Welcome everyone to the second event in our series, AI and the future of project management. Today's event is called The Human Edge, the Project Manager's Role in the Age of AI. And it covers how AI is affecting project managers and the leaders they work with within the development space. Our first event mostly covered the tech and tools side of AI. So we have been really looking forward to do a follow up that really covers the human side. And then again, it's great to see so many of you have decided to join us today. We are as always broadcasting from Hellerup in Copenhagen. So feel free to say hello in the chat and share where you are joining from. Before we dive into the agenda, we'll just begin with a brief introduction. My name is Rasmus Rydder. I'm heading up our strategy execution department. And I have a very strong passion for benefits and change management and how we as leaders of projects and portfolios can use AI, not only to boost our work, but really also to create more value. And with me, I have you, Executive Director of the Half Double Institute and a very strong voice within project leadership, Christina. Yeah. Thank you. And thank you for inviting me. Yes. My name is Christina Sire-Peterson and I am, as Rasmus mentioned, the Executive Director of the Half Double Institute. I've worked in project management my whole career. It's given me a great career. So I share Rasmus' passion for project management, for governance structures, for portfolios, and how to get the most value out of projects. I think we share that passion. Absolutely. And I'm actually the co-founder of this Danish method called the Half Double methodology. But I have also been looking at AI very much in the last couple of years and trying to really hone in on where does that leave the project manager? Where does it leave the PMO director? Where does it leave the sponsors and everybody who is in an organization busy with making projects succeed? So I'm really looking forward to our talk today. Yes, it's going to be great. I also just want to mention that after the webinar, you will receive an email with the presentation and a link to the recording. That was it for the introduction. Now we will push into the agenda and we're going to start out with a brief recap from the first event to set the scene for today. And I just want to stress upfront that having seen the first event is not a prerequisite for diving into this one. Then Christina will take over and share perspective on project management and she will also show how AI is moving the bottlenecks within development. Then it is time for the big conversation of today. We will start by discussing how AI will affect leadership in development organizations and the space around the project manager. And then we will discuss how AI will affect the project manager and the project manager's role. And finally, we have made some space for questions. And so on the topic of questions, we really hope that you have a lot of questions and comments and reflections and that you would like to share them. Please do so in the chat. It makes the next half hour more interesting for everybody. We also hope that we, of course, during the presentation and our conversation will cover some of the questions. But as I just said, we plan for questions in the end. So please just leave your comments or questions in the chat. What we don't have time to answer, we will cover up. We will cover in a follow up email after the webinar. All right, let's dive into it. The first part here is, of course, the brief recap from our last session. And fundamentally, projects exist to take us to a better place where we create more value. And so when we are discussing the impact of AI on project management, it is interesting to see where we start in terms of value creation. And this is why we start this event by taking a quick view of the results from our 20, 24 survey predicting transformation, success and failure, where we uncovered how much value we are creating with our change and transformation projects. And this is sort of the key slide from from that survey. And what we did was that we asked organizations, what percentage of the benefits potential do you think your organization is realizing in your transformations? And as you can see on the slide, 18% of those 145 respondents said that in their organization, they're actually realizing less than 20% of the benefits potential. And frankly, I think that's wild. But we can also see that only 3% of those same 145 respondents said that in their organization, they actually hit the mark and realize 80% or more of the benefits potential. And every time I present these results, I can't figure out what's worse. But in any case, I think we can agree that this is not great. So the next big question is, of course, why? Why are we in this situation? And I could really just go on and on about the why. But in short, we settle for technical success and financial failure. And this slide shows what drives benefit management or impact. And each factor has a statistically significant impact on our ability to create value. And so let's see. Let's start out by looking at what actually works. Because projects succeed with the technical delivery part in 81% of all organizations. So, you know, that's super important and great news. What is less great is that benefit management, benefit ownership, change management, that's where we really get into trouble. And, you know, we could keep on deep diving into this. But again, the super fast version is that we struggle with everything that has to do with humans. And it's classical change management. It's getting senior stakeholders into a room to discuss some prioritized benefits and eventually get them to own the benefits as well. And of course, benefit management also covers some technical elements like estimations. But that's not that difficult. The difficult part is the stakeholder part. Getting senior stakeholders involved and getting them to make choices about value creation and prioritization. All right. Now we know why we are in trouble in terms of value creation. The final part of the recap is about how AI impacts efficiency and quality. And here's some numbers from recent research from OECD and MIT that shows the benefits of AI is actually real. And what I want you to see here is that we've been used to, you know, often choosing between either high quality or higher efficiency. And the great thing here is that there's a dual effect. So we both get efficiency and better quality. And not surprisingly for anyone who has ever worked with AI, the use of AI creates an even bigger impact for the tasks that are content heavy. And so these numbers, of course, indicate that AI could be a real service and not solving solving everything we do, but having a really big impact on the technical delivery part. And that is, of course, the part that we were already good at. And I think maybe, Kristina, it's time for you to come back on stage because maybe that's a good tee up for you and your perspectives on project management. Yes. Yeah. Thank you. Well, as Rasmus just explained and also showed the evidence, we are still not realizing the benefits of all our projects. And if you look at the way that we do projects, a lot of us use different kind of project models. We use either Prince2 or SAIP, agile setups. Some of you may even use the half double methodology. I hope that. But if we look at that, most of the work that we do in projects to execute the projects consists of two layers. The first layer is what I call the artifact layer. That means that those are the things that we produce in order to drive the project forward. It could be a project charter, project description. It could be a Gantt chart with your plans, status reports. We do risk analysis. We do resource management. All of those artifacts that we produce to control the project, to execute the project and to communicate about the project. The second layer is what I would call the people layer. And that's all of the things that, in my opinion, actually makes the project succeed in the end. That is showing up, putting your team together, communicating to different stakeholders about the project, reading the room, making sure that each project participants have the relevant task, that they understand why they're doing the project, that they are developing and learning and hopefully also having some fun through the project. So if you look at any project and any project model that organizations are using out there, I want to say that in their sort of core, they all have these two layers. Some models stress the artifact layer a little bit more than the people layer. Some project management methodologies stress the people layer a little more than the artifact layer. Some project management methodologies, but both, all of them have these two layers. My contention is that AI will take over, or at least assist to a great degree, in what I call the artifact layer, that will be able to produce a Gantt chart. They will be able to produce your slides for a status report. They will be able to do a lot of the analysis of the portfolio. What they cannot replace at the moment is the people layer. Showing up for your project group, reading the room, going out to your stakeholders and ensuring that the project is still relevant to the organization, that the stakeholders feel heard and involved. All of those things we still need people to do. And I think that's a very central core because as Rasmus said a little bit earlier, it seems that we are actually quite successful in delivering the technical specifications of the project. But what makes a project successful is all the other things, the change management, the stakeholder management. And that belongs squarely in the people layer. So I think the role for the project manager is still very much there. But I think we have to really focus and hone in on the people skills. Because AI is going to assist us and it's going to be more efficient in many ways to do the artifact layer. Especially in large and complex projects, it can be really hard to do a plan. You may have a lot of vendors from all over the world. You have a lot of stakeholders. You have a big organization. So you need to focus on the people layer because AI is going to help you a lot. And that's, I want to say, good news. Let's have a look at an adjacent field, another profession, where I think AI has affected that profession the most here in the beginning. And that is software development. A recent study that I came across measured how effective software developers were at their work. And you could, they divided it into sort of three parts of the process. One was writing code. And you can see here that the numbers were that they were 180% more effective at writing codes. But if you look at how many projects they ended up building, they were 50% more effective. Still a lot more, but a lot less than 180. And when you see what was actually released, it's only 30%. And I think what that says is that there is still a need for humans to be reviewing. Marketing still needs to market whatever they're shipping. So you could look at it this way, that before one of the bottlenecks was how many software developers do you need to write a lot of code. So there were bottlenecks, human bottlenecks in all of the process. But now the bottleneck has moved. Now the bottleneck is people. Now that, of course, is a good and bad news. It means that, okay, now we can be a lot productive in software development in the front end, but we still need humans. So are we getting actually more efficient? That is something that organizations need to look at. But on the other hand, you also need to think about that there will still be a need for human judgment, ethnics. All of those things that need a human touch is still necessary. And now actually humans become the bottleneck. So I think there are still going to be jobs for software developers. They are just going to be doing something else. All right. Now we are ready to continue our conversation in this slightly different setting. And Christina, I've been looking very much forward to asking you some questions about this. Yes. And I think when we start about, we start with the leadership part and what should leaders do, then my first question is, what should leaders do? Yes. That's a good question. And we just saw a little bit from software development. Now software development and project management is not the same. No. But I think we can take some trends from that. So leaders, first of all, you showed in your study that there are efficiency gains to be had from using AI. Sure. But I think they don't come just on their own, just from sitting and waiting. So first of all, leaders will have to make sure that their organizations actually use AI and they have to start harvesting those efficiency gains. They are not just going to come in their lap. So I think that's the first thing. Okay. Make sure that the organization actually harvests the at least 15 to 30 percent efficiency gain you showed in your study. Yeah. Okay. So I guess that's a bit easier said than done. Yes. But let's imagine for now that the organization actually manages to do that. What should they do with all that time? Yeah. And I think that's going to be a real dilemma for leaders because their inclination is probably going to be to do more work. Right? Probably, yes. You're sitting there as a portfolio manager. You realize you have a 20 percent efficiency gain. The temptation would probably be to do 20 percent more projects. Probably, yes. Probably. But then we get more of the same like you showed. Then we might realize we get more technical deliveries without the benefit realization. So my challenge to the leaders would be think about how you can apply those 20 percent extra time that you got into the people layer, into the actual change management effort. Yeah. But it's going to be a paradox because I'm sure the pressure from some parts of the organization is going to be about, you know, making more sausage in the sausage factory. Exactly. Because I think, I mean, my first reflection would at least be, you know, how do you convince the CFO that this is a good idea? Yes. How? Because, you know, if you are in a tightly, if you're in the tightly run company or you're a little bit squeezed, then the CFO might think, okay, you know, there's 20 percent savings. That's great. That's a fire 20 percent and cash in on that. Yes. And it's the obvious choice, of course. At least for some. Yes. That's why leadership is hard. Exactly. Sometimes you make a non-obvious choice. And what I would say to that CFO, Rasmus, is, all right, well, then you get 20 percent more of the same. Yeah. And actually, it is the definition of insanity to do the same over and over again and expect a different result. That is true. So then you get 80 percent more technical deliveries. Yes. Without the benefits. Yes. And then we're nowhere. Okay. But, I mean, if we should then get sort of a little bit more practical. Yes. So, could you, you know, can we get a little bit more hands on, you know, what should the leaders actually do? Yes. So, there is this old saying in management that you get what you measure. Yes. Right? So, I think maybe now leaders have a golden opportunity to look at their KPIs and look a little bit at their incentive structure. And maybe see how can we actually incentivize benefit realization. Yes. I know you've studied that. I've studied that a lot, yes. So, maybe actually the world hasn't changed that much except now the leaders get a present. Yeah. And they have to do something intelligently. Yeah. And that could be trying to say, okay, how do we measure the KPIs so that we skew towards benefit realization and not just more technical deliverables. So, it's going to be difficult, but that's what they get their money for, right, is to make difficult decisions. Exactly. So, I think there's a golden opportunity here, but it requires a conscious and sometimes counterintuitive choice. Yeah. So, you need to stop and think before you just spend this 20% additional time or whatever we're going to get on more projects. Yes. What strikes me is when you really think about it, then it's sort of the size of the change. Yes. Because we're both, we're changing how we work to get that benefit from AI. Yes. And then on top of that, you're saying, okay, we need to do something different with all the time we now sort of free up. Yes. So, you know, how do you as an organization go about that? Because it sounds like that we are encouraging two sort of parallel transformations. Yes. No, I think it's going to be a change and I think Bill Gates just came out with a little article saying this is probably the biggest change in human history. And I think it requires a lot of conscious leadership. But also, I think there's a two-pronged strategy and I've noticed that in Implement, we sort of have that. It's about making an AI strategy top down about how do we use it, but it's also for the individual employees to throw themselves into it and start using it. Sure. And I think it requires both a bottom-up effort from the individual employees and the people managers to encourage the employees to use it, to try it out, but also from the leadership to actually really, really think hard about these questions. Yes. So, we don't just harvest it in more busy work. Yes. That's going to be the challenge. Yeah. And, you know, we're both sort of project and transformation nerds. So, I think also a reflection could be that we really need to look at this as a change and a transformation and maybe give it the governance that we would give other very large transformations or change projects, right? Yes. To make sure that we get it right. Yes. And make sure that we have the conversations about where we are going and how and so on, right? Because, you know, hoping that we'll just change the way we work is probably not the best strategy. No. I agree with you. So, it requires conscious effort. It requires conscious effort. Yes. For sure. All right. Being conscious of time. Yes. So, I'm just trying to sort of recap a little bit on these first part of the conversation leadership in the age of AI. I think summing up would be that the first thing you need to do as a leader within the development space is to make sure that you harvest the benefits from AI. Make sure that you, of course, provide the tools that your organization needs and whatever agents or skills or process redesign projects that you need in order to harvest the benefits and maybe most importantly help your team develop. And then as Christina said multiple times think about how you use that additional time because now is the opportunity to do something else and do the projects the way they should be done. So, we're going to focus on benefits and that's actually maybe also the sort of the last point. Have a look at the incentives because we know that is what is also driving behavior and we need to start looking at benefits more than just output and deliverables. All right. Then maybe this is the point of the agenda that most people have been looking forward to because now we're going to talk about the project managers and I think I'd like to start this conversation a little bit in the same way. So, if you're a project manager, what should you do? Yes. Well, I want to say I think my answer is a little bit the same as for the leaders. Yeah. Start using AI. Yes. The only way you harvest the efficiency gain is of course by using it and hopefully there'll be an AI policy and hopefully your organization will give you the tools and the training. But then it's about diving in. That is not a gift that's going to be put in your lap. It's a gift that you harvest yourself. Yeah. So, throw yourself at AI and start using it and figure out how it can make you more efficient. Yeah. I think that would be my first advice. Yeah. And I guess that's not one way to do that because there are different possibilities in different organizations. Yes. Okay. I think that would be the first thing to do. That's the first thing. Yeah. The next question is also a little bit similar to what we asked the leaders. So, what should they do with this additional time? Yes. Spend it on people. Spend it on people. Yes. Okay. That's it. It's quite simple. And in that way, you could say that a lot has changed and will change with AI. I think we both agree on that. But in some ways, the conditions for successful projects have not changed. It's all about people. It's all about people. If there's one thing projects have in common across sectors and countries and types of projects, they all consist of people. Agree. So, once you have harvested some of those efficiency gains by making AI helping you with all the artifacts, spend that 20% of extra time on your stakeholders, on your project team, on your surroundings, looking out for benefits, looking out for your impact. But again, you may feel some pressure for the organization to just do one more project, right? So, what are we going to do about that is a good question. But it is going to be probably quite difficult. Yeah. That's what I would do as a project manager. In some way, the two of us have a bit of a privileged jobs in terms of, you know, having a great deal of autonomy on how we spend our time. Yes. I have to ask you, Christina. Have you sort of spent that additional time that you gained from using AI on people? Well, I teach. I didn't say I could do. But, yeah, that's a good question. I think I have. I see some things getting easier in my work. Yes. Producing slides, presentations, calculating things in Excel. And I really do try to, one habit I've created, I use Claude a lot. Yes. And while it's working, I always make sure to get up from my desk. Yes. You know, it may take it five, ten minutes, even that short interaction. Yeah. I go get a coffee with a colleague. So, that might be a good thing to practice. Don't just sit there staring at the AI agent working. Yeah. Get up and talk while it's working. I think that's the perfect combination. Yeah. I must admit, when I came up with this question and I asked myself the same question. And if I'm going to be really honest, I haven't been super conscious about what I did with that additional time. No. But I think maybe this is a good time for us to reflect on it. Yes. And maybe, hopefully, that reflection has been passed on. I know that we're beginning to be a little bit squeezed on time. Yes. But, you know, will AI be the end of project management? I don't think so. The trouble is, all professions will probably say that. You know, lawyers will say it's not the end of being a lawyer and doctors will say it's not. So, you know, AI is moving so fast. It's really hard to predict. But we see some trends. Yeah. If you're going to be a project manager, really focus and hone in on your people skills. Yes. You are going to still be indispensable. Yeah. Yeah. So, no, I don't think it's the end of the project manager. Okay. Maybe sort of the final question on project managers. I mean, I've met a lot of project management and sort of if I'm a bit crude, you know, I can divide them into sort of, you know, more business oriented and more technical oriented project managers. So, what will I do if I'm actually sort of more of a technical project oriented project manager? Because I guess we are, you know, in the space where, you know, things are really changing. Yes. I think your world is going to change if you're a technical project manager. I want to say two things. You have to practice. Yeah. You know, how did you get good at technical project management? You probably practiced. Yes. So, how do you get good at people management and stakeholder management? Yes. So, I think making a conscious choice to try to practice, then you're going to get better. You may never love it. No. But I think you should. And if that really does not appeal to you, maybe consider having a role like a technical project manager. I still think there's going to be someone like we saw the code reviewers in the study. Yes. Maybe think about that role instead. Yeah. All right. So, to try and quickly summarize this conversation about the project manager role, I think maybe sort of the good advice here is get good at it fast. Invest the time in becoming or getting on top of this. And then the second advice is invest your time in people. And then maybe the third is always leave a human touch because that is what makes your work special. Yes. And maybe a final remark, Christina, because then I think maybe we have time for one or two questions. Yeah. I think we're going to run out of time actually. But yeah, I think the summary of our talk has been, yes, AI frees up capacity. We don't know how much yet. But that alone will not make a big difference. And second of all, the capacity does not free up itself. It requires conscious choice. And AI may present us with this unique opportunity. It might be the gift that we've been waiting for to become more efficient at our work. And please, please, please spend that time putting humans first. Humans have always been at the center of projects and they still will be. But now we really get a chance to focus on the human side. And you and I, Rasmus, through our careers has realized that's actually the difference between successful and unsuccessful projects. That is true. I think, you know, we're kind of running out of time. Yes. And I can see that there's actually quite a few very interesting questions. But I think what we need to do is to pick up on them and answer them in the email that we will use as follow up on this. Yes. Because I think time is, it's around nine o'clock. So I think it's time for us, Christina, to say thank you very much for today. I hope you enjoyed your conversations. We will share the recording and slides very soon and follow up on all your questions.