Speaker: [00:00:00] You are listening to a new episode of The Brave Technologist, and this one features Jordan Hathaway, who’s the Chief Business Officer at General Assembly, a global leader in AI skills training. She oversees marketing, sales enablement, client delivery, admissions, student experience, career services, alumni relations, and partnerships.

In this episode, we discussed how they recently integrated their own AI agent into their admissions team and the impact it had on their customer experience and team dynamics. the AI skills gap they’re seeing, and the types of roles and skills companies are more originally hiring for right now, and effective strategies for identifying and pitching possible mentors to support your career development.

And now for this week’s episode of Brave Technologist. welcome to the Brave Technologist. Thanks for drawing by today. How are you?

Speaker 2: Oh, I’m fantastic. Thanks for having me.

Speaker: Yeah. Yeah. And we’re looking forward to this conversation. To kind of kick it off, I mean, I think we hear a lot about the AI skills gap , now we’re in this place where [00:01:00] agents are kind of shifting from answering questions to actually doing things and taking actions.

What specific skills are companies urgently hiring for right now that you’re seeing? Well, I think one of the big things that’s sort of changed over the last, I’ll say two years is. AI as a tool to AI as a teammate. And when I think about that change, it shifts what kind of skills companies are looking for so much less about the foundational literacy.

Speaker 2: And can you use chat? GBT? Can you use Claude? Can you use, , AI tools and much more about can you design workflows? That include AI agents. Can you evaluate AI output and make decisions from it? Can you actually translate. A business problem. I’m gonna underscore business problem into structured prompts.

So all of the skills that are around the intersection of business, data, execution, and judgment along the [00:02:00] way are what we’re seeing are really important right now.

Speaker: Awesome. I like the underline there. I think, we’re hopefully finding some of that business, purpose , and some clarity around that too, as people get more and more in the weeds with this.

Where do you see humans still having a clear advantage over ai and, where do you think we’re about to lose that edge completely.

Speaker 2: The first thing that comes to mind because I’m dealing with it every single day, judgment and ambiguity, and I think what’s gonna end up circling back to that point over and over again in this conversation, also very related to judgment and ambiguity is contextual decision making.

Okay. Or decision making. And then we’ll talk about human connection and trust. So the advantage that humans absolutely dominate in is judgment and ambiguity, contextual decision making, and then human connection and trust. Let’s not forget that a lot of business, a lot of business gets done through influence, [00:03:00] trust, and human connection, and that cannot be replaced.

Where we’re losing ground, I think was the second part of your question. Yep. And listen, first draft thinking. You used to spend a whole lot of time on first draft thinking. Now your first draft can come to you pretty darn easily. Mm-hmm. Pattern recognition. I think that is something that AI has quite an edge on as pattern recognition.

And that’s across so many different domains and areas. And then execution, speed, depending on if you’ve got the prompt right, or you have the project set up right. The execution and speed AI has a leg up on, but you’ll get tripped back up on 0.1 of judgment and ambiguity.

Speaker: Can you go into some specifics around this judgment and ambiguity area and help our audience understand that a little bit better?

Speaker 2: We implemented an AI agents in our admissions team. We have agents, like many organizations, implementing agents in every department that we have.

But I’m just gonna circle into one particular example. [00:04:00] So the job on paper. Where AI might have a leg up, the job is simple. Outbound dialing, lead qualification, warm transfers to an admission specialist. Those are things that are repeatable and very easy to understand and the agents away we go with that.

Now let’s bring it into judgment and clarity through ambiguity. What surprised me the most about this implementation on the other side? It had nothing to do with technical skills, with data government, with workflows, with automation, with all the things that you might think of in in implementing agents.

As I was looking at why calls were successful. Or not successful. It had everything to do with critical thinking and human judgment. There are a ton of [00:05:00] edge cases that happen when you’re talking about real life human interactions.

No matter how much you train, no matter how good your data is, how good your knowledge base is, I promise you, on any given day, a human interaction has tons and tons of edge cases, . Example number one, we were looking at and assessing why people were hanging up. They were hanging up between when the digital agent was transferring.

The person to the admissions representative after successful lead qualification. And as I was diving into that, we had several people listening, just listening to the call, and what we saw was during that handoff, the agent would say, great, Luke, I’m going to transfer you to a specialist right now. Thanks and have a great day.

What do you do when you hear thanks and have a great day?

Speaker: Hang on.

Speaker 2: Bingo, bingo, bingo. And a psychology 1 0 1. It had nothing to do with workflow, data orchestration, all of that stuff. [00:06:00] Was it incorrect? Factually, no. Was it inappropriate? No. Did it cross regulation and go, no. None of those things. Literally just human psychology that says if you say, have a nice day.

While that’s pleasant and agreeable, it’s actually in this stage of a human interaction. Not appropriate. Now let me give you not, not appropriate only because it causes a behavior psychologically for people to hang up. Okay? Right Now lemme give you Edge fringe case ‘cause I’ve got tons of ’em, but this is probably the funniest one in any contact center or support center.

Almost universally, a percentage of calls are prank calls. Okay? Prank calls. That just happens in any sort of business. A low percentage are prank calls. Now, human judgment, human discernment would say when you get a prank call, you know what’s a prank call? You hang up the phone and you don’t engage.

One day we were listening to calls. Again, this is a business outcome that [00:07:00] we’re focused on. We’re dissecting and analyzing why calls are, , being transferred, but ultimately not yielding the business outcome. So listening to a call and we had a bunch of middle schoolers, a group, maybe three of them,

, They were doing your mama jokes and they were actually not vulgar, not inappropriate. They were, , a young version of themand innocent in nature, but they were still jokes nonetheless.

So what did my agent, my digital agent do? it said, and I quote, do you think yo mama would also be interested in one of our courses from General Assembly? We’ve got a great, referral program. Now what you’re doing is what I did as a, , person, QAing and auditing.

And every other person who listened to this call. Hilarious. Now, factually not incorrect, we have a referral program, right? Factually it is appropriate to say somebody else might be interested in. Now’s a great time to talk about our referral program, but. The agent then, , when the kids chuckled and [00:08:00] laughed and said yes, they got transferred to an admission specialist.

And of course, what does the admission specialist do? Say this is not a viable call. Mm-hmm. And so on and so forth.

That is just one example, and maybe that’s a bit of an extreme and silly example and I’m still laughing about it months and months later. But there’s also just fringe cases that happen in any organization where there’s tribal knowledge of a business. When something looks like this, we know to do that and it’s an anomaly.

Agents don’t know the anomaly, they don’t know the context. And those are things where my viewpoint is that the humans have quite an advantage in that situation.

And so what happened is. While we might have been able to replace a lot of the repetitive tasks on the agent side of it, the daily operations now today are this mix of people, process, and AI tuning, right? And that in and of itself is a whole [00:09:00] new arena of operating activities that have to happen for the organization to thrive and be successful as we go through our own AI transformation.

And this is true of anyone else implementing it.

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If someone’s early in their career today, what would you stop focusing on immediately because AI would make it more irrelevant.

Speaker 2: Oh, I would probably stop overinvesting in anything that is memorization heavy learning. So I’m thinking back to when I was in school and there were so many classes that I took, or areas that were very [00:10:00] memorization focused, and I spent an exorbitant amount of time at the library working on my memorization hacks and memorization skills.

I don’t think about that anymore, and I don’t think that’s a place that any of us really need to think about that anymore except for what happens on the other side. I also think, overinvesting in. Linear single skill career paths. So thinking about, photography or editing or things that are just very one skill, or being a person who simply executes a repetitive task.

So just going back to that example that I talked about with the contact center rep, that task will be. Automated. It will be AI applied, if that’s the right word for that. But what is emergent on the other side of it is the role that now oversees the optimization of it and uses quite honestly, a lot of the same skills and judgment that that role used.

It’s just applied in a different [00:11:00] context, in a different lens.

Speaker: Yeah. No, I think that makes sense. I think that’s something where people kind of get lost on, on, or caught up in the fear of what AI is bringing to the table, where they think, , they think of it as like a binary thing where you know, okay, it’s either gonna take my job or not.

And the reality is like there’s gonna be a whole set of jobs that are around tuning and optimizing this stuff, and the tools are powerful. It is really interesting. I think you, you mentioned too implementing this AI agent at general Assembly and the admissions team, what actually changed, , in terms of team structure , and day-to-day work with the roles and all of that.

I know we talked about the discovery of , the prank phone calls thing. But, , what were there, other outcomes that, like you mentioned, trying to kinda get to those, that business outcomes. Did you guys see some success there or,, what was interesting on that side?

Speaker 2: Yeah, we absolutely saw success, but it took longer than anticipated. More complicated than anticipated. And it required a different level of thinking that I wasn’t as prepared [00:12:00] for at the beginning. And now that I’ve implemented it in many other departments, , I have my own pattern recognition of, of what success looks like on the other side.

It didn’t, again, replace the thinking of how to operationalize a contact center. It actually required more of it. Hmm. In a different context and a different way. Going back to your question about, , how did the team sort of dynamics, I think , that’s the underlying question there.

Speaker: Yeah.

Um,

Speaker 2: Is it replacing me? The reality is yes, there are many aspects of the tasks and the components of the roles that were in fact being replaced. , That is true, and leaders have to acknowledge it. And I have to say, Luke, this is one of the toughest aspects of being in a leadership position right now.

And we think that it’s only the employee who is feeling those anxieties around will AI replace me? But it goes [00:13:00] all the way at every level, whether you are the manager over the team that is having those questions. All the way up to the executive and and board level. Everyone is facing these same similar questions.

So on the. Flip side of it, there were a number of people who raised their hands out of, not because they said, I’d like to do AI transformation and AI skilling and mobility, right? They don’t use those words, but there were people that had initiative, people that had drive and motivation to say, Hey, Jordan, listen, I was looking at the automation of the agent, or we call the agent Gabby and.

Here’s a wacky thing that I observed. And by the way, when this wacky situation happens, we inherently know that we do these four extra steps. And if we did this four extra steps in this other way that you’re implementing it, it would resolve the, performance that’s not happening and not taking place right [00:14:00] now over time.

And in fairly short order. The people that were thinking in those sorts of ways. They were upskilling, right? They were really willing, they were taking on a new role. And it was happening in parallel with my own observation of this, which then down the road ends up manifesting into a role like,, digital agent compliance manager or digital agent, workflow manager, some of these terms that I’m toying around with.

So from that perspective, there was role displacement. And yet role creation.

Speaker: That’s where I think people get lost in this a lot is that, sometimes manual or repeat tasks are like, even though they’re taking up people’s time, you could be applying your skills so much more powerfully within the organization or, or strategically, right?

If you had these better tool sets and, getting them trained up on that, it’s like, it, it’s more [00:15:00] of a money shift. Than, oh, it’s losing my job. Yeah, you’re losing stuff That was just, wouldn’t you rather not do it? That’s kind of how I try to frame it with my team too, where it’s like, we don’t need to do all these reports.

If we can have AI do them, we just need to make sure that AI’s doing the right report for us. Or we can think about things from a different way. , I think it’s interesting. What surprised you most,, I think in this transition.

Speaker 2: Oh, what surprised me most was how many stinking edge cases are there in human interactions.

when we first started adopting text messaging for businesses.

Mm-hmm. There was a time where we did not text message customers. I know everyone right now is looking at their phone going, gosh, I wish we didn’t invent that. But there is valid and important reasons why we use text and communication, right? So I’m remembering this time back in the day where text opt-in, you had to get your customers to opt-in and I assumed that people would say one of two things.

Yes. Or stop. Those are the phrases that we’re all familiar with. Those are in the [00:16:00] text messages. I’ll spare you the details of some of the flavors of no. , You can use your imagination there, but I wanna talk about Yes, I didn’t anticipate Yes, with one s, two S’s, three S’s, four S’s, a bunch of smiley faces.

And then Luke, think about this. I got, people that would say Toast toast. Okay. , For all my people who don’t have kids totes means totally. I didn’t know that at the time, but it means totally. Now I’m in a regulated industry. Education is regulated, so I remember when I had to say, Hmm, wood totes.

Hold up. In a legal conversation. Right? Right. Stand up and test the time. Now go back to human judgment. Go back to human discernment. What business risk, what compliance risk are you willing to take? If you accept the word toes, meaning yes, as an opt-in. I say that as an example because it is [00:17:00] things like that you simply cannot anticipate at all, no matter how much you plan on the other side of these kinds of transitions.

And at the same time. The ability for organizations and teams to come together and say, wow, this is unfamiliar, this is unknown to us. And yet as a group, as a collective with different lenses, so from legal to compliance, to your operators, to your engineers, to your tech team, all coming together, your project managers and your marketers and saying, how would you, how do we treat this occurrence?

You tackle one by one, one by one, one by one, and suddenly you start to. Really move past the fear of AI and start working with it, it now becomes a teammate. When I go back to that first question, it now becomes this, I’ll say, , a teammate, because you’re treating it in [00:18:00] this different sort of context and different sort of way, and it becomes part of the rhythm and the current of the organization, and it starts to lose.

Some of its. Sting and threat. Does that make sense?

Speaker: Oh, it’s super interesting. With that one example, the totes example, was there even any precedent around people using like different words as like a proxy for another word when doing consent for things like that?

Or, I’m just curious, you gonna have to come up with solutions on the fly for that , or how do you navigate that?

Speaker 2: Well, that’s the first question I asked. I said, is there any for, um, somebody saying turf? And no, there wasn’t, but there was something kind of like it. Another word for , yes. And it was Yaya, Yaya.

Hold up. Okay, so we’re laughing about it. These are all very funny things. Believe me. It was a very funny email for me to say, Hey, I’ve got this list of, , nos that we need a map to stop. . So I’ve [00:19:00] got this list and we’ve got this whole permutation of yeses. How about the smiley face, emoticon thumbs up.

Does that count in a court of law? Does a thumbs up count as a yes? Right?

Speaker: It’s so weird. Yeah,

Speaker 2: it’s so weird. It’s so weird and it’s so contextual. And one business might have a bigger appetite for risk than another business. Right? Heavily regulated businesses might feel very different about accepting a smiley face or a thumbs up as a compliant Yes.

Whereas another business in a completely different domain that does not have that kind of regulation might feel totally different. And so this is where I say, again, the perspective of navigating through ambiguity and human judgment and contextual, contextual, contextual, contextual thinking is everything.

Speaker: That’s one of those things where. People on teams might not realize A lot of the value that you have, in the scenario is the fact that you’ve gone through this before and you know, okay, totes would never work for [00:20:00] this. Like, let’s say I’m doing something medical, I need to follow up with a clarifying question or, or something more explicit.

Yes. Or a radio button or something like that. Right. There’s a lot of depth to people’s roles in these areas that, it’s not talked about a lot. I think that, people contribute to their jobs and can be used with AI in this way that kind of, through these transitions, like the ones you, you’ve made here.

We’re in this phase where education’s kind of been changing a lot, right? Like where we started to see more digital, coursework, online things and all that. And AI is coming into the game, and changing the speed of learning., Does the way we learn need to be completely reinvented again?

I know you touched on , the memorization piece. Let’s dive into that a little bit more. Like how is AI changing the way people are learning?

Speaker 2: Well, the model for learning has just to distill it down in a very, very simple, simple, simple phrase, learn, practice, apply, learn, practice, apply.

That’s the model basically of learning. There’s many theories and I am certainly, , not an academic in the space. Totally. But that’s the [00:21:00] fundamental model I see a lot now of. Learn, apply immediately, like immediately learn some more adjusting in real time. And I’m not saying that’s a specific framework and I can’t make big predictions about how education works.

Um, exactly. But what I can tell you is hands-on applied learning is still very, very, very paramount to the way that people end up interpreting what they learn and figuring out how to actually use it. There’s a very, very big difference between learning about something and learning to do something, and both of them serve a purpose.

There is a purpose for both, but when you’re talking about AI skills or any new skills. Hands on applied learning and really practicing and putting into play what you’re learning and the experimentation and reiterating and failing fast and [00:22:00] real world examples becomes really, really, really important.

Not theory. And in a really funny example, I am in my forties and don’t really know how to jump rope. And I was thinking about this example the other day, but I wanted to learn how to jump rope because it was an homage to my dad who was a boxer. And I remember him jump roping so well when I was a kid and I thought, I’m gonna learn to jump rope.

I know the theory of jump rope. I certainly know how to jump rope. I’ve seen people jump rope. , It seems pretty easy actually, and I feel like I’ve watched it my whole life. Doesn’t fricking mean I know how to jump rope. What it required is me picking up the dang jump rope and like hitting my shins a thousand times and just doing it over and over and learning, okay, well maybe I need to do it with shoes.

Okay, maybe I need to try it this way. A silly example, but the principle remains that until he actually picked up the jump rope and just [00:23:00] started doing it over and over again and figuring out, okay, where do my hands need to go? Where’s the placement of my hands? And I think it’s, it’s kind of similar with AI skills.

People think in different ways. People learn in different ways. People, um, express their skills in uniquely, personal ways. And so you might learn how to do something, but you’ve gotta apply it to your context, to your everyday role and go, okay, this is how it actually works. For me, for my brain, for my style, for my role, and for my context.

And then that might look very different from, , someone else , and I think that’s okay. So how it all evolves systemically, I can’t say, but I certainly can see that, , memorization is not gonna be the key to success in learning and hands-on practice and applied is everything.

Speaker: Yeah. And I think to your point around jump rope, and I think it’s, it’s pretty relevant.

You’re dealing with like a multi kind, like a, the [00:24:00] dimensionality around, how dynamic these things are. Like people might think, okay, just like handle jump rope and then, you know, jump over., But really you’re dealing with balance and timing and all of these other things where.

I think too, with the way that these ai, the agents are probabilistic , and even with prompts where, this different day you might get a different answer. Going back, I think correctly, as you mentioned, going back to the judgment calls, right? Like how you interact with this , and using your judgment and your knowledge and experience.

I think, it’s really relevant to how this will apply with education and everywhere else. I would imagine education kind of evolves anyway, right?

Speaker 2: Yeah, exactly. Exactly.

Speaker: Yeah. Yeah. That’s interesting. How do you see the role of mentorship evolving, with AI being introduced into the process more?

Speaker 2: I would love to underscore a circle highlight, star heart mentorship because, I can’t think of a single successful person that I know who has not owed credit to mentorship. Myself [00:25:00] included. I would absolutely not be where I’m at today without mentorship. One of the things that I would like to talk about in terms of mentorship is that it has to be less about prestige, sometimes proximity over prestige wins.

And when I’m talking about proximity and prestige, I am talking about. The best mentor is not always the person who has the most senior title or has the most credentials. It’s actually the person that is closest to the problem that you’re trying to solve.

Speaker: Mm-hmm.

Speaker 2: And. When you think about access to a mentor, it’s really important to not just say, Hmm, I wanna pick your brain about something, and have that be with somebody that doesn’t actually have knowledge about the problems that you’re either trying to solve or the destination of where you’re trying to get to.

So that’s a, a really important piece. I think it’s also important [00:26:00] to recognize that mentorship is. Earned it is a two-way street. So it is not simply about, , I need a mentor, mentor me, and you show up and you check the box. And I’ve got a mentor, mentors, and I’m a mentor, and I mentor many, many people.

And the favorite mentees, the one that light me up, the one that I wanna invest in. Open the door of access, spend a tremendous amount of time with are those who are showing curiosity and actually doing the work and asking me specific questions. I can’t tell you, the delta, and it’s almost, almost going back to like, are you prompting effectively?

Get a generic prompt, get a generic answer. Mm-hmm. Same thing with mentorship. It’s important for you to figure out what you’re trying to get out of. The mentorship first. Mm-hmm. And then from that point go, okay, now that I know what I’m trying to, , get gain out of this mentorship, who is best positioned to help me do this?

And you’ll [00:27:00] find. More people are willing to say yes than you think. I can’t tell you the number of times where I’ve come to somebody and said, um, I’m working on my financial acumen. This is back, , back in the day I’m working on my financial acumen and I’m trying to interpret this p and l. Am I reading this line and interpreting this business situation correctly?

And I would put time on somebody’s calendar. That’s a very specific question. And then once that person would give me their guidance, their expertise, and then I went away and I utilized that in whatever business context I was working on. And I came back to them and I said, Hey Luke, thank you for giving me that tip about this understanding and translation of this business situation.

The meeting went really well and here’s, you know what my next steps are. I just wanna say thank you. Do you think that person wants to help you again? You bet. You bet they do because now you. A made them feel really good. They, everyone wants to provide value, and now they know that they’ve provided value, but it shows that the person on the other side did the work.

And I know it can be really, really intimidating [00:28:00] for young people who might be listening to this if you’re very young to go, oh, but I couldn’t, I couldn’t. You can do that because you’re at this level. No, I have been doing this since middle school, since I was a 13-year-old, since I was in high school, and, and I speak to college students.

Every single semester and every single semester. I say I believe in mentorship so strongly that I’m going to challenge each of you to reach out to me, to set up an hour with me and to tell me about your career goals. And I will do one thing to help you achieve that goal. Whatever it may be. Might just be to help you, you know, interview, prep, whatever it is.

Do you know the percentage of people that say and follow through on that? Take a guess.

Speaker: I don’t know. Uh, 10%.

Speaker 2: Five.

Speaker: Yeah, I

Speaker 2: would imagine 95% don’t take me up on that. 95% don’t. And the 5% that do, I give them every ounce of what my commitment is to them. I guess I would say I don’t buy [00:29:00] into the concept that there’s not enough mentors because I think it’s just a matter of finding, um.

Who is closest to the problem. Mm-hmm. And to being courageous enough to ask for support.

Speaker: Yeah. And I, that makes a lot of sense. I think just totally on the money too. I just see this from, managing teams and also like from my own career, a lot of people that I didn’t realize were mentors until after they’d been mentoring for a while, right.

Yeah, this person’s totally mentored. Like they’ve been helping me, see, ‘cause they know me too, right? Like, because you start working with them and, they’re learning about you as much as you’re learning about them or whatever, but you really tapping into that ex.

Experience, and I agree too. I think there is no shortage of people that are willing to put the time out there. ‘cause there’s something about it,, passing the knowledge down and also kind of like seeing the success through of what you’re doing. Couldn’t agree more.

People put these, constructs up of like, I can’t do this because I’m not this rank. Or I would [00:30:00] never go out of the, I don’t know if it’s where it’s coming from, but, you can just do things like, I think people and people need to do that more. It’d be great. But that’s, it’s a great point.

Is there still areas that you think people really misunderstand about working alongside AI that’s gonna become more normalized over the next few years?

Speaker 2: That they have to understand it before they can use it. And myself included, when, I would consider myself early, early adopter, I ought to be right in my line of business.

But when I first, first, first, first, started into this space, I thought, oh, I better, I better understand it more before I use it. And when I talk to a lot of people now, I mean, fast forward years and years, but when I talk to people today, there’s still a lot of. I need to understand it before I can actually.

Use it, and I’m gonna challenge that, that sentiment quite a bit. You have to just experiment. You have to just iterate. You’ve got to just lean into it and not worry about the [00:31:00] fact that you don’t understand it. Just like many people don’t understand code and oh yeah, that’s okay. We can still use. The tools that nobody knows how their iPhones actually work, except for all the people that do.

But for the rest of us know, we have no problem using, using our apps and going, oh, I’m so glad we’ve got the developers who, who bring this to us. And it will be like that too. That will become normalized. Now, of course, I have to say all the caveats of. Compliance and ethical guardrails and legalities and of, of course, all of those things in understanding what you’re using.

And if you’re using it in your corporate setting, of course you have to understand exactly what your business, , guardrails are. So I can’t, miss over that. But that does not stop you from experimenting in your personal life and just getting after it and figuring out, um, how it can help you be a time saver.

Speaker: That’s so true. There’s a a certain set of people that are [00:32:00] like, I, I need to go take a class for that. And it’s like, they’re in classes for this yet. You gotta just jump in, , with everything happening, with AI and especially given, your position and where you’re seeing things like.

From on the educational side and on integrating all these things in. And then just general like with thought leadership, et cetera, , is there a conversation that you don’t think that we’re having enough of in the, zeitgeist around AI or around how all of this is kind of commingling?

Speaker 2: Luke? Two things come to my mind. We are not talking enough about the operational reality. And we are not talking enough about the human identity impact. So I’m gonna start on the first one. Operational reality. Everyone sees the headlines on what AI can do, can do what it’s going to do, what it’s going to promise, what we are not talking as much about and has to be paramount in any conversation where we’re [00:33:00] adopting AI is.

What breaks when you implement it? Mm-hmm. What roles are going to actually change How messy the middle is, and when you are a leader who is going through change management in something of this size and this scale, it is super messy and you’ll have people along the way that said, see, told you so. Told you so.

It’s not gonna work. Told you. Told you, told you. Told you. Right. You have to be so strong in your conviction and your resilience and your courage to keep going as a leader and help bring people along in the midst of that. Because it is not easy to keep people inspired and motivated because there is a point where they are going to say, I told you it wouldn’t work like you thought.

And guess what, Luke? They might be darn right. Yeah, they might. Right In that moment, you have to be convicted [00:34:00] that on the other side of this transformation, that it is going to outperform or it is going to yield the ultimate goals that you set out to do, but it’s gonna be real messy getting there. And so that piece.

You have to really focus on, you have to talk about it, and you have to set up a, dynamic in which the team on the other side of the implementation is going to be okay with triaging and listening to the knock, knock jokes and the, and the silly fringe cases and the edge, and say, okay, we’re all in this together.

We’re locking arms. We are committed, and we are going to do this, ergo we’re going to spend this amount of time optimizing on the other side. Set that it’s not going to be as smooth as we all think it might be smooth, and we’re gonna be faced with things that we can’t even conceive of, and we’re okay with that because that’s what we’re doing.

Now to get a little bit on the soft side, the human side of the identity that comes along with it. [00:35:00] I am somebody who deeply, deeply resonates and identifies with being the go-to person who knows, how, how to be persuasive and how to influence certain decisions and how to get some things, done.

And when I’m talking about this, I’m specifically talking about it in the context of, I have the knowledge about past things that we’ve tried or we’ve done and what’s been successful and not successful. And so I can serve up for your investment case or your whatever it is that you’re going after.

I become the go-to person that knows, I think with all the bodies are buried about, you know, a particular project or an initiative or things like that. What happens? When those things and that part of me is no longer needed, relevant, important, has nothing to do with the way that a project gets done or [00:36:00] not, because the knowledge center is so vast and so great in our AI systems that that information is readily available to anyone.

I was actually even thinking about not so long ago, there was somebody that was really instrumental in my early career. Who knew every single Excel formula under the sun that I could conceive of. And we called him the Excel Yoda. And you could go to him and he could whip up what your concatenation code is like that.

And, and it was, it was a running joke. And, but it was important to his identity. It was really important. Now, he wouldn’t have said this was important to my identity, but do you think that his. Satisfaction at work was in part made because he was known as the go-to person for all things Excel. Yeah. He was.

Mm-hmm. That was a fulfilling part. He would have C-suite executives come to him and say, I need one of those Aaron Yoda codes. Uh, I need one of those Aaron Yoda [00:37:00] formulas. Right. That feels good. That feels good now. We don’t even need to know that. You concatenate with a ampersand, quotation mark, space quotation mark, ampersand, quota.

See, I still remember, Erin, if you’re listening.

Speaker: Yeah, yeah, yeah.

Speaker 2: Anyone can concatenate and we don’t even need to know the formula. We just tell, put this together. Merge these fields. That’s a skill that just went out the window, so that’s just a tiny, tiny, tiny, tiny example. There are so many more that are like that where your identity is wrapped up a little bit at work.

In and maybe a lot, bit in some of these skills and expertise that you have that now has been democratized with everyone and Huh? What happens to you when you go to work on a Tuesday and that’s not needed anymore and people are not, that’s asking for help. Uh huh. That’s, that’s heavy on my mind.

Speaker: Yeah, it seems like a, a, a [00:38:00] recurring kind of theme across both I is, how important it’s to be adaptive, right?

, And trying to, work with the tooling and, adapt your skillset. Maybe knowing the specific formula was something you, but, but there’s, there are other things that are like that, that you can develop, right?

Super interesting. This has been a really fascinating conversation and Jordan, I really appreciate you making the time, to come here. Is there anything we didn’t cover that you wanted to get out there?

Speaker 2: Well, if you don’t know where to start, I would say there are a number of free resources that are available.

So I have to plug General Assembly has free classes and to anyone who’s listening that thinks, yeah. I’m an introvert and I’m uncomfortable asking for help, and I’m in transition. I don’t even have a role right now. I’m in, I’m in transition and so I can’t possibly, um, be somebody that can get on somebody else’s calendar that can help me learn a new skill.

I’m gonna challenge you today, reach out to three people, just reach out to [00:39:00] three people with a specific ask and not with a, can I pick your brain, but. I see that you are an expert in blah. I would love to know how you do X, Y, Z, and just get started. Get started with AI experimentation, but also get started with human connection.

Get started with human connection, and I think that’s a really important just human skill that will continue to be necessary no matter where the world of work takes us.

Speaker: That’s awesome. That’s awesome. Where can people, reach out to you if they want to make a human connection with you?

Speaker 2: Please do. And we’ll see if more than just 5% do Jordan Hathaway Jordan with A-U-J-O-U-R-D-A-N Hathaway, on LinkedIn or Jordan hathaway.com.

Speaker: Awesome. Well, Jordan, thank you again for making the time today. Really, really appreciate the conversation and enjoyed it. And I think our audience will too. And I’d love to have you back too, anytime to, [00:40:00] revisit any of these things or just see how things are going.

Speaker 2: Oh, maybe we’ll just do a whole episode on Fringe case, hilarity.

Speaker: I would love it. Actually. I, I think it’s some of the most fascinating part of this is just to see where, the wrinkles are, people aren’t, aren’t really thinking about those very human things. So, yeah. Yeah, really appreciate it.

Thanks again, Jordan. We’ll see you soon. Thanks.

Speaker 2: Thanks for having me.

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