Included in Socialmicole.com’s Most Inclusive HR Influencer List, Bennett Sung has contributed to the success of industry-leading recruiting technology brands, including VirtualEdge, Jobscience, MightyRecruiter, Koru, AllyO, and now Humanly.io.
In this episode, Bennett talks about AI’s current role in the recruiting process and the role it might play in the near future.
[0:00 – 5:23] Introduction
- Welcome, Bennett Sung!
- Today’s Topic: The Role of AI in Recruiting
[5:24 – 18:28] What’s Happening, Working, and Not Working in Recruiting Presently?
- The recruiting process hasn’t actually changed since the 1950’s
- Why haven’t we build a platform that aggregates the data points needed for job application?
[18:29 – 25:49] Conversational AI in the World of Recruiting
- Chat bots and the benefits they introduce to applicants
- Why aren’t companies using big data to gather information about their applicants?
[25:50 – 32:30] How Is AI Being Used in the Interview Stage?
- AI can now gather data from inside interviews
- Regressing data against candidate success
[32:31 – 36:21] Final Thoughts & Closing
- Start the candidate journey with a conversation
- Thank you for listening!
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Resources:
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“Hiring has never changed. The process itself never changes, right? They talk about disruption … but the actual transformation of the recruiting process really has remained consistent since the 1950s, when the CV was born or the resume was born.”
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“[AI in an interview] is a change management tool. This is not your traditional data collection compliance application anymore. So that is the initial purpose of AI, and it’s going to continue to build itself out so that it becomes much more effective in decision support.”
Announcer:
Here’s an experiment for you. Take passionate experts in human resource technology. Invite cross industry experts from inside and outside HR. Mix in what’s happening in people analytics today. Give them the technology to connect, hit record, pour their discussions into a beaker, mix thoroughly. And voila, you get the HR data labs podcast, where we explore the impact of data and analytics to your business. We may get passionate and even irreverent, that count on each episode challenging and enhancing your understanding of the way people data can be used to solve real world problems. Now, here’s your host, David Turetsky.
David Turetsky:
Hello, and welcome to the HR data labs podcast. I’m your host, David Turetsky. Like always, we try and find you the brilliant minds inside and outside the world of human resources to shed light on HR data, HR analytics and HR Technology. Today, as always, we have with us Dwight Brown from salary.com. Hey, Dwight, how are you?
Dwight Brown:
Good David, how you doing?
David Turetsky:
I’m doing great. And we have with us my great friend for many years. And we just calculated kind of over a decade. Bennett Sung. Bennett, how are you?
Bennett Sung:
I’m doing good. David, thank you so much for having me on your podcast.
David Turetsky:
Thank you very much for joining. I’ve known Bennett and I’ve always treated Bennett with care and respect because Bennett is a thought leader in the world of applicant tracking systems. HR is but mostly the recruiting process itself. And Bennett Why don’t you give a little background on yourself and where that all came from?
Bennett Sung:
Absolutely. So I started my journey I landed like most people, I landed in HR and recruiting technologies. So my career actually started off in the recruitment marketing space in, in Honolulu, Hawaii. So I decided to venture across the country to Boston, then to Seattle, settle down, settle down in the Pacific Northwest. And when the internet the dawn of the Internet came about recruitment marketing had to pivot. So primarily it meant that I needed to change career paths to the software space. And again, you know, coincidentally I landed in a human resource and Benefits Administration technology. And it really caught it took tickets own course from there sure venturing, as you mentioned, we spending a good portion of my career at ADP, learning the ropes of working with big companies, lots of salespeople, or messaging, etc. And then kind of you know, after that particular career path had come to a close, I really kind of took off in my really my product marketing, recruiting technology career path. So working for brands like Job Science, which got bought out by Bullhorn, Mighty Recruiter. So covered the landscape of recruiting technologies from applicant tracking systems, staffing technologies, to assessments, and now most recently to AI for recruiting. So that’s been my career path. And I’ve you know, I keep very laser focused on making sure that you know, that’s, that’s there’s the right course.
David Turetsky:
And it’s been fascinating working with you, because I actually have learned a tremendous amount from you over the years and anytime I needed to, especially when I was at ADP anytime I needed help understanding the world of applicant tracking systems, I would always go to my friend Bennett. So Bennett, the one thing that we ask every guest on the HR data labs podcast, what does no one know this one thing that no one know about you.
Bennett Sung:
So I my original intentions was never to be a marketing. My intention was to be a veterinarian. So you know, and so I enrolled into Animal Science classes at the University of Hawaii. And it’s an it’s unfortunate, unfortunate incident that one of the classes required me to dissect a pregnant cow. Oh, no, no. Because at that, I breathed in too much. And I inhaled way too much formaldehyde. And literally fainted on the cow job. That was my animal science career.
Dwight Brown:
Oh, I just got a mental picture of that.
Bennett Sung:
And then after that, I said, this is not going to be for me, even though I love animals and I still love animals. I don’t think I could really deal with this part of being a veterinarian. So I made a 360 pivot into economics, accounting, finally got to marketing and then finished my college career. So anyways, back out. Most people didn’t know that I had this passion for animals and that was I wanted to be
David Turetsky:
Sure, sure, sure. Okay. I can’t I have nothing to say, which is, as Dwight knows, that is, probably one of the rarest moments in the HR data labs podcast.
Dwight Brown:
I have never once seen your speechless David., Congratulations Bennett. You got the prize on that one.
David Turetsky:
Yeah, there you go Bennett. Leave it to Bennett for absolutely making me speechless. Okay, that’s, that’s
Bennett Sung:
That’s hilarious, awesome. that we’re gonna leave right there.
David Turetsky:
So our topic for today is fascinating, we’re gonna be talking about the role of artificial intelligence in the recruiting process. And like so many of us who have been in roles over the last two years, or three or even four, we’ve seen a tremendous change in the way in which recruiting systems talk to us deal with us, whether it’s AI and conversational AI, whether it’s AI in our candidate, the candidate process, actually entry of the the application. So this is a very fascinating topic, I can’t wait to talk about it.
Bennett Sung:
Same here, it’s been a lot of changes in recruiting technology, I think that is a great thing to see. But I think they consistency is the fact that hiring has never changed, the process itself never changes, right? There’s nobody who’s you they talk about disruption, you know, you’re disrupting and creating new technologies to create new experiences. But the actual transformation of the recruiting process really has remained consistent since the 1950s, when the CV was born, or the resume was born, right. And so it still required job seekers to hand a piece of paper over to the employer to look at their background. And then they make a judgment whether or not they want to hire here at that spot. And so, you know, from that state, from that early stage of the resume, being born to today, where you start to see and, and interact and engage with a ton of different types of technologies. The consistency has always been, you submit yourself, work on yourself a job, find yourself a job position, you’re applying for it, you get screened for it, you go through an interview process, you get go through a pre hired checklist, that you go through the offer and acceptance and boom, you’re working on that that minute. So, you know, in my, in my vision, the the world of where we’re living in is we’ve just we’ve introduced these waves of new technologies, right. So obviously, the first major change and change in recruiting happened when the internet kind of came to fruition that was really kind of became the new channel for classified advertisements, right, your, your, your your help wanted ads, that was one of the first major movements in change. But it wasn’t really we didn’t really change anything. We just shifted the 500 page, Boston Globe Sunday, how pocket section onto monster, you know,
David Turetsky:
Right, quite literally changed that way. Yeah.
Bennett Sung:
It’s just it was a simple pivot, right? It’s like, you know, you lost you, you know, you lost the beauty of all the branding that you got in the help wanted ads. But right now, you just saw a laundry list of descriptions, which made everybody be able to see what job opportunities. So that was, that was one of the first major waves. And then the the other waves that continue to come through, you know, we had then the emergence of social media. So all of a sudden, we coined a new term social recruiting. Right? It’s just recruiting using social media. It’s not a new type of recruiting. Right. It’s a new channel of recruiting and engaging with candidates and reaching more people.
David Turetsky:
And one would argue, Ben, if you looked at LinkedIn, it still is a separate entity. Yes, there are some jobs that are posted inside the social media of LinkedIn. Yeah. But LinkedIn is it has two separate channels. One, which is talk, which is the, you know, social media part. And the other part is or should be thought leadership isn’t. But the other part is, you know, I here’s the job board.
Bennett Sung:
Yeah. Yeah. I mean, and then, you know, then you have the mass array of millions and millions of candidates, or people you engage with to match them up. Right. So and then you began to, and then what came about after that was, you know, mobile. So mobile became a huge big required a lot of rethinking by technology providers, because now we have this very small device that we still need to present all this information. We needed to collect applications, you know, they didn’t at that point, they had not thought through, you know, oh, do I need to know, if somebody can really be able to type their entire your application on this low file. Yeah, right. But in things that will change, but of course, again, another wave, that wave, they tried to coin and phrase mobile recruiting. But again, it’s just recruiting using a mobile phone.
David Turetsky:
But remember, and we were there when we saw this happen, because I remember when QR codes, were starting to come out and you’d go to a store like TJ Maxx, or Marshalls, you know, and I’m using that brand, because I specifically remember those use cases there, where you’d walk in the front door, and it would say, we’re hiring. Here’s the QR code. Yeah. And you’d be taken through an experience. And, you know, I’m not, I’m not picking on those two companies specifically. But sometimes when you went in those stores, and you hit that QR code, you got the traditional experience of having to enter every single data point. And then you have the opposite. And I think this is one of the things you’re getting to, and so I’m sorry to if I’m pre empting. You but then you have the new experiences where they say, Well, do you just want to use your LinkedIn profile?
Bennett Sung:
Sure. Yeah. I mean, so that that’s the other kind of small wave that happened between happened after mobile was that whole one notion of one click Apply. Right, right. Like, I can use an existing profile that I created on LinkedIn, or maybe I tried to use my A Indeed, and Monster profiles to also kind of create that same experience. Interestingly enough, none of that took off. And really, it gave me because the it didn’t create, because it’s circumvented a lot of the applicant tracking processes that come afterwards, right? You know, like this pre minimum qualification questionnaire that comes with that. It’s like, okay, so now how do I figure out how to send the candidate do I need to send the candidate back to both fill out more information. And so it wasn’t a very seamless, integrated, seamless experience to the candidate as it could have been, but it was a good step forward and trying to simplify and get candidates into the phone.
David Turetsky:
But let’s talk about that for a second. Because I think it’s an important thing, the process of getting the candidate information. And this is from the application perspective, as well as enough demographic details to be able to make a judgment call, and the skills and all that other stuff should make a judgment call on if this person is the right person for me, you know, you’re going back, you’re talking going back to the 50s, when you typically had to have an offer, you had to have a sorry, a cover letter, say on offer, you had to have a cover letter. Plus, then you had to give your resume and cover letter was almost like your college essay, it had to be utter, thoughtful conversation with someone you don’t know about why this is a good position for you. When you know, you’re probably going through that Sunday, New York Times looking for a job, any job, you don’t know much about it, because the thing was literally a two by two or a three by three. You didn’t have a lot of detail, but you’re still applying for it. And again, going back to that process, we did those things so that we can present ourselves, right. Nowadays, we’re not presenting ourselves to a person and what our data needs to be complete enough so that you can as you’re talking about make that judgment call, why haven’t we evolved at all to be able to use those common data records that we have on the Internet, whether it’s in LinkedIn or other places that those records exist? Why haven’t we come up with one place, or one way of doing that, in order to be able to get enough demographic data to make that decision?
Bennett Sung:
Yeah, I mean, I think that is, that’s a big, that’s the that would be the biggest opportunity and big transformation, I think it would be so nice to have one central area that I can access all my personal and personal and professional information and use that in a way that’s useful in things like an application process or filling out a home loan, or filling out the crazy background check applicant, you know, application form. I mean, there’s a great, there’s so much redundancies still today in in the information that’s required and collected in not only hiring but all sorts of other things in life. And we still haven’t found a way to I think it maybe it’s the fear of data privacy challenges are such that may present itself to be who knows. I mean, again, I think there’s a lot of people don’t change. That’s the That’s the interesting that the changing of behavior is monumental. So, you know, I think when I look at recruiting specifically, again, everybody’s fearful of making any dramatic change. I mean, how many times decade over decade, every five years we have this conversation? Is the resume going to die, sir. Can the resume die soon, please? Well, but it’s just morphing is changing in its form. It’s changing. Its in its quality. I think the interesting thing that’s also surface is that there’s also this new realization is that recruiter behaviors also need to they kind of move with the times. And they need to also understand like, what is really predictive of success? You know, am I, you know, you know, does going to Harvard University differ than going to the University of Montana? You know, it’s, you know, they make all these biased decisions on brands of colleges,
David Turetsky:
or University of Hawaii.
Bennett Sung:
Yeah, my animal science studies, it’s like, so it’s like, what does? What is the brand of the college chef to do, do with your ability to succeed is as an employee, right? So there, so the so there’s all sorts of interesting kind of nuances around the resume, and specifically about the how it’s being used in the scoop in that initial, like, matching process, right. So the resume we know is written from a perspective of the job seeker, I’m going to tell you all about myself, I’ll try to put it into the language of, of the employer. But the reality is, it’s still very, very personal documents,
Dwight Brown:
I also have to wonder, in that process, and I’ve wondered this as an applicant, who are we building these processes to the benefit of? My perception is that this is really these are really processes that are to the benefit of the recruiter. Yeah, in other words, I get, I get 1000s of resumes, and I have to sit at my desk and go through these resumes. But we haven’t made it easy for the applicants, I still have to every time I ever applied for a job, I still have to fill out two hours worth of an application on a company’s website, attach my resume, you know, or are we building it for the applicant or for both? And so I wonder if that may not also factor in to the difficulties in the evolution that we that we’ve seen not evolving further than we have?
Bennett Sung:
Yeah, I think, you know, I think technology has always been designed, right has primarily been designed for the administrator, the recruiter, the back office purpose, right to make that process more to make that process a little bit more streamlined, and, and also for the increasing complexities of compliance reporting, but which is reason the reason the reason that ATS was given, no came about was, I needed to better track all these candidates. The Law tells me how to create this EEO one report, and I can’t go through hundreds of resumes and say, Oh, this one has applied for this position. This one I anticipate is this race and ethnicity and gender, it’s like, okay, so the applicant tracking system became, is a compliance tool. And it’s always been in that compliance nature. Now. So, you know, I think it’s tremendously important to collect as much information about the candidate and to collect that data in a way that can be used to help project future success as an employee. That’s the that’s the ultimate goal. That’s it’s that quality of hire, that we’re really trying to Northstar trying to continue to achieve and, and to figure out ourselves so that is the nature of the kind of the evolution of the recruiting process, the recruiting technologies that have helped support it. And now there’s a next generation new movement using AI.
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David Turetsky:
And that brings us up to our next topic, which is let’s talk about the world of conversational AI in the world of recruiting and how does it improve that experience for the recruiter for the applicant as well as for the overall organization?
Bennett Sung:
Yeah, conversational AI is a has was introduced primarily through chat bots, right? We all whatever your whatever your relationship is, with a chatbot, good or bad that chat bot has has survived is still surviving and living. And it’s seen across every part of the business. It’s being used in every part of the business for sure. I would say as Dwight, you had mentioned, the whole complexity as a candidate, where I feel the benefit of conversational AI is they put candidate first. This is conversational is these AI’s really designed? And its benefits come through for the candidate, right? You don’t have to register? You don’t you? You you can you have simple conversations through text messaging, I can, you know, go through the application process right within Facebook ads, I could use a QR code and be transitioned into a conversational application, which is just questions and answers. So it’s really tremendously been beneficial to the candidate. And interesting enough, regardless what conversational AI tool you use, and there’s been many of them, and we’re entering the second generation of, of chatbots. As we speak, the reality as candidates are big fans, you mean, we all read through the feedback, the feedback loops that come in, oh, my goodness, this is the this was such a fun experience. This was such an more, this was so much more efficient. This allowed me to give me give you more information about myself and myself forward in the best light. So we’ve never heard a we’ve never heard feedback from the candidate in that near real time.
David Turetsky:
But Bennett, let me ask you a stupid question. Yeah. Are they asking the old traditional process? Or is this a new process that does fill in all the gaps? But does it in a different way? Like, for example, we were talking before about the cover letter and the application? And the sorry, in the resume. And the application? Actually? Are they filling in the application and getting all the background and skills about the person in this conversation? And how long is it taking?
Bennett Sung:
You know, I mean, great question. I think that was its it, a lot of it, a lot of the questions, and the process hasn’t been fully redesigned. So you’re still being asked they a lot of different questions. The the I think the benefit here is it’s faster and easier to type in. And you can pick and choose your answers, for example. So there’s a lot of different variations of conversational AI. There’s conversational AI is where the candidate can just literally type in, type in a paragraph of information, the engine is going to say, Oh, you’re looking for a job in, in Honolulu, Hawaii as a salesperson. Okay, great. And, you know, that is, you know, one flavor, I think the more interesting flavor is that we’re all this conversation is now being structured. So we’re asking more, creating a more structured environment so that every candidate is put through the same experience, it’s actually a replacement of what I would say the phone screen is today. Sure, right. It’s usually the conversational chatbot comes in the minute that you apply. So the minute that you’ve applied, then I’m going to be circumvented back to say, I need for you to go start a conversation. We want to learn more about you, we’re gonna deep dive deeper to the experiences that you’ve just outlined in your application.
David Turetsky:
But but let me ask a stupid question, though. Instead of and I know where you’re talking about the started. That happens here. For many of us, when we get into the application process, something about this job is interesting to us, right? It’s almost like and I know, you’ve gone through these processes, too. But let’s think about consumer world where you’re applying for a car loan, or you’re applying or you like a car and you’re you’re going into look at the car, right? And you want to and they ask you for a bunch of details. But then it starts flipping to a conversation about what would you like in the car? And how do you get it? Yeah, and a lot of things are taken as a given. First of all, I have a license. Second of all, I can afford this third, there’s a lot of other things. But then it goes over and become serious, you provide your social security number, and now the conversation gets deeper and gets more serious. And in the same way, especially when we’re applying for a job, it has to get somewhat more serious, where we are giving our social security number, and then the company can use big data and find out all about us, you know, have they had jobs in the same, you know, background, they can go to work number and get all of the background from the person about where they’ve worked. And, you know, what the from and two dates are on those jobs? For sure. So there’s a bunch of big data that we can find out in the application process that we never, ever have to ask somebody about. Right? Why don’t we do that? I mean, is it FCRA? Is there a rule against doing things like
Bennett Sung:
There isn’t. I think we just haven’t explored that those levels of integration. touchpoints right. So I think right now, we’re still at the very elementary phase of leveraging data points from a job description to help create the CT screening questions. We are, you know, we haven’t, and we haven’t really kind of pulled in the other pieces of the ecosystem to help drive a lot more efficiencies and to amplify the existing data that we do have. That’s a great, great, great, great viewpoint for the future of conversational AI where we can actually pull in a lot of this data and not have to ask them and you know, I think there are we are we are seeing a little bit of that in terms of for example, a lot of the the integrations of conversational AI are two ways with your applicant tracking system so I can I will look you up if you’ve already applied for a job. I will look you up as you add to see what you know, are you still working, you know, at ADP, you know, or you’re still a product manager, we can ask those kinds of questions because we’re looking in, but we’re looking into the applicant tracking system for existing data that we can use so that we can verify because the real goal that most conversational AI tools are trying to get to is, are you interested? Are you qualified? And you’re available? That is, those are the three factors.
David Turetsky:
And that gets to Dwight’s question about helping the recruiter. Right. So all of this is being set up to help the recruiter get a stable of or a bench of good candidates for that role. Yes. And then the AI is going to rank those candidates by their usefulness. And by their their, as you said before, their probability of success,
Bennett Sung:
Right.
David Turetsky:
So let’s turn this around a little bit. And let’s talk about what’s happening in the world of AI at the interview stage, because if you have the stable of candidate to be had that bench of candidates, how does how does it help getting you through to actually finding the right one?
Bennett Sung:
Yeah, I think so. So this interview stage is been an enigma for, for, for its lifetime, we have not had a chance to understand what’s happening inside the interview. Until now. Now there are tool now there’s this new AI technology tools that sit inside your virtual digital interviews. And they are listening, transcribing. And ultimately will be analyzing the data
David Turetsky:
and even watching
Bennett Sung:
And sometimes watching. When Yeah, there are other circumstances of watching that one is a little bit more, a little bit more precarious. And a little bit more a little bit more dangerous if I was to say, because I learned very early on, when I made a detour into body worn cameras for free for police departments that yeah, they realize that facial detection was was a very discriminatory,
David Turetsky:
highly biased,
Bennett Sung:
highly biased, highly biased. So they just so though law enforcement said no to facial detection, and now just recently HR has said no to that, too. So this is a little different. So the tool that we the AI that we are we that is working with inside the interviewer, its intentions are pure are purely to surface behaviors, surface topics that are being talked about by the candidate. Trying to measure sentiment, you know, there’s a it’s a very interesting, it’s voice to text is one of the most complex technology applications today. It’s because if you think about it, all the requirements he sent me about just being part of a podcast, that’s that we have to tell mechanic oh, we need for you to set up this, this and this and this and this. So we can get the clearest audio possible. We have to do a lot of faulty audience.
David Turetsky:
But remember that this was there was an effort underway maybe five years ago or six years ago, because of some of the video interviewing technologies to actually send candidates a video camera.
Bennett Sung:
Oh, I remember those. I selected them. Yeah.
David Turetsky:
But there but but there is there there is, you know, you can tell every every person now and everybody has a smartphone now basically. Yeah, that has a camera. Yeah. So asking people to do video isn’t as much of a problem as it was maybe five years ago, even three years ago, maybe everybody has an iPad, while pretty much everybody is like either an iPad or a Samsung tablet or an iPhone or something. Right. So but so there is. So what you’re saying is, is that there is really good analysis happening. There’s data that’s being collected in the interview process to be able to predict is this person going to have the right culture fit, and the right skills as they’re talking? We’re kind of giving them a BS test to see if they’re, if they actually really know what they’re talking about. We can judge it based on their facial expressions based on their voice inflection. And based on that, as you were saying, that really important data that you’re collecting, which is supposed to go speech to text, right, and how do we judge and how do we get those nuggets out of that? Right?
Bennett Sung:
I think we’re still learning or a little early on and this technology being used to make decisions right, so it’s not going to immediately say it’s not that is a the future roadmap of interview intelligence. Today, interview intelligence is we’re still trying to understand we’re still trying to be do the basics of collecting viewing the transcription of voice to text. We are building models to help understand, you know, various behaviors like for example, interruptions If you as a recruiter interrupt the candidate, you know, we need to determine is that a good interruption? Or was that a bad interruption? Right? You know, so, so there is so there’s some there’s some science, there’s some additional science that he needs to happen and there’s some human, there’s still some human intervention that needs to go into those audio files and mark the data points as being, you know, a positive interruption, a negative interruption, is it, you know, are you showcasing patience, are you being impatient, and, and, and the likes, there are definitely some behaviors, how fast we’re all talking that is very easy to calculate. There’s other behaviors that are reflective of the interviewer’s style that we all are trying to measure, but it requires somebody who has a linguistics background to help understand the behavior of what they’re what they’re what they’re hearing, as to be whether or not that interruption is good or bad.
David Turetsky:
So so let me just try and summarize a little bit, because this is a really important part. We’re collecting a lot of data now. And at some point, we need to then regress that data against candidate success, like I’ll give just give me one metric that I like with looking at which is New Hire turnover. Right? So if we can’t, if we look at candidate success in New Hire turnover, versus the interviewer who brought them in, or the source of the of the source of the candidate or something like that, sure, then we’re learning not just for the sourcing or the, the sourcing side of the recruiting process, the interviewing process side, but maybe the entire process itself,
Bennett Sung:
Right. Yeah, absolutely. And I think this now what that we’ve exposed the interview, right, which is, and are building and collecting data, there’s a lot of insight, a lot of data points within the interview, that could be indicators of, you know, for acceptance rates, plus, you know, long, you know, the retention So, and most importantly, it’s also being used to help measure the efficacy is that they’re being that’s being put forth in interviewer and dei training. This is a, this is a change management tool, this is not your traditional data collection compliance application anymore. So that is, that is the that is the initial purpose of AI. And it’s going to continue to build itself out so that it becomes becomes much more effective in decision support.
David Turetsky:
So Bennett, we talked a lot about the current processes and where they’ve come from in the world of recruiting and recruiting technology. We’ve talked about how conversational AI has been introduced to the world of recruiting. And then we’ve talked about using AI in the interviewing stage. And I think you brought up some really phenomenal points that I think our listeners are gonna really love. Is there anything else you want to cover? Before we close?
Bennett Sung:
You know, I think that’s, I think the important thing is that organizations need to start at the candidate journey, with a conversation, give the candidates this opportunity to engage with you as a brand. And and you can use automation to get to the slate of candidates that you are using. So we leverage conversational AI, to really cast that wider net so that every single person that applied has had a conversation with you. That is mission critical. That is that is what that’s how you start building an inclusive workforce environment. So that is one thing that I love for organizations to really think through is that to create inclusivity is the starting point of your your diversity initiatives. It’s not the middle. So the if I was to reverse the acronyms that would start with the I. So that is what I feel. And that’s where compensated this whole topic of conversational AI can come into play. Because not in not all the candidates are being treated equal. Not all the candidates are being taught to. So at the early stage of the hiring processes, start the conversation, using conversational AI, to help to allow the candidate to feel that they’re being heard. And they are able to give their feedback. And that is what I like to leave the audience with.
David Turetsky:
Nothing is worse than just getting an email 10 seconds after you’ve hit the Apply button telling you that I’m telling you while you have phenomenal credentials. We’re looking at other candidates. Yes.
Bennett Sung:
The worst, the worst engagement conversation ever. For sure.
David Turetsky:
And it happens to all of us. Right. Exactly. Yeah. So Bennett, it’s been a pleasure. Thank you so much. You’re awesome. You
Bennett Sung:
appreciate as well. Thank you so much.
David Turetsky:
Do I thank you very much.
Dwight Brown:
Thanks for being with us. Bennett. This has been a fascinating topic. This is you know, I would say that next to seeing the doctor, to me, the most personal human process that any of us ever go through is the recruiting process. And so it’ll be interesting to see the intersection of AI with the human process and how we balance that. So thanks for being here and sharing your thoughts.
Bennett Sung:
Absolutely. It’s important to think about the whole the fact that AI plus humans when it’s not AI, or human only.
David Turetsky:
And there is a topic for a movie that we’re gonna be writing the script of. And it’s not dystopian, it will not be a dystopian movie. Yeah, for sure. Thank you. And thank you, everybody, for listening to the HR data labs podcast, we appreciate your support. If you found value in it, please hit the subscribe button. And if you know somebody else who might like this episode or other episodes, please send it their way. Thank you very much. Take care and stay safe.
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