The Breakthrough Hiring Show: Recruiting and Talent Acquisition Conversations
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Join our host, James Mackey, and guests as they discuss various topics, with episodes ranging from high-level thought leadership to the tactical implementation of process and technology.
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The Breakthrough Hiring Show: Recruiting and Talent Acquisition Conversations
EP 163: Exploring AI-Driven Hiring Solutions with Anil Dharni, CEO of Sense
Anil Dharni, CEO and Founder of Sense, joins host James Mackey to discuss how AI and automation are transforming the landscape of talent acquisition, streamlining recruitment processes while enhancing candidate engagement.
He shares insights about the importance of candidate experience, ethical AI use, and the need for HR leaders to adapt to emerging technological innovations to remain competitive in hiring.
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Hello, welcome to the Breakthrough Hiring Show. I'm your host, james Mackey, really excited about today's episode. We have Anil Darni, the co-founder and CEO of Sense, on the show. Anil, thanks for joining us.
Speaker 2:Yeah, thank you for having me Really appreciate it.
Speaker 1:Yeah, I'm very excited about this episode. I'm really excited to learn about what you're building over at Sense. To start us off, would you mind just giving us a high-level product overview on what you're building?
Speaker 2:Yeah, absolutely so. We've been building Sense for about eight years and we started the company. We are based out of San Francisco Bay Area, but we have a global footprint. We have teams in Europe, teams in India, and we are focused on talent, engagement, engagement and that. What that means is it can meet them. It can mean a mouthful, but it's focused around how are large companies with complex hiring needs basically simplifying their hiring needs and figuring out how to use automation and AI technology to basically create a great candidate experience and move candidates faster through the funnel? You know, that's, that's the the most important thing that is relevant for talent acquisition leaders today, especially in today's market.
Speaker 1:Yeah, and I'm looking at the website right now and it seems like it's a very comprehensive platform, so I'm excited to learn more about that. But just to take a step back, it would be really cool to learn a little bit more about you and the founding story. How did you come up with the idea to start Sense?
Speaker 2:Yeah, absolutely. You know I was. My career started in engineering, so I was building engineering products back in the boom days of the late 90s. You obviously probably don't remember that, but soon. You know I've been a serial entrepreneur started. This is my third startup done a couple of companies. I've done social networking. I've done mobile gaming, the last company that that's the one that we ended up selling.
Speaker 2:And after that, you know, I realized it's the people you hire that make or break the company. So how much fun would it be to start a company in the recruiting space, helping other organizations, especially larger organizations, helping them hire and find the right talent for their businesses as they're looking to grow and further expand. So that was the genesis. It's the same core team that started eight years back, the same folks that were building the mobile gaming company. I brought them on here and it's been quite the ride.
Speaker 2:We have about 1,000 customers and we have basically two pillars. I would say, like what really differentiates Sense versus other sort of HR tech vendors in the market is we truly have one segment that's focused on you know, you have that background around agencies and staffing companies, so we have a large footprint in that customer base. And then we have the second segment of the market, which is the direct hire the corporate side of the business right, so we do both because the solution is pretty flexible, and direct hire the corporate side of the business right. So we do both because the solution is pretty flexible and pretty broad, and it's been an exciting ride, yeah that's awesome.
Speaker 1:Are you still having fun? Is the big question.
Speaker 2:I think that's why I'm still here and that's why, like, the founding team is absolutely intact and we're having fun. We are signing up like just amazing logos. We are impacting a lot of folks. I think it's a two-sided problem, right, as you are very well familiar the whole thing around organizations and what are they trying to achieve and do. But it's also about the job seekers and how can we improve that and the interaction they have, not just with a particular job they're applying to, but also with the organization that they want to work ultimately with. So I think, every day I wake up and there are just so many challenges. But of course, with the advent of LMS and the new JNI technology, it's like really given, I think, a new set of tailwinds to the market and the opportunity that is in front of us.
Speaker 1:Yeah, absolutely. I always like to ask that question whenever somebody starts a business in recruiting. It's a really cool space that I think now is actually the most exciting time to be in it. I mean, at least in my career and recruiting, which I guess at this point for my first job in the industry. I guess it was about 12, 13 years ago, so at least from what I seen, I got started. I think my first job in staffing was like 2012.
Speaker 1:And this is definitely at least from where I've been working in the industry definitely the most exciting time, I think, with LLMs and some of the innovation that's coming out of the space, and how quickly it's happening is pretty amazing. That's right. I couldn't read more. Well, look, I mean I'm just trying to figure out where to start right, Because I'm on your website and there's a lot. I mean, your product suite is really comprehensive and there's a lot of really cool stuff here that I'd love to dial in on. Maybe we could start with what you would consider to be your core product. Maybe the largest percentage of your customer segment is utilizing the most. I mean, what is that?
Speaker 2:Yeah, yeah, no, I can jump into that. I think we can start with the buyer's perspective, right, that's what you also alluded to at the start of the conversation. But when you think about these large organizations and the director of TA or the VP of TA who's coming to us and trying to have a conversation, the need of the hour today definitely seems to be around recruitment automation. So which is all around. I need to drive more and more efficient recruiting process. I need to make sure that my recruiters are happy and productive and they're delivering on the ROI and the outcome that we are ultimately focused on. So that's the mindset through which TA leaders are coming and the product where that we build a core product is.
Speaker 2:Basically, think about it as a workflow orchestration engine. So it takes your different recruiting processes and it digitizes them and brings that online through basically a WYSIWYG editor, right? So you can just drag and drop your processes, whether those processes lie in the ADS or they lie on the CRM, and what kind of through those processes, what kind of communication and engagement you want to run on the candidate side, right? So think of it as a core orchestration engine. So that's the bread and butter of what we sell and some of these companies will come to us and say, hey, I want to basically automate from all the way from hello to hire to onboarding to the first day and onwards, especially in high volume hiring use cases. It's like my first 90 day of employee engagement is also pretty important because I have a pretty high attrition rate there, right?
Speaker 2:So all the way, the entire talent lifecycle automation is why people come to us and within that there's a whole slew of products that we can go into, you know, all the way from AI chatbots to voice, ai for screening sort of use cases, two-way text messaging, mass text messaging for recruiters so they get the superpower through which they can do these kind of mass broadcasts, and it's all human operated. There's no automation in that piece. To you know, moving the candidate all throughout the candidate journey and making sure that. You know, from a competitive perspective, this organization has a massive lead over their competitors who are?
Speaker 2:not using our platform.
Speaker 1:Absolutely so. Talk to me. I'd love to learn more about the workflow and integration with Epic and tracking systems, because it does seem like you're able to plug in automation throughout the entire talent acquisition funnel, and it seems like you also mentioned that it sounds like this could be even going into onboarding and post hiring. So this is also an HR product for HR teams, is that right?
Speaker 2:Yeah, so it depends on where the customer wants to push us. Yeah, so the answer is yes, but very much focused more on the engagement and communication aspect of it versus, like, the transactional. So we are not like we won't have DocuSign, we won't do like on document onboarding, right, that's all. Whatever the system that the customer is using will take care of it. But the problem is you are aware of, like, whether it's assessments, whether it's background checks, whether it's interviews, it's all about the follow-up. It's all about the problem, right, it's about nudging the recruiter, it's about nudging the candidate, it's about nudging the hiring manager, right, and that's where these processes are broken.
Speaker 2:It truly takes a village to get a candidate, to get a lead to become a candidate, to get a lead to become a candidate, to get from a candidate to actually make the hire right. And that's the gap that we initially saw and that's why I go to market initially you know, eight years back, seven years back was all around staffing industry. So we looked at the staffing industry and we said, okay, high volume recruiting, but it's recruiting in professional, in admin, clerical, in light industrial, in blue collar, it's all over the map, but it's high volume. And what's truly missing is a new age recruitment automation system that can come in it can be an overlay on top of the existing applicant tracking systems, and that's how we work. And then that brings the firepower of speed to the hiring process, of better conversion rates to the hiring process, right?
Speaker 2:The second realization we had is one really needs a multimodal strategy, right? So the way you communicate to the candidate or with the candidate is not around just email driven, which is what's still, you know, even if you squint your eyes and you look at a lot of like CRMs today, they are still email focused. But in these markets things are changing so fast, right? So it's email, it's text-based, it's chatbot interaction, it's voice interaction, so it really doesn't matter, it's through WhatsApp. You just choose your mode of how you want to engage with the candidate and we can enable that, unleash that power, right? So we've thought about it in those multiple dimensions when we built the product out.
Speaker 1:Got it and so I totally understand the use case for staffing and recruiting. I think that's a really smart place to start right, because it is particularly for high volume, right, high volume searches. They're working with several. You know a lot of customers, particularly staffing firms, that are focused on high volume industries, right, like industrial, as you mentioned. They do have high volume needs. They have a lot of roles. They're always hiring.
Speaker 1:It's literally their business to hire, right? So if they're in business, they're hiring their services companies, meaning they have lower margins. So they're really looking at how to scale effectively, which is incredibly hard because a lot of staffing companies can increase top line growth but they're making the same amount or less money. So they're often early adopters to automation type of technology like you're building. So there's a lot of value there. But I'm sure, as you've learned, it's like the use case isn't just relevant to staffing and recruiting. It's for in-house corporate teams as well. Are you noticing, like your corporate customers? Is there kind of like? You know, some features and products are more so leveraged by corporate teams and then some are more so leveraged by staffing. I'd be curious to learn more about that.
Speaker 2:Yeah, great question and something that we learn every day, differences between the two. But you know, I would say the staffing companies and the agencies are pushing the boundaries of what we can do, and for obvious reasons, exactly what you nailed right, which is this, is your bread and butter right. You make a placement, you make revenue, right, yeah, and the staffing company that makes the placement first gets that revenue right. Otherwise, all that effort that you spend trying to get that candidate submitted and hired went to waste because another staffing company came in and placed the hire for you right. So it's highly competitive. Speed is of the essence. Quality is still of the essence, because you still need to find the needle in the haystack as fast as you can. You alluded to light industrial, but let me tell you nearly 35% of that business is professional. So it surprises us. But when you're hiring 1,000, 2,000, 3,000 professionals every single year, that's still high volume hiring for you, right? So it's interesting that we still play very well in the knowledge workspace.
Speaker 2:So back to your question around corporate versus staffing. Corporate is just a laggard. They are going to adopt exactly the same things. Give them a few years, because they are going to come to the same exact conclusion. So I'll tell you. A great example is we launched Voice AI, so it's a voice product. You know, there are still some gray lines and boundaries as to what is compliant, what's not compliant. The staffing will push right, whereas a corporation will say, or direct hire will say, hey, wait a minute, I'm a large corporation, this could put me at some risk, so I need more time to evaluate this new technology. It's so brand new that I need some proof points. I need my infosec, I need my IT guys, I need my legal, I need my compliance stuff to come in first and then give me a green signal. So that could take a year, that could take a couple of years, but you know what it's coming. There's no stopping it. But I think that's the big difference that we see.
Speaker 2:Number two I will say is and I think you are very familiar with this is inbound use case versus outbound. So in corporate, what has happened is, I feel a lot of HR tech vendors has pushed this notion of like it's all about spending as much money as you can on job boards, because it's like it's as if they've convinced them that you don't have a database of candidates. You might be one of the largest automakers in the world, but they have convinced successfully you don't have. So all your focus and all your investment in technology needs to be on inbound Capture everyone that's coming through your career side, through job boards, where you're spending tens of millions of dollars and you need to put a lot of technology on there.
Speaker 2:And then the question we come to them because we have a staffing background, is like what the hell are you doing with your outbound right Database of potentially millions of candidates and do you even understand the quality of that database? Because let me tell you, when we work with staffing companies, they don't care about the inbound use case. They are all about why would I give Indeed LinkedIn a single dollar when I have 10 million candidates already sitting in my database right? So I think it's a little bit of an education, a little bit of a mountain for us to climb. But once they realize that all the techniques of typical marketing outbound use cases, sales, outbound use cases that exist that direct hires can start leveraging on their own internal databases, I think it unlocks a huge untapped potential today in the market. So that's what we are pretty excited about. But that's also the difference between the two segments.
Speaker 1:Yeah, yeah, that's. That's really cool. So what are some of the more recent features, products that you've been rolling out to customers that there seems to be the most excitement around?
Speaker 2:Yeah, I mean there's a whole slew around actually the capabilities of the orchestration engine, right. So, as you can imagine, like in the past, the orchestration engine was built just like the RPAs, or robotic process automation companies that you all might be familiar with. It's like you know, if an action is taken, then do this, if something happens, then do this. So it's all very rules-based automation. So eight years back, when we launched the product, that's exactly what we were doing, right? That's exactly what HubSpot does, exactly what we do, that's what RPAs do. So we are moving from a rules-based world. And then, you know, then in the recent years, when the LLMs came out, it's become pretty much a goal-based world. So we are moving into the world of, like the same engine orchestration engine that was hard-coded to some extent and then could do all these communications and automations, which is still great and added a lot of efficiency. Now we got to move to a world where it's all goal-based and agent-ticket nature and that's truly exciting and it's like it's going to unleash a slew of business use cases that in the past were very hard to stitch together for companies and organizations. And suddenly I think the eyes are opening and the art of what's possible is coming true, right? So what does that mean? That basically means I can tell you like.
Speaker 2:So, for example, if you're screening a candidate, right In the past it would be ask them question A, then ask B, then C, then D, and there's a linear progression of what you could do right.
Speaker 2:And if in the chat, the candidate would say, hey, I have a different question to ask you, the chatbot would just unwrap.
Speaker 2:It's like, okay, what just happened here and I don't understand it and I'm going to hand you to a human or it's just a failed chatbot experience.
Speaker 2:Right Now, with chatbot or voice, we have this amazing rule base hey, listen, you can have a conversation if you want, especially voice if you want, for 30 minutes with this candidate, but I want you to ask these five knockout questions one way or the other, because that's the goal. The ultimate goal is, is this candidate like an example in light industry could be? You know, if they can't stand on their feet for more than eight hours, we shouldn't really be wasting our time talking to that candidate, right? So now the candidate can spend 15 minutes asking different questions which might not be related to that one that screening knockout question but in the end, the AI is smart enough to bring them back to that question to ensure that that knockout question is in fact asked right. So I think the goal-based world is turning out to be super exciting for us and there are so many use cases for us to unlock that right there.
Speaker 1:Yeah, that's a lot of really cool things that the LLM is able to do and have more of those interactive conversations and loop back, as you mentioned too, which is really, really cool, and I'm just looking at again, like your website, and there's just so many different areas that we can dial into. I'm curious on just to pivot a little bit and talk about sourcing. I'm wondering if we could dial into that for a minute. I mean, I'm talking right now because you're mentioning accessing candidate pools that are existent in CR CRMs. You know there's some companies doing some innovative things on the sourcing side. Particularly, there's two episodes that folks should check out Steve Vartel with Gem. They're doing a lot of cool stuff with sourcing right now Different functionality, different products and features coming out and also the Workable episode. We had the CEO of Workable on the show, nikos, and he was able to break down different AI solutions that they're incorporating into their product.
Speaker 2:But yeah, I'm curious about, from a sourcing motion, how you're thinking about leveraging LLMs to provide better sourcing experiences and automation and speed and quality and that type of thing. Yeah, yeah, I mean, that's so much to unpack over there, but I can start with a simple thing. But first, before we get started on that, one caveat is we do not bring a database to our customers, right? So we assume that they have certain sources of candidates where they can access them. The whole database world is riddled with issues and, I think, ethical challenges, frankly speaking, from where people have sourced those databases. So we try and stay out of that.
Speaker 2:So what we see is one is opening up the aperture for the organizations in terms of, you know, giving candidates the opportunity to apply in multiple ways, right, so it could be QR codes, text to apply career site. They could be sitting to your point, they could be sitting in the CRM, they could be sitting in other databases. There's also access to job board databases and things like that, right? So I think one layer one has to build as an HR tech vendor is like how do you bring all those databases together in a unified search experience so when a job description comes in, you understand what it's looking for, then you can go and run a really highly qualified search on those multiple sources of data.
Speaker 2:So you have your passive candidates that might be in any of these data sets, you have your active candidates that are applying inbound, and then you have to mesh together and search in that context and if searching is just not enough, once you search, then you need to quickly reach out to them as fast as humanly possible. And that's where the automation layer comes in right. So all the way, extracting the job description from the job description, extracting like what is the semantic search that needs to be run on these databases, is the semantic search that needs to be run on these databases, and then from there coming up with a list and hopefully that list at least is ranked in some way or the other right. So you might have 100, 200 candidates that match the profile but you're looking for now. Once you have that list, then you need to reach out to them within seconds. So that's where the whole flow runs.
Speaker 2:So you use an app to create a job description. From the job description you extract the search. From the search you search on all these databases. That whole flow is just an agentic flow, right. And then you start the communication flow. So now you start reaching out to those candidates. Maybe you need to reach out to 100. So you start going one by one and you start having either chat conversations or voice conversations.
Speaker 2:Now what we've understood from chat is chat is great and it serves this purpose. Not everyone wants to be on the phone and have a conversation in public or even at home, but the chat responses are pretty short in nature, so the amount of information you can actually get out of the candidate is limited. So we've had a very successful chatbot. There are other chatbot companies. It's great, but text is limited and voice definitely leads to a richer conversation and you can build a much richer profile about the candidate through a voice conversation. So anyway, but going back to sort of these knockout screening questions, then those happen and then you have kind of create a shortlist that your recruiters can work off of. So hopefully, you know I've explained at least some part of the sourcing side that is, yeah, that can basically become super fast and agentic in nature.
Speaker 1:But yeah, that was what I'm curious about, because that's I'm seeing a lot of use cases around sourcing as well, and then going into this pre-screening use case, which I think is really interesting, right, and so I'm curious, though, from when you're getting into more so the pre-screening where the AI agent is reaching out to folks. What about from a consent perspective? Is there anything that folks have to consent in? And then, like, how, how do you ensure that you're able to do that for your customers so that your agent can actually do the outreach?
Speaker 2:Yeah, so, so, absolutely so. These databases, hopefully. Yeah, the only way we work with them is you know if the client has gotten the permission to reach out to that. So, and we follow GDPR and all those compliance and depending even on sometimes which state you're dealing with, which country you're dealing with, so it's all localized and we comply with that. And voice is also a new territory. Like just because you got consent for text doesn't really mean that you got consent for voice. So there are nuances there. But I think even there, like the way it's pretty nice, like the orchestration layer allows you to text somebody and say, hey, I'm going to call you in a few minutes, would that be okay to have a conversation, and then the person gives you consent and then you have a conversation, right? So at all points we are obviously following the opt-in and opt-out criteria, which is either maintained by the ATS or maintained by us.
Speaker 2:And it's bidirectional sync at any given time. Of course we don't go blind, we don't go and get into a database where they have no consent and we start spamming. That's the worst experience possible. Yeah, yeah, yeah.
Speaker 1:But you also mentioned for outbound. So I guess for is there a use case for when folks are like recruiters are doing outreach to candidates, like, let's say, doing like human touch, like reach, linkedin and mails, to then run into prescript Like how does how does the workflow work if that's like it's new outbound source candidates?
Speaker 2:Yeah, so you know. So there is the. As you know, all the CRMs and ADSs have come. You know, those are 20 year old technologies, 25 year old technologies. They've come into the world with a view of, like it's a recruiter or a sourcer use case, right, that's the insertion point. I want recruiters to come in and do a take certain action, sources to come in and take certain action. We came in from an automation first world so we believe in the future. Like recruiters don't need to hang out for hours in the CRM or the ATS. Either way, they don't like those, right? So everything that I can do whether I'm negotiating, whether I am selling them the position of what the value proposition of this job is, and the company it can all be done offline without the ATS or the CRM involved. Right, and now the AI is just helping record all those calls and put them out right. So, yeah, so our approach is different, you know. So we do allow for if the recruiter or the sourcer comes in and has to build a talent pool first manually, they can do it with the platform. Ideally, we just like to build everything that is just fully automated, right?
Speaker 2:The future is pretty simple. You have your annual operating plan and your annual hiring plan uploaded in the system. Adss have that capability. From that you extract all the roles people are hiring for. You know exactly the roles that are coming up. You potentially know the roles in which month they need to hire. You have the data to tell you like how, what kind of lead times you need. You automatically create those talent pools. A recruiter doesn't need to do anything and you start building those talent pools and start messaging and asking those people for having conversations.
Speaker 2:You're having voice conversations, chatbot conversations. Hey, I know you were a silver medalist six months back. Where are you today? What's your job title? Would you be looking for an opportunity in the next six months? And you know which candidates are warm, which candidates are hot, which candidates are actually cold. They don't want you to talk to them again, you know. So that's where all of this is headed, and I think there's a mix of AI and human power will always remain, but I think the human, the amount that will be human powered, will keep diminishing over time. At least, that's our perspective.
Speaker 1:Okay, yeah, for sure. What about? So can we dial in on AI evaluation and how you're thinking about that? So you have pre-screening right For just like the top knockout questions, right, which is a fairly straightforward use case, and then you start to get a little bit down funnel and I'm wondering if that's a use case you're thinking about.
Speaker 1:I think we're seeing probably more demand top of funnel or you probably have more data surrounding this than I do, but my assumption is top of funnel because it's higher volume. It's just a lot more work, right. And then when you get like particularly for professional service knowledge work, by the time you get to like a second or a third round, potentially per opening you have fewer candidates. So maybe there's potentially, arguably maybe, a little less demand in the market currently for down funnel evaluations, and maybe there's also a trust aspect and maybe there's also concerns around potential upcoming regulation or whatever else. I don't know. I don't know. I mean, I guess I'm just thinking like how are you thinking about like deeper evaluation use cases and is that something, an area you're going to play?
Speaker 2:yeah, no, I think I think you kind of nailed it like. The first part is top of the funnel, is a very much an efficiency use case, right, and? And also a use case where the TA leaders are realizing my recruiters are getting bogged down by the sheer scale of candidates that are coming in. There's just no way, and I think if you're a TA leader that has not made an investment or is not thinking of making an investment at the top of the funnel to do something about it, I think you're failing your organization, right. So you need to like, really bring some of these technologies, because they give you efficiency, they give you scale, they reduce the noise, so, but there's no value, not necessarily like code evaluation happening right. And, by the way, this is valid not just for professional roles, even for high volume roles.
Speaker 2:In high volume roles, let me tell you, like, if you're a large BPO center, you are spending tens of millions of dollars on assessment tools center. You are spending tens of millions of dollars on assessment tools. They don't want the noise to hit the assessment tool, they don't want fraudulent applicants to hit the assessment tool. They want to keep screening, keep knocking out people as much as they can to only have a select list of candidates. At the end of the day, in professional it might be I just need 10, like, give me really well screened folks that could match this role and then I will go super deep with them. That's why I also want my hiring managers to spend a lot more time right.
Speaker 2:So, yeah, so to your point. There is this screening happening, there is the noise reduction happening at the top, and companies are investing massively. But they're also trying to avoid costs that come later, either cost to the hiring manager, cost for assessments, cost for background checks, and if they are low margin businesses that need that rapid hiring, they really want to reduce any of this OPEX that they can. So it's a real ROI pitch for them. On the evaluation scale at the bottom, towards sort of the as you get closer to the hiring, yes, I think interview tools that are at least able to transcribe what happened in the interview and very quickly sort of compare those to the scorecards that people might have to use to evaluate. You know the final. Maybe it's the silver medalist, the final person they help picking, but at the end of the day it's still core human in nature, according to us, and it will probably remain there, at least in knowledge, work and professional, for a while, that's not going away.
Speaker 2:Yeah, I mean, that's a good thing, and I think that's a good thing. You want it that way.
Speaker 1:Yeah, yeah, I think so.
Speaker 1:I mean, I think that's the conclusion that I've come to as well just from hosting this series, talking with a lot of brilliant CEOs such as yourself, right, learning from folks who have a lot of access to data and are constantly having these very high level strategic conversations with customers, and so I think that that makes a lot of sense for me.
Speaker 1:Another question that I have is you know, you have companies like BrightHire and Pillar, companies that are doing like I suppose at this point they're calling it interview intelligence, essentially joining, co-piloting, zoom interviews, which I'm sure, if their CEOs heard me explain it that way, maybe I should let me just, it's a lot more than that, right, but basically they're joining recruiters on the interviews. They have all the role requirements on the back end and they're essentially they have all the custom questions and packaging the data. To summarize, you know how well, how many of the questions that folks answer and hey, did you forget to ask some? So they push that along to the next interview. They're, of course, generating job descriptions and doing some of those generation tasks too for customer questions, but they're really ensuring that candidates are going through a consistent, thorough, comprehensive interview process. Is that an area that your team is building in that direction, or are you staying away from that?
Speaker 2:Yeah, we are staying away from that, but we like aspects of that. We help enable that in the way. We have a large number of customers that have bought our interview automation scheduling product, which is a very big need of the hour, right. So you're talking about the actual interview process and the interview itself. I'm talking about the process around the interview, which is key because hiring managers are frustrated, recruiters are frustrated, candidates are frustrated Everybody's scheduling, rescheduling hiring manager are not prepared and they're coming into these interviews right. So that's a big problem, huge pain point. We have some of the largest healthcare hospital systems that we sell that product to. We have, I think, the fourth largest, the fifth largest automaker that is using us. So tons of examples. The example is as simple as that. I have 20,000 hiring managers. Please fix interview automation scheduling for them right.
Speaker 2:So it's such a natural extension of an automation platform. Right Now. Your point where you're coming to is we've seen this work like consistency is required Beyond consistency. That's why chatbots and voice AI is great. It's asking the exact same question over and over again to tens of thousands of candidates for that particular role. That's great. It's removing actually bias from that screening right that used to exist. So same thing is absolutely true, probably for the interview platforms, like at least they're helping the hiring managers ask the same questions and be consistent, I think. Number two instead of talking about the candidates, it's important to talk about the hiring managers. We know so many hiring managers even probably me we have gaps when we interview candidates. We are probably not asking the questions the right way.
Speaker 1:There's a lot of training that we can provide to these hiring managers, especially the first-time hiring managers, and if there's an AI tool, ai co-pilot that's helping me train my own internal manager teams to ask better questions, to put the best foot forward. That's a great win, I think. Yeah, I mean, I think it's like almost to some extent there, and that's a whole other side of the product too that I'm glad you're bringing up, because BrightHire and Pillar are also focused on essentially like evaluating what the hiring managers are doing. I think even BrightHire has a fair amount of this functionality where they're actually, I think, data like, even even like stuff like did you start the interview on time? Did you ask questions the right way? Did you spend five minutes talking about the football game you watched on Sunday and not really dialing into the screening questions? Right, just helping them stay organized and producing a score? Almost like, maybe like a gong right On the sales side they're doing some of that kind of analytics functionality on the call itself, which I think is pretty cool too.
Speaker 1:But yeah, it seems like they've gone like the interview intelligence, like co-pilot, packaging data, showing the candidates answers, responses, how relevant it is to the role, requirements, and then also providing analytics and data on how the hiring team is doing interviewing. You know cause I? You know it is having consistent interviews. Having hiring managers perform at a high level when it comes to hiring and interviewing is challenging, we interviewers. Having hiring managers perform at a high level when it comes to hiring and interviewing is challenging. We had Daniel Chait on the show a lot, the co-founder and CEO of Greenhouse. There's something funny he always likes to say is hiring managers struggle with two things hiring and managing Right.
Speaker 2:So you know, yeah, no, no, absolutely.
Speaker 1:And the same thing.
Speaker 2:Like you know, when we have the text message, we have a whole text messaging suite. Right, as I told you, the recruiters have. We try and measure the same thing. We try and measure how many messages they send, what kind of speed do they have? How fast do they respond to candidates? Which recruiters have better response rates? And we are also trying to just give these sort of signals back to the TA teams.
Speaker 2:And now the teams are like okay, it's not the recruiter that is pointing the finger. Now it's like I actually have a transcribed chat of yours in an interview of Mr Hiring Manager and here are the things that went wrong. Right, so I would like you to improve, because that's leading to a poor candidate experience for us. Right, so that loop, like our automation, can measure the candidate experience at any point point before the interview, before the screen, after the screen, after the interview, after the disposition, the final decision was made and we can give that intelligence back. Right, so it's super important and I think LLMs and AI is really going to help change the game, as long as they're not making the final decision, we are all good, but yeah, you know, I think it's a truly exciting time.
Speaker 1:Yeah, it's a lot of really cool, really cool stuff happening for sure.
Speaker 1:What about, from a regulation perspective, for companies that are a little bit concerned about incorporating AI into their workflow? Right, I mean, like right now, for instance, like one thing I've brought up several times on the show is, like workday right now there's a class action lawsuit for their AI system, potentially like discriminating folks over several different reasons, right, but you know, people are, I think some folks might be a little bit concerned about applying AI technology and I'm wondering, like you know, I think evaluation is a key sticking point on AI making a final decision, or, and I'm curious to get your thoughts on that I mean, I think, honestly, maybe there's nuance to the definition of what we consider evaluating Right and, and you know, is it considered? For instance, like, is it considered? Maybe it's it's more so evaluating if, if ai is generating role requirements, generating a jd, and then basically deciding what the role requirements are and then evaluating against those, maybe, maybe that's what evaluating is.
Speaker 1:Maybe, if a hiring team puts together the role requirements and then essentially, the is it and the AI is evaluating or is interviewing for that, Is that really evaluating or is that just matching Right, like, and so I'm just wondering, like, how do you think like the nuances is like where? Where do you think regulation is going to kind of fall in this in the evaluation AI decision-making piece?
Speaker 2:Well, yeah, yeah, I'm no EUOC expert or regulation expert, so I won't, I won't make any statements around that, but yeah, I mean, listen, it's fairly complicated. It does to your point. It does start from the JD. Everything falls flat from the JD, the noise starts from the JD. You just copy something from the job description, from Google or some past job description, and then you just run with it. Right, that's how the screening questions are generated. That's how the screening questions get on a chatbot, that's how they get on a voice AI. You know, if you made the mistake in the job description, everything flows from there. Right, that is the freaking reality. Right, and nobody wants to accept it, right, but that is the reality. So you know, the kind of things that we are experimenting is like imagine a hiring manager able to talk to a voice agent and tell them exactly who they're looking for, the core skills I'm looking for. Hey, it is still a forklift operator, or it is still a cashier at a franchise. But like, this is what makes this particular role special, or this is what makes this location special, you know, and let me tell you more about it. And can the voice agent capture that and really humanize the job description where, yes, there are certain knockout things like you have to be here, but here's where I can give you more context around that role, even though you know I'm Bath and Body Works and you're Lululemon and you're Nike store, but it's still a store job. But here's what makes us truly differentiated, right?
Speaker 2:So we are going to try and push the boundaries on that front, but I did want to make the point like, job descriptions are the worst and they start the whole problem chain, right, but there is bias, potential bias. So what are we measuring it against? I think the regulations need to understand we are measuring against humans, and I think it's too much bent on. Hey, ai is biased, but humans are not. The reality is, when we take the human data and we feed it into AI, ai is horribly biased. That's when we realize, oh shit, we shouldn't be talking about which school they went to, what was their name, what was their gender, what was their location. We have to keep all the data out before we train the models. Right and well, that data lies.
Speaker 2:When, as soon as that resume hits a human, there's no way the human's not looking at it, right? So I think one is truly figuring out what has to be screened out of these resumes. The good news is for a lot of jobs and I would say I don't know whether you know the stats like literally 65% of the world's workforce is not on LinkedIn. How many of those people actually have a resume? So what we are matching towards and what we are training our models towards is what I would call progressive matching, and what that means is there might not be a resume. There's a good chance that there won't be a resume, right?
Speaker 2:So I want to understand who you are. I want to have a conversation based on the skills that the job is saying it absolutely must have, but then I'm trying to understand your preferences. You know, can you work overtime? Can you do the shift or not? Can you work on the weekends? Do you even have a transport to get to work? I'm trying to understand who is Anil as a person, and even if you tell me I've done customer support work before, okay, but were you in a situation where there was a blow up? Can you talk to me more about that? So, trying to drill a little bit deeper than just what a resume might have, and even if you have a resume, a resume is just another data point to use, but you've got to use the job seekers' preferences, what they expect. You've got to take into account what the manager is saying and then try and intersect it.
Speaker 2:So the future, we believe, is try and remove the biases from the training models as much as you can. Get yourself audited all the time in real time. Hopefully Make the AI more explainable. If the AI matches a certain person to a role, can it actually explain why it did that? And then, if you make it completely blind, will it match? Will it match the same person again? You know that's where it gets very tricky. So I think explainability is a huge sort of actually a killer feature if you can get it going in your product, and then you need to make sure that that explanation satisfies the auditors at some point when you audit it right or can pass a bias audit right. So that's those are all the investments that at Sense we've made to make sure that we are doing it the right way and we are following at least the local regulations.
Speaker 2:I will say one last thing which people miss. We are so focused constantly on candidates, inbound candidates coming in, screening them. It's not just screening and dispositioning them and saying you're rejected, it's actually screening them. You know, it's not just screening and dispositioning them and saying you're rejected, it's actually screening them, saying that you're not a good fit. But here are three other jobs where you are a better fit for and let me help you apply to those jobs right, especially in the high volume scenario, because that's truly possible. But even if you're a technology company, maybe you apply for an AI job and you're not a great fit for the AI job. You don't have that level of expertise that was required, but maybe I can deflect you to a regular software engineer job and get you hired there.
Speaker 2:So the whole point of the system that we are trying to build is and actually our data is proving 70% of the candidates that apply to a job are not a good fit for that job. They just think they are, they believe, they want to believe that they are. It's a aspirational apply, it's not a true apply. But if we can deflect them and put them in roles and jobs and show them the jobs that they might be a better fit at, we can actually improve the conversion rates, we can improve the fill rates and we can make both sides happy. Right, we can explain to them why. Why are we taking the action that we are taking? So that's our vision and that's why we think the candidate experience also needs to change. The good news is a lot of TA leaders understand that and they know that that's what's actually happening in the market.
Speaker 1:Lot of TA leaders understand that and they know that that's what's actually happening in the market. Yeah, wow, this has been an incredibly insightful conversation. I've certainly learned a whole lot. I know our audience is going to really enjoy this and I would actually one day like to continue the conversation if you want to come back on the show man. You just shared so much great insight with us. This has really been fantastic. I think it's really incredible what you're building over. At Sense, it seems like a really impressive platform, a lot of different functionality to help out through the entire workflow. So I'm really excited to continue to follow what your team is building and it sounds it just sounds all like really impressive and incredibly helpful to your customers. So, anyways, thank you for coming on and sharing a little bit about what you're doing.
Speaker 2:Likewise Thanks, james, and let's stay connected.
Speaker 1:Yeah, absolutely, and for everybody else tuning in. Thank you so much for joining us and we'll talk to you soon. Take care.
