AI has a People Problem — Not a Technology Problem

This transcript has been generated by AI and may contain slight discrepancies from the audio or video recording.

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Alex: Talent is at the center of every organization. AI is reshaping how we hire, develop, and retain it, and for many, the technology feels intimidating. Some professionals wonder if they have the right skills when it comes to working with AI. Others feel like imposters in conversations about AI strategy and adoption.

Today, we're joined by Alicia Sanchez, Chief AI Officer of MPF Federal, a federal training and consulting firm, to explore how leaders and professionals can move from [00:01:00] AI uncertainty to confidence, from hesitation to action. We'll discuss the human strengths that make AI adoption possible, how to gain real-world confidence without being a technical expert, and what business and HR leaders can do to create cultures where people feel empowered to con- collaborate and contribute with AI.

Alicia, welcome to the AI+HI Project.

Alicia Sanchez: Thank you so much for having me. It's been an amazing experience being here this week.

Alex: Wonderful. We've got a fascinating conversation ahead of us right after this brief message.

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Alicia Sanchez: SHRM

Alex: So, you know, uh, uh, it stands out to me that you, on LinkedIn and a variety of different social platforms, have talked about this imposter syndrome that many feel when it comes to being a technical expert with AI and AI strategy. What, what prompted that? What was it that gave you that vibe and that need to share that with the world?

Alicia Sanchez: You know, I, I think that I am probably not the only one, but the reason that I was so public about it was because I came from a heavy technical background. I have a PhD in modeling and simulation. I spent almost 20 years building video games for the government. Modeling is essentially the, the baby steps of machine learning, and I found myself in a situation, I was working for a health insurance company doing enterprise AI, and I was really [00:03:00] struggling with the technology, and I was thinking, "If I'm feeling this way, this has to be more pervasive.

This can't be something that a lot of people aren't feeling." And I think it's indicative of some of the very early training. It was very hard to break into AI spaces initially because all of the training was so technical. It was 100% developer set focused, and if you weren't going to be a developer, there wasn't a whole lot for you.

And so I think that that was a darker time certainly that a lot of people resonated with, but I think it was th- at that moment that I realized that that's where the opportunity really was for me, that I didn't have to be able to code to understand the combination of technology and strategy.

Alex: You know, uh, I, I'm fascinated by this imposter syndrome 'cause I myself have a little bit of that imposter syndrome.

I'll be one of the first to say that, right? And, um, uh, I'm, I'm trained in some of the same spaces that you're trained in, right? My doctorate's in organizational [00:04:00] psychology, industrial organizational psychology, which sounds like it's people-oriented, but it's data sciences, right? Yeah. And I, uh, I struggle even with that first layer of AI that I was dealing with and AI strategy.

I still to this day don't feel like I've fully gotten there. Now, I, I feel much better than I did a year ago or two years ago, but it was fascinating to me to see that imposter syndrome, that I was being turned to for decisions, and in many cases it was like- I don't know. You tell me, right? Let's figure it out.

And, uh, I, I, I wonder what was that one experience? Where was it that you started to make that move towards feeling a little more confident?

Alicia Sanchez: I'm so, I'm so glad you asked, Alex, because I'm so excited to hear that you had an IO background also. My undergrad was IO focused, and I think that that was part of it.

I had spent an entire career at this point focused on humans and how they learn and how they adapt to organizational behaviors, and that was the light bulb moment, that humans created AI [00:05:00] in homage to the only thing that we could, to humans. And so as learning and development professionals, as HR professionals, as people who have spent their careers focused on humans, we should have the inside edge.

We should be the ones who understand this more deeply than others. But there's just this thin layer of vocabulary, the, the thin layer of technology that prevents it from feeling like we actually do have something to contribute in this space. And it's because the, the engineering folks, they don't speak the same language.

Yeah. And once we get on the same page, I think it's really easy to find that there's so much ability to make impact in the space.

Alex: I'll ask you another question that deals a little bit with enthusiasm, excitement, and at the same time, a little bit of uncertainty, right? There are a lot of professionals out there in the world who are excited about and curious about the use of AI, and at the same time, they're un- they're feeling uncertain, right?

There's obviously the, the big thing that we all think about is displacement, but in reality it's also the imposter syndrome. How do I use it in a way that [00:06:00] makes me better, that makes me more effective? And, uh, I, I guess my question for you is, when you think about that, how is it that you think many of these professionals are creating that balance or really making it so that they become, they overcome that uncertainty?

Alicia Sanchez: I think there's, I think there's a lot to unpack there because I think that we see different types of resistance. Mm-hmm. Some of it is uncertainty, some of it is displacement. But I think there's the very real fear that AI will ultimately replace humans in the things that we know are so importantly human, in cognitive, uh, abilities, in empathy, in the areas that have always made us who we are.

And I don't think that that is going to be something that we see, at least in the interim, right? We're not going to get a highly nuanced situation or a relationship-based AI in front of us. But the, that entire perspective is only, I think, part of the [00:07:00] reality that we're going to face if we don't learn ourselves to harness it.

And so I think it's incredibly important for us to understand that... I, I think it's a little bit like when you have-- or when you're resistant to getting a pet, right? Because you know that pet is going to run your life- Yeah ... when it's a puppy. You know you're gonna have to walk it. You know you're gonna have to take it out.

It's gonna rely on you for feeding and caring and all of those things. And I think AI can feel a little bit like that because you're making a commitment, and I think it's a commitment that has to be made. I don't think we can deny any longer that this is going to be part of the future that we're all going to be in.

But I think it's also very easy to feel overwhelmed by the scale of it and to feel like we could never actually be a dominant force in it because there were so many topics for all of our careers that we're like, "I can be the expert of this. I can understand more than anyone about this." But [00:08:00] when something changes every week, nobody can be the expert.

Nobody can keep up. I mean, we see amazingly talented, incredibly smart people who do have a really foundational understanding of what the state of the art is at any given moment, but that's only because they're coming at it from a foundational place of understanding what's capable at that moment. And as we see advances like we have in the most recent weeks with some of the really great agentic work coming out, we're able to shift in themes, not in exacts.

I'm sure if we pose the technology to any of our experts, uh, something obscure, no one would know. So it's easy to feel like you're behind, and it's easy to feel like you're replaceable until you really take a look inward and realize that, that there's something innately human about the work that we're going to continue to do that couldn't be replaced by machines.

Alex: You know, I'm, I'm, I'm, uh, I, I, I think about it all the time, right? 'Cause it's obviously the core of much of [00:09:00] the work that we've done. I g- I lead SHRM's thought leadership group, and so I get the opportunity to kind of see what HR professionals and working Americans are thinking. And I'm always surprised because our own data speaks to the fact that 62% of working Americans actually want their employers to teach them more, develop their skills.

They're not thinking first about the displacement. It's certainly in their minds, right? But what they're saying is, "Teach me more because I wanna develop the skills. I don't wanna feel like an imposter." The other thing that's fascinating is 51% of them say, "I either have no experience or I am very much a beginner."

And when you ask them things about, like, beginning, right? They don't think about things like automation. They don't think about machine learning. They don't think about NLP. They're not thinking about any of those types of things. What they're talking about is, "I've taken an email, I've dropped it into ChatGPT, and it helped me revise it.

It helped me edit it," right? That's great. That's not what we're talking about here, right? So [00:10:00] Yeah. I'll, I'll jump into another thought here, right? 'Cause I, I, in reading and doing the prep work that we talked about, uh, one of the things that stood out to me was actually this notion of people feeling like an imposters because they're less technically savvy or fluent than others, right?

It's that comparative kind of analysis that we all go through. Uh, in the HR profession, I've been working with the HR profession for almost 20 years. I was a re- I am a recovering HR professional myself for, uh, LAN and LATAM for many years. Uh, but one of the things that, that strikes me is I, um, I, I used to do speeches all the time talking about the HR profession and how it's evolving, right?

And one of the things that stood out to me was I'd say, "How many of you, when you hear an HR professional say, 'I joined HR because I'm a people person,' right?" And a lot of people heard that and said, "Oh, that makes complete sense," right? But for me, what I realized f- fairly early on in my own research was that was code for, "I don't like numbers," [00:11:00] right?

"I don't feel strong in numbers." And that's when I'd get a good laugh from about 80% of the HR professionals in the room, 'cause whether you like it or not, HR is a numbers business, right? And I, I, I wonder what is the corollary there that you see between that kind of thing, that euphemism that we use in HR, and this euphemism that you see here where I feel like an imposter because I'm less technically fluent.

Alicia Sanchez: It's such an interesting perspective on this, and I haven't heard this one. My sister recently told me she was taking a, a real estate exam to be a broker, and she said, "Oh, I, I can't do math." And I was like, "No, no, no, don't tell yourself that. Don't say those words to yourself. You are perfectly capable of doing math.

You don't prefer math. You don't like math, but you're doing math all the time. You're just not categorizing it that way." And so I love that, that that is something that HR professionals say. I actually haven't heard that. And for me, you know, because I [00:12:00] came from a simulation background and I grew up in Central Florida.

When I say grew up, I mean went to college there, and that is the modeling and simulation capital of the world. The reason that the Defense Department does simulation there is because it is a combination of training and then entertainment, right? But we use simulations for things that people can't otherwise have experiences with because they're either too dangerous, they happen too infrequently, or they're too expensive for someone to fail at.

And it's those experiences that bridge the gap from a novice to an expert, and it's all of those robust things that happen between, and that's part of what people aren't getting- Mm-hmm ... in AI yet, right? We don't have the continuum, and there are some concerns that this continuum could be broken as AI starts to replace performance In, in the human way.

So is performance going to remain a signal of competency in humans if some of the performance can happen? But [00:13:00] when we take this all the way back to the, the, the thought that I might not be good at math, I might not be good at computers, I don't understand it, there's nothing that's actually standing in the way of humans except the, the need to stop and take a moment and learn some of those cornerstone things.

Okay, this is what an LLM is. This is what gen AI is. And so there's always gonna be some of that vocabulary learning, but motivation is going to trump the, the ability or not under any circumstance. We used to think a lot in terms of learning styles, and that motivation, I think, is a more important construct when we think about what it's gonna take to learn something, right?

And I think that there's something there with AI right now if we were to pull the thread on perhaps performance is no longer the indicator of competency. Perhaps now we have these zero-click responses that we didn't have. You know, Google used to be, for, for the record, Google used to put in a [00:14:00] term, and it would give you all of the related links, and you still had to click those links and decide which were important and marry those thoughts with your own.

Now we get this zero-click answer, and I think there's a lot of concern about what that's gonna do to motivation to learn in the future. Uh, motivation to learn has always been sort of a, a healthy mix of extrinsic and intrinsic, and I think that that's going to shift in a way that we're really going to need to keep an eye on it because, you know, doesn't really matter ultimately what format you give it to me in.

I'm gonna have to want it. I'm gonna have to need it. And finding the right balance between those when it comes to AI is hard to do because the future is so nebulous. We don't know what we're really going to need for the future of AI. We don't know what we need to learn at this point, and there's no great mapping of that.

Alex: You know, I think about that, and I, I always think about the questions that I get asked by HR leaders, HR professionals along the [00:15:00] way, right? And one thing that strikes me is, uh, you know, at the top of the food chain, I get asked the question all the time: What is the, the strategic pivot that I need to make so that I am showing that I am much more advanced in the world of AI?

What is also interesting is professionals, just, you know, the people who are really the, the, the nuts and bolts of this profession, they ask all the time, "How do I get more proficient? Tell me the first two or three steps that I need to engage to show that I am proficient." In a weird sort of way, it's the same question from different parts, different levels of, of the profession.

I, I wonder what you would say is how do you help somebody go from hesitant to I'm fluent enough to then I'm gonna really d- divest myself of this imposter syndrome?

Alicia Sanchez: You know, it, it's, I call it going from curious to dangerous, right? And how do you get there in 60 seconds? And I think that obviously there's always [00:16:00] been this element of tinkering that helps people really understand what's going on, and it's something that I always look for when we're hiring, right?

Is this a tinkerer? Because if they are, they're showing the passion. They're showing that they're into this. But I think that that initial step is the hardest part, that finding something that makes sense to me because we've all been through this, this failure that happens if you try to use AI to write a first draft of something.

You know you've committed twice as much time as you would've if you had just followed a process. And so I think that as we mature in our own experiences with this technology, we start to gain that insider information of, "Oh, I know it's great for this, but I know it's not great for this, and I know that this is where it should be."

But how do we signal that upward additionally? And I think that this is where organizations sometimes struggle. I think that right now there's a lot of movement towards AI, and there's a lot of curiosity ab- about AI, but there's [00:17:00] also this, this compulsion to keep up with the Joneses. Those-- That company's doing an AI, and look at what they're doing with it.

We're seeing all these people. It's in the news every day, so let's just bring in some AI, and we'll see what happens. And of course, we know that that is not typically, uh, well, that that is typically a pretty good way to fail- Yeah ... at this because, you know, first of all, y-you're not gonna get an ROI out, out of something that you just toss a tool into.

You can't manage what you can't measure, and if you can't measure it, then you're in trouble. And so one of the things that we try to do to get people to move into the right direction that helps them become much more confident in their AI skills is to ask the organization to give them goals that are mission-focused, mission-bound, outcome-driven, so that they both know that they're moving in the right direction and that they're not just sort of squirreling off into, into the abyss, which it's easy to go down the rabbit hole.

Very easy to do, yeah. We love those rabbit holes. But also so they [00:18:00] feel like they're contributing and to set up recognition for people who do take strides, but we can't just drop a tool and say, "Well, we'll see what happens." Let's give some goals, and I think that that's the way people move more quickly away from imposter syndrome into, "Hey, I'm actually making something happen," because there's so much space left for these orgs to do amazing things, to still be frontier firms.

Mm-hmm. And it sometimes feels like you're behind, and sometimes that creates a rush that doesn't need to be there.

Alex: I, I wonder where are you seeing organizations really succeed in incentivizing that but also incentivizing the notion of bringing that, the truly human traits forward in, in the work that they do with AI, in the work that employers do with AI?

Alicia Sanchez: I think that there's, there's pockets of, of greatness everywhere in this, and I think that, that one of the first things that we have to do is take a very user-centered approach to this, right? Uh, we've seen organizations where they might throw a money [00:19:00] award up once a week, and nobody actually goes for it because that's not when-- what's important to them.

They want recognition. They want certificates on their wall. They want titles. They want responsibility. And so, any organization that's hoping to create something a little more organic, a, a little more bottom-up, I think first has to do the work of understanding what really is motivating for their people and then making sure that they provide them with the tools that they do need.

And, you know, it's not just dropping the tools again, but making sure that in context they're able to do that. But when we think about what we see from the, the ability to really create these innovative companies, to create these frameworks, to break down the silos that stand between us and excellence, and playing that devil's advocate and the, the angel on the shoulder, I think that there's going to be a little bit more than what we've previously seen or what [00:20:00] we've previously expected related to the ability for those companies to understand what their place is going to be.

Where are they going? They've gotta have their strategy before their technology, and to really do the task mapping, to do the work, to understand when it's appropriate to use AI and RPA and AI-based automation, when you're going to need a human in the loop, what should be human only, how to really harness the power of those and to ensure that as you move forward from a place and time, that you're also designing systems that have resilience, that aren't going to exclude future job paths.

And I think there's a, a small story here that I always think is interesting. For me, I was recently concerned with coders, and I think that we're going to see that with vibe coding and the ability for organizations to fail fast and to... And that means win fast, right? If an [00:21:00] organization can get to the point where they can make a fail decision, that means that they've opened aperture for wins also.

But I was concerned about what comes next. If we start eliminating the need for junior employees to code, then what we're saying is the people who used to code, they're now gonna be reviewing the code. They're gonna be overseeing the code, and we'll see AI doing some of that too. But are we creating a gap in the skill if we don't have the junior coders anymore?

Can the Code reviewers now do a good enough job at cognitive discernment because they've had that experience that they wouldn't be able to do if they didn't have it. So are we creating this knowledge gap? And then someone brought up to me accounting, right? There was a time when you walked around with a ledger, and then Excel came along, and there wasn't a gap.

Like, people trusted that immediately. They understood they could go in there, they could see the algorithms, they could see what was going on. It wasn't such a dramatic gap. But it really changes the perspective I think we need to have about the [00:22:00] evolution of our work and our workplaces. And when we think about how we're currently in a race to add AI to our existing workflows, I think we're missing the boat by not exploding our workflows completely and starting over from a baseline of, this is now our minimal capability, and we can move in a whole different direction from here.

Alex: Yeah. I'm always surprised by how organizations immediately think, "I'm gonna go ahead and turn on the levers on the system that I have," and say, "Let's, you know, uh, it's a new tool, that it's an AI-based tool," and it seems like the easy approach, right? But the easy approach sometimes sets you back, right? A- and in reality, let's look at our entire, you know, business process requirements and say, "What do I need to be doing?

What is it that I need to be exploring here? How do I actually achieve things? And then where is it that I build an AI kind of mentality around that," right? Uh, so I, I, I do wanna talk to you a little bit about that because we have some data, SHRM has some data, that [00:23:00] speaks to the fact that a, a significant number of organizations do not feel like their AI integration is successful, right?

Only 17% of HR professionals say the AI integration efforts or transformation efforts within their organizations are what they would call highly successful, and they're very loose about what they mean by highly successful, right? What does that say about this notion of human confidence that comes with the use of AI integration and, and the use of AI in general?

But more importantly, what does that also tell us about just where transformations fall down?

Alicia Sanchez: I, I think that the rubber hitting the road is where we're really going to see organizations succeed or fail. And I think that what we're starting to see in these trends is that there's a lot of standard change management.

I think that AI is a, a tool, and I think that we're looking at a moment in time that is so wrought with opportunity that I, I hope another one comes along in our lifetime, [00:24:00] but I don't know if it ever will. An opportunity to really rethink the way that we do business at such a fundamental level. But I think that we, we haven't eaten our own dog food, right?

We haven't followed the processes that we ourselves have told everyone, like, "Oh, it's not working? Did you do this? Oh, did you think about..." So it's, it's like when the IT calls and they're like, "Did you restart it?" And you're like- Ugh ... "Oh." Yeah. "I'll call you back." Yeah. And you never do.

Alex: Yeah, we've all had that conversation.

Alicia Sanchez: We've all had it. And so, so I think it's very easy to get caught up, but I, I do think that organizations may not see initial value, and I think that that's very hard. And I think that, again, we're looking at a situation where they are putting technology before strategy, and they haven't done the work to really rethink what the future could or should be for them, and how that might be something that might be a massive departure.

I know it's really easy for organizations to say, "Oh, we're gonna implement [00:25:00] AI, and people are gonna have so much more free time." Mm-hmm. It's like, free time to do what? To do what exactly? What are they gonna do with that time? Yeah. And we know that historically, that people do fill that time with more meaningful things and are able to do more things.

But we see organizations not start at the foundational levels with communicating to people what their goals are, to helping them upskill in the way that makes sense given their context, right? There's no one size fits all for any of these organizations. And so really taking a, a slower approach sometimes, a longer pre-production phase, if you will, can pay off immensely.

But I don't think there's any doubt that we're gonna see this trajectory continue to rise.

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Alicia Sanchez: SHRM.

Alex: So, you know, I'm, I'm gonna round this out with a, a question, right? And it's really the, uh, the ultimate question that we get to here, which is when thinking about business leaders, right, and specifically business leaders with a concentration in HR, what do you think we need to impart upon them so that they're creating those environments that, you know, foster that psychological safety, foster the kinds of openness to experimentation, although, you know, we used to call it openness to experience, right?

[00:27:00] Uh, and learning environments. What do, what do they need to do to do that in the era of AI?

Alicia Sanchez: I think that the, one of the most important things, and this, this really rounds out the whole conversation beautifully, so thank you, Alex, again- Yeah, of course ... for asking me this question. Yeah. 'Cause it gets back to imposter syndrome as well.

I think the most important thing for leaders to espouse right now is radical transparency. To not just be the one who is explaining why this is important, but be willing to share your own failures, to be willing to share that you don't know it all. You, you'll almost never hear someone say, "Well, who could keep up with everything that changes?

It changes every day," until now, until this technology has created a level playing field that, uh, is a little bit terrifying, I have to say, for leaders as well. How do they, how do they remain the master of this domain when they are being, every day, challenged by people who have information that they don't have and have this knowledge that they [00:28:00] might not be able to get?

And it's really important that we think through how we create a gas- a glass box of our own experience, how we ensure that people can look in and that we can see out in a way that we haven't really been able to do before, and I think this is the moment where it all comes together.

Alex: Hmm. So I'm gonna throw you a curveball now.

Alicia Sanchez: Oh, yay. Okay?

Alex: Two curveballs, actually.

Alicia Sanchez: Excellent.

Alex: So, first curveball is really how do you change people's frame of mind so that they're talking and feeling about AI differently than what they do, that starting with that hesitation?

Alicia Sanchez: That's a great question. And I think the way that I've, I've done it and the way I make it successful is just by the demystification of it, right?

Like, let's not even use that word. Let's-- What would be the best possible thing that could happen? What would be the problem that you wanna solve, the solution if we had magic? Because in a lot of ways, there is a little pixie dust and a little wand [00:29:00] that we could evoke here. And how do we get from here to there without putting in this word that has evoked fear in the path?

And then peeling back the layers of, okay, well, if AI could handle this part, if AI did this part, if we could use a little machine learning here and a little LLM there, would this meet your need? And if it does, would you be willing to really engage with it? And so for me, it's all about taking the very human need-based, problem-based approach.

Alex: Mm-hmm. So here's a question for you, and I'm just gonna, uh, throw this out, and this is a total curveball, right? We obviously are both IO psychologists. We both have worked in the world of simulation and training. What is your favorite simulation you've ever worked on?

Alicia Sanchez: So there are two projects that in, stand out, stand out incredibly.

For me, my dissertation was virtual field trips, and it was so interesting because it was reading second graders in Florida, and two of the books, one of them was on the Iditarod, and one of them was on [00:30:00] camping. Yeah. And you know that we don't camp in Florida. Yeah. There's too many bugs. It's 1,000 degrees in your tent.

You're gonna... It's like having a whole, whole different kind of experience. But we also can't actually understand why dogs would pull a sled on ice, because we've never seen snow. And so for me, the ability to transport people to have experiences in places that they wouldn't otherwise get to go, uh, has always been my, my number one use of that technology.

Is it as rich of an experience? No. Do they still wanna go there? I hope so. Mm. Because I don't wanna replace those things in person either. But I think that that's the power of simulation that has always been meaningful. So the coolest thing I've ever seen, which is completely unclassified, I have to, I have to include that information, was a hyper-directed sound, uh, speaker.

So I could pump sound to you or to anyone in this room individually. If you stood next to them, couldn't hear it. [00:31:00] If you stood on the other side, stood behind them, couldn't hear it. I could literally hyper-funnel sound so that I could message someone across a crowded room at a concert, uh, uh, for intel and for war fighting purposes.

Yeah. There's a lot of different use cases- Oh, yeah ... that we could entertain, but it was so neat. So I got to run a little experiment at, at the Institute of Simulation and Training in Orlando, uh, where we would bring in people, and we would just, "Hey, can, can you hear it? Could you speak clearly to them?" And you could, and it was just the coolest thing, and I don't think they ever really monopolized on that technology.

But I sure would like one for girlfriends, right, at the bar, like, "Hey."

Alex: Oh. I

Alicia Sanchez: would love that. See this. Wouldn't it be so cool? That'd be really cool.

Alex: It's like, there's

Alicia Sanchez: a commercial application here.

Alex: Alicia, this has been such a joy. I've enjoyed spending this time with you, and I've enjoyed listening to you talk about all the cool things that you've been working on.

Thank you so much for being here with us.

Alicia Sanchez: The pleasure's been mine, uh, all mine, Alex.

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Alicia Sanchez: SHRM