#70: Signal vs. Noise: Navigating the Crowded TA Tech Landscape

1 00:00:00,320 --> 00:00:02,640 SPEAKER_00: Welcome to the Changing State of Talent 2 00:00:02,640 --> 00:00:05,599 Acquisition podcast with your host, Grant Thorta. 3 00:00:05,919 --> 00:00:08,800 Each episode brings you unfiltered conversations about 4 00:00:08,800 --> 00:00:12,160 the tools, trends, and technologies impacting the 5 00:00:12,160 --> 00:00:13,839 future of talent acquisition. 6 00:00:14,000 --> 00:00:17,679 Our guests share their stories on what's working, what's hype, 7 00:00:17,839 --> 00:00:21,199 and what's actually helping companies hire better and grow 8 00:00:21,199 --> 00:00:21,600 faster. 9 00:00:21,839 --> 00:00:23,839 Have feedback or want to join the show? 10 00:00:24,079 --> 00:00:26,960 Head on over to Telivity.com to learn more. 11 00:00:27,199 --> 00:00:29,760 But now we're on to this week's episode. 12 00:00:30,160 --> 00:00:32,799 SPEAKER_02: All right, and we're back with another episode of the 13 00:00:32,799 --> 00:00:35,679 Changing State of Town Acquisition podcast. 14 00:00:36,000 --> 00:00:39,600 Slightly different format with some exciting news over the last 15 00:00:39,600 --> 00:00:43,439 couple weeks with Change Day getting acquired and joining the 16 00:00:43,439 --> 00:00:44,640 Recruitics family. 17 00:00:44,880 --> 00:00:48,000 Our next guest is Mark Tomasino. 18 00:00:48,240 --> 00:00:52,640 Mark, I've known for the better part of boy, uh two decades at 19 00:00:52,640 --> 00:00:53,119 this point. 20 00:00:53,280 --> 00:00:56,799 And Mark is now in his role as director of partnerships. 21 00:00:56,880 --> 00:01:00,079 Uh we'll call it special projects still over at Telivity. 22 00:01:00,320 --> 00:01:03,039 Mark, we'd love for you to share a little bit more about your 23 00:01:03,039 --> 00:01:05,680 career journey, maybe some of the pivotal moments that you 24 00:01:05,680 --> 00:01:08,560 know brought you into the Telivity or changed a team 25 00:01:08,560 --> 00:01:09,280 across the board. 26 00:01:10,400 --> 00:01:10,799 SPEAKER_01: Sure. 27 00:01:11,280 --> 00:01:12,400 Thanks for having me, Graham. 28 00:01:12,640 --> 00:01:15,519 Honored to be a guest finally on this pod. 29 00:01:16,000 --> 00:01:19,519 And um, yeah, a little bit about me is I've been in the industry 30 00:01:19,519 --> 00:01:21,680 since 2006, first job out of college. 31 00:01:21,760 --> 00:01:25,760 I made 125 cold calls a day at Career Builder, and I managed to 32 00:01:25,760 --> 00:01:29,599 not quit or get fired, which is more than most people could say 33 00:01:29,599 --> 00:01:30,480 at the time. 34 00:01:30,879 --> 00:01:35,359 And uh, you know, so I ended up doing all levels of sales, you 35 00:01:35,359 --> 00:01:39,040 know, from SB up to the largest global employers. 36 00:01:39,200 --> 00:01:41,680 When career builders started buying a bunch of different 37 00:01:41,680 --> 00:01:45,040 software and technology to bring to market, I was one of the 38 00:01:45,040 --> 00:01:48,079 early solution architects, uh, which is Graham, how you and I 39 00:01:48,079 --> 00:01:50,959 know each other, of working closely, of uh bringing that to 40 00:01:50,959 --> 00:01:52,560 market and working with different employers, tech 41 00:01:52,719 --> 00:01:55,200 stacks, uh, which really piqued my interest into the broader 42 00:01:55,200 --> 00:01:56,959 world of HR Tech and integrations. 43 00:01:57,280 --> 00:02:00,239 We were acquired by private equity and 2017. 44 00:02:00,480 --> 00:02:03,359 I then was kind of an internal sales consultant between the 45 00:02:03,359 --> 00:02:06,319 different portfolio companies that are owners owned. 46 00:02:06,560 --> 00:02:10,400 And finally in 2020, I became vice president of partnerships, 47 00:02:10,560 --> 00:02:15,360 which I oversaw our reseller integrations and agency partner 48 00:02:15,520 --> 00:02:18,479 programs amongst some other things, kind of building on all 49 00:02:18,560 --> 00:02:19,919 the things that came before that. 50 00:02:20,080 --> 00:02:23,120 I at the time I had a two-year-old son and I had a 51 00:02:23,120 --> 00:02:28,000 planned sort of career break and uh to spend more time with my 52 00:02:28,000 --> 00:02:28,639 growing family. 53 00:02:28,800 --> 00:02:32,560 So took a break from the workforce for a few years, but 54 00:02:32,639 --> 00:02:37,680 uh Graham, you know, you at Change State uh brought me over 55 00:02:38,000 --> 00:02:41,520 to help on a consulting basis with some HR Tech partnerships. 56 00:02:41,599 --> 00:02:44,639 And no surprise we got along so well and enjoyed working that we 57 00:02:44,639 --> 00:02:45,680 flipped that to full time. 58 00:02:45,759 --> 00:02:48,879 And now here we are, having grown change state and now 59 00:02:48,879 --> 00:02:51,360 joined the recruits family and over at Telivity. 60 00:02:51,599 --> 00:02:53,199 SPEAKER_02: Yeah, well, you know, I think you're 61 00:02:53,199 --> 00:02:55,599 underselling, and we're gonna get into it, you know, your 62 00:02:55,599 --> 00:02:58,879 background and knowledge of you know the HR Tech space, you 63 00:02:58,879 --> 00:03:00,639 know, in particular in the pre-hire world. 64 00:03:00,719 --> 00:03:04,719 And you know, I I always you know talk about what gives me 65 00:03:04,719 --> 00:03:07,759 energy, and I think there's a lot of problems you know to 66 00:03:07,759 --> 00:03:09,439 solve standing in front of a whiteboard. 67 00:03:09,599 --> 00:03:11,919 And you know, I think that's kind of where we cut our teeth 68 00:03:11,919 --> 00:03:12,639 in general. 69 00:03:12,879 --> 00:03:15,520 But you know, we're gonna dive deep into HR Tech. 70 00:03:15,759 --> 00:03:19,680 So I guess a great starting point, Mark, maybe is we just 71 00:03:19,680 --> 00:03:23,439 got back from HR Tech, and I think it's fair to say we 72 00:03:23,439 --> 00:03:26,639 probably couldn't walk uh you know uh 10 feet without seeing 73 00:03:26,639 --> 00:03:29,439 another uh AI-powered booth. 74 00:03:29,599 --> 00:03:32,479 And you know, you were kind of my co-pilot through all of this. 75 00:03:32,639 --> 00:03:35,520 So, you know, what was your biggest takeaway from HR Tech 76 00:03:35,680 --> 00:03:36,240 from the conference? 77 00:03:36,400 --> 00:03:39,439 What kind of stood out as maybe you know, genuinely, you know, 78 00:03:39,599 --> 00:03:43,599 innovative versus just uh well, we'll call it well-marketed, you 79 00:03:43,599 --> 00:03:44,560 know, noise? 80 00:03:45,120 --> 00:03:45,680 SPEAKER_01: Sure. 81 00:03:46,000 --> 00:03:49,120 Uh yeah, I mean, you you see the big players, although I would 82 00:03:49,120 --> 00:03:51,919 say the big players have a smaller presence there than 83 00:03:51,919 --> 00:03:55,120 maybe historic, you know, and I'm talking like the big HCM 84 00:03:55,120 --> 00:03:59,680 systems, but what you did see was a ton of startups. 85 00:03:59,919 --> 00:04:03,759 So like they have a whole startup area that was just you 86 00:04:03,759 --> 00:04:06,719 know folks that have point solutions trying to solve very 87 00:04:06,719 --> 00:04:08,080 specific problems in the space. 88 00:04:08,240 --> 00:04:09,759 So that was interesting. 89 00:04:09,840 --> 00:04:11,680 So there's a lot of emergent players. 90 00:04:11,840 --> 00:04:15,360 I would say like a really common one that I was almost surprised 91 00:04:15,360 --> 00:04:18,240 to see how many were you know trying to go about solving the 92 00:04:18,240 --> 00:04:20,639 same thing were these AI interview tools. 93 00:04:20,800 --> 00:04:24,319 Um, and so you know, we can probably get into the nuance of 94 00:04:24,319 --> 00:04:26,240 that a little bit and some things I learned, but you know, 95 00:04:26,399 --> 00:04:29,519 doing being able to do quick side-by-sides of companies that 96 00:04:29,519 --> 00:04:31,920 are essentially going about solving the same problem in the 97 00:04:31,920 --> 00:04:34,639 same way, but trying to peel back that onion and try to 98 00:04:34,639 --> 00:04:38,399 figure out who's who and what's what and who who has a novel way 99 00:04:38,399 --> 00:04:40,240 of solving this problem and who's really just putting a 100 00:04:40,240 --> 00:04:42,800 wrapper around the same back-end stuff. 101 00:04:43,120 --> 00:04:43,360 SPEAKER_02: Yeah. 102 00:04:43,519 --> 00:04:48,000 Well, you know, I I'll also say in the startup pavilion and you 103 00:04:48,000 --> 00:04:50,879 know, just in booths in general, maybe you maybe you saw this 104 00:04:50,879 --> 00:04:51,839 too, maybe you didn't. 105 00:04:52,000 --> 00:04:56,639 You know, I think that you know, we saw a lot of new solutions 106 00:04:57,040 --> 00:04:59,920 and you know, coming from founders who maybe haven't 107 00:04:59,920 --> 00:05:01,839 worked in traditional TA, right? 108 00:05:02,000 --> 00:05:05,920 And so, you know, we talk a lot about, you know, everyone talks 109 00:05:05,920 --> 00:05:08,879 about Tesla, you know, coming from outside of the you know, 110 00:05:09,040 --> 00:05:12,480 the vehicle industry, Uber, you know, people coming from outside 111 00:05:12,480 --> 00:05:16,079 of you know the transportation, like disrupting industries as a 112 00:05:16,079 --> 00:05:16,240 whole. 113 00:05:16,319 --> 00:05:19,279 And like, you know, I do hope, and I think there's you know, 114 00:05:19,360 --> 00:05:22,160 some very good, you know, there's a lot of energy behind 115 00:05:22,240 --> 00:05:24,480 you know, people coming from outside TA. 116 00:05:24,639 --> 00:05:27,920 And I'm hoping that you know we start to bring more fresh 117 00:05:27,920 --> 00:05:31,199 thinking into you know TA instead of some of the recaps 118 00:05:31,199 --> 00:05:33,839 that I read where it was, hey, yeah, guess what? 119 00:05:34,000 --> 00:05:35,360 Like someone bought someone else. 120 00:05:35,519 --> 00:05:38,319 I realize that might be ironic given that we just got acquired, 121 00:05:38,399 --> 00:05:42,639 but hey, like some big, massive applicant tracking system um or 122 00:05:42,639 --> 00:05:45,680 HRS system just bolted on another tool. 123 00:05:45,839 --> 00:05:48,959 So, you know, I'm just wondering, like, fresh thinking, 124 00:05:49,120 --> 00:05:51,519 do you feel like there's a little bit of a different energy 125 00:05:51,519 --> 00:05:54,800 in HR Tech and you know in the industry than maybe in years 126 00:05:54,800 --> 00:05:55,120 past? 127 00:05:55,519 --> 00:05:56,800 SPEAKER_01: Yeah, I would say that. 128 00:05:56,959 --> 00:05:59,279 I mean, you know, it's it's a double-edged sword. 129 00:05:59,360 --> 00:06:03,600 You have this emergence of AI, and so everyone's gonna use the 130 00:06:03,600 --> 00:06:06,639 same words, and you gotta try to figure out who's actually going 131 00:06:06,639 --> 00:06:07,920 about solving the problems. 132 00:06:08,160 --> 00:06:11,199 And you'll probably hear me refer to this several times on 133 00:06:11,199 --> 00:06:14,639 the pod, but it it all goes back to problems. 134 00:06:14,959 --> 00:06:18,000 And so the founders that I'm energized by or the products 135 00:06:18,000 --> 00:06:22,480 that I found most interesting are the ones that understand the 136 00:06:22,480 --> 00:06:25,279 problem that they're trying to solve, and they can articulate 137 00:06:25,279 --> 00:06:26,959 it when asked directly. 138 00:06:27,199 --> 00:06:28,480 What problem do you solve? 139 00:06:28,639 --> 00:06:31,759 What are the use cases that you're finding the most success 140 00:06:32,079 --> 00:06:32,639 with clients? 141 00:06:32,879 --> 00:06:34,480 And they know exactly what they're doing. 142 00:06:34,879 --> 00:06:37,759 You know, almost like starting from first principles of hey, 143 00:06:38,000 --> 00:06:39,120 you know, what's really the problem? 144 00:06:39,279 --> 00:06:40,879 What's what's the underlying problem here? 145 00:06:41,279 --> 00:06:43,759 So to me, that's how you you kind of cut through the noise. 146 00:06:44,079 --> 00:06:46,720 And the inverse of that would be if you talk to somebody and it 147 00:06:46,720 --> 00:06:49,759 sounds like they just keep adding features so they can keep 148 00:06:49,759 --> 00:06:51,360 up on an RFP checklist. 149 00:06:51,600 --> 00:06:54,319 Oh, yeah, we do this and we do that, and we do this over here 150 00:06:54,399 --> 00:06:54,720 too. 151 00:06:54,959 --> 00:06:57,439 And it's almost like you look lose sight of like, yeah, but 152 00:06:57,439 --> 00:06:58,720 what what do you stand for? 153 00:06:58,879 --> 00:07:00,079 Like, what's the ethos here? 154 00:07:00,160 --> 00:07:03,120 What what's the what's the fundamental problem that that 155 00:07:03,120 --> 00:07:04,240 you're trying to address? 156 00:07:04,720 --> 00:07:07,439 Because sometimes it can feel like our fundamental problem is 157 00:07:07,759 --> 00:07:11,120 we need to win more RFPs, or we need to we need to attract 158 00:07:11,120 --> 00:07:11,680 investors. 159 00:07:11,839 --> 00:07:14,480 So we need to use these words or say that we can do these big 160 00:07:14,480 --> 00:07:16,560 things because this other company said they could do those 161 00:07:16,639 --> 00:07:18,240 and they got a billion dollar valuation. 162 00:07:18,399 --> 00:07:21,519 So yeah, I always be rooted in into the problems, and that's 163 00:07:21,600 --> 00:07:24,720 that's what differentiates the solutions for me, personally. 164 00:07:25,040 --> 00:07:25,279 SPEAKER_02: All right. 165 00:07:25,439 --> 00:07:28,079 Well, you know, let's let's stick on this problems path 166 00:07:28,160 --> 00:07:30,639 because you know I want to get into our marketplace a little 167 00:07:30,639 --> 00:07:34,079 bit, but you know, I think that you know you've kind of you know 168 00:07:34,160 --> 00:07:36,560 framed this in a unique way for me, right? 169 00:07:36,639 --> 00:07:39,839 And that's as we're walking through these booths, we're 170 00:07:39,839 --> 00:07:42,399 talking to a lot of vendors talking about how we're gonna 171 00:07:42,399 --> 00:07:43,839 use AI to solve problems. 172 00:07:43,920 --> 00:07:46,800 And I think you know, there's probably a little bit of irony 173 00:07:46,800 --> 00:07:51,279 in here in that you know, job seekers are you know using AI to 174 00:07:51,279 --> 00:07:53,120 apply to a lot more jobs. 175 00:07:53,360 --> 00:07:57,600 And you know, I think you know, we've created a lot of volume 176 00:07:57,600 --> 00:08:02,079 problems for companies in the sense that we see a large, a 177 00:08:02,079 --> 00:08:05,439 much larger volume of candidates or applicant flow than we have 178 00:08:05,439 --> 00:08:06,079 historically. 179 00:08:06,240 --> 00:08:10,000 And so, you know, on some level, I think you know, we're using AI 180 00:08:10,000 --> 00:08:12,720 to solve problems that have been created by AI. 181 00:08:12,879 --> 00:08:15,120 Yeah, but what's your perspective of you know the 182 00:08:15,120 --> 00:08:17,680 bigger problems that you're hearing companies solving coming 183 00:08:17,680 --> 00:08:20,160 out of HR Tech or trying to solve maybe? 184 00:08:20,560 --> 00:08:23,120 SPEAKER_01: Yeah, I think you're you're touching on something 185 00:08:23,120 --> 00:08:23,279 there. 186 00:08:23,360 --> 00:08:26,079 I call it the AI arms race and recruiting. 187 00:08:26,240 --> 00:08:28,879 But yeah, let's talk about the employer side. 188 00:08:29,120 --> 00:08:33,840 So, you know, pretty quickly after ChatGPT rolled out, people 189 00:08:33,919 --> 00:08:38,399 started using it for things like you know, drafting emails and 190 00:08:38,399 --> 00:08:41,759 letters and in mails and things of that nature, so candidate 191 00:08:41,759 --> 00:08:42,559 outreach. 192 00:08:42,720 --> 00:08:45,679 And then they thought, oh, you know, this could probably make 193 00:08:45,679 --> 00:08:49,039 my job description a little more robust and you know fleshed out. 194 00:08:49,200 --> 00:08:51,919 So now it's it's rewriting the job descriptions. 195 00:08:52,240 --> 00:08:56,000 And oh, well, it also can help me filter and match, although 196 00:08:56,080 --> 00:08:58,240 those tools have been around for a while, but you know, they've 197 00:08:58,240 --> 00:09:00,960 definitely grown their providence and uh and more so. 198 00:09:01,039 --> 00:09:03,679 So I don't even have to like go through these resumes per se, 199 00:09:03,759 --> 00:09:06,480 because now I can have a tool level screen and float the best 200 00:09:06,480 --> 00:09:07,919 ones towards the top. 201 00:09:08,159 --> 00:09:10,799 And you know, that's all celebrated in the name of 202 00:09:10,799 --> 00:09:14,000 efficiency, but you know, it's not not fun when the other side 203 00:09:14,000 --> 00:09:14,799 has it too. 204 00:09:14,960 --> 00:09:19,360 Um, which then you have then you have uh you know candidates who 205 00:09:19,360 --> 00:09:23,360 are like, well, hey, I can use this, I'll rewrite my resume to 206 00:09:23,360 --> 00:09:25,200 perfectly match this job description. 207 00:09:25,440 --> 00:09:29,919 Okay, and you can send that out, you know, help help me uh answer 208 00:09:29,919 --> 00:09:31,600 these interview questions, right? 209 00:09:31,679 --> 00:09:34,320 And those tools are just getting more and more sophisticated. 210 00:09:34,480 --> 00:09:36,000 And yeah, send off my resume. 211 00:09:36,240 --> 00:09:39,440 Oh, congratulations, I you applied to a thousand jobs while 212 00:09:39,440 --> 00:09:40,799 you were sleeping, right? 213 00:09:41,120 --> 00:09:44,960 And now now you do have uh not only a volume problem, but you 214 00:09:44,960 --> 00:09:46,159 have a noise problem. 215 00:09:46,480 --> 00:09:50,159 Because what is an algorithm, a matching algorithm, no matter 216 00:09:50,159 --> 00:09:54,399 how sophisticated it might be, what's it supposed to do when it 217 00:09:54,399 --> 00:09:58,559 has you know hundred resumes that were tailor-made to the job 218 00:09:58,559 --> 00:10:02,879 description if that is the underlying matching criteria, 219 00:10:03,200 --> 00:10:03,519 right? 220 00:10:03,759 --> 00:10:06,159 It's like you know, congratulations, here's you 221 00:10:06,159 --> 00:10:07,759 know, a bunch of hundred percent matches. 222 00:10:07,919 --> 00:10:09,279 Well that's no longer useful. 223 00:10:09,519 --> 00:10:12,000 And now you have way more to go through. 224 00:10:12,320 --> 00:10:14,320 Okay, so what do you do? 225 00:10:14,960 --> 00:10:18,639 Well, now you have a whole category of solutions, also AI, 226 00:10:18,960 --> 00:10:20,720 that is is going to interview them. 227 00:10:21,200 --> 00:10:25,120 Because hey, I I don't have enough information and context 228 00:10:25,120 --> 00:10:28,559 to f to know who of this are are actually the best. 229 00:10:28,879 --> 00:10:32,080 But you know what, you know what I could do is if I had an 230 00:10:32,080 --> 00:10:35,440 ability to have a conversation with them, I can get more 231 00:10:35,440 --> 00:10:36,720 information on that candidate. 232 00:10:36,799 --> 00:10:39,600 And now you know then I could facilitate a better match. 233 00:10:39,759 --> 00:10:42,720 So I'm trying to get more information and context by way 234 00:10:42,720 --> 00:10:44,240 of conversation interviewing. 235 00:10:44,399 --> 00:10:48,080 But well, shoot, my stack of applicants is a thousand, two 236 00:10:48,159 --> 00:10:49,039 thousand tall. 237 00:10:49,279 --> 00:10:50,720 I can't interview all those folks. 238 00:10:50,879 --> 00:10:52,159 Ah, but AI can. 239 00:10:52,720 --> 00:10:53,039 Okay. 240 00:10:53,440 --> 00:10:56,559 So now we're gonna feed all of these candidates who may or may 241 00:10:56,559 --> 00:10:59,279 not deserve or should be interviewed, but we're gonna 242 00:10:59,279 --> 00:11:01,519 funnel them through this AI interview tool. 243 00:11:01,679 --> 00:11:05,360 Um, and on the other end, the hope is that now I've I've 244 00:11:05,360 --> 00:11:08,320 really sifted through and I've I've truly identified, you know, 245 00:11:08,399 --> 00:11:11,679 the best quality and most engaged and the best fit for our 246 00:11:11,679 --> 00:11:14,559 organization and for this role, you know, by by way of this. 247 00:11:14,639 --> 00:11:18,720 But like I just described a whole apparatus that like just 248 00:11:18,720 --> 00:11:20,879 wasn't there what, three years ago? 249 00:11:21,120 --> 00:11:22,960 And that's a lot of noise to sift through. 250 00:11:23,039 --> 00:11:25,360 And it makes me question, be like, wait a minute, what are we 251 00:11:25,360 --> 00:11:26,000 doing here? 252 00:11:26,399 --> 00:11:28,399 SPEAKER_02: Well, I guess you know, it reminds me of you know, 253 00:11:28,480 --> 00:11:31,039 maybe a decade ago, and I think you heard this in our past life. 254 00:11:31,200 --> 00:11:34,799 Like, you know, first the problem was uh, hey, a job ads, 255 00:11:34,960 --> 00:11:35,840 job ads are bad. 256 00:11:35,919 --> 00:11:38,080 You know, so your job ads are best case, that's a lie. 257 00:11:38,159 --> 00:11:41,039 You know, resumes are are poor, you know, people put you know, 258 00:11:41,120 --> 00:11:43,200 arguably some some fluff in resumes. 259 00:11:43,279 --> 00:11:46,639 So, you know, resumes and and job ads are both, you know, two 260 00:11:46,799 --> 00:11:47,919 false documents. 261 00:11:48,000 --> 00:11:50,799 You know, so you're matching one lie and with another lie. 262 00:11:50,960 --> 00:11:53,440 And, you know, so it's not a surprise that our matching 263 00:11:53,440 --> 00:11:54,639 algorithms are on, also. 264 00:11:54,720 --> 00:11:57,600 And I think, you know, this whole idea of context as we're 265 00:11:57,600 --> 00:12:01,679 getting into you know moving people through the process is 266 00:12:01,679 --> 00:12:04,080 one that I don't know, I'm probably more keen to follow a 267 00:12:04,080 --> 00:12:06,879 little bit too, because I think you're right that like, you 268 00:12:06,879 --> 00:12:09,120 know, it is becoming increasingly more difficult to 269 00:12:09,120 --> 00:12:12,480 match a generic is the word I use, even though I I hesitate, 270 00:12:12,720 --> 00:12:16,320 but a generic profile with a generic job description and try 271 00:12:16,320 --> 00:12:18,960 and figure out of a thousand people that come through, you 272 00:12:18,960 --> 00:12:20,960 know, who really is going to be the best fit. 273 00:12:21,279 --> 00:12:21,600 SPEAKER_01: Right. 274 00:12:21,759 --> 00:12:22,080 Yeah. 275 00:12:22,320 --> 00:12:24,960 You know, another term I I discovered yesterday, and it was 276 00:12:25,039 --> 00:12:30,399 a real uh brilliant article, but it it's basically this age of 277 00:12:30,399 --> 00:12:35,679 quick hit video and algorithm generate content and in gen AI. 278 00:12:35,840 --> 00:12:38,879 It's really compression and it's like a compression of the 279 00:12:38,879 --> 00:12:39,679 average. 280 00:12:39,919 --> 00:12:42,159 So, which just makes everything look the same. 281 00:12:42,240 --> 00:12:44,000 And you get like change blindness. 282 00:12:44,080 --> 00:12:46,399 You can't, you know, you're trying to compare two things 283 00:12:46,399 --> 00:12:50,639 that are like really, really similar, sounding, looking, etc. 284 00:12:50,960 --> 00:12:54,159 And the you know, the high fidelity, the signal, like the 285 00:12:54,159 --> 00:12:57,519 true uniqueness, the raw file or material, that that's what 286 00:12:57,519 --> 00:12:58,960 doesn't really exist anymore. 287 00:12:59,120 --> 00:13:00,960 Which, you know, we can talk about that later. 288 00:13:01,039 --> 00:13:04,000 I think that that might be the counter uh example 289 00:13:04,240 --> 00:13:07,200 differentiator that that employers and you know really 290 00:13:07,200 --> 00:13:08,480 anyone can use to stand out. 291 00:13:08,720 --> 00:13:11,519 I would love to dig in on context a little bit, if we can. 292 00:13:11,759 --> 00:13:12,399 Yeah, yeah. 293 00:13:12,559 --> 00:13:13,919 Well, where where do you want to take it? 294 00:13:14,159 --> 00:13:17,759 So this was my this was my big aha doing a lot of side-by-side 295 00:13:17,759 --> 00:13:18,399 comparisons. 296 00:13:18,559 --> 00:13:21,759 And as you can tell, I'm a little skeptical of the AI 297 00:13:21,759 --> 00:13:22,559 interviewing tools. 298 00:13:22,720 --> 00:13:25,679 Not that they can't work really well or be, you know, super 299 00:13:25,679 --> 00:13:26,320 useful. 300 00:13:26,559 --> 00:13:31,440 It's just it's the emergence of the solution seems to be trying 301 00:13:31,440 --> 00:13:35,600 to combat a problem of our own making, of you know, AI 302 00:13:35,759 --> 00:13:38,159 everything to you know, create that volume problem that you're 303 00:13:38,159 --> 00:13:38,720 talking about. 304 00:13:38,879 --> 00:13:42,639 But I do see these things you know having real great potential 305 00:13:42,639 --> 00:13:45,039 and can help a number of our clients who we interact with 306 00:13:45,039 --> 00:13:47,919 every day, you know, get through that tunnel and and find the 307 00:13:47,919 --> 00:13:49,600 best quality candidates. 308 00:13:49,840 --> 00:13:54,000 However, context is really what matters. 309 00:13:54,480 --> 00:13:59,360 And you know, I demoed probably six to eight of these tools at 310 00:13:59,360 --> 00:14:05,360 the expo, and some of them it's you know, you upload your job 311 00:14:05,360 --> 00:14:08,799 description, and then from there, the AI interview tool 312 00:14:08,799 --> 00:14:11,679 will analyze that and come up with interview questions that it 313 00:14:11,679 --> 00:14:15,039 can ask to susto if somebody matches the qualifications to 314 00:14:15,039 --> 00:14:15,519 the job. 315 00:14:15,600 --> 00:14:18,879 And so then they're gonna have some either plain voice or a 316 00:14:18,879 --> 00:14:19,919 video avatar. 317 00:14:20,080 --> 00:14:22,559 Uh, you can kind of pick your flavor there, and then that's 318 00:14:22,559 --> 00:14:24,639 what's gonna have the conversation with the candidate, 319 00:14:24,799 --> 00:14:27,679 and they're gonna ask these questions and hopefully without 320 00:14:27,679 --> 00:14:29,840 not too much lag time, and they're gonna ask those 321 00:14:29,840 --> 00:14:30,080 questions. 322 00:14:30,159 --> 00:14:33,120 And now you're basically conducting that first screen 323 00:14:33,120 --> 00:14:36,320 interview with the candidate, uh, but uh AI is doing it 324 00:14:36,320 --> 00:14:39,039 instead of a recruiter, so you can do thousands of these a day 325 00:14:39,120 --> 00:14:41,279 instead of you know dozens, maybe. 326 00:14:41,600 --> 00:14:46,240 So that's the solution, but the context that is fed into them is 327 00:14:46,240 --> 00:14:47,919 what I think is the differentiator. 328 00:14:48,320 --> 00:14:51,919 So some tools is like upload job description and go. 329 00:14:52,080 --> 00:14:54,720 Some of them are well, we start with the job description, but 330 00:14:54,720 --> 00:14:58,080 look, now you can toggle on this or enter this, you know, you can 331 00:14:58,399 --> 00:15:01,919 you can edit, you know, and kind of create your flow after doing 332 00:15:01,919 --> 00:15:04,559 it, which okay, that's that's good. 333 00:15:05,039 --> 00:15:06,159 Or better at least. 334 00:15:06,320 --> 00:15:09,519 But I think the best solutions and the ones that are most 335 00:15:09,519 --> 00:15:14,559 interesting to me are the ones that take in the full context of 336 00:15:14,559 --> 00:15:15,519 the organization. 337 00:15:16,320 --> 00:15:21,679 Meaning, like, what if you could upload all of your you know, 338 00:15:21,840 --> 00:15:25,519 recruiting best practices that you've established, and you 339 00:15:25,519 --> 00:15:28,639 know, the interview questions that you have said, you know, 340 00:15:28,720 --> 00:15:32,639 like that that's what we like to ask, or your um what you 341 00:15:32,639 --> 00:15:36,080 consider to be your employer brand differentiators, um your 342 00:15:36,080 --> 00:15:40,240 employee handbook, your core values, sample candidates who 343 00:15:40,240 --> 00:15:42,720 have worked out really, really great in your organization and 344 00:15:42,799 --> 00:15:45,600 you know what what their background is, the conversation 345 00:15:45,600 --> 00:15:48,320 that you have with the hiring manager in your intake meeting. 346 00:15:48,559 --> 00:15:52,000 Okay, now we've now we've included a lot more context. 347 00:15:52,240 --> 00:15:55,919 And whether it's a human or AI, they're gonna do a lot better 348 00:15:56,080 --> 00:15:59,600 because now they have more context to conduct uh a better 349 00:15:59,600 --> 00:16:02,159 interview and to facilitate a better match. 350 00:16:02,399 --> 00:16:07,200 And so I think that's it, is you have to you if you're going to 351 00:16:07,200 --> 00:16:12,240 use AI as a layer to try to speed up things that humans you 352 00:16:12,240 --> 00:16:16,080 know have been doing or could be doing, the the underlying 353 00:16:16,080 --> 00:16:19,360 context that they rely on is super important. 354 00:16:19,600 --> 00:16:22,080 But the catch is that is no different. 355 00:16:22,639 --> 00:16:25,440 Whether it's a robot doing it or if it's a human doing it, 356 00:16:25,600 --> 00:16:28,000 everyone performs better with more context. 357 00:16:28,240 --> 00:16:29,600 Yeah, I'll pause there. 358 00:16:30,000 --> 00:16:33,679 SPEAKER_02: Well, and I have a few different ways I want to 359 00:16:33,679 --> 00:16:35,679 kind of crystallize this, Mark. 360 00:16:35,840 --> 00:16:39,600 So, first, goals a better match, you know, we're talking about AI 361 00:16:39,600 --> 00:16:41,759 interviews in you know, in particular, and like, you know, 362 00:16:41,840 --> 00:16:44,799 I would argue that that's you know, by some definition, a new 363 00:16:44,799 --> 00:16:46,720 category of solutions, right? 364 00:16:46,960 --> 00:16:50,639 And I would also say that like, you know, the way we're thinking 365 00:16:50,639 --> 00:16:53,919 about our uh Telity marketplace, and we'll maybe talk about this 366 00:16:53,919 --> 00:16:57,120 a little bit, is you know, really shouldn't be focused on 367 00:16:57,120 --> 00:17:00,559 what category does something live in, but what problems are 368 00:17:00,559 --> 00:17:01,440 we trying to solve? 369 00:17:01,600 --> 00:17:06,079 And like, you know, hearing you describe feeding in all these 370 00:17:06,079 --> 00:17:08,480 additional contextual points, you know, your brand 371 00:17:08,480 --> 00:17:11,599 differentiators, your handbook, you know, your green breast 372 00:17:11,680 --> 00:17:16,160 practices and so on, like, you know, it almost feels like AI 373 00:17:16,160 --> 00:17:20,559 interviews, you know, kind of blend in with assessments. 374 00:17:20,720 --> 00:17:24,000 And, you know, maybe it's uh it's just a new type or a new 375 00:17:24,000 --> 00:17:26,079 form of candidate assessments. 376 00:17:26,160 --> 00:17:30,880 And like, hey, would you feel different if you're a candidate 377 00:17:31,039 --> 00:17:33,839 and you're going through the interview process and like, hey, 378 00:17:33,920 --> 00:17:37,200 it's not an AI interview necessarily, but like, hey, like 379 00:17:37,279 --> 00:17:40,319 this is our AI assisted uh, you know, candidate assessment. 380 00:17:40,400 --> 00:17:42,079 It's part of our process. 381 00:17:42,319 --> 00:17:46,880 Like, boy, like, does that make like are we, you know, so are we 382 00:17:46,880 --> 00:17:50,160 thinking about you know this in a way that is easy, you know, 383 00:17:50,240 --> 00:17:53,599 that is maybe just you know, almost off-putting for 384 00:17:53,599 --> 00:17:57,759 candidates or anyone when you hear, hey, it's AI, everyone 385 00:17:57,759 --> 00:17:58,880 shudders a little bit. 386 00:17:59,039 --> 00:18:02,319 But you know, arguably, like, we're not that far off from 387 00:18:02,559 --> 00:18:04,960 having this just be a new way to assess candidates. 388 00:18:05,119 --> 00:18:07,680 Is it just assessments under a different sort of cloak? 389 00:18:08,000 --> 00:18:10,559 SPEAKER_01: Yeah, I think it's probably off-putting if you call 390 00:18:10,559 --> 00:18:13,200 it an interview, and it's less off-putting if you call it an 391 00:18:13,200 --> 00:18:16,160 assessment, um, is the truth. 392 00:18:16,400 --> 00:18:19,440 Um, if I were trying to communicate this to a candidate, 393 00:18:19,519 --> 00:18:23,039 if I'm an employer, the way I would describe it is we get a 394 00:18:23,039 --> 00:18:24,240 lot of job applications. 395 00:18:24,400 --> 00:18:28,240 And while we would want to spend time with every single one, 396 00:18:28,480 --> 00:18:30,400 that's just not possible. 397 00:18:30,559 --> 00:18:34,400 However, we do believe in allowing all of our candidates 398 00:18:34,720 --> 00:18:38,799 to share their full story, you know, to get beyond just the 399 00:18:38,799 --> 00:18:42,799 resume, so that way we can have more information to see if 400 00:18:42,799 --> 00:18:45,359 you're a good match at our organization or elsewhere. 401 00:18:45,519 --> 00:18:48,559 You know, so we're using this tool that, you know, to allow 402 00:18:48,559 --> 00:18:51,599 you to put your best foot forward in a way that a uh a 403 00:18:51,599 --> 00:18:54,480 sterile application or resume can't, right? 404 00:18:54,640 --> 00:18:57,759 So that's you know, that's that's how I would convey it to 405 00:18:57,839 --> 00:18:59,119 uh to a candidate. 406 00:18:59,279 --> 00:19:01,359 Now, on the employer side, I think you're right. 407 00:19:01,440 --> 00:19:03,920 You know, we you you think of it as like the initial phone 408 00:19:03,920 --> 00:19:06,480 screen, you know, is really what it's it's kind of doing. 409 00:19:06,880 --> 00:19:11,920 But these things do have the capability to essentially spin 410 00:19:11,920 --> 00:19:15,920 up assessments, you know, type questions, you know, because 411 00:19:15,920 --> 00:19:18,480 they can be behavioral questions and then they, you know, they 412 00:19:18,480 --> 00:19:20,400 can be hard skill type questions. 413 00:19:20,640 --> 00:19:24,000 So, you know, what we consider to be like, oh, you reach this 414 00:19:24,000 --> 00:19:26,160 stage and now this assessment gets triggered. 415 00:19:26,319 --> 00:19:30,240 I do wonder if this is kind of a merging of two stages in the 416 00:19:30,240 --> 00:19:33,279 workflow, and it just to help speed up the process and get to 417 00:19:33,279 --> 00:19:34,880 point B a bit quicker. 418 00:19:35,039 --> 00:19:38,240 And I do wonder too, if you have an assessment that you like, you 419 00:19:38,240 --> 00:19:40,640 know, how how can you marry those things so that assessment 420 00:19:40,640 --> 00:19:43,440 gets completed and more of this conversational AI experience 421 00:19:43,599 --> 00:19:46,720 versus, hey, go through and you know, type out the information 422 00:19:46,720 --> 00:19:49,279 and check the box and you know, et cetera. 423 00:19:49,440 --> 00:19:52,400 Because, you know, if you can get all that done in one fell 424 00:19:52,400 --> 00:19:55,440 swoop, then maybe that's the maybe that's the answer uh 425 00:19:55,519 --> 00:19:55,839 there. 426 00:19:55,920 --> 00:19:58,160 And but then you also want to think about consistency. 427 00:19:58,319 --> 00:20:01,119 So do you, you know, you probably don't want AI just 428 00:20:01,119 --> 00:20:06,240 making up ad hoc assessments by you know by the position and you 429 00:20:06,240 --> 00:20:08,480 know, whatever the hiring manager had to say, you know, 430 00:20:08,640 --> 00:20:11,519 per se, you know, you I don't want to necessarily get into it, 431 00:20:11,599 --> 00:20:14,160 but you might be opening a can of worms with compliance and 432 00:20:14,319 --> 00:20:16,559 yeah, and just following a standardized procedure there. 433 00:20:16,640 --> 00:20:17,920 But but yeah, I think you're right. 434 00:20:18,000 --> 00:20:20,799 I think I think there could be a convergence of what we're 435 00:20:20,799 --> 00:20:23,680 calling AI interviewing with actually we're talking about 436 00:20:23,680 --> 00:20:24,400 assessments. 437 00:20:24,720 --> 00:20:27,039 SPEAKER_02: Oh, and I also think like, you know, when we think 438 00:20:27,039 --> 00:20:30,240 about where we're finding budget, you know, sure, AI 439 00:20:30,240 --> 00:20:32,960 interviews like it's taken, you know, we're saving headcount in 440 00:20:32,960 --> 00:20:33,759 theory, right? 441 00:20:33,920 --> 00:20:36,880 But like, boy, there's a lot of dollars in assessments too. 442 00:20:37,119 --> 00:20:40,000 And, you know, everyone's always trying to figure out where the 443 00:20:40,000 --> 00:20:41,119 money's gonna come from. 444 00:20:41,359 --> 00:20:44,240 And I wouldn't gloss over the fact that like, boy, there's 445 00:20:44,240 --> 00:20:47,119 gonna be some you know definite overlap opportunities with these 446 00:20:47,119 --> 00:20:50,640 AI interview tools, you know, and assessments and where, you 447 00:20:50,640 --> 00:20:53,119 know, uh everything's in the spirit of giving the better can 448 00:20:53,440 --> 00:20:57,519 the right candidates a chance to you know better align themselves 449 00:20:57,519 --> 00:20:59,599 with opportunities that they're applying to, right? 450 00:21:00,000 --> 00:21:02,799 Well, you know, I want I do want to talk a little bit about a few 451 00:21:02,799 --> 00:21:04,400 things, you know, in the marketplace. 452 00:21:04,480 --> 00:21:06,880 I but I before we get there, I'm gonna ask one more question 453 00:21:06,960 --> 00:21:07,599 though, Mark. 454 00:21:07,759 --> 00:21:13,119 So when we talk about all these tools at HR Tech, you know, I 455 00:21:13,119 --> 00:21:15,359 think you you brought up an interesting point. 456 00:21:15,599 --> 00:21:19,039 And I'm gonna paraphrase it and I'm gonna let you explain it. 457 00:21:19,200 --> 00:21:22,640 But when we think about differentiators, you know, you 458 00:21:22,640 --> 00:21:24,799 see, we see a lot of data providers. 459 00:21:24,960 --> 00:21:29,279 Talk about the data providers at HR Tech and what you're seeing 460 00:21:29,279 --> 00:21:33,839 with overlap or maybe you know, sharing um or you know, where 461 00:21:33,839 --> 00:21:36,480 people are getting their data from to build some of these 462 00:21:36,480 --> 00:21:36,880 tools. 463 00:21:37,039 --> 00:21:39,759 You know, and I know that's kind of a leading question, but um, 464 00:21:39,920 --> 00:21:42,720 you know, give me um give me your give me your thoughts, you 465 00:21:42,720 --> 00:21:45,039 know, when it talks to where people are getting their data 466 00:21:45,039 --> 00:21:48,160 from and how we should be thinking about that as TA 467 00:21:48,160 --> 00:21:49,039 practitioners. 468 00:21:49,519 --> 00:21:49,920 SPEAKER_01: Sure. 469 00:21:50,160 --> 00:21:53,440 Okay, so I'm gonna put this in a few different categories. 470 00:21:53,519 --> 00:21:57,839 And one I'm gonna call the candidate sourcing slash profile 471 00:21:57,839 --> 00:21:59,519 search uh tools. 472 00:21:59,759 --> 00:22:03,599 And you know, for the sake of this, I'm gonna avoid naming any 473 00:22:03,599 --> 00:22:05,680 specific providers or vendors. 474 00:22:05,759 --> 00:22:08,720 Um, but you know, I can describe the category. 475 00:22:08,880 --> 00:22:11,440 Like these would be tools that recruiters log into. 476 00:22:11,759 --> 00:22:14,240 Uh they're trying to search for candidates. 477 00:22:14,400 --> 00:22:16,559 There's there's gonna be you know a certain amount of 478 00:22:16,559 --> 00:22:21,359 matching features, and these tools are now adding um agentic 479 00:22:21,440 --> 00:22:21,759 AI. 480 00:22:21,839 --> 00:22:24,720 So if your recruiters don't want to use it, well, we'll have an 481 00:22:24,720 --> 00:22:27,759 AI use it and search for the candidates and message them and 482 00:22:27,759 --> 00:22:29,039 get them to apply, et cetera. 483 00:22:29,440 --> 00:22:34,880 These are basically a set of tools that are like um we'll 484 00:22:34,880 --> 00:22:37,920 call them LinkedIn competitors, or hey, you know, you're 485 00:22:37,920 --> 00:22:41,599 spending a lot of money over on LinkedIn, you know, here's a you 486 00:22:41,680 --> 00:22:44,319 maybe you could find the same candidates and others, you know, 487 00:22:44,400 --> 00:22:45,039 in this tool. 488 00:22:45,279 --> 00:22:48,480 I guess the breakdown like for the data question, well, where 489 00:22:48,480 --> 00:22:50,559 does that data come from in these tools? 490 00:22:50,880 --> 00:22:54,960 And the answer is most of them are LinkedIn profiles. 491 00:22:55,279 --> 00:22:57,599 Um like they are. 492 00:22:57,759 --> 00:23:00,400 Like that's that's like here's a person that does this job. 493 00:23:00,559 --> 00:23:04,960 Now that is supplemented with data behind the scenes. 494 00:23:05,039 --> 00:23:08,400 And there's probably about a handful, and really a couple 495 00:23:08,480 --> 00:23:10,559 that power the data behind the scenes. 496 00:23:10,640 --> 00:23:13,279 And this is where they're gonna bring in the phone number and 497 00:23:13,279 --> 00:23:16,480 the personal email address and maybe some other context bits 498 00:23:16,480 --> 00:23:18,319 that they've left on you know other websites. 499 00:23:18,559 --> 00:23:21,119 This is where you'll get like GitHub also thrown in there for 500 00:23:21,119 --> 00:23:21,839 the tech positions. 501 00:23:21,920 --> 00:23:24,400 That's why they always demo a Java developer because you got 502 00:23:24,400 --> 00:23:27,519 the hard skills and you get stuff you know over uh from 503 00:23:27,519 --> 00:23:28,160 GitHub. 504 00:23:28,319 --> 00:23:30,559 Now, these tools can be super valuable and they all have their 505 00:23:30,559 --> 00:23:33,759 own way and workflows and user interfaces that I think are are 506 00:23:33,759 --> 00:23:38,880 really slick, but like they can't they can they can't invent 507 00:23:38,880 --> 00:23:39,440 candidates. 508 00:23:39,519 --> 00:23:42,880 They don't have proprietor proprietary databases where 509 00:23:42,880 --> 00:23:44,480 candidates have uploaded their information. 510 00:23:44,640 --> 00:23:45,599 They're scraping. 511 00:23:45,839 --> 00:23:48,640 And you know, like it or not, the biggest source of that 512 00:23:48,640 --> 00:23:51,519 scrape data is LinkedIn, and then it's enriched with these 513 00:23:51,519 --> 00:23:52,559 other data sources. 514 00:23:52,880 --> 00:23:56,880 So I guess that's that's one, but like, you know, they can put 515 00:23:56,880 --> 00:23:59,920 whatever hundred hundreds of millions, a billion candidates 516 00:23:59,920 --> 00:24:02,240 on their on their pitch decks and these sorts of things. 517 00:24:02,480 --> 00:24:03,519 It's really the same. 518 00:24:03,599 --> 00:24:07,599 It's the same candidate pools, enriched with similar data sets. 519 00:24:07,759 --> 00:24:11,039 What's going to make those solutions different is the user 520 00:24:11,039 --> 00:24:13,839 interface, the different communication tools, and like 521 00:24:13,920 --> 00:24:16,240 you know, doesn't plug in with your existing workflow. 522 00:24:16,559 --> 00:24:19,599 So that's one, but I'm wondering if you were thinking of other 523 00:24:19,599 --> 00:24:20,720 types of data providers. 524 00:24:21,039 --> 00:24:23,200 SPEAKER_02: Oh no, I think that's I think that's exactly 525 00:24:23,200 --> 00:24:23,440 it. 526 00:24:23,519 --> 00:24:27,119 Like, you know, everyone talks about you know the billions of 527 00:24:27,119 --> 00:24:29,119 different data sources that they're pulling things from. 528 00:24:29,200 --> 00:24:31,359 And I think, you know, if we're being honest, it's you know, 529 00:24:31,440 --> 00:24:33,599 everyone's fishing from the same ocean, right? 530 00:24:33,839 --> 00:24:38,000 And you know, I think uh I think cunliness is one piece, but I I 531 00:24:38,079 --> 00:24:40,400 I think that there's a lot of overlap that we don't really 532 00:24:40,400 --> 00:24:41,680 talk about in this space. 533 00:24:42,079 --> 00:24:42,400 SPEAKER_01: Yeah. 534 00:24:42,640 --> 00:24:45,519 There is another category data provider I do want to highlight. 535 00:24:45,759 --> 00:24:49,440 And these this is what I would call going back to the context, 536 00:24:49,759 --> 00:24:54,160 one of the foundational layers of data that people talk about 537 00:24:54,319 --> 00:24:58,559 but don't seem to execute on a lot, or at least they they 538 00:24:58,559 --> 00:25:01,759 understand the concept, but they're not really understand 539 00:25:01,759 --> 00:25:02,640 what that means. 540 00:25:02,799 --> 00:25:05,359 And it has to do with skills taxonomy. 541 00:25:05,839 --> 00:25:10,160 So going through the exercise of really understanding your 542 00:25:10,160 --> 00:25:13,039 organization and the roles within it, and this, not not 543 00:25:13,039 --> 00:25:16,319 just the job title and the job descriptions, but what are the 544 00:25:16,319 --> 00:25:18,960 actual skills needed to complete this work? 545 00:25:19,119 --> 00:25:22,240 And where does your organization want to go and what skills are 546 00:25:22,240 --> 00:25:23,200 needed for that? 547 00:25:23,519 --> 00:25:27,680 Well, you have your big HCMs like a workday or SAP, and 548 00:25:27,680 --> 00:25:32,079 there's there's areas there for skill data, uh, but it's not 549 00:25:32,079 --> 00:25:33,519 populated like out of the box. 550 00:25:33,599 --> 00:25:35,039 Like that's something you got to go do. 551 00:25:35,200 --> 00:25:37,599 And you can kind of like brainstorm and think and start 552 00:25:37,599 --> 00:25:40,799 typing in skills there, but there are data providers that 553 00:25:40,799 --> 00:25:44,799 provide this framework of skills taxonomy, which so essentially 554 00:25:44,960 --> 00:25:48,400 like think about like really completing your work, your 555 00:25:48,400 --> 00:25:52,480 internal workforce data with skills, and then that informing, 556 00:25:52,720 --> 00:25:58,240 i.e., context, of okay, talent mobility, who do we need to go 557 00:25:58,240 --> 00:25:59,279 get from outside? 558 00:25:59,440 --> 00:26:01,119 You know, what are the skills necessary? 559 00:26:01,200 --> 00:26:05,039 And now that's gonna facilitate better matching and queries like 560 00:26:05,119 --> 00:26:07,839 on those whatever technology you have layered on top of that. 561 00:26:08,079 --> 00:26:12,160 So I would add that in the context bucket, and these 562 00:26:12,160 --> 00:26:15,920 skills, skill type data providers can provide that 563 00:26:15,920 --> 00:26:18,960 enrichment so everything else performs better. 564 00:26:19,279 --> 00:26:22,240 SPEAKER_02: Yeah, I think we're gonna see quite a shift in um 565 00:26:22,240 --> 00:26:25,599 skills demand over the next you know year to decade. 566 00:26:25,759 --> 00:26:29,279 You know, we talk about uh, you know, the example I forever give 567 00:26:29,359 --> 00:26:32,160 as a, you know, when I went to IU, it was you got to get an 568 00:26:32,160 --> 00:26:33,039 informatics degree. 569 00:26:33,119 --> 00:26:34,160 That's what my mom said. 570 00:26:34,319 --> 00:26:37,440 No one knew what it was, but like, hey, it was had to do with 571 00:26:37,440 --> 00:26:40,880 computers, maybe, you know, and and and obviously I didn't get 572 00:26:40,880 --> 00:26:42,799 an informatics degree, and here we are. 573 00:26:42,960 --> 00:26:45,920 Um but hey, 10 years later, what was the number one degree people 574 00:26:45,920 --> 00:26:47,039 were looking to hire for? 575 00:26:47,279 --> 00:26:48,960 People with informatics degrees. 576 00:26:49,119 --> 00:26:51,759 You know, then like, hey, last year it was prompt engineers. 577 00:26:51,920 --> 00:26:54,319 You know, I think you know, the interesting piece you know that 578 00:26:54,319 --> 00:26:57,359 I'm following is you know, I think we saw people putting a 579 00:26:57,359 --> 00:27:00,319 microphone in front of a lot of folks over at HR Tech saying, 580 00:27:00,400 --> 00:27:02,799 hey, what skills do you think are gonna be most in demand in 581 00:27:02,799 --> 00:27:04,799 the next, you know, next five years? 582 00:27:04,960 --> 00:27:09,200 And my answer is probably contrarian, and it's uh reading 583 00:27:09,200 --> 00:27:11,759 and writing, critical thing, being able to critically think, 584 00:27:11,920 --> 00:27:12,880 being able to speak. 585 00:27:13,039 --> 00:27:15,920 I that is those are the skills that are gonna be in demand. 586 00:27:16,079 --> 00:27:19,839 You know, everyone can you know drop an article into Chat GPT 587 00:27:19,839 --> 00:27:23,440 and say, hey, turn this into a LinkedIn post. 588 00:27:23,599 --> 00:27:26,880 And like uh, you know, the old adage is hey, I I know what it 589 00:27:26,880 --> 00:27:27,920 is when I see it. 590 00:27:28,079 --> 00:27:30,319 You know, hey, I I I know what it is. 591 00:27:30,480 --> 00:27:33,200 Um, you know, human wrote it, you know, versus if it was 592 00:27:33,200 --> 00:27:36,240 written by you know ChatGPT and just popped out. 593 00:27:36,319 --> 00:27:39,680 And I think you know, we're gonna see a pretty big increase 594 00:27:39,680 --> 00:27:43,200 in demand for critical thinking, you know, reading, or well, 595 00:27:43,279 --> 00:27:45,839 maybe reading is not the right one, but writing and speaking. 596 00:27:46,000 --> 00:27:48,559 Anyway, that's a that's a rabbit hole we don't need to go down 597 00:27:48,559 --> 00:27:49,200 today. 598 00:27:49,440 --> 00:27:50,640 Um, probably. 599 00:27:50,880 --> 00:27:51,119 All right. 600 00:27:51,200 --> 00:27:54,240 So coming out, you know, coming out of HRTAC, you know, a lot of 601 00:27:54,240 --> 00:27:58,799 pressure for uh from clients from executive leadership 602 00:27:58,880 --> 00:28:01,119 saying, hey, I got this great report from Gardner. 603 00:28:01,279 --> 00:28:03,440 It says we need to do something with AI. 604 00:28:03,920 --> 00:28:08,400 And I would say it's a pretty overwhelming landscape. 605 00:28:08,559 --> 00:28:12,400 You know, how do you think about helping, how do you think about 606 00:28:12,400 --> 00:28:14,799 evaluating maybe AI solutions first? 607 00:28:14,880 --> 00:28:17,920 And you know, if you're a if you're a buyer, um, you know, 608 00:28:18,079 --> 00:28:21,359 what are some of the questions that you know you ask that kind 609 00:28:21,359 --> 00:28:23,440 of cuts through the marketing speak, Mark? 610 00:28:23,599 --> 00:28:26,079 You know, helping a how how would you help a buyer thinking 611 00:28:26,079 --> 00:28:29,759 about you know workflow problems rather than you know technology 612 00:28:29,759 --> 00:28:31,039 features or benefits? 613 00:28:31,440 --> 00:28:36,000 SPEAKER_01: Yeah, I guess first first I would say don't pay 614 00:28:36,000 --> 00:28:37,359 attention to the Joneses. 615 00:28:38,559 --> 00:28:42,960 I do think there's an element of that is part of the marketing 616 00:28:42,960 --> 00:28:46,480 rush is to make everyone feel like they're not participating 617 00:28:46,480 --> 00:28:47,759 in some sort of party. 618 00:28:47,920 --> 00:28:50,799 And if you don't do this, you're you're gonna get left behind. 619 00:28:51,119 --> 00:28:55,359 I you know, like yes, you need to be evaluating at all times 620 00:28:55,359 --> 00:28:58,880 how you can um improve your process and be efficient and 621 00:28:58,880 --> 00:29:02,000 learn about new tools and leverage points, you know, to 622 00:29:02,000 --> 00:29:02,640 apply to your business. 623 00:29:02,720 --> 00:29:04,880 So I'm not I'm not saying like just put your head in the sand 624 00:29:04,880 --> 00:29:08,079 and pretend like this isn't one of the most amazing times in 625 00:29:08,079 --> 00:29:10,000 technology innovation because it is. 626 00:29:10,400 --> 00:29:14,240 But what I'm what I am saying is is like, well, I heard so-and-so 627 00:29:14,400 --> 00:29:17,519 is using this you know, piece of tech or that piece of tech. 628 00:29:17,759 --> 00:29:20,559 You know, we need to do that too, or you get pressure from 629 00:29:20,559 --> 00:29:23,680 your executive team, like, we need to use AI. 630 00:29:23,920 --> 00:29:27,440 And what's absent of a lot of these conversations, and we get 631 00:29:27,440 --> 00:29:32,400 inquiries like this all the time, is it it's like they're 632 00:29:32,400 --> 00:29:36,079 vaguely in an area, you know, they've been dropped in this 633 00:29:36,079 --> 00:29:39,039 territory, but they don't know where to go. 634 00:29:39,279 --> 00:29:43,759 Like they don't know is it is it west, is it north, is it east, 635 00:29:43,920 --> 00:29:47,039 you know, is it through you know this door behind that tree? 636 00:29:47,279 --> 00:29:51,200 So um and then but the reason is is they haven't they haven't 637 00:29:51,200 --> 00:29:54,400 slowed down and just thought you go back to the critical 638 00:29:54,400 --> 00:29:54,799 thinking. 639 00:29:55,039 --> 00:29:56,319 What is the problem? 640 00:29:56,880 --> 00:29:58,880 What problem are you trying to solve? 641 00:29:59,119 --> 00:30:03,920 And so I guess now I'll I'll get into like what's the framework 642 00:30:03,920 --> 00:30:07,359 that I've been using and uh I I think will be really helpful of 643 00:30:07,440 --> 00:30:10,160 of helping TA leaders think through this, but also helping 644 00:30:10,160 --> 00:30:13,759 partners, you know, vendor solutions clarify, you know, 645 00:30:13,920 --> 00:30:15,759 their pitch and thinking. 646 00:30:16,079 --> 00:30:19,200 But um I can't take credit for this, and if we have time, I'll 647 00:30:19,200 --> 00:30:20,480 tell you the story where it came from. 648 00:30:20,720 --> 00:30:22,960 But there's this is called the five questions. 649 00:30:23,200 --> 00:30:26,480 Um I've added a six because I think it's important in this day 650 00:30:26,480 --> 00:30:26,720 and age. 651 00:30:27,039 --> 00:30:28,799 SPEAKER_02: It's not to your framework now, Mark. 652 00:30:28,960 --> 00:30:29,680 See, it's six. 653 00:30:29,839 --> 00:30:30,640 It's six questions. 654 00:30:30,799 --> 00:30:31,599 So look at that. 655 00:30:31,759 --> 00:30:33,440 So now it's the Thomas Tino method. 656 00:30:33,519 --> 00:30:33,839 There we go. 657 00:30:34,000 --> 00:30:35,039 SPEAKER_01: Oh, great, perfect. 658 00:30:35,200 --> 00:30:36,960 Um, no, I can't take credit for this. 659 00:30:37,119 --> 00:30:41,200 Um, but uh or you know, I will say I use it and I have refined 660 00:30:41,200 --> 00:30:41,920 it over the years. 661 00:30:42,160 --> 00:30:44,559 So the first thing is what is the problem? 662 00:30:44,960 --> 00:30:48,000 And then I would add it, I'd add a kind of an addendum to that. 663 00:30:48,079 --> 00:30:50,240 And what is the impact of that problem? 664 00:30:50,559 --> 00:30:54,559 Because if you're in a business, every business has a lot of 665 00:30:54,559 --> 00:30:55,119 problems. 666 00:30:55,440 --> 00:30:58,160 But you have to quantify that problem, and and that way you 667 00:30:58,160 --> 00:31:00,400 can prioritize which problems you're trying to solve. 668 00:31:00,720 --> 00:31:05,920 Number two is why is what I'm doing today not working? 669 00:31:06,319 --> 00:31:08,400 What is it about your current situation? 670 00:31:08,640 --> 00:31:11,119 Maybe it's your current tech stack, maybe it's your current 671 00:31:11,119 --> 00:31:13,759 process, maybe it's the people you have or the roles and 672 00:31:13,759 --> 00:31:17,279 responsibilities you've divvied up, but like why is what I'm 673 00:31:17,279 --> 00:31:18,240 doing today not working? 674 00:31:18,400 --> 00:31:20,240 And that's where you get to the root cause. 675 00:31:20,400 --> 00:31:22,400 So really it's like the first one is what's my problem? 676 00:31:22,480 --> 00:31:25,119 Like, what are the symptoms and how bad are they? 677 00:31:25,440 --> 00:31:28,160 Two is why am I experiencing those symptoms? 678 00:31:28,319 --> 00:31:31,680 And if you can't get clear on one and two, do not pass go. 679 00:31:32,079 --> 00:31:34,079 Because then you're gonna end up talking with a bunch of 680 00:31:34,079 --> 00:31:36,720 solutions and they're gonna put all sorts of ideas in your head 681 00:31:36,720 --> 00:31:38,559 of what your problem may or may not be. 682 00:31:38,720 --> 00:31:42,400 And if you are, you know, end up buying one that's not actually 683 00:31:42,400 --> 00:31:45,680 mapped to the root cause or the problem, you're gonna burn a lot 684 00:31:45,680 --> 00:31:49,200 of capital, a lot of time, and a whole year business cycle before 685 00:31:49,200 --> 00:31:50,400 you even realize that. 686 00:31:50,720 --> 00:31:53,920 So, like that is the critical point is the one and two. 687 00:31:54,079 --> 00:31:56,720 What's my problem and why is what I'm doing today not 688 00:31:56,720 --> 00:31:57,039 working? 689 00:31:57,519 --> 00:31:57,839 Okay. 690 00:31:58,559 --> 00:32:02,160 From there, now you can go and look for solutions. 691 00:32:02,640 --> 00:32:06,640 And um, you can make sure that if you're talking to a solution, 692 00:32:06,799 --> 00:32:10,720 that they match your problem situation, they match your one 693 00:32:10,720 --> 00:32:11,359 and two. 694 00:32:11,759 --> 00:32:14,880 But three, what will you help me do differently? 695 00:32:15,119 --> 00:32:17,279 And this is where you get to differentiators. 696 00:32:17,839 --> 00:32:18,960 A lot of solutions. 697 00:32:19,200 --> 00:32:20,720 Okay, you say you can solve my problem. 698 00:32:20,880 --> 00:32:22,640 What are you gonna help me do differently than I'm doing 699 00:32:22,640 --> 00:32:22,880 today? 700 00:32:22,960 --> 00:32:25,519 And how does that compare to other solutions that are in the 701 00:32:25,519 --> 00:32:25,839 market? 702 00:32:26,559 --> 00:32:30,960 Number four, why should I believe you if you're talking to 703 00:32:30,960 --> 00:32:31,599 a solution? 704 00:32:31,839 --> 00:32:34,960 And you know, that can sound a little crass maybe, but really 705 00:32:35,039 --> 00:32:38,000 what you're asking is like, what's the believability here? 706 00:32:38,240 --> 00:32:41,759 Are you somebody who just started and I'm customer number 707 00:32:41,759 --> 00:32:42,000 one? 708 00:32:42,240 --> 00:32:43,920 Have you been in business for a while? 709 00:32:44,079 --> 00:32:45,039 Where have you had success? 710 00:32:45,200 --> 00:32:46,960 Do you have case studies, testimonials? 711 00:32:47,119 --> 00:32:48,960 Do you have reviews that aren't just paid for? 712 00:32:49,119 --> 00:32:50,400 Like, can I talk to somebody? 713 00:32:50,480 --> 00:32:51,839 Like you have a referral. 714 00:32:52,079 --> 00:32:54,240 So that's why, why should I believe you? 715 00:32:54,319 --> 00:32:56,319 That's your trust and credibility if you're a solution 716 00:32:56,319 --> 00:33:01,359 provider, and that's your BS detector if you are a buyer of a 717 00:33:01,359 --> 00:33:02,079 solution. 718 00:33:02,480 --> 00:33:07,119 Number five, what are the costs, ROI, and roll-up planning? 719 00:33:07,279 --> 00:33:08,799 So, what's the pricing model here? 720 00:33:08,880 --> 00:33:12,559 You know, is it per user, per module, is it subscription, 721 00:33:12,720 --> 00:33:13,920 annual, you know, et cetera. 722 00:33:14,079 --> 00:33:15,200 What's the ROI? 723 00:33:15,359 --> 00:33:16,880 This goes back to problem. 724 00:33:17,039 --> 00:33:20,400 The question number one what is the impact of this problem 725 00:33:20,400 --> 00:33:21,519 having on my business? 726 00:33:21,759 --> 00:33:27,200 If you can't connect the ROI of that solution to your original 727 00:33:27,200 --> 00:33:30,480 problem statement and the impact, like that that step is 728 00:33:30,480 --> 00:33:32,960 often missed, but that's how you get stuff done internally. 729 00:33:33,119 --> 00:33:35,599 That's how you're gonna sell it to the board or to your your 730 00:33:35,599 --> 00:33:38,640 CHRO or who's ever making the final decision on that. 731 00:33:38,720 --> 00:33:42,160 You have to be able to show the ROI as it relates to your 732 00:33:42,160 --> 00:33:42,400 problem. 733 00:33:42,559 --> 00:33:43,839 And then rollout plan. 734 00:33:43,920 --> 00:33:46,480 Is this like going live by the end of the week? 735 00:33:46,799 --> 00:33:48,319 Is this the three, four months? 736 00:33:48,559 --> 00:33:50,160 Do I need to bring in a project manager? 737 00:33:50,319 --> 00:33:51,680 Do you provide a project manager? 738 00:33:51,839 --> 00:33:53,039 What happens after I'm live? 739 00:33:53,200 --> 00:33:56,079 What sort of ongoing support, training, customer success, and 740 00:33:56,079 --> 00:33:57,039 those sorts of things? 741 00:33:57,279 --> 00:34:00,799 And those things can mitigate your risk of like, hey, it was 742 00:34:00,799 --> 00:34:02,640 the right solution, wasn't stood upright. 743 00:34:02,799 --> 00:34:05,440 It was the right solution, but man, that support, you know, 744 00:34:05,519 --> 00:34:08,000 it's just not where we needed, or we didn't have the ongoing 745 00:34:08,000 --> 00:34:08,480 training. 746 00:34:08,800 --> 00:34:09,280 All right. 747 00:34:09,599 --> 00:34:14,960 So that leads to number six is what are my risks and how can I 748 00:34:14,960 --> 00:34:15,840 mitigate them? 749 00:34:16,239 --> 00:34:18,880 And some of that is getting the rollout plan right. 750 00:34:19,280 --> 00:34:21,519 But the other ones, this is gonna have to do with data 751 00:34:21,519 --> 00:34:23,519 security, uh, compliance. 752 00:34:23,679 --> 00:34:26,639 And you know, in the age of AI, like, you know, we need to have 753 00:34:26,639 --> 00:34:30,239 a conversation about hey, what are you opening me up to in 754 00:34:30,239 --> 00:34:34,960 terms of you know bias, emerging law and compliance and those 755 00:34:34,960 --> 00:34:35,599 sorts of things? 756 00:34:36,239 --> 00:34:39,440 So those are I would call now the six questions. 757 00:34:39,760 --> 00:34:44,800 But that's how I talk to partners to evaluate them, to 758 00:34:45,119 --> 00:34:48,559 you know, create this idea of a short list of who we feel like 759 00:34:48,559 --> 00:34:50,719 we can trust to introduce customers to. 760 00:34:50,880 --> 00:34:53,119 And on the flip side, when we're talking with customers, this is 761 00:34:53,119 --> 00:34:56,239 how we can help clarify their thinking about you know what 762 00:34:56,239 --> 00:34:57,440 they're actually trying to solve. 763 00:34:57,679 --> 00:34:58,000 SPEAKER_02: Yeah. 764 00:34:58,079 --> 00:35:01,280 Well, I think uh you and I went through those same uh five 765 00:35:01,280 --> 00:35:05,679 question trainings uh for a well a number a number of years, I'd 766 00:35:05,679 --> 00:35:05,840 say. 767 00:35:05,920 --> 00:35:08,320 And I think that you really has just helped frame you know how 768 00:35:08,320 --> 00:35:10,880 we approach a lot of our problem solving too, which has been 769 00:35:10,880 --> 00:35:11,440 super fun. 770 00:35:11,599 --> 00:35:15,760 So um, okay, well, I know that we're past time. 771 00:35:16,239 --> 00:35:19,920 I don't think we're gonna get into our marketplace, Mark, but 772 00:35:20,000 --> 00:35:23,199 you know, I'm gonna say, you know, I got two questions to 773 00:35:23,199 --> 00:35:23,760 close with. 774 00:35:24,000 --> 00:35:27,679 One, looking forward, so you know, which what's maybe your 775 00:35:27,679 --> 00:35:31,440 most contrarian prediction about talent acquisition technology? 776 00:35:31,599 --> 00:35:35,280 You know, what is what's everyone think will happen that 777 00:35:35,440 --> 00:35:38,480 something that you might be might might strongly disagree 778 00:35:38,480 --> 00:35:38,800 with? 779 00:35:39,280 --> 00:35:44,320 SPEAKER_01: Um so I would say there's there's a lot of fear, 780 00:35:44,480 --> 00:35:45,519 uncertainty. 781 00:35:45,760 --> 00:35:47,920 You know, AI is gonna take all of our jobs. 782 00:35:48,079 --> 00:35:51,360 And um particularly it's like the like the idea that there's 783 00:35:51,360 --> 00:35:55,280 gonna be this superintelligence or AGI that could become 784 00:35:55,280 --> 00:35:58,079 malevolent and want to turn us all into paperclips. 785 00:35:58,239 --> 00:36:01,039 And if we don't, you know, if we don't stop this now or get 786 00:36:01,039 --> 00:36:02,639 alignment, you know, we're all doomed. 787 00:36:02,800 --> 00:36:05,119 I I have strong reasons for believing, you know, that 788 00:36:05,119 --> 00:36:06,079 stuff's not true. 789 00:36:06,559 --> 00:36:10,960 But really, it it boils down to of just having a fundamental 790 00:36:10,960 --> 00:36:14,719 fundamental understanding of what it means to be human and 791 00:36:14,719 --> 00:36:21,599 what humans uniquely do, and what AI or any robots or you 792 00:36:21,599 --> 00:36:25,280 know computer programs, what they are, and what they're meant 793 00:36:25,280 --> 00:36:25,760 to do. 794 00:36:26,000 --> 00:36:29,519 And so I'm not scared of that, and that's probably a separate 795 00:36:29,519 --> 00:36:32,239 podcast of digging in the exact reasons why. 796 00:36:32,400 --> 00:36:35,599 But because I'm not scared of that, I also view that as the 797 00:36:35,599 --> 00:36:37,440 the quintessential opportunity here. 798 00:36:37,679 --> 00:36:41,599 There will be tasks that AI will do, and they will do them 799 00:36:41,599 --> 00:36:44,880 faster, better, cheaper, and at scale that you just can't do as 800 00:36:44,880 --> 00:36:45,519 a human. 801 00:36:45,760 --> 00:36:47,440 And that's a good thing. 802 00:36:47,679 --> 00:36:51,920 There are going to be things that are uniquely human, and 803 00:36:51,920 --> 00:36:56,639 that is the only place where employers, candidates, 804 00:36:57,039 --> 00:37:00,480 businesses of all shapes and sizes, and just being a person 805 00:37:00,480 --> 00:37:03,440 in this world, that's where you differentiate yourself. 806 00:37:03,599 --> 00:37:07,599 And I I feel like there's going to be a counter movement, or, 807 00:37:07,679 --> 00:37:12,800 you know, all the everything will be AI'd, or you know, 808 00:37:12,880 --> 00:37:15,119 there's going to be agents doing all this stuff. 809 00:37:15,360 --> 00:37:19,119 And really where you're going to stand out is um doing the things 810 00:37:19,119 --> 00:37:21,519 that only humans can do, i.e., critical thinking, like you 811 00:37:21,519 --> 00:37:26,000 alluded to before, storytelling, uh, making uh making things that 812 00:37:26,000 --> 00:37:28,639 are compelling and providing a human connection. 813 00:37:28,800 --> 00:37:31,119 And I think that's where you'll see people win. 814 00:37:31,440 --> 00:37:32,800 SPEAKER_02: Yeah, well, I guess great. 815 00:37:32,880 --> 00:37:35,440 Well, yeah, last question, you know, I think you bring some 816 00:37:35,440 --> 00:37:38,559 pretty uh, I think you have a very unique lens, and I know 817 00:37:38,639 --> 00:37:42,079 hey, whatever everyone's reading or listening to kind of drives 818 00:37:42,079 --> 00:37:42,880 how they learn. 819 00:37:43,039 --> 00:37:45,679 So what are you reading or listening to these days, Mark? 820 00:37:45,760 --> 00:37:48,719 And you know, maybe like where do you go when you're trying to 821 00:37:48,719 --> 00:37:52,719 learn or stay ahead of our our landscape or this AI landscape 822 00:37:52,719 --> 00:37:53,199 in general? 823 00:37:53,599 --> 00:37:53,840 SPEAKER_01: Yeah. 824 00:37:54,000 --> 00:37:56,400 Well, given the nature of what I do, I'm plugged into the 825 00:37:56,400 --> 00:37:56,639 industry. 826 00:37:56,719 --> 00:37:58,800 So I'm always talking with industry peers and you know 827 00:37:58,880 --> 00:38:00,639 folks at these different providers. 828 00:38:00,880 --> 00:38:03,840 So that's certainly a source of information that you know it's 829 00:38:04,159 --> 00:38:06,880 not everyone has time to do that because it's not their job. 830 00:38:07,119 --> 00:38:11,119 I would say more broadly is I really, I really love taking 831 00:38:11,119 --> 00:38:12,239 ideas from everywhere. 832 00:38:12,400 --> 00:38:15,840 And so not specifically, you know, industry or you know, 833 00:38:15,920 --> 00:38:19,760 directly, but like, you know, these are you call them your 834 00:38:19,760 --> 00:38:22,880 social media follows, but I I pride myself on finding 835 00:38:22,880 --> 00:38:26,719 independent thinkers who I admire intellectually, and then 836 00:38:26,719 --> 00:38:29,360 I love it when they disagree with each other on whatever 837 00:38:29,360 --> 00:38:33,519 topic it is, because that gives me an opportunity to like weigh 838 00:38:33,519 --> 00:38:36,639 two really good arguments and then decide between those. 839 00:38:36,800 --> 00:38:39,039 And so I guess I don't want to give you a list of people to 840 00:38:39,039 --> 00:38:41,519 follow or anything like that, but I I would urge others, you 841 00:38:41,519 --> 00:38:44,239 know, like you know, if you find yourself in an echo chamber 842 00:38:44,239 --> 00:38:46,960 where everyone seems to be agreeing with them themselves, 843 00:38:47,199 --> 00:38:49,679 step outside of that because there's always smart people on 844 00:38:49,679 --> 00:38:53,199 both sides of an argument, and getting good arguments from two 845 00:38:53,199 --> 00:38:56,960 sides helps you find a better, you know, the more correct 846 00:38:56,960 --> 00:38:58,159 answer ultimately. 847 00:38:58,320 --> 00:39:01,920 And a book I'm reading, and I recommend it to everybody, but 848 00:39:01,920 --> 00:39:04,079 it's you know, it takes a lot to get your brain wrapped around. 849 00:39:04,239 --> 00:39:07,039 It's called The Beginning of Infinity by David Deutsch. 850 00:39:07,519 --> 00:39:09,840 And you might want to start with some podcast interviews. 851 00:39:10,000 --> 00:39:15,679 His last name is spelled uh D-E-U-T-S-C-H, but gives you a 852 00:39:15,679 --> 00:39:18,480 real kind of what I was talking before of giving the foundation 853 00:39:18,480 --> 00:39:21,920 of understanding what it means to be human, what is knowledge, 854 00:39:22,079 --> 00:39:26,159 uh, how do we grow knowledge and you know why we shouldn't be so 855 00:39:26,159 --> 00:39:27,920 worried about the robots taking over. 856 00:39:28,239 --> 00:39:30,880 SPEAKER_02: Yeah, so just light, light reading, you know, for 857 00:39:30,880 --> 00:39:34,400 your for your for your afternoon coffee, or maybe the perfect 858 00:39:34,400 --> 00:39:37,360 book if uh you know you if you're if you can't sleep. 859 00:39:37,679 --> 00:39:41,119 No, David Deutsch is now on my Twitter feed every day, uh since 860 00:39:41,119 --> 00:39:42,719 you and I have been having those conversations. 861 00:39:42,800 --> 00:39:44,159 So fantastic recommendation. 862 00:39:44,239 --> 00:39:47,599 And again, I go back to like you know, the energy at HR Tech is 863 00:39:47,920 --> 00:39:50,880 you know, it it is good that we have people coming in from 864 00:39:50,880 --> 00:39:53,360 outside of the TA space and building products. 865 00:39:53,599 --> 00:39:58,159 And you know, I think sure, adding a new uh widget or 866 00:39:58,159 --> 00:40:02,159 feature to you know to an ATS or HRS is something that you know 867 00:40:02,239 --> 00:40:03,840 these large vendors want to talk about. 868 00:40:03,920 --> 00:40:05,840 But like, you know, I think we're gonna have some really 869 00:40:05,840 --> 00:40:08,800 good ideas come from people who are not, you know, in the weeds 870 00:40:08,960 --> 00:40:09,679 every day, too. 871 00:40:09,840 --> 00:40:12,639 And you know, I love that you know, we are all trying to 872 00:40:12,639 --> 00:40:15,760 leave, you know, to learn from outside of our industry, period. 873 00:40:16,000 --> 00:40:16,239 All right. 874 00:40:16,320 --> 00:40:18,320 Well, I think that's a good place to put a pin in this one, 875 00:40:18,400 --> 00:40:18,639 Mark. 876 00:40:18,800 --> 00:40:22,559 Well, I think our six questions has to turn into a webinar at 877 00:40:22,559 --> 00:40:25,519 some point too, because you know, I I think we both get 878 00:40:25,519 --> 00:40:28,960 energized talking about problem solving and and our approach to 879 00:40:28,960 --> 00:40:29,119 it. 880 00:40:29,199 --> 00:40:32,239 So maybe we'll add that one into the old ticker also. 881 00:40:32,559 --> 00:40:35,599 Well, till next time, we'll link everything in the show notes and 882 00:40:35,599 --> 00:40:36,559 you'll know where to find us. 883 00:40:36,639 --> 00:40:39,199 But yeah, thanks, Mark, as always, for this and the 884 00:40:39,199 --> 00:40:41,920 continual journey we're on with now with Tellivity. 885 00:40:42,000 --> 00:40:42,880 So onward. 886 00:40:43,199 --> 00:40:45,360 SPEAKER_01: Thanks for having me and looking forward to it. 887 00:40:45,440 --> 00:40:46,800 And I'll see you in the next one. 888 00:40:47,039 --> 00:40:48,880 All right, thanks for tuning in. 889 00:40:49,039 --> 00:40:53,119 SPEAKER_02: As always, head on over to changestate.io or shoot 890 00:40:53,119 --> 00:40:55,360 us a note on all the social media. 891 00:40:55,440 --> 00:40:58,960 We'd love to hear from you, and we'll check you guys next week.