Recruiting Future with Matt Alder - What's Next For Talent Acquisition, HR & Hiring?

Ep 649: Building A Personalized Candidate Experience

Improving the candidate experience has been a perpetual goal in recruiting. Despite the best intentions, the improvement process's effectiveness still ebbs and flows with the changes in supply and demand in the labor market. However, are things finally about to change? Ever since the exponential acceleration in the development of AI, I've been excited about the possibility of technology delivering a genuinely personalized candidate experience at scale. So, what does this vision look like? Which elements are already possible, and what do TA leaders need to do to make it a reality? My guest this week is Don Tomlinson, CTO at Daxtra. In our conversation, Don gives us a refreshingly pragmatic view of the AI use cases that can combine to build a personalized candidate experience and highlights pitfalls and dangers to be aware of. In the interview, we discuss: Using AI to diversify talent pools Taking out the background noise Personalizing the candidate's experience in real-time Bespoke communication at scale Automatically tailoring the hiring process to individual circumstances. Transparency The dangers of losing the human touch Efficiencies, investment, and ROI Build vs Buy, Core Vs Complementary AI self-auditing to improve strategy and process What will the future look like? Follow this podcast on Apple Podcasts.

Duration:: 21m
Broadcast on:: 01 Nov 2024
Audio Format:: other

Ever since the exponential acceleration in the development of AI, I've been excited about the possibility of technology delivering a genuinely personalized candidate experience at scale.

So, what does this vision look like? Which elements are already possible, and what do TA leaders need to do to make it a reality?

My guest this week is Don Tomlinson, CTO at Daxtra. In our conversation, Don gives us a refreshingly pragmatic view of the AI use cases that can combine to build a personalized candidate experience and highlights pitfalls and dangers to be aware of.

In the interview, we discuss:

Using AI to diversify talent pools

Taking out the background noise

Personalizing the candidate's experience in real-time

Bespoke communication at scale

Automatically tailoring the hiring process to individual circumstances.

Transparency

The dangers of losing the human touch

Efficiencies, investment, and ROI

Build vs Buy, Core Vs Complementary

AI self-auditing to improve strategy and process

What will the future look like?

Follow this podcast on Apple Podcasts.

Tower acquisition is going through an unprecedented transformation and many of you are likely in the middle of planning your strategies as we move through 2024 towards 2025. We all know that operating models, change management and aligning TA with corporate objectives are essential parts of any transformation strategy but with the market and AI technologies in particular evolving so rapidly there's a real risk that your strategy could quickly become outdated. That's where strategic foresight comes in. It's a proven methodology that helps you build credible future scenarios, create agile strategies and most importantly have a proactive influence on what the future of tele acquisition looks like both within your organization and across the industry. I know you're busy so I've created a concise online course that breaks down strategic foresight into easy-to-learn tools specifically designed for TA transformation. It's quick to implement and will keep you ahead of the curve. You can learn more by visiting mattalder.me/course that's mattalder.me/course. There's really never been a better time to shape the future of tele acquisition so don't miss this opportunity to make a lasting impact. Hi there, welcome to episode 649, a recruiting feature with me, Matt Alder. Improving the candidate experience has been a perpetual goal in recruiting. Despite the best of intentions, the effectiveness of the improvement process still ebbs and flows with changes in the supply and demand of the labour market. However, are things finally about to change. Ever since the exponential acceleration in the development of AI, I've been excited about the possibility of technology delivering a genuinely personalized candidate experience at scale. So what does this vision look like? Which elements are already possible and what do TA leaders need to do to make it a reality? My guest this week is Don Tomlinson, CTO at Daxtra. In our conversation, Don gives us a refreshingly pragmatic view on the AI use cases that can be combined to build a personalized candidate experience, as well as highlighting the pitfalls and dangers to be aware of. Hi Don and welcome to the podcast. An absolute pleasure to have you on the show. Please could you introduce yourself and tell us what you do? My name is Don Tomlinson. I'm the CTO at Daxtra. For those of you who don't know what Daxtra is, we're a resume parsing and then AI search and match organization that connects candidates to recruiters, to jobs. I'm really trying to make that a more efficient process and utilizing AI to help make that more efficient. Fantastic. Daxch has been around quite some time haven't you? Yeah. So Andre and Steve found the company back in the early 2000s. It's went through a lot of transition over the years, as you can imagine. But yeah, it's a really, really great company to be a part of. Fantastic. So obviously, you mentioned AI there and AI is the biggest thing that everyone's talking about when it comes to acquisition at the moment. And obviously, it's a big part of what you do as a recruiting tech business. What do you see as the big advantages that AI can bring to recruiting? So Matt, I will be honest, I am very optimistic about how AI can help recruiting. And there's a real advantage is that recruiting organizations can take advantage of. First thing, and probably the biggest area, is just reducing admin work. There is so much opportunity to remove the redundancy of the recruiting job and allow our recruiters to focus on more of that high priority work of building relationships and selling the organization. And I think that's a huge advantage. The other one is just speeding up the screening process, especially when you have high volume areas, you can really utilize AI to speed that process up. I think AI will really help organizations start to diversify their talent pools. AI can strip away a lot of the background noise that recruiters may pay attention to and really focus on how are we matching candidates to job descriptions, looking at skills and work history, and not some of those other areas. And so I think that that will one diversify that talent pool, but it also eliminates some of the biases. And then other areas around just sourcing candidates faster, better assessing of qualifications, and then also being able to scale communication. I think those are probably the biggest advantages I see that AI can bring to the recruiting process. No, absolutely. I mean, that makes perfect sense. Are you seeing any of that kind of already sort of coming through and happening in the work that you're doing? Oh, yeah, absolutely. So when we, you know, as we are starting to match candidates to jobs, we want to make sure that we're providing match scores for recruiters so that they can start to focus on those those candidates, most likely to one fit the job role. Also, those that are maybe looking for opportunities. There's a lot of data that AI can utilize that starts to match candidates, which then helps recruiters focus in the right area. I suppose there's also a big potential impact on the candidate experience here, isn't there? Yeah, there is. I mean, so the candidates, I think candidates are really going to benefit from from AI. First of all, you know, they're going to get more of a hyper personalized experience. AI can help understand the candidate and the role and start to build content to make sure that each candidate has that own experience. It can streamline the application process, you know, based off of the data that we have about a candidate, it may be able to skip certain steps or prioritize certain steps in the application process. From a candidate, I think one of the most frustrating things for candidates is what they don't know. Updates around the process, where are they at in the funnel and so forth. And with AI, you can provide more real time updates every time you're moved from one area to the other. AI can help and generate the types of messages that candidates want to hear from. And the other area, and I'm starting to see a lot more of this, is when you're looking at an organization, rather than going out and looking for jobs that an organization may have posted, it's more of how do you match yourself to jobs that may be open. So rather than starting with a job in mind, start with your profile in mind and AI can help match you to the opportunities with an in an organization. I think that's a real benefit because sometimes candidates don't realize they're a good fit for certain jobs. And AI can actually help match them to those opportunities and give them maybe a different career trajectory. Yeah, absolutely. I think the interesting thing about this is for years we've sort of had this argument about candidates want to deal with humans, they don't want to deal with machines. But I think what we're seeing here is the potential for machines to do things that humans just can't do, which will radically improve the candidate experience. Yeah, exactly. And so by utilizing some of these models, we're opening up new worlds to candidates that just quite frankly, I don't think individuals have the ability to fully understand all of the opportunity. However, AI can look at the candidate and really start to build some of those career plans for the individuals. I think that's really powerful for candidates. No, absolutely. And you said right at the beginning of the conversation that you're very optimistic about AI and time acquisition and how it's going to move things forward. But what are the dangers? What are the pitfalls? What should people be sort of looking out for right now? Yeah, so I think the first and foremost, the biggest one is that loss of human touch. So when the hiring process becomes impersonal, then we start to lose candidates. So we want to still give candidates that white glove treatment and really make them feel that they matter. And sometimes when you use AI, we can lose that feeling. I think there's a danger of losing the variable quality of candidates. So sometimes when you look at AI, it may start to say this is the ideal candidate. So it might overlook certain candidates that might be ideal for the role because it's using more of that machine learning and it's not knowing enough about the person. And so I think that's a pitfall. One of the pitfalls that organizations definitely need to be wary of is what the investment is. So yes, there are efficiencies to be gained, but that needs to be budgeted for around what the investment looks like and really making sure that you are getting an ROI on what you're looking to achieve. And then two other things is one is you look data is learned from. And if there is biases within the data, you're just going to perpetuate that. And so you really have to make sure that you're starting with clean data. And then the last thing that you need to look at as well as just making sure there's no liability issues with your AI algorithms, because that can be expensive if you're not where you need to be from a compliance standpoint. And just to pick up on one of those things there, we were sort of talking just now about that kind of hyper personalized candidate experience, the AI kind of generating content and messages to kind of help people. How much do you think these large language models are going to kind of improve as we move forward, which sounds a kind of a weird question because 18 months ago, we hadn't seen anything like this before. Now we sort of take it for granted and pick holes in it. And the reason that I asked that is obviously lots of people are using Generative AI to generate job descriptions and LinkedIn posts and messages. But it's kind of in terms of losing that that human touch, they don't always sound as authentic as they could be. Do you think the technology is going to improve to make that happen? Or do you think there still needs to be a kind of a big bit of kind of human intervention in that messaging? So I think in the initial stages, I think that with as much data as we are collecting around the experience, we will be able to make those experiences, although driven by AI, still make them personal. The one great thing that I love about AI is it's always learning. And as we start to utilize Generative AI to build the experience in real time, those are just going to get more and more understanding of what the candidates want and what the candidates need to hear. And it is going to become a much more personal hiring experience. And it can be tailored to the individual. And I think that's what's really powerful, is that it's constantly learning. And over the next couple of years, I think we're going to make drastic improvements in that process. Kind of following on from that point, everyone has access to large language models, generative AI in some form or another, even if that's just baked into something like LinkedIn or Google. There's obviously a lot of experimentation going on. I know that number of TA teams are building their own, building their own tools, very often using those publicly available resources. When should people sort of be leveraging existing tools? And when should they be building their own? Where's the kind of dividing line here, if you're trying to learn about this and experiment, how it can work in your company? Yeah, great question. So what are the things in the build versus buy decision? I think even before you can get to that point, we're going to, organizations really need to just, first of all, assess what their in-house expertise is. Do you have the ability, the expertise, those individuals that can really drive the creation and really optimize your strategy? And also then, how is it going to impact resourcing within your organization? Once you've really thought through that, the dividing line for me that I would probably move towards is, decide if this is core or complementary to your business. For complementary problems, for complementary features, I would move towards buying them. It addresses problems with low hanging fruit. It's an area where you probably have vendors who have been focused on solving that problem for you, and you're going to be able to quickly go to market. However, on the core side, if it's core to your business, that's where you may want to think about building it instead. If it's key to your business operations, then you may want to make this more specific than something you can buy off the shelf. And so, again, that's probably where you want to utilize that in-house expertise. You may bring in a consultant or two to help with the process. But the dividing line for me would be, just understand, is the problem you're trying to solve? Is it core to your business or complementary? If it's complementary, probably lean towards buying it? If it's core to your business, lean towards building it. Yeah, I think it's interesting. It reminds me a little bit of about 10, 15 years ago, maybe even longer ago, when lots of employers were building their own ATS systems, because it seemed like an easy thing to do. And I think I could be wrong, but in 2024, I doubt any employers who are an ATS company are building their own ATS systems anymore. So, it's interesting how that evolves. Yeah, exactly. And so, and that's where I think as we get more and more AI tools, that the decision is probably going to become easier for a lot of organizations, because there are going to be more options on what is part of that complementary side. And so, but again, thinking about, are you solving a unique problem that most organizations don't have? Or is this something that, yeah, probably a lot of organizations have, like your example of an ATS, everybody needs an ATS. Somebody out there is probably building one that multiple organizations can use. Absolutely. And I suppose you've touched on some of these things already in the conversation. But lots of TA leaders out there, looking at the landscape, looking at the market, it's kind of very confusing in terms of the products that are available and the direction that things are kind of moving in. What advice would you give to TA leaders when it comes to AI in terms of having the right to strategy and making the kind of the right decisions for the future? Yeah, so that's one of the tricky questions is, you know, what wind should recruit recruiters use AI and what should they be looking out for? The first thing I think they need to make sure they're doing is utilizing AI and the data that they're receiving to make better, more informed decisions, but don't take away the ability for you to make the decision itself. So when AI starts making the decisions for you, that can be where you lead down a rabbit hole and maybe into some negative consequences. Always think about the impact of each party that is impacted. So what I mean by that is you can have a new AI feature that is going to create a lot of efficiencies for a recruiter. But that's only one of the parties that's impacted. You also have to think of what other parties are involved. And so sometimes you got to think about the candidate or sometimes you have to think about the hiring manager. What is the impact to those other constituents that are involved in the decision? So think of it more holistically and not just what you need for your role. The other thing that you have to really think about is how, what is the quality of the data that you're utilizing? As you know, garbage data in will create garbage data out. Make sure that when your organization is taking on an AI process, that you're using good data, because again, AI is just going to learn from the data it has. And then probably the last thing I would say is at any point you're introducing AI, think about looking for ways to enhance the relationship between the candidate, the recruiter, and the organization. Everything should be about selling to that candidate, but making sure that candidate's experience is great. And so always looking on, is this going to enhance the relationship or is this going to hurt the relationship? And if you kind of keep those things in mind, I think recruiters will be pretty successful in bringing in AI technologies. And as a final question for you, what does the future look like? What do you think recruiting is going to be like in three to five years time? So it's interesting to start to think about what that's going to look like. A lot of AI today is building efficiencies, it's summarizing data to make data decisions. So I think when we look out three to five years, first of all, we're going to be able to get into an area where we have more, what I call effortless recruiting features. So effortless recruiting, to me, is the ability to start interacting with candidates even before a recruiter has had to go in and screen the individuals. So you have automated candidate searches, predictive analytics around what candidates would be most likely to be looking for a job. But those are the types of features that are going to become, they're going to go to the forefront. I also think that in three to five years, we're going to need AI to validate candidates better. So looking at AI powered assessments, enhanced video interviews, and real time feedback, and so forth, you know, AI is, you know, you mentioned an efficiency earlier, like creating job descriptions. Well, just as easily, we can start to have candidates that will build resumes to match job descriptions. So us validating candidates is going to be very, very important. And I think that's where AI is going to make huge strides, is to make sure that even when we see a resume that looks like the perfect candidate, we still need to vet that. I think AI will help us validate that. We're going to have more data in the decision process. And the other area, and I think this is another reason to be optimistic, is I think AI will start to audit itself better over time. So be able to suggest areas of improvement within the process. So not only will AI help, you know, make those efficiencies, but it cannot start to audit itself and help you as an organization pivot some of those strategies. So you can take better advantage of the technologies you're using. Don, thank you very much for joining me. Absolutely. It's been a pleasure to catch up again, Matt. My thanks to Don. You can follow this podcast on Apple podcasts on Spotify, or wherever you get your podcasts. You can search all the past episodes at recruitingfuture.com. On that site, you can also subscribe to our weekly newsletter, Recruiting Future Feast, and get the inside track on everything that's coming up on the show. Thanks very much for listening. I'll be back next time, and I hope you'll join me. [Music]