Episode 577 - Keeping the Human in the Loop - with Denise Pereira

Speaker A: This episode is sponsored by Greenhouse, the leading hiring platform. Helping companies to get measurably better at hiring AI.

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Speaker C: Get ready. It's Tarpod time. We talk talent recruitment and everything in between. So strap in and prepare yourself for a dose of knowledge tied up in a ribbon of fun. Now please join your hosts and industry leaders, Lauren Sharp and Craig Watson.

Speaker B: Hi, everyone, and welcome to tarpod. I'm Craig.

Speaker A: And I'm Lauren.

Speaker D: How are you, Craig O.

Speaker B: It's been a long day with you, but we're getting there.

Speaker D: Every day is a long day with you, I can tell you now. And that giggle in the background is a fabulous guest. The one and only Denise Pereira, senior, uh, global talent sourcing partner at Zendesk. Also the founder at Kaleidosaurus, not Kaleidoscope, the fractional table recruitment lead and also podcast host of the Crafty Source.

Speaker B: Is there anything you don't do?

Speaker D: Good point.

Speaker E: Yeah, I don't cook, but I've started

Speaker D: baking, so that sounds okay. Yeah. Cooking optional in our house.

Speaker B: We're so lucky to have you with us today, Denise, because when anyone in Australia, or even apac talks about sourcing, they think of you.

Speaker D: I know. You are the sourcing queen.

Speaker E: I don't know why they do that. I don't know why they do that.

Speaker D: Because you're pretty bloody good at it and you're pretty bloody vocal about it. So what we thought we'd do is get you in to talk about sourcing. And in an AI driven talent acquisition world, we are now. So what the fuck are we doing?

Speaker B: How much is it? Has sourcing changed in the last year to 18 months?

Speaker E: That's a good question. And if I think. First of all, thank you so much for bringing me back. I think the last time I was on here was actually with Matt when our, uh, roles were impacted in our last gig. And then we started doing our own thing from there. So it's been a while. Things have changed drastically. I think the biggest change has been that there is a chance that sourcing could go away in the couple of years, at least from what I'm seeing. We follow people like Glenn Cathy, Vanessa Rath, there's Jan Tegsi. These are the. At the forefront of what's happening in AI. So, um, if I look at when we break down sourcing. Sourcing has so many caveats to it. Right. There's the engagement path, there's a search part. What AI is doing well but just not well enough is yes, it can go out and it can search for people, yes, it can engage with people. But I still feel like that's where the human connection plays probably the biggest part in everything that's happening in sourcing. Right. Because with people being bombarded with the same messages, people are like, I want something fresh, I want something different. I still want to talk to a person at the end of it. I don't think it's drastically changed anything just yet, but that's in play and that's going to change drastically. I think we. If you. If you look at 2022 to 2024, it was all around prompting becoming m. Better at prompts. Now we've moved into agentic AI.

Speaker B: Yep.

Speaker E: In the past two years. Right. And if you put sourcing on top of that, people are now looking at how do I make agents who are going to do all the groundwork for me, whether that means me going out and shoving a JD in telling it what a pretty ideal profile looks like. Go and scrape the web to find all these people where they're sitting, not just on LinkedIn, but other social sites. Engage with them. Get that back into the fall intake meetings. This is where I feel like it's made a small change, but it hasn't made a drastic change. I'll talk about bullying here because when you go back to sourcing, it's all about people always think about bullying. Because that's where I don't think AI has perfected how you can actually search properly. My own example being when I try and do a Boolean search, I will use AI for it, just to see what output it gives me. But I still have to tell it what to do in the backend. Use parentheses, use quotation marks, if it's more than two words, et cetera. So I don't think it's drastically changed just yet. But I think with the way AI is evolving, there's going to be a lot of different changes happening in the backend for us. And to be honest, it does make me a little scared for sources in general because typically resources come from a research background, right? A lot of them do, yes. And we prefer the manual hard work of going and finding people where you typically wouldn't be able to find them. And so far from what I've been seeing, AI is just scratching the surface, at least for me from a sourcing perspective. But it's getting a lot more better.

Speaker D: So is it finding like when you're comparing it to the bullying search, are you finding more passive candidates in any way or is it just bringing up the same stuff?

Speaker E: I, uh, wouldn't say it's just passive candidates. Right. Why do we do sourcing to begin with is to obviously look at how do we widen our pool than what we've already got from a job description percent so I don't think it's just passive candidates is definitely given some results that I hadn't really looked at from a candidate profile perspective. Again, it goes back to how you're doing your search. I don't think it's just about passive candidate. I think it's pretty much everyone. And to be honest, I haven't. The agents that I've created so far, they'll go and find me people, let's say if it's in a hard to fill market like India, right, where there's constant churn of applicants coming through, I've got like a daily digest running in the back end that it will go, it will provide me people. But where is it bringing all these people from? LinkedIn. Everything ties back into LinkedIn. It still hasn't perfected. How do we go and source on Behance or Dribble or Kaggle until you actually specify where you want them to X ray or find candidates from.

Speaker B: It's interesting, isn't it, because you made a couple of points there. One that it's that you fear for the future of sources. But then you also said that there are parts of the process which need that human touch and that AI hasn't got to. And I would suggest that it's going to be very difficult for AI to be in an area where there's that negotiation, negotiation back and forth and really getting under the hood and asking questions that yeah, maybe sourcing as a function might get a bit smaller because there'd be less people doing it, but those that use a combination of that human element and AI tools that work and use them to their benefit are probably going to get better and really have a strong career pathway.

Speaker E: 100%, Craig. And I think we are all seeing this everywhere. Where TA teams are inundated with work. They're doing pretty much two, three people's work right now. Right. And we're always being thrown at use AI, but nobody's telling us how to use AI in recruitment. If we look at TA as an example, I forgot what the data was. But now more and more people that were skeptical actually coming to the table and this is where they're using a lot more gen take AI than actually prompting. That makes sense. And I feel like there's, if I take my own example where I'm currently I'm at Zendesk and we have a massive gen AI community where we are all actually, we have things where we have AI upskilling. So we actually bring in our engineers to show us how to add an MCP to our uh, agentic agent. How can we do better? So I always say start with the low hanging fruit. And that's what I did for myself is we're starting with the low hanging fruit and then building from there. So yeah.

Speaker D: So um, on that front then there's always AI and agent are built around what we put into them, we create them. So are we seeing any biases being embedded into AI sourcing tools?

Speaker E: That's a good question. A lot of the sourcing tools that I've been playing around with, again, this is why that human in the loop is so important, right? Because AI can absolutely amplify that bias. If I look at what I've been working on and the tools I've used, I have to check everything that I'm doing because by habit, by default, we are a creature of control. We like to know what's going in, what's going out. So from that aspect, look, is there bias? I'm sure there is, but we should be the ones checking for that bias. And how do we kind of mitigate some of that in the back end? And I guess again it goes back to what you said, Lauren is all around the prompts that we are feeding it, the Personas that we are giving it. I just created one that is, I call it the job Description energizer. Right. Because when we look at our job descriptions, they're boring as fuck. So I wanted to energize it and I gave it a very detailed prompt and I said to it, I was like, remove bias. I wanted to make sure that everyone, whoever is relevant can apply to it. Especially from a diversity. Everybody wants more women, but nobody knows how to do it properly. Right. So I fed it a lot of that. And I started to see that the job description, the output uh, that it was giving me, obviously I was going through everything I was sharing with the hiring manager getting their input and what we've seen is an uptake in more relevant profiles because I'm also sharing that same thing with people that I'm reaching out to. So my point with that is that no matter what happens, how far we get into AI, I think at the end of it us making sure that we are that buffer in between is probably going to be the most important thing in the output that we are receiving.

Speaker B: Yeah. Do you think that say you were using AI tools in sourcing versus not using AI tools in sourcing to the people with the AI tools at the moment have any advantage?

Speaker E: 100% you get more time back in your day, right. You have more time to do what you haven't had time to do. Again, I can only speak of my experience. So if I look at a, ah, note taker as an example, I've recently had hand surgery, I can barely type that note taker has given me time back. So we use candidate FYI as an example and that note taker is so number one we're doing a lot of beta testing with it because it's a new tool for us so we have to report back on what's working, what's not. And so far it's been working brilliantly. So if I'm having five calls in a day, if before it used to take me about 20, 25 minutes to get everything together to make sure that for one candidate I'm now saving about hour or more on just putting my feedback together and submitting that to hiring managers. I've got a Google gem set up so I take those notes and I tell it to condense it into a one pager. Uh, for the hiring manager I obviously give it my intake notes as well and then I just pass it on to the hiring manager, it saves me from typing. So that's just one small example of how we are using AI uh to make our lives a lot more easier. If I look at from a sourcing perspective I feel like we still need to get there as much as we talk about hyper personalization because when I talk about low hanging fruits, I'm talking talking about job descriptions, I'm talking about uh, profiles, I'm talking about outreach messages, right? So if I go into outreach messages we talk about hyper personalization but people don't realize hyper personalization takes time. I was doing sourcing this morning, I was still doing It a bit manually and that took me a good two hours to look at candidate profiles and I think I got through about 13 people. I hyper personalized everything for every candidate and then I was like, this is not a good use of my time. I need to find something better that is still going to make sure that I have a human in the loop where I'm reading things, but also to take that away from me in a way. So I'm still figuring that out.

Speaker D: That was actually my next question was how do you balance that human centered candidate experience when we're still trying to use AI for efficiency. So it's a really tough balance at the moment, isn't it? Because I'm seeing that AI, what you've said, you're giving incredibly great examples of how it's making your life easier and those types of things. But we're in the human business and people have to keep remembering bring that.

Speaker E: I was actually I did this sort of showcase with the TA team and I was talking about the different aspects of sourcing and I used to actually run this in my trainings as well through kaleidosource was the different facets we have of sourcing. Engagement probably has become to me at least right now with all this AI stuff that we have under the hood. That's probably the most crucial is the engagement part. So I'll give you another example of that. So for some of the roles I'm working on, I'm actually giving candidates the option to create that consistency of connection through engagement. I'm actually giving them an opportunity instead of talking to me, hey, here's the hiring manager. This is their diary. Book sometime and have a chat with them. So that way they don't feel like, hey, this is just another sequence or a follow up or a uh, drip feed campaign happening in the back end. I still want them to feel like yes, they are being approached by a person. Yes, we use AI to create some of the stuff, but that's only to help us to make sure we are putting out the right content to you. Right. And trying to create a connection in the right way with you. So there are still different ways where we can have that human connection in the loop. Everything still needs to go through. I say, look, if you feel like you've got a good working rhythm where you feel like you don't need to check what's happening, that's well and good, but you have to feed it a lot for that to start picking up on your Persona, your tone. You've got to keep Talking to it every now and then.

Speaker D: So what's it, how's it moving in? Uh, you're developing talent pools before a role even opens up. Are you utilizing AI in developing those pools as well?

Speaker E: Yes. So we are going to be. We're actually going to start doing that. We have a couple of tools that we are about to implement. We are right now in the sandbox testing phase. With that, it's going to allow us to create not only campaigns, but one thing I've always talked about is there's a difference in talent pools and there's a difference in pipelining.

Speaker C: Oh, yeah.

Speaker E: So till your business doesn't know what the hell they're hiring for, you don't know what needs to go where. And this is where workforce planning comes into play. And I don't know of any company that does workforce planning really well. Everyone is very reactive from that perspective. One of the things we're talking about is not everyone is interested in a role. What are we going to do with those candidates who said, hey, I'm not interested now, but then reach out to me in three, six, nine months? How do we nurture and keep them engaged in between all of that? And this is where AI is probably going to play the biggest role for us is we're going to start creating content calendars. This is something that I had brought up when I used to work with Matt at Tori is how do we keep them engaged? Yeah, not everyone wants to know about a job, but they also want to know what's happening in your company. Right. First we want to run surveys with people that have already opted in and figure out, okay, if you were to hear from us, what do you want to see? What do you want to hear? What's going to be relevant for you? Then we want to bring up a six month sort of content calendar and use that to keep people engaged. We're going to look at things like roundtables again. The human element is going to be alive in that. Right. How do we have roundtables? How do we get sponsorship from exec teams on this to say that, hey, we also want to be part and parcel of this because it's not just recruitment's job to keep these people warm and keep them engaged throughout the process. But again, like you said, talent pool or talent pipeline, it's a long haul game. We are actually going to be. We've just signed off a deal with gem sourcing. I've only seen parts of it, I haven't used it, so I'm really keen to see how we're going to actually utilize that in terms of keeping people engaged through that. And I think it's very top of. It's very heavy in terms of top of funnel because someone has to constantly be on the pulse of that. You can't. It just can't be a set and forget. And I don't even think drip feed campaigns are going to be good enough for that, really. It's only going to be one part of it. Right. And this is where that's serving your candidates to know what is it that they want to hear from you? What do they want to hear about? Is it podcasts? It could be podcasts, it could be the latest news, it could be the newest announcement of acquisitions that are happening. If I look at, from a sales perspective, these things are actually nuggets for them from a competitor standpoint. Like they could be working at a service now or a snowflake or whatever. And we are telling them what our recent announcements are, who we are acquiring. That's intel for them.

Speaker B: Yeah. Ah, I'm going to go on a different tack here for a minute. Denise, just off air, you told us that recently you've changed function, that you're sourcing in a little bit or added an extra function. So somebody's had years and years, years of specializing in a sector or in an area and now moving into a different function with different types of roles in different areas. How hard has it been and what process have you taken from what you knew and new bits that you've added to try to get this right?

Speaker E: Yeah, I love that question and I'm glad someone actually asked me that, besides my missus. So

Speaker D: now that you're an old woman, old married woman, I, uh, I'm surprised you actually took talk to each other. Isn't that what happens, Craig, after so long of marriage, you stop talking to each other?

Speaker B: Yeah. It's only natural.

Speaker E: Yeah. Everything happens through texting now, doesn't it? Then the next room, just text them. Can I get a glass of water? Yeah. Look, it's. Personally, for me, it's been hard and different. I'll say different rather than hard. Right. Because nothing's too hard if you really put our minds to it. I think given where we were even last year, to where we are now, a whole different company now. What I have, and this is where I'll say AI has actually helped me quite a lot. One is I've been talking, sitting and talking to people. So this is where I've used connections and tech to get what I need to get to make me better at my job. Obviously, talking to the people in the business to really understand the lay of the land. Where were we six months ago? Where are we now? But AI actually helped me because I can't always be on the phone or on a call or on a zoom call with everyone all the time. We use Gemini. We have access to all of the AI tools here. So using different AI tools to, number one, understand what our job family architecture is, what those roles actually mean in layman's terms. Coming not coming from a sales background, that really helped me. Let's say if I'm going into discovery call with a hiring manager, I already had a very good understanding in layman's terms of what they're actually looking for. That helped us in terms of not utilizing that 15, 20 minutes, half an hour on just them explaining to me what the hell that role is. What I also did was based off my understanding of that I already had about 10, 12 profiles, and this actually happened this morning. I had about 12 profiles to take to that call and said, hey, just based off what I know and my own understanding, is this what we are looking for? And of those 12, there were seven profiles that they said look good. So I was at least hitting 75 of what it was. So that's where AI helps really well in obviously the information. I don't know about you all, but I don't even Google things anymore. And when you Google things, it automatically takes you to AI.

Speaker B: Yeah.

Speaker E: So I just ask Claude. I'll ask Perplexity. I'll ask Gemini just to make sure that the three different angles they're giving me actually make sense to myself as well. And if you don't understand something, constantly ask them, badger them, till it actually makes sense in your mind. So I hope that, yeah, kind of.

Speaker D: It is also showing the evolution of the value proposition as a talent acquisition professional as well, because, um, we are having to change the way we do things and how we source and. And finding all of those things as well. It's actually. Yeah. It's an interesting, um, interesting journey, I've got to say.

Speaker E: Yeah. Lauren, if you're. Lauren, if you remember when you and I spoke about two, three weeks ago, I actually mentioned to you, so we use lovable over here as well. And I love experimenting with Lovable because I know all about quote. So I'm like, okay, I'm gonna. I'm gonna use that. So I built thinking of Zendesk, thinking of competitors. Everyone's always asking for what's happening in the lay of the land. This and that. So I actually built this intel sort of dashboard for us where I've connected my perplexity to it. In the back end, it was using GPT, not chip. Yeah, it was using Gemini. But what it started to do was I was running out of AI credits because of it. And it allowed me to build something in real time that now I can take to hiring managers and say, hey, you've asked me for abc, but the market right now is telling me that we should be leaning more to xyz. So it tells you hiring intent, which of your competitors are hiring. So you get an understanding of, okay, if I'm looking for a bdr, but three of my competitors are similar. Ah, title but something different. Maybe we need to be pivoting, maybe we are going somewhere wrong. So it really gives you this whole overview of who our competitors are, uh, which region, role breakdown, family breakdown, what jobs are we losing to AI, what jobs are being hired because of AI. So that helps us sort of pivot and navigate, uh, that even in an early conversation with hiring managers. So there are things that we can be doing. Again, it goes back to what is our pain point, right. What problem are we trying to solve? Sometimes I think about low hanging fruit, but then um, I'm like, everybody's trying to solve for the same, same problem in recruitment, right?

Speaker D: So what? Okay, this is more of an ethical question.

Speaker B: Careful. She tries to catch people out with these ones.

Speaker D: That's because you've got no ethics and morals.

Speaker B: So I'll answer for you if you like.

Speaker D: He is a void of humanity. So where do you draw the line ethically when AI is making or heavily influencing a sourcing decision about people?

Speaker E: That's a really good question. I think it comes down, look, it comes down to cross referencing for me, right? So I'll give you an example. It happened recently. I was doing a bit of a. Not a scorecard but a comparison against candidates who have, who are in the process versus passive candidates. And one of the candidates that I liked, it actually spat out some information about me that wasn't true. So I went back and I looked at the profile because it says something that they came from a different background than they hadn't. And I actually had to correct it in the back end and it said, oh yeah, thank you for picking that up. And this is where we can very easily get swayed if we are not checking that information. Right. So that cross referencing. So from an ethical standpoint, had I connected or contacted that candidate and referenced that company and their background that the AI said to me, me, I would have been on a whole different side of it. Right. I would have actually looked like an idiot reaching out to someone quoting something that actually wasn't even in their profile. So. Yeah, sorry.

Speaker D: Interesting.

Speaker A: It's not always right, is it?

Speaker D: It's amazing how many people are ah, taking whatever AI spills at them as gospel as truth. It's not truth. You have to, you have to check the sources.

Speaker E: Yeah. I think it's also because people are, how should I say this?

Speaker D: Lazy.

Speaker E: Lazy. We all about convenience. So uh, sometimes it's okay if the first two lines make sense. The rest of it is going to make sense from an ethical standpoint. We use everything at zendesk. We have ChatGPT, we have. It's all within Zendesk's instance. So even from a data privacy perspective we don't have to worry about anything. It's all within our own instance. Nothing is going out. Everything is coming in and staying there as is as well.

Speaker B: Yeah. Hey Denise. And we've talked a fair bit about this in the past about role re architecture and workforce planning. Can you see a world where the sourcing function gets involved at analyzing existing roles to see what the future of that role is in a business?

Speaker E: 100%. I don't know if you all know Joe Atkinson, but he runs if I'm not mistaken. And just recently they were talking about the role of a talent engineer. And that is going to become something I 100% believe for us to really up our game and be seen as a value add to the business, especially using AI, we all need to become talent engineers. Irrespective of whether we're just focusing on sourcing or whether we're just focusing on recruitment or it's the first bank together. Everything is going to be top of funnel. Right. We are going to become advisors in a very different way than what we are used to right now. And AI is going to play a big part in that. So yeah, I definitely think sourcing, all of that is going to be wrapped into. I don't even think that is just going to be recruitment and sourcing anymore. I think it's going to be a talent engineer who's pretty much taking a function from A to B using AI. Like how do we get you as a team from here to there? You're skeptical. Why are you skeptical? Let's take that skepticism away and actually give you something to believe in.

Speaker B: Yeah. Interesting.

Speaker A: Interesting. That's.

Speaker D: This is a whole new theory out There I love new concepts, Denise. God, I love that.

Speaker E: It's not me, that's Joe Atkinson and the team. But I really, I was listening to their talk on YouTube and I, I seriously believe that's where we are, that's where we are headed. Because if you think about automation, if you think about stuff, this is something anybody and everybody, even a coordinator level can now start doing. Right. So how do we show that we off value to the business?

Speaker B: Yeah. And that's it. At the end of the day, businesses are looking for value and forever our ah, function's been seen as like a cost center or a support mechanism. And you need to have strategy. We need to be able to. Engineering is a lovely way to describe it. Is that 100%.

Speaker A: Yeah.

Speaker D: So while we're talking about this, the cost center, et cetera, you mentioned earlier you're chewing through tokens and now they've changed these models on pricing, etc. That we all suck it into our $30 a month. There's no longer $30 a month, is it? And yeah, so for AI sourcing tools, what at the moment out there is truly delivering on roi. And separate it from the noise. Separate that from noise because there's so much effing noise. Right. Everyone's got the new beauty thing, but what is actually working? What is the best roi?

Speaker E: And I think if you want to talk about roi, then obviously using what your company has internally is the best way to start because it's free to you. Even if I look at us over here, we have token usage, but I obviously don't use it as much as engineers do or prod people do. Use what you can that's available to you without you having to spend money. Again, again, how many of us are actually building versus using right now? So when I'm talking about building, I'm talking about authentic agents working in the backend 24, 7 in the loop. Right. I don't even have that. Uh, yes, it does its own thing in the backend, but I know for a fact I'm not using a lot of token usage in the backend. So again it comes down, I have my own personal tools as well, which I prefer using because I don't want to be told off for using too much. So I use my own stuff in the backend. But I also know not everyone will have the luxury or want to do that. So if you're worried about that, go with what's already being used in the company. Right. Because it's kind of, you have more your hands are not as tight as you would if it's coming out from your own pocket, if that makes sense.

Speaker D: That's very true, but you make a

Speaker B: good point there, because we are talking, uh, a lot about token usage at the moment, but general AI use won't exhaust your tokens. It's if you. If you're in that build mode. Generally.

Speaker E: Generally depends.

Speaker D: Oh, Denise, please elaborate because we've been in opposite corners on this, Craigo, and I have, so please come in.

Speaker B: Yeah, you better come up down on my side, otherwise you'll be trouble.

Speaker D: My side. She's my friend. More than your friend. Okay, we're claiming friends now.

Speaker B: You're right.

Speaker E: I can only talk about Claude code because that's what I'm using and that's where I hit my tokens the most. But again, that's my own paid version because I'm still trying to be up and running with Zendesk as one in the back end using their terminal. But yeah, I know with thought code, whatever I've been using, I pay, what, 20, 30 bucks or something like that? And depending on what model you use as well, is where your token usage comes into play. I know within less than eight hours I've hit my token and I wasn't even building. I was just going back and forth on ideas with it. Um, yeah, I don't know, Craig, if I'm on your side or Lauren's side on this. I think this is one of those things. And I was telling Lords last week, there was a podcast I listened to, and they were actually talking about token usage and how companies are, like, going nuts over that.

Speaker D: You for that? I have. I. I'm listening to that daily podcast now. Thank you for that. Tell our listeners about this podcast.

Speaker E: Oh, I forgot the name of it because I just did a search on AI and it came up and this guy, every day for 20, 25 minutes, he does a daily brief on what's happening in the AI world, what's happening within companies, like your metas of the world, other companies, what are they doing? And one of the ones that I listened to that I shared with Laws was around token usage.

Speaker D: It's called. And if, uh, it's called the AI Breakdown.

Speaker E: Yeah, okay. Yeah, that's the one. Yeah.

Speaker D: Yeah.

Speaker E: And Craig, you should listen to it and maybe then you. Laws and I can get onto another call and decide who's right in that space.

Speaker D: So every day they will lead, like anything from around 30 minutes to 5, 5 minutes to 30 minutes, they'll talk about what's going on in this week in AI, and it just. It just summarize and takes out some of the noise, and it's been really good. I've listened to a few episodes. Episodes over the weekend since you put me on last week, and very much for that.

Speaker E: You're welcome.

Speaker B: Hey, Denise, given that you are, uh, that sourcing guru and you are over AI in our space better than most, would you be open if anyone listening wants to have a chat to you and was a bit lost in their own workplace, would you be open to having a chat to them?

Speaker E: Oh, yeah. Look, I 100% would love to, if anyone wants to even partner, because to be honest, I'm still finding my feet with AI. Who isn't overwhelmed by AI right now, right? Everybody is. You barely blink. You go and take a shower, and 10 new models have come out. Yeah. If anybody's up for even partnering, brainstorming how can we make our community better? I'm all. I'm up for it.

Speaker D: It is. You're right, though. You and I even spoke about this a few weeks ago when we were chatting and just catching up about, like, uh, you said to me, do you feel lost? And I said, absolutely. It's changing so quickly. Just when I get my handle on one thing, it moves and it's obsolete. And it just. Sometimes I'm just sitting back going, I'm just going to po. Pause for a while and then see what emerges as the winner. I don't have the brain.

Speaker B: Wait till the war's over. Don't get involved in the war. Just wait till it's over pretty much and go with the winner.

Speaker D: I'm just gonna sit on the sidelines and then and wait because my brain just can't handle too much more of this flying.

Speaker E: Yeah. Yeah, 100%. In fact, the other day, I don't know, I was reading somewhere on Instagram, someone said, if you're thinking of doing any kind of AI courses, don't waste your money. There's no point in it. Because by the time you finish your AI course and you get your certificate in your hand, there's 10 new courses that come up. And I was like, oh, my God. That's actually pretty good, because I started doing. I remember when prompt engineering was a thing. I went on Udemy and I did a prompt engineering course. Do you think I use it? No, I don't. Because so much has changed since then.

Speaker D: I know. You just tell the agents now to create your prompts, correct?

Speaker E: One hundred percent. One m. Hundred percent. Yeah.

Speaker B: Hey, Denise. Thanks. So much for spending time with us today. It's been wonderful.

Speaker D: Insightful, Very insightful.

Speaker B: And I think that this could be a conversation that we revisit every so often throughout the year because, uh, like you said, things are changing so rapidly. But thanks for taking time out of your busy day today.

Speaker E: No worries. And thank you so much, both Craig and Lois. And I'm sure I'm going to catch you all around the trap soon.

Speaker A: Absolutely. So, everybody, that's a goodbye from me

Speaker B: and a goodbye for me. Today's episode comes from Greenhouse software companies

Speaker A: that use greenhouse surface real qualified candidates faster streamline interviewing and make confident decisions. Supported by AI built on structured hiring.

Speaker B: Visit greenhouse.com to learn more.

Speaker C: More.

Speaker B: And don't forget to tell them Tarod sent you.

Speaker C: Thanks for listening to Tarod. And please don't forget to subscribe. And make sure you listen to the outtakes at the end of the episode. They're usually the best bit.

Speaker E: Not too bad. What the hell is this? Neuter. Is that mine? Let me remove it.

Speaker D: I that is your neuter.

Speaker E: I don't even use it.

Speaker D: You need to neuter your ner.

Speaker B: It came in

Speaker D: poppies down there. And Nice is over there.

Speaker E: Hello.

Speaker D: And Bonnie's over there. So we've got all the dogs. All of our pets, Bonnie, Poppy and Harvey

Speaker C: J.