Hook, Line, and Skillfishing — When Candidates Look Better on Paper

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

Nichol: Every HR professional has lived this nightmare. The resume looks perfect. The interview goes brilliantly. You think you found your next star performer? Then after one month on the job, you quickly realize the capabilities simply are not there.

Welcome to a special live conversation of Honest HR, where we turn the real issues facing today's HR departments into honest conversations with actionable insights. I'm your host, Nicole Belyna. Let's get honest.

A phenomenon what SHRM is calling skill fishing is becoming more common as candidates are creating resumes to appear highly qualified without underlying skills to match. SHRM checked in with over 2,000 US workers and HR professionals, and the numbers will say it all. Three out of five workers, about 63%, say they've worked alongside someone who looked great on paper or nailed the interview, but just didn't have the skills once they got the job.

It gets even trickier thanks to AI tools, padded resumes, and a little clever personal branding. HR pros are seeing the shift too. Almost nine out of 10 say AI makes it way easier for people to seem more capable than they really are.

To help us make sense of all of this and share what we can actually do about it, we're joined by Alex Alonso (Alonso), SHRM's Chief Knowledge Officer, and AJ Farage, the founder of Source X, known as the Google Maps of Recruiting and also two of my favorite people. Welcome to Honest HR, Alex and AJ.

AJ Faraj: Thank you, Nicole. Happy to be here.

Nichol: Yeah, it's a pleasure to have both of you. So let's dig into these questions. I've got lots of questions for you in the next hour here. So, Alex, I wanted to start with you. SHRM coined the term skill fishing to describe this hiring challenge. Can you walk us through what skill fishing actually looks like in practice? And how it differs from traditional hiring mistakes?

Alonso: Yeah. You know, it's sort of funny because this is the one that's very personal for me. I'm the one that actually first pulled together the words skill fishing. One of the things that strikes me is we started to see an increase in the number of individuals who are looking even better on paper than what you might have imagined.

For me, this was very personal because I hire a variety of researchers. My background is as an organizational psychologist and I lead SHRM's thought leadership functions. I lead our knowledge center in a variety of other groups as well. And I remember thinking to myself, what are the conditions that actually lead to someone coming in and looking so great on paper and somehow falling, having it all fall apart?

For me it was actually more than personal because we had actually hired somebody, not here at SHRM, but at a prior employer. I remember hiring someone who was so dang perfect. Came from the top rated program, had a PhD. Finished their PhD in four years, and had a dissertation that had won awards.

Then the minute that they got here within four weeks, we knew they couldn't open an Excel spreadsheet. They couldn't analyze data. And more importantly, even given a menial task, like something associated with reverse coding survey items and reverse coding survey data, which is simply taking what are negatively worded items and reversing the scores on them so that they are positively aligned with the rest of the data. They couldn't figure out, they needed guidance from three different people to get them to the point where they could do that.

That, to me, was my first experience with what I was calling back then, catfishing or being catfished by a candidate. Anybody who's ever watched MTV knows what catfishing is. We've all watched the show to some degree in the relationship context, but to me this was being catfished by skills. In the workplace context, this person looked like a 10 out of 10, and in reality they were probably a 0.5 out of 10. They weren't anything like what we had experienced.

To me, what we're seeing is that is on the rise in ways that we've never experienced before, and we've got some data to actually back that up.

Nichol: It's a lot to take in. So AJ, I would love to hear from you as a highly experienced recruiter, we've heard that you've had your fair share of skill fishing incidents. What are some of the most surprising incidents you've come across?

AJ Faraj: Yeah. So this is a great question Nicole, and thank you for having me today. I think one of the things that I want to talk about before I answer that question is the socioeconomic factors and drivers behind skill fishing.

I've actually investigated this phenomena because when I was building one of my companies, Wadi Tech over 10 years ago, I quickly realized that there's a lot of clients of ours that were experiencing skill fishing. Alex, congratulations on creating that term because it's actually today being amplified by AI to your point.

The socioeconomic driver started, at least based on my research and me going into great terms to actually do this research, is that during the dotcom boom, there was a huge gap in terms of talent. There is a lot of shortage for companies that are growing out of Silicon Valley to hire talent. So there was a layer and a gap for organizations that were going to come and help a lot of these employers put a lot of engineers in a lot of these seats.

There was a full blanket of companies that came in and they have identified this particular loophole. What they did is that they were able to focus on maybe engineers who had one year or two years of experience. They put them in boot camps, they amplified their resumes with skills and they created some fraudulent resumes and they've trained them how to interview for positions and ultimately that definitely have impacted a lot of the employers and organizations in which a lot of these folks actually came and worked.

But one thing to also focus on is a lot of these employers from 10, 15 years ago, they really had to take a lot of this talent. And knowing that some of these folks did not have five, seven years of engineering experience, but they really had to fill the seats because they had programs to deliver on. They had software to write and so on and so forth.

So this is a phenomena that has definitely impacted the market and it's been going on now. My experience has been heavily impacted on the technical front. And so we've hired candidates who've had a lot of people interviewed for them, and another person showed up to work. That has happened. People who have obviously lip syncing. We've seen all that. We've hired candidate one time who was supposed to be an engineer. And then we came to realize that he was basically searched online for every task that was given to them. And so it was definitely impacting a lot of the projects and ultimately that we had to let them go.

The impact on organizations is tremendous. And I've had so many cases in which I had hired someone who just was not purely vetted.

Nichol: During the tech boom, there was tremendous pressure to get people in seat. And so there's super fast hiring, and so there's that possibility where you're really putting your organization at risk because you're like, Hey, I gotta get this person in the seat, otherwise we don't get the job or we don't get paid. We can't bill our clients.

So that certainly can lead to more skill fishing incidents, but certainly you've also pointed out that this isn't a new phenomenon. But would you say that we are seeing an increase in skill fishing incidents today?

AJ Faraj: Yeah, I think a lot of organizations, and the reason for it is that based on my experience, at least over the past 10, 15 years, the impact was really most likely due to the technical positions. But now with AI, you are seeing that in non-technical positions, and so you're seeing a lot more organizations being impacted, whether it's an administrative, it's a marketing position and or any other type of position. And the reason for that is because of the availability of a lot of the AI tools that act as co-pilots to a lot of folks on the market by saying, you know what, I'll just put this on my resume and then when I get the job, I'll probably ask ChatGPT to help me out.

Nichol: That's right.

Alonso: To AJ's point, one of the things that strikes me is we're seeing, for instance, that more than half of HR professionals are saying that it's the advent of these tools that are helping potential employees or candidates actually jump or add the skills representation that makes them look much more competitive in market. And it's happening in jobs that aren't technical, but it's happening with the technical skill in those jobs.

So perfect example of that. As a marketer in my space, we've seen it with a variety of data scientists who claim that they have the skills that they don't really have, or we start to see them really build in work samples that indicate what is happening or what they can produce when they themselves never actually produced it.

Nichol: Yeah. So Alex, you identified three key forces accelerating skill fishing. There was credential inflation, performance theater and AI amplification. Which of these do you see as the biggest threat to hiring accuracy?

Alonso: The biggest threat to this is actually the performance theater as I think of it. It's the ability to demonstrate that you've performed something when in fact you have never actually done that. And it's happening in large part because of the AI tools that we're talking about.

Now, I'm not somebody who's a proponent of doing away with AI by any stretch. I tend to lean more on the side of being much more liberal with what happens in terms of the use of AI. But one of the things that I think is we're starting to see is this notion of divesting ourselves of the kinds of assessments that are, the traditional assessments that we've seen around performance and performance theater.

So much so that when you look at our data, 51% of HR professionals are now using what is a probationary period. In other words, being very clear about, you are gonna go join us, but you've got a probationary period and it's expanding across all types of jobs.

When I look at our data, what that speaks to though is if you were to go back to the height of the labor market when it was basically two jobs for every individual that was unemployed. Back in 2022, we were doing away with those all the way around. We were hiring a warm body to make sure that they could come in, learn how to do the job.

Today, because the labor market has shifted and we've got basically one-to-one or even fewer jobs than there are unemployed individuals. What we find ourselves doing is being much more prescriptive by saying, we're gonna build in these probationary periods. And it's that ability to build in that probationary period that makes it so that we have a little bit of wiggle room, but you're seeing them in jobs that you would never have expected in any way.

AJ Faraj: Another point that we're seeing today. And I'm not sure, Nicole and Alex, if you've seen it, which is a lot of talent are actually offering two week trial. I don't know if you've seen this. This is a new phenomena that is actually coming where I'm willing to work for two weeks. You can try me out and see for yourself.

Alonso: Yeah. And not only are we seeing that AJ, but one of the things that we're seeing is this notion of work samples, but doing work samples in person and bringing in the opportunity to try a work sample given one that is fully in person performed and prescribed within a week long period at an employer. The idea that we will actually pay for that, but you're gonna go ahead and demonstrate that you're able to do that and it is quite literally performance theater with the notion that you are gonna prove that you can perform.

Nichol: Yeah. AJ, we have a question from the audience. If you don't mind, how are you navigating the influx of bot applications and resume making, and how is that impacting the use of AI screening tools within an applicant tracking system? And then the last part is how will recruitment change because of this?

AJ Faraj: Yeah, awesome question. So the first thing I would like to say is in the era of AI things are moving fast, organizations are adapting. And one of the things that we need to do in talent acquisition is redesign our interview process. I've been talking a lot about this across organizations.

The past days of doing a blanket type policy when it comes to interviewing candidates should no longer be applied here. I think number one, there's a lot of bots applying on behalf of candidates. Resumes are being completely edited by AI or even drafted by AI. All that is going to be creating tremendous overload on teams that is going to be gaming the system. And it's going to create a lot more skill fishing, where you're hiring talent who skill fished you because you're just overworked and you've made perhaps the wrong hiring decision.

So when it comes to the redesign of interviews, it really starts with, well, we know everyone in the next probably couple of years is going to be using AI to come through that funnel. So ultimately what companies and what we're seeing the startup world is doing, and what we're doing here at Source X is really combating that by creating layers of interviews where candidates actually cannot just come in and just have the bot apply on their behalf. They're going to be going through like an AI interview. They're going to go through multiple potential challenges right at the front gate before we even consider them and actually have to speak to them internally. So that's one way.

The other way is that you really need to understand the domain that you're operating in. Whether you're hiring technical talent, you cannot really interview someone who's technical, similar to someone who's in the marketing or any other type position. And that is because of the realities, not only AI, but remote work. A lot of organizations continue to operate remotely. And so that's another amplification of this particular problem that we're talking about today.

Nichol: Yeah. So I mean, beyond just the frustration of bad hire, certainly there's very real organizational costs of skill fishing that leaders might not even immediately recognize. And you've just pointed a few of those out. Alex, you also mentioned that demonstration now matters more than declaration. What specific methods have you seen work effectively for validating real capabilities during the hiring process?

Alonso: So nothing has ever really supplanted the notion of a work sample. At the end of the day, a true blue demonstration of the ability to do a job is absolutely the most valuable predictor of the performance criteria we've ever seen.

But what I find in particular we are seeing is really the rise of, as AJ highlighted, really sort of an AI-based interview, but in reality it's an AI-based assessment center. And it's the ability to see how people are tackling complex problems or problems that are multifaceted and multi-layered in an assessment paradigm. Meaning that the AI tools are building ways to test how well people can tackle multiple issues.

In the old school days, we used to call these assessment centers or role play exercises or things like leaderless group discussions that in baskets exercise, take your pick. AI allows us to do that and actually assess it in a much more efficient fashion. And for second order jobs, those jobs that are that second layer of professionalism, those types of things are actually much more effective at helping us do a bigger throughput and do much more throughput when it comes to assessing a candidate pool.

One of the things that we see people really engaging in now is the notion of building those tools and taking those tools into account when trying to assess a candidate. If you look at those types of tools, they're becoming much more prevalent. So much so that I would argue just in our own investment strategy here at SHRM, we've seen a rise of almost a hundred new startups in the last six months in this space, and that's a group that is growing almost significantly every day.

Nichol: So according to SHRM's skills first movement report, relevant work experience, demonstrated skills and competencies were ranked among the top factors in hiring positions. That makes sense. So AJ and Alex work sample tests and skill demonstrations sound great in theory, but they can be a little time intensive. So how can organizations balance through vetting with maintaining that efficient hiring process?

AJ Faraj: So I think one of the things that we need to really think about as folks that we're hiring continuously is again, I'm gonna talk about redesigning that interview process, but I think one thing I wanna highlight is that the future is going to be AI doing a lot of the mundane or easy tasks that a lot of folks are going to be hired to do.

You might hire someone to, I was giving an example the other day to someone, if you're hiring someone in marketing to just write. A lot of people will disagree with me, but to write something AI can definitely help you write something that seems very, very good. So you don't necessarily need to hire someone to just write.

So ultimately what that does is create a gap, and that gap is specifically why are you hiring that person? What are some of the tasks and duties that they have to actually implement on day to day in which they are going to be successful. And so that's where the design of that interview process is going to come into the picture, which is, what are you exactly interviewing me for? Let's not do it like a blanket policy. Tell me about yourself. What have you done in the past?

I think it's more of AI is gonna be doing probably 60% of the things that you're gonna be doing. Let's talk about four things that you're gonna be doing every single day that AI is definitely not gonna be a part of, in which you're gonna convince me that you're gonna be actually a hundred % on the dot there. So that's one way to think about it.

The other thing that we probably need to take into consideration is the fact that when you're looking at different types of positions, you need to take into consideration how to interview talent in the sense of a technical coder. Today with all the AI tools on the market, you are probably gonna be skill fished to some degree. So we need to redesign our process.

We're seeing it every day on social media, how people are trying to game the system and a lot of developers are trying to game the system on purpose. And so the question is that how do you redesign your process? If going to apply for a realtor job, you go into a center where they check you and you have to sit in front of computer and do it. I'm not saying this is the best way to do it, but I think there are ways in which we need to really think about how do you interview a marketer? How do you interview a developer? How do you interview a doctor?

Nichol: Yeah. Yeah. For sure.

Alonso: If I could just chime in there, one of the things that strikes me is AJ's absolutely right in that the criteria that we use for most jobs and selection today are gonna completely change. And I think of this as specifically when it comes to, does the person have skill proficiency or not? Are we hiring for that? And realistically, in the future, we're not gonna be hiring for that.

What we're gonna be hiring is the capability to develop proficiency, meaning how well will somebody learn, how well will somebody, how long will it take for them to become proficient as opposed to being somebody who already brings the skills to bear.

What that results in, in many cases is we're gonna be hiring consistently for somebody who can learn quickly and porting them to the kind of job that we think they can do based upon that. Now, there are limitations, of course. Regulatory reasons make it so I'm not gonna hire just anyone to be a doctor. I'm gonna need somebody who is a doctor. Somebody who is an engineer.

But at the end of the day, what that does is it changes the criteria that we're using to select humans. It also changes the way that we select those humans, and then more importantly, it's gonna change the way that HR departments are evaluated. Time to fill is not going to be the key metric that we think about when it comes to talent acquisition. It's not even gonna be quality of hire. It will actually be time to proficiency.

Nichol: Right? Yeah. And people's learning agility.

Alonso: Yeah.

Nichol: So Alex, what role should the provisional periods play, or those trial projects play in modern hiring and how can they be structured fairly for both employers and candidates? And I can see, just to kind of add, I can see a couple questions from the audience who are concerned about really kind of implementing that and operationalizing it in a day to day.

Alonso: Yeah. You know, there are the practical implications that go with those. The first is that they are really, how fair is it to actually build something in like that when you've got a variety of jobs that are available right there, and it becomes really onerous if you are a large employer and you have a variety of different jobs that you're trying to fill and there isn't consistency across those jobs. Let's say that they're varied in the types of roles that you have.

This is the case for what we call a universal skills model, where you're basically looking at a base layer of skills that are the kind that you wanna bring into the organization and then over time you'll assess further for using more traditional methods for proficiency and skills proficiency.

I think about examples that I've seen over the years, whether it was through Tyson's food or in airlines, or even in places like Walmart, where you see that universal competency model that they used to call it now, their universal skills framework, and what they're doing is they're assessing at a base layer. What it is that somebody brings to bear in those skills. And those skills are things like ability to build learning, agility, ability to pivot, ability to do the things that make you effective in a changing environment or an environment where skills become obsolete very quickly or change very quickly.

That's the first piece of it, I think. And those are the ones where if you're doing those on mass, I think you keep them general and they exist across all jobs. The idea being that it's universal across all jobs. What then becomes the second layer is you wanna build in valid instrumentation or valid selection instruments that are the kind, that actually tackle the what is those technical components that are really the hard technical components.

Now, keep in mind that AI does start to bring you closer to marrying those two together, but you're building a combination or kind of a multiple hurdle approach. The last thing that I'll say is those are the pragmatic considerations. The legal ones and the validity ones are always something that we should encounter. And as an IO psychologist, I am someone who's always gonna advocate for what is legal considerations and validity based considerations.

The one factor that always stands out to me when thinking about AI-based tools or other tools is it's very easy to develop construct validity in those cases because it's pulling from what is a known source in many cases as far as what are the sources out there that exist for valid knowledge bases. The harder part is tying it regularly to a criterion and correlating it to that criterion so that you can predict what the result you got from that assessment. Which is very much random towards the criterion that you're trying to develop, which is less random. It's performance and so that's the tricky piece. But if you can rest your laurels on at least that first content or construct validation, that's a good start.

AJ Faraj: I think one thing I would like to add to that, and in terms of hiring, mass hiring and the industries in which we're operating in, I think skill fishing may be applicable in specific industries rather than industries that would require some kind of a universal skill matrix.

What I mean by that, so we operate in different countries and we operate, for example, in the GCC, they definitely hire en mass for some positions and they bring folks from different countries and whatnot. They do have skilled fishing, believe it or not, that the skill fishing that they're facing and the way they go through about verification of identity and things like that, is primarily around power skills. So it's not necessarily things that are going to be completely impacting an organization. These are things that could potentially be taught to a human versus something, you're bringing someone at the mid-management level or even in a senior management level where it could be devastation for the company in terms of cost, in terms of the replica of effects that may incur by the company.

Nichol: Yeah. So AJ, as AI tools become more sophisticated, how do you see the skill fishing challenge evolving and how can HR leaders be prepared for more?

AJ Faraj: So there's a few things that we need to really think about when it comes to that. I think, as I said earlier, I mean, I keep talking about this, the redesign of interviews, and the reason I talk a lot about this, and that's because something that we really had to encounter across both of my companies.

And so one of the things that I talk about, you really have to stop for a second and think about the market landscape of talent. You're saying to yourself, well, talent are people, people are adjusting. They're using AI to apply. They're using bots. Humans need to have jobs. It's a given. There is a gap in the marketplace. There's jobs that are coming on the market where there isn't enough talent for these jobs.

And so one of the things that you really need to think about from an organizational perspective, number one is upskilling. I think that's a pretty big thing that a lot of companies need to take into consideration, especially when it comes to very tactical positions within an organization. So upskilling is number one.

Another one that I actually just wrote, which is think about anyone that you are going to be hiring into your organization as a freelancer. And typically if you look at it from that particular lens. I'm an entrepreneur. I hire a lot of folks in my organizations, and so every person that comes into the picture is definitely going to be impacting everybody in that particular circle.

So what I think about it is I think about every particular hire that comes into the organization as a freelancer. So when you hire a freelancer, what happens is that time is of the essence. So earlier Alex mentioned something, which is the provisionary period, which is about 60 days or 90 days in different states and so on, different countries. And so for me it's about how do you hire someone and what exactly they need to deliver from day one to make sure that they have not skill fished me.

So if you hire a developer and he's charging you a lot of money as a freelancer, in the second day it's gonna work or not. So you're gonna have to think about that very, very well when you're doing that.

Number three, I think with the adjustments of the workforce and what we're seeing today, and Nicole, you probably know this about me, I have talked a lot about AI interviews and using AI into your hiring process. I'm a big advocate for that. So there has been, if you look at this particular movement, which is AI hiring. About a year and a half ago, there was a lot of pushback by organizations across the United States, in some countries around the world. But today the adoption rate is tremendous. We're seeing a lot more organizations adopting it simply because of things that we're talking about today. High volume skill fishing. It's mundane work. People are trying to move faster and so on and so forth.

So I think if you implement the right tools in your company. If you redesign your interview process and you redesign how you hire folks. And finally, you'll look at the talent from my point of view as freelancer, which is time is of the essence because AI is gonna be on your side anyways, so the expectation is going to double. And so that's what you probably need to put in place to avoid skill fishing when using AI tools on the market today.

Nichol: Yeah. So I mean, in short, it can't be business as usual, but it's really about taking a look at your current interview process and understanding where it makes sense to weave in AI tools, maybe assessments, the work product as Alex mentioned. And it is a combination of those things as it makes sense for your organization and your organization's culture.

Alonso: You know, one thing that I would add to what AJ was saying is in many cases what we found about that freelancer kind of perspective is organizations that take that perspective are much less likely to see skill fishing in high volumes.

If you were to look at our data today, we see that 13% of employers, meaning organizations are saying that they see skill fishing happening at a very often level, meaning that it's happening across more than 50% of the jobs that they have, and it's happening at a rate that is close to 50% of the candidates that they see. That is scary to me. I think about someone in your role, Nicole.

Nichol: Agree.

Alonso: Imagine basically you're flipping a coin over and over again, praying that this person is gonna be effective and that they are actually able to do this regardless of that. So the first layer is thinking of people as freelancers the minute that they get there, because you are not wedded to those individuals, but two, you need to know right up front whether or not they actually have those skills. And you see it right up front because you can change the employment paradigm very quickly.

The second thing that we see is when thinking about people that you bring in, don't just think of them as a freelancer. That is somebody who will do that one role. When you're assessing individuals and assessing candidates, ask yourself, what three roles can they fill? Meaning if they are someone who is a freelancer, but they have high skill in something else. Could I apply those skills? Could they learn, could they actually tackle more than the one role that we've sort of looked at them for?

And by applying that every, what three jobs rule can this person accomplish, it immediately makes it so that you're assessing not just an individual for their fit, you're assessing them more for what it is that they can actually do from a learning capacity.

Nichol: Yeah. And I've always sort of had the mindset when I'm interviewing someone, which is, you may be interviewing somebody for a particular role, but I always like to look beyond the role. Like, where else do I see this person? Because it isn't, I mean, to your point, you can't just look at what's in front of you today. It's you're looking ahead and across your organization all the time.

AJ Faraj: I think one thing, Nicole, if I may, add one thing, just give some examples of what we've done in the past also. So one thing hiring managers need to think about today is that how did this candidate actually get to my desk? In terms of like, how did I actually, why am I interviewing this person? What are the steps that they've gone through early on and how can I actually trust the process?

So I think the hiring managers today in organizations also, because there's a lot of burden on them when you're hiring someone between time to onboard them between the project delivery and so on. It's a big, you're driving a lot of impact, and so as a hiring manager, you really need to think about that and be a part of auditing that the steps in which the candidate actually landed on your desk. This is very critical.

For example, we had early on year, a couple years ago when we implemented our first AI interviewer, we implemented it for one specific project, believe it or not. And that's how it started. It was for Salesforce, Salesforce implementation. There's a lot of skill fishing in that particular space. Tremendous talent. But there is also a lot of folks who are skill fishing in that particular space.

And so what we've done is that we really had to audit how we are doing business and how we're hiring a lot of developers in that particular space, simply because hiring managers were saying, you're wasting our time with a lot of these talent. You're sending us the wrong talent.

And so we really had to stop, look at the technical recruiters that we had and say, well, how can we enable you? So we built the AI interviewer. And the recruiter would actually, this is something for folks on this call to take into consideration sometimes you might actually have to have a recruiter who's not necessarily technical. If we're talking about technicalities, attend an interview with an AI recruiter, if that makes sense. The AI is doing the interview. They're there administering the call and looking at the talent, especially in an environment. That's why I said early you really need to understand where are you operating, where are the risks in that particular market? And how do you deploy and optimize your process accordingly.

So that's some of the things that we've done early on and we've reduced that skill fishing in that particular, for some of these Salesforce projects tremendously by implementing and optimizing our process.

Alonso: I know you're preaching to the choir here, AJ, because Nicole, I can tell you right now, I'm sure is thinking, oh my God, hiring Salesforce architects, hiring Salesforce professionals is one of the hardest things in the world, and she knows this because of my team and my team leads data architecture here at SHRM and data governance. And so we see this repeatedly. In fact, I've never seen a series of skills that lead not just to skill fishing, but also to ghosting so quickly. I've never seen anything like that in my life.

Nichol: For both of you, if you could change just one thing about how organizations hire tomorrow, what would it be?

Alonso: AJ, you're the expert. I'll let you jump in.

AJ Faraj: I think one of the things that I have noticed, building Source X, which is a completely an AI platform for hiring, but one of the things that I have noticed is that AI interviews, if they are designed by whether the hiring manager or the recruiter, they typically have these folks question exactly what they want to ask. So it goes back to the redesign of interviews.

A lot of times when you're meeting someone face to face, you end up creating questions on the spot. It's back and forth. So you come up with new questions and why not? When you're doing an AI interview, and I'm gonna be an advocate for anybody that is going come through your funnel, you must have a layer of defense, and that is going to be a first, an AI screening or two AI screenings before they even speak to someone on your team.

But I think what if I were to change one thing? Is that what exactly are you going to be asking candidates as soon as they come through that door? And did you really think about these questions very well and the exact answers that you're expecting? And I think if you are able to design that very, very well, it's going to save you a lot of headache down the road. And I think that's one of the things that I like to change is that every company needs to stop and say, what are some of the questions we need to ask for that type of position to make sure that we are not wasting any time at the step after that particular screening.

Alonso: For me, the one piece of advice that I would offer is turn AI into your advantage. And I know that's hard to do when you think about municipalities and different states and even the federal government, the way that they're approaching employee selection and thinking about bias when it comes to AI algorithms and things like that and selection.

But what I would actually advocate for is there is no greater test of judgment than situational judgment and or forced choice if given two options, which is the better option of what I can accomplish and. AI allows you to do that much more effectively and much more quickly. The interesting thing about that it is, it is also very much tied to how well somebody is performing or capable of performing a given task. So to me, that's the first layer of what I would do.

The second layer is I would almost immediately advocate that you build in something around learning orientation and learning agility as part of your overall selection process and your selection tools.

Nichol: Agree a hundred %. Perfect. Well, I appreciate both of your perspectives. We kind of started on a topic, skill fishing, which is not a great experience, and you both have given us some really great positive outlook. You know, insights about hiring, the just the potential of being able to look at candidates, not just from one job, but from a variety of different opportunities.

And so obviously AI presents its challenges, but I think we can all agree that the positives far outweigh the negatives. So with that, I want to thank both of you, Alex and AJ for joining us with this very special live recording of Honest HR.

Alonso: Thank you, Nicole. It's been a pleasure. Thank you, Nicole.

Nichol: I had a lot of fun. So that's gonna do it for this week's episode, and we will catch you next time.