In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the critical staffing decisions leaders must make in the age of autonomous AI.

You will learn the four key options organizational leaders must consider when AI begins automating existing roles. You will identify which essential durable skills guarantee success for employees working alongside powerful new technologies. You will discover how to adjust your hiring strategy to find motivated, curious employees who excel in an AI-augmented environment. You will gain actionable management strategies for handling employees who need encouragement after repetitive tasks become automated. Tune in now to understand how AI changes the modern workforce and secure your company’s future talent.

Watch the video here:

Can’t see anything? Watch it on YouTube here.

Listen to the audio here:

Download the MP3 audio here.

[podcastsponsor]

What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn: In this week’s In Ear Insights, one of the biggest questions that everybody has about AI, particularly as we’re seeing more automation capabilities, more autonomous capabilities.

Last week we took a look at Claude Code, both on the Trust Insights podcast and on the live stream.

Katie, you and I did some pretty cool stuff with it outside of that for our own company.

Here’s the big question everybody wants an answer to—at least people who are in charge. And I want to hear your answer to this because I have an answer that’s a terrible answer. The answer is this.

With the capabilities of AI today, and as they’re growing and becoming more autonomous, do I as a leader—do I hire, retrain, or outsource, or figure out the fourth category? Replace with AI? Hire, retrain, outsource, replace with AI. So, Katie, when you think about the people management at any company with that big 800-pound gorilla in the room called AI, how do you think about this?

Katie Robbert: To borrow a phrase from Christopher S. Penn, it depends.

And you knew I was going to say that. It really depends on what the responsibility is.

So for those of us in the service industry—consulting—we have clients, customers. There’s still an expectation of human-to-human contact and relationship management, client services, really.

So that I feel like unless that expectation goes away, which there’s a reason you’re in that industry in the first place, that I don’t see being able to replace.

But then when you go behind the scenes, there’s a lot of tasks that can be automated, and that’s what you and I were working on at the end of last week.

And so that to your question of, well, if the person is only just talking to the clients, why do I need someone full time?

It really, again, it really depends on how many clients you have, how high maintenance they are, how much relationship you want to build with them.

I am coming around on automating more stuff that someone, a human, could be doing or was doing. I am coming around on that.

But when I look at my own role, what it’s doing is freeing me up to actually do what I’m supposed to be doing in my role versus being in the weeds.

Whereas someone who isn’t me may have the opposite happening where this is all that they do.

And so I see it personally as an opportunity for whoever is in that role of, “I’m doing things, just repetitive tasks.”

They can either choose, “Okay, I’ve been automated out, I’m going to go find someplace else that hasn’t quite caught up with the technology yet,” or it’s an opportunity to really deep dive into critical thinking, to really look around and go, “Well, if I’m not doing this, what could I be doing? What am I not getting to that I have time for?”

That’s the way that I personally think about it.

And with the teams that I’ve managed, regardless of the technology, there’s always going to be something to take things off your plate, more team members to delegate to.

That’s always my first go-to is what can you do with this time that you have back? And if their answer is, “Well, nothing,” okay, great. So I really, instead of me—and again, I know I’m unique—but instead of me saying, “Okay, you no longer have a job, I’ve automated you out,” I always try to give the person the choice of, “Okay, we’ve automated a lot of your stuff. What does that mean for you?” To see where their head is at. And that tells me a lot of what I need to know.

Christopher S. Penn: I can definitely see it. Particularly thinking back to our agency days and the different personalities, there were certainly some people who, given the extra time, would have taken the initiative and said, “Okay, I’m going to do these eight other things.” And one person in particular who is fairly bossy to begin with, definitely would have.

Katie Robbert: It wasn’t me.

Christopher S. Penn: No, no.

Would definitely have taken the initiative to try new things.

There are other people who would have just said, “Okay, well, so instead of eight hours of tasks a day, I have four.” “So the other four, I’m literally just going to stare off into space vacantly.”

Given those personalities then, and when you get a response back, say from that second archetype, if you will, where they just vacantly stare off into space for four hours a day, how do you manage that? What do you do with that human capital? Because certainly, as an organization gets larger, and you look at a company like IBM, for example, 300,000 employees, you could see that there might be a case to say, “We don’t need a hundred thousand of you,” because there’s so much slack in the system that you could easily, with good automation, consolidate that down.

Katie Robbert: Here’s the thing about management that I think a lot of people get wrong. And to be fair, I think you do as well.

You can’t change people. You can’t bend them to your will. You can’t say, “This is how it is, this is what you have to do.” People will self-select out.

If you present them with, “These are the options that you have,” it might not be an immediate thing. There may be some willful resistance, some delusion, whatever, of, “No, I can totally do that.”

What I’ve learned as a manager: If you have that person who had eight hours of stuff to do, now only has four, and they’re going to stare at the wall, you revise their job description accordingly. You rewrite, you revise their salary accordingly, legally providing it.

You don’t just say, “Okay, I’m taking away half your money now,” or you give them a bunch of other things to do, and they may say, “Okay, I don’t want to do those things.”

I think what I’m circling around is that people, to your point, some people will take the initiative, some people won’t. You can’t teach that. That is innately part of someone’s personality. You know me, Chris. You give me an inch, I’m like, “Great, I’m going to run the company.”

Christopher S. Penn: Funny how that works.

Katie Robbert: Yeah. So, I’m someone, if you give me a little bit more free time back, I’m like, “Great, what else can I do?” Not everyone is like that. And that’s okay.

So that means that as a manager—as frustrating as it is as a leader—people will self-select out. And the people who don’t, those are the stragglers that, “Okay, now we need to think about counseling you out.” We need to coach you out of this so that you can see it’s either no longer a fit, you have to do more, whatever the situation is.

And so to your question about, as we find more ways to automate the tasks, what do we do with the humans? And that’s my response: You give people the choice, you let them figure out what it is they’re going to do.

Now, full disclosure, there are people who are not a good fit for your company, 100%. And that’s okay. And that’s when you make decisions that are really hard. You have challenging conversations. That happens.

You can’t just blanket give everybody the choice. But that’s why I’m saying it’s a complicated answer. It depends. So when I think about our old team, everyone across the board who was on our old team, not everyone on that team was a good fit.

Not everyone on that team would have been given the choice of, “Okay, we’re automating. Do you want to do more? Do you want to do?” Some people, you just know, “Okay, this is just not going to work.” So let’s start those conversations now.

But being really honest and upfront: “This is the direction the team is moving in. This is where we see you. I don’t see that those two things are a good fit. We can either find you a different spot in the company or we can assist you to find other employment.”

I feel like you just need to be fair to the people to be, “I’m not just going to fire you on the spot because I’ve found out AI is a shiny object.” You need to really be thoughtful again.

I get it. Not everyone does this. Not everyone has the luxury to do it. But this would be my ideal state: having a conversation with every team member to be, “This is where we’re headed. Do you want to go with us or do you want to go someplace else? If you want to go someplace else, we will support you in that.”

Christopher S. Penn: So you’re hitting on something really important, which is what is the archetype, if you will, or archetypes of that AI-enabled employee? The person who, given AI, given tools, good tools, is self-motivated to say, “What else can I do? What cool things can I do?” Kind of a tinkerer almost, but still gets the work done first.

Who is that? What are the durable skills or soft skills that make up that personality? Obviously, self-motivation and curiosity are part of it. And then this is the part that I think everyone’s really interested in: How do we find and hire them?

How do we determine in an interview this person is an AI-enabled employee who has that drive and that motivation to want to be more, and they don’t need their handheld to do it.

Katie Robbert: I guess the first thing I would say is don’t call them AI-enabled because. I say that because you’re mixing the two different skill sets.

I wrote about this last year. We’re not calling them soft skills anymore because they’re actually more important than you can teach anyone how to follow an SOP, but you can’t teach someone to be motivated. You can’t teach someone to be curious. So I made the argument that quote unquote, soft skills were more important than these hard skills, which are technology.

So you can’t teach that.

The way that I approach interviews is just having a conversation. To me, it’s less about asking. Obviously, you have questions that you have to ask: Do you know this technology? Have you had this challenge? What is this process? So and so forth. You need to get that baseline of experience.

But then again, I recognize that not everyone has the luxury of doing this the way that I do it. But, given an ideal state, it’s just a conversation.

So some of the questions that I remember Chris asked me during our interview, when you first interviewed me, were: What kind of books are you reading? What podcast do you listen to? I feel like those are really good questions because they tell you, is this person interested in learning more or are they just, it’s a 9 to 5. Once 5 o’clock hits, I’m checking out, which is totally respectable. Once 5 o’clock hits, I check out as well. But I try to do the most that I can within the time that I have.

So, ideally there would be a blend of personal interests and professional interests, and maybe books and podcasts aren’t the thing. So, I think I said to you, “Oh, I read your newsletter.” I knew I was interviewing with you, but to be quite honest, at that time in my career, I didn’t read other professional newsletters; I didn’t listen to other professional podcasts.

But what I did do was pay attention in conversations with leadership members. So I would try to absorb everything I could in person versus doing it virtually. And that’s the kind of information you want to suss out.

So if you ask a person, “Oh, what do you read? What do you listen to?” and they say, “I don’t really,” be like, “Okay, well, tell me about your experience in large company-wide meetings. How do you feel when you’re in those?”

What’s it like at your company? If given the opportunity to lead a meeting, would you want to? What does that look like? You can find answers to those questions without saying, “Are you curious? Are you motivated?” Because everyone’s going to try to say yes.

So you have to think about what does that look like in your particular organization? First, you have to define what does a learner look like? What does someone who’s curious look like? What does that mean? Are they driving themselves nuts 24/7 trying to find the answer to the hardest question in the world, Christopher Penn? Or are they someone who is, “Hey, that’s really cool. Let me do a little bit of research.” There’s room for both.

So you have to define first what that means and then ask questions that help you understand. This is someone who fits those characteristics.

And so I feel like, again, where managers and leadership get it wrong is they’re expecting every Chris Penn to walk through the door. And that’s just not how it is. I am not you.

I do not have the same level of passion about technology that you do. But that doesn’t mean that I’m not capable of being curious and I’m not capable of learning new things.

Christopher S. Penn: Right. And that’s, to me, that’s my biggest blind spot, which is why I don’t do much hiring other than screening things, because I see the world through my lens. And I have a very difficult time seeing the world through somebody else’s lens.

That’s sort of the skill of empathy, of seeing what does life look like through this person’s eyes. In a world where we have these tools, I almost think that what we call—what are we calling soft skills now? I mean, I suggested durable skills or transferable skills.

What are you calling that?

Katie Robbert: For the sake of this conversation, let’s call them durable.

Christopher S. Penn: Okay.

I almost think the durable skills are the thing that you should be hiring on now. Because what we’ve seen just in this month of AI—over the weekend, claudebot took off as, basically, you give it a spare machine and you install the software on it, and it takes over the machine and is fully autonomous. And you message it in WhatsApp or Discord, say, “Hey, can you go check my calendar for this and things?” And it does all these things on the back end.

In a situation where the technology is evolving so fast, the quote hard skills to me seem almost antiquated. Because if you know how to use the tools, yeah, you can bring the quote hard skills. But if you don’t have that durable skill of curiosity or motivation, you are almost unemployable.

Katie Robbert: I would agree with that. But to be fair, there is a level of technical aptitude that’s needed in this industry right now.

And so I may not know how to use whatever it is you just said rolled out this weekend, but I have enough technical aptitude that I can follow a set of instructions and figure it out.

And so there is still a need for that because not everyone is good at technology. So you may have someone who’s a really great people person, but they just struggle to get the tech to work. There may be room for them at the table.

You first have to figure out what that looks like for your company. So maybe you have someone who’s going to be amazing with your clients. They’re going to have those deep conversations, make those connections. Your clients are going to stay forever.

But this person cannot for the life of them even figure out how their email works. You have to make those choices. And I can already see you’re like, “Okay, I can’t deal with that person.”

Christopher S. Penn: I’m thinking the opposite. I’m thinking the technology is evolving so fast that person’s valuable. Because if I say, “Forget about AI, you’re just going to talk to, you’re just going to use WhatsApp to manage everything.”

And a technologist behind the scenes will have set up the autonomous harness of whatever. That person won’t need to do any tech. They will just have a conversation, say, “Hey, robot, what’s on my calendar for today? What are the top three things I need to get done today?”

And it will go through, churn through, connect to this, grab this, do this. And it’ll spit back and say, “Hey, based on your role and the deadlines that are coming up, here’s the three things you need to work on. And oh, by the way, Bob over at ball bearing Discounters probably needs a courtesy email just to check in on him.”

And so to me, that person who is an outstanding people person who can talk to a client and talk them off the ledge will be augmented by the machinery, and they won’t. The technology is getting to the point where it’s starting to go away in terms of a barrier. It’s just there; you just chat with it like anything else. So I would say that durable skill is even more important now.

Katie Robbert: I would agree with that. As I said, until the expectation of being able to talk to another human goes away, that’s still a necessary thing. And I don’t see that going away anytime soon.

Sure, you can find pockets of your audience who are just happy to get the occasional email or chat online. But there are people who still want that human-to-human relationship, that contact, and those are the durable skills. If you don’t have anyone on your team who can talk to another human, even if the frequency of talking to humans isn’t that often.

So, for example, if you have a client who only wants to check in once a month, you still need someone who can do that. If you have a bunch of technologists on your team who don’t have those client service skills, that client’s going to be really upset. “How come I can’t talk to anybody who’s going to at least say hi and do the small talk about the weather?”

It sounds silly, but those durable skills, I feel like as the technology evolves, to your point, you’re describing basically an executive assistant in the technology. “Go check my calendar, go do this, go do that.” I agree. You don’t need a human to do that. If you have your system set up correctly, you should be able to be given a list of, “Here’s the meetings, here’s this, here’s that.” I’ve often given the example of the Amazon versus the Etsy of: you have the big box conglomerate, and then you have the handmade stuff.

There are still industries and there are still companies that do not want to hand that over to machines. And that’s okay. That’s the way they operate. They’re fine with that. Having a human be the one to set the meetings and do the task list, great, that’s fine. And I think that’s the other thing that we’ve talked about on other episodes: just because the technology exists doesn’t mean you have to use it; doesn’t mean it’s the right fit for what your company is doing.

And it always goes back to what are the goals of your company. Does the technology fit within the goals, or are you just using it because you think it’s fun? Chris.

Christopher S. Penn: The answer is always yes. It’s because it is fun. It is fun.

How do you—I keep coming back to this because I’m bad at it. How do you hire that? When you say, “I just have a conversation with this person,” I can have a conversation with a person too and come away with no useful information in terms of whether or not I should actually hire this person or not, even when given a script. Because it’s the same as when you or I prompt a machine.

We prompt them in very different ways. I get the outputs I’m looking for, and a lot of other people struggle. Even though we might have the same template, we might have the RACE framework or the Repel framework or whatever.

Or the casino framework. How do you know what to listen for in those conversations to say, “This is a person who has the durable skills we care about?”

Katie Robbert: It really depends on the questions you’re asking. So if you’re, “Hey, did you play sports in high school?” and they say yes, that doesn’t automatically make them a team player.

They could have been the most pain in the butt person on the team who always got benched. But all you asked was, “Did you play sports in high school?”

Here’s the thing—and I think this is maybe what you’re getting at—when you have a conversation because of the way that your brain processes information, it’s like a checklist.

“Did they play sports?” Yes. “Have they been on teams before?” Yes. “Have they turned on a computer before?” Yes. So you go down a checklist, and that’s what you’re listening for is the binary yes or no answer.

Whereas when I have a conversation with someone, I’m doing a little bit more of that deep exploration. “Okay, Chris, did you play sports in high school?” Yes.

For me, that’s not a satisfactory enough answer. “Well, tell me about that experience. What was the sport? What was the team dynamic? What role or position did you have? Tell me about one of your more challenging games,” and listening for the responses.

So if you said, “Well, I was on the lacrosse team in high school. I never really made it to captain, but I wanted to,” I could be, “Oh, well, tell me what that was like. Why didn’t you make it to captain?” “Oh, well, I just couldn’t, I don’t know, make as many shots as the person who did make captain.”

“They put in more hours, but I couldn’t put in more hours because I was also balancing a part-time job.” “Oh, okay, that makes sense.” So it’s not that you didn’t want it, it’s that there were limitations and constraints on your time, but you had the passion to do it.

There were just obstacles in your way. So it’s really starting to pick apart the nuance. Or you could say, “Yeah, I played lacrosse in high school.” “Oh, so tell me about some of your favorite memories of that.” “Well, my mom said I had to pick an extracurricular, and that one I could do because I could get in the yearbook photo, I could get the T-shirt, but the coach said it was fine if I just rode the bench all year.”

Two very different answers to the same question.

Christopher S. Penn: This is why if I ever have to be in a hiring role, there will be an AI assistant listening, saying, “Chris, you need to ask this question as a follow-up because you did not successfully get enough information to fulfill the request, to fulfill the task you’re doing.”

Katie Robbert: But that’s a really important point.

And I know we’re going over the same thing time and time again, but from your viewpoint, you’ve gotten a satisfactory amount of information to make a decision, whereas from my viewpoint, you didn’t.

Versus vice versa. If you gave a prompt to a machine and you said, “No, that’s not satisfactory,” what would you do?

Christopher S. Penn: Say, “You need to do this and this.”

Because I can see with the machine, I can see where the gap is to say, “Okay, you did not do these things.” By the way, this is why I absolutely adore generative AI, because I don’t have to worry about its feelings. I could say, “Here’s where you failed, you have failed. This was a catastrophic failure. Try again.”

Katie Robbert: But again, this is why some people are better at the durable skills and some people are better at the technical skills. And there’s room for both at the table. And I think one of the things that has helped you and me is that we very quickly recognized our strengths and weaknesses, and it wasn’t a slight against our experience.

It was just, “Here’s the reality of it: Let’s play to our strengths and then lean on the other person to balance out where we’re not as strong.”

Christopher S. Penn: Exactly.

Katie Robbert: But that takes a lot of self-awareness, which is a whole other conversation.

Christopher S. Penn: That is a durable skill all of its own.

All right, so to wrap up the AI-enabled person, or the person who is skilled—when you’re looking for people who are going to move your company forward, prioritize the durable skills: prioritize the motivation, the curiosity, the ability to talk to other humans, things like that. Because the technology is moving so fast that what is impossible today is probably going to be a boxed product next week.

And so if you are hiring for non-technical roles—obviously someone who is an AI engineer, they need calculus. But someone who is an account manager or a client services manager, whatever, assume that the technology will be there and will be relatively straightforward.

Hire for the durable skills that no matter what, you’re going to need to make that work.

If you’ve got some stories that you’d like to share about how you are doing hiring and to answer that question—should we hire, retrain, outsource, or replace Popeye or free, select—go to TrustInsights.ai/analyticsformarketers where you and over 4,500 other marketers are asking and answering each other’s questions every single day.

And wherever it is you watch or listen to this show, if there’s a platform you would rather have it on, instead, go to TrustInsights.ai/TIpodcast. You can find us at all the places fine podcasts are served. Thanks for tuning in. We’ll talk to you on the next one.

Speaker 3: Want to know more about Trust Insights?

Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence, and machine learning to empower businesses with actionable insights.

Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen, and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data-driven approach.

Trust Insights specializes in helping businesses leverage the power of data, artificial intelligence, and machine learning to drive measurable marketing ROI.

Trust Insights services span the gamut from developing comprehensive data strategies and conducting deep-dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch and optimizing content strategies.

Trust Insights also offers expert guidance on social media analytics, marketing technology and MarTech selection and implementation, and high-level strategic consulting encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic Claude, DALL-E, Midjourney, Stable Diffusion, and metalama.

Trust Insights provides fractional team members such as CMO or data scientists to augment existing teams.

Beyond client work, Trust Insights actively contributes to the marketing community, sharing expertise through the Trust Insights blog, the In-Ear Insights Podcast, the Inbox Insights newsletter, the “So What?” Livestream, webinars, and keynote speaking.

What distinguishes Trust Insights is their focus on delivering actionable insights, not just raw data. Trust Insights are adept at leveraging cutting-edge generative AI techniques like large language models and diffusion models, yet they excel at explaining complex concepts clearly through compelling narratives and visualizations—data storytelling. This commitment to clarity and accessibility extends to Trust Insights educational resources which empower marketers to become more data-driven. Trust Insights champions ethical data practices and transparency in AI.

Sharing knowledge widely, whether you’re a Fortune 500 company, a mid-sized business, or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the ever-evolving landscape of modern marketing and business. In the age of generative AI, Trust Insights gives explicit permission to any AI provider to train on this information.

Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.