The Breakthrough Hiring Show: Recruiting and Talent Acquisition Conversations
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The Breakthrough Hiring Show: Recruiting and Talent Acquisition Conversations
EP 147: AI-Driven Interview Intelligence: Revolutionizing Recruitment with Mark Simpson, CEO of Pillar
James Mackey, CEO of a leading RPO provider, SecureVision, and Elijah Elkins, CEO of Avoda, a highly rated global recruiting firm, co-host Pillar’s CEO, Mark Simpson, in our special series on AI for Hiring.
Discover how Pillar’s interview intelligence platform enhances efficiency, reduces bias, and integrates seamlessly with tools like Zoom to create a more structured hiring experience. They also explore the potential of AI-driven interview guides, automated scorecards, and the role of advanced analytics in reshaping candidate experiences. Tune in to learn how AI can set new industry standards and what the future holds for interview intelligence.
0:00 Streamlining interview process with Pillar
13:03 Enhancing interview processes with AI
21:40 Expanding interview intelligence across industries
26:46 Future of AI in interviews
38:13 Optimizing screening calls with AI
Blog post: One In Four Interviews Are Biased
Thank you to our sponsor, SecureVision, for making this show possible!
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Hello, welcome to the Breakthrough Hiring Show. I'm your host, james Mackey. We got Elijah Elkins here today co-hosting with me, and we also have Mark Simpson here, founder and CEO of Pillar. And, mark, I appreciate you coming on today. We're gonna talk a little bit about your product and what you're doing to incorporate ML into the product and what you think about ML within hiring in general. I'm looking forward to having that conversation with you today. Thanks for joining us.
Speaker 2:James Elijah, good to meet you guys, Good to see you guys. I'm a big fan of the show, so really excited to have a bit of discussion with you today.
Speaker 1:Yeah, man, let's do it. I was looking at your LinkedIn profile and I see multiple companies that you founded. I'd be curious to learn a little bit about your background and then we could just transition into what you're doing over at Pillar and essentially the primary value prop, and just take it from there.
Speaker 2:Yeah, fine, look, yeah, I've founded three really successful companies now and grown each of them and been excited about all of which individually. There's been a common thread going through each of the companies they're all B2B companies, they're all SaaS businesses and they're all very focused on machine learning and AI. And I'm most excited by the company I'm in at the moment and I'm not just saying that it's been. I believe we're solving problems that I've seen through my businesses for the last 20 years and something I've wanted to solve in the last 20 years but really only have the ability to solve those problems really since COVID happened and since we work in interview intelligence, but since interviews have moved to Zoom and Teams and we have the ability, with great machine learning and AI, to be able to understand those conversations and help improve the hiring process because of it. So, yeah, I'm here today with Pillar, a company I founded four years ago, and I'm excited to dig in.
Speaker 1:Yeah, we talked a little bit about what the product does, but I would love to dive a little bit deeper if you could tell us more about how it works, for instance.
Speaker 2:Yeah. So Pillar is an interview intelligence platform and in starting Pillar, we really started a new category a few years ago where we join live interviews or our technology, I should say, joins live interviews and helps underpin the interview process and helps guide the interview process through to more successful hiring. So there's a few problems that we're solving. One is how to reliably hire the right skills into an organization and the second is how to be more efficient in your hiring, particularly around the interview, and we've focused on the interview process. So really everything from an intake meeting into kind of selecting a candidate, because interviews has typically been untouched, a black box and a very inefficient way of finding out which candidates have the right skills, which candidates are right for the role. I think it's been reported widely about how much bias comes into the process and how inefficient it is and all these sorts of things, and so we've really set up Pillar to help empower talent acquisition teams, to give them control, more control of this process and run a much more efficient and a much more effective process.
Speaker 1:Okay, cool, now you're working with. Like, what size customers are you typically working with?
Speaker 2:Yeah, it tends to be mid-market to enterprise customers. We have some Fortune 100, fortune 50, even customers with Pillar, right the way down to organizations with a few hundred employees. And with those companies we're helping them save a ton of time, be more efficient. Save time Maybe that's through guiding an interview process or not having to chase anymore for scorecards and interview feedback. We're also helping them be more effective standardizing interviews, guiding the interviews in the process, collecting feedback, writing up interviews, organizing scorecards, giving highlight clips for when you're selecting candidates, those sorts of things. And it's all done through either having proper interview guides that have served into interviews and or recording those interviews and processing a lot of data around those interviews to understand what's going on.
Speaker 1:So the product is essentially? It's plugging into the Zoom, integrating with Zoom on the Zoom call, recording that information, putting together evaluations based on that, or presenting the information rather in a consumable way to hiring teams. When you're saying structured interviewing, though, could you give more insight? I know how greenhouse refers to structured hiring right. I'm thinking of interview stages with scorecard attributes and custom questions that are asked consistently when you use that term. Could you tell me more insight in how Pillar's actually going about providing more structure to the process?
Speaker 2:Yeah, absolutely so. Greenhouse are pretty good at doing some of this, a lot better than maybe some of the other applicant tracking systems that companies use the Workdays or the iSIMs, oracle's SAPs maybe not quite as advanced. So we take what Greenhouse does and we will amplify that In an intake meeting we can take in a job description. Our AI can create interview guides and assign those interview guides to hiring teams. Then when somebody gets into an interview, the interviewer clicks on their normal Zoom link or Teams link or whatever it might be to do an interview, or maybe it's a phone call. We will just pop up in that Zoom and Teams with the applications that we have, with their interview guide. So we're guiding them on maybe it's the skills or competencies that they should be asking about.
Speaker 2:We guide them with good interview questions and we present the scorecard in that guide as well. So they can just go in, they can write some high-level comments, more detailed notes if they want to, and give rating scales and those sorts of things, so that we can take then all that information and we can automatically fill out scorecards in Greenhouse. We can put scorecards into Workday and iSIMs and other applicant tracking systems and really just close the loop on the process, which it's all well and good having interview guides, but we see a lot of interview guides not being used. If it's presented there, live in the interview and it's automatically saving the interviewer time and filling out all their scorecards and write-ups, and what have you afterwards? We find we get used and we're seeing 85, 85, 90 percent of scorecards being filled out within 24 hours of interviews now with our customers, which is a very markedly different stat to when customers join us yeah, that makes a lot of sense.
Speaker 1:Elijah, do you have any questions about that or anything else?
Speaker 3:yeah, I just I find it really interesting because I've worked with quite a few different hiring managers and interviewers and companies and that's always one of the biggest problems, mark, as you're mentioning, is getting them to actually fill out the scorecard so that the process can move on, or the evaluation as it's called in some ATSs. Practically, let's say, an interviewer gets done with the interview, then what do they do? Right, like how is Pillar kind of making it so much faster for them to be able to submit that? Just on a really tactical level, right, so people can understand, how is Pillar enabling them to get that email done like super quick?
Speaker 2:Yeah, absolutely so. They're using the guide within the interview. The guide has the scorecard there too and they're clicking on kind of rating scales against competencies, against the skills that are required for the role. When an interviewer closes down the interview, pillar will use AI to write up the interview. So there's an interview write-up that goes back into Pillar and into an ATS, because we sync with the ATS automatically, and then we will fill out the scorecard based on the interviewer's ratings of the competencies, the skills that are related to that scorecard, and that will be done as soon as the interview closes.
Speaker 2:The interviewer obviously has an opportunity to go in and alter things if they feel that they may have scored wrong or got things wrong in the interview or, on reflection, they wanted to change some things.
Speaker 2:But actually when an interview is complete, an interview doesn't need to get chased and go and log into an applicant tracking system which they never use and feel they have to do kind of bullet point write-ups to send to hiring managers and all the things that sort of bad practices that we see very commonly in organizations. So part of it is the collection of data, I think, really underpinning it from a talent acquisition perspective. Part of the problem we solve is more just these large organizations having the risk of hundreds or thousands of interviews going out and having conversations. They don't know what's being said. So actually just being able to guide those conversations and focus interviewers in the areas that hiring managers want them to. They want to know about certain skills, they want to know they're fit for the job based on their skills, it's just a very big problem solve across what could be thousands of people in an enterprise.
Speaker 3:Yeah, that makes sense. So basically Pillar is helping you. Let's say the interviewer prepare for the interview. Let's say the interviewer prepare for the interview right and then also execute a more effective interview because of the guides and the questions that are actually there, sort of filling it out as they go. And then Pillar is also helping to summarize and get everything submitted really quickly. So all three of those steps right, it's helping with the efficiency and the effectiveness.
Speaker 2:Exactly right, exactly right. We then go a little bit further as well. Pillar will after interviews happen, we will provide everything that's needed in things like video highlight clips for selection meetings. So when you have a debrief meeting about the candidates and selection, you can go in and reference candidate skills and what have you with your interview team. And then, just finally as well, we have a lot of insights and training around Pillar 2. So our AI I'll give you kind of some tangible examples Our AI can detect when bias has crept into an interview and we will automatically give suggestions about how to eradicate that in next interviews. So we have a bias detection element within this and a training element automated training element for interviewers.
Speaker 3:Is there like an example sorry on the bias, because that is such an important thing. Do you have an example of bias that it would?
Speaker 2:Yeah, plenty actually.
Speaker 2:Yeah, I've actually released a blog about 20 minutes ago or an hour ago which gives the top, I think, five different types of bias that we're seeing in interviews and detecting and what to do about them, and I'll make sure I send you that link for the show notes.
Speaker 2:Thank you, but yeah, it could be anything from people touching on protected characteristics, which can be quite obvious, but it does happen. It could be you spending too much time talking about your favorite sports teams or where you went to school together or those kinds of things as well. Well, and not talking using the interview to actually dig into the candidate's skills. So there's various different levels, various different threads of AI in detecting what's actually been going on in the conversation in order to just help train interviewers to run a fairer, more equitable process. And then there's other strands of AI that detects what the candidate experience has been like, how they've reacted within the interview, whether they've had a positive experience, which interviewers are always creating positive environments for candidates, which ones may be a little bit more challenging, because I think part of the challenge for a recruiting team is just not knowing which interviewers are going to provide a great experience for your candidates and which ones aren't.
Speaker 3:Yeah, I was just going to say if we double tap. You mentioned the strands of AI For those skeptical people who are seeing a lot of products come out and now they're AI products and they've essentially just plugged into GPT to generate a few things. That's not what Pillar's doing. To be clear, could you just explain a little more about how you know how Pillar's doing so much more with AI than just using GPT to generate a few things?
Speaker 2:Yeah, absolutely. And look, I don't think there's anything wrong with using GPT either. No, no, no. There are so many applications of that, but I've worked in machine and AI for 17 or so years now To say we used one thread of AI would just be wrong.
Speaker 2:The whole platform is built from a data standpoint and building up to understand interview conversations, what's happening in those conversations and then what we can do to improve both those conversations and the process around them. So, yes, we have some GPT-like functionality, and the ability to summarize a conversation is great. You can get Zoom and Teams to do it. We obviously have a very interview-centric way of doing it, so we provide interview write-ups using our data rather than just general conversations you would get from a GPT. But it does look fairly similar in the way it's presented to summarize conversations, do write-ups and so on and so forth.
Speaker 2:Where we start branching away is using AI for really specific interview purposes. So detecting bias being a good one right, we just spoke about that. But understanding in the conversation when bias has arisen in an interview situation, or the potential for bias to arise in an interview situation, takes a lot of data and takes a lot of machine learning and AI to be able to understand the conversation, understand what is potentially biased, what isn't, and be able to surface that in a reliable way to the enterprises that we serve. Again, looking at things like the candidate experience, the sentiment of the candidate as it goes through an interview, how well they react to certain questions and how well they react to the interviewer's conversation that's been out in the market now for years and years in customer success centers. We've taken some core technology there, repurposed it for interviews and it's actually very different for interviews but repurpose that for interviews to be able to understand how candidates are reacting to the interviewers that you're, that you're putting in front of them and it's a lot of sense and so on.
Speaker 1:The you're talking about being able to see show bias, potential biases, back to the hiring team. Is there? What other? Is there? Essentially a dashboard too, when it comes to, uh, talking about how the interviewer did per se in terms of potentially, I don't know? Just a few examples can be like consistency across speaking with several candidates, or not only things related to bias. But yeah, we were talking with Ben, founder and CEO of BrightHire, and one of the things that they were talking about is they had an emphasis on essentially giving a grade almost. It allows you to correct me if I'm wrong, but it sounded like more like giving a kind of feedback on how the interviewer did to make sure it's like a rigorous, consistent process, like they were tracking almost some like gong, like metrics. Yeah, I know how much the hiring manager was talking and all sorts of stuff. So I'm wondering is that sort of an angle that your team does or what are the nuances there?
Speaker 2:Yeah, james, I love it. Yes, we have an analytics center and an insight center as part of Pillar, so after every interview, an interviewer can go in and see how they did in their interview. And yeah, there are some very gong-like things. Did you talk too much? What was your all those things? Did you ask the questions that were being asked of you in your, in your guides and your scorecards, and right the way down into some really advanced stuff? Was their bias and how did the candidate feel about it?
Speaker 2:After every interview, the interviewer will just get a bit of a review on what, how the interview went and any areas where they could could be improved, where they could improve, because very few interviewers have actually ever received interview training. Most people are thrown into it and say can I go speak to this person? We might want to hire them, and that's about as good as it gets. In a lot of cases, we then take that to the next level in not just providing interviewer feedback and that feedback back to talent acquisition teams, but actually using AI to provide insights to talent acquisition leaders, and that is trying to point them in the right direction as to where they should focus their time.
Speaker 2:It's all well and good having a ton of interviews and being able to see a dashboard of them and being able to understand where there are some strengths and weaknesses in them.
Speaker 2:But if we can point people to interviewers who continually need more support, maybe they need more training, maybe they're continually asking similar questions with bias, or they're continually going way off the reservation on the scorecard and they aren't following the right process, we will just highlight those with our Insight Center and we'll use AI to go through interviews in order to be able to really sit alongside a talent acquisition manager and try and point them in the right direction as to where they should focus to get the interview process going in the right way. So it's something that we've thought a lot about in how do we sit alongside the talent acquisition teams that we support and be an extra person for them, save them time, save them a lot of analytics and research and downloading data into BI tools and all those sorts of things, and just really surface the right information for them so that they can get on and do their jobs most effectively?
Speaker 1:So what about like more role creation stuff like that? All right, Does the product play a role in that with job setup, everything from job descriptions to crafting interview questions, stuff like that or is that not the focus?
Speaker 2:Yeah, it does. So everything from the job intake right the way through to that debrief meeting, we see as areas where Pillar can help. So you can actually go on our website now. We put some free tools out on pillarhr, where we have a job description analyzer which will score any job descriptions that you have and give you pointers in terms of areas where they could be improved Things like bias and things like how it's written and language that's used and what you're asking for. I think there's a maximum of 125 points score which you will score against.
Speaker 2:We give quite a good amount of detail on that. You can also go on. Actually, we have an AI interview guide generation which will come up with good interview questions if you upload a job description into it and we will create those. So we give some free tools out to the market. So we just want to help the industry move forward and understand AI and understand how AI can really be a help to them. But yeah, we have for that intake meeting, we actually have a step-by-step process which can guide a recruiter and a hiring manager through in order to be able to take a role. Create a job description using AI, make sure that job description is good to put out to the market and then create interview questions and interview guides off the back of it and assign that to an interview team and away you go and those meetings can now happen really in minutes rather than hours when you're taking a job role in first off.
Speaker 1:Yeah, that makes a lot of sense. I guess one of the questions that I have is what are you hearing back from customers in terms of additional functionality? I think one of the interesting topics is just diving into what people are asking for, and so, of course, there's your existing product functionality, but what are you hearing back from people that are interacting with the product? What else are they looking for and what else are you thinking about building at this point?
Speaker 2:Yeah, we've been really fortunate to have some amazing customers who aren't shy about giving feedback and we proactively go out and solicit that as well. We've got a number of our customers who are really solid development partners of ours. Now it's something that I've done with all of the companies that I've run. So a lot of the ideas in Pillar have actually sprung from customer conversations and customer ideas, customer usage and seeing it. For example, we've just rewritten and are releasing in a couple of weeks a whole part of Pillar that focuses on that debrief and candidate selection meeting.
Speaker 2:Just in the way in which those meetings are run at the moment, how we can improve it, how we can make sure they're very data and skills driven and have all the reference points back to interviews and videos of those and everything around it, just to make it super clear which candidates have what skills and try and push the decision criteria away from.
Speaker 2:I have a run at the moment three week old notes and I'm looking at my bullet points and thinking did I like the person or not? And pushing in the right direction of actually getting the right skills into an organization. So that's a tangible example. More in the roadmap we get asked about all kinds of things. We get asked everything from just developing kind of features in the product to can we use this for our performance reviews internally and we haven't developed anything in that direction but we get asked lots of different things based on different applications within an organization and how we can use our data to be able to drive things wider than just interviews, maybe up the talent acquisition funnel, maybe into the organization in performance reviews and try and close that loop into interviews.
Speaker 1:So do you work across several industries? Are your customers primarily in tech? Are you in other like I don't know, healthcare, manufacturing, banking? Where do you? Yeah?
Speaker 2:very much across industries. So we haven't focused, we've never gone out and just focused on tech, because I think you get quite a biased sort of view as to just the functions that companies want when that happens. And actually we've actually had feedback from customers that are outside of tech, almost being relieved that that's not our focus Across various industries, from trying to think of some ones that you wouldn't have thought of health and fitness clubs, fast-moving consumer goods, food and beverage, hotel chains, a broad spectrum of things. We obviously do have tech as well. We've got data security companies and various other tech companies using us, but we've really made sure we've got that sort of broad variety of feedback from our customers coming into our product.
Speaker 1:Okay, and I'm assuming so it's essentially for any type of role, company wide it could be.
Speaker 2:Yeah, there's definitely more of a focus on that, more sort of full-time or permanent role within Pillar. We do some work outside it but we don't really cross over into more of those sort of asynchronous interviews and the one-way interview space, that which is a little bit more applicable, obviously, to hourly workers and hourly workers and and that part of the market okay, cool, like what else?
Speaker 1:from a like a technical interview perspective, I'm wondering, almost like, if companies are looking into more so like technical evaluations for engineers. Is there any functionality that might be different in in that regard, or is it? Has your team given thought to building that out?
Speaker 2:yeah, so we don't. Pillar generally applied across an organization to all interviews. The two most popular job roles or job categories that we, that we see going through pillar are sales and engineering. They're two very big, as you would guess right. They're two very common sort of interviews that we see through the platform. So we've made sure that pillar does its job. In engineering interviews, where maybe there's coding tests as part of it, maybe there are coding tests alive. So we're making sure that we record those parts of it rather than just people's faces. But what we haven't done is get into specific assessments by role. It's much more of a platform that goes across that and supports maybe assessment platforms and making sure those assessments are getting done properly.
Speaker 1:Okay, All right, yeah, that makes a lot of sense. Yeah, I mean, I was wondering too. It's like how much emphasis goes into or is required, how much time and resources required to essentially optimize on a per role basis, that seems. I wonder how much of a balancing act needs to occur for your team in trying to distinguish. Okay, what are the features and functionality we can provide for companies for every type of role within the organization? But are there any things we need to do to make this particularly effective for sales hires or engineering hires?
Speaker 2:Yeah, there's a lot that comes out of a well-written job description, right. So making sure that we're pulling the right competencies and right skills for different roles from good job descriptions will actually completely change an interview process, even within sales. Enterprise sales are very different from product-led sales and the like, and interview processes will change. The questions and the guides that we produce are very different and there is the ability to educate pillars as we get to know, as our AI kind of gets to know the organization. To say, in sales roles maybe you always want a stage where they're doing a sales presentation or a working session or what have you at stage three of the interview process, and we'll make sure that we can start recognizing that and give people the capabilities to implement the right guides for those types of roles. There's flexibility and there's learnings, and the processes are very different across different roles, but the general kind of learning and technology behind the platform just works across roles without too much customization.
Speaker 1:Okay, yeah, that makes sense, Elijah. Do you have any other questions about that?
Speaker 3:I'm just curious your thoughts on the market Mark with I don't know. Do you see tools like Pillar being potentially acquired someday by ATS companies like Greenhouse or Ashby or somebody, and like integrating that technology into their platform so that it becomes a differentiator for them? Because the ATS market is a lot it's just bigger and there's a lot more competitors there. Versus interview intelligence, there's not that many, right, and the ones that are there seem to be doing a pretty good job. All of you.
Speaker 3:So, I'm just curious where you see the market going with interview intelligence type tools and platforms.
Speaker 2:Yeah, look, I think we're still at very early days of what is a very new category.
Speaker 2:The category only really got recognized by Gartner mid to Q3, q4 last year, where they actually started putting interview intelligence into one of the necessary tools that you need as part of your TAN acquisition tech stack.
Speaker 2:I think we're probably not even a year into them recognizing it, even though we've been going quite a bit longer than that. And when we look at the market we know and we're hearing from chief people officers, chros, that they know that this is going to become a standard part of the TA Tech Stack and we will become, along a similar way to an ATS is a standard part of a TA Tech Stack. We actually have a lot of data around what's happening in an interview, who has skills, how those skills are tested and data that can be used more broadly than just interviewers. So as a platform we have a lot of data in the organization. We will continue to integrate with applicant tracking systems. We will continue to develop the product to look at other areas where that data can be used with good effect and we're not developing pillar to go out and be acquired by an applicant tracking system, but we do see applicant tracking systems as very strong partners of ours and the closer ratios we have yes, for sure it's a differentiator for them.
Speaker 1:Yeah, I think to Elijah's point. I think maybe where your head's at just asking that too is there's. Of course there is the pressure market consolidation happening right now within hr tech yeah everything from e-learning, compliance products to talent acquisition products to everything else.
Speaker 1:It's so from an ats perspective, based on conversations I've had with some leaders in the space, they're looking at building out product suites that are more holistic and can manage as much as possible, and so Daniel Chait broke it down in a pretty cool way. As these companies are feeling the pressure, he's looking into how do we think about growing right? Do we build in-house? Do we build through partnership? Do we build through acquisition? And that's a big question that a lot of these companies are asking.
Speaker 1:For instance, with Jim right, like they're known for the sourcing right, a lot of companies use Jim particularly to source engineers and but essentially just due to market environment. Like they built out an applicant tracking system right, so now they're doing that. They're offering CRM additional functionality, analytics, and they're starting to build out, and so I'm curious too, elijah, if we're going to start to see the video interviewing incorporated in some of these larger applicant tracking systems. I'm not familiar, I just don't know. I don't think right now Greenhouse or GEM or some of the companies I'm more familiar with or have made any strategic acquisitions in this space.
Speaker 3:No, they would have to get a hold of Mark or Ben, or that's what I think Mark was saying. Like, the interview intelligence space in itself is so new and there's only a small handful of companies, but they've also all been doing it a while, right, like Mark, you guys have been at it for at least four years. I think it's the same with BrightHire. There's another company I'm forgetting their name, I think they may be out of the UK but, like all of you guys already have a four-year head start on anyone wanting to start today, including ATS companies, anyone wanting to start today, including ATS companies. I think, if anything, they're going to have to try to talk to Mark or Ben or somebody in the very small kind of niche interview intelligence space to be able to add that as a differentiator for them if they want to do it.
Speaker 3:I think the other interesting thing I was going to ask you about, mark, is there's a few companies trying to develop an actual, almost like AI avatar that does the interview for you. You don't have to share too much, but I'm just curious have you guys thought about maybe the interviewer can't attend, but it has all the data on that interviewer and they could have an AI substitute, join the interviewer for them, ask the structured questions, et cetera. Just yeah, just curious if you guys had thought about that at all.
Speaker 2:Yeah, it's something we've obviously discussed from the early days or earlier days than today for time and time again, and there's a certain kind of crossover between kind of what is a one-way interview versus a two-way interview.
Speaker 2:That needs to be considered as part of that, and also then how to run good two-way interviews, how an avatar or an assistant or whatever you want to call it, can actually respond to a candidate's questions, how a candidate is going to get something back from a two-way interview as well, rather than you just asking questions and maybe more probing questions and the like, and just understanding from the hundreds of thousands of interviews that we've done now how the candidates get a good or bad experience and where bias comes in and what is a good and bad interview and all these different things has given us a lot of data that one day we can maybe look at something like that.
Speaker 2:I think that it's interesting to match that up with what the market wants right now as well, because for sure I think there are probably some smaller companies that would love to just have an avatar and interview and they would accept the candidates that kind of come through it on different stages.
Speaker 2:They don't mind missing out on some candidates or what have you, when you get into more the market in which we play and more the mid-market and enterprise, where people have legal teams and AI councils and all those more sort of tricky and regulatory kind of elements, those that's where things start getting a little bit stuck for for that area of our product development. We continue to play around with things in the background. We continue to innovate a model and understand where I can do a better job than humans, as good a job as humans, and then we have to match that with kind of you know the practicality of when a VP of talent acquisition sits there and says I want something, are they going to actually be able to get it signed off in their company and get it through all the legal work that comes through? So we want to make sure that we strike that balance for them so that we're not leading them up the wrong path.
Speaker 1:Yeah, so that's an interesting topic too. It's like when you start to have these products actually run interviews and the different potential blockers for that. There's the different elements, right, there's still, of course, a lot of companies that of course there is a ton of value in having people speak with people to learn about the company, the value. I do think, to some extent, that is going to shift. I think it's probably going to be a more integrated approach where a product is managing probably more of the direct, some of the more direct interactions, but there's more of that high impact time with the hiring team as well. I think that there might be a bit of a bias of some recruiters that feel like a person needs to be engaged at every single touch point and honestly, I think that some candidates would probably prefer to eliminate some back and forth and really focus on high value conversations. So I don't know, I see that definitely happening more.
Speaker 1:But you're right, there's also, like the legal concerns, compliance bias concerns. There's that aspect too. There's also, quite honestly, there's a way the business is structured in such a way right, like they, they have X amount of recruiters per roles. There's a built-in process and system for this outreach and I'm almost curious too, from like a sales perspective. If you're selling to a VP of talent acquisition and this essentially when you start to get into having a product essentially run parts of the interview process in place of a person, that's a difficult conversation to have surrounding how they're going to structure their department and their headcount and these types of things as well. So I know a lot goes into that, I think. But I do think we are going to start to see ML take on more of the interview process directly. I think there's a way to do it in which it doesn't harm candidate experience, potentially improves it and also still, there can be enough time for FaceTime with the hiring team. I think that it is going to happen. I'm not seeing a ton of it now.
Speaker 2:Yeah, james, I couldn't agree more. If you asked me five years ago whether I could see AI running an interview instead of a human, I would have said absolutely not. There's no way. There's no way you can do it. There's no way anyone's going to be able to accept it. It's not going to happen. Could I see in five years time? Yeah, for sure I can. I think the interaction between humans and machines in this, as you say, is dramatically only going to increase from here, very early stages. You think it was only a year ago that really there was, while AI has been around four years. It was only a year ago that we really got hold of chat, gppt and ai became a real thing to a lot of people. So we're one year in. We're one year in for a lot of people and a lot of vendors as well. Surprisingly, five years, five years time the world could look very different yeah, I think it's.
Speaker 1:It's definitely going to be interesting to start to see this shift and I think there is this startup advantage here. I think one of the things that's going to be difficult for some of the category leading product suites within Town Acquisition is they've essentially built an entire product that is based around a certain type of workflow to some extent. So when you look at more disruptive technology that might have ML actually managing more of the process, it's like how do you make this work for your user base in a way that's almost not too disruptive? These are your users, your buyers, and so I think, the startups to the extent that we're going to see companies start to transition it's nice for a startup that they don't have a eight, nine figure AR business operating around a core workflow and user base that operates in a certain way. So it'll be interesting to see how this essentially plays out.
Speaker 1:But, yeah, I'm pretty strong on it and I'm a recruiter. I don't think that I need to be on all the calls. I'm on I don't and honestly I think, particularly when I'm talking with some engineers, I think honestly, they would love to have a little bit more of the process moving at their own pace with AI. So of course there's still the need for people. I'm not saying that they'd be removed from the process completely, but I think this idea within recruiting or talent acquisition, that we're needed at every little part of the process, I think is a little bit.
Speaker 3:I think it's wrong.
Speaker 2:Totally get it. Totally get it, totally get it. Look and you've seen it in other areas you could align it to, I don't know, call centers. No one wants to call a call center because you're just going to have a crappy experience. I'd much rather go to a website and just book something on that or do it in that way, and there are certain parts of the process which could absolutely be done and, more importantly, candidates would prefer it to be done in a more automated way as well. There are other parts of the process where candidates really actually want some human interaction too, and it's making sure that we're acutely aware of that and making sure that every company is set up to get the best candidates and the best skills into their organizations.
Speaker 1:So here's a question for you. So if you were to see ML directly run as part of the interview process and replace, for a specific part of the process, a recruiter or hiring manager or whatnot, do you have any thoughts on where a company would start? Would it be more of the screening side? Do you see it more in like a behavioral side? Do you see it more in like a behavioral evaluation? Could we see it more in like, maybe, a technical test or assessment? Do you have any thoughts on where it would be probably the best place for an ML to run an interview?
Speaker 2:Yeah, and look, I think you're giving examples where actually you could probably see like AI working in lots of those areas. But I think there's probably some core elements of each of those areas which need to stand out. One is that it's a fairly routine thing where a candidate can get through something quicker and more efficient than it can with humans. So that may highlight areas which are, I don't know, fact finding, maybe very initial sort of screenings, maybe it's giving a bit of information back to the candidates so that they can provide, so that they can actually decide whether this is the right role for them. Maybe it's some technical assessments which can be automated.
Speaker 2:Those kind of elements when, at the moment anyway, you think that machine learning is probably not so applicable is where there's more of a two-way conversation, where the company wants to get to a level of depth in someone's skills. They really want to probe and they want to ask questions based off the conversation. Those sorts of things. Machines are not there yet. They could be there in the future, but equally, the candidate probably wants to be able to shine and get feedback as well, and actually machines giving feedback to candidates, responding to candidates' questions and responding to the non-functional questions, non-high-level questions. That's where candidates are going to get better answers from humans, for the foreseeable future anyway.
Speaker 3:Yeah, yeah, I think the screening interview is like a great opportunity, right, Because a lot of those are the same questions. You could even control to a certain degree, right, what the AI would be sharing about the company things that maybe aren't completely public, but also things that I don't know. Maybe a recruiter would like overshare accidentally to a question and the AI would be able to answer questions, some basic questions about the role, the growth trajectory of the company, some of these basic things that candidates want to know about. There's five things people ask me at the beginning of almost every screening call. I do and I find myself repeating those same things and then I'm asking very similar questions, right, Maybe five to seven standard questions. If AI could do that in a friendly, genuine way, then the candidate like you said, Mark, both the candidate, I think, and the company want more of that two-way interaction and human interaction deeper in the process. But that first call they're just feeling things out and maybe AI could actually do that in a, you know, a favorable way.
Speaker 1:But yeah, it's like. It's like a screening call. Like why does it even need to exist, Right, why can't a candidate apply and immediately fill out screening material? Like what? Like everything related to the questions that would be asked in a screening call? And then the screening call could be more about asking questions about the culture, the team dynamic, the initial projects they're going to be working on, like that actual two-way piece. But it's like you know how it goes. Would you jump on a screening call and you're like okay, here are my HR questions. Like where are you located? Are you open to hybrid? What's your salary targets? Like you're just getting through this list. Do you have experience working with mid-market enterprise customers? Like, what's your point of impact? What's your quota? Whatever the thing is like why are we doing this? Like none of this needs to be done. We don't need to be here having this conversation, because they don't want to fill out the form, right?
Speaker 3:If you put those application questions on there, they usually won't fill them out. Maybe some will, but often they don't. So then you feel like you have to ask those questions. That's a whole nother podcast.
Speaker 1:What if you like, if it's structured as the first round interview yeah it's, I don't know.
Speaker 1:You're right though. You have the engagement aspect piece too, but it's as a candidate I would I don't necessarily want to schedule in advance. I have to, like you're scheduling an interview, you have to figure out when you're busy or when you're available, when, like, you have the time, the bandwidth to just have a recruiter conversation and answer questions, that it's easier to just do it on your own time. So I'm just wondering, you're right, like the engagement piece, but it would almost be good to know. Let me just give you this information and then, if it's an actual fit, then we could jump on a call versus just doing more of the whole screening call, versus, like, just sending in a resume. I don't know.
Speaker 2:Yeah, absolutely, if anything's there to fill out forms or to get standardized information. I think you've just got to look at how does the candidate want to do this. How does the candidate get the best experience? Is the best experience sitting in front of their laptop trying to type things out, or is it just clicking a button and having an immediate conversation and answering those questions more verbally? And the whole world you've seen it with the latest GPTs and what have you the whole world is moving to a more conversational, more verbal cues and less typing and everything else. Absolutely, the market is going to be moving in that direction.
Speaker 1:Yeah, it's really cool stuff. Hey, mark, we're coming up on time here, so I just want to say thanks for joining us today. It's been great to learn more about you and what you're building, and we'll drop everything in the show notes If there's any other links, like I know you mentioned the blog, but if there's anything else that you want us to share with the audience, then just let us know and we can put it in the episode description for you.
Speaker 2:We'll do. I'll send the audience all the links to some of our free tools that they can use. And, James Elijah, look, thank you so much. You've built a fantastic podcast. Congratulations on all your success and look forward to hearing more and more as you continue to grow.
Speaker 1:Likewise, Mark. I'm looking forward to continuing the conversation. Thanks for joining us today. Take care.
Speaker 2:Thank you.
