Talent Development and the Evolving Project Leader Role

Speaker A: Welcome to Witham Sounding Board, a podcast sharing powerful business tips, insights and trends for those seeking to become a rock star in their industry.

Speaker B: We're coming back to the Witham Sounding Board. This is the part seven of the PMO series that we've been put on for the past few months. Today we have Kim Gordon and Sarah Carroll that you've already met. We're also going to be welcomed by Laurent Tessier. Laurent, do you want to say a few words?

Speaker C: Hey, Logan.

Speaker B: Yeah.

Speaker C: Thank you for inviting me. I've been listening to all these podcasts. It's been great and now it's great to be part of it.

Speaker B: Wonderful. This will be the last one we've covered. AI, data systems, hybrid methodologies, remote teams, cybersecurity. Today we're going to close out the series with what I think is most consequential topic of all the people. With that being said, I know something you wanted to get into. You have a story you want to say?

Speaker D: Yeah, thanks, Logan. So today's topic is all about talent development in the pmo, uh, space. Right. And I think the, uh, way we can kind of start this off and explore is around what does the profession get wrong and what has it historically rewarded and you know, where is it really going, especially now that we're really modernizing with AI and where we have more tools at our disposal that are really going to shift the gears to more judgment based and reporting work as opposed to retroactive reporting activities. Right. So the historically the PMO space has been there's two extremes, right? You could be as strategic as you want, or you can almost be as administrative as you want. Right. And then, you know, artificially so there's a low barrier to entry where it seems just a task management type of role and function. And sometimes that's all you really need on very small projects or ticket management. Right. But it's often confused to just being only limited to that. Right? So there's so much more to it. So you can consider a true PMO a strategic risk management center, a way to advance initiatives that transform your organization, right? When you kind of segment it to just administrative work getting tasks done around the project, the PMO is more of a commodity rather than a value add. So today's topic I'm really excited to kind of explore about how we can really emphasize the strategic part of the PMO while really automating the administrative parts that have defined it for a very long time.

Speaker B: Hey Sarah, Is Kim's story unusual or is it a pattern you see across

Speaker A: organizations yeah, uh, not unusual at all. I actually like how Kim brought this up because I think a lot of times it's actually, it's a common challenge because there's this like, perception people have that PMs are really just scheduled trackers, coordinators, you know, whatever it may be. But that's such a small part of our job. At the end of the day, a project isn't successful because status reports went out on time. Right. It's successful because it delivered a business outcome. So if a, ah, pm, for example, can't actually articulate why a project exists, what problem it's solving, then they're really just task managers and that's not the job at all. So priorities are constantly changing, requirements are changing, stakeholders are always changing their minds, as we know. So it's like if you don't understand that business purpose behind what you're delivering, then you can't actually adapt when those changes happen. So you know, you can keep your schedule green the entire time, but that doesn't really tell you anything at the end of the day because you might not be delivering something the business doesn't actually need. So you need that skill set that balances both, you know, being able to coordinate and also be more of that strategic project manager as well.

Speaker B: Totally, Laurent. Here's what strikes me with that story. The skills that PM got right, the tracking, the reporting, the dashboard discipline, those are exactly what AI is best at replacing. Is that a coincidence?

Speaker C: No, I think, uh, the field is evolving and AI is allowing us to not get rid of the admin stuff, but it's taking it over and it's making it so as a project manager you have to really think through the other parts of the job. And like Kim and Sarah were kind of talking to, it could be that that was kind of viewed as your entire job if you're just doing the admin pieces. But with AI, uh coming into play, it's going to push everybody to really rethink either what they're their job is or to they already knew that there was more to it, more strategy to it, tying it to, for example, the business outcomes and they're going to be able to spend more time in that direction.

Speaker B: Professional Project manager. Track it, record it, keep the recs at a screen. You're doing your job. Did we train a generation of PMs optimize for the wrong thing?

Speaker D: I think so. I think there's a, uh, broader misconception and I'm willing to hear what the others have to say too, but it's Almost a trap. Right. We think that the project, if it's going green, right? That reflects on the project manager and rewarding great, great job. You know, you did great on the project manager. If you reverse the thinking, it is the project manager's job to flag red at the right time, to anticipate risks before they happen. So I often tell my project managers a, uh, kind of like a counterintuitive statement. The redder it is, the better job you're doing. Not necessarily because of how they're managing it, but the fact that they're seeing it, detecting those things early and they're not ignoring little seeds of behavior in a project or little seeds of patterns that can lead to actual issues later on.

Speaker A: Right.

Speaker D: So I think we've been artificially conflating a green project and incentivizing only to report the green parts of it, sort of like on the project manager's performance. And it's should be the universe. So maybe I'll hand it to Sara Laurent, which one you guys want to take that?

Speaker A: I agree. I think I agree like to an extent, because I think we definitely have spent like so much time training PMs, that yes, you need to emphasize the schedules and the status reports, governance, all that stuff. But those skills, like, they are important. So I don't want to discount that they're important because without them, projects can become super chaotic, as we know. So I don't think the problem is necessarily that we taught those things, but the problem is that along the way, people started confusing those activities that are almost just expected with actual project outcomes. And like Kim was saying, uh, a green project plan, that's not the goal. It's like business value. Are you actually delivering value to the business? So all those things like the schedule, the rate log, status reports, those are just tools to achieve that outcome. So it's not that their PMs are trained on the wrong things, it's just that we also forgot to teach. In addition to that, maybe the project management is also like, uh, a leadership role, not just an administrative and coordinator role.

Speaker C: I agree with Kim and Sarah. The idea of the status reports and whether something is green or red is. It's a synthesis of the information that is going on in the project as a whole. And it's meant to be a tool to do something. And before we make a decision about doing something, we need to apply judgment on whether what we are seeing, either A is real, right? We need to check the data, or if it's something that's big enough that we need to take action. If we do need to take action, what should that action be? That's the bad part that I think, Sorry, I was kind of hinting at the status reports and all that is important because it's that data gathering step and then the other two steps are the piece that lead to the better business outcomes into a project really being successful, even if it went over budget or over schedule.

Speaker B: Kim, I'm going to come to you for this one and it could be a quick answer, but what is the version of that PDM, um, who actually makes it in 2026? What changed about how they think?

Speaker D: You know, I think AI is. But before I kind of describe the characteristics I think AI is bringing to the surface and the necessity of changing. But uh, it's not necessarily anything that new. Right. So I think the future of PMing is going to be predominantly proactive and forward thinking strategists really. It's the PM is able to understand what the project is delivering and understand what quality means in the delivery and what does that mean for the organization like Sarah has been alluding to about business outcomes. Right. It's the roi. How does this project actually fit in the organization? So we have to ask ourselves questions around the project, not just in the project. How does the project fit the bigger picture in an organization's goals and strategies? Right. And you know, with the. I think the future is also that organizations are going to become more and more project driven to drive their growth. So growth is going to be more and more imperative the more that technology is advancing and the more competitive the landscape becomes. Right. So PM right now, unfortunately in the marketplace, PM is like cybersecurity. You only realize you need it once you get hit by a million dollar breach. Right. And then you realize, oh, maybe we should have some controls on it. That's going to be, I think something of the past where technology is going to force kind of like a cybersecurity technology is going to force the adoption of cybersecurity measures proactively just because of the increased risk. Same thing with a uh, project driven organization. They're going to need to adopt that proactively. So I think organizations are going to evolve to have that into their infrastructure and then the PMs are going to be forced to evolve. The ones that are kind of been stuck in the administrative mode, they're going to have to start thinking outside the box and not just retroactively reporting what, what happened. Right. They're going to be influencing the direction of the project. They're going to be asking about interdependent season. They're going to make sure we're carrying forward lessons learned and again, not new, but it's something that is going to. We're now have the pressures to, for all of us to evolve that way.

Speaker B: So that was a great analogy. Thank you for that. Laurent, I want to give you the floor for a minute because we talk about AI changing project management in almost every episode of this series, but you've actually done the implementations. When a PMO deploys AI tools in their project environment, what actually changes for the PM on day one and what turns out to be hype.

Speaker C: So usually, as we've been talking about, the admin work is the first thing that goes to AI. The tools are not perfect. So as a team, really, but as the project manager, typically you're kind of the one who is going to be the first line of defense for making sure that you have accurate information. Like we've been talking about this idea that all of this data that we're gathering and synthesizing and reformatting in different ways is to help us to reach a successful business outcome. And those admin layers get put in by whatever tooling it is that you've got available, whether it's something that's out of the box, like Asana, uh, being able to use its AI tooling to gather information and then to give you those nice status reports. But you, you then need to make sure that that information is accurate makes sense so that you can take that next step that everybody's been talking about, which is taking action, applying your judgment based on your experience, all of the complexities of what's going on that may not actually have been captured in, uh, emails, et cetera, and deciding what actions you're going to be taking.

Speaker B: Sarah, I'll come back to you in a second for a follow up to that. But when you're. Kim, for you, when your team started using these tools, what did you expect to happen and what actually happened with AI?

Speaker D: Ah, you know, it's for a disclaimer. It's going to sound like I'm a, uh, Debbie Downer on it, but I'm actually really huge enthusiast. It's just, I'm kind of realist about where all are not right. So with AI, it's as good as the data and quality you put into it. So I think the biggest pitfall is that we outsource thinking sometimes to AI instead of treating it like, uh. I guess another analogy is instead of treating it like your intern, right you wouldn't take your intern's output and be like, okay, this is the final product. You're going to review it thoroughly, you're going to critique it, you're going to say, hey, fix this, fix that. You're going to have a vision for what you want, right? I think the pitfalls, just training our mindset around how we use AI to enable our efficiency without losing our competency, that's the hard part. So, and then this goes for pmo, but anything else really, where if we use AI to train our managerial skill sets, it's really no different than delegating to an intern or a staff or something like that, right? Because you need to have a vision. You need to be able to communicate clearly what that vision is. You need to be able to iterate and have conversations on it. And then, um, it's a lot of the same skill sets. So I think when we're getting into using AI to enable PUMA reporting to aggregate information, we treat it with the same CrossFit verified mindset that we would with anyone else we're delegating to. And if I think that's attainable and it'll train everyone's mindsets to have more of that managerial scope. And we need to ask ourselves, is this true? And kind of look at source documentation, right? We still have to own claims. I can put pretty graphics on a status report, but I need to be able to justify where I got my statistics from. So I need to speak to that. I need to be able to answer questions on that. And I think that goes for any professional. But for pmo, it's just so critical because so many things happen in a project and change, and then we need to make very informed decisions.

Speaker B: Nice. Sarah, this question is going to be for you. So, from the organizations you advise, what is the gap between what they expect AI to do for the PM talent and what does it actually do?

Speaker A: First, I agree with everything Laurent and Kim said. And maybe just to add on to what Kim said, I think sometimes the biggest gap is that organizations, there's a lot of fear around AI, and you know what it's going to do to the workforce. And I think they think AI is going to make PMs unnecessary, potentially. But what I actually think is it can make, you know, really great project managers even more valuable. To Kim's point, you know, AI can create status reports, it can summarize meetings, it can do all of those types of tasks. But what it can't do is it can't walk into a room full of clients or, you know, important stakeholders and understand all their competing priorities or their requirements. And it can't help organization actually make a good management decision. So yes, AI can take over all those fundamental mechanical tasks of project management, but the human side and like the leadership side that Kim was talking about is just going to become even more important, not less important.

Speaker B: Great response, Sarah. Thank you for that. Laurent. Uh, back to you. Here's a question I really want to push on. Does AI raise the ceiling for great project managers, lower the floor for weak ones or both?

Speaker C: I think it's a little bit of both. The great project managers, they have an understanding of all of the different pieces that they need to do to be great. And usually the admin stuff that we're saying is going to is already being handed off to AI is just the time consuming activity that they need to go through and does that data gathering step. But isn't really the most important parts and they already know that. For someone who is on the, yeah, I would say more like junior kind of learning how things are going, I think that it, it helps them to look like they're doing better than what they, their skill sets might actually be at. Because the AI is providing such a high amount of support with the admin tasks. But it is going to stop at that layer where it's just doing the data gathering. And as much as you can ask whatever your favorite AI is to have an understanding and to ask it what the judgment is on, is this really a risk? It's not going to give you the answer that a seasoned project manager will. A little bit of a double edged sword there because for someone who's just starting out or is on the, on the weaker side of their PM skills, they'll look on the surface like they might be getting an improvement. But unless they start transitioning to having better judgment and all of the strategy that everybody's been talking about, uh, they're, they're not going to get better results.

Speaker B: Kim, that last point, does that match what you're seeing when you're evaluating your team's own performance?

Speaker D: For sure. And then, you know, the way we try to help show that AI is going to be used as a tool but not kind of replace. What we're doing is we're trying to develop internal scorecards, right? And then it's almost like the AI has all the information, it doesn't necessarily have the judgment. So we're trying to experiment with scorecards that can kind of give us a threshold of okay, what are the parameters that we need to manage and look at and what does good look like, what does bad look like? And then obviously there's judgment in between.

Speaker A: Right.

Speaker D: But if we have those thresholds, then we're baking in into the AI bots that we do use an element of judgment that can help us. But also the main thing is I think it helps actually train ourselves how we think this way. Like if I got a cheat sheet just for, not even for pmo, but just for executive management and it was in the form of a scorecard, what does good look like, what does bad look like, what am I managing towards and where are the drivers behind it? That's such a simple framework to follow and really upskill. Right. And expand your thinking. And I think the cool thing is if we use the scorecard approach to bake judgment to AI, we're also baking judgment more into ourselves. We're learning from it as to how do we take a top down view. So I find it an excellent training opportunity to compensate for any of the shortcuts we're trying to take with AI right now.

Speaker B: So if the role is changing and AI is reshaping, which parts of it still belong to humans, what does the skills profile of a high performing project leader actually look like in 2026? Sarah, I know you mapped this out and I know there's a table that we'll kind of share after this podcast, but walk us through it and Laurent can please add any flavor that you see along the way. But Sarah, if you want to talk us through this.

Speaker A: Yeah, sure thing. So when we look at the future of project management, you can kind of think about it in two different dimensions. The first one is the technical piece, technical enablement, and then second one is leadership depth. So the technical enablement side is really all of the skills that modern PMs need to operate effectively in today's environment, like understanding data, being comfortable with AI, having awareness of risks and cybersecurity, all these things that we've already talked about. And honestly those are kind of becoming like table stakes. They're important, but they are really just expected skills that you should have as a PM at the base level. And then that second dimension is where things get interesting, the leadership depth dimension. That's where I think the biggest differentiator in PMs will be, because that's really more of the people skills, like the ability to communicate strategically, to navigate ambiguity, to influence people, to develop people, and to really help your clients or whatever projects you're working on, to really help them move through the changes. So these are more of the strategic people skills. And what's interesting is that as technology continues to to evolve, those leadership capabilities are actually going to become more valuable, not less valuable. So, yes, AI can help you with gathering information, summarizing data, automating tasks, but like we talked about before, can't walk into a room full of important stakeholders and create alignment. So, you know, it can't help a team navy uncertainty like, you know, you get the best. I know I've kind of like belabored this point. So when people ask what the future PM looks like, I don't really think it's someone who's purely technical or purely a leader. It's really someone who combines both of these dimensions. So technical enablement, that's the basics, like, that's what gets you in the door. But the leadership depth, that's what really differentiates you.

Speaker B: Wonderful. Laurent, over to you. So, on the technical side, AI tool proficiency is on this list. What does genuine proficiency look like in practice? Not just I use copilot, but real proficiency.

Speaker C: I think one of the first things would be to, to go beyond what the AI tools provide you as a button, summarizing your email, summarizing a document. All of those things are things that are so easy to do now that at this point, it's not even, I mean, you're using AI, but it's not even close to proficiency. Being able to understand what it is that you are trying to achieve, properly managing what context your AI tool is going to be using to achieve that goal, whether that's creating a deliverable or creating a report for yourself or, you know, we've been talking about the status reports for a lot here, but making sure that the AI is not just searching everything, but that you're managing the context appropriately to get a better answer for whatever it is that you're trying to do that goes beyond just the standard usage that a lot of people have started out with. And for those that have not spent a lot of time with AI, that might be where they still are, but it'll be important to go beyond that, to continue experimenting so that you can ultimately get a good understanding of the boundary of where and when and how you should be using AI. And the best way to have that understanding is to experiment and to just use the tools.

Speaker B: Agreed. Kim, which of these skills is hardest to develop an existing team, and which ones do people consistently underestimate?

Speaker D: I think the skills that are hardest to develop are definitely the people skills, because you have to be able to read the room in real time. You have to understand what levers you can pull, what you can't. There's a lot behind change management, right? Sometimes your key resistors you want to make into your change champions to make them feel included, to have ownership. All that is, uh, exercise and persuasion and getting buy in and negotiation. And AI can enable these things by giving you ideas, maybe having some ideas around facilitating those discussions, right? But then you're the one actually connecting with the other person and then the other person wants to know that you're actually thinking through these things and are invested into it. You're not just being fed something to kind of repeat to them and you're not really thinking about it, right? So it's really about those people skills and getting into the room like Sarah alluded to, and being able to also influence the direction of a project. So the PM is in a unique role where you simultaneously see from a bird's eye view what this executive stakeholders want. You also see what the functional team is doing, the development teams are doing. And you have to speak basically three different languages with all your different stakeholder groups. So some are going to be at a high level, some are at a detailed level. And you're going to need to be able to connect the dots by speaking all those languages and influence each of the groups and each of them have different priorities and agendas. Sometimes they're not always aligned with each other and that's a whole different skill set. I think that sometimes again the trap of kind of going to the PM field is a, uh, by the book, PM kind of adheres to standard rigid processes. And Sarah's had the unfortunate experience of being my pm. You know, even though I've been in the PM field for a while, you know, I've never been one kind of standard. I've been, I have had to be dynamic. I've had to kind of think on the fly, both from a consulting standpoint, but even from a PM standpoint, right? So it's like there's this misconception about standardization and rigidity and the PM is not really as connected to knowing the details on a project. That doesn't have to be the case at all. I think the dynamic pm, the one that is able to understand the context of a project, not just report on it, but that all that stuff are different skill sets. That's going to take some time and then AI can enable that learning, but it can't replace it totally.

Speaker B: And I'll throw this up to the team. The uncomfortable question is, are These their skills on the old PM profile that are not actually getting in the way things PMs are good at that are working against them in this environment.

Speaker A: Yeah, I think there's probably a few of these, but one that comes to mind right now maybe like over planning. And I can relate, uh, to this as a pm. Like obviously you want to control as much as you can, you want things to go as smoothly as possible, but sometimes you spend so much time trying to create certainty and then you just, you lose the ability to adapt when things change. And realistically like things are going to change, especially if you're working on large projects. So yeah, that's one thing that I can think of.

Speaker B: Nar Kim, anything you want to add?

Speaker D: Yeah, I can go. I know I mentioned rigidity as one and I think maybe just to add a new one, it's kind of just accepting at face value certain things just because you're not the expert and not saying that you should produce an unqualified opinion. But needing to dig in right is going to be really important, especially if you're on an unfamiliar type of project or an unfamiliar industry or whatever the case is. It's really digging in and then sometimes there's a tendency to take a face value what you're hearing.

Speaker C: One of the things that I was just thinking about as I'm hearing the responses from you all is the direction that the PM role is shifting to in some ways runs counter to the folks that would probably be attracted to the role in the first place, where you're having to be very detailed with keeping track of all of the information which, like we were talking about the tasks and decisions, et cetera, and that's all being taken over by AI. So I think the, the role that someone is going to be or not the role, the, the way that the profession is evolving is going to run counter to a lot of the people that have been attracted to the role because so much of it was basic data gathering. And now I think it's going to be a difficult choice for some people.

Speaker B: Yeah. So Project Manager is becoming a title that covers an increasingly wide range of actual jobs. A PM running a regulatory implementation at bank and a PM running an agile project team at a startup are doing fundamentally different work. Sarah, I know you've met five emerging archetypes that are starting to define where the role is going. Walk us through them.

Speaker A: I think one thing that we've noticed when we talk about the quote unquote future Project Manager is we're not really talking about one type of person anymore. There's a few different archetypes, as you said, there's five. And I think most people that are listening to this podcast will probably recognize at least one person on their team as we go through all of these. But the first one would be the AI augmented pm. So this is someone who's, they've really figured out how to use AI to its best ability. They're using it to test plans, identify risk, draft communications. They're using AI as a thought partner and not as, you know, an ultimate decision maker. Then there's the delivery strategists. These are some of the best people to work with because they are never losing sight of that business outcome that we've talked about. So they can connect every single milestone and task and requirement back to something that leadership actually cares about, which is super important. So for them, they're really outcome oriented. Then another one that we're seeing a lot is the distributed architect. So this person understands that high performing team, remote hybrid, whatever, they don't just happen naturally. They're super intentional about communication, decision making, documentation, and just keeping people aligned in general, whether it's across time zones, different functions, different teams. They're not really just managing the team, they're really designing how the team is operating. Then there is the talent multiplier. These are the PMs who, they're not just measuring success by what they deliver, but by how many people that they're developing along the way. So every project becomes an opportunity for these types of people to coach, to mentor and to build capabilities. And so they're really creating that next generation of PMs while still managing their current project. So it's definitely a lot. And then finally there is the risk integrated pm. So this is someone who they're treating risk, security, compliance, all of that is a part of delivery. They're not as a separate work stream or separate conversations. So they're constantly asking, you know, what could impact this? Are we addressing it early enough? What are the mitigations? So it's really a mindset that's becoming important, especially if you're working in industries that are highly regulated, of course. So, yeah, so what's interesting about all these is that they're not mutually exclusive. You know, the best PMs usually are going to have some elements of all of these archetypes. But like I said, yes, if people listening to this podcast are thinking about Maybe their strongest PMs, I bet you can already put them into at least one of these buckets.

Speaker B: So, Laurent, question For you, the AI augmented pm, you've actually worked with these people and have become this, what does it look like in real life? What does it take to actually get there?

Speaker C: What does it take to get there? I would argue it takes a lot of experimentation. In order for you to be able to effectively use AI as a thought partner, you're going to need to have a good understanding of how you interact with whatever AI tool it is that you have available to you, the types of answers that it gives back to you, how you can push back on whatever it gives back. We've talked about judgment this whole time, but the idea is that you have that judgment so you can get to the answer that you are looking for, whatever that happens to be. And the only way that I have found and from m what I'm seeing all over the Internet is you just have to experiment. You have to get your hands dirty. And the more experimentation that you do, specifically if it can be something that is outside of the norm, that will help you to really learn. Going outside, for example of, I'll say simply creating a status report, it'll really help you to get an understanding of how you can best leverage the tool. So it's really about getting those, those hours in on just practice. You'll start to pick up on it very similarly to if you were just start working with somebody. And the more you work with that person, especially if it's on different types of projects, the more of an understanding you're going to have about how that person thinks and the best you're going to be able to work together.

Speaker B: Great response. I'm going to open up the floor. I'm going to ask a question. So what archetype on this list does not actually exist yet in most PMs

Speaker C: but should for a lot of product managers that we're working in offices. Right. So the idea of having a fully remote team is just something that some people just haven't had the opportunity to do. And it certainly has its pros and cons. But when we're talking about being able to use AI to do a lot of this admin work, it means that the information needs to be digitized in some way, needs to have been a meeting that was recorded, a teams or a slack message or an email or something. So being able to architect the way that the information flows on a team for something like a remote team or an in person team, and you want that information to be consumable by AI, it's actually very similar exercise. You want to have a good let's say team etiquette and hygiene around responding to messages with whatever the solution was. For example, so that everybody, in this case, everybody, the humans and the AIs know that maybe a task was completed or that a solution was, we had come up with a solution or we have not come up with a solution, whatever the case might be. And it's a little bit harder to do that when it's so easy to simply walk over to somebody's desk because you're not really forced into that situation. But I think as AI becomes more and more responsible for all that end work, we need to get into the habit of having all of the information be available.

Speaker B: Sarah Kim, anything you wanted to add?

Speaker D: I guess the name would be. I, uh, probably have to refine it a little bit. But maybe like the enabler, I think sometimes what I observe is that the PM and the consulting teams or delivery teams are kind of seen as two separate opposing forces. And then really the PM is an enabler. And that could look like many different things. So oftentimes delivery teams have different priorities or maybe they don't know the goals for the week. So it's really just making sure everyone understands, you know, what are they working on? Maybe it's carving out time for the delivery teams. I know sometimes, a lot of times, um, it's helpful with clients as well as our own teams that third party vendors getting a platform where they can all kind of do a working session together and then at the end kind of get an output as opposed to expecting them to kind of squeeze things in throughout the week. So it's kind of putting it down, different tactics to make it easy to do delivery work and kind of removing those blockers. And it's a way to make sure we keep the momentum going forward and getting the output. So I think enablement is really key.

Speaker B: Great, let's move on. So let's talk about development. Kim, I know you have seen this on the PMO development programs. We've seen it fail. What is that pattern?

Speaker D: Sure, it's again, I think it kind of harkens back to the administrative piece, but then also not allocating sufficient budget in a project or across all your projects for that control mechanism to have sufficient PM support. So sometimes that's the first place organizations want to cut. But it's important for PMO teams to take a firm stance and say, hey, this is our risk mitigation measure. And then you are at risk of having overages on budget or discontinuity between different projects. Right. Seeing it as dispensable, when really it's the control mechanism that's going to save you more in the end. And getting the right talent, right, getting the right strategists on at the table, those different archetypes make sure that they're efficient and then they can help do the people management side of the project, not kind of just execute it like a checklist. That's going to be the most important piece. I think also it's just PMO is not just its own role. I think PM skillsets should be integrated into every single role in an organization. So a director should have PMO skill sets, a consultant should have PMO skill sets. Every single level can benefit from project, uh, management mindset can bring. So I think cross training is there and I think it's just not siloing. So I think cross functional training in PMO is so important. So I think everyone kind of has a slice of a pie in the project. It's very educational when you kind of show everyone, hey, this is what's going on from a 360 view, right? This is. And then now they're no longer just noticing their slice of the project. They understand what the broader implications are, everything that's going around. I think just that cross functional integration and then everyone has that PM mindset is so essential in an organization. You don't have to be a PM to have that mindset.

Speaker B: Sarah, I want to ask you real quickly, I know you have some evidence about what moves the needle. Can you go into that a little bit more?

Speaker A: So it's what people are referring to as the 70, 2010 principle. So what that means is 70% of real skill development comes from stretch assignments on, you know, real, actual projects. But then 20% does come from mentoring and coaching, types of relationships, and then 10% from actual formal training. So formal training does matter, but, uh, it is the smallest piece of it, you know, not the whole program.

Speaker B: Great. Laurent, I do want to close out the development questions, but what about AI tool proficiency specifically? Because I hear from a lot of PMOs that they are sending their PMs to AI training and checking a box. Is that working?

Speaker C: The AI training is step one. I think that's kind of what the others were alluding to. You should have training, but that's not where it ends. It's a small piece of it, especially because a lot of the trainings are focused on the really basic foundational pieces which are important. But it needs to go beyond that. And as people are learning the AI, uh, boundaries that I mentioned earlier, and they're going through the experimentation, it's going to be necessary for them to have guidance along the way.

Speaker B: Next topic I want to talk about is interviewing, hiring. If the skills and archetypes have changed, the way you evaluate candidates has to change, too. I'm going to ask all three of you the same questions, and I want to know where you agree and where you don't. Starting with the most loaded one, the pmp. Still a job requirement, yes or no? Kim, you want to go first?

Speaker D: Oh, man. Controversial opinion. No.

Speaker A: Yeah, I think I'm going to have to agree with Kim. I don't think it should be a universal requirement, but, I mean, I still think it has value. Like, yeah, PMP could tell you. Yeah, right. It could tell you. Okay, someone clearly has taken the time to invest and learn and understand all the fundamentals of being a pm, but what it doesn't tell you is whether they can actually use that and influence people or navigate ambiguity on a project, things like that. So I think it could be a good signal, but it's not really a differentiator. Like, really differentiator is what you've actually done with those skills.

Speaker B: Agreed. Laurent, anything you want to add?

Speaker C: I agree with what Kevin, Sarah, there, great.

Speaker B: Let me push on AI fluency specifically, because I think this is where hiring is most behind. Laurent, uh, what question do you find it? Find out if someone actually knows how to work with AI tools, not just whether they've used them.

Speaker C: I want to know what someone has built using AI, and I kind of alluded to this a little bit earlier when I was saying, you know, going beyond what you've got as a button on your screen. What is it that someone has built using the AI tools that were made available to them to either handle some routine tasks because their organization maybe didn't have a tool that kind of did it for them, that's been productized or something that provides you with a report that helps you to apply judgment, a way for you to take that next step that we were discussing, the strategy and the judgment piece. What have you used AI to create to help you with those endeavors? It's a great way to learn how up to date people are, how comfortable they are with kind of pushing the boundaries of what most people do with AI and to explain how they came to recreate the tool, which gives you an insight into that process.

Speaker B: Yeah, completely agree on that. So we're going to close the content portion of this episode differently than what we've done before on the other podcast. But I want an unpopular opinion from each of you, something you actually believe about project management talent that you would not say in a client meeting. Kim.

Speaker D: Oh boy, I'm full of unpopular opinions. But I guess it would have to be that the capability gap in the PMOs is really not at the junior level, it's more at the senior level. And then we are going to need to find a way to upskill both sets where you've learned on the junior side through kind of the data gathering and the administrative stuff that you learn judgment along the way. And then the seniors take, uh, that learning and learn to apply judgment over time where they don't need to do that as much.

Speaker C: Right.

Speaker D: And then it's solving the gap between how we now train juniors up with, we're training with theory more than experience going through those. We can't just throw them in and say, hey, produce a judgment call on this. So I think we're going to have to solve for what training is going to look like, because Sarah alluded to it that formal Training is only 10% of learning. Right. And then now with our stretch projects, we're going to ask more sophisticated ways of managing a product with less time, because now we're AI enabled. How do we solve the capability gap? And that's, that's a big question for sure.

Speaker B: Sarah.

Speaker A: Yeah, I don't know if this is really an unpopular opinion, but I think it is. So my unpopular, um, opinion is that I don't think AI is really this big threat to project management that everyone thinks it is. I think the bigger threat is taking the exact same processes that we've had for years and years and just doing them faster with AI. I see people using AI to generate status reports, quicker to write meeting notes, all that efficiency stuff, which is great. But if we're not actually taking a step back and figuring out whether those activities are still the best use of the PM's time, then we're missing a much bigger opportunity. So I think the real value of AI isn't just that efficiency. It's giving us a chance to really step back and rethink how project management even works in the first place and what we can do to make it better.

Speaker C: I think for those simple projects, there won't be a human project manager. Uh, the project teams, generally speaking, are going to shrink as they get AI enabled. As a project manager, you'll have more projects that'll be more complex because you'll be AI enabled and there's small kind of routine or maintenance types of projects. It'll be the people who are the doers and AI or several AI agents, however you want to slice it. But AI helping to keep them on track and there won't really be someone to provide that day to day oversight. It'll be at a higher level.

Speaker B: I do think that's the right note to end on for this episode for the series. To everyone that has followed along in all seven parts. You've seen all the thankful content we put out. You listen to the podcast, you've read the blogs.

Speaker C: Thank you. We are, we value this.

Speaker B: We we value these kind of interactions. And if you have any questions about pmo, if there's anything we can help out with on the Witham team, please let us know. To my fellow co stars, thank you guys for joining again today.

Speaker A: Thanks Logan.

Speaker B: Thanks guys.

Speaker C: Thank you everyone. This was fun.

Speaker A: Thanks for joining us. Be sure to subscribe to our podcast so you'll be first in line to

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