Skills, AI And The Future Of Hiring

[00:00:00] Support for this podcast comes from Harvard, the industry-leading hiring solution helping organisations optimise their talent decisions. Rooted in over 35 years of rich data insights backed by IO psychology and cognitive science,

[00:00:18] Harvard delivers a suite of automated solutions that enable organisations to engage, hire and develop the right talent in a fast and fundamentally less biased way. Visit harver.com to learn how you can take the smart path to the right talent.

[00:00:44] There's been more of scientific discovery, more of technical advancement and material progress in your lifetime and mine, than in all the ages of history. Hi there, this is Matt Alder. Welcome to episode 566 of the Recruiting Future podcast. The rapid development of AI and the increasing sophistication of skills-based hiring methodologies

[00:01:12] are two potent forces shaping the future of talent acquisition. So, what kind of future can we look forward to? And will we see the increase in talent mobility and the decrease in bias that we need to make hiring more equitable and ensure employers have better access to the talent they need? My guest this week is Ben Poor, Chief Customer Officer at Harvard. Ben is a highly experienced IO psychologist,

[00:01:40] who's been working to reduce bias in the hiring process. Ben's knowledge and experience have given him expert insights that can help us to understand what the future of talent acquisition looks like when you combine the power of AI with the potential of skills-based hiring. Hi Ben, and welcome to the podcast. Hello, great to be here. An absolute pleasure to have you on the show. Please could you introduce yourself and tell us what you do?

[00:02:08] Ben Poor, Chief Customer Officer of Harvard, Father of three boys and have a degree in industrial organizational psychology. My career has been spent connecting data throughout the HR lifecycle to help organizations make better decisions, teams operate better, and ultimately individuals to find the right jobs for themselves. So in my day to day, I work with my team to maximize the intersection of technology and selection for our clients.

[00:02:36] We ensure that there is a balance of talent acquisition professionals getting a good throughput of qualified and diverse applicants. Fantastic stuff. Tell us a little bit more about Harvard and how it achieves that. Harvard is a SaaS company that helps HR measure the traits and skills needed to be successful in a job, while automating routine decision rules using a configurable technology to adapt to talent acquisition and talent management needs.

[00:03:03] We do this with a modern look and feel that provides candidates and recruiters with an informative and accessible solution. Our goal is to make sure talent professionals have the data and information to make better and faster decisions in their hiring funnel and for talent mobility. We act as part of the overall ecosystem of the talent acquisition lifecycle to ensure fair and valid hiring. And our customers use this information to inform individual development plans for their new hires.

[00:03:31] And talk us through the biggest challenges that you're seeing happening in the talent market at the moment. Yeah, yeah, it's been an evolving process. Crazy few years. You know, there's this constant tension between organizations really, you know, not really understanding what specifically they need at this moment. And then knowing how to evaluate those skill sets correctly, and then ultimately communicating with the applicant.

[00:03:57] And then at the same time, there's been this tooling to make sure applicants can apply easily and quickly, even though they might not really be interested in the job. So it creates this bottleneck of organizations feeling like they have all these warm leads and applicants feeling like they're constantly applying and getting rejected. While the whole time, you know, it kind of feels like a shell game.

[00:04:20] Because we see this huge trend of providers and clients wanting faster hiring and lowering the bar to entry into the funnel. But then they're not really matching people to jobs and not understanding the skills that organizations need. So they're losing these people at a higher rate. And it creates this continuous feedback loop of fast in fast out. You know, I was really thinking it's been quite a shift from just two years ago.

[00:04:46] And, you know, I wanted to ask you, what do you think has changed? And what have we learned from this change? Yeah, that's a really interesting question because it's been a very difficult market to explain really in the last two or three years. There's so much going on in lots of different places. So I think we've got different applicant flows going on, huge amounts of disruption in the industry.

[00:05:10] So, you know, lots of talent acquisition professionals being hired and then laid off, which can't be very good for continuity of strategy. But I think that that kind of variance in flow in pipelines is one of the things that's got people really thinking about skills and how we, you know, how we kind of properly match people to properly match people to jobs. So very much kind of echoing what you're seeing. But I think there is a way to go yet.

[00:05:39] I think we can see the solution, but there's a way to go yet before it's kind of implemented by everybody. I don't know if you'd agree with that. Yeah, definitely. I mean, I've spent, you know, 20 years in recruitment and hiring. And it's really interesting because there are these ebbs and flows, right? There are times when we need people quickly and we can lower our barrier to entry. And then there are times when the applicant pool is slim and we need to make sure that we're evaluating for the right skill sets.

[00:06:09] And this time is unique in that it's kind of both happening at once. You know, organizations, they don't want to lose these applicants. It's a tight labor market. But at the same time, they want to make sure that they're getting people with the right skill sets. And I think that's the promise of skills based hiring. It kind of broadens the applicant pool to say you don't necessarily need this type of experience in this area to be successful in this job. Absolutely.

[00:06:39] The skills based hiring thing is really, really interesting. I mean, how do you think the evolution is going? And what is it that's going to really drive employers to, you know, to sort of, what is it that's going to increase the uptake amongst employers to moving towards skills based hiring? Yeah, you know, it's interesting because I started my career about 20 years ago and thought we were moving to skills based hiring back then.

[00:07:06] And as a field, we love rebranding things to make them sound new. You know, and from my perspective, when we discuss skills based hiring, what we're really talking about is whether the person has the knowledge, skill, ability, personality and motivation to do the job. Now, these are all nerdy I.O. terms that make perfect sense to us. But we recognize it's way too confusing for someone to try to make a hiring decision, especially quickly, to break it down to this level of detail.

[00:07:35] And I think that's why the rise of competencies in the early 2000s and now skills are the naming conventions. Basic principle is that people have transferable skills that span job families. I'm guessing you didn't grow up dreaming of running a successful podcast, but your skills align with success in this arena. The skills you possess or have developed for this are probably being curious, good facilitation skills, good organization skills, etc.

[00:08:05] Now, these skills could have made you just as successful in another field that requires it. So skills based hiring is really expanding people's minds that you don't have to have experience or even education in that specific area to be successful in the job. When we look at it this way, we open up way more opportunities for our candidates, recruiters and employees.

[00:08:25] And we get more into talent mobility and being able to match a larger group of employees to the skill sets that we need in the job. One of the things I wanted to focus on is bias in all of this. Now, we always talk about bias in hiring as something that's kind of always been there. Why do you think hiring historically has always contained this element of bias?

[00:08:54] Yeah, well, humans are biased, right? Machines are also biased because they're built by humans. But the nice thing about machines is that you can track and measure everything it does and actually identify and improve the fairness. Whereas with humans, there's non-conscious bias. So they're not even aware that the inputs they're using to make judgments are at fault.

[00:09:19] So time and time again, we see structured interviews are effective to reduce bias and predict success. But it's not because you're asking all applicants the same questions. It's because you're using the same rating schema or scales to make the judgment on the knowledge and skills of the applicant. And without defining the rating you're using, the person's going to fault to a pseudo similar to me effect, where they'll judge the person based on what they've experienced in the past.

[00:09:49] So if they've seen someone with this strength or even weakness in the past be successful in the job, they'll either inflate that strength or downplay that weakness. And if they've seen someone fail with that weakness in the past, they'll inflate how bad that weakness is. And all this is done in an instant. And what we know about humans is you're doing that in an instant, but then you'll use your logic after the fact to back up your decisions.

[00:10:14] And to improve fairness and reduce bias, I equate it to like a crime scene. The ways to reduce bias are to provide discrete evidence that improves your probability of making the right decision or following the leads. So how someone scores on an assessment is one piece of evidence. How they do in an interview is another. The qualifications on a resume is another. All right. So people who are high in all three of these areas are most likely going to be a good hire.

[00:10:42] We even recommend hiring managers don't review resumes before an interview because it will bias their thinking and ratings. And if the resume biases the interview, now you go from having three pieces of evidence down to two because they're influencing one another. So it really is using these different pieces to make the right decision. On that topic of bias, do you think that AI is something that can reduce bias? Yes.

[00:11:11] Ultimately, I do think it is something that can reduce bias. I think that, you know, with any new technology, you're getting new data and information. And again, you can, that means an increase of it. And just like with any normal distribution, there's an increase of good data and information and bad data and information.

[00:11:35] And that's where that human in the loop concept comes from, because it really is going to be more of experts understanding where they can identify the good data from the bad data. And that, you know, determining what are the outcomes and skill sets that I'm measuring for so that they can determine where the bias might be influencing decisions. I'm going to come back to that kind of balance between humans and machines in a second.

[00:12:04] But before I do, I mean, do you think that AI is going to make a difference in the hiring process overall as we move forward? Yes. I think, you know, just like any advancement in technology, it's going to shape jobs. You know, I think that the fear that people are going to lose jobs, what we've seen from any advancement is that it creates new jobs. And the promise to it is that it gets rid of the more mundane and repetitive tasks that we all do.

[00:12:33] So from an efficiency standpoint, an example of a benefit to these large language models specifically is that it creates a new opportunity for less technical people to use qualitative data or free text data instead of just quantitative data. So some people, they get really freaked out by numbers. But when it's shown in a visualization or in a text based format, their anxiety over numbers decreases and their problem solving skills kick in.

[00:13:00] And I think that is one of the promises that it opens up the opportunity to use more data information to make informed decisions. Instead of spending time collecting, cleaning and organizing data and information, they can focus their time on what the data means and how to improve decision making based on it.

[00:13:20] Ultimately, for talent acquisition professionals, I think it'll focus their jobs more on the social and strategic aspects because the more administrative mundane tasks can be automated. And now they can spend more time diving in with applicants and with hiring managers to understand critically what is needed and making sure that they're solutioning for that.

[00:13:45] So you're kind of not suggesting that AI is going to replace recruiters, but their role is going to fundamentally change. How long do you think that's going to take? Because it's such a big discussion at the moment as to whether AI is going to cost lots of recruiters their jobs. And it seems like there will be this kind of chaotic transition phase until we really know what that looks like. What do you think the timescales are? What do you think is going to happen? I don't think it's as fast as people think.

[00:14:13] You know, I spend time at a lot of different kind of recruitment events. And it's interesting to see the tried and true practices are still there, you know, from 40, 30, 20 years ago. You know, that skill set, those tactics. I think the technology just helps to speed up some processes, again, automate some processes. But no, I don't think it will replace recruiters.

[00:14:40] But it is likely to change recruiters jobs. And I think, you know, now we're starting to see the change. But I do think it's going to take multiple years as we figure out what works and what doesn't. Again, this is a relatively new technology for the masses. I'd say it's an evolution.

[00:15:01] And even though it seemed like a big bang at the end of last year of this whole new technology, this has been a slow evolution of natural language processing and these large language models. It's just that now it's more open to the masses. And I think recruiters are going to be able to test ways. How is it going to make their lives more efficient? You know, I even think about, you know, the way we use subject matter experts.

[00:15:29] So a hiring manager, when you're trying to define their needs, you might go to them if you're in a small organization or if it's a new job, you're going to go with a blank slate and just start asking questions to really determine, you know, what are the tasks performed here? What are the competencies or skills needed? But now you have an opportunity to have a base set. You can go to these large language models or things like O-Net and pull data to get the first draft and then review with the subject matter expert.

[00:15:58] So now all of a sudden you're spending your time refining to really determine what they need versus collecting of data. Now, what you said earlier about people hating numbers, but they are giving them the ability to analyze data and look at pictures and all those kind of things. That really resonated with me because that's me. I'm very bad at putting numbers together, but fascinated by looking for patterns and seeing what it can tell us.

[00:16:26] You mentioned a few ways that talent acquisition professionals can make use of data. Maybe if you could sort of expand on that for us a little bit, what do the people listening need to think about in terms of how they use data in their jobs in the future? I mean, the first step is just track it consistently. You know, I can't tell you how many times I've worked with an HR leader and they say they can't trust the data so they can't make inferences from it.

[00:16:52] Well, until you start tracking it consistently, you can't really identify what is faulty in the data and what's not. So, once you start tracking it consistently, you have to start connecting data. So, I'm a psychologist, right? And I really believe in hypothesis testing. So, I think that, you know, a hypothesis you might have is I think these three to five skills are related to successful performance on the job. Okay, well, what data do I have to measure those skills?

[00:17:22] And then what data do I have to show that performance has improved? We talk about this as the predictor criterion relationship. Once you determine that performance has improved, you can determine how much it has improved and then show the business the return on investment of your team's work. Now, performance is a difficult one. So, I typically recommend start small. You know, did they show up on day one? Did they complete onboarding?

[00:17:51] Have they taken LMS courses? You know, how many courses have they taken? Did they make it through the first 90 days? Did they get promoted? You know, so it's really building relationships with talent management professionals in the business so that you can align strategies and connect data. So, you can start saying the information that we're measuring here is related to the outcomes that you're experiencing.

[00:18:17] And then once you set that connection up, you can have this continuous feedback loop where you start saying, okay, people who did well in these skills achieve these outcomes. People who are poor on these skills achieve these poor outcomes. So, you start refining your assessment methodology to make sure that you're hiring the right people for the jobs that you're looking for.

[00:18:43] So, final question and really, again, summary of what you've been talking about so far, but pull this all together for us. What do you think the future might look like? Where could talent acquisition be in two or three years time? It's funny because as I've gotten older, I realized two or three years, it seems like a long time, but it really isn't. I think generative AI will be more part of our working environment.

[00:19:09] And the promise is the gap between people who are tech savvy and those that aren't will get smaller. I really do believe recruiters will be more strategic in the coming years because those mundane tasks will go away. You know, I hope, but again, it's been 20 years that I've been hoping that talent acquisition professionals and more importantly, hiring managers will realize that transferable skills are what's critical.

[00:19:34] And not that a person had specific experience in this industry over this amount of time. You know, we know the reason we ask people to draft and submit resumes is because we're making a leap that their education and experience proves that they have inherited the knowledge and skills needed to perform the work. But as we know, just because a person has experience, let alone the inflation that we know happens in resumes, it doesn't mean that they've actually attained those skills.

[00:20:05] So we've always known that there are ways to measure a person's skill level in certain critical areas so we can match people to jobs. And my hope is that we rely more on that than, you know, a piece of paper that provides, again, one piece of evidence. Ben, thank you very much for talking to me. Thank you, Matt. It's been a great time. My thanks to Ben.

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