The Digital Project Manager

The Playbook for Creating AI Teammates That Actually Work

• Galen Low

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Building AI teammates isn’t a future-state fantasy—it’s already happening. Megan Ratcliff shares how she tackled resource constraints in SaaS marketing by creating a custom AI ecosystem that filled key gaps across content, strategy, and cross-functional alignment. The result? Less time on execution, more space for strategic leadership.

This conversation brings grounded insight into how AI can be used to replace tasks, not people—while creating opportunities to reimagine roles entirely. From demystifying the learning curve to managing team adoption and navigating the future of work, Megan offers a clear-eyed look at how to use AI meaningfully without losing the human judgment that drives results.

Resources from this episode:

Galen Low:

What made you decide to pull the trigger on creating AI teammates instead of doing something more conventional?

Megan Ratcliff:

I was the head of demand at the time, and I didn't have a lot of resources. I didn't have a lot of cash. I didn't have a lot of time and I didn't have a lot of team members. So I started by building out the first teammate, which was a campaign copywriter. Then I started to build strategists to help me think.

Galen Low:

Will building a team of AI agents automatically put you and your teams out of a job?

Megan Ratcliff:

Yes, and... because what is going to happen is that you're going to be able to build a system of tools that supports the work that you do from an execution standpoint, and you can also use these tools to help uplevel your strategic influence on the organization. So what you should be doing is using AI to replace your current job while building your new job. That is what the future of this looks like.

Galen Low:

I heard of someone who actually puts their team of agents on their resume and basically interviews and sells themselves as a team to potential employers. Valid or fake news?

Megan Ratcliff:

Valid. I've seen this before. If I'm hiring a marketer, I'm looking to see how AI enabled they are. So if you came to your interview and you said, here's the team of agents I'm bringing with me, I would be like...

Galen Low:

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. Let's get into it. Alright, in this episode we're talking about what it's actually like to build and manage a team of AI teammates — not in the future... right now, using today's technology. To do that, we're going to focus on the SaaS marketing space, and then we're gonna zoom out from that into a set of actionable tips and tricks for any team-based collaboration. With me today is Megan Ratcliff, a marketing specialist who did exactly what we're talking about in her previous life at SaaS Career Tech company, Dice. As the Head of Marketing at Dice, Megan effectively built AI teammates to fill the otherwise universally accepted gaps between the holy trifecta of marketing, sales, and customer success — effectively building the bridge between those teams to drive revenue growth. Today, Megan is a hands-on go-to-market consultant and coach pulling people out of the AI pit of despair. At Clarity & Motion Collective, she leverages her specialized AI ecosystem across multiple verticals to amplify organizational capabilities while maintaining the human connection that drives real business relationships. Megan, thanks for being with me here today.

Megan Ratcliff:

Thanks for having me.

Galen Low:

I'm really excited about this. I loved our chat so far. I loved our chats in the green room. I'm excited to dive in because this is such an important topic that I see in my LinkedIn feed. You know, it's you're gonna be managing teams of AI teammates. This is gonna be the future, and then you were referred to me from a mutual contact of ours and she was like, Megan's done this. You should talk to her. I'm like, yes, I should talk to her. To start off, I wanted to just like set the scene around one big hairy beast of a question that is on everyone's mind when they read the title of this episode, but then I'd like to zoom out from that and talk about three things. Firstly, I wanted to talk about the problems that you set out to solve and what your learning curve looked like as you dip your toe into the deep end of AI. Then I'd like to get your hot take on a few headlines and whether they are myth. Exaggerations or maybe just like absolute truths when it comes to AI teammates. And lastly, I'd like to explore what the future of teamwork looks like alongside AI teammates not too far into the future, the two to three year horizon line.

Megan Ratcliff:

Sounds great.

Galen Low:

Let me start out with my big, hairy question, so to speak. Will building a team of AI agents automatically put you and your teams out of a job, and if so, what is the right job for humans in 2026 and beyond?

Megan Ratcliff:

Good question. It is a hairy question and there is no like definitive answer I would say. No or yes. And because what is going to happen is that you're going to be able to build a system of tools that supports the work that you do from an execution standpoint, and you can also use these tools to help up level your strategic influence on the organization. So what you should be doing is using AI to replace your current job. While building your new job. That is what the future of this looks like. So smart marketers, project managers and the like are using AI to replace some of the stuff that they are doing now so that they can get into more strategic work.

Galen Low:

And is that valued at organizations right now? Like I've worked in several agencies and I've had some really great bosses and a lot of my bosses, that was the first advice they gave to me. They were like, listen, your job is to make yourself redundant. Find something new to do within the organization. I was like, cool. And I feel lucky because that actually they were true and honest to their word. I made my way up the ladder. But I've heard stories from others who are like, yeah, they're like, oh, I'd like to do this now. And they're like, yeah, we don't need that. Could you just keep doing the mundane stuff you were doing? Or if you've automated it, then I don't know if there's a place for you in this organization, like is that a risk in this day and age, or do you feel like the trend with your clients and the organizations that you work with, are they yeah, we want everyone to level up. We want them to come to us with ideas and we want them to sort of shape new jobs that maybe don't exist right now?

Megan Ratcliff:

Yeah, I think smart organizations are investing in their people right now so that they can upskill them because then instead of having a bunch of. People that are doers. You have a bunch of people who built systems that do and that they can think to build new systems, right? So I would say if you're in an organization that just wants you to maintain the status quo, go ahead and build your system to do your job for you. And then go find an organization that values what you're doing. So I don't know. And there will be people too that are happy doing what they've been doing for the last five or 10 years and like, great, continue doing that. But for everybody else, I think it's really important to be thinking about what is the next step that you wanna take and where do you wanna learn and where do you wanna grow? Like what are you curious about? Go explore that. Because that's what's gonna expand your scope of influence in the organization that you're at.

Galen Low:

I like that sort of growth mindset call out. And I mean, the devil's advocate in me, it was like, you're like, yeah, it's fine if you wanna keep doing what you're doing. And I'm like, is it though? Part of me is like what we're seeing online, what we're seeing organizations do sometimes related to AI, sometimes not. It's sort of like a resistance to status quo or stagnation. Some people will see it as, I don't even know if that's a question, but that's really kind of like, is that really?

Megan Ratcliff:

Yeah. If you are one of those people who likes what you're doing and it's task oriented, and you're doing the same thing repeatedly over and over again, you will get away with that for probably two more years, and then you're gonna not have a job anymore. And so it's one of those things where there's a little bit of a timeline here. And if you are not thinking about how to replace your job with AI and building your next job, you're a little bit behind. And so it's time to do a little bit of research and figure out how you can implement some tools to help uplevel your skills. I was actually talking to somebody earlier today and they were talking about certifications for their team for AI, and I was like, cool, that's like getting certified in the internet. So, that's the inflection point that we're at right now is if you were resistant to the internet, you know, like you didn't fare that well. The last round this time is the same thing. If you are resistant to this technology and there's lots of reasons to be right, it is a scary technology. You know, there are environmental implications, like there's all kinds of things. But if you're totally resistant and you're not exploring this at all, you're gonna end up like somebody who was ignoring the internet.

Galen Low:

That's a really good point about, yeah, the internet. I think your point is interesting about the certifications. I think right now, I mean my hot take is, and also on a timeline of probably about two years or less. The certifications are serving a purpose to be like, Hey, I am embracing this. Don't just take me at my word. I put in some work and I've got this thing that says, I care enough about the future that I'm exploring this. And then I think you're right. I think it's gonna be like I can type. That's great. That's what table stakes. That'll be, you know, two years from now or so. And I also like that, like if I'm picking up what you're putting down, I'm stringing two things together here. But it's okay if you don't want to like conquer the world, climb the ladder, become super strategic. But you do need that mindset of like building systems, even if you're going laterally, right? Or it's like, I like my task oriented stuff. And now I can do more of it because I'll build systems across and I can scale horizontally, not just climb the ladder and try and become a CEO.

Megan Ratcliff:

Yeah, and I think that's totally fine. And one of the things that we also talk about is not everyone needs to be completely AI fluent. Learning AI is like learning another language. And so there will be maybe 10 to 20% of the population that becomes AI fluent, and they're the ones that are like building all kinds of things that you have never dreamed of. And then everyone else needs to be able to like order a glass of water and like find the bathroom. You know what I mean? You need to be able to do a few things really well to be able to get through this moment. But yeah, that's my hot take, I guess.

Galen Low:

No, honestly, I like in like when you say it out loud, it's actually quite sensible. You know, earlier I said like typing a stable. But in a way it's like when you look at it as comparing it to the internet, the advent of the internet, it's like, well, sending an email is kind of table stakes, Zapier make.com, stuff like that. Automation is it's a different tier. And then yes, if you want to like code and be an engineer and architect the internet, that's a whole different level as well. But all of these levels exist within it.

Megan Ratcliff:

Yeah. There's a level that's like the baseline level, which is can you interact with. An LLM chat bot. And like, can you use it effectively? That is like the equivalent of ordering your glass of water that's totally acceptable and something that's going to be critically important over the next year or two.

Galen Low:

I love that. I totally agree. It's actually probably a good segue. I wondered if we could like zoom out a bit. You went on your own journey through many of these stages. I don't think you would maybe consider yourself like an engineer or a coder today, but certainly you did not have a technical background coming into this. But you came from the world of SaaS, I'm gonna lightly call it, but in the world of SaaS, like it's actually really difficult to get marketing and sales and customer success on the same page, rowing in the same direction, singing from the same songbook. Some organizations might have like a chief revenue officer that strings all these things together at the top. But in many cases these teams of usually peers with different bosses and different goals like need to make money happen. And at the top of the episode, I mentioned that you had built out a team of AI teammates to address this problem, but I wanted to like unpack that a bit. I was wondering if there is a specific problem or set of problems that you were setting out to solve. By doing this. And what made you decide to pull the trigger on creating AI teammates instead of doing something more conventional, like doing some training or having a lunch and learn series?

Megan Ratcliff:

Yeah, for sure. Big gigantic question that you asked in any, anyone that has worked in corporate America understands that there's like so many things going on at any one time. So for productivity reasons, having like a training or a lunch and learn to get everybody aligned, we'd have those. We tried to do it all the time. They were just really unsuccessful. You're trying to like cram four teams together while still maintaining like your individual like identity as the miniature organization inside of your larger organization. That's hard. People have an identity tied to the thing that they do, right? And so the advent of the teammate for me came from, I was the head of demand at the time, and so I was doing that job and I didn't have a lot of resources. I didn't have a lot of cash. I didn't have a lot of time and I didn't have a lot of team members, human team members to do the work. So like we didn't have copywriters. We had like a little bit of help on the design side, but not a ton. But in terms of like campaign strategy and sales enablement, material making and that kind of thing, we were running really low on resources, and so I started. By building out the first teammate, which was a campaign copywriter, which is a great place for anyone new to AI to start is content creation. It's really easy because you can understand what good looks like. You can get it started right away. The LLMs are naturally made to do that for you. So that was my first stop. And then as I started working with it and refining it and making it shippable, making it good enough, I started thinking about like, could I be structuring my campaigns in a smarter way? So then I started to build strategists to help me think, because I was not a classically trained demand gen person. I came from a marketing agency as an account director. Yes. The skills transfer a little bit, but like there were things I was tasked to do that I didn't have like hands-on experience doing. So I kind of had to fake it till I made it. And then I was building teammates to help augment where my gaps were in my skillset. So then we started working on, there was a product launch that was coming up and there was an ICP defined for this. Then we launched it and it went differently than we all thought. And the people we thought would buy that product were not buying it. And instead, a separate group of people was buying that. And I was like, that's so weird. Somebody should do something about that. We probably need to redefine our ICP. And then everyone was like looking at each other and I was like, who's gonna do that? Nobody owns the go to market. So then I was like, why not me? I will do it. So I built a go to market strategist. And the whole purpose of it was to help me figure this out, right? So I fed it information about what our conversion rates were, typically, who our ideal customer profile was. And then I took the buying data from the accounts that were buying from us. No, PII ever just as like a anyone that's listening, I never put PII into AI and neither should you. But anyways, I had it analyzed like these are the accounts that are buying from us. Compare this to who we thought was gonna buy from us. What's the difference between these two? And we started to create a new profile of our buyer. And I was like, oh, this totally makes sense that this is like, what is actually transpiring. And I was like, are there some tests that we could run to help us validate this? And so we started running tests and so that go-to-market strategist, teammate that I built started to break down the silos between marketing sales enablement, rev ops, so that we could have kind of one single source, and that teammate was shared among everyone so everyone could go consult it, and it was trained and built on a shared intelligence layer, which was really critically important. And so once you start to build that shared intelligence layer, and then you have a shared orchestration layer, shared teammates that you guys are using, and then you kind of have a shared understanding of the judgment, the human judgment that you're using to evaluate those other two layers. That's when you break down the silos between the organizations and you start moving as one, and the AI is your connective tissue that is then carrying you forward. So that's how we did it.

Galen Low:

I love that because it also ties back to the thing you said earlier, which is that as humans we have like identity tied to the work that we do. Our teams are like micro organizations within larger organizations and sometimes that's the gap. We're not sharing the same information. We're not aligning on a worldview or even our understanding of the customer, but if you have this shared intelligence layer that everyone is using, that everyone's feeding into, of course, no PII in there, nothing personally identifiable, but taking that data and understanding what the picture is and having conversations and dialogue around that, like that is the bridge. I was actually really curious because I'm like, how does this string everything together? It makes sense now as like this tool that's almost like a shared resource that learns from all the teams. Exactly. I also like the idea that I come from agency world as well, and then kind of like startup ish world where everything's lean and like, yes, of course you would like to have a copywriter, like a marketing copywriter, conversion copywriter. You'd like to have a strategist. You'd like to have 11 people on your team to take a product to market and build demand for it. But usually you can't. And so it relies on these like unicorn roles. And I've also seen that go wrong where it's like, great, our unicorn left, let's go find another one. And you're like, guess what? That was maybe the only one versus, and maybe this is the real appeal of AI and why it's taking off right now, at least at a sort of the work level. Is that it can help support some of the gaps that you individually have like identified within yourself. You'd be like, I've, I'm not a copywriter, but guess what? LLMs are good at language and they've trained on lots of copywriting, some of which is probably bad, and you do need to correct it and have an understanding of what you want to say and how you wanna say. Because I think everyone listening and definitely you get a whole bunch of like LinkedIn outreach or emails that are clearly AI generated and automated and operating at scale, and we'd want something different for ourselves, I guess. But I like that idea that it's like, okay, I don't have the skill. I'm the only one who can do it. I'll raise my hand to do it. But also I think I need to build a bit of a system knowing that I don't possess the skill. Clearly no one else on the team does otherwise, you know, or is not willing to wield it. No one raised their hand. So we need to kind of build this up. I think that's a super cool way of looking at it, a super healthy way of looking at it. I want to come back there, but first I actually wanted to ask you about your learning curve. I mentioned earlier you don't consider yourself as someone who's got a technical background. You did not do like computer science and university. Can you talk to me about what the learning curve was like for you? Like what did it take? To figure this all out. Yeah. What resources did you leverage and like how did you find the time even to build and operationalize your AI teammates?

Megan Ratcliff:

Yeah, for sure. And yeah, I don't have a technical background. I went to school for marketing. I was in the business college and then I worked at a marketing agency as an account director for 10 years. And so I had been around a lot of engineers. I had been around a lot of technical people, but I am not. Somebody who's gonna go build something in JSON or HTML or React like I am not ever gonna do that. It is Greek to me. So the way that I got started was I started working with a woman named Lisa. And she was brought on as a fractional CMO at DICE to just help kind of light the flashlight for us on like, here's what is possible. And I just really took to her and I started learning from her. And it was at one point, 'cause we were trying to figure out use cases for me because I was like, I've been around it. ChatGPT had been out a few months and I was like. How is it really useful? I've just been asking it like kind of silly questions I know about hallucinations, whatever, and I'm just like, it's fun to play with, but I haven't figured out how to incorporate this in my real job. And so we started thinking and I was like, well, my first kind of stop is the content side because like I am drowning and I'm not a copywriter and I need help there. Immediately she's like, okay, let's build one. Do you wanna build a custom GPT with me? And I was like, I can do that. And she was like, you can do that. And I was like, cool. So she showed me some instructions that she had built and she goes, why don't you take a run at it? Why don't you try to build your own instructions? And so back in the olden days of building Custom GPTs, I like wrote my own instructions on my keyboard and I gave it to her and she reviewed it and gave me some feedback, and then I edited it. And then I built it and I started using it. And then I'd iterate on it and train it and like do kinds of things with it. And then all of a sudden it was like pretty honed and I was getting pretty good things. And then I was like, what else could I build? And then my brain started going like in creative overdrive. And I was like, what can't I build? And so I started building, like I remember going and I had an idea, I'm an ideas person. AI really rewards ideas people. So if you're an ideas person, like now is your time. But I had an idea and I was like, oh, I really wanna run this by the president of dice. Like I think he'll love it. And then I was like, wait, I don't know the president of DICE very well. Like I think, I don't even know if I've ever had a direct conversation before. So then I was like, I kind of wanna test my idea before I go and like make myself look like a fool. So I took transcripts from all hands meetings he had given, I created a job description for president of da. I took like language from emails he had written, and I created a simulator that was like, okay, here's our president. This is like, you know what his job is, what he cares about, here's his language patterns and like I'm guessing at his personality here. And so then I created that, and then I gave it my idea and then it pushed back on it and was like, I hate this idea. It's terrible. And I was like, oh no. Then I was like, how do I get a yes from him? And so then it helped me reframe it in a way that would be like more aligned with what he cares about. And so we didn't change the sentiment, we just changed the way that it was framed. And then I pitched that idea and then it went really well. And then post, I was like, Hey, I have to tell you something that I did and I don't wanna freak you out, but I created a simulator of you to test an idea. And he was like, oh my gosh. That's like so smart. And then it turned out that we were actually building his personal brand as part of our content strategy. And I was like, we can use your simulator as the backbone to create content for you. That sounds like you. And he was like. Brilliant. We're gonna reuse this thing. And then I was like, what other simulators should I be building? So I built my personal simulator, and then I started building persona simulators so that we could get instant real-time feedback from our personas. I will also mention if anyone is listening and is like, great idea, build an AI persona. No, you cannot build an AI persona based on AI information. You have to have actual real information about those people. You have had to have done the research. Interviews qualitative and quantitative data. That's what feeds a persona simulator and makes it better. So I started creating persona simulators so that we could run campaign copy by those. And so then the world just started like shaping, and I would keep some teammates, I would ditch some teammates, and then I started linking them together and I was like, oh, this is so much better than like copying and pasting like context from one to the next. And when I started doing that, I started getting really efficient. So you're asking, when did you have time to do this? And it was like in the margins. It was like in between meetings. If I had 15 minutes, I would go work on something and then I would prompt and like maybe start a set of instructions, go to my meeting, come back, see what the output was, refine, update, whatever. So like some teammates, they would take me a day to build because I was like doing it in between meetings. Sometimes it would take me longer. It just kind of depended. But over time, I built an ecosystem in probably four months that started doing my job pretty well for me. And then I had more time to innovate, so that's why I was able to raise my hand and say, I wanna do this go to market thing because I have the time to do it.

Galen Low:

I think you dispelled a myth for me there, which as soon as you said it, I knew it to be true, but I was like, I'm like, gosh, it's hard to sit down and like have a full thought. And like I think I approach it as though I'm coding, even though I've like I am a very poor coder if even that these days. But I think I approach it that way. I'm like, I'm gonna need to sit down and crack my knuckles and really think this through. I'm gonna need four hours. When am I gonna find half a day to do this? And you're like, yeah. I was actually just in between meetings, like tweaking things and seeing what happens. I like that because I think that is a, it's a far more realistic, maybe pragmatic, especially. Someone from an account management background and like myself from a project management background, we're back to back. We're securing in between meetings, you know, and how do you find the time? When are you gonna find a day of no meetings to get this all done? But it doesn't mean that you can't. Because you could just be watering a plant. I don't know if that's a very good analogy, but you know, it's like you can make progress on it and eventually there's an ROI because if you're, they're actually useful tools. Like you said, there's some that you chucked, you're like, eh, no. Good discard. But overall ROI was that you ended up with more time and could feel like you had the capacity to raise your hand and say, I think I can like do more here for this team because of the efficiencies that I've built. And I think that's really cool. Earlier on, I maybe misspoke. I used the word agents and so I thought maybe I'd just ask here, because you know, everyone's talking about a agentic, this and that, and I think maybe that's like at the core of where there's some anxiety, right? Agents being autonomous, AI making decisions on its own based on triggers, talking to other tools. It seems like a train with no one at the helm going as fast as it can. I could see why people have anxiety about it. Actually, are we talking about agents here? We're talking about like custom GPTs. Like if we're not talking about agents, is that where the path leads or is that not necessarily the only way? Like are you on step two of nine for creating a team of only AI teammates with no humans?

Megan Ratcliff:

Yeah. Agents are being like tossed around. Really freely right now. And an agent is an AI tool that is acting autonomously on its own. And I think when people say they're making agents on LinkedIn, they're actually just making a teammate, which is a custom GPT with a human in the loop. So are we headed to a place where agents actual true agentic AI is taking over in some cases? Yes. In other cases, no.'cause the judgment layer that I talked about earlier is really important. Only humans have taste and only humans can like have the like specific nuance and historical reference and like information that an AI can't pick up. AI is not emotional, and so that's where the human comes in. And I've been like thinking and hearing a lot about adding agentic AI into a specific workflow. And in some lanes of work it makes sense to do an entire workflow end to end with agentic AI. And that's like data related or something. But when you are in a creative field, having a human in the loop is critically important. Otherwise it's sloppy. McDonald's actually had an AI ad that was produced in Europe and it was bad, and people really pushed back and they pulled it because it wasn't good. And that's where like the human element of it is so critically important. So if you are sitting in a role with, you know, a workflow that you're thinking about and. You wanna start getting into automation? It's fun. I have automations going on my behalf all the time, but I'm automating the edges of those workflows, not the heart of those workflows. And so I might be AI assisted at the heart of those workflows, but I am still human in the loop. So if you're thinking about like, I need to clean some data before I create a report with that data, you might automate some of that data cleansing. Then you might pull it through, have it start to create the report, but then you are gonna put your own specific human judgment in there. So end to end. Sometimes human in the loop most times.

Galen Low:

I like the word you used, not just judgment, you also used the word taste. And I've never heard anyone describe it that way, but it resonated with me because. And I'm gonna connect it back to something you said earlier, which is that like you can't just ask your LLM, you can't just go, AI, please create a persona out of nothing of my CEO, of my ICP of this and that. We need to feed it with the right information. We need to train it and we need to sort of tweak, optimize, and some of the, what we call taste, I think is also like. And I'm thinking like these of taste, not like having good or bad taste of something, but it's just like that flavoring. Sometimes that nuance is maybe because it's something that is, as you say, emotional and not necessarily something that AI will get or pick up on, or B, we just didn't tell AI that thing. So you might wanna still have that when it's as important as presenting something to your CEO. I wouldn't even like hire a very talented human to just autonomously without any input from me and without running anything by me. Create a business model, pitch for my CEO and present it. I would never do that. Not any human, like run it by me. And that's why like that human in the loop thing is so important and in my mind, like natural and okay.'cause I think there's some shaming going on too online where it's like, agentic AI is out there. If you are still at the keyboard, you're doing something wrong. And I think that's it's a bit toxic and I think it makes people feel like. They are behind getting rid of the human workforce. Yeah. And they're like being shamed for it. And you're like, I don't know if that's the only fate for everything. And like you said, absolutely some things that we should automate, some things that we should orchestrate, but also things that are not there yet, maybe never will be in terms of like the role of the human in the loop and what that actually means.

Megan Ratcliff:

Yeah. Well, and if you think about just modern technology in general, like people used to wash their clothes by hand. Now we have washers and we have dryers. What we all want is like a robot to fold our clothes for us, that would be like a totally agentic system for laundry. But if we're sitting in reality, you are putting human in the loop, putting your clothes in the washing machine, you are setting the dial, you're letting it run. When it's done, you are physically moving it. You're deciding what's going in the dryer, what's getting hung up. That is a judgment called by you, the human. You are setting the dial. You are deciding how long this is gonna dry for. And then you're deciding when you're pulling it out and when you're folding and where that's gonna go. So even in that process with modern technology, you are human in the loop at every step, but you're using machines in two places to save you time. This is the same exact thing where you are deciding like what's worth your time and what's not. There's going to be processes that we're doing manually right now that just don't make sense. But there's going to be processes that are critically important that humans maintain, and that has to do with case, it has to do with judgment, it has to do with ethics. Those types of things.

Galen Low:

Even to take an analogy way too far, but you know, now there's the all-in-one washer and dryers. It's like, yeah, why are you actually moving your clothes into a different thing? We can engineer that. But then there is the ultimate decision making, the ultimate human judgment when it comes to laundry, which is like from a press or like mixed fabrics. Like what am I setting this to? What is the actual difference? Do I even know? But still, it's that thing where you want human discretion so you don't end up with like. A shirt that is three sizes too small all of a sudden.

Megan Ratcliff:

Or what are you pulling out of the dryer before it dries? You gotta get those leggings outta there. You can't dry those.

Galen Low:

Ladies and gentlemen, AI is just laundry, actually.

Megan Ratcliff:

Yes, just laundry.

Galen Low:

I like this human in the loop bit. I noticed that in your posts and your work, the human elements is key. As I'm listening to you tell this story, you know, and like having these teams start using these tools, like there is like I imagine a change management component to like get teams to like shift gears and view. These like AI tools as their teammates. What does it even look like to like. Get people to wrap their heads around this and get buy-in and also like what kind of pushback did you find yourself on the receiving end of?

Megan Ratcliff:

Yeah, for sure. And now that I help marketing teams in general, I've got a lot more exposure to the change management side. And the first step is dispelling the fear. Because a lot of people are scared about this and I work with marketing teams and, you know, content writers are freaked out because, you know, that's the easiest path forward. That's how I got started was content. And so. What we do is we dispel the fear and we show them what the limitations of AI are, where human taste comes in. And also like, yeah, this can probably write a press release for you, but you're gonna have to like give it the good inputs and then now you can elevate yourself to a content strategist and you can do more strategic work and you can get better inputs. You can do research. Quickly in the right places. And so, yeah, step one, dispel the fear. AI's not gonna take your job. It may take components of it. And you have the ability right now to reimagine what your future job looks like. Like how many people do you know have a job that they love end to end, they love the whole thing. Not very many, right? If we're being honest, it's not very many people are like, I had the best day ever, every single day. And so when we talk about reimagining what's possible, this is their opportunity to decide, okay, here's what I really like about my job. Here's what I wanna keep. Here's what I really don't like. Let's figure out how to offload that to a teammate, or even automate that, because then I can focus more on the things that I love to do. So that's how we start to kind of reshape. The mindset shift is so important because without it, without reimagining the work, nothing fundamentally changes. You're just doing the same work, maybe a little faster, but you're not gonna see any real productivity gains. You're not gonna learn that much. But when you start to reimagine what that work even is, that's when you start to become like limitless. And that's when like the world opens up. So it starts with fear. Let's dispel the fear.

Galen Low:

I like that. Dispelling the fear bit. I'm curious if, I think there's like this individualistic lens that we tend to put on things. Everyone right now it's like you can event reimagine you and your individual role, and then we're starting to see this sort of like discord in homogeneity. Like the corporate structure wants some homogeneity. So it's like, okay, well you like writing press releases and you're not gonna use the AI tools to write your press releases. And this other person who has the same title as you. Wants to not write press releases ever again, but still wants to hold onto this other piece of their job. Suddenly you have these two roles that have the same title that are technically not doing the same things, or maybe they are doing the same things because they've augmented their capabilities using their AI teammates so that they actually kind of are, I don't know if there's a question in there, but like, do you find that there's like this, like a risk of special snowflake roles as we reimagine our futures?

Megan Ratcliff:

And so that's actually you were gonna ask me later about what the future looks like, and this is the future. Galen, is that organizations that are gonna be successful in this transformation are fundamentally re-imagining work and their thinking in terms of outcomes. So what are the outcomes that we are responsible for? Let's create workflows that align to those outcomes, and then let's assign the people that have the skills. To do that work to that workflow. So what you're going to find is a breakdown of the hierarchy, and what you're gonna see is the change over to a what's called a work chart. And so people will get together to do the work that they're best suited for, and then once that work is finished, they disassemble and then they assemble in a different way to do other work. And so that's how you break down the silos that hinder people from true collaboration inside of companies and they start doing work that actually matters. So think about a company that's really, like every company says that they're customer centric, and it's not true. Think about if you would actually rally around becoming a customer-centric organization and you created a work stream that's solely focused on that, and you assign the people who are closest to the customer to make that happen, that is a major shift in thinking. And when you start to do that, you are going to find more like super individual contributors for sure. But people's skills are going to be what carries them forward. Because right now, like you and I could have the same title and vastly different skill sets, and that's a good thing, right? Get rid of the title, keep the skills, and go do the work that's best aligned with what you're good at and what you're interested in.

Galen Low:

I really like that it lends itself to a transformation that I think we've been trying to do for a while, which is like become skills-based organizations, but even like as like the workforce is also still prone to look at things through a job title lens. Even if organizations say or are trying to become skills-based, I like this idea of a work chart. I think the most empowering thing that I heard you say there was, first of all, I could only help but picture nanobots coming together to like make a shape and then do a job and then like disband and do a different thing. And then I was like, wait a minute. With my project manager hat on, I'm like, that's like a project. You know, you kind of come together temporarily and like put your skills to use to achieve a goal and then maybe disband or go onto a different project. That team might look different. What you do on that project might look different and that's probably okay. And then I also, like, I'm combining that with the idea that, yeah, sometimes you are gonna have a team where you're like, nobody's a copywriter on this team. What are we gonna do? And at least for now, especially when it comes to like language. You've got your like copywriting, AI teammate can come and support. It's good at language processing, it's good at natural language processing. It's been trained on a bunch of stuff. You can tweak it to taste, but suddenly you've got this team that can actually, you know, get the job done even if it actually was missing some skills. And I think that's actually a really empowering view on like the future of work.

Megan Ratcliff:

Yeah, for sure. Well, and I think too, like understanding the outcome that you're going towards, the skillset you already have on your team helps you identify what teammates you even need. You don't need to build AI for the sake of AI. You should build it to do a specific thing. And when you get your organization, you have your outcomes, now you have your work stream set up and you've assigned, you know, the people with the skills best able to do that job. There's gonna be gaps. And so how do you fill those gaps? You build a teammate to fill those gaps, and so that's how you identify what's needed.

Galen Low:

I like that. And like, you know, in a human perspective, or at least a human personnel perspective, sometimes we do run into that situation where you're like, I can't justify a full-time coffee writer. And then you're like, I guess I'm gonna share it with like eight other teams. And then it just doesn't quite work that the sort of put the training on their understanding of the product, their understanding of the material, like it's spread across, there's all this context switching. So there are areas where like that can be really helpful. Yeah, totally. I know we like talked about the future now and I think it's fine. So what I wanted to do was land out with a little game that I'm calling. AI myth. The idea is that I throw out something that sounds like it could be a myth and you give me your hot take on it and you can say like agree or disagree, and then we'll like expand on it.

Megan Ratcliff:

Okay, great. I can't wait.

Galen Low:

Here's the first one. Managing agents or AI teammates is just like managing people.

Megan Ratcliff:

Myth. False. AI teammates don't have emotions, so you don't have to manage those. But you should do a review of your agents probably at a regular interval quarterly, probably twice a year in some cases. So you still should do a diagnostic review of your teammate, but. It is not the same as a human. It does not have emotions.

Galen Low:

I hadn't even thought about that. Like the performance review, quarterly performance review with your AI teammate. I love that. You should. Alright, next one is, this AI tool can replace your whole marketing team.

Megan Ratcliff:

No, false. False. Any tool claiming it's gonna replace your marketing team is marketing. It's bad marketing at the least. No, you still need humans because again, humans have emotions, humans have taste, humans have judgment, and humans hold nuance that an AI could never do. Plus you need somebody to like pick up the things and move them around and deploy the things, and you need somebody to like control the robots. So no, it won't replace your marketing team. It will make them better.

Galen Low:

And I could probably identify in my inbox the people who did replace their marketing team with just AI sometimes lacks that nuance and taste. Okay. The next one is kind of a tinfoil hat. One, AI agents will rise up and take your job, and that's what the government wants.

Megan Ratcliff:

That's the bananas. It depends on what government, where you live.

Galen Low:

Fair?

Megan Ratcliff:

No. You as a human have the ability to direct AI, and you have the ability to try and see if AI will take your job, but in the process, you will create your new job. Will people lose jobs in some cases? Yeah, they already have. That's unfortunate. Are new jobs being added every single day? Yes, because they're changing. So again, like the advent of the internet, jobs are just fundamentally shifting and changing and it's up to you to decide like are you gonna change to?

Galen Low:

Yeah. It's actually funny because like there's this confluence of the dialogue around labor market shortages and like shortfalls in population growth. And you're like, okay, well I just I'm interpreting this, but I'm like, oh yeah, the government wants AI to be able to do some of these jobs because frankly, we just aren't giving birth to enough new humans for us to take care of that other generation without just like offing the the older generation. But then to your point, transformation is taking place. It's creating new jobs. It's not. Even if you know the world of robotics and the world of automation and the world of AI is, you know, going to install this layer that takes existing jobs, but it's not very human to be like, cool. Now we'll just like sit and eat grapes and do nothing and it's gonna be a post work culture. There's gonna be more to be done.

Megan Ratcliff:

Yep, exactly.

Galen Low:

Next one isn't really a myth, but I heard of someone who actually puts their team of agents on their resume and basically like interviews and sells themselves. As a team to potential employers. Valid or fake news?

Megan Ratcliff:

Valid. I've seen this before, so I don't hate it actually, because if I'm hiring, let's just say I am hiring a marketer. I'm looking to see how AI enabled they are because the future of marketing is AI fluent and AI enabled. So if you came to your interview and you said, hi, I'm so and so, and here's the team of agents I'm bringing with me, or the team of teammates I'm bringing with me, I would be like, fascinating. Like walk me through your thought process in building these. How are they helping you? What workflows are you doing, and how are you like leveraging these because that could be a real asset to your team. So not fake news. I've seen it. I don't hate it.

Galen Low:

I don't hate it either. And I think it's like, I like that you took the angle of fluency. It shows fluency. You know, earlier we talked about certifications. Is that going to be a thing in AI two years from now? Maybe not, but definitely the show me the thing you built and that shows your capability. It shows the way you think about work. It shows your ability to kind of create systems and orchestrate, even if you're not technical. I also like this idea, although I don't know if this is how it would work in practice, but it's like. Knowing your weakness, that interview question. Okay, what's something that is a weakness of yours? And you might be like, okay, well actually I'm a really crap copywriter, but I also have built an agent that is a pretty good copywriter and maybe it's that faux pa of turning that question around. So it seems like it's a positive, but also it is legitimately in some cases right now, able to like be like, I filled the skill gap. By doing this thing that actually is quite acceptable these days.

Megan Ratcliff:

Yeah, and maybe the new question isn't what is your weakness? It's what teammates have you built to augment your skills?

Galen Low:

I like that.

Megan Ratcliff:

I dunno, I don't think anyone's gonna ask it, but maybe in the future somebody will.

Galen Low:

Honestly, I think like we should create that. Questions to ask in 2028 in your job interview when hiring for a marketing role. Yes. Just for even more fun before we wrap, do you have a question for me?

Megan Ratcliff:

I do have a question for you. So you're talking to PMs all the time and you're talking to kind of a wide range of folks here. So what is the biggest gap you're seeing between what leadership thinks AI adoption looks like and what's actually happening on the ground?

Galen Low:

That's a great question. So for me, I think there's like stereotypes around project people, right? It's like you're all process robots, you know, you want everything neat and tidy and in a box. You know, in, in my world, they're all digital PMs. They work with technology all the time. Shouldn't this tool just do the thing? Like, isn't it just like set a radio button, press okay, suddenly we have agent workflows. So I think it gets. Simplified at the top. I think there's a stereotype that all PMs are process people, and now there's like this countervailing force around PMs recognizing that actually a lot of their value is human. It's like human relationships. It's nuance, it's negotiation. It is, you know, building relationships, it's building loyalty and then leveraging loyalty frankly, to get something done. It's much more of this like. Orchestration of collaboration and now they're kind of like at odds because not every PM thinks of themselves as technical and able to build a system no matter what they do. It will take longer than what a lot of leadership teams think it should. There isn't a lot of guidance, I think, and I do wanna come back to that because what I loved about your story was that. You had a mentor. Right? Like someone who was there in the organization also in your space, A CMO who was like, yeah, let's like here's what I know now you try and let's see what we can build. And I guess that's my long way of saying that. I think there's a mismatch in expectations of like what the skills are that a PM has that are valuable multiplied against a sort of vacuum of mentorship and leadership and guidance. I think a lot of people are just quietly trying to figure out by themselves and probably like you said it earlier, you were like, I built some teammates and I threw them away. And I think a lot of folks who are doing that right now probably think they're failing. They're like, oh, I just invested like three days building this thing and I put it in the bin. I am definitely not gonna have a job next year. Whereas actually, that is progress. That's what we want. Yeah. That is actually answering the mail on what an organization or what a leadership team wants from their PMs.

Megan Ratcliff:

Yeah.

Galen Low:

And I think that's important to call out.

Megan Ratcliff:

Yeah. And like just the education and training piece of it is so critically important because. All of these organizations have expectations coming down to the people that are like, you must increase your AI usage by whatever percent. Or we must get 20% more efficient. Or, you know, they're adding KPIs to these people without any like real training, without any one-on-one training because. Every person's job is different. The use cases are different. So if you give a blanket, here's how to uset GPT, or here's how to use Gemini training, that's not that useful to people. They need like actual fluency skills. They need to be trained on those. And then it's so helpful if they have somebody that they can talk about their work with to help them even like get started. It's like you are pushing that first rock for them and with them, and then once they get going they become unstoppable. But they just need that like little bit of help first.

Galen Low:

I love that. Yeah. And I totally agree, especially on the project manager front. You know, we are problem solvers. You get us going, we'll figure it out. But coming, going from like inertia, like the stasis into like momentum, there's a lot of imposter syndrome. And then the other thing I love that you said is that yeah, you do kind of need to, like, it almost isn't good enough to have like an AI mentor. You had an AI marketing mentor who understands the depth and breadth of marketing and also AI, and I think that's like an important inflection. I hadn't thought about that. That's really cool. Megan, thanks so much for spending the time with me today. I had a lot of fun. Thank you for playing my MythBuster Orama game. For folks who wanna learn more about you, where can they go?

Megan Ratcliff:

You're best able to find me on the LinkedIn, so search Megan Ratcliff and you'll run into me.

Galen Low:

Awesome. I'll include a link to your profile in the show notes. And thanks again. Thanks for your time. This was a lot of fun.

Megan Ratcliff:

Thanks for having me.

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 on over to thedigitalprojectmanager.com. Until next time, thanks for listening.