3Sixty Insights #HRTechChat with Jennifer Ravalli, Head of Marketing at PandoLogic

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Jennifer Ravalli was our guest for the latest episode of the 3Sixty Insights #HRTechChat podcast. Following long stints at ADP and, most recently, iCIMS, Jenn joined PandoLogic in September 2020 to head all marketing efforts.

Jenn and I met and first spoke at the Spring 2021 HR Technology Conference & Exposition. It’s when we delved specifically into PandoLogic’s solution, a programmatic job advertising platform with underpinnings in artificial intelligence and available on the iCIMS marketplace.

Naturally, this episode of #HRTechChat covers much ground in the areas of AI, the future of work, and more — among our favorite topics at 3Sixty Insights. (Go here and here for our blogging on these ideas.)

Jenn sees AI transforming the roles of recruiting (and sourcing, for that matter). Drawing on lessons learned from the analysis of massive data sets, AI will eventually become just plain better than even the most seasoned, savvy, innovative professionals at certain things in recruiting and sourcing.

Some of that will be the machine learning part of things — the repetitive things recruiters would rather not do anyway. But AI will also be better at other things, such as being far more accurate than human intuition when it comes to knowing where the best candidates for an open requisition are likely to be and where they’re likely to look.

Who exactly these candidates are or should be is another thing AI will get better and better at determining, eventually better than any human ever could be. And AI will make organizations better at producing job descriptions that attract the most qualified, most diverse candidates for the job.

In sum, soft skills, skills such as people skills, will grow ever more critical to recruiting professionals’ success as their day-to-day work grows to encompass mostly this aspect of their roles.

And that’s just a small peek into my conversation with Jenn. I encourage you to give this episode a listen.

Our #HRTechChat Series is also available as a podcast on the following platforms:

See a service missing that you use? Let our team know by emailing research@3SixtyInsights.com

Transcript

Brent Skinner 00:02
Well welcome everyone to the next episode of HR tech chat. And we have with us today as our guest is Jennifer Ravalli, who is Vice President of Marketing at PandoLogic. Welcome, Jennifer.

Jennifer Ravalli 00:19
Thanks, Brent. It’s a pleasure to be here today.

Brent Skinner 00:22
Yeah, yeah, we, we had an awesome conversation. It’s, it’s crazy to think that it was actually two months ago now. But, uh, at HR tech conference, the virtual one in the spring, we had a fantastic conversation, and you went through a lot of what you folks are doing at PandoLogic. And we, and we ended up getting into a, if I recall correctly, a pretty philosophical conversation, I think around, you know, how AI and machine learning, machine learning and NLP and all that stuff, right, is really going to affect and have a have a massive impact on what talent acquisition is, like, in the, in the, I’d say now not too distant future, although it’s not tomorrow. Some of it’s here already. But you know, some of it’s kind of science fiction seems science fiction, but uh, with as with so many other things in today’s world, we’re, we’re pretty close to the science fiction at this point. So where would you like to start?

Jennifer Ravalli 01:26
You know, I think really, it’s a really interesting discussion. And, to me, it’s all about data fueled change, and really seeing that data take shape in talent acquisition overall. And in the life of a recruiter. I think this is a very similar transition to what you saw in marketing 10 years ago, where things really went from being very gut instinct led to being extremely data driven. Now, you know, everything from not only where you advertise, or how you seek out the right Client Profile, but even you know, the creative process has gone very much to kind of a data driven model. And I think you’re seeing the same thing happen in the talent acquisition space today. And really, a lot of early adopters have taken to this very well in terms of bringing AI, natural language processing and machine learning into their recruitment process. But there’s still a lot of ways to go in terms of adoption there. And I think a lot of that really comes from fear, and kind of a fear of loss of control, kind of with kind of having data kind of take you to those places. But I think a lot of that’s going to start to change very quickly, particularly with job ad recovery on the rise. And you know, what’s really also very interesting now is, you know, we’re seeing that, you know, jobs are picking up, but people are having a really hard time getting the talent they need right now. And, you know, a lot of that has to do with, you know, certainly dynamics, you know, fear of change for the job seeker, as well as just being able to, you know, think about, you know, whether they want to take the risk at this point, you know, given kind of where we are. But I think it’s really hitting a pivot point where people need more data, they need more AI and machine learning fuel tools, in order to get to that talent that they really need to kind of bring to the table. So it’s very interesting time right now. But I think that transition point is really starting to happen. And a lot of that has to do with the data that’s now available, and the ability to be able to take that and learn from it on an ongoing basis.

Brent Skinner 03:44
I can’t imagine having to handle or adjust to the coming tsunami of job applications, if we were in the 70s right now, right? That’s a reference point is, you know, is what was it like in the 70s versus now, right? And that’s where you get your real contrast is, I mean, a Can you give us there’s so much in what you just shared? I have several questions, I don’t want to and as we talk about the things and you know, in order here, but what I’d love to what I’m curious about right now, first is can you give us an example of how the AI Well, I’m just gonna go off on a super mini rant right now. Listen to decide, you know, AI is often used as kind of an umbrella term for some things that aren’t necessarily AI but are very sophisticated and useful. For instance, machine learning, natural language processing, this, you know, and then in the have, you know, real, like artificial intelligence, and some of this stuff is kind of lower level, but it’s still very sophisticated. So, with that in mind, with these new fangled, you know, more certain Far more sophisticated computing capabilities? How is that? How is that helping right now?

Jennifer Ravalli 05:08
Sure. Um, so I’m trying to think of a really good example. How we can really think about I’m going to use sourcing, actually, as an example. Um, you know, most recruiters make decisions kind of, hey, like, I know, based on my prior experience that this job is hard to fill or easy to fill, you know, is it something I should advertise? Or is it something I can do on my own, and if not, they kind of have preferential places that they may want to place those ads. And it’s definitely a very personal example, kind of given what we do. But I think it’s very relevant to what we’re talking about. But that series of decisions that that recruiter or sorcerer makes, has a huge impact on the outcome. And often, they’re very fraught with bias, and use a fairly limited scope of data. When we introduce AI into that equation, we can really streamline those decisions, the application of AI is really around the decision making of being able to kind of change on a dime, make those decisions very quickly, and leverage that data that it’s learning from, to be able to continue to change and adapt that reasoning. So when we introduce that AI into the equation, we can streamline, and we can also make those decisions much faster, and on a much larger scale. So if we were to evaluate those jobs at PandoLogic, we would look at them, we do something called a predictive Performance Report, which basically, before we ever spend a dime, we say, based on all of our data of over 10 years of being in the programmatic market, supply and demand, etc, we’re actually pulling in all of that data to say, actually, this job is much harder to fill than this one. So here’s where you should place your dollars. And you need to be placing it in a variety of places, in order to be able to get to those candidates and meet them where they are. And you know, what’s very interesting is while most of the time, folks would, you know, advertise maybe in three or four places, we have over 1000, places that we’re placing those jobs based on where we see that talent residing, and based on real time data, that we’re making a decision to place a dad remove an ad, or actually impact the cost, in order to be able to get that best outcome. And so when we apply that philosophy, we’re able to actually really save around 3% of what they would invest normally, in order to get that candidate. So those are some of the real life applications of AI that can really see tremendous results, and also help to attract a larger scope of candidates, then maybe that recruiters individual decisions might make on their own.

Brent Skinner 07:55
That’s really interesting. And so a couple of things. So a, I mean, we’re talking about a fundamental, when we when we back up a little bit here, so you look at recruiting as a profession, right, and recruiting professional, a really good recruiting professional, you know, they’ve sort of developed, for lack of a better term, just to keep to keep this moving right now, a sixth sense, you know, or kind of, like just a, just an intuition, or a, just a, we could even say just sort of a fine understanding of the types of roles that they work with, and the company that they work for. Right. And so they kind of they’ve developed this over time. And, and, and some recruiters perform better than others. And you could argue that that might be one of the reasons, right. But what you’re talking about, is, is kind of a this is a strong word, and I’ll use it anyway. It’s kind of a usurpation of that. Right? It’s, it’s, so you have a recruiter that has an intuition for or believes very strongly, they have an intuition for what is needed to fill a certain role. And you could apply, I mean, the Royal you, you could apply AI to that, right? And come up with a completely different conclusion. And, and this is where this is where he kind of changing, you know, this is where the recruiting, I think, maybe loses some of some of its some of the art of it, right. Okay. So, so, so how do you see recruiting the profession of recruiting, evolving, to adapt to live side by side with this AI? Because some of the, what you’re talking about that was the word So looking for is the art of the art of recruiting? Is it? Was there an art in the first place? Right? Or was it just kind of a, you know, a, just did the recruiter think there was an art to it, but maybe there wasn’t? Do you see where I’m going with this?

Jennifer Ravalli 10:15
Yeah, I’m a huge believer in the balance of art and science, particularly coming from a marketing background. Um, you know, certainly like my ability to craft messaging to be able to get to that right person at the right time, you know, is a lot of art, but then validated by science, right. And I think that that’s where we’re going to see recruiting go as well. You know, you still need instinct, when it comes to talking to that candidate and being able to, you know, place that right? person, it’s going to be a true cultural add to the organization. But when we think of a lot of the work that AI should be doing, at times, or could be doing for them, it’s often in the places where it’s more areas where they may not want to be spending a lot of their time. Okay. And I think that’s, and I think that’s a big difference, I think there’s a fear of AI, that it’s going to take away jobs and being able to, you know, kind of replace people. But that’s really not kind of where I believe AI is going, it really should be more of a companion, you know, similar marketing, there are different roles, now you focus on different things, then maybe you did 10 years ago when you didn’t have AI to help you. But when you think about having that AI, particularly, it’s at the front end of the process, it allows you to really have that recruiter focus on what is truly that more human mission, which is, you know, once you kind of start to get that initial field of candidates, making sure that you’re getting the right person in the role having a really strong candidate experience to support that, you know, it’s interesting, we as talent acquisition, as a group, right, the Royal we have the industry, talk a lot about candidate experience, and, you know, different loops and things of that nature. But when we’re actually doing some work now, around kind of job seekers, and how they’re really feeling and their sentiment, and you know, there’s some more really great stuff to come, but like high level, like, they don’t see the same way we do. And if we were to think about where a recruiter can spend their time, it’s really honing in on providing that support to the candidate making that candidate experience really personalized, and bringing them through for both the hiring manager and the candidate, versus some of this work that kind of drives the initial kind of pool. You know, the other thing is, you’ll also in thinking about, you know, driving more diversity into the workforce, you know, if you don’t source for that in the first place, like, you’re not going to end up with a clean slate of candidates that, you know, may represent a really diverse population, you know, and technology can really help eliminate bias in that area. So I think it’s really should be about working hand in hand, and being able to take the benefit of the AI on the front end. But then take that magic that the recruiter brings to the table that art that gut that instinct to really put that perfect person in the hands and deliver them safely to the hiring manager to really impact the business. Yeah,

Brent Skinner 13:26
I love that, you know, in it. Because that’s what it comes down to, you know that there’s a need for a human interaction there. for for the business task at hand to feel satisfying to the to all the stakeholders involved. So there’s that human, that human interaction, I think that is necessary. And so

Brent Skinner 13:56
what you just described sources, I mean, this is going to make source sourcing folks who focus on sourcing as their role, this is going to make them very, very efficient. And it is occurred to me that they, you’re, what you’re describing is, essentially, we’ll just use the word automation, because it’s this really kind of, I think, a different word for it. I’ve heard it as process. There’s terms for it that I’ve heard that are escaping me at the moment, but something around process, but we’re talking about the automation of much higher, much more complex processes, then, you know, we have automation of you know, like payroll processing and other things. That’s it, that’s essentially lower level automation, we’ve almost, you know, completely won the game when it comes to that lower level automation. You know, not everybody has the tools, but the tools that can do it exist, right? Whereas, what you’re talking about is is, again, I’m using automating as a placeholder term here, but you’re talking about automating much higher level more sophisticated complex processes and workflows. In the decision making is the other thing that that I wanted to make sure that we hit on here, that really struck me, you’re improving or assisting or neither of those is necessarily the necessarily the right word here, either. But, but you’re you’re enhancing the decision making, that they would make, to make it more accurate or more applicable or pertinent, make it make the accurate result more pertinent. And that is, that’s something that that’s occurred to me previously, in my own writings. on the blog, 360 insights is this idea that AI is, well, we talked about human decision making work. Let’s look at because I want to get into job descriptions, too. Because I, we had, we talked a lot about that, but let’s, okay, I’m a human, I write, I write my own CV, or, or maybe I hire someone else to, to write it for me, or to provide heavy input into my own writing and my CV, right. It’s humans evolved, you know, it’s our best guess, sort of an analog for, for pure objectivity in terms of understanding of my own career, I mean, I have an opinion, a bias about my own career, it doesn’t even have to be, it doesn’t even have to be for anybody that that opinion, that bias doesn’t have to be particularly arrogant or, or anything like that. It’s just a bias. And I look back on my career. And from my perspective, I think that a, b and c is are relevant in XYZ might not be as relevant, right. And again, I’m also Furthermore, I’m looking at my credentials, and sort of my experience, and my expertise versus not necessarily, you know, recognizing my soft skills are talking about those in my, in my CV, so, so my CV is just is just wrought with, you know, just these biases. And I don’t want to say that inaccuracy is because that’s not the word, but these biases are these, this information that may not be relevant to the role that is open, right. And then when I’m an organization writing a job description, it’s kind of the same thing. As an organization,

Brent Skinner 17:35
I think,

Brent Skinner 17:36
I have an idea that I may be very emotionally attached to, that I think this particular open roll needs. Right? And that may not that, you know, it’s really shot in the dark. Well, it’s a little bit better than a shot in the dark. But it’s, it’s certainly not nowhere near as accurate or as assuming as objective, as an AI would be in sort of looking at the data, right? If you have a large enough data set, sorry, I’m ranting here. But the second love this topic is this, that AI with a large enough data set of set of data around that particular kind of role would be able to develop or provide heavy input into the development of a job description that would, you know, ultimately be much more objective and relevant to the actual needs for that role for that organization. So yeah, so we have two things, we have a CV, that’s, that’s maybe irrelevant, and maybe, you know, it’s not inaccurate, but it’s irrelevant, right? potentially very much. So the same thing for the job description. So we’re sort of the like, the blind leading the blind. And that’s what we’ve been doing forever, right. And it’s not the proof, we certainly haven’t failed because we have hired people, I’m talking in aggregate. In all of industry over decades of time, we’ve hired plenty of people that did a great job, employing people didn’t do a great job. It’s been you know, that’s just been the reality of it. Now, we’re talking about potentially completely different scenario. So with that backdrop, and I’m, I’m sorry, I think I went on too long there. But, but was talking about job descriptions for a moment here. What’s your reaction to that?

Jennifer Ravalli 19:20
I’m super excited to talk about this. I had like a million thoughts coming through my mind as you were sharing. Um, so first of all, I think the resume angle is really interesting, because I don’t think anybody has even has even thought to solve that yet. And, you know, there’s a lot of opportunity there. You know, I know personally, I was thinking about myself, and you know, when I look at my CV, you know, how do I look at it? And what I always find is, I downplay a lot of things that I’ve done, right, which is a very female trait. Oh, can I really take credit for that? With that really my work and I actually in one point, when I was in Reviewing for a job years ago, you know, I used a lot of weed. And the the hiring manager who’s actually probably my biggest sponsor overall in my career was like, well, youth we a lot, like, tell me what you did specifically. And I’m like, you know, these are all things that I lead, and yet I am almost making them less, you know, granted, that means that I support the fact that I work with a team and that, you know, we don’t get work done entirely on our own. But it really you see that very much come through, particularly when you look at the difference in how women create resumes, and how men create resumes, and also how they respond to job descriptions. So those two things really don’t do go hand in hand. One interesting thing, in particularly think about the use of AI in recruiting in NLP as well, is really when we say throw away the resume. And when we start to do things through conversational AI, to get people to talk about themselves in different ways than they may have, if they were simply putting together a resume and thinking about their accomplishments, their achievements, and their and their credentials. But really talking about what they did, what lights them up, and what excites them about what they’re looking to do next. So I think that there’s some really interesting work to be done, particularly on the resume. But you know, there’s some, certainly some movement using conversational AI, not that it’s perfect yet, but I think it helps. And then as we think about how we store that information, and you know, maybe a little bit crazy, and as we’ve thought about, you know, kind of very much into the future, how that mess, how that information that we’re sharing, through those avenues becomes more of a profile for the job seeker and how they can kind of move forward in their careers using the things that they’ve shared, when they’re not sharing them in to be on a piece of paper, but in having conversation. So I think that there’s a lot to be done there. And who maybe that’s maybe that’s my next job is creating a company that does that, but

Brent Skinner 22:08
that company before you, right? I just realized I let the cat out of the bag here.

Brent Skinner 22:17
So, you know, the velocity networks foundation does some stuff around this and, and it wouldn’t it be interesting, you know, now we’re, we’re talking far out stuff, but I it it, you know, I could see it happening is you had maybe each individual, you know, in an increasingly gig economy gig like economy in the future, right? It and we that’s a whole rabbit hole. And we’ll get into right now, because we don’t have enough time. But it’s a day long conversation. But you have individuals in the future who have an AI assistant or, or companion, you could call it an AI companion that maybe that’s a little creepy, but it was an AI assistant, or, you know, an AI prosthetic, right? That, that helps them. They’re working so many gigs, so many projects at such a high velocity just from one project to the next. Right. And you have the AI that’s, that’s, that’s discerning, determining who’s best for the next projects, right. And so you have as an individual, you have an AI program, that’s, that’s constantly re compiling your background, to present to the, to the, to the project and need your potential, you know, your potential relevance for that project. Right. And, and that’s something that, you know, at some point, it look 10, not 10 years ago, about eight years ago, I transitioned myself, I transitioned from being consultant I was caught doing contract work with all sorts of people, to one of the vendors, large vendors in the space basically gave me an offer that I could not refuse, in a good way, not in The Godfather way. Anyway. And so I had to compile my CV, I had to go back in all these projects and figure out and it had to be they wanted something kind of exhausted and that and then of course, the the background check was apps was an absolutely i mean, that was a nightmare, because it was it was all positive. I was hired and all that. But that was a real crazy process going through background screening, as well, right, because I had to go back and figure out all this stuff that I hadn’t thought to keep track of at the time, because I never thought it was going to be relevant. And so I’m glossing over it a little bit. But the whole point is that it would have been nice also to have had a blockchain, kind of just pull it all in that gives all of that to so. So the future I think, you know, and I was sort of inferring from something you said pretty Obviously, just a moment ago, I was inferring blockchain. I didn’t know if that’s maybe what you meant. But yeah, blockchain and AI is going to be huge. And maybe this is what role do you think this this? I wasn’t expecting to? that we’d get into this today. But psychometrics, what do you? What roles Do you think those will play? In this sort of hypothetical? Not so hypothetical future?

Jennifer Ravalli 25:26
Yeah, I mean, that’s really interesting. I think we’re seeing a lot of that. And I think this will also apply very much when it comes to behavior and the chat and reacting to you, I want to make sure we get to job descriptions as well. But um, the when you look at behavior, and it was funny, we were meeting with someone, and as we were showing them, our platform, you know, one of the things that we do is extrapolate thing, extrapolate job descriptions into more common language using specific variables. And what that then shows is the behavior of an applicant and what they respond to and what they don’t, right. And when we think about bringing behavior into evaluation for a job, and things of that nature, you know, I think that there’s, there’s been a lot of work there. And I think some folks have bought in and some folks have not, but there’s, we look at that from a marketing perspective, with such detail, in terms of, you know, if you can get to psychometrics in being able to build a profile of your customer, you know, this is very relevant to health care, when you think of, you know, particularly people who are, you know, more Advent to take risks versus others, right, and being able to then build that profile using data using the AI to build that, you’re able to make really much better decisions about the placement of those, those potential employers or members that you’re bringing into, per se, a health plan, right. So very similarly, you know, when we think about people who are taking risks, who are willing to take risks, in a job, you know, whether or not they make sense in a startup environment versus a more stable environment, like, these are all really great tools to be able to add to kind of look at the value of, of the while, I would say, I hate to say fit of a position, because I really think you should always look at candidates as how are they going to add to your organization versus fit in your organization. But really kind of profiling out and understanding how that person is going to add value in terms of the way that they make decisions, and the way that they behave in certain scenarios. And I think that there’s a lot of work being done in that area. But I’m not necessarily seeing a lot of people pick that up in terms of except maybe potentially towards the end of a process, you know, when it’s down to kind of two specific candidates and trying to kind of make a decision between them. But I think we can do a lot there probably in the future in terms of as we build those, and curate those profiles, whether feel through kind of, you know, the language that they’re providing, and then attaching that through blockchain? Or, you know, or as well as through kind of assessments and things later on in the process. Yeah,

Brent Skinner 28:19
all great stuff. You know, what, again, that goes back to what something you were saying earlier around. I think that you said, you said, quote, I think that we can prepare for it. I think that we can apply AI to the front end of the process and recruiting to really help the recruiter be more effective. Far that further down the stream. Applying the second Max, as opposed to, you know, once you’ve gotten whittled it down to two employees, right, potential new hires. Applying that at the outset. Everybody, right. Yeah, a lot earlier. Let’s talk about job descriptions. What how our job descriptions? You know, what was the state of job descriptions today? What was it? What are the challenges? What do we need to do? And, and how can AI help?

Jennifer Ravalli 29:10
Yeah, so I think we can all agree that job descriptions are generally pretty awful.

Brent Skinner 29:18
depressing to read, I mean, to me, like, Oh, I know, I’m not right for this. Which is often the wrong conclusion to make.

Jennifer Ravalli 29:30
Yeah, exactly. Um, you know, we actually had a great meeting with a company yesterday. And that and I can, it’s okay, if I mentioned them, for sure. So it’s get optimal and they’re doing some really fantastic work in this area. But like a tidbit they shared with us that I I knew but with this outstanding is over 50% of every job description is plagiarized. So basically, every time so Somebody is looking to kind of create a new job description, what are they doing, they’re searching on the internet for similar ones, you’re bringing together pieces of the things they like versus what they don’t and building that job description. In addition, many companies think that they need to hire unicorns instead of sales people, right. So, you know, there may be they’re gonna give interesting titles, you know, in terms of like, you know, things like value engineer, versus like a, you know, solution consultant, or something of that nature that is more common. And, you know, and what we find is, when we, when we ingest those job descriptions, you know, we find that we take about 68 different variables within a job description and evaluate that, to see how we can make that more responsive through SEO and, you know, through kind of the the placement, and what we find is when we eliminate all the jargon, the job description performs much better, and people actually apply for it, right. So it’s not the unicorn, it’s, you know, we’re actually looking for a salesperson, but it’s a salesperson for a tech company and not Walmart, right, and being able to make that distinction. And sometimes the way job descriptions are written, it isn’t that easy to actually tell the difference. So it’s really important, I think, to that, but then the other part of it is really the bias of who applies. And, you know, I think there’s a lot of great work going on in this area. And it’s like, six women will not apply for a job unless they feel like they are 100%, hitting every single area on that job description, whereas men will apply when they feel like they’re 60%. Ready, I ready enough, I’m going to take that step, a lot of that is about the construction of the job description, you know, too long, too many bolded requirements, and the language that’s in that job description. And that it can go more masculine or feminine, it may actually, you know, completely, completely, not at all have any impact on a woman who will say, I don’t want that job at all, just given maybe a couple of words within there. So there’s a lot of room for change. And it’s exciting to see vendors who are coming to the table and taking the data that is a service that’s available to be able to say, this needs to be less biased language, you know, we need to remove kind of gendered language from here. And then we also need to think about streamlining this description so that more people will apply. And it’s amazing to see the behavior applied to that when you actually put that into the market. And you see the changes in how people respond to them.

Brent Skinner 32:49
So I’m getting excited there. Because I’m looking at the time we do need to sort of land the plane here, but and I knew this conversation would go this way knew that we’d run out of time. But you say about DNI I mean, this, you know, you know, so there’s all sorts of ways that an organization can equip itself to, to to be better at DNI, right? And, in one of those assume is D, D, E, and di now, excuse me, but, but one of those ways is, is assuming number these ways may not have been readily apparent at first blush, but one way is to apply AI to your job description writing process. Right. Right there. It’s interesting, and I have heard these, these statistics before that you shared in it, it’s very interesting, you know, you know, that that is, you know, how do you solve for that? Well, part of that is changing how the job description is written. Also, part of it is just, you know, is just changing attitudes or helping women to, to think you’re a little bit more in to realize that you can apply for a job, even if you’re not 100% qualified, but, but we’re getting back to some of that sort of, sort of authoritarian language that comes out of job descriptions in general. And, you know, what, whether or not it’s necessarily helping any one group to be more represented, it’s even if you just think about the fact Hey, let’s make our job description sound less authoritarian. I mean, that that, in and of itself is is a That sounds great to me. I think that would sound great to anybody. Right. So that’s another way of looking at it.

34:41
Yeah, yeah, it definitely is, but I think it’s also like, okay, so I want to make my job description more friendly, less authoritarian, but I don’t necessarily know based on my own judgment that if I say I’m looking for someone who’s a risk taker, like that, that’s going to take 50% of the population say No, no thanks, I need something really stable. And most important to me right now, right. And, you know, when you introduce AI, and where you’ve seen people take action or not into the equation, you can look out for some of those things that are just not so easily identifiable, and really be able to make a step forward in that,

Brent Skinner 35:20
you know, yesterday, we had our, our global executive Advisory Council symposium. And we had a few folks online, we had a few presentations and some interactive, free, open forums, in one of the presentations yesterday was around neuro diversity. And this idea of, you know, the world is built around a sort of the neuro typical way of thinking, this, this gets beyond, you know, gender and all that it’s just, you know, we’re talking about, you know, people on the spectrum that they don’t call it Asperger’s anymore, but you have, you know, autism light, I guess, would be called Asperger’s. And then, and then other people that are farther along the spectrum, but these are the, these are neuro you could call them neuro atypical but sadly, if there’s a neuro diversity there, in, in, you look at that there’s an analogy here, so or a parallel thing here, right, you look at you just Google, the term leadership, and all this stuff comes up, right. And it you know, your typical person with who’s on the spectrum isn’t necessarily going to be a leader in an organization. But when you use leadership type language in, say, a job description, you may be dissuading people who would possibly be very good to have on your team for specific tasks, like really good. from, from even applying, like, there’s the, I don’t have it in front of me. But there’s data out there that says, you know, that if you have, they’re actually, which to me was SAP, a while back, they did a, they actually decided to, with some intentionality there, they intentionally built some teams that has some neurodiverse people and people on the spectrum, and they found that those teams actually performed 30% better, because some of these neuro diverse folks are really, really good at focusing on a task and solving for problems. And, and so if you don’t have them on your team, you, you may actually be screwing yourself, you know, so you. So you need to. So there’s all sorts of people that, that you dissuade, from joining your company, inadvertently do this through the language you’re using. And if you apply AI to that, to that, um, that challenge, you may find yourself attracting talent that’s going to make you

Brent Skinner 37:49
better.

Jennifer Ravalli 37:50
Absolutely. And, you know, I really think and I look at that as inclusion, right, you know, they talk about like, diversity can be seen with inclusion is felt. And when we think about diversity, there’s so many dimensions to it. And, you know, when we think about folks who are disabled, as well, like they don’t necessarily disclose that someone may not disclose that they’re neurodiverse, they just may keep that to themselves, and then not apply. And when you think about how you take all of those dimensions of diversity and apply them to a team, you get such a better outcome. And that’s really where, you know, one thing that you I talked a lot about with some practitioners is thinking about, you know, everybody has the question, is this a cultural fit, when we really always need to be asking instead, what is a cultural is a cultural ad. And, you know, as we think about that, that allows us to open up folks to have different perspectives, different backgrounds, that may not necessarily be like exactly what we were expecting to be the perfect person for that role. And, you know, I think it’s amazing when we think about how we can take data, to be able to help us understand what those nuances are, and then really kind of drive home bringing a truly a workforce that’s going to deliver the results and deliver the types of organizations that we all want to work for.

Brent Skinner 39:18
Yeah. And in what you just described is sort of a microcosm of this idea that, that we have our own sort of subjective ideas of what’s going to work and it’s not that we’re, we’re purposely being subjective, it’s just we have our limitations as humans right, and to be able to apply AI to the process to get to more of an objective conclusion that may be more relevant to the to the future of the organization is, is is always a good thing. We are, we do need to conclude our conversation. This has been fantastic. You know, we should do this again. Some time,

Jennifer Ravalli 40:01
I would love that I really had a great time today and thank you so much for having me, Brent. Oh,

Brent Skinner 40:06
absolutely is It is our pleasure. Thank you so much, Chad. Take care.

 

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