1 00:00:03,040 --> 00:00:05,800 Speaker 1: Welcome to the Business of Tech powered by two Degrees Business. 2 00:00:05,800 --> 00:00:08,880 Speaker 1: I'm Peter Griffin, and this week I'm delving into the 3 00:00:09,039 --> 00:00:13,399 Speaker 1: future of work, or more precisely, the future of your 4 00:00:13,520 --> 00:00:17,360 Speaker 1: efforts to get a job, because AI is changing the 5 00:00:17,480 --> 00:00:21,720 Speaker 1: hiring landscape quicker than you probably think. If you've ever 6 00:00:21,760 --> 00:00:25,759 Speaker 1: wondered about how algorithms are re shaping who gets noticed 7 00:00:25,840 --> 00:00:29,400 Speaker 1: in the pile of cvs, or what employers are looking 8 00:00:29,440 --> 00:00:32,800 Speaker 1: for and recruits in the era of AI, you'll want 9 00:00:32,800 --> 00:00:34,840 Speaker 1: to keep listening. I'm joined on a business of tech 10 00:00:34,880 --> 00:00:39,360 Speaker 1: this week by Kara Smith, managing director al tauroa for 11 00:00:39,440 --> 00:00:45,320 Speaker 1: the global recruitment giant Talent, and Jack Jorgensen, general Manager Data, 12 00:00:45,479 --> 00:00:50,000 Speaker 1: AI and Innovation at AVOC, the talent owned tech company 13 00:00:50,040 --> 00:00:52,720 Speaker 1: sort of their sister company that does all the tech 14 00:00:52,920 --> 00:00:57,040 Speaker 1: underpinning their HR and recruitment efforts. Kara told me that 15 00:00:57,160 --> 00:01:01,560 Speaker 1: applications are coming into Talent in record numbers at the moment. 16 00:01:02,240 --> 00:01:05,720 Speaker 1: It's a soft market, so any decent position is attracting 17 00:01:05,959 --> 00:01:11,440 Speaker 1: huge interest across the recruitment industry. AI is scanning mountains 18 00:01:11,480 --> 00:01:16,280 Speaker 1: of applications and filtering candidates before a human ever looks 19 00:01:16,319 --> 00:01:19,119 Speaker 1: at them. So what does that mean for companies trying 20 00:01:19,160 --> 00:01:23,600 Speaker 1: to find real talent amidst the noise? More importantly, what 21 00:01:23,640 --> 00:01:26,120 Speaker 1: can job seekers actually do to stand out from the 22 00:01:26,200 --> 00:01:32,080 Speaker 1: crowd of algorithm optimized cvs. Kara and Jack breakdown why 23 00:01:32,120 --> 00:01:34,680 Speaker 1: it's no longer enough to just hit apply and why 24 00:01:34,920 --> 00:01:39,800 Speaker 1: personal branding and proactive networking matter more than ever whether 25 00:01:39,800 --> 00:01:42,759 Speaker 1: you're hiring, hustling, or just hoping to keep your career 26 00:01:43,160 --> 00:01:47,039 Speaker 1: future proof. Stick around for some really useful advice. Here's 27 00:01:47,080 --> 00:01:55,760 Speaker 1: the interview with Carra Smith and Jack Jordansen. Cara and Jack, 28 00:01:55,840 --> 00:01:57,040 Speaker 1: Welcome to the Business of Tech. 29 00:01:57,080 --> 00:02:00,640 Speaker 2: How are you doing? Very good? Thank you whatever, very good, 30 00:02:00,680 --> 00:02:01,120 Speaker 2: good morning. 31 00:02:01,640 --> 00:02:05,160 Speaker 1: Yeah, you're an Australia Jack coming in from Melbourne, so 32 00:02:05,200 --> 00:02:08,440 Speaker 1: thanks for taking the time. Now you're from Avoc, which 33 00:02:08,480 --> 00:02:11,960 Speaker 1: is a sister company to Talent, the big recruitment company. 34 00:02:12,200 --> 00:02:14,880 Speaker 3: Yeah that's right. Yeah, we work with Talent on project 35 00:02:14,880 --> 00:02:15,679 Speaker 3: delivery right. 36 00:02:15,800 --> 00:02:18,720 Speaker 1: Okay, So we're going to get into some of the 37 00:02:18,800 --> 00:02:22,720 Speaker 1: technical aspects of recruitment and the buzzword at the moment, 38 00:02:22,720 --> 00:02:25,880 Speaker 1: it really is around artificial intelligence and the impact that 39 00:02:26,040 --> 00:02:29,200 Speaker 1: is having. But Cara, it would be great for you 40 00:02:29,280 --> 00:02:32,000 Speaker 1: to give us an overview. You've been at Talent well 41 00:02:32,000 --> 00:02:34,800 Speaker 1: over a decade now in the recruitment game for at 42 00:02:34,880 --> 00:02:38,360 Speaker 1: least a couple of decades. Keen to get your perspective 43 00:02:38,440 --> 00:02:40,440 Speaker 1: on what is going on out there at the moment 44 00:02:40,520 --> 00:02:43,600 Speaker 1: in the New Zealand market. It has been a tough, 45 00:02:43,919 --> 00:02:49,800 Speaker 1: tough year, but you're seeing more applications than ever before. 46 00:02:50,560 --> 00:02:52,480 Speaker 1: And this is what we're sort of hearing as well 47 00:02:52,520 --> 00:02:55,320 Speaker 1: at Business Desk is that any half decent job that 48 00:02:55,400 --> 00:02:59,720 Speaker 1: goes up is just mobbed with people. So there's a 49 00:02:59,720 --> 00:03:02,400 Speaker 1: lot of people, you know, two hundred people sometimes applying 50 00:03:02,440 --> 00:03:06,680 Speaker 1: from mid grade communications manager role, which is I've just 51 00:03:06,760 --> 00:03:09,520 Speaker 1: never seen before. A lot of my friends who have 52 00:03:09,639 --> 00:03:13,480 Speaker 1: been made redundant from journalism are finding themselves you're really 53 00:03:13,680 --> 00:03:16,600 Speaker 1: missing out on jobs that maybe five years ago they 54 00:03:16,600 --> 00:03:19,120 Speaker 1: would have got. So can you get your perspective on 55 00:03:19,840 --> 00:03:23,960 Speaker 1: what's happening with applications and the amounts of decent jobs 56 00:03:24,000 --> 00:03:26,720 Speaker 1: that are out there and how candidates are sort of 57 00:03:26,800 --> 00:03:28,240 Speaker 1: navigating this at the moment. 58 00:03:28,360 --> 00:03:31,519 Speaker 4: Well, firstly from a market perspective, you know, I think 59 00:03:31,560 --> 00:03:33,639 Speaker 4: we all went away at Christmas and we're like, you know, 60 00:03:33,680 --> 00:03:37,400 Speaker 4: we're going to come back in January and the market's 61 00:03:37,400 --> 00:03:40,080 Speaker 4: going to have magically rebounded, and. 62 00:03:40,640 --> 00:03:42,120 Speaker 2: Obviously that never happened. 63 00:03:42,640 --> 00:03:45,000 Speaker 4: And look, I'm getting to a point and I believe 64 00:03:45,000 --> 00:03:47,440 Speaker 4: a lot of our clients are getting to a point 65 00:03:47,720 --> 00:03:51,000 Speaker 4: where I don't think that's going to happen. I think 66 00:03:51,040 --> 00:03:54,320 Speaker 4: we need to accept that within reason, this is now 67 00:03:54,400 --> 00:03:57,320 Speaker 4: our new normal. And therefore, what does that mean for 68 00:03:57,400 --> 00:03:58,960 Speaker 4: candidates who are looking for jobs? 69 00:03:59,360 --> 00:04:00,160 Speaker 2: So you are. 70 00:04:00,600 --> 00:04:04,520 Speaker 4: Application rates have been on the increase for about actually 71 00:04:04,520 --> 00:04:09,560 Speaker 4: eighteen months now and continue to climb every month. And anecdotally, 72 00:04:09,640 --> 00:04:13,880 Speaker 4: you know, our clients are just absolutely swamped with applications, 73 00:04:13,680 --> 00:04:16,760 Speaker 4: as are we as well. So what that does is 74 00:04:16,800 --> 00:04:19,120 Speaker 4: it creates I think it creates two things. 75 00:04:19,240 --> 00:04:21,640 Speaker 2: One, it creates an incredible amount of noise in. 76 00:04:21,560 --> 00:04:24,559 Speaker 4: The market, and I think secondly, it creates a false 77 00:04:24,600 --> 00:04:28,880 Speaker 4: perception that there are lots of fantastically skilled and suitable 78 00:04:28,920 --> 00:04:33,600 Speaker 4: candidates for the role that you are advertising and recruiting for. 79 00:04:33,960 --> 00:04:37,440 Speaker 4: And I don't really think that's the case. It's noise. 80 00:04:37,839 --> 00:04:41,440 Speaker 4: There might be gems within that noise, but we're not 81 00:04:41,600 --> 00:04:48,159 Speaker 4: in a hugely candidate rich market at the highly skilled area. 82 00:04:48,720 --> 00:04:50,920 Speaker 4: And when you add the complexity on top of that 83 00:04:50,920 --> 00:04:56,160 Speaker 4: that obviously from an AI and automation overall digital transformation perspective. 84 00:04:56,520 --> 00:05:00,520 Speaker 2: The skill sets that are needed are really grown very 85 00:05:00,640 --> 00:05:01,200 Speaker 2: very fast. 86 00:05:01,240 --> 00:05:05,200 Speaker 4: That's changing, right, and people quite simply just don't necessarily 87 00:05:05,240 --> 00:05:07,400 Speaker 4: have all those skills yet because we haven't been involved 88 00:05:07,400 --> 00:05:10,960 Speaker 4: in those types of projects yet. So your overall question 89 00:05:11,279 --> 00:05:16,279 Speaker 4: was what how are candidates? How are they navigating that? Look, 90 00:05:16,320 --> 00:05:19,160 Speaker 4: I think I still talk to candidates that are overly 91 00:05:19,600 --> 00:05:24,360 Speaker 4: reliant on job boards, They are overly reliant on applying 92 00:05:24,360 --> 00:05:25,880 Speaker 4: for a job, and they just leave it at that, 93 00:05:26,120 --> 00:05:29,039 Speaker 4: and that is just not going to be successful. They 94 00:05:29,200 --> 00:05:33,400 Speaker 4: must use a multi pronged approach. For a market as 95 00:05:33,440 --> 00:05:36,120 Speaker 4: small as I'm coming to you from Tamakimkodo, Auckland. So 96 00:05:36,400 --> 00:05:38,880 Speaker 4: for a market as small as Auckland, for a market 97 00:05:38,880 --> 00:05:41,960 Speaker 4: as small as New Zealand, your personal brand right now 98 00:05:42,040 --> 00:05:45,520 Speaker 4: is absolutely everything. Because if a hiring manager opens up 99 00:05:45,560 --> 00:05:48,920 Speaker 4: their their inbox or their ATS and says sees five 100 00:05:49,040 --> 00:05:52,440 Speaker 4: hundred cvs whether it's right or wrong, They're going to 101 00:05:52,560 --> 00:05:55,560 Speaker 4: naturally go who do I know and hear or what 102 00:05:55,800 --> 00:05:59,200 Speaker 4: partner can I partner with that knows someone that can 103 00:05:59,279 --> 00:06:02,679 Speaker 4: point me to so the right people or the people 104 00:06:02,680 --> 00:06:04,600 Speaker 4: they think are right, they might not be right. I 105 00:06:04,680 --> 00:06:07,520 Speaker 4: guess that's an important part rather than just dealing with 106 00:06:07,560 --> 00:06:08,600 Speaker 4: all of us noise. 107 00:06:08,480 --> 00:06:10,920 Speaker 1: And look, talent does quite a bit of recruitment in 108 00:06:11,240 --> 00:06:14,919 Speaker 1: the tech related space, a lot of contract roles, particularly 109 00:06:14,920 --> 00:06:19,440 Speaker 1: with Microsoft, HP and the like, you recruit for those 110 00:06:19,680 --> 00:06:24,160 Speaker 1: technology stacks. What are you seeing specifically around tech recruitment? 111 00:06:24,200 --> 00:06:26,960 Speaker 1: What we're sort of hearing overseas is really the tech 112 00:06:26,960 --> 00:06:29,040 Speaker 1: industry is eating its own dog food when it comes 113 00:06:29,080 --> 00:06:33,560 Speaker 1: to artificial intelligence and seeing big efficiencies there in software 114 00:06:33,600 --> 00:06:36,560 Speaker 1: development and the like, and that is definitely leading to 115 00:06:37,279 --> 00:06:41,360 Speaker 1: job losses but also slower recruitment as well. As we've 116 00:06:41,400 --> 00:06:44,200 Speaker 1: always had quite a tight market when it comes to 117 00:06:44,320 --> 00:06:46,240 Speaker 1: tech talent in New Zealand. It's hard to get really 118 00:06:46,240 --> 00:06:49,479 Speaker 1: good people and we've leaned on immigration to get the 119 00:06:49,520 --> 00:06:51,320 Speaker 1: people we need. What are you seeing at the moment 120 00:06:51,400 --> 00:06:52,680 Speaker 1: specifically in tech. 121 00:06:52,600 --> 00:06:55,680 Speaker 4: Specifically for tech, We're seeing a growing need of what 122 00:06:55,760 --> 00:06:59,160 Speaker 4: I would say the skills around what's going to be 123 00:06:59,200 --> 00:07:02,039 Speaker 4: happening is happening with in certain AI initiative, So that 124 00:07:02,080 --> 00:07:05,159 Speaker 4: could be solutions, architecture, that could be data, that could 125 00:07:05,160 --> 00:07:10,680 Speaker 4: be governance, that could be people who have advised pilot programs, 126 00:07:11,200 --> 00:07:16,080 Speaker 4: you know, successfully before into scaling implementations. We're seeing a 127 00:07:16,160 --> 00:07:19,960 Speaker 4: need for leaders who are change agents. We're seeing quite 128 00:07:20,000 --> 00:07:22,640 Speaker 4: a lot of reorgit that leadership layer as well. And 129 00:07:22,720 --> 00:07:26,760 Speaker 4: from a software development perspective, yes, we are kind of 130 00:07:26,760 --> 00:07:28,960 Speaker 4: seeing a bit of a what I would almost call 131 00:07:29,000 --> 00:07:34,040 Speaker 4: like a sinking lid recruitment policy. That the new normal 132 00:07:34,240 --> 00:07:37,360 Speaker 4: or the new desired future state is that people will 133 00:07:37,400 --> 00:07:40,880 Speaker 4: have leaner teams and you will have software development teams 134 00:07:40,960 --> 00:07:45,600 Speaker 4: who are far more productive due to you know, advances 135 00:07:45,600 --> 00:07:48,800 Speaker 4: in certain AI tools, et cetera. And so from again 136 00:07:48,840 --> 00:07:51,960 Speaker 4: going back to the perspective for the candidate, the candidate 137 00:07:52,000 --> 00:07:55,320 Speaker 4: themselves really need to be investing in do you have 138 00:07:56,080 --> 00:07:59,480 Speaker 4: do you understand how to utilize those tools to make 139 00:07:59,520 --> 00:08:00,840 Speaker 4: yourself more productive? 140 00:08:01,240 --> 00:08:02,400 Speaker 2: And does that change? 141 00:08:02,520 --> 00:08:05,240 Speaker 4: And I think it is changing the nature of So 142 00:08:05,280 --> 00:08:09,400 Speaker 4: then what does a really good tech candidate do If 143 00:08:09,600 --> 00:08:14,840 Speaker 4: ultimately you're using tools to do the more commoditized tasks, well, 144 00:08:14,880 --> 00:08:20,040 Speaker 4: then your real strength is in creativity, problem solving, change management, 145 00:08:20,600 --> 00:08:24,080 Speaker 4: stakeholder management. And I think that actually is the bigger 146 00:08:24,240 --> 00:08:26,480 Speaker 4: is the bigger change and is going to be the 147 00:08:26,480 --> 00:08:27,960 Speaker 4: bigger pressure in the market. 148 00:08:28,320 --> 00:08:30,240 Speaker 2: In order to develop those skills. 149 00:08:29,840 --> 00:08:34,559 Speaker 1: AI is changing every industry. The AI Forum's latest survey 150 00:08:34,760 --> 00:08:38,520 Speaker 1: of businesses found the number of businesses that said they'd 151 00:08:38,600 --> 00:08:41,560 Speaker 1: let staff go as a result of AI had double. 152 00:08:41,600 --> 00:08:45,360 Speaker 1: It's still small, about fourteen percent, but it's also saying 153 00:08:45,400 --> 00:08:48,839 Speaker 1: it's opening opportunities for existing staff, which goes to your 154 00:08:48,840 --> 00:08:51,439 Speaker 1: point that the skill set is changing and you need 155 00:08:51,480 --> 00:08:54,439 Speaker 1: to be ready for that. Jack from your point of view, 156 00:08:54,520 --> 00:08:59,920 Speaker 1: you know these Karen mentioned these ATS's applicant tracking system 157 00:09:00,120 --> 00:09:03,520 Speaker 1: which probably fill candidates with drid, you know as they 158 00:09:03,559 --> 00:09:07,360 Speaker 1: tried to get through with their application and actually get 159 00:09:07,360 --> 00:09:10,400 Speaker 1: in front of someone. What are these systems and how 160 00:09:10,440 --> 00:09:13,880 Speaker 1: widely used are they now? Buy big recruiters, Every. 161 00:09:13,760 --> 00:09:16,760 Speaker 3: Big recruit is using it. You've got you know, in 162 00:09:16,800 --> 00:09:19,520 Speaker 3: the New Zealand market, you know, five hundred applicants going 163 00:09:19,559 --> 00:09:22,360 Speaker 3: for one posting, straining market, it could be thousands of 164 00:09:22,360 --> 00:09:25,920 Speaker 3: applicants going for a job posting. And if you're not 165 00:09:26,120 --> 00:09:28,760 Speaker 3: using a system to try to clear through that volume, 166 00:09:28,920 --> 00:09:31,760 Speaker 3: then you would have to have you know, thousands of 167 00:09:31,800 --> 00:09:36,360 Speaker 3: people reviewing CBS for a job posting. So these ATS systems, 168 00:09:36,960 --> 00:09:39,840 Speaker 3: as a gross simplification of what they do is that 169 00:09:39,880 --> 00:09:45,280 Speaker 3: they will collect and store applicant information pase that information 170 00:09:45,480 --> 00:09:48,559 Speaker 3: to look for keywords, and then they score that applicant 171 00:09:48,600 --> 00:09:52,559 Speaker 3: against the job posting. They then help manage that candidate 172 00:09:52,679 --> 00:09:56,079 Speaker 3: through the progression of the hiring pipeline, you know, scheduling, interviews, 173 00:09:56,240 --> 00:09:59,440 Speaker 3: rejection notices. It's a centralized system to manage that component. 174 00:10:00,080 --> 00:10:03,520 Speaker 3: The end, the recruiting firm will have time to hierometrics 175 00:10:03,960 --> 00:10:07,240 Speaker 3: the source of hiometrics cost per hiometrics. I think it's 176 00:10:07,240 --> 00:10:11,200 Speaker 3: important for candidates to understand that they are being put 177 00:10:11,240 --> 00:10:15,000 Speaker 3: into these systems because from a sheer volume perspective, it 178 00:10:15,120 --> 00:10:17,760 Speaker 3: just has to be done. Having a CV or a 179 00:10:17,800 --> 00:10:21,079 Speaker 3: resume that is optimized for those is definitely going to 180 00:10:21,120 --> 00:10:25,920 Speaker 3: help bring your chances up to the top of the pack. However, 181 00:10:25,960 --> 00:10:29,960 Speaker 3: there are obviously some caveats and some ethics concerns that 182 00:10:30,040 --> 00:10:31,079 Speaker 3: kind of play into that as well. 183 00:10:31,160 --> 00:10:35,000 Speaker 1: Yeah, so the sorts of things these ATIS systems are 184 00:10:35,760 --> 00:10:38,480 Speaker 1: looking for. Keywords are a key one, so looking for 185 00:10:38,600 --> 00:10:43,000 Speaker 1: common section headings, formats, things like that, and then you've 186 00:10:43,040 --> 00:10:46,240 Speaker 1: got optical character recognition. If someone sends into maybe a 187 00:10:46,280 --> 00:10:49,880 Speaker 1: PDF for something with information and even images and there, 188 00:10:49,920 --> 00:10:52,280 Speaker 1: the systems can extract that and look at it, and 189 00:10:52,320 --> 00:10:57,720 Speaker 1: then you've got artificial intelligence there as well, extracting information 190 00:10:58,000 --> 00:11:01,480 Speaker 1: inferring meaning from the context. In that I guess that's 191 00:11:01,520 --> 00:11:05,679 Speaker 1: the really interesting one. How well that AI is at 192 00:11:05,880 --> 00:11:09,240 Speaker 1: among five hundred cvs going, Ah, this person has the 193 00:11:09,280 --> 00:11:11,520 Speaker 1: hard skills, but also the soft skills that are really 194 00:11:11,520 --> 00:11:12,920 Speaker 1: going to suit this role. 195 00:11:13,200 --> 00:11:16,000 Speaker 3: Yeah, I think it's still got a long way to 196 00:11:16,000 --> 00:11:20,280 Speaker 3: go in regards to identifying those soft skills, especially so 197 00:11:20,559 --> 00:11:23,280 Speaker 3: as if I was to look at hiring a candidate 198 00:11:23,720 --> 00:11:26,520 Speaker 3: or going and putting a CV through myself, I'm not 199 00:11:26,559 --> 00:11:28,960 Speaker 3: there to focus on the skills that I actually have. Now, 200 00:11:29,240 --> 00:11:32,480 Speaker 3: I'm there to optimize my CV for a system. So 201 00:11:33,040 --> 00:11:37,360 Speaker 3: it's more about how do I optimize my ranking as 202 00:11:37,360 --> 00:11:39,360 Speaker 3: opposed to how do I showcase who I am as 203 00:11:39,360 --> 00:11:42,840 Speaker 3: an individual? And what that does is those systems are 204 00:11:42,840 --> 00:11:46,440 Speaker 3: going to start overlooking, you know, potential in candidates, because 205 00:11:46,600 --> 00:11:51,280 Speaker 3: AI can't identify potential in a human being unconventional candidates, 206 00:11:51,480 --> 00:11:56,280 Speaker 3: so any transferable skills unique non traditional candidates. You know, 207 00:11:56,360 --> 00:11:59,080 Speaker 3: if we look at your journalistic colleagues that you mentioning 208 00:11:59,120 --> 00:12:01,840 Speaker 3: before missing out of jobs that they would have normally 209 00:12:01,840 --> 00:12:05,360 Speaker 3: gotten in the past. You know, these systems, you know 210 00:12:05,840 --> 00:12:08,600 Speaker 3: someone may have mentioned two or three things more than them, 211 00:12:08,760 --> 00:12:11,160 Speaker 3: or they've missed a single keyword on their CV which 212 00:12:11,160 --> 00:12:14,120 Speaker 3: has de ranked them significantly. But they might be really 213 00:12:14,160 --> 00:12:17,319 Speaker 3: really good for the job anyway. And AI I don't 214 00:12:17,320 --> 00:12:20,440 Speaker 3: believe is going to make any major leaps and bounds 215 00:12:20,480 --> 00:12:24,640 Speaker 3: in this regard in the near future, because even though 216 00:12:24,679 --> 00:12:30,240 Speaker 3: you do have video analysis that's looking at para language 217 00:12:30,280 --> 00:12:34,720 Speaker 3: analysis in videos, which obviously rides a very fine ethical 218 00:12:34,760 --> 00:12:37,679 Speaker 3: line for biometric data collection, it's still not going to 219 00:12:37,679 --> 00:12:41,160 Speaker 3: be anywhere near as good as a recruiter who knows 220 00:12:41,200 --> 00:12:45,120 Speaker 3: the candidates, who understands the people, who understands your organization 221 00:12:45,520 --> 00:12:48,000 Speaker 3: and what the culture is there and what they need 222 00:12:48,320 --> 00:12:52,280 Speaker 3: as a business, and finding the right balance of the 223 00:12:52,320 --> 00:12:54,840 Speaker 3: technical or the hard skill sets that are required for 224 00:12:54,880 --> 00:12:59,560 Speaker 3: that role, but also the person and the mentality behind 225 00:13:00,400 --> 00:13:04,280 Speaker 3: what that organization needs. 226 00:13:08,760 --> 00:13:11,640 Speaker 1: The logical thing for me, I think, faced with this, 227 00:13:12,400 --> 00:13:14,000 Speaker 1: God forbid, I have to go out there into the 228 00:13:14,000 --> 00:13:16,319 Speaker 1: workforce in a soft market at the moment, but I 229 00:13:16,400 --> 00:13:19,640 Speaker 1: would probably go to chat GPT to write my CV 230 00:13:19,800 --> 00:13:23,840 Speaker 1: for me and prompt it to create a CV and 231 00:13:23,880 --> 00:13:27,199 Speaker 1: a cover letter that is going to be optimized for 232 00:13:27,400 --> 00:13:30,440 Speaker 1: putting my best foot forward. Are candidates doing that, and 233 00:13:30,520 --> 00:13:32,920 Speaker 1: are you seeing that you starting to see the same 234 00:13:33,000 --> 00:13:38,080 Speaker 1: sort of tropes and style emerging in cvs and cover letters. 235 00:13:38,200 --> 00:13:40,360 Speaker 2: Yes and no, but it's definitely growing. 236 00:13:40,760 --> 00:13:43,720 Speaker 4: I think what concerns me, and I talk to clients 237 00:13:43,720 --> 00:13:46,760 Speaker 4: about this all the time, is if everyone starts doing that. 238 00:13:46,880 --> 00:13:48,839 Speaker 4: I get why we're doing that, because we're trying to 239 00:13:48,920 --> 00:13:51,480 Speaker 4: hit the matches and we're trying to be higher ranked 240 00:13:51,480 --> 00:13:55,600 Speaker 4: in an ATS or a job board or maybe even 241 00:13:55,600 --> 00:13:59,120 Speaker 4: a hiring managers taking fifteen cvs they've received from their 242 00:13:59,160 --> 00:14:02,319 Speaker 4: recruitment partner and they're going, hey, I'm going to chuck 243 00:14:02,360 --> 00:14:05,280 Speaker 4: this into my co pilot or something similar, and who 244 00:14:05,280 --> 00:14:07,720 Speaker 4: would you recommend that I interview. I have no doubt 245 00:14:07,760 --> 00:14:10,240 Speaker 4: all of these things are happening because quite simply, people 246 00:14:10,240 --> 00:14:12,520 Speaker 4: are playing with the tools and they're low on time. 247 00:14:12,840 --> 00:14:13,839 Speaker 2: So what worries me. 248 00:14:14,040 --> 00:14:17,480 Speaker 4: Is just the the uniformity that starts to that starts 249 00:14:17,520 --> 00:14:19,480 Speaker 4: to happen. And so I have to bring it back 250 00:14:19,520 --> 00:14:22,720 Speaker 4: to a point that I made before that a candidate 251 00:14:22,920 --> 00:14:24,920 Speaker 4: must take you know, I guess the bull by the 252 00:14:24,920 --> 00:14:28,480 Speaker 4: horns more. They must have a multi pronged approach. They 253 00:14:28,560 --> 00:14:30,840 Speaker 4: must be thinking about their network, they must be thinking 254 00:14:30,920 --> 00:14:34,520 Speaker 4: about their personal brand. They must be thinking about can 255 00:14:34,560 --> 00:14:37,480 Speaker 4: I can I get an internal referral at that organization 256 00:14:38,040 --> 00:14:41,560 Speaker 4: as well? You know, so that there it's it can't 257 00:14:41,680 --> 00:14:44,400 Speaker 4: if I think of it just solely relies on their CV. 258 00:14:45,160 --> 00:14:49,640 Speaker 4: And if that then becomes solely relying on word matching, 259 00:14:49,800 --> 00:14:53,840 Speaker 4: whether that's after you've engaged with the recruiter or before 260 00:14:54,200 --> 00:14:57,200 Speaker 4: you've engaged with a recruiter, that that to me is 261 00:14:57,240 --> 00:14:59,280 Speaker 4: a concern. So you've got to really think about what 262 00:14:59,320 --> 00:15:02,200 Speaker 4: are the things that in your control. Another thing I 263 00:15:02,240 --> 00:15:04,280 Speaker 4: would say, and this might sound a bit funny, when 264 00:15:04,320 --> 00:15:06,360 Speaker 4: we you know, say, you know, spell check your resume 265 00:15:06,440 --> 00:15:09,520 Speaker 4: and grammar check everything. I'm starting to almost like the 266 00:15:09,560 --> 00:15:13,520 Speaker 4: odd grammar, the old grammar mistake. And my parents are 267 00:15:13,520 --> 00:15:16,120 Speaker 4: teachers and they've drilled the stuff into me my whole life. 268 00:15:16,200 --> 00:15:18,280 Speaker 4: But you know what, because then you know it's authentic. 269 00:15:18,760 --> 00:15:21,040 Speaker 4: Then you know it's it's like that's a human being 270 00:15:21,080 --> 00:15:23,320 Speaker 4: that's written that, and they've made a little mistake. Well, 271 00:15:23,520 --> 00:15:25,800 Speaker 4: you know, mistakes are human and we kind of want 272 00:15:25,840 --> 00:15:28,920 Speaker 4: those humans. And then the other thing I would say 273 00:15:29,160 --> 00:15:32,720 Speaker 4: more to a client is if you're if you are 274 00:15:32,840 --> 00:15:35,920 Speaker 4: taking cvs and putting it into some kind of ranking system, 275 00:15:36,200 --> 00:15:40,880 Speaker 4: and if you're only working from that judgment, then you know, 276 00:15:41,040 --> 00:15:43,280 Speaker 4: tell me the last time you hired someone that was 277 00:15:43,520 --> 00:15:46,960 Speaker 4: a ten out of ten and hung around a long time, 278 00:15:47,120 --> 00:15:50,360 Speaker 4: was actually engaged, actually learned a lot. It's not going 279 00:15:50,440 --> 00:15:52,680 Speaker 4: to be the case. We know when we take a 280 00:15:52,720 --> 00:15:55,240 Speaker 4: new role, if we've got that edge, if we've got 281 00:15:55,240 --> 00:15:57,960 Speaker 4: that gap, if we have to learn something, we know, 282 00:15:58,080 --> 00:16:00,600 Speaker 4: we're more engaged in the hiring manager to gets more 283 00:16:00,640 --> 00:16:03,520 Speaker 4: out of that candidate as well. So that's the part 284 00:16:03,160 --> 00:16:06,800 Speaker 4: about potential, that's the part about the full picture, and 285 00:16:06,840 --> 00:16:08,320 Speaker 4: an ATS is not going to be able to give 286 00:16:08,320 --> 00:16:08,680 Speaker 4: that to you. 287 00:16:08,840 --> 00:16:12,960 Speaker 1: Yeah, so the human in the loop is really important. 288 00:16:13,080 --> 00:16:14,600 Speaker 1: And so what does it look like now for one 289 00:16:14,600 --> 00:16:18,120 Speaker 1: of your typical agents, you know, potentially looking at for 290 00:16:18,240 --> 00:16:22,760 Speaker 1: great candidates for it contracting roles for instance. So they're 291 00:16:22,920 --> 00:16:26,480 Speaker 1: getting hundreds of applicants. So is the system sort of 292 00:16:26,560 --> 00:16:29,400 Speaker 1: creating a shortlist based on rankings, based on all the 293 00:16:29,400 --> 00:16:31,480 Speaker 1: things we've been talking about, some of those keywords, some 294 00:16:31,560 --> 00:16:35,720 Speaker 1: of the AI inference, and then they're wading through a 295 00:16:35,760 --> 00:16:37,480 Speaker 1: short list of candidates. 296 00:16:36,960 --> 00:16:40,600 Speaker 4: Only if and I'm answering from a talent perspective, now, 297 00:16:41,120 --> 00:16:44,200 Speaker 4: only if we needed to go to that stage. 298 00:16:44,400 --> 00:16:46,360 Speaker 2: I would call that an escalated search. 299 00:16:46,680 --> 00:16:48,600 Speaker 4: So going back to the fact that over the last quarter, 300 00:16:48,680 --> 00:16:51,400 Speaker 4: eighty two percent of all of our successful placements have 301 00:16:51,560 --> 00:16:55,520 Speaker 4: not included a job board ashole, and that's because we 302 00:16:55,800 --> 00:16:58,440 Speaker 4: every single time a role comes in, we're going to 303 00:16:58,480 --> 00:17:01,840 Speaker 4: our networks, to the collect actives networks as well. Every 304 00:17:01,920 --> 00:17:04,680 Speaker 4: contract role that comes into the Talent New Zealand business, 305 00:17:05,000 --> 00:17:07,439 Speaker 4: we raise it and we go, hey, who do you know? 306 00:17:07,600 --> 00:17:10,800 Speaker 4: So that networking is so so important. We then go 307 00:17:10,960 --> 00:17:14,560 Speaker 4: to all of our talent pools, to the people that 308 00:17:14,640 --> 00:17:19,240 Speaker 4: have contracted with us before, et cetera. And most of 309 00:17:19,320 --> 00:17:22,240 Speaker 4: the time I would hesitate to say two thirds of 310 00:17:22,240 --> 00:17:23,439 Speaker 4: the time we. 311 00:17:23,320 --> 00:17:26,320 Speaker 2: Can have a short list from that poll. 312 00:17:26,920 --> 00:17:29,399 Speaker 4: Only then would we need to go outside of that, 313 00:17:29,440 --> 00:17:31,879 Speaker 4: and that might be for your new your niche skills, 314 00:17:31,880 --> 00:17:35,480 Speaker 4: you're more in demand skills, et cetera. And then it's 315 00:17:35,560 --> 00:17:40,879 Speaker 4: probably more direct headhunting. Again, another thing we've actually started 316 00:17:40,920 --> 00:17:43,479 Speaker 4: doing because again we're trying to fight through that noise 317 00:17:43,720 --> 00:17:47,600 Speaker 4: and fundamentally give our clients more confidence that they can 318 00:17:47,680 --> 00:17:50,200 Speaker 4: move on this candidate. It's a real person that we've 319 00:17:50,480 --> 00:17:53,240 Speaker 4: worked with before, et ceteras. We're actually taking reference checks 320 00:17:53,320 --> 00:17:57,080 Speaker 4: up front, so we're now going we need more information 321 00:17:57,280 --> 00:18:00,640 Speaker 4: to give to our clients to say you have done 322 00:18:00,640 --> 00:18:02,919 Speaker 4: a project like that before that you've you know, what 323 00:18:03,000 --> 00:18:06,520 Speaker 4: are your strengths to paint the candidate as a human 324 00:18:06,720 --> 00:18:10,720 Speaker 4: being with real strengths and real potential. That's fundamentally what 325 00:18:10,760 --> 00:18:12,400 Speaker 4: we're doing right now and with seeing some really good 326 00:18:12,400 --> 00:18:12,879 Speaker 4: things from it. 327 00:18:13,080 --> 00:18:16,080 Speaker 1: So do you recommend that people who are looking for 328 00:18:16,200 --> 00:18:21,719 Speaker 1: jobs actually go approach recruitment agencies or companies directly and 329 00:18:21,880 --> 00:18:25,800 Speaker 1: just say, look, I'm on the lookout for work. I 330 00:18:25,840 --> 00:18:28,440 Speaker 1: think some people actually go to talent agencies and register 331 00:18:28,680 --> 00:18:31,640 Speaker 1: themselves with them, so you know, contractors and they likes 332 00:18:31,680 --> 00:18:35,359 Speaker 1: so that you're on their radar. Is that something that 333 00:18:35,400 --> 00:18:38,160 Speaker 1: people should definitely in this environment be doing, actually reaching 334 00:18:38,160 --> 00:18:40,600 Speaker 1: out proactively, even if they don't they're not looking for 335 00:18:40,760 --> 00:18:43,720 Speaker 1: job today but maybe in the near future. 336 00:18:43,840 --> 00:18:46,000 Speaker 4: Certainly so I would, And this is where I would 337 00:18:46,080 --> 00:18:48,920 Speaker 4: be using the likes of chat GPT for I would 338 00:18:48,920 --> 00:18:51,760 Speaker 4: be going, you know, these are my key strengths, these 339 00:18:51,760 --> 00:18:54,880 Speaker 4: are the types of opportunities I'm looking in for. What 340 00:18:55,000 --> 00:18:58,159 Speaker 4: recruitment agencies are specialists in that? So if it's in 341 00:18:58,200 --> 00:19:02,440 Speaker 4: New Zealand talent specialists, tech and transformation recruitment, and then 342 00:19:02,640 --> 00:19:06,080 Speaker 4: going proactively going to that organization and saying, hey, keen 343 00:19:06,160 --> 00:19:08,320 Speaker 4: to pop into your office, keen to you know, shout 344 00:19:08,320 --> 00:19:09,919 Speaker 4: you a cup of coffee. This is the magic of 345 00:19:09,920 --> 00:19:12,640 Speaker 4: New Zealand right where we're so close and we're we're 346 00:19:12,680 --> 00:19:15,280 Speaker 4: this kind of community. So you have to use it 347 00:19:15,280 --> 00:19:18,160 Speaker 4: to your advantage. Really keen to talk to you proactively 348 00:19:18,240 --> 00:19:20,960 Speaker 4: around the types of opportunities I'm interested in, you know, 349 00:19:21,160 --> 00:19:24,080 Speaker 4: by strength some of my successful projects, et cetera, and 350 00:19:24,280 --> 00:19:27,400 Speaker 4: learn about what you're seeing in the market. I would 351 00:19:27,440 --> 00:19:30,480 Speaker 4: be trying to do everything a little bit in advance 352 00:19:31,240 --> 00:19:35,040 Speaker 4: because often when we are engaging, well not often every 353 00:19:35,080 --> 00:19:38,000 Speaker 4: time when we are, when we do receive a role 354 00:19:38,320 --> 00:19:41,240 Speaker 4: from a hiring manager, we have to move at speed. 355 00:19:42,000 --> 00:19:45,520 Speaker 4: So that's why we don't always put up an AD 356 00:19:45,640 --> 00:19:47,840 Speaker 4: because it slows us down. We have to know the 357 00:19:47,840 --> 00:19:50,199 Speaker 4: people already and we invest a lot of time at 358 00:19:50,240 --> 00:19:54,080 Speaker 4: talent meeting, you know, candidates ahead of the curve. 359 00:19:54,160 --> 00:19:56,480 Speaker 1: Traditionally, the good thing about applying to a job a 360 00:19:56,600 --> 00:19:59,960 Speaker 1: job AD is it sort of seemed on the outside 361 00:20:00,000 --> 00:20:02,760 Speaker 1: anyway like it was going to be a merit based decision. 362 00:20:02,920 --> 00:20:05,520 Speaker 1: The recruiters would look at a group of people and 363 00:20:05,880 --> 00:20:09,520 Speaker 1: people they didn't know or expect to apply for this job, 364 00:20:09,680 --> 00:20:11,960 Speaker 1: and everyone got a fair crack at it. I think 365 00:20:11,960 --> 00:20:14,000 Speaker 1: a lot of people would feel a little bit dirty, 366 00:20:14,080 --> 00:20:17,919 Speaker 1: really sort of trying to make approaches over DMS on 367 00:20:18,000 --> 00:20:21,040 Speaker 1: LinkedIn or whatever. But that's the reality of it. You've 368 00:20:21,040 --> 00:20:23,640 Speaker 1: got to have this multi prong approach. But Jack from 369 00:20:23,680 --> 00:20:26,440 Speaker 1: your point of view, with AI now in the loop 370 00:20:26,480 --> 00:20:30,280 Speaker 1: looking at some of these applications, I guess the real 371 00:20:30,320 --> 00:20:33,560 Speaker 1: concern for a talent agency for a company is if 372 00:20:33,600 --> 00:20:37,080 Speaker 1: the system shows bias against someone you know. There have 373 00:20:37,200 --> 00:20:40,560 Speaker 1: been some high profile cases as a chap in the US. 374 00:20:41,000 --> 00:20:44,040 Speaker 1: Derek Mobley, he was an IT guy in North Carolina. 375 00:20:44,080 --> 00:20:47,440 Speaker 1: He applied for over one hundred jobs, didn't get anything, 376 00:20:47,920 --> 00:20:51,919 Speaker 1: applied to work Day a software company, and got rejected. 377 00:20:52,200 --> 00:20:55,639 Speaker 1: Man of color with a disability, and he suspected that 378 00:20:56,040 --> 00:20:59,320 Speaker 1: the system was flagging him as not appropriate. He took 379 00:20:59,359 --> 00:21:02,160 Speaker 1: that to court. It's ongoing. I think there have been 380 00:21:02,280 --> 00:21:04,840 Speaker 1: a number of high profile cases like this where people 381 00:21:04,840 --> 00:21:08,560 Speaker 1: are going I'm getting nowhere and I'm getting eliminated at 382 00:21:08,600 --> 00:21:11,680 Speaker 1: an early stage. So how do you deal with that 383 00:21:11,760 --> 00:21:14,440 Speaker 1: from a bias point of view, making sure that you're 384 00:21:14,480 --> 00:21:17,840 Speaker 1: not the system is not eliminating people who may be 385 00:21:18,080 --> 00:21:21,200 Speaker 1: really good but on the surface don't seem appropriate. 386 00:21:21,440 --> 00:21:23,480 Speaker 3: If I had an answer to that question, I think 387 00:21:23,520 --> 00:21:25,719 Speaker 3: I'd be in a different job. So yeah, I mean, 388 00:21:25,720 --> 00:21:28,560 Speaker 3: you're absolutely right. One of the biggest challenges in that 389 00:21:28,800 --> 00:21:32,160 Speaker 3: is the large language model, specifically so your chat GPT 390 00:21:32,280 --> 00:21:36,399 Speaker 3: style AI is really opaque in its decision making and 391 00:21:36,440 --> 00:21:41,160 Speaker 3: so challenging that AI's decision to understand why a particular 392 00:21:41,200 --> 00:21:44,240 Speaker 3: candidate was rejected is near on impossible. At the moment, 393 00:21:44,520 --> 00:21:47,080 Speaker 3: there are tools that are being developed to actually understand 394 00:21:47,480 --> 00:21:49,640 Speaker 3: the chain of thought in a bit of a deeper way. 395 00:21:50,400 --> 00:21:54,120 Speaker 3: We would have seen in older AIS before the kind 396 00:21:54,160 --> 00:21:58,040 Speaker 3: of transformer AI model started coming out in twenty seventeen, 397 00:21:58,520 --> 00:22:02,399 Speaker 3: those decision trees were very static. But with large language models, 398 00:22:02,440 --> 00:22:06,480 Speaker 3: it is very, very opaque. From a bias perspective, AI 399 00:22:06,560 --> 00:22:11,640 Speaker 3: doesn't eliminate bias, it almost codifies it. And so if 400 00:22:11,640 --> 00:22:14,800 Speaker 3: we're looking at the historical data that that AI model 401 00:22:14,800 --> 00:22:17,119 Speaker 3: has been trained on, and then for a company who 402 00:22:17,160 --> 00:22:21,920 Speaker 3: is predominantly hired people from let's say MIT, then it's 403 00:22:21,920 --> 00:22:26,720 Speaker 3: going to prioritize those traits and in turn amplify the 404 00:22:26,760 --> 00:22:29,280 Speaker 3: bias within the system. It's kind of like an echo 405 00:22:29,320 --> 00:22:31,960 Speaker 3: effect where it's you know, this person's from MIT, so 406 00:22:32,000 --> 00:22:34,280 Speaker 3: they get boosted and they get the next person gets 407 00:22:34,280 --> 00:22:36,919 Speaker 3: boosted because of that. So pulling the bias out of 408 00:22:36,960 --> 00:22:40,960 Speaker 3: those models and getting them to be a lot more 409 00:22:41,320 --> 00:22:44,879 Speaker 3: down the middle is a really really big challenge. But 410 00:22:44,960 --> 00:22:46,720 Speaker 3: on top of that, if you do get it down 411 00:22:46,760 --> 00:22:49,280 Speaker 3: the middle, then it's going to promote down the middle candidates. 412 00:22:49,400 --> 00:22:51,560 Speaker 3: That doesn't necessarily mean it's going to be the best 413 00:22:51,600 --> 00:22:55,720 Speaker 3: fit for your role, for what your organization needs. And 414 00:22:55,760 --> 00:22:59,520 Speaker 3: so I think that there's really two things to consider. 415 00:23:00,160 --> 00:23:03,440 Speaker 3: First is that AI is fantastic as a use case 416 00:23:03,520 --> 00:23:06,360 Speaker 3: to pull the wheat from the chaff. So if you've 417 00:23:06,359 --> 00:23:09,199 Speaker 3: got people who've never worked in that industry before, you 418 00:23:09,200 --> 00:23:12,520 Speaker 3: know they've had a role that's in supply chain and 419 00:23:12,520 --> 00:23:14,840 Speaker 3: now they're going for a finance director, you know they 420 00:23:14,920 --> 00:23:17,440 Speaker 3: might not have the skill sets to complement that role, 421 00:23:17,520 --> 00:23:20,560 Speaker 3: and AI can quite quickly remove those candidates from the pool. 422 00:23:21,080 --> 00:23:24,919 Speaker 3: But then you do really need someone with experience and 423 00:23:24,960 --> 00:23:27,600 Speaker 3: an understanding of your organization and an understanding of those 424 00:23:27,600 --> 00:23:31,359 Speaker 3: candidates to ensure that that bias doesn't, you know, move 425 00:23:31,400 --> 00:23:36,280 Speaker 3: beyond that initial culling and allow those candidates to progress successfully. 426 00:23:41,080 --> 00:23:44,120 Speaker 1: Do you envisage JACK the future of recruitment where it's 427 00:23:44,119 --> 00:23:48,680 Speaker 1: sort of like almost a fully automated process from job 428 00:23:48,760 --> 00:23:52,440 Speaker 1: posting to application to sort of onboarding, where a lot 429 00:23:52,480 --> 00:23:57,639 Speaker 1: of that is effectively AI augmented, but you know, a 430 00:23:57,640 --> 00:24:00,480 Speaker 1: lot of it is very automated. 431 00:24:00,680 --> 00:24:03,959 Speaker 3: I mean, there are definitely roles where that is applicable. 432 00:24:05,080 --> 00:24:07,679 Speaker 3: There are definitely sections of the market where I think 433 00:24:07,720 --> 00:24:11,520 Speaker 3: that that's definitely a use case to support that narrative. 434 00:24:12,040 --> 00:24:17,400 Speaker 3: For example, entry level positions. You know, in Australia specifically, traditionally, 435 00:24:17,640 --> 00:24:20,679 Speaker 3: as I was growing up, everybody worked in either a 436 00:24:20,680 --> 00:24:23,880 Speaker 3: department store or at in fast food as the kind 437 00:24:23,920 --> 00:24:27,080 Speaker 3: of their first jobs, and I think those roles are 438 00:24:27,359 --> 00:24:30,840 Speaker 3: perfect for that kind of automated process once you start 439 00:24:30,880 --> 00:24:35,320 Speaker 3: getting into the more I guess career centric job positions. 440 00:24:35,920 --> 00:24:38,879 Speaker 3: I don't believe that AI has a part to play 441 00:24:38,960 --> 00:24:43,560 Speaker 3: in the entire process. However, it's definitely got benefits along 442 00:24:43,800 --> 00:24:47,840 Speaker 3: the journey of higher to actually placing that candidate. 443 00:24:47,880 --> 00:24:50,640 Speaker 1: I've heard a bit about I haven't seen an example 444 00:24:50,680 --> 00:24:53,480 Speaker 1: of it, but sort of pre screening interviews that are 445 00:24:53,480 --> 00:24:55,840 Speaker 1: done with AI. So it may not even be a 446 00:24:55,920 --> 00:24:59,879 Speaker 1: recruiter on the call, but someone will be as to 447 00:25:00,040 --> 00:25:03,359 Speaker 1: log onto a platform as answer a series of questions 448 00:25:03,440 --> 00:25:07,399 Speaker 1: and in the AI analyzes their responses. Is that something 449 00:25:07,440 --> 00:25:09,240 Speaker 1: that's becoming more common in the industry? 450 00:25:09,320 --> 00:25:13,600 Speaker 3: It is, so what they're looking at there is typically 451 00:25:14,200 --> 00:25:17,719 Speaker 3: the is the candidate baseline applicable for that, asking them 452 00:25:17,800 --> 00:25:20,600 Speaker 3: kind of general questions about you know, who they are, 453 00:25:20,680 --> 00:25:23,359 Speaker 3: are they a citizen? Are they legally allowed to work? 454 00:25:24,119 --> 00:25:27,760 Speaker 3: They're also looking at para language, so how the candidate 455 00:25:27,880 --> 00:25:32,240 Speaker 3: speaks their intonation. It's not just you know, text on 456 00:25:32,280 --> 00:25:34,800 Speaker 3: a page. Then it's it's you know, are they're confident 457 00:25:34,840 --> 00:25:38,280 Speaker 3: in how they're talking, which I think rides a pretty 458 00:25:38,280 --> 00:25:42,520 Speaker 3: fine line around kind of ethics and the use of 459 00:25:42,520 --> 00:25:45,679 Speaker 3: biometric data in that regard. There's also a bit of 460 00:25:45,720 --> 00:25:49,560 Speaker 3: retric yet to see any actual numbers on it, but 461 00:25:49,600 --> 00:25:51,960 Speaker 3: there is retric to say that it is used for 462 00:25:52,040 --> 00:25:57,040 Speaker 3: screening out candidates in ways that allows certain organizations to 463 00:25:57,080 --> 00:26:01,199 Speaker 3: be more discriminatory. So as an example, if they're you know, 464 00:26:01,880 --> 00:26:04,800 Speaker 3: they may not they may need someone who can speak 465 00:26:04,920 --> 00:26:07,880 Speaker 3: English at a very very high level, and they're using 466 00:26:07,960 --> 00:26:11,760 Speaker 3: that video platform to help remove candidates from there. Personally, 467 00:26:11,840 --> 00:26:14,800 Speaker 3: I've gone through an interview process many years ago where 468 00:26:15,000 --> 00:26:17,879 Speaker 3: they use this technology. They wanted me to do an 469 00:26:17,920 --> 00:26:21,680 Speaker 3: interview like that, and I flat refused. In my opinion, 470 00:26:22,080 --> 00:26:25,800 Speaker 3: if I'm interviewing for your organization, interviewing for a role, 471 00:26:26,240 --> 00:26:28,320 Speaker 3: and I'm giving you that respect, I would expect the 472 00:26:28,320 --> 00:26:30,800 Speaker 3: same respect in return that I would like to speak 473 00:26:30,800 --> 00:26:33,840 Speaker 3: to a human being. Because I'm while I'm being interviewed 474 00:26:33,880 --> 00:26:35,679 Speaker 3: to see if I'm the right fit for your organization, 475 00:26:36,080 --> 00:26:38,440 Speaker 3: I also want to interview the organization to make sure 476 00:26:38,440 --> 00:26:39,439 Speaker 3: that it's the right fit for me. 477 00:26:39,520 --> 00:26:41,960 Speaker 1: But what do you do, Cara, when you're applying for 478 00:26:41,960 --> 00:26:46,200 Speaker 1: a job and these processes are built into the system 479 00:26:46,480 --> 00:26:49,920 Speaker 1: and you're uncomfortable about it, it's sort of hard to 480 00:26:50,240 --> 00:26:53,199 Speaker 1: go around them. As maybe even inappropriate to try and 481 00:26:53,200 --> 00:26:53,800 Speaker 1: go around them. 482 00:26:53,960 --> 00:26:56,600 Speaker 4: I think that's a values question for the individual, and 483 00:26:56,920 --> 00:26:59,600 Speaker 4: whatever the answer is, I would respect it fundamentally if 484 00:26:59,640 --> 00:27:02,480 Speaker 4: you don't to engage in a certain process, whether you 485 00:27:02,480 --> 00:27:05,479 Speaker 4: know and that you know, just just recently our clients 486 00:27:05,520 --> 00:27:08,879 Speaker 4: are doing more face to face interviews again, and you know, 487 00:27:09,040 --> 00:27:12,119 Speaker 4: and you know COVID, and immediately post COVID that was 488 00:27:12,200 --> 00:27:16,280 Speaker 4: more online. So even that change, which is kind of 489 00:27:16,280 --> 00:27:20,160 Speaker 4: a more traditional change, even that change, we had candidates say, 490 00:27:20,600 --> 00:27:22,359 Speaker 4: you know, I don't really see the need to go 491 00:27:22,400 --> 00:27:24,879 Speaker 4: into the organization, you know, I would prefer to do 492 00:27:24,920 --> 00:27:28,920 Speaker 4: this online, et cetera. So there's always changes and processes 493 00:27:29,040 --> 00:27:31,600 Speaker 4: and it's up to the candidate to engage or not. 494 00:27:32,119 --> 00:27:34,400 Speaker 4: I did want to go back back a step if 495 00:27:34,440 --> 00:27:36,880 Speaker 4: I if I could, because I picked up on your 496 00:27:36,920 --> 00:27:41,080 Speaker 4: point around when you said that you think you had 497 00:27:41,119 --> 00:27:43,639 Speaker 4: a really fair shot. You know, there's an ad, you 498 00:27:43,720 --> 00:27:44,680 Speaker 4: respond to the ad. 499 00:27:44,920 --> 00:27:45,520 Speaker 2: You hope that. 500 00:27:45,520 --> 00:27:51,159 Speaker 4: Everyone had been verified and looked at equally and fairly. 501 00:27:51,720 --> 00:27:55,600 Speaker 4: And I think a really important part for candidates to 502 00:27:55,720 --> 00:27:58,960 Speaker 4: remember whether it is fair or not, is that at 503 00:27:59,000 --> 00:28:02,160 Speaker 4: the point that you're in aging with a recruitment consultant, 504 00:28:02,720 --> 00:28:06,520 Speaker 4: it's often an engagement that is non exclusive, and so 505 00:28:06,640 --> 00:28:10,080 Speaker 4: by the nature of that, there's an element of speed 506 00:28:10,840 --> 00:28:14,040 Speaker 4: and effectiveness that needs to take place. So I would 507 00:28:14,080 --> 00:28:17,840 Speaker 4: just it's just the realities of that. However, I will 508 00:28:17,880 --> 00:28:21,399 Speaker 4: say we are working a lot more with clients who 509 00:28:21,640 --> 00:28:25,960 Speaker 4: are asking us to canvas exclusively, and often we're being 510 00:28:26,080 --> 00:28:31,000 Speaker 4: retained in this manner, canvas could be multi region. Say, 511 00:28:31,040 --> 00:28:33,400 Speaker 4: for example, we want you to look in both markets. 512 00:28:33,800 --> 00:28:37,880 Speaker 4: We're going to utilize you as our exclusive partner. We're 513 00:28:37,920 --> 00:28:40,280 Speaker 4: going to give you two weeks to run this process. 514 00:28:40,720 --> 00:28:43,400 Speaker 4: We want you to provide us with a balance shortlist. 515 00:28:43,720 --> 00:28:46,480 Speaker 4: We would love to see you know, Wahan, their Malori, Pacifica, 516 00:28:46,880 --> 00:28:49,920 Speaker 4: you know, perhaps it's neurodiverse candidates as well. We'd love 517 00:28:49,920 --> 00:28:53,440 Speaker 4: to see a longer and more balance shortlist. And so 518 00:28:54,160 --> 00:28:56,200 Speaker 4: we need to see more leadership like that from our 519 00:28:56,240 --> 00:28:58,640 Speaker 4: clients as well, to say we're not going to have 520 00:28:58,720 --> 00:29:01,240 Speaker 4: you go for a race for for eight hours. We're 521 00:29:01,280 --> 00:29:04,040 Speaker 4: going to partner with you early. We're going to provide 522 00:29:04,080 --> 00:29:06,120 Speaker 4: you with the full picture, and we're going to enable 523 00:29:06,160 --> 00:29:08,400 Speaker 4: you to go out and run that process. And when 524 00:29:08,440 --> 00:29:10,760 Speaker 4: we are working in that manner, we are having a 525 00:29:10,840 --> 00:29:14,760 Speaker 4: talent incredibly successful results for the client. So that's just 526 00:29:14,800 --> 00:29:16,200 Speaker 4: another thing to keep in mind as well. 527 00:29:16,360 --> 00:29:19,440 Speaker 1: So whether people like it or not, really you sort 528 00:29:19,440 --> 00:29:22,400 Speaker 1: of have to be algorithm friendly in terms of the 529 00:29:22,440 --> 00:29:27,760 Speaker 1: information you're assembling for recruitment companies or employers to look at. 530 00:29:28,000 --> 00:29:28,200 Speaker 2: Jack. 531 00:29:28,240 --> 00:29:31,080 Speaker 1: Do you think you know ultimately we're all going to 532 00:29:31,160 --> 00:29:35,280 Speaker 1: have to sort of curate our own data sets that 533 00:29:36,160 --> 00:29:40,280 Speaker 1: fits those requirements, everything from how we design our LinkedIn 534 00:29:40,720 --> 00:29:44,600 Speaker 1: pages to our websites. You know, like ten twenty years ago, 535 00:29:44,600 --> 00:29:46,960 Speaker 1: it was all when you were trying to get your 536 00:29:46,960 --> 00:29:49,880 Speaker 1: website noticed. It was all about search engine optimization. You 537 00:29:49,960 --> 00:29:54,920 Speaker 1: had to be algorithm friendly when you designed your website. Essentially, 538 00:29:55,080 --> 00:29:57,440 Speaker 1: we are having to do that now for how we 539 00:29:57,480 --> 00:29:59,560 Speaker 1: represent ourselves to potential employers. 540 00:29:59,600 --> 00:30:02,280 Speaker 3: Yeah, and if I wind back the clock to the 541 00:30:02,480 --> 00:30:05,120 Speaker 3: let's say the nineteen seventies and you went in for 542 00:30:05,160 --> 00:30:09,239 Speaker 3: a prospective job, you would dress nicely, make sure that 543 00:30:09,280 --> 00:30:12,160 Speaker 3: you had a haircut, and you're presenting yourself as the 544 00:30:12,160 --> 00:30:14,520 Speaker 3: best possible self that you can be for that interview 545 00:30:14,880 --> 00:30:18,280 Speaker 3: and for that engagement. I think that that's just changed 546 00:30:18,320 --> 00:30:24,920 Speaker 3: over the years into a CV cover letter style presentation. 547 00:30:25,280 --> 00:30:28,120 Speaker 3: Then LinkedIn came through, and now we're into a different 548 00:30:28,120 --> 00:30:31,880 Speaker 3: space of curating your image. It's just shifted from a 549 00:30:31,920 --> 00:30:35,760 Speaker 3: physical space into a more digital one. So you absolutely 550 00:30:35,760 --> 00:30:38,440 Speaker 3: do need to be curating your online presence and profile, 551 00:30:38,600 --> 00:30:42,720 Speaker 3: specifically for the future and for roles that you want 552 00:30:42,960 --> 00:30:46,640 Speaker 3: in five to ten years time. The data that's being 553 00:30:46,680 --> 00:30:49,000 Speaker 3: harvested now is going to be on the books for 554 00:30:49,080 --> 00:30:52,040 Speaker 3: quite a long time. And so if you've got social 555 00:30:52,120 --> 00:30:55,360 Speaker 3: media presence that's not conducive to who you want to 556 00:30:55,360 --> 00:30:57,440 Speaker 3: be in ten years, you may want to clean that 557 00:30:57,560 --> 00:31:00,200 Speaker 3: up so that it's not coming back to or a 558 00:31:00,240 --> 00:31:02,720 Speaker 3: recruiter in ten or fifteen years and they're looking at 559 00:31:02,760 --> 00:31:07,520 Speaker 3: it going so you said this, but yeah, absolutely, curating 560 00:31:07,520 --> 00:31:12,040 Speaker 3: your profile, curating who you are and who your presence is. 561 00:31:12,080 --> 00:31:15,520 Speaker 3: But I think it's very important not to overcurate the 562 00:31:15,520 --> 00:31:18,360 Speaker 3: point where it's not authentic to who you are and 563 00:31:18,480 --> 00:31:23,360 Speaker 3: to your own capability, because then you're selling something that 564 00:31:24,080 --> 00:31:28,200 Speaker 3: is completely misleading. And you know, while we can look 565 00:31:28,240 --> 00:31:31,440 Speaker 3: at it and say, well, you know, companies and organizations 566 00:31:31,440 --> 00:31:34,400 Speaker 3: shouldn't be using AI to do this process, you know, 567 00:31:34,520 --> 00:31:36,640 Speaker 3: in the same token, we should also say the candidates 568 00:31:36,640 --> 00:31:40,160 Speaker 3: shouldn't be using AI. However, when push comes to shove, 569 00:31:40,200 --> 00:31:42,800 Speaker 3: it's going to happen in both directions. It's about, you know, 570 00:31:43,000 --> 00:31:45,800 Speaker 3: do I make myself as ethical as I can be online? 571 00:31:46,280 --> 00:31:48,120 Speaker 3: Do I make it, you know, present who I am 572 00:31:48,200 --> 00:31:51,240 Speaker 3: and get hired for what I can present to the world, 573 00:31:52,080 --> 00:31:57,680 Speaker 3: versus providing some artificially inflated capability statement which in three 574 00:31:57,680 --> 00:31:59,840 Speaker 3: to six months is going to turn out to completely 575 00:32:00,120 --> 00:32:02,080 Speaker 3: in your image, especially in the market the size of 576 00:32:02,080 --> 00:32:04,360 Speaker 3: New Zealand, where word we'll get around quickly. 577 00:32:04,440 --> 00:32:08,280 Speaker 1: And as always I've having hired a few people myself, 578 00:32:08,320 --> 00:32:10,960 Speaker 1: there's always the you know, what is different, the different 579 00:32:10,960 --> 00:32:14,520 Speaker 1: approach someone's taken, and which is quite refreshing in a 580 00:32:14,600 --> 00:32:18,400 Speaker 1: sea of sort of templated cvs you see on LinkedIn, 581 00:32:18,920 --> 00:32:21,200 Speaker 1: a lot of people in the tech sector are going 582 00:32:21,480 --> 00:32:23,920 Speaker 1: posting their CV. This is the CV that got me 583 00:32:24,000 --> 00:32:27,680 Speaker 1: jobs at Google, Uber and Amazon, and they're usually the 584 00:32:27,720 --> 00:32:31,040 Speaker 1: most basic. One page is so it seems you know 585 00:32:31,080 --> 00:32:33,840 Speaker 1: all the strategies you can do in that. What what 586 00:32:33,920 --> 00:32:37,720 Speaker 1: really appeals to people is just give me the basics, 587 00:32:37,920 --> 00:32:40,120 Speaker 1: state it plainly, don't try and dress it up too much. 588 00:32:40,200 --> 00:32:42,880 Speaker 1: Is that what you feel, Caro, when you're looking at 589 00:32:42,880 --> 00:32:45,840 Speaker 1: a pile of cvs, Give it to me simply. You 590 00:32:45,960 --> 00:32:47,200 Speaker 1: sell yourself on a page. 591 00:32:47,240 --> 00:32:48,840 Speaker 4: Yes, And you know, going back to one of the 592 00:32:48,840 --> 00:32:52,280 Speaker 4: first things I said, hiring managers and recruitment companies and 593 00:32:52,680 --> 00:32:56,479 Speaker 4: large organized talent acquisition teams and organizations are just dealing 594 00:32:56,560 --> 00:33:01,360 Speaker 4: with so much information and so much noise. The more succinct, 595 00:33:01,920 --> 00:33:06,040 Speaker 4: the more precise and correct. Go to Jack's point as well, 596 00:33:06,080 --> 00:33:08,400 Speaker 4: don't say things that aren't real because it will be 597 00:33:08,440 --> 00:33:11,520 Speaker 4: found out at some point throughout the process. The better, 598 00:33:12,000 --> 00:33:15,720 Speaker 4: whether that's one, two, three pages, you know, you know, 599 00:33:15,720 --> 00:33:18,239 Speaker 4: it's kind of arguably up for the candidate, but it 600 00:33:18,320 --> 00:33:19,600 Speaker 4: must be specific. 601 00:33:19,640 --> 00:33:21,720 Speaker 1: And just finally, going back to what you were saying 602 00:33:21,800 --> 00:33:24,920 Speaker 1: at the start, that the types of projects you're recruiting 603 00:33:24,960 --> 00:33:27,760 Speaker 1: for are slightly different now and that's the influence of 604 00:33:27,880 --> 00:33:31,400 Speaker 1: artificial intelligence. And we've been told, you know, the mundane, 605 00:33:31,680 --> 00:33:35,920 Speaker 1: the manual, the repetitive is going to disappear increasingly. So 606 00:33:36,040 --> 00:33:39,080 Speaker 1: your skill set needs to be the soft skills around 607 00:33:39,120 --> 00:33:43,880 Speaker 1: working with others, collaborating, overseeing the AI agent. Things like 608 00:33:43,960 --> 00:33:48,520 Speaker 1: quality assessments and assurance are really important. So in terms 609 00:33:48,520 --> 00:33:52,760 Speaker 1: of how you navigate this job market dynamic that is changing, 610 00:33:52,920 --> 00:33:55,720 Speaker 1: what should I do as a mid forties guy who's 611 00:33:55,760 --> 00:33:59,800 Speaker 1: been a wordsmith my entire life, but AI is coming 612 00:33:59,800 --> 00:34:02,040 Speaker 1: for that industry. Should I be going out and doing 613 00:34:02,600 --> 00:34:06,560 Speaker 1: free courses from MIT and that around prompt engineering and 614 00:34:06,680 --> 00:34:10,840 Speaker 1: so showing that I have some interest in upskilling in AI. 615 00:34:11,040 --> 00:34:14,640 Speaker 1: Or should I be doing soft skill type skill development, 616 00:34:14,680 --> 00:34:18,360 Speaker 1: doing team leading and that sort of thing, what do 617 00:34:18,400 --> 00:34:20,480 Speaker 1: you need to have on your CV for the algorithm 618 00:34:20,480 --> 00:34:22,600 Speaker 1: to go? This person is thinking about the future. 619 00:34:22,600 --> 00:34:24,800 Speaker 4: I think in the short term people definitely need to 620 00:34:24,840 --> 00:34:28,680 Speaker 4: be taking responsibility of upskilling themselves about AI a one 621 00:34:28,760 --> 00:34:32,480 Speaker 4: hundred percent. I'm currently doing micro credential right now in 622 00:34:32,520 --> 00:34:37,200 Speaker 4: Disruptive Technology focused on AI with Francis Valentine at Academy 623 00:34:37,360 --> 00:34:40,680 Speaker 4: X and speaking of you know, journalists there are and 624 00:34:40,920 --> 00:34:44,440 Speaker 4: the creative industry in general. There are a significant amount 625 00:34:44,560 --> 00:34:48,560 Speaker 4: of adult students around the forty to fifty year age 626 00:34:49,160 --> 00:34:53,400 Speaker 4: from the film commission partaking in this cohort. So that 627 00:34:53,520 --> 00:34:58,520 Speaker 4: is incredibly important that everyone takes responsibility for that, and 628 00:34:58,560 --> 00:35:02,799 Speaker 4: then in parallel, yes, really thinking about how does that 629 00:35:02,880 --> 00:35:06,600 Speaker 4: augment your work now so that you can do more 630 00:35:06,719 --> 00:35:10,560 Speaker 4: of those human lead things. So as an example, at Talent, 631 00:35:10,640 --> 00:35:13,359 Speaker 4: we're currently kicking off a pilot, you know, looking at 632 00:35:13,360 --> 00:35:16,960 Speaker 4: some different LMS for the executive team. I've created a 633 00:35:16,960 --> 00:35:20,560 Speaker 4: baseline for myself to go, this is how much time 634 00:35:20,600 --> 00:35:23,319 Speaker 4: I spend with my team now and really, you know, 635 00:35:23,640 --> 00:35:26,880 Speaker 4: in really important conversations, this is how much time I 636 00:35:26,920 --> 00:35:28,439 Speaker 4: spend with clients right now. 637 00:35:28,800 --> 00:35:31,640 Speaker 2: I'm wanting to now get better at using you. 638 00:35:31,600 --> 00:35:35,120 Speaker 4: Know, these productivity tools etc. So that I can spend 639 00:35:35,120 --> 00:35:38,080 Speaker 4: more time in front of clients with my team and 640 00:35:38,160 --> 00:35:40,840 Speaker 4: basically having more impact than before. 641 00:35:40,920 --> 00:35:43,960 Speaker 1: Well, lots of good tips there and some great insights 642 00:35:44,000 --> 00:35:46,879 Speaker 1: into what's going on in the New Zealand market. Caras, 643 00:35:47,000 --> 00:35:49,239 Speaker 1: so thanks so much, Thanks so much, Jack for coming 644 00:35:49,239 --> 00:35:50,040 Speaker 1: on the Business of Tech. 645 00:35:50,080 --> 00:35:50,799 Speaker 2: Thank you very much. 646 00:35:57,120 --> 00:36:00,799 Speaker 1: Thanks to Carra Smith and Jack Jorgensen. Really interesting my 647 00:36:01,040 --> 00:36:05,840 Speaker 1: key takeaways. Really AI is shaking up recruitment on every level, 648 00:36:05,920 --> 00:36:08,759 Speaker 1: making it more critical than ever to craft a really 649 00:36:08,800 --> 00:36:12,399 Speaker 1: good CV that's friendly not only to the algorithms but 650 00:36:12,480 --> 00:36:14,200 Speaker 1: to the humans as well. Who are going to get 651 00:36:14,200 --> 00:36:16,680 Speaker 1: a shortlisted bunch of cvs and you really want to 652 00:36:16,719 --> 00:36:20,600 Speaker 1: stand out there. But you know, and this sounds obvious. 653 00:36:20,960 --> 00:36:23,280 Speaker 1: I know a lot of friends who are simply fired 654 00:36:23,280 --> 00:36:26,120 Speaker 1: in an application for a job this year and been 655 00:36:26,160 --> 00:36:29,000 Speaker 1: a bit indignant about not even getting an acknowledgment off 656 00:36:29,000 --> 00:36:32,920 Speaker 1: it or not making the shortlist. You can't just stop 657 00:36:32,960 --> 00:36:37,280 Speaker 1: with the application. You need to cultivate your personal brand, 658 00:36:37,680 --> 00:36:42,560 Speaker 1: tap into networks, start upskilling in the AI and soft 659 00:36:42,600 --> 00:36:46,879 Speaker 1: skills that are now the real differentiators and show recruiters 660 00:36:47,080 --> 00:36:52,120 Speaker 1: and companies' perspective employers that you're willing to upskill and reskill. 661 00:36:52,239 --> 00:36:55,399 Speaker 1: That's what they want to see. The smartest candidates are 662 00:36:55,480 --> 00:36:59,480 Speaker 1: moving beyond the job board rat race, you know, going 663 00:36:59,520 --> 00:37:03,880 Speaker 1: to seek and trade me jobs, making direct connections, and 664 00:37:03,920 --> 00:37:08,759 Speaker 1: showing both technical acumen and genuine human potential. I think 665 00:37:08,760 --> 00:37:12,160 Speaker 1: in the tech sector, which talent works in, this is 666 00:37:12,360 --> 00:37:15,439 Speaker 1: all the more important that you have obviously those those 667 00:37:15,480 --> 00:37:18,759 Speaker 1: technical skills, but you have all the other skills, the 668 00:37:18,760 --> 00:37:21,920 Speaker 1: soft skills that they want, as well as that aptitude 669 00:37:22,000 --> 00:37:25,280 Speaker 1: and AI and that willingness to of skill in AI. 670 00:37:25,719 --> 00:37:29,200 Speaker 1: I think it's really interesting that while recruiters are using AI, 671 00:37:29,760 --> 00:37:33,759 Speaker 1: they recognize the serious limitations off it for picking who 672 00:37:33,800 --> 00:37:36,600 Speaker 1: the best fit candidates are going to be. It's sort 673 00:37:36,640 --> 00:37:39,840 Speaker 1: of refreshing really that Karen and Jack are advocating, you know, 674 00:37:39,880 --> 00:37:42,640 Speaker 1: the traditional way of doing things, getting out there, hustling, 675 00:37:42,760 --> 00:37:45,799 Speaker 1: working any angle that will give you an edge. That's 676 00:37:45,840 --> 00:37:47,680 Speaker 1: it for the Business of Tech this week. Show notes 677 00:37:47,680 --> 00:37:49,880 Speaker 1: are in the podcast section at business esk dot co 678 00:37:50,160 --> 00:37:53,000 Speaker 1: dot nz. Thanks so much to our loyal sponsor two 679 00:37:53,080 --> 00:37:56,759 Speaker 1: degrees No. I'll catch you next Thursday streaming on iHeartRadio. 680 00:37:57,080 --> 00:37:59,440 Speaker 1: We're your favorite podcast app for another episode of the 681 00:37:59,440 --> 00:38:01,120 Speaker 1: Business of Time tick catch you then