Lydia Dishman 0:00
Thank you so much, Steve, and thanks everyone who has joined me for this panel, and everyone in the audience giving you a shout out. I understand that this is the most wonderful time of the year with open enrollment, so I do appreciate that you’re spending some time with us today. So we’re talking about integrating AI into talent acquisition, and we know that the potential is there to revolutionize the hiring process, offering significant efficiency gains and cost savings. Sounds too good to be true, but research shows that 67% of HR professionals believe that AI does streamline recruitment by automating repetitive tasks, those terrible things we don’t want to ever do again, while organizations as a whole report up to a 30% reduction in hiring costs. So the shift is redefining how companies source, assess and onboard talent, and that paves the way for even more data driven decision making and personalized candidate experiences. The findings highlight how AI transforms the traditional recruitment process, but there are ethical concerns such as bias and certainly the lack of human touch. Where are the people in this equation? So there are some crucial considerations as we approach making more and more of these decisions, to integrate AI into our processes. So with that in mind, we’re going to talk to these five experts working in space right now. I’m going to ask them each to introduce themselves and tell us one thing that you personally think is currently standing in the way of wider AI adoption in these processes. So Jenny, let’s start with you.
Jenny Cotie Kangas 1:50
All right. Well, thank you, everybody for having me here today. My name is Jenny Cotie Kangas. I am a director of talent center transformation for Eightfold. And the question, what do I think is holding people back? I think our design bias for how we’ve historically solved talent acquisition problems, is likely one of the biggest things that’s holding us back. So when we’re so stuck with how we’ve always done it, it can be hard to break from that and try something new. So that would be my answer to that, and I’ll pass to the next person.
Lydia Dishman 2:24
That’s great, and thank you for saying that. I do believe that even though we know intellectually, the only constant is change, we as humans actually loathe to change anything because it feels comfortable and we don’t like to be out of our comfort zone. So let’s go on to Leslie.
Lesli Stasiek 2:44
Hi. Thanks so much for having me. Nice to see everyone. I’m Leslie Stashek. I am a Senior Director of Human Resources with syncora, and I think one of the biggest barriers preventing us from adopting more AI in this space is really developing a strategy around it and a longer term roadmap that’s very intentional, that optimizes both the investment user experience and the implementation, and I think that that is directly correlated also to knowledge about the tools in the in the process itself.
Lydia Dishman 3:21
Aalso a really important point on creating a roadmap requires such an investment of time at the outset, and it’s like, oh, well, I can invest the time or I could just do it the way I’ve always done it. So definitely. So thank you for that. Kristen?
Kristen Baller 3:42
Yeah, thank you for having me. Kristen Baller, I am the head of talent acquisition for DISH Network, or formerly DISH Network, and I would say one of the things that’s really holding I have seen hold us back, is we’ve really, actually redefined the role of talent acquisition, and when we think about the role of the recruiter, historically, a recruiter has been an analyst. They analyze a resume to understand if somebody’s a fit for the role. And now we run it, we go into an interview, and we analyze what the individual is sharing with us, and with the input of AI, our role of being an analyst is now evolving and allowing us to be more of a consultant and an advisor to the business. And so finding our way and our footing through that, it can sometimes feel like, are we being replaced, and which isn’t the trip like, which isn’t actually the case. And so there’s a little bit of misconception of how AI is going to support talent acquisition and its future?
Lydia Dishman 4:43
Yeah, absolutely. Thank you for pointing that out. I do think that there is a sort of, maybe a very thin, but definitely their undercurrent of fear, maybe just a tiny little bit. So thank you for that, Angie.
Mike Aronson 5:13
Mike Aaronson, I work with Johnson Controls. I lead our global operations team, which is essentially tools, technology and process. You know, I’d say, you know, not too different from what others have shared. I just think the fear of the unknown, you know, unwilling to let go of what’s comfortable. You know, not understanding how it’s more of a displacement rather than an elimination. You know, tasks may be different. It’s more of an aid to help do these jobs, you know, transactions, but it’s actually in addition. It allows more strategic work, right? It allows more valuable added conversation. So it’s less about touching buttons and more about having conversations.
Lydia Dishman 6:00
Oh, I love that. Maybe we need to trademark that less about touching buttons. Um, thank you. Thank you for adding that. Um, Angie, I’m going to try one more time before we sort of start diving in, and then you can join as you’re able, perhaps with technical difficulties, but we will, we will keep on, and we’ll come back to Angie in a minute. So Jenny, I wonder if you wouldn’t mind setting the table for us just a little bit. So for HR and talent acquisition professionals who are just at the beginning of exploring what resources to use and how to integrate them. What do you see as the best place to start, and what should they focus on accomplishing in those early stages? Great question.
Jenny Cotie Kangas 6:49
So in those early stages, you’re just beginning on this journey, the most important thing that we’ve found, and for context, I work with very large scale organizations and help them go through transformation and change. And my role, and I would say the most important thing, is to fall in love with the problem you have. And so it’s not about chasing after the technology. It’s actually about really, really starting to seek to understand what are the problems that we have, and being able to collect those dots and then calibrate in terms of order of importance, like, what is the most, the largest priority for me, for my users, for my team, to be able to tackle, and then using that calibrated list is the area that you kind of go into. So it’s much less about like, Let’s go search for the tech. It’s more of like, let’s search internally. Let’s look in the mirror and figure out where our problems are.
Lydia Dishman 7:39
Yes, inventories are always a great place to start. And if you don’t mind, just a quick follow up, because when I was listening to JJ presentation before this one, there was a note about who needs to be involved, and if you were thinking about which stakeholders were going to be at the table, because obviously HR is not operating and operating in a vacuum. So who would you suggest sits at the table? So
Jenny Cotie Kangas 8:05
That answer is going to differ based on the company. So one of our organizations I’m working with right now has 400,000 people. They have over 2000 people that are recruiting themselves. And so what their cross sectional team looks like might be different than somebody who’s a smaller organization, but typically you’re going to want a cross section of your hiring managers, your actual recruiters, the people who are doing the work, your operations folks, people from the business it finance like you’re going to bring those together, and they’re kind of going to become your like your Avengers team, for how you’re Going to dream, team, not a website fits all answer to that. It’ll differ based on the organization. But it really comes down to, again, that mirror aspect of seeking to understand what’s our problem in that process, you should also be seeking to understand who are the people that we should be bringing along to solve that problem, and always, always making sure that people who are actually your users, your recruiters, your hiring managers. Have a seat at that table because they’re the ones that can be using that service or solving those problems too.
Lydia Dishman 9:11
Excellent. Looks like Angie’s back. So perhaps we can come back to have you just introduce yourself really quickly. Maybe hopefully. I just wanted you to introduce yourself briefly, and then we’re we’ve already jumped into the questions,
Angie Lombardo 10:15
Operations Director for Arcadis, and I’m Denver, Colorado.
Lydia Dishman 10:18
Okay, awesome. Thank you. All right, so let’s go to my next question, because I’m sort of trying to take our audience on a little bit of a journey here, Kristen, tell me some more about what best practices are for using AI to attract candidates. Let’s start at the beginning there.
Kristen Baller 10:44
Kind of coming off Jenny, I think, really thinking through what problem are we trying to solve, but also a what’s i What I’ve learned being supporting more of the being on the internal side of talent acquisition versus like a vendor, is starting to actually understand what are our How are our vendors, the individuals were already partnered with, how are they leveraging? Ai, what is already in our products that is happening today, and that has been incredibly helpful in identifying where and where else we might be? Might we leverage that? Or where else do we need to lean in from potentially additional external parts of talent. Excuse me, within talent acquisition, but when we’re thinking about AI and areas that we could potentially use or areas that we’re looking at, a lot of the areas right now that my team has been focused on is within our employer branding and recruitment marketing space, and really starting to think through, how are we starting to identify predictive analytics of our candidates behaviors or where they hate like, where are they? Where do we target these individuals, as well as some of thinking through how, looking at our programmatic spend. So when we’re pushing jobs to job boards, how are we making sure that we’re optimizing the but the job boards where we’re receiving the best candidates, and candidates that when I say the best candidates, excuse me, but the candidates that move through the process, are we just receiving 1000 candidates, but only one candidate is making it to hire, are we? And starting to optimize there. And as we are evolving, then we’re starting to see, you know, okay, now we’ve looked at employer brand and our recruitment marketing, where might we be able to leverage it within our sourcing, or some of our our different programs, and then moving into, you know, how are our recruiters able to leverage this? How are we able to truly free up some of the recruiters’ time so that they’re able to focus on the candidate experience? Candidate experience has become, I think, a pain point across all organizations, as ghosting is increasing individuals feeling like they’re not being communicated on a timely basis throughout the interview crisis. And so how might we be able to free up our recruiters time so that they can focus on engaging and partnering with the candidate throughout their journey. And so that’s, I would say, from where we are as an organization, the places that we’re leaning into and trying to better understand.
Lydia Dishman 13:29
Just a quick follow up there, because I kind of stuck on this candidate behavior thing. And so if you’re able to see where someone is clicking through, for example, and how long they’re staying with the application process, are you able to use that data to sort of flex and adjust the application process?
Kristen Baller 13:54
So where we’re looking at right now is, where are candidates? Where are they spending the most time on our website? If somebody comes and they’re clicking through trying to better understand our positions at our corporate offices, then how are we providing them more information within the corporate space and about the benefits that they might see on site? Or if we have, we have technicians that are in the field, if they’re looking at diving into information within the page, how are we understanding what they’re looking at and then additional information that we can feed them so that they are engaged and want to apply and start that application process. And so what we’ve learned through this journey is we really don’t have to educate or we don’t want to educate the candidate on the organizational structure of the organization. We want to educate them enough to get them to apply, and then to be able to connect with a recruiter to get a holistic understanding of what the role is and why. They want to work here, and what opportunity looks like?
Lydia Dishman 15:05
That sort of brings me to my next question. And Leslie, I’m going to put you on the spot for this one. There is this sense of, you know, we use the AI for this, and then we move it to where the human takes over. So can you talk about how your team is balancing using AI with human interaction? Yeah, I
Lesli Stasiek 15:26
I think that doing that is so critically important. We can’t just swing the pendulum to one side. So for us, at sync core, we’re really just starting our journey. We use phenom as our CRM, and that’s helping us to build our pipeline internally and externally, but we definitely still have that personal touch from the recruiter as well as the hiring manager to really help candidates understand, is this the right place for me to be as well as managers understand, is this the right place for the candidate to be and really get a feel for the culture as well as the role itself. So we’re really allowing that process to be optimized. But at the top of the funnel, we’re driving efficiencies with AI simply because of the volume of applications we get on a daily basis for all of our roles. It’s just not even fees. We don’t have enough recruiters to go through everything, so we’re using the AI at the top of the funnel, and then keeping the human touch as we sort of filter through to get really to the applicants we want to spend some time with. And then, you know, down the road, like I hope, we can leverage this even more internally to really empower employees and give them the resources to build their, you know, their careers as they go on, and then have driven better discussions. So the AI helps the employee have the right tools and resources to to own it, but then the human touch of having the right discussions with, you know, HR and the hiring managers within the organization. So I think it’s going to be a critical balance. I don’t think we should. You should never, you know, just go, go one way or another. I think they can work very nicely in harmony together. Mm, hmm, absolutely.
Lydia Dishman 17:09
And it also brings up Mike’s point about buttons versus conversations, and absolutely, Mike, I wonder if you can chime in here and talk about some ways to use AI to assess if you’re if these processes and tasks are being performed efficiently. Because everybody’s used the word efficiently so far.
Mike Aronson 17:33
I mean, you know, you look at the end process of talent acquisition, and there are different milestones on the journey that have become so transactional. And you know, at its core recruiting is to get a job, find a candidate like it’s overly simplified in that respect. And so what we’ve done is we’ve done some time studies to understand, in order to understand the effectiveness, you have to understand what you’re saving. And so looking at the end and process and saying, This is how long this takes, this, how long this takes, this so long this takes, and then identifying where you could potentially have, you know, some sort of automation or AI impact that. And so for a practical example, you know, a job, a position, becomes open, and now it’s all right town acquisition has the job. You need a job description. If we don’t have a job description, we’re chasing the job description. There’s job description generators, you know, there you can use any of the tools that are, you know, chat, GPT or Gemini or co-pilot, any of that sort of stuff. And so something that used to take potentially days or even a week, we can get done in five minutes. And now that’s time saving back into you know, day to day you look at, you talked about programmatic job distribution, rather than saying, all right, I gotta go post on LinkedIn and indeed, and all these different places, the algorithm is going to determine what’s effective. It’s going to put in front of the right candidates. And then you measure the ROI, and you continue to tweak, you know, there’s tools that measure so again, that’s time saving for a recruiter. There’s an efficiency created in that. I mean, there’s along the journey, whether it’s tools that rate candidates, they aren’t great, or grade candidates, not necessarily ranking. There’s a lot of you know laws and you know policy around the bias, but ultimately fetching candidates and pulling them into and saying, Here’s your job. These are the top three candidates that already exist. So it saves time on sourcing. It’s, you know, interview scheduling. Now it’s, you don’t have to go back and forth. This doesn’t work for me. This works for the manager. What about the candidate? So having, you know, those time saving dynamics, you know, we’re at the point now where it’s like, all right? And we have a lot of that in our current process. What we don’t have is, you know, recruiters doing their phone screens now they’ve got to write up their summary and send it to the manager. Well, there’s tools that summarize conversations. There’s tools that you can put in your phone notes, phone screen, notes, job resume, say, summarize my conversation. And the experience now you’re not spending, you know, 3040 minutes writing up a phone screen, notes, sending what you uncovered, and so doing that time studying and understanding. Okay, this used to take us a day. It takes us five minutes. We’ve just given efficiency to the recruiters. And so it’s really about understanding where the end to end is. And again, it’s not replacing the person. It’s making the job a bit easier so that they can have a conversation around this. Is why I think this person’s good. I, you know, I saw somebody like, you know, just having much more consultative approach, rather than push the paper, get the response, not interested, back to it and be kind of going through that whole cycle again.
Lydia Dishman 20:43
And I do think, though, that a lot of folks are not really great at giving a fair estimate of how long a task will take them. They either think it’s way longer than it actually does take, or they just say, Oh yeah, that’s five minutes. And then, you know, 25 minutes later, they’re still but so do you have a hot tip for people to kind of crack out of that?
Mike Aronson 21:09
No, I mean, if you know, because, again, using the, you know, chasing a job description, they’re not spending two full days chasing a job description. They’re waiting for something else, and it’s never an exact science. I mean, right? You can put ballpark figures into efficiencies and say, You know what? How long should this take? In reality it should take five minutes. We have a repository, or there’s already something on hand, but there’s not. And so it’s really, the fact is, AI makes it almost instantaneous, so any sort of adoption is going to save time. So yes, you know, back and forth with scheduled interviews, we actually know. So there are tools that will give you how long it’s taken you to schedule through AI, and most of them are, at least in our experience, text messages like 10 minutes. You know, you know WhatsApp is like seven minutes. There’s no way, unless you’re on the phone with a man with a candidate, and you’ve already gotten the green light to schedule whoever you want, that it’s going to be faster than that, right? You know, there are automatic efficiencies that are created, even if you don’t have the exact time.
Lydia Dishman 22:14
Well, that’s good advice, indeed. Angie, I want to come to you next. Hopefully your connection is cooperative. I am supportive. Today, I came across a really interesting thing from one of my contributors talking about the paper ceiling. We’ve all heard about the glass ceiling, but the paper ceiling is when you are holding somebody down because they don’t have the pedigree, but they may actually have the skills. So talk to me about how AI can help with skill based hiring and Talent Management, sure.
Angie Lombardo 22:45
So in our organization, we actually did implement an AI platform that sits on top of our ATS that is very helpful, and we’re making that shift towards skills hiring. We’ve actually had two very big success stories lately, which is exciting, because it takes time. But what the platform helps us do is, when you open a job, it pulls over the skills, and then you can calibrate, and the system will search based on those skills, and it’ll help you rank the talent pool, which is not someone who would apply, and your applicants. And it helps, as Mike was saying, it helps. It really helps with the task. I mean, everything has a human touch, right? So this just helps in efficiency, and it really helps in our regions, where we have 1000s of applicants, like India and the Middle East, where we’ll open a job for a week and get 2000 applicants. Well, this system is very helpful because it narrows down based on skill. So you can start reaching out to the skills that match, not necessarily just the CV that matches, but it is a shift for your manager, I have to say. So normally, you know, a lot of times you open a job and you send them someone that has the skills, and they say, well, this doesn’t match the job description. And you say, but it does. It has these. You know, this person has 10 years of project management, they have five years of this. They have these certifications. So it takes some education, but the system does really help with that, because it can, it can paint this visual picture for the manager based on their qualifications and the skills they need, who’s available, and sometimes it’s two people. So we need to expand those parameters, and it helps the team, because it helps them focus on skills, and it makes their job more efficient, because they can use instead of just individually looking at 1000s of resumes, they can start with the people who match the skills and then work from there. Yeah, so it’s been a really big help for us. It’s still a journey, a learning journey, but we recently just hired someone back to do a job that isn’t really a job that’s in the market. Truthfully, it’s a job that we it’s unique to us. And originally, the manager said, No, the resume doesn’t match. And I pushed really hard, and we ended up hiring this person because they’re actually. A great match based on their skills. It was a win for us. But it does take time to adjust, for sure, well, and
Lydia Dishman 25:07
I think that that plays into, you know, what Jenny said at the beginning, which is, you know, you’re in a comfort zone. You like the way that things are right. You’ve always done them that way. But I do think that this is sort of an adjacent issue to what bias humans bring to the table, and unfortunately, what bias some AI tools can bring, because when you’re matching for skills, theoretically that should be an unbiased process. You either have this skill or you don’t. So, right? So are you finding that that’s helpful that way too?
Angie Lombardo 25:44
It’s becoming helpful as we’ve had it for eight months now. Initially, it didn’t, because everyone, just like Jenny said, they’re like, Well, this is how I work. I don’t want to, why would I look at this, you know? But because we’ve had a few success stories, it is helping for people to look at a job differently than they would before. I wouldn’t. I would love to say it eliminates bias. I can’t say that it’s but I do think
Lydia Dishman 26:08
It’s okay. 111, step at a time.
Angie Lombardo 26:12
I think it eliminates bias in just relying on a job description. But well, you know, it helps. It certainly helps.
Lydia Dishman 26:20
Thank you, Kristen, you want to add what you’re what you just said.
Kristen Baller 26:24
We’re forcing the business to really look at it. Looking at jobs differently than we have before is so important, because what jobs look like 10 years ago versus what they look like today? The entire landscape has candidates. The landscape has changed, and as we evolve into these skills based hiring, some of this AI is actually working like it’s really working for the candidate. Some of the different pieces that involve AI that we leverage through our vendors allows us to go back to a hiring manager and say, Hey, this little unicorn that you’re dreaming about that what you will think, we’re only going to pay $30,000 for a year. It’s actually right. There’s actually only this amount, this number of people in the market, and they actually are probably going to cost you 200,000 plus a year. And it allows us, as talent acquisition, to really level set with the business and helping them understand what their what they can afford, what is their true needs, and helping them understand what the market, what’s in the market, and where are candidates who, where, and you know what they’re doing with other employers.
Lydia Dishman 27:38
Don’t folks in HR call those people purple squirrels. And where am I hallucinating on that one, it’s a purple squirrel. Yeah, okay. Just hadn’t heard that term in a while, so wanted to make sure I was still up to date on the lingo. I want to just unpack the bias piece just a little bit more. Jenny, would you indulge me here? I know that I’ve been covering AI for over a decade, particularly as it was, you know, first used in hiring. It was so interesting, as I said, theoretically, that it was going to eliminate bias. But then we started seeing how the AI wasn’t quite working up to speed, if you will, to truly eliminate bias. So where are we at right now with that?
Jenny Cotie Kangas 28:21
What you’re referring to? So historically, there were some situations where machine learning was being used. So machine learning looks at past decisions and then replicates past decisions, but if we’re replicating past decisions that were not the right decisions or were biased in nature, as a result, you can end up only promoting white males, or you can not, you know, promote women of color, right? Like, there’s certain situations where we could be perpetuating the wrong thing, and that’s a really important piece to remember with AI, right? So AI can be biased, right? It’s all about how you’re using it and how you’re coding it, and so if you’re not putting the proper guardrails in, or if you don’t have, like the vendor partnership, and they can show you your bias audits and all these different pieces, those should be significant red flags. But the other thing that I always want to remind people of when we’re talking about bias is the idea that the most impenetrable black box is the human brain. So if I can make a decision one day when I’m really hungry and a different decision when I’m not hungry, about somebody’s opportunity for a role, we’ve got a problem. And so that’s where this AI is starting to come into place, right? Like what Angie said, of being able to leverage the AI to look at that 1000 different candidates and to shortlist the 1000 down to the 20 right, like when we’re leveraging AI to do that on the basis of skills. Typically, that slate that we get, that 2020 candidates we get, is very, very diverse, because when we look at skills, instead of looking. As you know that external, you know identifiable characteristics, the outcome is diversity versus like diversity as an add on. So I’m kind of all over the place here.
Lydia Dishman 30:10
But no, no, you’re good for all.
Jenny Cotie Kangas 30:12
The story is, AI can be biased. It’s really, really important to make sure that you’re partnering with the right tech vendors who understand not just how their tech is today, but you gotta ask, as we’re going, what guardrails you’re gonna put in place to make sure that we’re not making the wrong decisions, right? Because, again, like that situation that we were talking about in the beginning at the beginning of this, we don’t want to perpetuate the wrong decisions. We’re trying to use AI for good, right?
Lydia Dishman 30:37
Yes, absolutely, AI as a companion tool. But yeah, I do remember having these conversations, and there was a lot about, what is the data set going in to make sure that what the output is is not biased, and what is it being trained on?
Jenny Cotie Kangas 30:56
Yes, which is also when we’re talking about job descriptions, and leveraging Gen AI for job descriptions. It’s always important to run that through some sort of anti bias tool, or like a Textio or something, because you have to remember the data again that was being coded in that Gen AI was typically biased data, right? So it’s typically more mail leaning. It’s not as inclusive as there could be emotionally, there could be charged words in there, because it’s looking at patterns from the past in order to predict the future. But we don’t want to just perpetuate the things we’ve always done in the past to the future. We want to actually kind of reset right and recalibrate and make sure that we’re creating job descriptions, job advertisements that are truly inclusive. And so even when we have some of these tools out there, it’s important to sometimes layer other tools on top of it to make sure that some of those pieces because, as you said, Lydia, it’s bad data in it can be bad data out, right?
Lydia Dishman 31:48
Yeah, absolutely. Leslie, you were nodding, and it reminded me of something that you said in your previous reply about career development and identifying internal talent, so help us understand more about how we can use that internally versus just using it for external candidates. Yeah, I
Lesli Stasiek 32:12
I actually think that’s one of maybe the undervalued areas. I mean, currently, I think everybody’s thinking externally, but it’s far less expensive to develop talent internally than to go out and source a new candidate. You typically have to pay them more, and then you lose time as they get up to speed. So if you can leverage it to develop, you know, younger talent into future leaders in your organization, I think that that’s where you can see exponential benefits. Different platforms have different things, but like a talent marketplace, where individuals employees can go look to see what opportunities there are. They can see, not just roles, but opportunities for them to maybe be on a project and just different things that they can do to continue to learn and develop and to connect across the organization. We have 46,000 employees. There’s just absolutely no way that leaders know everyone that might be a possible candidate for their role. And we really are. Are. One of our big initiatives is to drive holistic experience across the organization for our future leaders. So back to what Jenny was talking about, and if you’re just looking at what happened historically, you might not pick somebody for a role because they don’t have any experience in that business unit, but that’s very intentional. We don’t want them to bring over transferable skills, and then they can get experience, and then they become a more enterprise minded leader in the long run, which is like the longer term pictures. So tools like this allow us to look at not just career pathing within a function, but across multiple functions or business units. It allows us to match mentors across the business which you get. You drive that connectivity, and you drive that networking, and then you just can grow your pipeline and your succession planning exponentially.
Lydia Dishman 34:10
I think, yeah, absolutely. And I think it’s also important for people already inside the organization to be able to feel like they can progress in their career, whether they want to go sideways or up, and let’s not even start with the jungle gym, because that was another discussion for another time, but, but I do think that it is really valuable to to internal talent, to be able to feel like the company is investing in them.
Lesli Stasiek 34:40
It’s one of the biggest indicators of engagement and retention. So it’s worth every dollar spent, every minute a manager has a conversation with an employee about it. So I 100% agree, and we see that our engagement surveys are, you know, whether people will. Indicate if they don’t feel like they’re getting enough career development opportunities and they want more.
Lydia Dishman 35:05
Absolutely.
Lesli Stasiek 35:10
And people are very vocal when they they are, I’ll tell you exactly what they think.
Lydia Dishman 35:12
Yes, exactly. Mike, I want to come back to you because, you know, Leslie just mentioned minutes, and I keep coming back to you for the efficiency question, but talk to me about finding a sort of balance between efficiencies, as you were talking about, but also providing a really good candidate experience, because you don’t want to cut it too short in the you know, just for the sake of saving some time and moving on to the next thing. So how do you handle that?
Mike Aronson 35:40
Yeah. I mean, you know, one of the kinds of byproducts of artificial intelligence or automation that we have is everything that is, you know, to some degree, digital can be tracked, so you can see how your candidates engage with you. I think Kristen was talking about understanding behaviors of the candidates. So for a long time, I think town acquisition used to say, well, this is what candidates want to see. This is what we’re going to put on our careers page. This is going to be what you know, we’re going to highlight and show. This is information we need on the application, so we’re going to ask for it. But that is not considering the candidate’s experience. That’s not considering what they actually want. And so a lot of what we’ve done is, and I’ve told our teams, like, Listen, if we can measure it, it’s going to tell us something, and it makes no sense to do nothing with it. Right? If you can see where the candidates are coming from, we have a chat bot on our careers page that candidates ask questions. I want to know what questions they’re asking. I want to know what questions we don’t have the answers to, because that’s clearly what we need to optimize and create an efficiency on. I want to know the questions that we have, that we never get asked. And so now we’re catering our experience to what the candidates are actually telling us where they’re coming from. You know, we had some, you know, real life examples of, you know, who entered, who interacted with that chat bot? And in our mind, there was a perception that production workers will never interview, will not enter, interact, like other other roles with the you know, technology. They just want a resume. They don’t even have email addresses. All these crazy anecdotes that were proven completely false, like the highest volume jobs that we get interacting with our AI chat bot is the production workers, because they can do it through text message. They can do it through I mean, there’s a variety of different ways that they engage. And so rather than trying to solve problems that we think exist, we are trying to solve the problems that our candidates are telling us, and so we had a very large career page with a bunch of different tabs, and the main page and the job search was all where anybody was really going to so what’s the point of having all these other things that’s just focusing on what the candidates want to see? Our application had, I kid you not, almost 48 fields that could be filled by a candidate. And he said, Well, what? What do we need at the time that they’re applying? Just because we need it for payroll or we need it for the candidates, let’s get an offer. Let’s collect data when the candidates, when we need to collect it, not when just put it all at the front end of the process and give a really poor experience. And so we’ve been able to take our time filling out applications down from like 1314 minutes to two and a half minutes. And that’s just candidate experience. Like, try to put everything we do now I said, Listen, our strategy is candidate experience. Yes, we’re recruiting. Yes, we do talent acquisition internally, all of these different things, but the center of what we’re doing has to be the candidate’s experience, because they’re not going to change their behaviors. We have to change ours, right? There’s too many candidates out there that are doing what they want, and if it takes too long, I usually the example, unrelated sound acquisition, if you go to a career or if you go to anything that you are going to buy, if I have to create an account, I’m leaving the page like I don’t, I wouldn’t move forward as guest, continue as a guest.
Lydia Dishman 39:06
Yeah, as you know.
Mike Aronson 39:09
Why do people use Amazon so regularly? Because it’s so easy. Yeah? So if we don’t use those concepts in the process, then we’re sorry, bearing our own interests. Not the candidates. And ultimately, we have no role if we don’t have candidates. And so candidate experience really has to be at the center of everything you do. And you need to listen to the candidates. You need to use the data, and they’ll just say, Oh, that’s interesting. A lot of people come from India, or a lot of people are, you know, logging on from this particular country or a region. Say, well, all right, well, maybe we need to do more branding in the regions where they’re not coming from. Use it to attract the candidates in a way that is informed by the candidates.
Lydia Dishman 39:51
I would love for you to talk to all the physicians offices who ask you to fill out the same information on a piece of paper. But also online. So just keep that in mind, that’s your $60,000 idea. Angie, I want to come back to you because Mike mentioned India and you were talking about how many, how many candidates you have coming in. So how are you using this to optimize the candidate experience as well?
Angie Lombardo 40:21
It’s been really helpful in our candidate experience, because the platform, because it’s really streamlined interview scheduling, which I know Mike also talked about, it’s people can self schedule based on your calendar, which I realize is very basic functionality, but we
Lydia Dishman 40:37
didn’t save a lot somewhere.
Angie Lombardo 40:40
And then you can also schedule a panel interview, because you can see everybody’s calendar and just set it all up in one click. So it’s really helpful, especially for those big countries that get all the applicants, they can really, you know, narrow down to the top 10 or the top 20, screen at screen, and then screen out and move on. And it just makes the process significantly more efficient, because it takes out all of that back and forth, emailing, definitely, yeah. So it’s been great. It’s been great for us, and we continue to optimize the candidate experience as much as Yeah.
Lydia Dishman 41:13
Okay, so we have a couple minutes left. Let’s do a speed round, if you will. And looking forward, what should we be aware of in the coming year? What issues, challenges? What have you, Jenny, putting you on the spot first, but we’re going to go around quickly.
Jenny Cotie Kangas 41:33
I have the first one. Okay. What issues do we have? I think one of the big issues that we should have is, how do we democratize the ability to identify our skills in the context of work. So if you can’t identify your skills with a front end to be able to collect those dots in a resume, you can’t do all the things downstream. So how do we actually work to democratize that aspect in our talent acquisition experiences?
Lydia Dishman 41:58
Okay? Kristen, 30 seconds. No pressure.
Kristen Baller 42:05
We have a lot of subject matter experts like Jenny or Michael myself that are focusing or working within the AI space, but when we think about it at a greater scale, and I think education, that’s what we’re going to have to really think about. We’re giving all these teams a Ferrari, but nobody’s taught them how to drive it. And so all that education where we can find ourselves in dicey situations. And so how do we continue to educate at scale? Yeah,
Lydia Dishman 42:31
absolutely. And I love that analogy. Thanks for that.
Lesli Stasiek 42:37
Leslie, yeah, I would just say continued change management. I think education is the first piece of that, but you can educate and create awareness. That doesn’t mean that behaviors are going to change. So ensuring that you have a great change management plan to pull all of that through as you go implement, I’ve seen some systems implemented where they have all the shiny bells and whistles and people are excited, and then six months later, they’re still not being optimized.
Lydia Dishman 43:09
So that would be something that I would say definitely continue to plan for great we’re building here.
Mike Aronson 43:13
As we know it, will look different a year from now, five years from now, and so our teams have to really elevate themselves and learn. I always say as if AI will not replace total jobs, but people who don’t use AI will be replaced with people who do. And so having the ability to upskill yourself learn different competencies, move on from the tax tactical transactional work, because that’s where talent town acquisition is going. And it may just be talent in the end, acquisition is part of workforce planning and acquisition and development and all of that. And so we have to make ourselves ready to get there.
Lydia Dishman 43:59
Great Angie, take us home quickly.
Angie Lombardo 44:00
Those are all really great points. I would say the one thing to just be aware of is that the more that we use AI, we do have to just be careful that we’re not just using AI to do our work, but we are putting that human touch on it before we put anything out to be viewed. Great.
Lydia Dishman 44:17
That’s great advice to end on. And so Jenny, Leslie, Kristen, Angie and Mike, thank you so much for spending this time with me, and I am just absolutely thrilled once again to have facilitated a really engaging conversation. Over to you, Steve, you.
