Julia Levy Live on Shally's Alley

Somewhere around 1 PM, and you are now live on Shelly's Alley. We have a repeat offender this time, my good friend and former boss at Fiserv, Julia. Julia, you're back a second time. You didn't get enough last time. What's going on?

Well, you said offender, and I do offend people sometimes.

The shoe fits.

I don't know.

I guess so. You know that, what, what are they— what's that saying? You don't make an omelet without breaking a few eggs, right?

I'm a little cracked, that's for sure.

I think the two of us have made a lot of omelets.

Yes. And I'd say, um, back in our FYSERV time, boy, we had a lot of these types of discussions, just talking shop and new tools and tech and, you know, yeah, it's always good to meet.

That's true, we did. And we— there's been a lot of change since that, but there's also been a lot of the same old, same old recycled stuff. I went to HR Tech last year and was really shocked at how— I don't know if I told you this before, but it just seemed like a lot of these companies basically took the sign down off of their booth and put up a new one, and that's it. Like, it, you know, it's like they renamed the company, but it's the same company and it's the same business, and, and, um, just not a lot changed. They just added the keywords AI, um, DEI, and, um, workforce management or something like that. And it's a new product, but it's the same product. It's just called something else with 3 new keywords.

The spin, the spin, right? Yeah, sounds like some of our politicians, but I won't get into that.

Uh, yeah, that's exactly right. First time, what would the post be about? The post, I'm not really sure. First time with you, what would the post should be about? Not sure what that question means, but I think if the question is what is the show about, well, we wanted to talk about the candidate experience, and in particular, so something that, that Julia's championed for a long time is the candidate experience, especially because I don't mind saying this, Julia also helps people out, uh, with career advice and, you know, resume writing and things like that. It's just so— it's kind of like a— I don't know, it's a hobby. It's because it's not a business, right? You— no, you do it as a friend and colleague to people. Um, even runs a networking group in Atlanta for that. So it's something that Julie has been really passionate about over the years. And, um, you know, when I first started working with her, it wasn't really top of my mind because my perspective was always finding hard-to-find talent. So the candidate experience for me was almost like, like a given, right? When you reach out to somebody and you invite them to apply, it's, it's a white glove candidate experience. Um, but there's this other side of like people that apply that you didn't necessarily invite, and, and they sometimes have a terrible candidate experience. So, um, I learned a lot about that from, from working with Julia and Nowadays, there's a lot of conversation around automation and the candidate experience. So what do you find? What is the state? Let's start with what is the current state today of candidate experience and technology, like the intersection of those two when they come together? What's that looking like now?

I think that there's still a lot of poor candidate experience processes. And, and ours isn't where I want it to be, but that I always think it's that, um, what I would call the black hole of the application process. And then once someone's applied, that black hole of waiting. Am I going to hear from someone? Is there an actual recruiter going to look at my profile and screen my resume, or is a robot doing it? And so there's a lot of questions around The intersection of using technology and the application process, that's something that's really, I think, important to get right. At Commscope, we're just starting a journey to really improve that. We have some SLAs for our recruiting team around that period of the application came in and when they look and review and how people, how recruiters manage their applications. So if you have a job and we know it takes 30 applications to fill an engineering role, and you've got 100 applications in there, maybe you don't— you should shut the posting down, go through those 100, if you need more, then reopen it, right?

Rather than setting, letting people sit and wait. Yeah, it's, um, that's a good point. So the SLA is really in place to discourage the black hole that people refer to. And it's more about like just getting back to people, right? It's not that you have to get back to everybody in a certain way. It's that you are being more mindful and respectful of applicants and closing things out and, you know, being more— what's the word I'm looking for? Like, timely.

Yeah.

With applicants, right? Because I mean, as an applicant myself, I would rather hear, hey, no thanks, than like nothing.

Yeah.

You know, at our former employer, the— when I got there, that candidate black hole was 105 days.

So imagine that people didn't get a response, right?

Nothing before a recruiter would touch them or look at majority of people. And over time we've reduced that to about 14 days, which to me is more manageable. Although I think as we look at the different generations in the workforce, I think that immediacy is really critical and could be a game changer to any organization if people are doing that candidate management in their requisitions quickly, because speed is really key to success in many instances.

So this is interesting. Um, I, I need it, you know, I, I've built my own sourcing team a few times, and recently here in the last year, um, I've had to do that again. And, and I've— and I get to do that, I should say, right? Because it's, it's always a fun experience and it's always interesting, and I learn a lot when I build a sourcing team. Um, I inherited only 2 people in the team here. So, uh, one of them was a contractor and found a full-time job, and unfortunately they, they left very amicably. We, we couldn't offer them a full-time job, so, um, you know, they're welcome back anytime. But that meant that basically I had to, you know, start over from scratch. When I first started, I couldn't, I couldn't get anybody to apply to my job. I had the job posted for months and nobody applied. I was like emailing people, hey, who do you know? And, and asking people. And finally I got my team up and running, um, and then I lost a member of the team, so I had to backfill that position somewhere closer to like the end of summer. And that time when I post the job, I'm not kidding, Julia, I got— so the difference was I got zero applicants for sourcing And I had to beg, plead, and scratch to get anybody. And then towards the end of the summer, I posted the job and I got 500 applicants.

Wow.

Overnight. And talk about the candidate experience. I was like, what am I gonna do? I can't eat the dog food. Like, I can't do what you just said. And, and, and get back to people quickly because there's no way I can get through 500 resumes.

That must have been a really good job description.

I don't know if it was the job description or the sign of the times, or it was just very strange. Um, so what I ended up doing, and, and I hope this— don't, don't shoot the messenger, but I had to do something— what I ended up doing was I wrote a message to everybody that applied with— through the ATS I could do that— and I essentially sent them out a message that said, Thank you very much for your application. We've received your application. We are interested in talking to you, but first we'd like to know a little bit more about you. We'd like to get to know you a little bit better. And there was a link that said, please complete this form. They click on the link. They would then go to an ATS, a greenhouse form, where they would answer 7 questions. And yes, I know it's a form, and yes, it's questions, but what I committed to doing, because I couldn't do it with the 500 applicants, was anybody who took the trouble to actually complete that, they definitely got a personal message from me. Not an automated message, but a personal. So I'm like, all right, at least I told all 500 people, we got your application, you know. But I can't look at 500 resumes in, in a timely way. I mean, I eventually could, but So then, um, only about 100 or so people actually completed the form. To me, that was sort of a— well, it was a way out of having to review all 500 resumes because I kind of figured, all right— and by the way, I also sent them a reminder, so it wasn't like you get one chance, you know. Everybody got that, we got your resume, please fill out this form. And then those who didn't fill it out, a week or so later, got a, we'd still like to talk to you if you're still interested, you know. If we don't hear back from you, we'll just assume that you are no longer available, right? So that was my assumption is some people applied for jobs and they applied for several jobs and they got a job. And so they're not interested in talking to me right now. So that was my solution to it. But there's no AI there. That was Shelly AI, you know, it was like, okay, as they came in, I got a Slack message that a new applicant would come in, I would quickly look at their profile. And now I could determine, okay, to senior, to junior, not— and I would message them quickly back, hey, thanks for filling out the form, wanted you to know I did read your form, we're not proceeding because, or we are proceeding because. That was a lot easier to manage. And that wasn't 100 overnight, that was like 100 total over time. So what do you think about something like that, that's essentially kind of brute force but maybe a little bit smarter? Is that a bad candidate experience because I'm telling an applicant to come back and reapply?

Or so I've been at companies where we had multi-stage application processes.

Okay.

So you could— some applicant tracking systems have the ability to put some of those kind of pre-screener questions up front immediately. So yeah, as part of the application, they could be asking it. You may get some dropout, right, that people are like, I don't want to answer these 7 questions. So you could do it up front, which would then you know, could then highlight people that really meet the qualifications that you're looking for. And if someone doesn't meet them, then you give them that notice, right? So that's one thing depending on someone's applicant tracking system, if they can do that. But I don't think there's anything wrong. Listen, I've— I work at companies where we don't have really deep pockets and we have to make pennies stretch. And when we can do things like that, I mean, you still had it in the system. It was a form. You could still manage that data. It's still providing a good candidate experience.

There's an applicant flow log. It's legit. The questions I asked were related to the job. They weren't discriminatory or related to anything that would—

Yeah, there's definitely low-tech ways of doing that. That's lower tech than maybe having some sort of AI that can go back or answer those questions, you know, ask those questions or infer from someone's resume if someone, is a little more qualified than what you're looking for. There's a whole host of tools and technologies that are out there. I've just not always had the privilege of having the pockets to be able to afford them all.

Right, that's right. Yeah. And when you have something like Greenhouse, you know, not promoting or knocking anybody, but an applicant tracking system or a system of record, like, Greenhouse that has tons of, one of the things that their claim to fame is they have tons of API relationships or just the fact that they have a very open API. If there's an inexpensive solution out there, that's a good solution, it will easily plug into Greenhouse versus you have some ATSs where they're actually the opposite, you know, draconian in their closeness where you have to have a million dollars and all kinds of stuff in order to get an API with them. So I think that may be part of what the technology is that we're looking for in the future. But in your opinion, what is good AI and what is bad AI related to the candidate experience?

Um, I think there's probably two different pieces of it. Is one from a candidate's lens, and then the other one— I guess three. I always say kind of three legs. You have the candidate You have the recruiter or sourcer, and then you have the hiring manager.

Okay.

And so some AI might just focus on candidates, some may address all three.

So one of the AIs that we're going to be rolling out this year is called Paradox.

And, uh, that's a chat AI, right?

It's a chat and automation. So we're looking to use— so some of— if there's job seekers looking at this, you might have seen some companies that have Olivia or some sort of chat that pops up in somewhere on the career site. What's great about a tool like Paradox, and there are others that are out there that we've looked at that are either focused on the TA space or maybe more generalist applications. We use some internally, but we went with Paradox because of their expertise in the talent acquisition space. So one of the things from that candidate lens that a conversational AI or chatbot can do really well is answer a lot of the candidate questions. What I found is I've built very robust career sites that have candidate resource pages and content right, that candidates can go to. But what you find out is that many candidates don't go and look throughout your career site.

In other words, the information is there if you look for it, but they don't look for it.

So, right.

And we try—

I've always tried to make it intuitive as to where that information would be if they have the questions. And we are so used to using a search bar to type in your question and find the information that you need. So having that candidate care, that experience is really great because a lot of the questions that someone might have as they're trying to decide if they even want to go through your application process can be answered in a conversational way with links to more information and data that can help them make a decision if your company is the right company for them or even what jobs you have available, available. So Or matching the job, upload your resume, and then the AI can tell you which jobs are a fit based on your experiences.

So rather than maybe digging through, if you have a lot of job postings, digging through the job postings and maybe even attempting to interpret job titles that may be unique to that company that don't really translate well or easily, then the, the System is really more like a concierge in a way that, okay, based on your background, here's some jobs to look at. They may not be the ones you want, but how about try these first?

Yeah.

And then the kind of questions that they ask are what, benefits or—

Yeah, it could be benefits or about the CEO. It could be about the hiring process, locations. We've— we're just in the early stages of kicking this off. We have a list of like 400, 500 questions. That are pretty typical for— I feel like I'm air quoting a lot. Sorry.

How did you— Yeah, it's because you think you're on the phone. But yeah, how did you come up with that list? Like, did you just gather questions over time?

So they, they gave us a list of questions and then we went to our recruiters and our recruiting coordinators. And this is all pre-work we're trying to do ahead of really kicking off this implementation. But we've asked our recruiters, what are the questions you get asked most by candidates? And then same thing from our recruiting coordinators that do all of our interview scheduling. So one piece of the puzzle is that upfront candidate care and trying to answer the questions that are most frequently asked and provide as much information as you can and help that search process. Where the automation comes is the conversation AI will enable candidates to apply through the conversational AI. So then you get this great experience because today someone clicks on a job posting, reads through it, clicks apply, and then they land on a page that says create an account or sign in through our ATS, which is not ideal.

And then they have to create a profile and then apply.

You answer a bunch of questions that we don't make it easy.

And I know that if I'm not making it easy for people, they're not going to be applying and we're losing out on people. And a lot of companies still haven't gotten the application process right. We've been working on it over the last couple of years, but this is going to enable us to, through chat, through chat on the website, through a QR code and texting on your phone or WhatsApp.

So you text and it replies back. Okay.

We'll be able to have a conversation and take that application all the way through. So it will skip some of those steps that we have in our more manual process.

So some of that chat is like, you know, what's your current employer? How long have you been there? So it's interviewing them based on the questions that would normally be on a form.

A little bit.

Yeah.

Upload your resume and then it parses some of that information in so you don't have to necessarily fill in whatever.

Thanks. Or more.

More about your contact information. Again, you don't want someone to be sitting there 10 minutes in a chat. So we have to be mindful of that. And look, I think if someone doesn't complete their application, when they come back, the conversational AI can say, welcome back, Shelly, pick something, finish your application and things like that. Once the recruiter reviews somebody, they can then say, I wanna set Shelly up for an interview and we can automate that process with an integration to Microsoft. To the calendar system. Yep. And that's not just with the recruiter and the candidate or prospect, it's also can be done with hiring manager interviews. So after the recruiter screens someone, they can say, Shelly would be great to speak to this hiring manager. And get you set up. And that is automated through the system. It'll look at open schedules, find the best time. If somebody needs to cancel or reschedule, it'll automate that. Someone hasn't responded, they'll remind them. They'll remind you right before the interview. So there's a lot of process automation that the, the system can do.

Does that, Essentially, does that ease the burden of the coordinator or almost replace the coordinator?

I think it eases the burden. I don't, I mean, in some instances that could be the case of replacing, but I don't think it replaces. It just gives them the opportunity to work on some other value add, like our—

Rather than back and forth. How about 5? Well, I'm busy. No, I can't make it. Right. So it takes, it takes that sort of, you know, email tennis or texting tennis back and forth out of the equation. And also, if you have to cancel or you have to reschedule, okay.

So there's always going to be complex interviews that a system might not be able to handle or things that go wrong. It's not AI replacing recruiters or coordinators or things like that. It's AI plus the recruiter or coordinator, and it's just working a little differently, um, in my perspective.

So that's the candidate. So the candidate is, um, don't want to recap here. So from the candidate's point of view, they get to, um, have matched, be matched to the, to jobs. They can ask questions rather than, you know, poking around on a website. So it's easier, it's more immediate, it's friendlier. So facilitates that from the recruiter and coordinator's point of view. It's accelerating the scheduling and all those other things that are like we just talked about. And from the hiring manager, what is the, the good side of this? Or do they really not know? They don't notice it. It just things happen faster.

I think they'll notice that they have things scheduled on their calendars. If they need to reschedule, they can just mark in there that I need to reschedule. I think it'll say automate a little bit of that as well.

Okay, now what's the bad? So that's— so all of this sounds great. I mean, like, I'm ready to buy, you know, um, I don't know how much it costs, but I'm— this sounds really good. I get definitely the benefit of answering questions, asking questions, facilitating the application. But what's the ugly side when, when this doesn't work, or what, what can happen that goes the other way?

I think it's when you use AI to make decisions on candidates, like where an AI is really auto-screening and dispositioning people. That can be problematic because they're—

I mean, we all have inherent biases, but you do need to make sure that whatever AI you're using, that you understand the, the backend of it and that you have checks and balances to make sure that there's not bias that's unintentional brought into your processes. Looking at the, you know, there's from a TA practitioner perspective, if I think on like put my operations hat on, there's the upkeep of any system looking at all of the, you could overcomplicate things. I mean, we've done it in ATSs before.

So facilitating it becomes— there's more hurdles and— okay.

Yeah.

So I think it's things like that that I find probably might keep me up at night a little more than the overall construct of using it and what it could do to help automate processes. I think there's a lot that I haven't even considered yet with the tool that we could be doing or we could add. It's like, I don't know what I don't know yet. So as we get into this implementation process, there may be things that I say, aha, and look to see if in like some sort of phase 2 or iteration of the process that we can continue to evolve.

Evolve it. Yeah. You'll know more from the experience the candidates are having. I'm assuming there's an escape valve somewhere where, you know, the chat application has a, let me get a human kind of, oh, this is out of my scope, you know, that kind of thing.

I think that how we'll, for sure there's that. I think how we're going to implement it is any candidate has an option. You can use it and we're going to have it front and center so that someone knows it's there and it's our preferred method for to get someone in because I know it will be a better experience. But if someone doesn't want to deal with AI at all, they can choose not to and just go through our normal process, which will continue to iterate and improve. I think the other day— yep.

So yeah, no, go ahead. The other—

I think the other danger around some of the AI So I know we talked offline about ChatGPT and some of the technology that's coming out, and I do think there's some dangers there in how candidates might use the ChatGPT or some of these to try and game a system. And I think that's something that employers will need to look at But I think there's a lot of benefits for a tool like that as well. So it can help you write a resume, or as a recruiter, you could use it if a hiring manager is talking about a technology you don't understand. You could ask ChatGPT to explain what cloud computing is, or, you know, the Wi-Fi 10 or any of those things, and it can provide you some information. But I think it's still a little new from some perspectives of how accurate is the information that it's providing you.

Yeah, the accuracy is definitely in question. But in that way, I think the earliest iteration of something that brought up that dichotomy was Watson with their first-generation AI that they went public with and their approximation engine. What Watson was doing, I don't think they do this anymore, But what they were doing at the time was they had a percentage threshold where Watson would provide an answer to the question that approximated 70% certainty. So what a computer is supposed to do, or what we know a computer to do, is it gives you the exact answer. I mean, if you're using a calculator and you put in this times this and hit enter, it's gonna give you exactly this. That is an absolute, right? There's no gray area. But what Watson was doing was essentially saying, "Yeah, it's about this." And that's actually how human beings think. We don't think in absolutes. We think we know the answer, but we're secure enough in thinking that's the correct answer that we say it as if it was a fact. But it may not necessarily be a fact. It could be a fact to us, or we could be almost sure it's a fact, but it sounds like a good answer. And so that's what Watson was doing. I think the risk with ChatGPT is exactly the same, is it is not an approximation engine, but in the same way, it is providing you information based on how you frame the question, which actually matters, changes the output, believe it or not. It's specifically how you ask it and what you say when you ask it. And it also is providing you information only based on a specific model, which is the other part of it. So it really isn't, it's actually not AI yet. It's still a language model. So we think that it's giving us an answer, but it's only giving us an answer in this context. The context is huge. The dataset is huge. And that's what makes it so powerful that people are impressed with it, is the amount of information that they're summarizing. But you can't take it for granted. There's got to be checks and balances where it seems like, the system that you're implementing is more, you know, there's a definitive answer because it's been provided by the model and it's contextual to that specific environment. This is about candidates and jobs and applications. Nobody's going to ask, you know, Olivia about mathematical formulas or to explain cloud computing. So it doesn't have to do that.

So yeah, when it's more, I don't know, it's a closed environment, we're providing that data and information to it. To provide the answers to candidates. Whereas ChatGPT is much— I was asking, I was playing around with it. Now I was trying to play around with it. I set up some, some brainstorming sessions with some of my team members and we were getting locked out of it constantly.

So it was a real disappointment that we weren't able to really as a group kind of test it together and see some of the things it could do.

But I was, I was asking it, As I played around with it, asked it to rewrite my LinkedIn summary. And so, you know, that was cool. Or write a job description of a software engineer and how much does a software engineer make? And it pulled some Glassdoor data. Now, I didn't validate that data based on what my recruiters see in real time. So that could be interesting to do. But there's some, some great potential implications for it.

I'm going to post something here. Let's see if it comes up. There we go.

For anyone that's listening live, I'd be curious to see if anyone's played around with it.

Yeah. I'd love to know what people are doing. I just posted, so you might want to try this, beta.openai.com/playground. That one's not locking people out because It is the test environment, um, and a lot fewer people are actually on there. Still leaving it open so you can still get to it. If you did register for an account and you logged in with your account prior, now you can put in your email address and it'll send you an unlock code so you don't get that locked out code. It's still free, but the Playground is actually a good place. I'll tell you what I recently did. Um, so this was Stephanie's idea, my wife's idea. Uh, we were talking about ChatGPT and she's a, she's a recruiter. At T-Mobile. And she was like, well, what I wish I could do is I wish I could take a resume and compare it to a job and be able to like be more informed for when the hiring manager says this person's not qualified, be able to like be more informed on that by, by comparing this. So on a lark, I was like, that sounds like an interesting proposal, so let's see if it's possible, right? So what I did was never done this before. I was like, all right, here's a resume. And I went through about 3 iterations of this. First time it said— the first time the answer was like, I'm sorry, I cannot answer this because I don't know this and that. So I was like, okay, rephrase the question. Same information, different question. It came back with kind of like an, um, this doesn't appear, you know, like I'm not really sure, it doesn't appear. But then the third time's the charm. I said, compare this profile and the URL to the profile and match it against the requirements for this job. So compare this profile matched against the requirements for this job, enter, and then it came back and said the profile for this person matches the requirements for this job because this person has this many years of experience in this has skills in this area that are in the— and like, it literally went a side-by-side comparison, and it wrote a 2-sentence paragraph that very convincingly told me that this resume is a potential match. Now, it doesn't say that the hiring manager is going to interview the candidate, but it does say they match. Now, that is one of those things that you're thinking, wait a minute, are we not supposed to be doing this? But is, is this really accurate? And if you read what it said all it's doing is it's paraphrasing the, the rec and it's paraphrasing the resume. And the text of both actually does match. So at least from a textual analysis point of view, it's not wrong.

I did something similar, Shelley. So I'm laughing because of course our minds think alike in that, right?

That's weird.

I did something very similar and I used— I've used myself as the guinea pig on a lot of things.

Yeah, right.

Like talent acquisition requisition, like job description. And I used mine and you could— I even did one where I just said, what are the skills of a recruiter or talent acquisition person? So it looks like Leanne's listening or listening in or watching.

And she—

so I have signed up for Bing, but I haven't tried it yet. And glad to see you're playing around with it as well. Yeah.

Yeah, so that is the built-in, in case people are wondering, um, the built-in ChatGPT because Microsoft invested in them and have added it to their search engine, to the, to Microsoft's Bing search engine. So now if you go to bing.com and you're logged in, keep in mind that there are, there's a, there's a Bing that's available to anybody anonymously just bing.com. But if you use your Hotmail or whatever Microsoft account and you log in, you, um, you earn rewards and, uh, it keeps your history so that it can serve up like news articles and things like that that are relevant. Well, what they've done is they've taken this ChatGPT and added it there. So now when you go to Bing and you're logged in, it says ask me anything, and it's taking on more of a conversational form rather than what you used to do, which would be type in a bunch of keywords, you know, uh, Southwest Airlines flights or whatever. Now you can say the question that you asked before, what is cloud computing, and it's actually using ChatGPT. So it's, it's not the same as ChatGPT, but it is attempting to answer questions using the conversational capabilities of ChatGPT, which is smart.

Hi Dana, thanks for the The next— so, Shelley, Dana, who just commented, is, uh, someone I worked with many moons ago at MetLife. So, um, glad to see— I knew she was coming. Um, I, I don't know that ChatGPT could help someone update a resume, their resume, in the sense of you could probably give it some projects or facts that you worked on and then ask it as a candidate to help you create something for your resume. So I think it could be helpful for candidates in that way. But I think it's still— as recruiters, we're still going to have to really screen and probe to understand someone's true experience.

I find in technology this is definitely a point of contention where Engineers, software engineers, developers, they're working on something like they're learning Python right now. That's what they're working on, they're interested in, they're learning it. But at work, they're developing in Java. On their resume, their Java experience is clear, but they don't put their Python experience on their resume until they've mastered it. So it's like what the engineer is interested in now, which is Python, would be highly relevant to this job. If we knew that they're developing in Python and were interested in it, it would be a good recruiting point. But we don't get to see that because all we see is Java because they haven't updated their resume since they're just learning it. And that's like a disconnect. So you call the candidate up and have a Java job and they go, "Meh, I don't really want any more Java jobs. I'm interested in Python now." But, you know, they didn't update their resume and they don't want to update their resume because they don't have any experience with Python yet. So that's kind of an interesting thing. That's why a lot of us in sourcing, we do more than just look at the resume. We look at their GitHub and Stack Overflow because that tells us more about what they're doing now, and we can interpret that a little bit easier. Now that's maybe just unique to software engineering. Not all fields are, you know, as I would say, continuously changing, um, at least not as rapidly, right? I mean, software developers are learning a new language every couple of years basically.

So yeah, yeah, that's, uh, I wish, I wish there was an easy way. Resumes are everyone's biggest headache and nightmare.

Yeah, that's like, we need to like give that person a, a reprimand for inventing resumes. This is the worst idea ever. I mean, it helps nobody, you know? Like, where's that?

I need to figure out whatever the next best thing is, and then I can quit my job and make millions.

Yeah, right. Yeah. Well, the CV, the CV wasn't any better. I mean, the CV, the curriculum vitae, which had your family history and was 27 pages long. I mean, imagine applying for a job with that. So that's not any better either. I don't know. It's—

but I think that the— just even that, talking about the resume and people not reflecting their, their most relevant experience goes back to my passion around candidate experience and trying to educate people on best practices. And I'm going to go on a rant for a second. I put— for those of you that know me and are watching and have looked at my recent posts, there was a career counselor. So I'm gonna quote on that one because I— the woman, I think the woman has her own business, but she did a TikTok that went viral. And I think a little bit of it is that, you know, the clickbait of, but it was like 5 things you should lie about as a candidate.

Right. So this is a professional career counselor telling people to lie about these things. And because she wanted people to click on it. But when they go read the article, it's not really exactly what she's saying.

Yeah, well, she is saying it's not necessarily that— well, she said lie about the job, your job title, which I have a problem with, but some of the others were more on how to better answer a question like, where do you see yourself in 5 years? So she's saying if you plan on going to law school, lie about it and don't say you're going to law school. Say, I see myself in your job or whatever that answer, the more politically correct corporate answer.

Tell people what they want to hear.

Yeah. And so it's a little bit about that, but the job title, I was like, well, first of all, just candidates saying, oh, look, this career counselor, this career coach told me it's okay to lie about my job title.

Yeah, I just think of George Santos, right?

Like, talking about a verifiable piece of information. I mean, the work number— doesn't the work number say dates of employment and job title?

Yeah.

I mean, that's like me.

I, I years ago got Taleo system admin training experience, and I did do some sysadmin work in my TA Ops role, but I had a sysadmin on my team, Ben. And when I, I wouldn't call myself a system administrator. If I was applying for a sysadmin job, I wouldn't say I was a sysadmin. I would highlight all the things that I did that were similar or supported, you know, as a sysadmin would do. So I can talk about all that relevant experience, but I sure wouldn't change my job title.

It's okay if what your internal company calls the job versus what is more—

that's a very good point. Yeah, I run into that all the time. And I, when I did a lot of training, teaching recruiters, I would often use the example of— I'm not making this— it's 20 years old, but I'm not making it up. Systems Implementation Design Engineer was the actual job title. Systems Implementation Design Engineer. The job was Tier 3 help desk. That completely doesn't even, like, you know, so you're out there looking for a systems implementation design engineer that doesn't exist because only this company uses that job title and they use it to describe essentially an escalated help desk. Like, you know, they're not the help desk Tier 1, they're not the, the, the escalated one, they're the engineering part of the help desk, which is You know, and the implementation part of it had to do because it was a rollout of an application. So that's— it was like, wow, let's obfuscate this job title as much as we possibly can. Um, but anyways, so in conclusion, I, uh, next time I need to hire a sourcer, I should not hire ChatGPT.

Probably not.

Probably not. Okay, all right. But if I need to hire a recruiter, I should hire Olivia.

Probably not.

Or what was her name? Paradox is— Paradox is Olivia.

Is Paradox. Yeah, there's a couple others out there, although I don't know if they're all still out there yet or if they've been acquired by other companies. There's been a little bit of movement, um, in that industry.

But that's good because that means that, you know, if something's good and it's too small, it gets absorbed by another company that can support it, the technology moves forward. But, you know, otherwise, because too many choices is also analysis paralysis, right?

Yeah.

But you know what, living up to our, our pattern, I, I kept you over time again. Uh, one, one more time. I should probably just tell people the show is 45 minutes instead of 30 minutes, and then, and then I'll go over by 15 minutes and be an hour. So yeah. Thank you so much for coming on a second time. I really appreciate your time and being here and sharing your insider's view of chatbots and AI and all that stuff. Is there anything you want to leave the audience with as you part? A word of wisdom to think about or mull over over the weekend? Hmm.

I mean, I'd say play around with ChatGPT and some of the tools, and I'd love to hear what people think about it. Some of the different applications that you might find for it. I mean, look up a recipe. I bet it could tell some good recipes and things like that.

You never know.

I'd love to know how people are using it. And for those watching, if we're not connected on LinkedIn, feel free to reach out. I always love talking shop, and as Shelly knows, I talk a lot sometimes.

Nah, it's all good. But yeah, definitely an open connector. And if you're in the Atlanta area, and in the business, um, a good resource for, for career connections and things like that. Well, thanks a lot, Julia. Happy weekend, everybody. We'll see you next Friday. Bye everyone.

Thanks, Shelley.