The Digital Project Manager

How to Lead AI Organizational Change in a Complex Enterprise

• Galen Low

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When AI enters the enterprise, the work isn’t just about the tech—it’s about culture, collaboration, and courage. In this episode, Galen chats with Deborah Ketai, a program and change management leader who helped a Fortune 5 healthcare organization align its people, systems, and culture around AI. Together, they unpack how she built a community of practice that broke down silos, reduced knowledge debt, and created space for cross-training, collaboration, and smarter risk management.

From talent strategy to trust and transparency, Deborah shares what it really takes to sustain AI-driven change inside complex organizations—and what PMs need to learn now to stay ahead as their roles evolve.

Resources from this episode:

Galen Low:

Is it worth the effort to try to break down silos and align attitudes around AI within a massive organization? Is it even possible?

Deborah Ketai:

If they skip the step, they're going to incur a lot of debt, technical debt, knowledge debt that is gonna make future projects more expensive and more difficult?

Galen Low:

You had called out that it wasn't just generative AI, in fact, this is more like foundational change.

Deborah Ketai:

We had really three goals — talent, mobility, and retention to encourage collaboration between these AI teams so that they're not reinventing the wheel. And the third is a cultural shift, encouraging business champions to consider AI solutions to their business challenges.

Galen Low:

Some folks who are being asked to do a dual role. Do all projects need change management?

Deborah Ketai:

Not all projects require change management. The skillsets are different but overlapping. Program managers need to get closer to where strategy is formed to be transparent in reflecting back to leadership where they're likely to be misalignments.

Galen Low:

Welcome to The Digital Project Manager podcast — the show that helps delivery leaders work smarter, deliver faster, and lead better in the age of AI. I'm Galen, and every week we dive into real world strategies, new tools, proven frameworks, and the occasional war story from the project front lines. Whether you're steering massive transformation projects, wrangling AI workflows, we're just trying to keep the chaos under control. You're in the right place. Let's get into it. Okay, today we're peering behind the curtain of how a program manager and her team created a community of practice within a massive healthcare enterprise that helped remove silos and align attitudes around AI within the business. My guest today is that very program manager, Deborah Ketai. Deborah is a certified organizational change manager and program manager most recently for a massive US-based healthcare group, where she built thriving communities around AI, DEI, and data transmission controls. Deborah has also been very active in building the project management community as a board member and programs portfolio lead for a regional chapter of PMI. In this episode, we're gonna cover what Deborah's day-to-day looked like as a program manager leading a major organizational change initiative around AI, whether AI and machine learning related organizational change needs to be treated differently, what teams of any size can borrow from the playbooks of enterprises undergoing massive AI transformations, and whether the role of a project or program manager will evolve to incorporate change management and what that might mean for the PM skillset. Deborah, thank you so much for being here today.

Deborah Ketai:

Thank you for the opportunity, Galen.

Galen Low:

I'm so excited about this conversation because you are just someone who has been like very deeply involved and embedded as a program lead and change manager for a massive healthcare enterprise just as AI was beginning to enter the fray. In our conversations leading up to this, you've mentioned things like breaking down silos between divisions and stakeholder groups, running training sessions and hackathons, addressing fears around risk and compliance, and also just getting AI practitioners and maybe the AI practitioner wannabes all on the same page. So I thought maybe I'd just start with like one hot question that I think my listeners want to know. My question is this. Is it worth the effort and energy to try to break down silos and align attitudes around AI within a massive enterprise organization? Is it even possible? And what would happen if an organization like the one you were working with skip that step completely?

Deborah Ketai:

That's really a good question, Galen, and I'm glad you asked it. I think alignment is going to be critical and breaking down silos is gonna be a big piece of that. First of all, companies are going to need to establish data and AI governance. They're going to need to align their resources and expectations around developing or buying AI tools. And if they want to have an AI first culture, and they're going to have to, if their competitors are doing it, they're going to need to think about what that means for their employees, both now and in the future. So at a bare minimum, they're going to need to break down silos to avoid the risk of cybersecurity incidents. If they skip the step of breaking down silos, they're also going to incur a lot of what we call debt, technical debt, knowledge, debt, and so on. That is gonna make future projects more expensive and more difficult. I think there are three areas in which I've broken down silos. You really mentioned all of them. AI, system operating controls, and as community engagement, lead of the second largest employee resource group at United Health Group. And also to some extent through a mentorship program, breaking down silos after mergers and acquisitions. So I think that there are a number of different steps involved and skills involved in breaking down silos that project managers and change managers need to focus on. One is creating empathy among different groups by familiarizing them with each other. Cross-training, job shadowing, mentoring and coaching. Leadership needs to create and get buy-in on the vision of what it would look like and what the benefits would be of breaking down the silos. They need to create and concentrate on touch points, processes, and communication channels that bring the silos together. And they also need to concentrate on eliminating obstacles to bringing people together such as different data sources is one of the big ones.

Galen Low:

What I really like about your answer there is, I was kind of thinking of it as like breaking down silos as in like helping different groups throughout an enterprise understand one another. I like the framing of, it's kind of everybody's problem. It is a team effort. It's not just about kumbaya. Do we all understand each other and get along? It's also that when it comes down to like data governance and compliance and security, like everyone is in some way accountable for their own piece, and everyone does need to be sort of singing from the same songbook and that whole idea of aligning and agreeing on. What the impact or outcome is of that alignment needs to be agreed and understood at the leadership level as well, so that it's not just this sort of cultural shift, which is a vague word that we are using all the time, especially around AI, but actually it is about risk. It's actually about the ROI, I guess, on having that togetherness, having that unity. Also the cost of getting that wrong, not having those conversations and then incurring a risk or having a risk realized that could be a very expensive and damaging risk. That could probably be avoided if people were kind of on the same page. I like how like tactile that is.

Deborah Ketai:

It can be both squishy and tactile.

Galen Low:

I like that. Squishy and tactile and the tech component too.

Deborah Ketai:

She is a kind of tactile, so that's, there's that.

Galen Low:

I love that. I wonder if we could zoom out a bit. You have been, as you were alluding to, like your previous role at a large US healthcare group, you know, your responsibility was actually for leading change management programs and initiatives relating to a lot of things, and I think most recently, AI and machine learning. And it sort of comes back to, you know, this curiosity I have, just what did your day-to-day look like in this role and what outcomes were expected of you?

Deborah Ketai:

As often happens with change management, my biggest effort was probably the upfront effort of defining what the goals were, what the success metrics were going to be, who was going to be involved and so on. And we had really three goals — talent, mobility and retention across the AI space. So this is a huge Fortune five company. AI is happening in pockets all over. And let me be clear that when I talk about AI in this context, it's not just generative AI, it's also predictive analytics. It's wearables and sensors, it's Internet of things. My second goal for the program was to encourage collaboration between these pockets of AI. So that they're not reinventing the wheel. And the third is a cultural shift, encouraging business champions to consider AI solutions to their business challenges. So in the beginning, I spent a lot of time identifying and segmenting the stakeholder groups. I partnered with an AI reporting and analytics specialist in order to do this. And determine their needs, find out how best to reach them. And then the rest of the program primarily involved creating opportunities for them to learn, share, and build community. Targeted to the different groups that those segments that I identified, so my projects within the program. Were threefold events such as an internal conference and hackathons content like podcasts, articles, presentations and community, internal social media and knowledge sharing. And then there was a fourth piece to this, which was risk management advising the governance committee on governance, compliance, and security.

Galen Low:

That is a big program. Yeah. It's funny because we often talk in my community about like, you know, getting sort of handed a directive and having to execute it, but you know, you were starting from further back. And not only that, but even what's interesting about it is the timeframe, you know, you had called out that it wasn't just generous of AI, in fact. At the time, generative AI was relatively new in the business world in terms of like everyday business application, but this is more like foundational change, anticipating that there will be greater change and more specific change in the future. It was more about sort of bringing people together and it's really interesting you've done it again, right, where like community can be a squishy thing, but actually the ROI and the risk management, I guess, of. Bringing people together so that they understand one another so that they can cross train, so that they can collaborate together, like it builds that foundation to then do whatever is next. That in itself is risk management. I really like that idea because I think the word community of practice, even just the word community at all gets thrown around and I think the value of community isn't broadly understood. It's not as scientific as you know, a lot of business leaders and executives would like. But I think you've kind of hit the nail on the head that by doing these things, you're creating a sort of fabric and unity and cohesion between disparate groups that understand the world a different way and trying to kind of get them on the same page. I really like that. I was thinking about two things, actually. One, I was thinking about what you had mentioned about business leaders and getting them aligned with like how to use this technology in the business. It's funny. Talking about it now in 2025 when everybody is chasing down every AI tool, but like did you find that you were convincing people that AI was actually a worthwhile thing to invest in and discuss?

Deborah Ketai:

Yes. I think that there were two levels of that. One was that very often. Business leaders simply had not considered AI as a potential solution to problems. Whether those problems were fraud within healthcare or diagnostic imaging, there were so many different potential areas where AI could be used and they just weren't thinking about it. The second step was then really to help connect them with people who were knowledgeable enough about it, that they could boil the AI concepts down for them and explain, alright, does this exist yet? Can it exist in the near future? And what are the risks, expenses, et cetera, and potential return of using these solutions. And then compare it because AI is never going to be the one size fits all, you know, golden key to the puzzle. So they still had to consider their other non-AI solutions as well.

Galen Low:

It's really interesting that like it brings into such sharp relief the importance of talent. And that's the first thing you mentioned, right, is you know, you actually took a data science ish analytical approach to identifying the talent and stakeholder groups across the enterprise to understand the groups you're dealing with and the skill sets that they have and the skill sets that are needed. To facilitate this like knowledge sharing so that everybody can kind of like level up, right? It almost like lifts all boats, especially in that realm of like the art of the possible is what we used to call it, right? Is we need to know what this technology can do. We have to understand that it's not a silver bullet. We have to understand how this fits into the ecosystem of other technology or other methods. And on top of that. Like in healthcare, the stakes are very high. You know, there is a lot of compliance to consider. There's a lot of, you know, regulation to consider, and fundamentally we're talking about the livelihood of human beings as the business. That's what's at stake there and that's why it's worth it to bring all these groups together and identify who knows the right things and who can train and cross train one another so that we make a stronger team that sort of understands one another, has that empathy has worked together and can pursue some of these, you know, solutions that might be AI like only or might be AI combined with other technology, or might be AI combined with different med tech or just process. Gosh, what a big lift. Out of curiosity, 'cause it is such a big program, how do you even begin to measure that change to know if it's working? And just as a backdrop, my organization, you know, we struggle, we want to get, bring everyone together, right? We want to have these hackathons. We did a week long shutdown just a few months ago to, you know, explore as teams, different AI solutions, you know, different approaches to how we can use vibe coding. But all along the way we're like. Is this gonna be worth it? We're gonna shut down. We're gonna spend all this time and energy to like have a kind of, you know, an outcome. That was pretty clear. We also struggled with that. How do we know if it worked? How do we know if it's working? What kinds of things were you measuring or asked to measure in terms of whether or not these, like cross training and educational sessions were working, or the hackathons were working?

Deborah Ketai:

I will acknowledge that the metrics were the most difficult part of the program for me. And a lot of that had to do with the fact that some of the data that we wanted to track simply was not captured in our HR and other systems. So for example, not only was there no way to track career mobility within the HR system, but because this was a Fortune five company that had grown largely through mergers and acquisitions. The relics of old HR systems still existed in the fact that the same type of role often had 15 different titles, job titles or job codes across the organization, and that made life very difficult. So we really often had to rely on very simple or proxy metrics. Not the kinds of things that you'd wanna do long-term. Long-term, we would've worked with HR to capture more data, but just simple things like engagement with content. So we implemented various kinds of web analytics on both the social media, the website, and so on the microsites. We also obviously captured things like conference attendance. We were very much interested in making sure, kind of as a side note, that as we were developing the AI practitioner community, that it be diverse. And so that wound up requiring all kinds of data, even as we were inviting speakers for this three day four track conference, making sure that the various presenters. Were different genders, different geographic locations globally, all kinds of diversity metrics that we also had different ages. And again, a lot of that data was kind of hit or miss. A lot of it was just plain missing and some of it was misleading. So had the program gone on longer and unfortunately it, it was cut short for a variety of reasons, having nothing to do with the program itself. But had it gone on longer, I think we would have been able to come up with ways of determining and tracking data that was more appropriate.

Galen Low:

I like that approach of like, what have we got to work with and how can we use that to gauge traction, I guess is kind of the way I'm thinking about it. Right now, I work in an organization where we can still wrap our arms around our staff and like, not physically, but what I mean is like we can still, we know that this individual over here is good at x and y. You know, we don't have a deep HRIS. This sort of skills matrix isn't deeply formalized. We just kind of know like this person would be good. They know their stuff. Maybe they can do a lunch and learn. When you talk about enterprise at scale, and I'm thinking about my time working for consultancy, like 500,000 employees, right? It's almost more similar, like when you talk about social media and these like events and these conferences, it is almost more like a sort of mass business model. Like you would, if you were planning a conference that was external or you know, building a social media platform and you're looking for engagement, you're looking for things like. Ticket sales and attendance. You have to look at this data in aggregate, and then you're hoping that they fill out that survey of on their way in or on their way out about who they were and who they are and what things they like to do, and that's the data you have to work with. I like that idea that if it continued on, absolutely, and I think even just like having an HR system that has more structured and clean data about people and what they're good at to sort of make decisions about community and events, but also just like staffing. I think it's the right idea. You almost need AI to build the next generation of stakeholder identification, I guess, at that scale. But I like that it's actually sort of signal based, right? Not trying to dig too deep into data you don't have and wishing that you had it and sort of being immobilized by it versus like being creative, working with what you've got. What you said about the job titles and the job codes is like, what a mess. Like I've worked in organizations that, you know, are like m and a is a weekly ritual, right? Just like a choir company every week. And it's so messy and there's so many things and we're like, how do we even know who we've got now? But yeah, I can sympathize with that effort of trying to figure out who does what and who should be at the table and who could speak at this event and trying not to like miss anybody. Right? Like why didn't you ask, you know, like this person who is our expert on this, I was like, wasn't in the system, sorry.

Deborah Ketai:

Right. And you know, on the other hand, whenever you're working with a technology associated change, you have the advantage of having user statistics of various kinds and analytics of various kinds that at least you can call on to find out who's using hugging face or whatever.

Galen Low:

Is hugging face a tool?

Deborah Ketai:

Hugging face provides these small areas to create your own generative AI models.

Galen Low:

Okay. Right, right. My head went directly to Ridley Scott's alien franchise. It's like aliens that jump up and grab your face. I was like, probably not what Deborah means.

Deborah Ketai:

Probably what the founders envisioned, right?

Galen Low:

Yeah, exactly. Could we just have a thing that jumps out at the screen and just like slurps data out of their brain? It strikes me that you're, you know, you're very passionate about change management and it's not just AI change management. You know, you've worked on de and I initiatives, you've worked on other sort of data initiatives. I'm just curious, like in your experience, does change management need to be treated differently when it comes to AI, or is it kind of the same playbook as other types of organizational change?

Deborah Ketai:

There are some differences. First of all, anything related to AI and ML is going to move faster and faster as time goes on. The learning curve is just exponential. I think that people who are going to manage change initiatives or projects or programs in the AI space need to get a sense of the foundations of data process, infrastructure, alignment with strategic goals, and for change management. You always need buy-in at the top. You need buy-in from middle management because they're gonna provide the what's in it for me. And you need mechanisms for the ground level employees to feed ideas back up and attitudes back up to the top. There's also the question of resistance to AI as a concept, whether it's the. Immediate reflex of, is my job secure? And the answer to that is maybe, maybe not. Is anybody's job secure in this day and age? There's a lack of understanding. There is often a lack of transparency, both around the use of AI within a company and simply the lack of transparency of the tools themselves and how they're making decisions, or if you're gonna allow them to make decisions, how they're coming up with their output. There's plenty of fear and risks involved, and so I think that there is a separate space within change management for people who are working on AI related change initiatives. But I think the basic tenets of change management are the same, regardless.

Galen Low:

That makes sense to me in terms of, yeah, the things you mentioned about buy-in and transparency. Yeah. What really got me was the pace of change and that it's not just like one change in organizations that I've worked in the past. There is like a change management team, right? And they're gonna manage that one change and then they walk away and. Come back whenever they're needed, right? For the next big change. But what we're talking about here is actually continuous change on sort of that like, I don't wanna say shaky ground, but you know, I think the thing you said about job security and fear of the technology and like the black box decision making within the technology, those are all things that sort of amplify how people experience change. Then it's exacerbated by the fact that it doesn't just end, you know? I'm like to take the metaphor too far, I'm picturing like hunger Games, right? You're like, okay, maybe if there's one, like one round and you make it, you're like, okay, I'm safe forever. Whereas that's not the case. It's gonna like kind of, it's continuous change. I once got chided for saying constant change 'cause constant, anyways, but the continuous change aspect is a thing that makes it. Complicated because it's not just a straight line anymore, like linear change management. It's like, it's cyclical. It's like a cycle of change that almost needs to be managed continuously because change isn't, you know, hasn't stopped.

Deborah Ketai:

Right. I mean, even within the AI, the individual AI efforts themselves. There's continual change. Your data is drifting as time goes on. So there are all kinds of different things that the project manager or the change manager or both need to take into account that are different based on the science involved. But as I said, that's kind of a small area relative to the foundations of project management and change management. Those are the same.

Galen Low:

Yeah, the frameworks are the same. The stuff that goes inside of it, there will always be, well, it couldn't make it too easy for ourselves, could we? It's not gonna be the same every time. There's gonna be nuance, there's gonna be complexities about what type of change it is.

Deborah Ketai:

And one of the complexities that I think people are just now starting to realize is that increasingly, particularly with generative AI, AI is itself a stakeholder. It has its own. You know, we're finding out that it has its own goals, its own attitudes, and it will act differently and sometimes contrary to the way you want it to based on those internal realities.

Galen Low:

That's interesting. I really like that. We've been talking in the community about almost like adding your AI tools to the like project communication plan because it becomes a source of truth or you know, a source of information. So we do need to communicate updates to it. You know, whether that's uploading meeting minutes and stuff up to like the tools that we're using so that they're up to date. I like that whole notion of like, they've got their own attitudes and perspectives on things. And you know, this time two years ago, it would've been like, that's sci-fi, but it's not really, you know, we're talking about agentic workflows. We're, you know, we're talking about pursuing a general artificial intelligence. We're not there yet, but even just now, we do need to kind of consider that because frankly, that's how it's built, right? To either a, especially in the case of generative AI, cater to an individual user's preference, but maybe also behave differently in front of another user, which I guess is probably the same with humans, but I like that idea that it needs to be taken into account, that it is a stakeholder, that it itself is changing and it itself needs change management in a way to understand the change that are going on around it.

Deborah Ketai:

I think, you know, it's very much in my mind, like belief in God. I don't think you have to believe in God. I don't think you have to believe in general artificial intelligence in order to recognize. That there are ways in which the world, or the ways in which artificial intelligence acts as though it had a personality or as though there were divine design. And so you need to take those things into account and see how you can limit guide, et cetera, or risk being overtaken by events and situations that you don't have any control over.

Galen Low:

I feel like we need to make some t-shirts that says AI works in mysterious ways. It's just not untrue. You know, like all along here I'm thinking of, you know, you and your background, your role as sort of program manager and change manager. You are deeply involved with the project management community and in my community. There's some folks who are being asked to kind of do a dual role, right? They're being asked to be project managers and change managers. In other words, it's hybridizing this sort of PM and CM roles, and it seems to be, at least from where we stand, it seems to be getting more common. I wanted to get your take because you've kind of lived. I guess first of all, like do all projects need change management and also do all project managers need to understand change management?

Deborah Ketai:

No, not all projects require change management. The orthodox view on that is basically that a project that is complex and that has. A risk of not realizing its benefits if people fail to adopt it once it's implemented. And I'm not just talking about technology projects there. Those are the projects that need change management. I don't think that it's reasonable to expect project managers to be change managers, but it is becoming the norm in a lot of organizations. The skill sets. Are different but overlapping. But what I tend to focus on is the reason why you should have separate roles is the timelines, because traditionally, project managers are not given the opportunity to follow through and really make sure that a change gets sustained.

Galen Low:

Interesting. Yeah.

Deborah Ketai:

That's really key. So unless your PMO or operations or whoever is governing your project management process is willing to recognize and fund the ability of the project managers to ensure change sustainment. I don't think it makes sense to have project managers do change management. But yes, they have to understand each other. I think it's going to become more and more the norm and more and more helpful to have integrated project and change management plans and for project managers who either are forced to or are interested in doing change management. Some of the things that would be really helpful to them to learn some are our basic concepts. To understand that successful change has to come from the top, the bottom, and the middle of the corporate hierarchy. To understand that, depending on your organization, you may need to be able to step into the role of communications lead. You may need to work with your company's learning and development function, or even play that part yourself. If your company doesn't have one, you may need to establish or re-engineer processes. So things that are not necessarily a strong focus of most project managers. They're there in project management, but they're not really a focus to the extent that they are for change managers. And the other thing is that both. Individual project managers and project management organizations need to be willing to and commit to putting in a lot of time and effort upfront on various kinds of assessments. So stakeholder assessments, impact assessments, readiness assessments. Without those, the change is not gonna be successful. The adoption's not going to be successful, or it's going to be successful at first and then slip back.

Galen Low:

I like that sort of time aspect of things. And even these sort of, you know, up top, when you were talking about your mission, your, you know, day to day it almost was so zoomed out and foundational and like almost. It was in itself an initiative, which arguably is a project, but also it probably set the foundation to spawn other projects, like for example. Coordinating the hackathon would be a project, right? That you would have someone ideally come in and take the lead on and deliver, you know, they could be in and out. You did all this sort of upfront foundation work to sort of set up the change and then afterwards. You know, there's a period of time longer than a project where change is measured. What I find interesting about it is that, you know, when we're talking to the folks at the Project Management Institute, one of the sort of big focuses now is driving outcomes and doing more than just managing the iron triangle and being more responsible for the value that we deliver. And I think that comes up as one of the arguments as well is like, but we're not always around to see that change manifest and like, you know, are we setting ourselves up for success if we're actually just moving onto the next project and actually have nothing to do with, you know, how that impact is measured over time. You know, are those things even compatible? I don't even know if this is a question in there.

Deborah Ketai:

Could it, I think there are a couple of thoughts in there. One that comes to mind is that in the initial focus on Agile, one of the core tenets was dedicated teams. Well, that was one of the first things that slid Yes, by the wayside when it was actually implemented in most large organizations. And the fact is somebody needs to be dedicated to following up on all this. You can call them part of the team. You can call them a team that sits kind of above the fray and communicates. With the teams that come and go, but somebody has to do that.

Galen Low:

I like that you framed it earlier as an investment. In other words, you know, a leadership team must be willing to invest in having someone who is that dedicated person or team that oversees it. And you know, I'm thinking about my time in consulting or you know, where we did have, it was a whole separate division that did sort of change management. As a separate layer, and now I'm starting to understand why is because, you know, their aspect was different. It started earlier and it ended later in terms of like managing change, measuring, you know, the impact of that change. And then there's this sort of the middle bit right where the project got done and delivered and it would be a. Probably non-viable investment for a business to say no, we'll just keep that project team on to like sit around and measure their results for the next 18 months and not do anything else. Right. And we need them to sort of shift along. But I like that idea that the investment can be in a dedicated person or a dedicated group that oversees change and that, you know, maybe the recommended direction is not that project managers also be change managers. But that perhaps they could switch hit, like maybe you could be the change manager role in a certain initiative and you could also be the project manager, maybe one at a time. Not both at the same time, because yeah, it's hard to be responsible for measuring impact if you just roll onto another project after a project goes live. I guess maybe that kind of like. It kind of triggers me to sort of zoom out a bit and I'm thinking about, you know, the role of a project or a program manager. From your perspective, how do you think the role of a program manager will evolve over the next three to five years? And maybe like, what skills should PMs be focusing on today so that they can be ready when it does?

Deborah Ketai:

For program managers and by program manager I'm going to define it just for the purposes of this question. As somebody who manages a set of related projects and as a set, the program may or may not have defined start and end date. It may be something that's ongoing. I think that program managers need to get closer to where strategy is formed and be able to influence strategy. And to get senior leadership to clarify priorities, goals, and to be transparent in reflecting back to leadership where there are likely to be misalignments, where investments are not likely to yield the appropriate return. Where that return is likely to be important in the long run, but no guys, it's not gonna give you 10 X in the next fiscal year. But it's still important. I also think that program managers are going to need to focus more on enterprise risk and by risk I'm including the classic, both negative risks and opportunities.

Galen Low:

I love that you did that because not a lot of people do. Right? The like opportunistic, like positive risks. And honestly, I think that makes sense, especially with that definition. I'm glad you defined it upfront because I'm kind of thinking of that role where you're a program owner, your program does not start and stop. It's not just like a collection of projects that you know will end at a certain date, but you actually sort of own this program. You have that perspective, right, to be looking beyond a certain date to be, you know, thinking of the business strategy, I guess my question would be, have you found that leadership teams want program managers at the strategic table? Or are program managers who are trying to be more strategic, kind of like fighting tooth and nail to get invited in? And if so, what's the journey look like there?

Deborah Ketai:

I'm going to use the classic project manager response. It depends. It depends on the leader, it depends on the organizational culture. And I also think a lot of times we have to be proactive. We can't just assume that if we're not invited in early, that it's because they don't want us there. Sometimes it's just nobody's thought about it. So over the course of the next 3, 5, 10 years, building relationships is going to become more and more an important function of project and program managers, both because of. AI impact on jobs in general. And because they're going to need to get closer and closer to that source of power and vision. And I think another thing that is going to happen over the next three to five years is the most successful project and program managers are going to be the ones that kind of blur the lines between business and it. The ones that are coming from an IT background are really going to have to learn what makes their business tick and what makes their industry tick. The ones that are coming from a business background are going to have to learn something about what enables the different aspects of their business to share data and information. If it's healthcare to, you know, to process claims, to collect money, they have to understand the basics of all those things, and so that line is going to blur as time goes on.

Galen Low:

I like that sort of almost how pragmatic it is, right? I think there's a lot of LinkedIn posts and other sort of media that's almost like you need to flip the switch tomorrow and become a strategic leader and be at the table, you know? Like now. But I appreciate that, you know, your view. It will take time, right? You need to kind of be that person who's flagging risks and showing that you understand the business or showing that you understand the technology in your current role, and then sort of stretch up and blend into that next role. And I think that's a really, like a realistic view of how this shift is actually happening. You know, everyone's like, well, it's gonna, AI is gonna level everyone up, and then we kind of stop there. But none of us really know what that means, but. That's what it means. It means, okay, like start building these skills now. Start building these relationships. Now, it's not that you aren't wanted at the table, it's just that people don't know that you should be at the table yet. That'll take time, and then yes, you will have this perspective. Ideally the bandwidth to have that perspective, to look at it from a business strategy and tech strategy lens, not just a project scope, project budget lens.

Deborah Ketai:

Right and I mean, think of it in terms of your own life as a project manager. You already know that there are other people who are not invited in early enough, whether it's the testing team, the compliance and security folks. We all have this problem of not being in early enough in the conversation. And if you need a wedge into that world, sometimes it's helpful just to ask, to be invited to meetings and promise you'll just be a fly on the wall. You are not going to engage, you're not, you know, whatever. But at least be there and have skip level meetings with your managers so that you can start percolating some of your ideas up or some of your concerns up.

Galen Low:

I like that It almost brings us full circle to what you're talking about at the beginning, which is sort of getting the right perspectives and talent in a room together, or have them exchange knowledge and cross train and sort of building community because you know, if we all are able to see it from one another's perspectives. Or even if we all start to see things a little bit from the same perspective, that's a tie that lifts all boats. You know, like that's what creates that cohesion. That's what gives us the ability to work together cohesively, to address the changes in front of us, address the challenges in front of us at speed and at scale.

Deborah Ketai:

I agree.

Galen Low:

Nice little bow. Deborah, thanks so much for this. Just for fun, do you have a question that you wanna ask me?

Deborah Ketai:

I do. I don't know if you're going to wanna answer it, but what is one thing that you would love to do with AI but are either afraid to or don't know how to do?

Galen Low:

Oh my gosh. Yeah. The list is many, but I'm very satisfied with the way a lot of my generative AI tools work as like a thought partner, and I haven't yet sort of unleashed it to be autonomous and not just agentic workflows, but like thinking of what I would do if I had a project coordinator or an assistant that's a human on my side to kind of take a ball and run with it with limited direction. And I know that's sort of what people are trying to build, but the way I'm seeing it right now is that a lot of the agentic workflows have more to do with intelligent automation than they do. Like sort of taking action. It's almost like this blend, like I wish I could sort of quote unquote prompt my assistant to go and do a few things across different tools. And I'm thinking of things like agent mode in ChatGPT, and maybe I need to play with it more. But it is this sort of like cross tool administration that is sort of by request, like as it happens, not necessarily sitting in the background as an AI agent doing its job. I just crave a sort of more multipurpose assistant who is somewhat autonomous and somewhat prompted like a bit of that blend to kind of go out and just shave some of that time off for me. You know, I dunno if that's like a very vague answer, but I just find myself sitting in between the folks who are building apps, you know, vibe, coding apps, folks who are building agents that just work in the background and do stuff and make decisions on their own. And prompting, which is not quite the same for me as having a. If I had a human assistant to go out and tackle some tasks for me, that would be great. I will prompt them. They will make some decisions themselves. They'll come back. You know, maybe that is how people are building agents right now, but I just haven't seen it yet.

Deborah Ketai:

Yeah. What keeps me from doing it, frankly, is lack of trust in where my data is going to go.

Galen Low:

Is that black box again. Maybe this is a whole other episode, but you know, I think that is one of the things that gives everyone pause. I was reading an article and I can't remember where it was for the life of me, but if I do remember, I'll put it in the show notes. But this notion of not adoption slippage, I can't remember the word, but it's just like people are kind of like hitting a wall somewhere where they're like, start using AI and then at a certain point, you know, only a certain percentage is using it for these types of things or at this frequency. And I think one of the things, it's not necessarily just. The technology or understanding it, but like the trust and also the transparency around data governance and compliance and where does this go and what am I allowed to do and you know, can I connect? Can I plug all these things together and will it be safe? Even just that thought, not even to going through the process, but even just that thought creates hesitation. But yeah, I relate to that completely. That's me as well. I'm like, what if it does it wrong? What if it's, you know, sending data everywhere? This all kind of happens in a black box. It could go sideways on me before it actually creates a benefit.

Deborah Ketai:

I was recently at a PMI Regional Leadership Conference, and one of the session breakout sessions was about AI and project management, and one of the things that came up was the question, is it ethical to have your chapters email blasts facilitated by AI.

Galen Low:

Interesting.

Deborah Ketai:

And the outcome of the discussion was that basically it was not ethical if it meant releasing your members' data.

Galen Low:

Right. Okay.

Deborah Ketai:

Into the void. So unless you were using some kind of completely internal tool, the answer was no.

Galen Low:

Isn't it interesting how it always kind of comes back to guardrails that we ourselves as humans, don't fully understand, right? They're discretionary decisions usually, and then there's, you know, policy and the sort of governance aspect, but we don't yet trust ourselves and our technology to kind of build those guardrails and put them in place so that we have that trust. Right. That's very interesting. I'd love to revisit that. I'm always, I'm interested in ideas and examples of how AI is being used in project management and otherwise and also what gives us pause, what obstacles we're facing. Often it's not the technology, it's actually the ethics.

Deborah Ketai:

Yes.

Galen Low:

And the trust. That's so interesting. Deborah, thank you so much for spending the time with me today. I really enjoyed myself. For folks listening, where can people learn more about you?

Deborah Ketai:

Definitely connect with me on LinkedIn. I'm the only Deborah Ketai on LinkedIn. And if you have the opportunity to add a note, mention the DPM podcast.

Galen Low:

Awesome. I will include a link to your LinkedIn. I'm happy that you're the only Deborah Ketai. I'm like one of two Galen Lows on LinkedIn, so I have envy right now. But I'll include that link in the show notes for folks to connect with Deborah. Deborah, thank you again.

Deborah Ketai:

Thank you so much, Galen.

Galen Low:

That's it for today's episode of The Digital Project Manager Podcast. If you enjoyed this conversation, make sure to subscribe wherever you're listening. And if you want even more tactical insights, case studies and playbooks, head over to thedigitalprojectmanager.com. Until next time, thanks for listening.