Building HR Technology for Tomorrow – ep.156

Dive into the innovative world of HR technology on this episode of The Jali Podcast, where host Melyssa Barrett explores groundbreaking strategies with Joel Quintella, CEO of Quintella Group. A leader in HR tech, Joel brings over 25 years of experience in enhancing HR processes for Fortune 500 companies through the integration of advanced technology and psychological insights. Discover how AI is being leveraged to eliminate biases in job descriptions and promote inclusive hiring practices. This episode sheds light on balancing technology with human outcomes to create diverse talent pipelines and drive equitable hiring solutions. Tune in to learn how tech is transforming HR into a more efficient, just, and inclusive field.

Melyssa Barrett:  Welcome to the Jali Podcast. I’m your host, Melyssa Barrett. This podcast is for those who are interested in the conversation around equity, diversity, and inclusion. Each week I’ll be interviewing a guest who has something special to share or is actively part of building solutions in the space. Let’s get started. Joel Quintella, CEO of Quintella Group, a minority owned business that’s transforming the HR tech world. He has a PhD in business psychology and a background in computer systems engineering. He’s not your typical HR guru. He is the tech genius streamlining HR processes for Fortune 500, 100 companies and tech giants. For more than 25 years, Joel’s been in the trenches of HR technology innovation, experiencing and propelling the evolution from clunky complex systems to efficient solutions. You will enjoy this podcast with Joel with his background in psychology. He has kind of one of a kind way of understanding how people and technology interact in the workplace. We do even have a conversation about artificial intelligence because he’s not just about creating sophisticated algorithms. He’s also focused on how using tech to help companies perfect their hiring game. And we all know that everybody’s looking at AI these days. So yes, I asked him about it. Join me as we talk about leveraging AI to eliminate bias and job descriptions, promote inclusive hiring. We talk about balancing technology and the human outcome in terms of creating diverse talent pipelines, and we even talk about some tech solutions for tracking and DEI. So check it out.

I am excited this week to hear from Joel Quinella, who is just a masterful whiz when it comes to HR technology, inclusion, all the work that you’re doing, I just think is phenomenal. And most of my audience knows, I love to celebrate people who are actually doing the work, not just talking about it, but actually doing the work. You have such an extensive background in psychology, computer systems, engineering, all of these things. And so I always like to start out with the story because everybody has a story. It’s like how did you get to where you are today as the leader, CEO of your own company?

Joel Quintella:  Yeah, that’s a good question. Maybe you were talking about your dad was an entrepreneur, which is cool. You got exposed to that. I didn’t. I grew up in the southwest Texas, right by the Rio Grande and grandfather had a farm. I just spent a lot of time there. I guess he was an entrepreneur as well. Yes. It just never occurred to me that I should own a company and anything else. It just didn’t come up in the, it had a great childhood, but just didn’t occur to me that maybe I should own a business until I started getting into school. And you were right. I started off as a computer systems engineer actually in high school. I don’t want to tell you the programs that I was using really date me.

Melyssa Barrett:  You look pretty young, so I don’t know.

Joel Quintella:  Thank you. Yeah, the checks in the mail. Thanks. Thank you for that. But I started there and then actually when I went to college in Arizona, I started off as a computer system engineer and I figured out quickly that there’s some really smart people in computer systems engineer and I didn’t quite fit and that level of intelligence was pretty high. But I actually started, so I took a psychology course, the one-on-one or whatever courses, and then it occurred to me that I didn’t have to study everything that they were saying was essentially putting a name to stuff that I already knew. It was just a perfect blend for me. It was just like, I dunno, a fish to water, whatever it is. So I switched my major to psychology and before that I was floundering and it took me a long time to graduate undergrad because I was just moving around, but when I hit psychology, I just blew through it.

And so my last two years of college were better grades and then I just started thinking about a graduate school. My professors were asking about it and I said, well, okay, yeah, I think so. But just like you were saying, a lot of my psychology professors and they were wonderful, but they were speaking a lot of stuff, but I always thought, does that even work in the real world? Second, but I don’t know if it really works. So I wanted to find something. One of the psychology department or branches that do something like that and that have to be something called industrial organizational psychology, but we just call it business psychology in general. So I went to the Ohio State University in Columbus because at the time, that’s where the top 10 programs were in the country, and it took me a long time to graduate undergrad as well, or graduate school as well. Again, I got into the classes. They were talking a lot of, I read tower, they were okay with spending an hour or two just I know just talking about theory, I’m going, does this even work? I quickly left after my master’s and then just started consulting. I had the tech background. I always migrated towards the tech. So that was in the nineties or so when the internet was just started to come. But I bounced around from different companies. I worked for Verizon many years

Melyssa Barrett:  Ago

Joel Quintella:  Before it was called Verizon, and then eventually in 2009 I got the opportunity to just put my money where my mouth is. I had a lot of thoughts about how to do technology. Mainly we do HR technology. You’re talking about most of it’s really complex. There are a lot of buttons, there are a lot of words. It takes you two weeks to learn how to do it, and even then you don’t really get the hang of it. So I decided to create some technology and specifically in the assessment technology, so HR assessments, like personality test, IQ test, stuff that people don’t like to take. I’ll share a bit more why it’s important, but I wanted to blend that science of selecting right people into a company. Most people do even interviews, you’re informal interviews, everybody thinks they’re great at interviewing people and they’re not.

Melyssa Barrett:  No, absolutely. There’s so many biases that come in when you think about even the interview process or how people absorb information and then recommend somebody for a position that in some cases you might want to take a chance on somebody, but then there are maybe more qualified individuals that you don’t want to give a chance. Social investing that you’ve combined the business psychology and the computer engineering assessment process to I think a address a lot of the, I guess I’ll call ’em the subjective challenge when it comes to HR technology, which is pretty awesome.

Joel Quintella:  Yeah, it’s pretty cool. It’s not rocket science and my wife hates when I say that because she’s going, well, it kind of is rocket science.

Melyssa Barrett:  It’s not rocket science to you maybe.

Joel Quintella:  Anyway, it’s pretty straightforward a selection. Where we play most of our company is where we sit in the hiring space. It is a probability game. You don’t know if you’re going to make the right decision when you’re talking to somebody or going through the hiring process. You know what they’re going to do in three months or six months. You have to make a guess, and there’s a way to increase your probability of making a better guess, and it’s what you’re talking about, which is let’s make sure, number one, you need to make sure you understand what’s required at that role. Every role has a, even people that you were in, the positions that you were in or even just managing you had a pretty good sense of, Hey, I need people that can do these five things and if you can’t do these five things or have this quality or the ability, you’re not going to do well,

Not a lot of people do that. They just wing it. Like you’re talking about, you wanted to go buy a job description, which is never any good. Anyway, so first you start with that, and again, if I were to ask you what are the five, whatever you would be able to tell me pretty quickly, we do that with hiring managers and then we ask other people as well that know what performance is like, and then we come up with a list and then we just decide, alright, how are we going to measure these things through the hiring process? So it’s a deliberate, okay, I’m going to use an assessment for these things. I can’t assess communication with an assessment, I got to do that in an interview, but here and then you work backwards and go, how can I get at that stuff?

Melyssa Barrett:  And it’s so interesting to me because when we think about human resources and recruiting specifically, we hear about so much when it comes to AI and how we are implementing ai, which to me makes me nervous about bias and all of those things. So what do you think some of the challenges that companies are facing when they start adopting technology into their process?

Joel Quintella:  Yeah, no, the technology itself is, most big companies have an applicant tracking system that allows it to move people through the pipeline. But one of the big issues is a candidate experience. They’re going to go through and you probably, I am sure your members are saying, yeah, I’ve applied to 50 jobs and I haven’t gotten an interview and they didn’t tell me what it is. I don’t have any idea what’s going on. So communication is very important. So AI does that really well. It actually can proactively go out and chat with your candidates, tell them this is what’s going on, answer any specific questions that they may have throughout the end process and that makes for a good candidate experience. And even other things like scheduling interviews,

Melyssa Barrett:  That’s

Joel Quintella:  A crazy mess. And you might have, you’d be surprised at how many huge companies do not have a scheduling program and they’re just doing it themselves. So it’s a whole team just trying hire manager, what’s your schedule candidates? What’s your schedule trying to put together, putting together this crazy email that says you guys have to go here, here. That’s crazy. AI can do that really, really well. So all of the eliminating some of those repetitive tasks that we have to do, AI can take those and streamline those, and that’s a huge help. Now, the problem that we are seeing, we see it, but not a lot of people are saying anything about it for some reason. And it’s the fact that now you’re starting to use AI to make decisions about the individuals.

This person, she would move forward, this one shouldn’t. Now that’s when you start getting into problems. And we play a lot in the structured interview stage. So once you’re screening folks out, you have a handful of folks that you want to select in, you got to make the right decision. And one way is this structured interview process, which takes out a lot of the subjectivity and forces hire managers to follow a strict instructions. What do you do before, even after you got to ask these questions because we know that you need this competency. This is very deliberate and you got to take these notes. You have a five point scale, you have to look at that, so you have to justify, so it reduces that discrimination on our side. We’re always looking for quality of hire. How do you do that? That’s the predictability. And then we’re also looking at discrimination because EOC is going to come after you, if not, anyway.

Melyssa Barrett:  Well, and I think you bring up a good point because there are so many ways that you can use AI and technology, but then there’s this whole human element that is because during the process, you’re also establishing some sort of trust and inclusion and

Joel Quintella:  Yes, well, you think you are.

Melyssa Barrett:  You hope you are

Joel Quintella:  Well. Exactly. Well, hope is not a strategy. You got to go ahead and when we help, we go into companies, my first question to them is, how do you know your hiring managers are making good decisions based on interviews? Don’t, we don’t know because they’re not tracking that information.

Melyssa Barrett:  In

Joel Quintella:  That tracking system. There’s a huge black hole, and it’s typically on the interview stuff. So companies don’t know who the panel members are. You leave that up to the hiring managers. Are they diverse? We don’t know right now. Then once you have interviewers, are they making good decisions specifically or are they discriminating against others They don’t know. Companies don’t necessarily because they’re not tracking that information. Interesting.

Melyssa Barrett:  Yeah.

Joel Quintella:  We decided to build a company that allows people to track that information. If you don’t have the data, you can’t make improvements. Yeah,

Melyssa Barrett:  No doubt. So then to that point, when you think about, I know you probably have innovative approaches that you use to help companies track and improve their DEI metrics even along the way, because everybody’s at least trying to have a diverse slate and achieve diversity representation,

Joel Quintella:  You don’t know. As a company, I’m always amazed by that, that companies don’t track that information. Like you’re talking about diverse panels, they have no idea. You can train your hiring managers, hey, or say, Hey, look, you need to have a diverse panel. You don’t know what they’re

Melyssa Barrett:  Doing.

Joel Quintella:  Even if you’re using structured interviews themselves, you can train hiring managers where you should, how to do these. You can give them this structured guide that says you need to follow this. You don’t know if they do. But during that conversation, we thought it’s important to track that information throughout this process itself. So we know are you making good decisions? But also was there any sort of discrimination? Do you have a department that says too old all the time or asked about kids or It’s easy to get into that territory, but we have to capture that information so that our clients can make better decisions and not get in trouble when they’re challenged because they’re huge companies. They’re going to be challenged, somebody’s going to sue them. You have to have this information to help your cause, right? Otherwise you lose, you got to give a lot of thousands of money. The other thing that we’re seeing on AI is AI is now listening into your interview. So as the interviewer asking these questions, you’re still following a structured process, but the AI is essentially transcribing everything that everybody’s saying

Melyssa Barrett:  And summarizing. Summarizing.

Joel Quintella:  Exactly. So taking that, so the idea is, hey, we got to take that off of the hiring manager or the interviewers because it is a lot of note taking when you use this competency based interviewing. So conceptually, that makes a lot of sense, right? Okay, transcribing, then summarizing and that summary should help the hiring manager make a better decision by looking at that five point scale. So that’s the idea. Now what we’re seeing or not seeing is that we’re aware that the top transcription models are biased. Is it going to transcribe and summarize my conversation the same way it’s going to transcribe yours or anybody else? It isn’t, but nobody cares for some reason what’s going on here overlooking or you have these companies are always saying, Hey, we’re non-biased. We’re checking our ai, and it’s not biased. And that’s not true. They’re not looking at that. They’re aware that they can say that Nobody’s going to challenge ’em. Nobody’s going to say anything about, I don’t believe your AI is not transcribing correctly, therefore we’re going to attest you. And they also know that if there are challenged, there are no consequences.

Melyssa Barrett:  Yeah, I was just going to say, and it’s interesting because I think you’re right. I think a lot of people don’t test out to even understand what the bias is, but clearly there is a way, and maybe you can speak to what kind of technology help there is to level that playing field and especially for underrepresented groups like women and male dominated industries and all of those

Joel Quintella:  Things. Exactly. I grew up in near the Rio Grande, so English is not necessarily my first language. I always say that I’m illiterate in at least two languages Spanish, and I know that I have a lot of grammatical errors as I’m talking. So I’m pretty concerned if an AI is transcribing and making decisions about what I’m saying, I don’t trust that is going to happen. So that’s the other side. So you have to look at two things. One is from that transcription summarizing quality of hire perspective, and the only way you can do anything about it is you actually have to check, you have to do a lot of testing to make sure that your AI is transcribing correctly. Now that’s a lot of work.

Melyssa Barrett:  Yeah, no doubt. And what an interesting example, because when my mother is an immigrant from Panama, and her first language, she learned English, but her first language is Spanish, but when we were growing up, she was like, absolutely not. You speak English.

Joel Quintella:  No accent. That’s exactly what my mother told me.

Melyssa Barrett:  And so you end up with different cultural challenges I think depending on where you’re going, what you’re applying for, all of those things. But I think when we think about hiring, we’re always thinking about competency-based hiring.

Joel Quintella:  Yeah,

Melyssa Barrett:  Exactly. And so what role does HR really play in promoting that equity for competency-based hiring practices?

Joel Quintella:  So they have to be very deliberate. It’s this structured thing you’re talking about to remove I guess biases, but just sure the candidates are being treated the same way. Ensure that they’re being asked questions around the same competencies, ensure that they’re being given the same assessments and that’s the way HR or talent acquisition mainly can help with that, with the bias. And what’s interesting, we talk a lot about DNI in general in the workplace, but talent acquisition is a unique function because it’s pretty easy to tell if you are discriminating doing the process. It’s just a simple audit of the process and there are consequences for discriminating. So it’s a little unique, which is cool. So this is a function to go after because they’re going to have to do it. It’s not whether I want to or not want to. If you don’t, you are going to be sued and you are going to lose period.

Melyssa Barrett:  Yeah,

Joel Quintella:  You want to. So talent acquisition departments are constantly focusing on that as well. So not like any other departments, succession planning, talent acquisition or talent management, I say that’s all fuzzy, but talent acquisition, you can actually measure that stuff. So there’s an accountability component to which is great, but it’s nice. It’s built into the process itself that the way you take it, the way you actually ensure that’s happening is this structured process. You have to know what the competencies are. You can’t just have one person tell you, you got to have five experts say this is what you need. And then you have to decide what are the assessments, how am I going to get at that fairly quickly and especially in interview, what are the competencies, what are the questions I need to ask that are based on those? So there’s a structured format. So it’s cool that it’s baked in a talent acquisition from a risk perspective, there’s a risk, right? And there’s a real risk,

Melyssa Barrett:  Right? Yeah.

Joel Quintella: Walmart, they’re going to come after you, right? Equal C loves to plaster who they’ve sued that much money they’re getting, but it is going to happen. So whether you like it or not, you have to deal with it as a talent acquisition professional.

Melyssa Barrett:

What trends are you seeing when it comes to workplace culture transformation? Because retention is always a big issue as well, and I know in some cases when we talk about talent acquisition, sometimes we forget that we have a whole workforce over here that’s already working for the company.

Joel Quintella:  Exactly. There’s huge push, I guess people call ’em talent marketplaces so that they’re realizing that finding internal folks that can do the job is a much better thing than actually trying to find external folks to do the job. So what clients or companies are doing is they’re moving to a skills-based model. So in the skills-based model, what they have to do is they actually go through and it’s a huge process by the way, so everybody’s talking about it, we’re going to do this, but it’s pretty tough to do as clients are finding out. It’s a little tougher to do than said, but the first thing they have to do is go through all of their positions. Again, this is a deliberate thing. You have to go through all their jobs. So you probably saw this as well in a company you could be five titles that say customer service, but they’re completely different and they’re big paid completely differently. So this process allows you to clean that up so that it’s easy to see if you’re customer service, you’re the only one, you’re being paid this way and there’s a clear line for how to go up the organization. So that helped quite a bit. There’s a clarity component, ambiguity, it’s always tough to deal with, but this puts a nice pressure. The second thing you have to do is actually do those models, the profiles for each of those jobs.

Again, that’s easier. A lot of

Melyssa Barrett:  Work,

Joel Quintella:  A lot of work. And number three actually is you have to measure your current employees. You have to know what skills they have, and that’s a huge undertaking as well. But you do that so that you can go to internal resources. You have a whole bunch of great employees already. Maybe they’re not even in the right position, but if you have a skills profile for a lateral position or something a little higher, you can find those employees based on the skills profile. Then proactively ask them, Hey, maybe this is a good job. What do you think? Or Here’s a project that needs your skills, what do you think? Which is pretty cool, right?

Melyssa Barrett:  Yeah,

Joel Quintella:  I think that’s an amazing thing.

Melyssa Barrett:  Well, and to keep it all up to date is a whole nother task though. It’s one thing to do it one time, have everybody load their skills in, and then it’s okay, I moved over here and I now have new skills.

Joel Quintella:  Exactly. Keep up with it. Which is tough, which is what I’m saying.

Melyssa Barrett:  Absolutely.

Joel Quintella:  It’s a great idea, but it’s pretty hard to do. Companies are finding out we do everything. So that profile is not only talent acquisition, but it’s everything else. So you identify high potentials within your company, you’re still going to use that profile, that scarce profile to figure out who should be a high potential throughout or who should go development or who should be in the succession plan. But if you do it, it works really well.

Melyssa Barrett:  So where do you see the future of HR tech heading? Because I know you have such an interesting background and perspective. We hear a lot of, I’ll call it withdrawal from diversity, equity and inclusion, and we have AI that’s coming in, which is doubling in capacity every couple of months and utilizing all these large machine learning models and all of that. Where do you think we’re going when we talk about the future of HR tech and talent acquisition and what skills are really needed in this new generation?

Joel Quintella:  Yeah, it’s crazy. It’s crazy. Some of the new titles that have come out and the kind of salaries these folks are getting a prompt engineers, which basically, how do I ask chat GBT to find the right stuff

Melyssa Barrett:  Or code this whole program or something?

Joel Quintella:  Yeah, they do hallucinate. They’re not, right? So you get people that are just trusting it and then you go the wrong way. But we’re noticing is a bit what we’re talking about and what it does really are these tasks, the repetitive task where it can go out and somebody asks a question and they can go out and find the answers fairly quickly and come back and go provide that answer fairly quickly. So it’s more of like a chat agent. So the way it’s moving and HR Tech is moving is that kind of thing. You don’t have to go search for different applications to try to find the answer. As an employee, you just sit there and you ask, Hey, what’s the policy for my PTO? And it’ll go and find it and ask you, which is really cool. That’s a huge savings. That’s a huge savings. Now the problem where it’s D is when it starts making decisions about who should get a promotion, who should go in for development, who should be hired? Now everybody says because they have to say this, right? Everybody understands, all vendors by the way, have to say, we should never use AI to make decisions. There always has to be a human involved. Everybody says that that’s not the way they do it in real life,

Melyssa Barrett:  Right? In practice, they’re doing something different.

Joel Quintella:  Their AI is making decisions, right? There is no human, and frankly, people are going to allow the AI to make decisions even if you tell them not to because it’s going to make recommendations for whatever. And sanction usually if you’re just doing this other repetitive task, it’s a really cool thing. It saves a ton of time and headache on the employee side or even the candidate side, but also on the employees, internal employees. That’s a pretty cool thing. But companies are getting into this other space, the hiring space,

Melyssa Barrett:  And I just think there’s such a challenge when it comes to transparency around AI because we want transparency. When somebody makes a decision about giving us a loan, and yet now we have no transparency into why they selected this person or

Joel Quintella:  People say the words, but they talk the talk, but never walk the walk. It’s so weird and I think it’s just there are no consequences. You can say whatever you want, then people are going to buy your product and who cares, right? If it

Melyssa Barrett:  Are they though.

Joel Quintella:  Yeah, if you’re working with Fortune five vendors, luckily you have other departments saying legal that’s going to come and say, you can’t do that. Come on man, that’s great. You can’t do that. You don’t have these vendors that are just saying a bunch of stuff and we’re running up against a lot of these kinds of folks working for Fortune five hundreds. Legal’s going to say something, you can’t do that. Or compliance, are you crazy? You going to get me in trouble here? Or data security or intellectual property or your data.

Melyssa Barrett:  Think

Joel Quintella:  About what’s happening. All that data’s coming in, especially if you’re in interviews and you’re transcribing everything that everybody’s saying. There’s a lot of IP in there, confidential information as well. What do you do as the data security now? It’s a whole new can of worms because now it’s all out there.

Melyssa Barrett:  It’s discoverable.

Joel Quintella:  Discoverable, exactly. There’s actually funny fortune. One of the things that we see is some Fortune five hundreds want their hire managers to actually write notes or type in notes into that guide and some say, no, I don’t want my hire managers to type any notes with any notes close to that structured interview guide. I just want them to do a final rating and justify that final rating and that’s it.

Melyssa Barrett:  Interesting.

Joel Quintella:  It’s a crazy thing. The other thing that I don’t think around the transparency, and again, I’m in the hiring space, so this quality of fire that I’m talking about is important and candidate experience is important as well. But what we’re noticing is that everybody says these respected HR research, whatever, are doing studies on candidate experience. And of course AI is a big topic and the question is, do you trust AI to make decisions during the hiring process? And we found that 65% or whatever it is, the number are do trust AI to make decisions. And I’m going, did you ask people of color? Do you have data that says non people of color said this? People of color said this. And the answer is no, they don’t have that information. In fact, I asked that kind of question in a webinar recently. The expert says, that’s an interesting question, we haven’t looked at it.

Melyssa Barrett:  Oh wow,

Joel Quintella:  What the hell that didn’t occur to you? Had an experience is important. Shouldn’t ask folks. So town negotiation position, I see they have to, whether you like it or not, they have to consider this DNI perspective, but it’s also a huge push on candidate experience, which makes a lot of sense. There’s a dollar amount attached to treating people accordingly. If you treat people poorly or Walmart, those people are not going to buy from you.

Melyssa Barrett:  Yeah,

Joel Quintella:  That’s a lot. Experiences is important, but nobody’s actually dividing that information going, wait a minute, do people of color trust AI to make decision as much as non people of color?

Melyssa Barrett:  Honestly,

Joel Quintella:  No. In fact, we know that. Did anybody actually ask that question? No. It almost, it is so obvious to people of color, I don’t want to get you in trouble. People of color

Melyssa Barrett:  Not going to get me in trouble.

Joel Quintella:  But if ask specifically, or maybe they do ask the data terrible. If Thatt reported all of these candidate experience, whatever folks going to do research, they say again, 65 or 70, whatever, nobody reports those on the data side, nobody reports are using AI. From the quality high perspective, there’s some issues because the transcribing, the summarizing component and the candidate experience, which everybody cares about is also pretty important. But nobody actually cares enough to check for the differences on how the way that people are. Some folks are measuring whether people of color or everybody trust AI in the process is what they do is essentially present one page transparency kind of a thing that says, this is what we do, this is ai, this is exactly how we’re using it. Who knows the whole page saying this is what we’re doing, transparency. And at the bottom is, do you want to opt in or opt out of this AI and what they’re saying, and you’ll appreciate this A data person, they’re saying that that’s the way we know that people of color trust the process because

Melyssa Barrett:  They check the box.

Joel Quintella:  They’re not opting out. I don’t think that’s the same thing.

Melyssa Barrett:  It’s definitely not the same thing

Joel Quintella:  Folks are using as a metric. I’m going, I don’t think that’s correct. And by the way, who’s going to be more worried about opting out people of color or non-people of color?

Melyssa Barrett:  For sure.

Joel Quintella:  Where’s that David? It isn’t anywhere. Right?

Melyssa Barrett:  I love this curiosity that you have. So I’m venturing to guess that Cantella, your company has a lot of thoughts and as you go about the assessment process that there’s a lot of integration that you can create based on the information that you have access to. So tell me a little bit about Quintella and what you do and your company.

Joel Quintella:  So we’re essentially an HR tech company that works with a lot of Fortune five hundreds to make sure that they’re hiring the best employees and not discriminating essentially. And what our system does is allow 10 acquisi professionals to do that structured process, that competency modeling. There’s a technology that has to make it really simple to go ask the questions of the hiring manager and five experts. If you do have an Excel spreadsheet, it’s a crazy mess. Our technology allows people to do that so they can all say, Hey, look, this is what the experts said. Do we agree? Do we not agree? And then our system allows clients to actually pick those assessments that they need for that competency model and especially the structuring of the component, not only tracking the data that nobody knows who’s on the panel, we want to know so we capture that information and report that information both proactively and reactively.

Melyssa Barrett:  So, and I’ll ask it this way, I think we talk about equity a lot, but I think in a lot of cases I talked a little bit about how when you’re thinking about retention and talent acquisition, sometimes we forget about the people who actually are working at the company already, but how do you ensure that a degree of equity is being pulled into the process for people that already work there versus an external candidate?

Joel Quintella:  So it is a similar thing and we always say, I can give you great people, but it’s up to you to keep them right? Because culture’s a huge thing and manager is a huge thing. Sure, that’s a big, we can only predict from the hiring side only so much, but it’s the same information or the same qualities that if you were correct then that’s what they require on the job, but there’s also a culture fit that you should be assessing. Now, here’s where I think this is the only way we are going to become less biased. There’s always going to be bias.

Melyssa Barrett:  This

Joel Quintella:  Is the only number one, the people that are developing ai, that team has to be diverse. If you’re not diverse, you have no idea what you don’t know.

Melyssa Barrett:  You’re not even asking the questions,

Joel Quintella:  You’re not even asking the question. But it’s so crazy. People of color can look at it and go, this is really obvious. How do you not know this? And even my friends that said, point something out. They go, wow, I didn’t even think about that. What do you mean you think about this it,

Melyssa Barrett:  Right? Those offensive commercials that you see, it’s like somebody had to watch this, right?

Joel Quintella:  They had to go through 50 people to approve this thing,

Melyssa Barrett:  Right?

Joel Quintella:  Nobody figured that out. Anyway, so it has to be diverse. And then the internal team who is implementing this AI has to be diverse as well. The third one is this whole after market of auditors, they’re going to come in and now audit the laws and hiring essentially say you have to have an external personality, whatever, but those auditor companies have to be diverse as well. But guess how many are not too many? And you’ve probably seen it, right?

Melyssa Barrett:  Interesting. And I love the fact that you all are providing assessments and bringing all that in because I think until we start answering the questions and measuring the impacts, I think people just disregard. If I can’t see it, if I don’t measure it, it doesn’t exist.

Joel Quintella:  Exactly. That’s one of the differences that we will go at that extra step

Melyssa Barrett:  Because

Joel Quintella:  We care about that extra step. Now is the company that’s not necessarily diverse or that group that’s doing the AI is not diverse. They’re going to shut their computers off and go, that’s days. That’s it. I’m gone. I’m probably going to do more analysis to make sure it’s correct because it’s important to me. It’s a motivation thing, right?

Melyssa Barrett:  Yeah.

Joel Quintella:  Again, it’s not rocket science, but you have to ask the questions and then you have to gather the data to make sure that if it’s important to you in any way, you have to do those extra steps to make sure that that’s where we’re different. We want to use ai, we’re using ai, but we’re going to ask those questions

Correctly. Is it not? How the transcription, is it transcribed correctly? How about the trust in the ai? We’re we’re going to ask color non people of color, do you trust it? Do you not? What do you think what happened? Let’s get their point of view and actually do something about it versus just whitewashing or whatever. That’s where we’re different. So other companies do similar things, set up assessments and technology, but we’re really concerned about what AI is doing and not doing, and we’re taking the steps to make sure and the kind of companies we work with, they think that’s important as well. Yeah,

Melyssa Barrett:  That’s awesome. And I was going to ask you for what kind of advice you would give for leaders that are transforming their organizations, but you gave a whole laundry list of things there. Is there anything else that you might highlight?

Joel Quintella:  I think it’s the one point is important. You have to have a diverse team. Nobody’s saying this by the way. I dunno why people dance around it. I always say this rule when you’re trying to find a Mexican food restaurant, and the rule is when you walk into the restaurant, and if you don’t look in the background, you don’t see two grandmothers look like they could be by grandmothers. You better walk out.

Melyssa Barrett:  Yep, no doubt. And who’s eating there?

Joel Quintella:  Okay, nope, I got to get out here. If it’s good enough for selecting a great Mexican food restaurant, why isn’t that good enough to build in your ai? A diverse team is cooking. Why is it not the same rule, which is higher stakes? Everybody knows this rule, but nobody transfers over here and that’s an important one. That’s the only way we’re going to get less bias. Your team who’s building it has to be diverse. The internal team who’s implementing this has to be diverse and the people who are auditing and monitoring have to be diverse and people are.

Melyssa Barrett:  So how do you think, I mean we have a lot of companies that are eliminating this whole chief diversity officer position. What’s your view on that?

Joel Quintella:  What we’re seeing is that they’re eliminating the name, but it’s still happening behind the scenes, which is great for us and it’s going to get a little worse in the next couple of years, but companies do find it important because there’s a lot of research that says if your team is diverse, you make more money.

Melyssa Barrett:  Everybody likes more money.

Joel Quintella:  Everybody likes me. If you’re Walmart or your client base is probably 40, 30% people of color, you better have people on your team that understand what that huge market wants. The companies do understand that, Hey, I better, that’s my client base. That’s a lot of money. I better find out what is happening so that I can make the money and the clients that don’t lose eventually.

Melyssa Barrett:  And as we come to a close, I think we’re all thinking about the workforce and what the workforce looks like when it comes to talent acquisition over the next five or 10 years. We have a lot of people that are nervous because jobs are going away, but we know that jobs are being created as well. And so what are your thoughts in terms of just upskilling that workforce and how that’s going to play into the talent acquisition space?

Joel Quintella:  Well, just this skills-based approach, which necessarily removes everything else from the picture, but not necessarily a competency or a trait that you’re required is can you do what’s required of the job? Ease, no color, essentially, if you can do that skills based approach, you’re going to minimize any of the bias. You’re just talking about who can do this job? I don’t care where you come from. I don’t care what your IQ is. Can you do this job? And if you can, that’s a great position for you. So skills-based approach does work. It’s a lot of work to do that, but it does help reduce that bias. We get away from the fact that there’s a lot of people of color that are pretty good what they do. So if you’re doing a skills based approach, you’re going to include more people of color in your process, and that’s everything. Talent acquisition, succession planning, development work, that’s still going to get better. So even though the names are going away, the internal processes are not going away

Melyssa Barrett:  Because

Joel Quintella:  There’s money attached to that. And if there’s money, you’re going to do something about it or you lose out. That’s a lot of money. And if you upset your candidates, people of color, they’re not going to go shop at Walmart or whatever, it’s paid on a Walmart, but they have millions of candidates coming through, they lose a hundred thousand candidates. That’s a lot of money for them, and they know that. So whether they believe in whatever it is, they are going to pay attention to that diversity. It’s not just because the EOC is going to sue you is because I’m going to lose money if I don’t do this correctly. When you do that, things change. Things happen.

Melyssa Barrett:  That’s awesome. And I know, let’s tell people how to get ahold of you and make sure that they’re aware of what you offer. I think as I started out, I love to celebrate people who are doing wonderful work. So I just appreciate the fact that we could have this conversation.

Joel Quintella:  I really appreciate you having me. I know that I say a few controversial whatever, but it has to be said, right?

Melyssa Barrett:  Absolutely.

Joel Quintella:  You can’t just dance around some of the issues, but

Melyssa Barrett:  I didn’t find them controversial, so maybe other people will.

Joel Quintella:  Exactly. Whenever I make comments and webinars, the only people who like them are people of color. There’s a thousand people in the webinar.

Melyssa Barrett:  Yes. We need those allies. Like we got to have the intention, and if they’re in positions, then we got to get ourselves in the positions.

Joel Quintella:  That’s annother thing of how we do it. But to reach me, you reach me, and then it’s Joel, JOEL at Q-U-I-N-T-E-L-A, dot io.

Melyssa Barrett:  Io. All

Joel Quintella:  Io.

Melyssa Barrett:  You got to be different, Joel.

Joel Quintella:  I’m an IO psychologist by the way, so just go there. We ran into that. It’s actually Indian Ocean is the

Melyssa Barrett:  Nice,

Joel Quintella:  Yeah, we founder anyway, so just reach out to me. You can find me on LinkedIn. Joel Canella, I think it is actually is Joel Canella is the LinkedIn page or give you an email. I’m happy to,

Melyssa Barrett:  Yes. I love it. I completely wish you continued blessings and hope we will continue to interact. I think there are so many opportunities in the world, especially as we think about the public sector and the private sector and how we’re using technology. There’s so many different uses for the perspectives that you bring, and I just want to thank you for all the work you’re doing in the

Joel Quintella:  World. No, I appreciate that because you’re just some pretty cool stuff.

Melyssa Barrett:  It is been a pleasure for sure. So thank you so much.

Joel Quintella:  Yeah, absolutely. Thank you.

Melyssa Barrett:  Thanks for joining me on the Jali Podcast. Please subscribe so you won’t miss an episode. See you next week.