How to Build Real AI Fluency Across Your Team

Galen Low: These days, almost everyone has the right things to say about AI. They've got the right vocabulary, they've got the right tools on their home screen, and they've got all the right certifications hung on the wall of their virtual background. But how can we as leaders tell who is truly AI fluent, who is faking it, and who is just too intimidated to ask for help?

And beyond that, how do we know if we are truly building AI fluency within our teams and within ourselves? And actually, what does AI fluency really mean anyway? To answer that, I've brought in a PM thought leader and educator with a doctorate in operations management to talk us through the ROI of true AI literacy and how to develop it.

We'll be diving into why it's so important to create a human-led operational layer on top of the executional layer that's being increasingly handled by AI, and we also dive into what makes a career durable now that technical skills are becoming commodified. Hope you enjoy the episode.

Welcome to the Digital Project Manager Podcast—the show that helps delivery leaders work smarter, deliver smoother, and lead their teams with confidence in the age of AI. I'm Galen, and every week we dive into real-world strategies, emerging trends, proven frameworks, and the occasional war story from the project front lines.

Whether you're steering massive transformation projects, wrangling AI workflows, or just trying to keep the chaos under control, you're in the right place. Oh, and if you like what you've been hearing from us lately, please consider following us wherever you're listening or watching, and maybe even leaving us a review. All right, let's get into it.

So today we're talking about what AI literacy really means in project and operations management, and why it's not enough for employers to just hope that their top talent learns AI on their own time. We're gonna be talking about the value of hands-on expert-led training when it comes to building AI literacy. We're gonna talk about the ROI of AI literate managers who also understand the business flywheel, and we're gonna talk about how leaders can distinguish between their truly AI fluent talent versus the ones faking it just to get ahead.

With me today is Justin Bateh, CEO and founder of AI Operators Lab and editor-in-chief of Tactical Memo. Justin has spent over two decades leading teams, managing projects and ops, and spearheading AI rollouts. Not only has he held chief operating officer and executive director roles, but he literally has a doctorate in operations management. And as a professor with 16 years in the classroom, he's taught over 13,000 students and professionals how to lead, operate, and build careers that last.

Every week, his free newsletter, Tactical Memo, delivers tactical playbooks on AI, project leadership, and career growth to over 18,000 operations leaders at companies like Amazon, Deloitte, and Google. And if you like what you hear today, it's the best place to get more of Justin in your inbox every week.

Justin, thanks so much for joining me today.

Justin Bateh: Hey, Galen. Thanks for having me. I appreciate it. I've been a big fan of what you've been doing here at Digital Project Manager for a long time, and I know I've contributed some articles in the past, and I think this is gonna be a fun one for me.

Galen Low: Yeah. No, I'm excited as well.

I'm always excited to talk to other educators as well. I think it's just like a whole different level of a conversation and craft. You know, you're someone that, like you said, you've been contributing with us. I love everything you do with us. You're a high-value asset to the Digital Project Manager, and honestly, you just bring a lot to the table.

Justin Bateh: Let me just warn you, I'm a, an educator, and I'm used to teaching, so you might have to cut me off because sometimes I can go on and on and on about topics that I really like, but don't feel like I would be insulted if you say, "Okay, Justin, that's enough. Let's move on to the next."

Galen Low: I appreciate you, but I also...

I think I'm, I'm excited to let you go off on a tangent just because, you know, you package information so well that I probably don't need to cut you off. I could probably just sit back and have my coffee.

Justin Bateh: Yeah, we'll see.

Galen Low: All right. Let's set the scene with a bit of a controversial question. So in the headlines today, it's becoming clear that the cost of AI tools, both like licenses and tokens, are a pretty major factor that's accelerating businesses' plans to make significant reductions in headcount.

You know, having both top talent, expensive talent, costly talent... anyways, talent in general and tools is just getting too expensive. But meanwhile, I continue to run into pockets of resistance where professionals, both early into career and in the middle of their career, and actually late career, are either refusing to use AI or are actively gaming and sometimes even thwarting their organization's AI transformation strategy.

So my question is this Which is more likely to survive in 2026/2027? A business with all the AI tools but not the skilled humans to use them well, or a business with all the skilled humans but no AI tools?

Justin Bateh: Oh, wow. I can see why you call that the hot question. But I'll tell you right away, skilled humans with no tools, and honestly, it's not even close.

Huh. Okay? Tools without skilled humans is just gonna be another cost center that we have to manage that's probably gonna be on a big subscription basis. So you're paying twice, once for the licenses, once for the mess. And one of the You mentioned that I package things so well. One of the ways that I like to describe this is it's like taking a, a swimmer and throwing them into the water for a race, right?

A good swimmer on their own can perform well on their own, right? A bad one can't. They're gonna sink. Now let's say we take that bad swimmer and we add goggles and fins and a nice fitting bathing suit, right? The good swimmer's just gonna swim better, but the bad one's still gonna be bad and sink. Right.

So I think the skilled humans without tools is also a temporary problem, right? A- as soon as companies will proceed with allocating the budget, they know that the human work is where things are actually at, and the tools will come naturally after that. So I think the, the, the thing is, tools versus people is the wrong question.

I think the winners are going to be the people who aren't buying tools or just training, but they're building the operating layer, and that's the people plus judgment asset.

Galen Low: That I love. And I actually really like the swimming analogy because, you know, it is a bit of deliberately spicy question where there probably isn't a right answer.

But what you said about a strong swimmer with tools, you know, with the right goggles, with the right swimwear, you know, with the flippers, they're off to the races. They're gonna do great. And there is a cost to expecting someone who cannot swim at all to just suddenly adopt these tools, right? The, to get the right gear, to get the right equipment.

And you know what? They might eventually become pretty decent swimmers, maybe not because of the equipment, but that can sort of help them, but it's costly. It actually could be more costly than the alternative because, again, you're paying twice, maybe even three times, right? The sort of figure-it-out-yourself, the license costs, and the tokens.

Right, right. It's like that's a, a pretty costly proposition.

Justin Bateh: Yeah. I led a swim team for a while as a volunteer, and one of the things that was the hardest part of running the swim team were the parents, okay? And I think anybody- Okay. ... who's been in youth sports will understand that. And it was essentially saying, "Look, you know, you're buying your kid all of this fancy equipment, but they still can't swim 25 yards across the pool."

All right? None of that is gonna help. So you have to get the reps in, the practice in, and so forth. And I just kinda wanted to give some context of where that came from.

Galen Low: No, I like it. It's actually funny because the analogy keeps going when you have a board or something like that, where the, you know, the pressure from the board is putting pressure on executive teams.

Executive teams are trying to, you know, get their staff to swim really well. But the expectation from the top is like, "Why haven't you gotten the team to swim super well, right?" You know. I know they can't swim, you know, as of yesterday, but they should be Olympic swimmers by now.

Justin Bateh: See it all the time.

Galen Low: That sort of continues on. Listen, thank you for humoring me with that question. Yeah, obviously things aren't that black and white. There's you know, a middle ground. You mentioned the operating layer, and that's really what I wanna talk about today. It's funny 'cause in my notes I've affectionately nicknamed you Doc Ops because you've got a doctorate in operations management, you are an active professor, but also the thing that I find most interesting about you is that you take time out to run a cohort-based learning program that helps project leaders and operators and managers develop AI fluency and also the executive skills to drive impact.

And while we were planning this episode, yeah that idea of an operations layer and the concept of you know, just like how meaningful ROI comes from AI, not just from the tools themselves, but from the way that they are used, from how you know, talent is mobilizing their own skill and layering that on top, and not even just like training around the tools, but like also this business understanding of how to mobilize AI in a way that actually adds business value.

I was wondering, can you talk to us a little bit about how true AI literacy creates this operational layer that sort of sits on top of the tools? What are human project managers, operators, and managers able to do that a full orchestra of AI tools maybe cannot?

Justin Bateh: Okay. Yeah, no, absolutely. So Galen, here's the thing that I think about.

AI is what I'll call the execution layer, right? It's gonna draft, it's gonna summarize, it's gonna flag risks, it's gonna generate a ton of options for us to choose from, right? And it's genuinely great at that. Pretty much better than all of us are, right? And it's getting cheaper and cheaper as the days go by.

The operating layer is everything that sits above that, right? And it comes down to really four things that the tools can't do, okay? One is deciding what's worth doing. You know, AI can generate 10 project plans like in 60 seconds, right? But it's not gonna be able to tell us which one to actually fund.

Two, owning the trade-offs, okay? For example, scope versus timeline versus the politics of everything that's going on, right? You know, AI is not gonna be able to attend the meeting that happens after the meeting where real decisions are actually made, okay? Three, accountability. This one's very simple. AI cannot be fired, so it can't be trusted with the final call.

Somebody's name has to be on the decision. And then four, on this operating layer, I have translation. Turning a cryptic executive ask into real work, and then turning that work back into a story that the executive can repeat on upward, right? That's half of project management right there. 100%. And a lot of us are already familiar with it, right?

And no tool does that. I'll kind of give you a concrete one, right? I teach people to start every project by pasting the scope into AI and asking what can go wrong, right? Great risk register, 60 seconds. But knowing which of those 15 risks is gonna be the one that actually kills the project, right, because you've watched that particular stakeholder behave this way for the last two years, that's the operating layer.

So the way I'd say it is the tools are getting cheaper every quarter. The operating layer is getting more valuable every quarter, and companies and individuals should invest accordingly.

Galen Low: I really like that especially the opportunity cost of it. And it's funny when you talk about the tools, and I think you're right.

I think in some use cases, at a certain level, the executional layer, right, it's better than most humans. And it's easy for us, even for me, to go like, "Oh, okay, well, I guess it's better at everything than me now." And I often forget that there's still a lot of real world context. You know, people use the word nuance, but it's just like stuff we haven't fed to the machines even, and how, you know, organizations operate, how humans interact together.

That translation thing I really like because, you know, arguably, there's a lot of people kind of using their LLMs to say, "Okay, well, listen, act as my, you know, very tough CTO stakeholder and beat this up." But that's still not quite the same as what you said, like having worked with this person for several months or even several years and knowing their personality and knowing the other priorities in the business and knowing...

the thing that really resonated with me was, like, knowing what's worth doing and you know, curating, selecting and understanding what the trade-offs are. When you frame it like that, that's massive for an organization because like we were talking about earlier, you know, you get a, you know, two dozen people who can't swim to save their own lives with all the swimming gear in the world, throw them into a pool And at a certain point, all of that faffing about, you could have made more progress just deciding to do the one thing where you did have the right talent who could do the job with the right tools, going fast instead of trying to tackle everything all at once and really getting nowhere.

Justin Bateh: Yeah. Oh, I agree 100%.

Galen Low: I guess maybe beyond that then, like we're talking about AI literacy, we're talking about this operational layer. Arguably, some of the things you talked about that are not like things about like knowing AI. Sure, we talked about like what AI is good at versus what humans are good at, but actually a lot of those things that you mentioned, you know, I couldn't go walk into, you know, whatever, just like a introduction to AI course and figure all that out for myself.

I think like I'm hard-pressed, I'm literally... I'm not baiting you to talk about your course, but like I'm hard-pressed to think of an opportunity where I could go in and be like, "Yeah, let me learn the technology," but also in the context of the people I work with and like the business mechanics and like what our priorities ought to be.

And I mean, maybe I should go there because like I think like the AI training space, arguably it's getting pretty saturated now. And even when it comes to really specific niches like project management or operations management, and I know you've got this cohort-based program, which again you know, I'm not trying to promote, I just really like the idea of it because it's, it's close to my heart, this notion of learning together, not just learning by ourselves on, you know, Udemy.

But I'm wondering, like how can employers, you know, distinguish between people who just watched a few videos and got a certificate versus the people who really understand the business and can actually orchestrate the technology in a way that makes an impact?

Justin Bateh: Yeah, no, and I'll come back to that. I just wanna kinda rewind just a tad bit to your comment about, you know, the training and knowing the tools a-and so forth, and it's almost like all of the hard skills that we were told to focus on, right?

At least in my generation growing up Are gone. Can be all be automated at this point. So now we're coming back to those original soft skills that people forgot. And what I have found with my course, I've run, I don't know, half a dozen to a dozen cohorts, have had hundreds of students come through, and we started off with generative AI, then we moved to agents, then we moved to agentic AI.

And now I would say that based upon corporate demand and student demand, about half of the course is about leading projects in the AI era, and that's been an interesting turn. It also helps make my content a little bit more evergreen because I'm not chasing different trends every month, right? But no, your question about, you know, companies looking...

What, what was it? Companies getting evidence or looking at evidence for fluency from folk.

Galen Low: Yeah. It's kinda like distinguishing between, you know, the folks who understand the technology and the business versus, you know, the people who are like, "Oh yeah," you know, "I've built, you know, four dozen agents, and I replaced four roles."

And, you know, maybe that's true, maybe it's not, and it's actually hard in this work context, in this marketplace to for leaders, for managers even, right, to be like, "Okay, that person's telling the truth. They really know what they're doing. They're gonna add value, and they know how to make an impact with this knowledge," versus the people who are just accruing basically AI trivia, but not really understanding how the business works and how they can mobilize that into value.

Justin Bateh: Yeah. So remember this as I answer your question, okay? Fluency has artifacts. Familiarity has vocabulary, right? And that's the whole test. This is one of my favorite questions in the whole space because there's just a one-word answer to it, and it's called receipts, right? Okay. A certificate says you sat through something, right?

A receipt says you've actually changed how you work. So the fluent person can show you a workflow that they've rebuilt, right? Ask them, and they answer it instantly and specifically. If you were to ask me, "Here's what I handed off to AI, here's what I kept, here's what I broke when I tried it," right? That last part matters, right?

Because if nothing broke, it's possible that they may not have actually done it. The certificate collector describes tools in general terms. I've seen this over and over again. They have vocabulary. They'll say things like, "I've been leveraging AI to drive efficiencies Me as an employer, that sentence would be a red flag.

Real users are gonna say things like, you know, "I stopped writing status updates from scratch. I bullet the raw updates and have it sort by risk." So if you're hiring or you're deciding who's gonna lead your AI initiatives, you don't wanna ask, "Do you know AI?" All right? Everybody's gonna say yes. Instead, you wanna ask the question, "Walk me through the last thing you stopped doing manually."

All right? The fluent person is gonna light up, right? The faker is gonna stall. And that's what I mean when I say that fluency has artifacts and not just vocabulary, and that really is the whole test in one thing for me.

Galen Low: I really like that. And I, I like the notion of receipts, and I really do that thing you said, which is they will describe what went wrong.

And I think that's something that doesn't get highlighted in the, you know, our AI zeitgeist right now, especially not in my LinkedIn feed. Well, okay, now more and more in my LinkedIn feed because I'm following really great people, but, you know, for a while there, it was just like, "Here's how I 10x the thing," and it was, you know, sort of very buzzwordy, and it made it look like, and indeed some people's first day, you know, with AI and trying to do a thing, looks like everything will go perfectly every time, right?

And so okay, that's easy. Yeah. Okay. Done. Not realizing that it's The next step or like day two onward, or you know, getting more complexity where you really have to sink your teeth in and it's not as easy to get a, like rewarding output. There's problems you run into. I really like that as a proof point, to the point where even if they're like, "Yeah, I never really actually got it off the ground 'cause these things were, you know, were going wrong.

I w- I'm still solving these problems," is even better than, you know, someone saying, "Oh, yeah, like I optimize my efficiency every day."

Justin Bateh: Right.

Galen Low: It's just you know, okay.

Justin Bateh: Yeah. I mean, it reminds me of when I was first learning how to interview other people, and I realized very quickly that the question shouldn't be, "Tell me how you would do this in this situation," because anybody can sit there and make up a nice, great scenario, right- Right

with a solution that they would do. But really digging into the historical context of what have you done in this situation and getting into the specifics, that really is gonna be a big teller for you.

Galen Low: I like that. Artifacts, not just the vocabulary. Like you said, it applies to more than just AI. It, it applies to even just assessing and evaluating, you know, someone's capabilities.

As I'm saying this, I'm like, I realize I framed it in a very negative way. I was like, "Oh, yeah, the fakers versus the people who actually know what they're doing." But also, there's the folks who are, you know, self-conscious, right? Like you said, everyone's gonna say, yes, they know AI, some because they're like braggadocios and, you know, they wanna get promoted and they'll just say anything, some because they're just trying not to get fired.

And maybe I flip that around actually, and say okay, well, you know, for leaders who are noticing that some people are, you know, saying they're like, "Yeah, it, AI's been making me more efficient," but you can sort of tell they have a knowledge gap, that they're kind of, you know, insecure about it. Not that they're trying to get that promotion and look better than their peers, but actually just trying to keep up and not look like they're behind their peers.

Yeah, what guidance would you give for a leader in that situation in terms of okay, yeah, maybe there is a way that, you know, we can Turn this vocabulary into artifacts, like maybe there's some learning to be had.

Justin Bateh: I think that a lot of companies are really hesitant to do that because they don't trust the privacy of the data that they're gonna put in, right?

There's a lot of stalling right now where organizations or, and managers and leadership will say, "You need to learn AI. AI is the future, but you can't do anything with it from our work." Right. Right? I see that a lot. I see in my cohorts and in my courses, companies that pay students to come in, and they sponsor them with employee reimbursement, but they are not allowed to share anything about what's happening at work.

So we end up providing them with scenario and test data to learn the skills and then hope that they can take those skills and apply them, you know, into their unique situation. So I don't know if that trust is gonna come sooner than later, but I definitely know that that's probably a big factor in that.

Now, with regards to the person who doesn't wanna get fired, wants to learn, I really think that in this case, self-education is gonna be something that you have to pursue. I don't think that you should be waiting on your employer to do the training. If you think about it, just a few years ago, ChatGPT wasn't allowed in the workforce, right?

Right, right. It was like, "Nobody can use AI here," right? It's like that in the colleges that I teach at right now. AI is out. AI is out. Well, a few years later, now AI is in. Right. And so that's gonna change as well. Hey, if you can't use Copilot right now, trust me, you'll be able to use Copilot in a couple of years.

If you can't use this in right now, you'll be able to use it, so learn now and learn on your own.

Galen Low: That's really interesting, and actually, it, it's a really good point about just confidential data and maybe just not even knowing what the policies are where you work, and that could really inhibit a team's ability to learn.

Yes, you're right. I'm still encountering, you know, industries and work contexts where, yeah AI is either not allowed or is sort of discouraged other than a few, you know, use cases until they can sort of build that trust and, and especially policy around it. But I agree on the point that a lot of folks who are like, "Yeah, I'm good at AI," but really they just haven't had any practice because it isn't clear, you know, what they can do with it.

Not just capabilities, but what data they can use. What are the use cases, and how, you know, how are they taking that specific constraints in their company and start using it in a meaningful way? So I think there's, there's a piece there where there's an onus on these organizations, any organization really, to just have, kind of have clear guardrails and guidelines for, like, how it should be used.

But I think you raise a really good point about the self-learning bit 'cause, you know, I mean, I wanna come back to that a little later on as well in terms of, you know, is self-learning the only way? But I do like the idea that- It kind of gets you practicing and actually using the tools and using the technology outside of the, you know, constraints of where your organization may be at.

And I think it's apt because then it's well, you are actually investing in yourself because things are gonna be changing. And frankly, you know, you may not continue to work at the same organization, you know, and get your gold pen and watch at the end. There's a lot of flux happening right now, and it's better to be able to use the technology in a context where you can to get those reps in and build confidence and get those receipts on, you know, what you actually have built.

I think that's really interesting.

Justin Bateh: Yeah. And you know, you mentioned it's not just about the capabilities, but I, I think that it is definitely a part of this has to do with people just not knowing everything that AI is capable of doing. Not that I know everything, but I know quite a bit more than my friend who asked me the other day, "Have you started using Claude yet?

It's really great." And I'm thinking like, "Dude, I've got skills and MD files and workflows automated and all these types of things, and you're just discovering Claude's chatbot," right? Or the person who I would talk to in a restaurant that would say, "I'm not so sure I trust AI yet. I don't know where this is going."

They're so far behind Okay. And so a lot of it does have to do with, you know, realizing what the capabilities are. Of course, within your restricted environment, I'd say 70% of my students come from restricted environments and, you know, half the job is figuring out what the rules are, and nobody really knows.

They're just restricted.

Galen Low: What I like about that is when you have conversations with other people, like it's easy to sit there in isolation, especially like me and my like, you know, LinkedIn feed. It would be very easy for me to just look at that and go, "Oh my gosh, am I behind?" Z- it's like people are so far ahead, and I think it's that dialogue with other people that really like highlights the fact that, yeah, right now there's a really big range.

And for good reason. Like you said, like some companies haven't figured it out. Some of them are like, "Listen, don't use it yet. We need to figure it out." Some of them are actively like, "Listen, we're not gonna use AI in our business right now. That's part of the strategy." And it creates a spread across different industries and different roles, and it stands to reason that like things are changing so fast that not everyone's gonna be at the same level.

But I like that idea of like dialoguing together and sort of sharing, you know, tips. And as much as it's kind of like, okay, well, oh, you're just opening Claude now? Like I've, you know, I've already got my markdown like locked. What are those folks missing? Or maybe flipped around, like what's so valuable to you about cohort-based learning?

Justin Bateh: I've taught for years on Udemy. I have 35,000 plus students on there. Totally have left that platform, but there's things running on there automatically now without me, right? And I did the short little video tutorials that people pay $9 for, and the completion rate is near zero, right? They just stack courses.

So I mean, I'll be honest, you can absolutely learn the features of tools by yourself. The videos are fine, prompt libraries are fine, if the goal is knowing like what buttons do Right? If that's your goal, then you don't need any hands-on training. But features aren't fluency, and we have to make sure that we distinguish those two.

Fluency is judgment, and judgment only develops in one way, just like the swimmer. Reps with feedback, okay? And solo learners don't get that, and they miss a few different things.

Galen Low: I like that, the reps with feedback. It's so apt. I like that you said earlier, it's not even about sort of learning the capabilities of the technology and keeping up with all the model updates.

It's like being able to lead in an AI context, which is a context that is always changing. We've been talking about yeah, like part of that journey to get the receipt is to troubleshoot, to have things go wrong, to have things that just don't work and push through. And yeah you know, I, I agree with you.

I think an individual could still learn a lot without sort of learning in a group situation or getting, you know, feedback as they do their reps. But yeah, isn't it a great way to learn faster and not just with your own sort of perspective on it? And I think that's the thing that's really been adding dimension for folks in their AI journey, is that, yeah, you could look at it with your blinders on from your own POV, and yeah, you'll get somewhere.

But I think it's like a great opportunity for us to be dialoguing with others and get input and feedback from others to kind of just open the aperture on the way that we're looking at things, and that's kind of like where the value is.

Justin Bateh: I wanna add to that because I think there are three key missing parts that people who use self-directed learning methods and, and download these courses and prompt libraries are missing, and it's context, calibration, and commitment.

You know, for example, with context, you can download a prompt library for free anywhere, but what does that do? It answers somebody else's questions, right? Not necessarily yours. In a cohort or in live hands-on training, you're working on your actual project, right? Calibration, here's the trap with AI. It always gives you something, and it always looks great, okay?

You don't know if your output is actually good until somebody else who's seen it 100 times tells you, right? That's literally what a teacher is for, right? Not information, but calibration, right? And then third would be the commitment part. As I mentioned, I've used to do the mini courses that people pay nine bucks for, and you have zero completion rate, right?

Right. And let's be real. Everybody listening's probably wa- has 40 other unwatched tutorials- ... saved somewhere, right? Yeah. Live cohorts finish because humans show up for humans, and that's a big part. I've taught over 13,000 people across the 16 years, and I'll tell you the patterns that I see. The ones who learn are never the ones with the best materials.

They're the ones with the reps, the feedback- In a room. And you know I like sports analogies, right? So I'm horrible at basketball, right? Having me watch LeBron James play basketball and then getting me the absolute best pair of basketball shoes isn't gonna solve my problem. Right. Right? But if you send me to a three-week basketball camp, I may come back a little bit better.

Galen Low: Yeah. I think that's a really apt analogy as well because it is getting the reps in literally, and I like what you said about, well, A, commitment. I totally agree. My course used to be cohort-based, and it really helped people pull through. It was not about me or even about the material, it was about their commitment to one another, you know, showing up, and like sort of building those relationships to get peer feedback as well.

But, you know, also, yeah, the idea that practice does make better. It's not even just best materials. It's what is the best environment for people to improve? And yeah, I do agree. I really like that. I like the calibration bit too because that that just, you know, that hits home completely.

The output from AI looks so good. And a lot of people, it creates doubt for them. They're like, "I guess this is what good looks like. I don't, I don't even know if I can provide feedback on it 'cause it looks so polished and done. I'm just gonna ship it off." But needing to get that feedback and build that, you know, critical thinking skill to look at the output from any model, from any period in our, you know, AI future to be able to say, "Okay, well, is this hitting the mark in terms of, you know, what I'm trying to achieve, my goals, you know, what impact looks like, what value looks like where I work?

Is this actually good, or is it just a bunch of good-looking words?"

Justin Bateh: Yeah, no, no, I agree. I mean, if you think about what educators and teachers are there for anyway, right? They are Typically more experienced in a subject than you are, right? And so anytime that I have you know, hired coaches or taken classes by real people in a subject that I didn't know too much about, but I wanted to become very good at, okay, I have sped up that process so much by seeking outside help.

And so that is essentially what that, you know, calibration is for. Would you rather take my years of experience or your years of experience and get all of the benefit of that in a few weeks? Or would you rather have the years of experience and wait?

Galen Low: Right.

Justin Bateh: So I know which one that I prefer.

Galen Low: That's a really good point.

It's an accelerator, really. You could probably figure it out on your own by touching the hot stove as many times as possible over the next two decades, but why, when you could talk to someone who already did that?

Justin Bateh: I tell my students, "I'm only maybe two years ahead of you."

This hasn't been going on for a long time.

Galen Low: Exactly, yeah.

Justin Bateh: In project management, I might be 20 years ahead of you, right? But in the AI space, I'm only a couple years ahead of you. It's not that much further along.

Galen Low: I'm glad you went there 'cause actually that was my next question. I'm like, "Well, you know, how many more years do people have?" We talk about these sort of job descriptions that we see where it's like, you know, "Must have 15 years of, you know, experience with generative AI."

Most of us don't have that unless you were working in a lab somewhere before ChatGPT was out in the public.

Justin Bateh: Yeah, I don't see how that's realistic.

Galen Low: So, but even that gap, right, of you know, I'm six months ahead, I'm a year ahead, I'm two years ahead, there's something that can be taught that will accelerate people who are, you know, either earlier in their journey or at a different point in their journey, it's still helpful.

Yeah. We're not looking for, you know, tenure track, you know, multi-decade AI experts right now. They probably do exist in other spaces, right? Other types of AI, but that's not what we're, we're talking about right now. We're talking about, you know, generative and agentic AI, you know, used in the workplace, these specific tools for these specific use cases.

Justin Bateh: Yeah, and use cases is the right word because, you know, in the AI Powered Project Management course, you know, one of the things that have evolved over time when I first started teaching it, let's say cohort one, okay, it was predominantly a project management course with generative AI. And cohort two, three, four was about the same.

Five, six, seven, eight, as I started to see, you know, things started changing more into agents, okay- And into everybody wanting to build an agent for themselves, right? We started moving into that direction, and then as agentic became more of a topic that people wanted to learn more, we actually, for a brief period of time, cohort eight and nine, almost changed into an engineering course.

Right. Almost. And I saw that, and then what happened, I started having systems engineers joining the call and asking me questions that I had no idea about, right? Because I don't have that technical background that way. I help people apply AI to whatever work, you know, their project or operations to be able to do it better.

So cohorts nine, 10, 11, 12, we're coming up on 13. We've really gone full circle now and brought that back to project management is the focus, not the AI tools. We're not building AI tools here. We're gonna use the tools that are on the market that we have access to and figure out how we can use those to g- be better at our job, and that's it.

That is it. And when we've done that, we've simplified a lot of concern from those who may not be as far ahead as those, and we've also kept out the systems engineers who realize that, you know, I mean, th-this isn't the course for you, right? And one of the things that we do, and that we stopped doing actually, is chasing tools.

I had probably two dozen tools early on that we covered, and I would say half of them are no longer exist. Right? Okay, at this point, you know, a year, year and a half later. And that's gonna continue to happen. So what we do is focus on Claude, ChatGPT, Copilot, and Gemini. Those four, and those four only, okay?

You didn't ask me, but I'll tell you why. The reason is because we have confidence that those are gonna be here a year from now. They have the backing, they have the funding, they have the current, you know, development and process. The other part is most people already have access to one of them. And then the third part is as they get better- You'll get better, right?

So I'd rather t- attach myself to a tool that I know is gonna be here in two or three years and continue to get better than to chase the shiny object, right, and, and learn it because it does great presentations. Well, most of those things that you can do that you used to have to have specialist tools for, you can do in these four major platforms.

Maybe one day it will be two major platforms, I don't know, as they start to, you know, consolidate and m- look at mergers and the market changes. But right now it's those four, and those four are the ones that we focus on.

Galen Low: I love that. And y- what, what I really like about that is I, I, I know we're talking about the cohort, but it's actually a microcosm of the indust- of, of the world right now, like the working world, in the sense that, A, yeah, like the tools are changing really fast.

Chasing them all down isn't the goal. The goal is to be able to apply the technology in a consistent and meaningful way to deliver more value for yourself and, you know, for, you know, whatever mission, project, or organization you're working for. And also, I like that what you said about the systems engineering, 'cause I'm seeing that in my community right now.

It's oh, do we all have to become, you know, coders and, you know, architects now? And the answer is maybe, but if we get too far away from the craft, which is, you know, operations or delivering projects well, right, or leading people, then... You know, there's another course for that, and that can be something that someone can assemble in their own little curriculum of saying, "Yeah, I'm quite technical.

I wanna learn how to, you know, do technical things. I wanna build infrastructure, not just, you know, enhance and augment my role." That's fine, too. But also, if you then start going too far away from it, then, you know, we lose the craft of project leadership, which arguably isn't just about building technology.

There's a lot more to that. There's a lot more... You said it earlier, right? Just like leading people, leading organizations in this age of AI is More important, being able to adapt to change, I guess, is the other thing, right? Is more important than mastering that one shiny object right now that might not be here next year.

So I think what you've done is smart.

Justin Bateh: Yeah, no, and we experienced just that at the halfway point, where we realized that I was bringing in AI experts from NASA. Okay? I was bringing in all sorts of great people.

And the course turned too technical, and it became more about building the AI tools as opposed to using the AI tools as a manager, as a business person, okay, as a leader.

And so we've totally reverted back that way and are bringing in more appropriate folks into the cohort. And really, life is a bit easier now because I'm not so concerned about having to keep up with, you know, every single specification change, you know. Or having systems engineers that are way technically smarter than me, and the course is stumping me.

Definitely, yeah. So I like to make it very clear about my strengths and what I do, and we've recalibrated that over the last couple of months.

Galen Low: I really like that. I wanted to come back to something we were talking about earlier, just the sort of self-learning, and I think I opened this by saying, "Well, yeah, listen you know, a lot of employers right now are kinda like, 'Yeah, you figure out AI.

That's on you.'" And you had this post on LinkedIn, and the headline was, "No one is coming to train you." And it was framed around this article, I think from Business Insider. It was talking about employers expecting employees to learn AI on their own time. And just in the context of everything we've talked about today, and we have talked about self-learning, but what should employers be doing to not leave AI fluency to chance?

Is that a now problem? Is that not a problem? Is that a maybe later after we rehire the humans problem? Could employers be doing more to be focusing that development of AI fluency instead of just leaving it to chance and having people, you know, maybe just watching a bunch of videos and just getting a bunch of certificates and thinking that they're AI fluent when actually they're not?

What can they do to sort of support their employees but also guide them towards the value that they're looking for?

Justin Bateh: I remember that post, "No one's coming to train you." So both things in what you described is a standoff, right? And they can both be true at the same time. One, employees should not wait for permission to upskill.

I think that's the case whether it's in AI or not, right? And then employers who leave fluency to chance right now are really making a mathematical error, okay? Those don't contradict each other, all right? I know, you know, your question was about for employers, and I would think of a few things that they should do if they would be willing to take my advice.

And one is train on workflows and not tools. The failed model that I see when I do corporate training outside of my cohorts, right, is buying licenses and hosting lunch and learns.

Galen Low: Right.

Justin Bateh: That model works... The model works for something that is more conceptual, right, and not hands-on. The model that works for AI is taking your five most expensive recurring workflows and rebuilding them with your people actually in the room with the people who run them.

Training the work, right, not the software, which I had mentioned, you know, us reverting back towards a focus on. And the other is, you know, measuring receipts and not completions. We talked about receipts earlier. You know, course completion rates through your training and development Division f- is confirmation of compliance, right?

Not confirmation of expertise. The metric that matters would be, "Galen, how many workflows have you changed this quarter?" And that's it. And then one of the things that irks me when I hear it, you know, is, "Hey, you need to go get training, but we're not paying you." You have to make it paid time, not homework.

Not homework. If you expect people to learn on nights and weekends, okay, you're selecting for people who have free weekends Okay. And free evenings, right? Not for their potential, right? Your best people aren't gonna do that homework for you. They're gonna start quietly interviewing somewhere else. And so to that point, here's the bottom line on that standoff, right?

It does have a guaranteed loser. Whether it's the employee or the employer, it's whoever waits. The individual or the company who moves first is gonna get ahead of their peers. The company that moves first is gonna get ahead of their competitors, and you really just have to pick a side of that trade-off.

Galen Low: I really like that. And I like that what you're saying is it doesn't have to be one or the other. If you're listening and you're like, "Listen, I, I wanna run ahead and just learn on my own. I'm not gonna wait for my employer to create some training program," you know, go and do that. Also, if you're an employer who is like, "Okay, well, you know, we are going to focus on use cases and hands-on workflow that actually adds value, and we're gonna measure that, and that's, you know, and we're gonna fund it," that's good, too.

That's gonna push you ahead. Doesn't mean that the people who are already learning on their own don't need it. It's a way to kind of calibrate, to use your word, right? Recalibrate how people are thinking about the work, and not training on just the tools themselves and where the buttons are, but actually the work.

Training on the work and how it adds value and what workflows are being transformed to, you know, get to this promised future of AI, not just get imbued with all of the sort of tool saturation.

Justin Bateh: Yeah. And I always tell folks, you know, and this might come from my operations background, you know, with a big believer in, you know, continuous improvement and the total quality management principles, but even a 1% increase is okay, right?

You know, you certainly don't wanna spend $100,000 on training for something that's gonna save you $5,000 a year. Right. Okay? I get that. Yeah. Right? Just do it manually or do it the way you've always done. But I do think that early on right now, as companies are testing out training or as employees are trying to prove that training would be necessary, would be to find some quick wins.

Find some quick wins, something that you can, with your own authority and within your restrictions, show that got better with AI Okay? And stack those, and that creates momentum, and eventually that momentum could lead to something bigger.

Galen Low: I like that. I like the increments, and I think it makes sense, and also probably very comforting to hear.

It, it is for me, right? Where you're like, "Okay, I, now I'm on the hook to, you know, 10x the business by building the best workflow in the world." Not necessarily.

Justin Bateh: Yeah, I've seen that a million times. Somebody reads a book, right, with a framework in it, and they come in the next day, and now everybody needs to know that framework and start incorporating it, right?

The next boss comes in, they've read a book- ... they've watched a documentary. Okay, they come in and now the whole department changes based around that. I did see it with the 10x stuff quite a bit. Yeah, I mean, that's just the nature of management.

Galen Low: Yeah. Transformation doesn't have to be fast and dramatic. It could be slow but valuable.

Justin Bateh: Yeah, for sure.

Galen Low: I wanted to land out on... I know I've angled this conversation around, you know, leaders and organizations and how they should be thinking about training. We've touched on individuals as well. But I know you're, you, you're very passionate about helping people, you know, build rewarding careers.

And so for the individual listener, like a project manager or the operator sort of watching this all unfold, what actually makes a career durable right now? If technical skills are depreciating as fast as, you know, we're talking about here, what is the thing that's, you know, accruing or compounding?

Justin Bateh: Right. So this might be probably my favorite question of the whole thing, and you've kind of, you've sensed that already based upon my prior work. So I may go on a soapbox, and if I do, feel free to, you know, rein me in. But let me give it to you straight the way I see it, right? Everything we've been taught about careers was to optimize one asset, skill.

Get good, work hard, gain the skills, and the career takes care of itself, right? And here's the thing that I've watched play out over and over and over again within organizations, and it's very uncomfortable to say, but the most skilled performer on the layoff list is still on the layoff list. Skill was never the whole game.

Now it's the entry fee, right? And right now, the fastest depreciating asset that you own is your skills, right? Because AI makes baseline competence nearly free, right? Being good at task stops being your differentiator. So to your question, what compounds? You know, I think there are some things that we really have to start teaching that kind of go back to the basics, and they would be a few things.

One would be relationships. Relationships compound. Today, I would be very surprised if somebody got hired by submitting an application online and then randomly hearing back. Okay? Nobody hires a resume, right? They hire a name that someone said out loud to them. Every opportunity that you'll probably get moving forward now is gonna arrive through a person.

Your reputation also compounds. Okay? I was thinking about this last night on a nonprofit board that I was on. Different story, I won't share it, but I had to kind of explain this to somebody. What gets said about you in the rooms that you're not in is very important. Your promotion is often pretty much decided in a meeting that you're not invited to, right?

You're just it's just announced to you by people who might be speaking on your behalf and repeating things that you never said or didn't mean it a certain way.

Galen Low: Right.

Justin Bateh: So you can either author that or leave it to chance, right? Your options also compound. Savings, market awareness, staying recruitable, right?

You can't negotiate anything that you can't walk away from, and that's gonna be a, a fact that a lot of us deal with, right? And then your story. Your story compounds. Okay? Whether you can articulate your impact in one clear sentence, right? Having invisible excellence and no excellence get paid the exact same.

Okay? So my advice to everybody listening would be keep your skills current, obviously, right? But understand that skill is the asset that everyone's already told you about, okay? The career that's gonna survive the next, I won't even say decade, I'll say three to five years, is built on those assets that I just mentioned, right?

That's the stuff that I talk about every week, you know, because I've watched too many talented people learn it too late, and I really think that, you know, skill is one of your assets, but it is now one of five that you should be focusing on.

Galen Low: It's so funny because you're right. It's hard to hear because we've been raised that way to be focused on specifically the hard skills.

And when you said invisible excellence pays the same as zero excellence, so it's I know a lot of my listeners can relate to that because, you know, in some of these roles, both operations, you know, project leadership, team leadership even, you know, they're sometimes invisible and thankless kind of roles.

And what's funny is you know, what you describe... I like the way you described it, right? The sort of relationships, reputation, how you tell your story. It-- there's a connective tissue between those three things. But I like your framing because I think, you know, said another way, a lot of people would be like, "Oh, now you have to play the politics game and to stay, you know, off the layoff list, and it's dirty Game of Thrones stuff."

It's not. It's just, it's relationships, reputation, and storytelling, right? It's actually the game. It's the current game. And it isn't, you know, bad either. It's still relationships between humans. It's still, you know, highlighting your value, taking pride in it, understanding what your value is and taking pride in it.

And yeah being visible enough to have your value talked about in rooms that you're not in that is just how it works. It's how it arguably always worked. And yeah I think sometimes people think that's icky, but the way you framed it is like, no, it just makes sense. This is just the dynamic of collaborating with human beings, you know, in an organizational structure that may or may not have, you know, deep hierarchy and, you know, performance management and, you know, executive decisions made in, you know, at the table that you're not at.

That is really valuable. I think it's really interesting. Is that something you cover in your courses?

Justin Bateh: There is a small part that we cover in the AI Powered Project Management course, but I have some more things coming out soon that I can't talk about exactly right now, but they will be focusing on the other assets.

And You know, you hit the nail. Reputation, relationships, storytelling. Don't forget about your options either. You have to have leverage. I would call it, I'd call it leverage instead of options, right? It's just the way the game is played, and that's how it is. It doesn't have, have to be how you operate in your personal life.

But in your work life, you're gonna have the need to operate that way if you expect some type of upward trajectory.

Galen Low: I like that. I also like that tease. I'm gonna have to have you back once you can talk about the new things that you're working on. In the meantime, yeah, I have to say, Tactical Memo is it, it's a, it's a lifeline for me.

There's such good value in there. Like you said, it is what you're talking about week on week as you sort of build, you know, your new, I was gonna say artifacts, but but new material, I guess. So I'm really excited about, you know, where that could go.

Justin Bateh: Yeah, no, I appreciate it, and when you told me that you were reading my emails and going, you were in the welcome sequence and how you get them, I was surprised.

I was glad to hear that.

Galen Low: It's a tough business sending a newsletter into a black hole and hoping and just looking at open rates alone, right?

Justin Bateh: Yeah, I get the metrics, but I never know who's behind those metrics.

Galen Low: Awesome. Justin, thank you so much for spending the time with me today. It's been a lot of fun.

You know, speaking of Tactical Memo and your course, you know, where can people learn more about you?

Justin Bateh: Yeah. So for Tactical Memo, comes out weekly. It's my newsletter. It's kind of covers exactly what we talked about today, tactical playbooks for AI project leadership and building careers that compound, right?

And so you can go to tacticalmemo.com/subscribe or just search Tactical Memo or find me on LinkedIn under Justin Bateh, DM me. You'll see the link there, and subscribe. It's free, and I think that you'll get quite a bit of value out of it.

Galen Low: It is a really good one. I will include all those links in the show notes for folks listening and watching. I'll include a link, Justin, to your, to your LinkedIn as well. And yeah, thanks again. This has been great.

Justin Bateh: All right. Thanks, Galen. It was a blast. I appreciate it. Thanks a lot for the great questions.

Galen Low: All right, folks. 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, create a free account with us at thedigitalprojectmanager.com. Until next time, thanks for listening.