because we saw that.

It's this role in that it takes an organization still is comprised of people who are trying to create value for other people. We haven't yet built an organization that is completely autonomous and has no people or maybe one person. People are working on that by the way. Until that point we are trying to, so it almost feels I wish, like my vision for what I believe the ideal state of the future organization is much longer and separate conversation.

I almost feel like the way that movie studios build movies is like an ideal state, because movies are temporary endeavors too. They build products and you could say a movie is not something that delivers constant value and you have to provide support. So I agree there's differences there, but when movie studios need to pull together people for a production we pull together the individual contributors, you have the producers and that sort of stuff, and then someone is managing this.

Process. Process to get to a very well defined end goal on budget, on time with the resource you have dealing with the complexity situation. And without those people, the movie would not been thinking about it organizational.

Galen Low: I like that. I like that a lot actually. And you know what, like it's I was talking earlier about, how it's been interesting to watch your journey.

It's also been interesting to watch PMI's Journey and you were just mentioning PM Expo. And without going too far off script, like my understandings is Pierre announced or described the MORE framework. I don't remember what it stands for, but the idea is that PMI is also has been recognizing.

Project management is bigger than a project manager. Title is bigger than the project management profession or world. It's about translating a vision into execution and that individual or those individuals who are responsible for that. You don't have to identify as project manager is, but the whole getting it done thing and making it deliver value is valuable and important.

And I know you've always believed that. So it's been interesting to watch those streams. Join philosophically, but also now you're a part of PMI and I'm like, wow, what a great fit. Like I think that's a really interesting evolution.

I wanted to come back to something you were saying, like with the movie studio thing, right? Earlier we were talking about Leapers and then we're talking about folks who understand the steps to translate a vision reliably into a more predictable outcome. When should an organization be hiring someone who might have the CPMAI designation versus say, a whole bunch of leapers.

What stages should they be looking at that? What are the right roles for them?

Ron Schmelzer: Obviously, from my selfish perspectives, I believe everybody should get CP master. So if you're a leaper and you wanna truly leap and get ahead, it's not just a matter of mastering the latest tools because that will be a bit of a constant chase.

The latest tools keep changing, but you should have, again, for yourself, this framework and how do you run and manage these things so that you yourself can be successful. And now as being part of PMI actually the cost driven way somewhere. $2,800 and $3,800. Now I'm talking about, that's very expensive, right?

Now, it's now that CPM is on the PMI platform, it took a little while to do it, but now you can actually get it directly from pmi.org. It's 699. Oh, wow. Like price. Yeah, and and it's this zone pricing too, so it's like even cheaper in, in different zones. Price should not be the barrier.

So we tell everybody, it's like. This is something that's gonna provide long-term value for you and your career. One, because it'll be a credential that other people can say, oh, this person knows what they're doing. They'll hire you for it, but also because it'll make you personally more effective. But in terms of augmenting the organization, I think it requires some form of learning, right?

Whether that's gonna be experiential learning, which is important too. These things are not at odds, so you could say. You call them hackathons, whatever it is, we need like opportunities. We need to give people the freedom and the psychological safety to be able to experiment and use tools that may or may not be.

Useful in the immediate short term, but the process of learning them and getting experience, that process by itself is highly valuable and will give the organization both the knowledge and the resilience it 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 some form is required. In this environment of change. So it's either experiential learning, we call formal forms of learning. CP A would be a formal form of learning. I think the one thing I would like to tell organizations is don't make up your own methodology.

If you're gonna get make a model methodology, let's just get one that already exists. You can learn it and then from that creates something else. But it's better to start from a position of knowledge than not to reinvent the wheel. I hate when that happens. The second thing I would like to say is that if you're hiring other people to help, if you're bringing in consultants, you're bringing in contractors, they do need to have some creds.

It's way too easy now, especially now with like how easy it's to use AI tools to be like, oh yeah, we're AI experts. How we've all been like cranking on prompts. It's big deal. It's so is my mom. It doesn't make her an a AI expert. So I would think that for us, some sort of certification, CPMAI, for those who are implementing parts, should be a requirement for those doing it.

Galen Low: I love that. One point there is now that you're part of PMI, there's that accountability thing. I had one client, the one sort of notable time when having a PMP certification was useful in my career. As a government client, they're like, we want them to have their PMP. The project manager needs to have their PMP Why?

So that we can complain about them to PMI if they don't do their job like we can lodge a complaint. Is that now part of it? Could someone be like, Hey, listen, I'm working with some someone who has a CPMAI certification, but turns out they have no idea what they're doing. Can they like lodge a complaint and report them at PMI?

Ron Schmelzer: You can't guarantee that people know what they're necessarily can know what they're doing when they, even if you give them the toolkit to, to do it. I think, yeah, the answer is yes. The throat to choke, there's a certification registry, there's a community being part of it, it's standards.

I think it's really important. Also, I think just having some sort of. Critical mass is in and of itself, there's some safety in numbers, right? Let's say there's another methodology somebody created. It may be great, but it's only a few people know it. Then the problem is that if you have a problem with that person, you can't quite go to other people and say are they doing it right?

Because they're, nobody else knows what that is, but it's that's why I think a lot of these methodologies have worked over time, even though some of them may be of questionable value. Lean Six Sigma, it's got some value, but not in every industry for a while. Some of these things have been 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 is better than no approach. And two, you get the sort of community of people who have built things around it. That's the biggest thing that we are actually really hopeful for and expecting that part of being a PMI is that we're not the only ones who need to create this ecosystem now.

Now we actually would love other people to come in here. Build on top of CPI create more materials, create more learning, create more training, create more opportunities. We don't wanna be in the monopolistic position of being the only people who sell training around this. So far that's the case because we haven't built the training partner program.

But in the future, that won't be the case. We want people to adopt this and say, maybe there's a CPI thing for pharmaceutical. That's done in a very particular way to deal with those things. I would say there's opportunity. I mean there's lots of opportunity here, to really get things right.

And as I mentioned, we really value project professionals are the doers, the people who are really trying to take vision and help make that execution a reliability.

Galen Low: It's funny because as you were talking the note I took just as food safe, I was like, is this just for project managers?

And your position was actually like. In a perfect world, like selfishly, everyone would have it. But then when I stopped and thought about it, I was like, that's actually right because let's say you're in the food business, you're a restaurant, or food preparation, whatever, food safe is that thing that's gonna underline everything to make sure that everyone is doing things in a sort of safe way.

And normally in a project management, I'd be like, it's not really safety, but when it comes to AI, for some reason it is, because it's a lot of responsibility. There's a lot of criticality to it, right? Like it's not just from my world, right? Building a website, we know how to control that. We know what it's gonna do.

This is actually like, how are we collectively making good decisions and following a shared approach based on other people's experience, our community, our global community experience with AI to make sure we don't either. Trigger the end of the world or even just build something that's actually really crap, so I really like that.

You mentioned something else though that I thought was really interesting. You said experiential also is valuable. And I know that, there's an academic lens, to some of these certifications. What is a good potent pairing, right? So you're gonna do CPMAI, it's not necessarily gonna be like, Hey, let's do a project together where you deliver, an AI driven solution.

But it might be good to take that learning. Maybe put it into practice somehow. What are some of the opportunities to do safe practice like HandsOn?

Ron Schmelzer: Yeah, that's so powerful. For right now we have what's called a workbook that we tell people as they're going through CPMAI to work through the workbook.

And the workbook is meant to be experiential. So it's take a problem that you're trying to solve. That was actually one of our little final little notes at the end of our leap or keynote was that like, what are you gonna do today? What's one thing you can do today? What's one process that you can change today?

It might be small and tiny, but maybe it's something that you do all the time. You can just like change that one thing and AI enable it and make your life happier. And then of course, really the biggest thing is to gain that experience, right? So the workbook, we do the same thing. It's go pick something, do something you're gonna do and let's run it through this CPMAI process and go through it from beginning to an end.

And then you'll see how 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 longer topic, but I think the way that the training is done and the way that people do workbooks is still a holdover from the past. I'd love to do is to AI enable the learning itself.

Yes. So that you're learning the whole idea of learning and doing. 'cause you're talking about there's the doing the experience part, and then there's the learning. And the problem is doing them either in isolation is problematic. Learning without doing is conceptuals theoretical. It's like when you go to college and you send the lecture and you're like, that's nice, but what am I gonna do with this?

And then you have the, just the doing. But then the doing without learning feels almost aimless sometimes. You're like. I will do some prompts. I'll use this tool, but what am I other than just playing with it?

Or maybe someone told me to use it, like what am I gonna use it? And maybe if I did, someone did tell me to use it.

I used it for that one thing and then a couple weeks later, I'm not doing it anymore. So it hasn't really fundamentally changed. So what we wanna do is combine the learning and doing together. That would be, I think, phenomenal. That's one of the things we're trying to really work out, which is that, okay, pull up CMPAI as your sidekick while you're doing your project. And work with it, work with CPMAI to say, Hey, I'm doing this project, what should I do next? Or What should I do first? And then CPMAI say what problem are you trying to solve? I gotta build my keynote slides, but I don't know where to start it.

Would you like me to help 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, the data part would ask you like, where's the data from? Can I get access to it? The next thing would be like let's prep this. Is this clean?

The next thing? Be like, okay, let's go ahead and build this now let's go test this. Now let's go deploy it. Then you could see how that works. I'm starting to see some organizations out there that are starting to do this blended learning, doing. Model and I'm keeping an eye on them for ideas to borrow to see how they do it.

But I think this would be great. I think every organization that has a learning component will need to think about how to integrate the learning and doing together.

Galen Low: No, I love that. And it's funny because like even in my world, from a training standpoint, our training actually is in order for it to be practice oriented and scenario based, it requires people, right?

It becomes like a service. Which, without going too into the details is difficult to scale, right? Yeah. Versus if we are training a model that can be that tough client or stakeholder and work and coach you through a conversation that like is a very practical but like directional learning, I'd love to see where that goes.

And honestly, I think you'd be a great person to be working at that with BMI, and like in the AI world overall.

Ron Schmelzer: See, now we're talking about transformation, right? This goes back full circle to where we started, which is that we're not talking about just implementing a tool in the way we're doing things now.

It's just rethinking the way things are doing. 'cause it might actually be more powerful for everybody. See, we tied all the subject topics together.

Galen Low: Ah, see that's how the pros do it. Ladies and gentlemen, Ron Schmelzer.

Honestly, Ron, thank you so much for spending the time with me today. Love having you on the show. Love what you're doing. And we're gonna have to have you back there. Were at least three other podcasts in there.

Ron Schmelzer: Oh, for sure. Always thrill to be talking to you and to sharing all this with your audience. Being out there and being helpful, I think we're all on this journey together.

Galen Low: Absolutely.

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