Ethical AI w/Chano Fernandez

[00:00:00] Welcome to the Recruitment Flex with Surge and Shelley I'm Surge and I'm Shelley and we talk all things recruitment starting right now.

[00:00:15] Bo Zhuhan, welcome to the Recruitment Flex Shutly so many great guests in 2024 already. We've got a big dog here we've got a big guests.

[00:00:28] Oh he's gonna appreciate you all in my dog, but anyways, I am very pleased to introduce our guest today, channel Fernandez, who is Co CEO of the company Angel, channel welcome so much to the show.

[00:00:45] Thank you so much for having me, and by the way Sir, to have a big dog at home. So, awesome you! Okay.

[00:00:51] What type of dog, I need to learn, he's allowed to know there is a lot, but yeah he's there is around 25 kilos, 85 60 pounds, whatever, so it's a big one, yeah.

[00:01:04] That's a big one, so chano you can you just share with the audience a little bit about who is chano and maybe give us a glimpse into your journey in HR Tech.

[00:01:13] Yeah thank you Shelley for the question, you wanna make it brief as you can tell for my accent. Clearly not native in English speaker.

[00:01:21] Boring is paying, live most of my life across Europe, starting my career in consulting and then he's always being enterprise be to be software since very early days right after McKinsey and he started mostly in HR Tech, clearly already at SCP 10 years at work day, are now continuing at ageful. So I've been trying to learn more about HR practitioners.

[00:01:49] Obviously, the software is a part of it, but I'm much more interested in how we can help HR leaders and CHROs and HR departments to do a better job for their employees and their work forces.

[00:02:02] That is a big decision and a big move to go from a world-renowned company like workday, you say workday and everyone in our industry knows exactly who they are.

[00:02:16] So I am going to put you on the spot here and ask you why would you move away from workday and why would you choose a full thing? There's a lot of HR Tech vendors out there. I'm sure you had your pick. Talk to us a bit about that your decision.

[00:02:32] Let me answer first the decision, right. I think part day give me an opportunity to help transform HR in the cloud and simply speaking, I believe and then convince a full is very well positioned to provide an opportunity to transform HR in AI, right? And we can talk a little bit about that work.

[00:02:53] You said workday and everybody will know who that company is. I'll tell you that in 2014, I was managing a large business at SAP and I moved to workday was a very small business in Europe, and it was around 400 million total revenue a year are 10, 2014 and I was calling customers and of course, maybe it was part of my English.

[00:03:17] But work what and what do you guys do? So clearly it's been a fascinating growth story and of course today would be well renowned and known company. But that was not the case back then.

[00:03:30] So I guess I can't see similarities on eight feet a few years down the road, but we have a hell of a lot of work ahead of us to get there.

[00:03:39] Okay, so when you look around the landscape tell us what was it? What are the similarities that you see in eightfold.

[00:03:47] And the opportunity. I would say try to be thoughtful when you make career deficiencies, right? I'm privileged that there are opportunities out there definitely.

[00:04:00] And I very openly share that first you need to be aligned with the purpose of the company and certainly align with what I fully trying to achieve, which is help everyone to create the right career for them in the world.

[00:04:13] Secondly, you need to share a common value system and that's tremendously important to me of course, then I would say potentially this is in particular order of prioritization, is there a great technology platform.

[00:04:28] Is there a solution these raternities here that is solving real business problems and use cases that I can understand and, of course, a full is providing that opportunity for me in AI.

[00:04:40] Do you think that you can attract right talent and create people on the bus that you can set a great north star and bushing and execute up on that one.

[00:04:49] Okay, and have some fun along the way. I like to build as I say a full is a bit more today. I was worked in 2014 when I joined.

[00:04:59] But I gave you a little bit the analogy that I liked to build in part and potentially the fast growth, acceleration, processes and learnings that grow through those experiences.

[00:05:09] You know, before joining a full ID, my homework because anyone can make a wrong division but I will not forgive myself for not doing the right utilities.

[00:05:18] So clearly I review a lot on the technology and I talk to customers and to partners and to industry analysts and I thought this seems like a fun opportunity to work on.

[00:05:26] Okay, I love the comparison of work day in Europe in 2014 and eight fold in 2024.

[00:05:36] Because Surgeonai were passionate about technology and HR tech and so we're probably a lot more familiar with eight fold than maybe a lot of our audience.

[00:05:47] Can you give us that elevator pitch? What is eight fold?

[00:05:51] Yeah, well, at least in eight fold today is potentially three times larger because in much more than it was worth a in Europe in 2014, right?

[00:06:00] So so much larger than that part. Of course, it'll fall as a whole, right?

[00:06:04] So a full is an enterprise telling Italian management platform as the end of the day in order to fulfill our purpose to provide the right career for everyone in the world.

[00:06:13] I think at the core, it does a great job in terms of creating a good much between job opportunities and skills, right?

[00:06:21] And doing some very automatic process. But of course, you know, it does a well-telling management and internal mobility and succession management, a lot of them telling acquisitions.

[00:06:32] So clearly, most of the elements and that's what I say a platform that you need to manage your talent they are there and clearly we're working on that full filming of that vision.

[00:06:43] So Cheno, I want to jump into Coseo like myself and Shelley have tried to do this at the podcast and it failed miserably because Shelley wouldn't agree to everything I said and we had to figure out a different solution.

[00:06:59] So you've been coseo at work day and now you're coseo at eightfold. How does that work? Who makes that final decision is that the one?

[00:07:10] Yeah. Yeah.

[00:07:12] Oh, I gave their eyes there ways to look at this. Right. The first thing is maybe I'm not good enough or mature enough to be as a socio. That's okay. We'll need to learn, I need to grow.

[00:07:23] So I guess there is a part of that one. Honestly, and we can talk about the one at work day, right?

[00:07:29] But this one was more by the side was my initial proposal when I started to talk to as you and it makes a lot of sense when you think about it, CO is a very lonely job and there are not many people sometimes that you can talk about a lot of things that are involving either

[00:07:46] but also people, decisions in the business so you have someone to partner with. When you can do it with someone that you can build some good trust, hopefully friendship, but there is complementary of skills in terms of where your passions and your experiences are, there is a good divide and conquer.

[00:08:06] There is a great opportunity for the company, right? Is it easy to make it work? No, but I would say we're focused both of us on doing the right things for the company and what matters to the business. We can have some really great discussions and hopefully make the company better location wise as well, starting Europe actually here so it's another great divide and conquer because the challenge for many of these, I wouldn't do the fall day. I wouldn't do the work day there is becoming truly global companies.

[00:08:34] I want to say these is many of the born US Silicon Valley companies. So that also helps.

[00:08:41] Thank you. I was always curious if you guys vacation together had your families come over in a Sunday. I guess you're living in different countries so that will be harder.

[00:08:52] I think it can become friends as well but I guess we have enough from each other that we need some breathing space between family some occasion as well.

[00:09:01] It makes sense. So let's jump into AI in the talent acquisition space, obviously it's an extremely hot topic that you've been in the space for a long time. Would like to get your perspective. How is AI transforming process and the way we work here in HR?

[00:09:21] I would say it's a simplest way to look at it search and this is not going to surprise anyone. I hope it starts with automation and productivity increases.

[00:09:31] Right. So you can think about how big parts of the recruiters tax for example, how can be automated. So they can focus on some of the high value are the activities or applying judgment on some final

[00:09:37] initial making were clearly machines will be lacking some elements there. As you can see on areas like the much in between job and opportunities and I would say false solutions will be at least providing a 90% accuracy today on standard matching of skills.

[00:10:00] If you compare the most smartest and experienced recruiters we do on a human perspective. You can think about scheduling of interviews or sourcing candidates. There are many processes in areas that as a simple way to look at it, it provides productivity increases and automation.

[00:10:18] If you think about AI in the mainstream, it's really only become popular I'd say last year with chat, GBT, genitrative, AI. Do you find there's more awareness now of AI tools like Atefall? Is it an easier conversation than it was two to three years ago?

[00:10:40] Yeah, definitely. Well the first thing you mentioned something very important, right? I would argue on one of the reasons why Atefall and what is said hopefully will provide me an opportunity to transform HR in AI is I believe that you need to have the right.

[00:10:56] technology platform in order to be able to do it. And of course,

[00:10:59] AFO was born in AI, right? It was born seven years ago, the way it was designed, the way it was

[00:11:05] thought out. You know, the companies were potentially born in the cloud, other companies were born

[00:11:10] in an earlier home, right? So as peration on it, you can think that you can do many things,

[00:11:16] but architecturally and technology is speaking, if you don't have the right architecture,

[00:11:20] there are some business problems that you might know, or use cases, you might just know

[00:11:24] might be able to fall firm. There is a limit on how much can you bend at the

[00:11:28] New York architecture to do something that has not been thought out of the site for since

[00:11:33] day one. Clearly after Chi Security, or there is an immense interest in terms of how can

[00:11:39] we be jumping in the AI cases and what it should be doing for my business or our business?

[00:11:46] And this is potentially one of those first technologies where now you also have a responsibility

[00:11:52] when it's said to have a sort of understanding on from an ethical perspective, yeah, you will

[00:11:57] know be the experts on what the algorithms are producing, but you need to ensure that there is a

[00:12:02] responsibility and accountability for you to use algorithms as clean as possible, as

[00:12:07] not by as possible, and as ethical as possible. That's a perfect segue way to where I wanted

[00:12:13] to go. Recently read the book The Algorithm when she did a deep dive on companies in this

[00:12:18] space and how they're leveraging AI. But what I found really interesting and really it kind

[00:12:23] of shocked me even though I'm in this industry and I spend a lot of time researching

[00:12:27] how much nail coil there is out there, right? And we're going to see a lot of it. If you go

[00:12:32] to HR Tech or any of these shows, everyone just stamped AI under product. It looks like they

[00:12:38] printed it off the day before and they made sure they had AI on the boot. So, but if we put

[00:12:45] that in perspective and reading that book, I made me a little bit nervous that a lot of

[00:12:50] these technologies, there is no checks and balance to make sure that these results have

[00:12:55] been accepted, that the real, what does eightfold do different than a lot of these companies

[00:13:03] that are launching AI products to make sure that it is mitigating bias and it's actually

[00:13:09] giving results that we can validate as well. What a great question. We do have a lot of

[00:13:15] check-in balances, but let me give you some examples, right? First of all, I would say

[00:13:19] I'm a true believer that we humans are biased by default, right? Most of us has some sort

[00:13:26] of bias. I'm a true believer that AI or technology useful good can help us to solve

[00:13:32] part of that project. Of course, we're very proud and that is something that again before

[00:13:37] uni-etteful that did a lot of to the audience on in terms of how we're looking at it from

[00:13:41] a trust and it is based, first of all, what would be the body we say, oh, we comply with

[00:13:46] all laws and regulations that are there? Yes. And now, right? So, I would say, for example,

[00:13:51] we are not a few companies that are complying with the newest regulation for New Year city

[00:13:56] laws. They are hiring. Yeah. And there may be only a couple that are complying so far,

[00:14:01] which are more stringent, right? Yeah. I would tell you that we are third party audited.

[00:14:07] And not many companies would be third party audited. They would say we have our internal

[00:14:11] checkout balance. It's for example, we are not going to be delivering a new model. If we are

[00:14:17] not certain that the previous model is completely working. But then of course, through

[00:14:21] some of these laws or regulations, and even working with our customers, basically need to

[00:14:26] prove things like what we call or it's called per-touration testing. So, let me give you an

[00:14:30] exam. At the end of the day, some of the main bias categories, which are usually gender

[00:14:36] race, nationality, disability, or not disability, you have an education degree, or you don't

[00:14:44] have an education degree. What we go through is I give the machine, let's say, a number

[00:14:51] of resumes. And then I go with the machine, and we do this with our customers, and we

[00:14:56] do this on our own tests. And now, I'm not channel any longer now, and call Maria. Now,

[00:15:03] I am from Venezuela. And now, I have a disability. And we do those changes, right? And

[00:15:09] we're exposed to those changes from the regulation. And if the machine was doing or the

[00:15:14] engine was telling me that I was good for this job, and doing a match between my

[00:15:20] skin and this job opportunity, not an ordinary longer channel, Maria, and I'm from Venezuela.

[00:15:26] And I don't have an education degree. It's still that job. Much needs to be one to one. And

[00:15:32] if it is not a one to one matching, again, with kind of these key, the masks, then the model

[00:15:38] does not work. And then we need to find where the problem is or what the challenge is. Some

[00:15:43] of our largest customers and regulated industries, we need to prove this, some of these

[00:15:48] regulations. We need to prove this. And again, it's a one to one ratio, we're not looking

[00:15:53] for anything less than that on some of these big categories, which give me confidence

[00:15:59] and in terms of creating the potential known by us, right job, can be simple. It's less

[00:16:06] known by us one that recruited us a person would produce to start with. So that's just an

[00:16:12] example, right? There are other things that have been done, but that's a true real example

[00:16:15] of how it works. I appreciate that we're starting to figure out that the due diligence

[00:16:20] and the liability is going to fall a lot on the end employer and not on the vendors. And

[00:16:25] I'm short, that's going to be different in every country. So if I am a practitioner

[00:16:31] or talent acquisition leader, I want to bring an AI solution to my company, eightfold

[00:16:36] as an example, what due diligence should I do to make sure that I'm alleviating risk for

[00:16:44] the organization and getting one of these tools in place? Yeah. What a great question, right?

[00:16:50] I would say first of all is, you know, a higher level criteria in terms of who on the

[00:16:55] vendor you would consider and then Atlanta, your question in terms of the most tactical,

[00:16:59] but it's still very important point of alleviating some of the risks and some of this

[00:17:03] concern, right? At a higher level search, I would say that, of course, you're looking

[00:17:08] for vendors that have a right vision from a technology perspective for you, that they can

[00:17:14] bring the right innovation, that they can have a completeness of a platform, that they

[00:17:19] are referentable meaning that there are customers that are happy with and are seeing value

[00:17:23] on adoption and some of the use cases. There are that at a lower level in terms of, you

[00:17:29] know, how happy and do with this, again, you may ask them for their certifications on

[00:17:34] their all these if they're done from a third party, you do your own tests. But with some

[00:17:39] of the customers, we've even done this exercise before in terms of your candidate pool.

[00:17:43] You know, what was the outcome in terms of the matching profiles and bias on the bias

[00:17:47] with the samples that I told you before, and what would it be with the likes of an A-4, right?

[00:17:51] And it's our correlation better to eliminate bias than all the data you have before in

[00:17:58] terms of what was produced from pure human beings. I would produce any better results

[00:18:03] that are in average we are because the ending is producing better one than you or are we

[00:18:07] not, right? So again, there is a more strategic aspect and then there is yet. You need

[00:18:13] certainly to comply with security, with privacy, with ethical way of approach into it. But then

[00:18:19] you need to be continuously all eating or testing as you are going forward, right? I'm

[00:18:24] then be very thorough that is there is any challenge on any model not to put any other

[00:18:29] model in production, because when we move more towards the narrative where I were or

[00:18:34] the challenge is stars is we may understand what I'm going to think one model and another

[00:18:39] are going to think another model but once we start connecting algorithms and models and generative

[00:18:45] AI starts working is where we may be a little bit more lost on what is this actually

[00:18:50] producing these outputs. You have a bigger problem to solve, make sure that the integral

[00:18:55] part of a smaller problem are all ethical right foundations, because once they start interacting

[00:19:00] with each other, wow, you know.

[00:19:03] I think the key point you said there are the one that I'm taking away and that we don't

[00:19:07] do, and I've been guilty of this is I implement something and then never look back of

[00:19:15] is it actually producing what it's supposed to do. I just go on autopilot and I just

[00:19:20] let it run. And this is something especially with AI that any practitioners listening,

[00:19:25] they should be auditing and taking a look every three, six, nine, 12 months. It's not

[00:19:30] a set in forget it type of solution.

[00:19:32] I think regulation is going to potentially go in as well on that direction, and it's going

[00:19:37] to force you to do it so, but I can tell you that, at least our largest customization

[00:19:41] regulated industries, finance services, life sciences and so on and so forth, they're forcing

[00:19:46] us to go back and prove that is happening exactly as expected once or twice a year.

[00:19:52] I just have a quick question because we talked about choosing a provider, what do

[00:19:56] diligence that you need to do? And this is fun for a lot of us. Like getting a new tool is

[00:20:03] just like new to me. But what's not as much fun is implementing a new tool. I don't

[00:20:11] even want to talk about implementing workday, that is, it's a project, right?

[00:20:17] Implementing eight full is is obviously probably not as deep, but what advice would you have

[00:20:21] for practitioner they've decided on eight full? What should they look at first before they

[00:20:27] start implementing? I would say as simple as, "I'm ready, and what is my readiness",

[00:20:32] right? There will be customers that potentially from a process perspective and I can tell

[00:20:37] you because I see these, they're not ready in terms of having acquisition or talent

[00:20:42] processes, or they would like to be in order of readiness. They might not be clear in terms

[00:20:48] of the use cases they are trying to address, right? There might not be clear in terms of

[00:20:53] the KPIs, visibility, young insights they want to have on the dashboard and reporting

[00:20:58] and how should they be implementing and configuring the solution, right. So it's part of

[00:21:03] the readiness. I don't think it goes that much is, yeah, it would be potentially initial

[00:21:08] implementation. As you say, that implementing a core HRIS, but of course, the technology

[00:21:13] may work, but we always been saying, "Yeah, you're readiness, and then you need to prepare

[00:21:18] for change management and you need to prepare for governance on the processes and how

[00:21:22] you're going to be making the decisions, so this implementation on how you're going to

[00:21:25] be making and maybe not training, how you're going to be thinking that you're going

[00:21:29] to be adopting innovation, because we produce a lot of innovation."

[00:21:33] Before customers were always asking, "Are you going to be annoyed if enough?" Now,

[00:21:37] especially with larger customers, part of the challenge is how do we expose innovation

[00:21:42] to saying, how do they understand the wonder brings value to them, and how are they going

[00:21:47] to be adopting that one? I'm even once he's being implemented, how are you going to be

[00:21:52] with the solution afterwards, are you going to be doing internally, is going to be a partner

[00:21:56] support the Jew? Because as we know, since the SAS solutions, they are life and they're continuously

[00:22:04] bringing new options for you to take home in terms of additional value. Some might be relevant to

[00:22:08] you. Some might not be relevant to you in terms of the business progress they're so big for.

[00:22:13] I want to come back to what I've been talking a lot about this year as well is the trends in 2024

[00:22:20] around talent intelligence. It's being discussed in many HR circles because once you have that

[00:22:27] intelligence, you now know where the holes are in your workforce. That is maybe one of the

[00:22:33] objectives, in terms of why you would go down this path for what is the KPI or the outcome. Because

[00:22:40] being able to identify where you need to upscale, reskill, or in extreme cases, rip and replace,

[00:22:48] companies need to know that information. You know, you talk about regulated industries where the

[00:22:53] banking industry is going. If you want to stay competitive, you'd better have a skilled workforce.

[00:22:59] That's certainly a conversation we heard a lot about. What advice for leaders would you have?

[00:23:06] In terms of once you have this information, what is it they need to be ready to do with it?

[00:23:12] First of all, going really into this direction of skills, which we're talking about. First of all,

[00:23:19] I believe that talent is everywhere opportunities are not right and sometimes with people with

[00:23:23] certain degrees or not. So I think it's going to provide much more opportunity hopefully for

[00:23:28] underserved backgrounds or underrepresented minorities, which I'm pretty excited about.

[00:23:35] Secondly, I was just talking to a customer this morning that was telling me we went through a

[00:23:40] riff process last year and usually through the riff process, we were able to reallocate 10% of the

[00:23:46] workforces because it was not being our first one right. It is a fortune of 100 company

[00:23:51] and we take full on our last riff process, we've been able to reallocate 35% of our people

[00:23:57] instead of 10%. And that's been because of our ability of understanding some of these skills that

[00:24:02] we had and that could be applied to other opportunities within the companies, which as you can

[00:24:07] imagine is very enlightening for me to hear because somehow you feel like you contributed from

[00:24:13] 10 to 35% on a bit of a riff, there were like 3,000 employees, there were quite a number of families

[00:24:18] there. And hopefully we did a little bit of our part to helping out to have an opportunity

[00:24:23] within that company that was their will. But clearly going through this process in terms of my

[00:24:29] skills today and the 2 B skills because when companies are trying to identify first is the

[00:24:33] taxonomy and what do I have my inventory today. But where we are going is more like

[00:24:39] tomorrow my strategy kind of initiatives is this and I understand that these are the skills that

[00:24:45] I'm going to be needing tomorrow and I know how thing. Hey, how am I going to be acquiring the

[00:24:49] end and what is the cost of not acquiring them, which ultimately is giving leader kind of a priority

[00:24:56] decision as well, or where they should be focusing on wire, which skills from where. And some

[00:25:02] we'll be developed internally, or what is the cost of not doing that. And that is where we're

[00:25:07] talking with companies on internal mobility that's where we're companies are trying to create

[00:25:12] into in projects or gigs, where people kind gets uncertain skills that they may need to develop

[00:25:18] a job. That is what we see going in the companies today, but it's clearly as easy as they

[00:25:24] would say, there is a lot of try to figure out right now on companies on how they will work since

[00:25:30] we've been talking for a few years and now in his case and implementing it. And there is the

[00:25:35] Trios, which is good trios in terms of if I'm providing it in temporary work projects to

[00:25:40] get more skills is and provide more internal mobility, what does it mean for my workforce development

[00:25:47] as a whole, and is that closing the gap towards the skills that I think we're going to be

[00:25:52] needing and for which initiatives and the ones that we do have today, right. It's a bit to

[00:25:57] early stage, but those are the discussions that there being have of course, needless to say, part of

[00:26:02] the skills are so training and education and where are they solutions like ours offering mentoring

[00:26:07] and the learnings you should be having. If your ambitions and aspirations are to pay to a point B

[00:26:12] on your career development, and you're on a point A on your career development, right? How you

[00:26:18] Mabelle that journey. How you get there? Yeah, I think that's the ultimate goal. That's where

[00:26:24] you go or talent managed. And it has been for several years that talent management, we're now

[00:26:30] calling it talent intelligence. I think it's that next level because the bigger the company,

[00:26:34] the more difficult it is. And is it easier to lay people off versus reallocate them? Yes.

[00:26:41] Reallocating them has so much to say about your commitment to community, it's better for brand.

[00:26:47] Absolutely. That is aspirational.

[00:26:52] There are people that are great cultural fit that they don't know already about your

[00:26:56] company. And there is an opportunity to reallocate because they fit on those skills.

[00:27:01] That's also a great win for the company. Right. And that person is going to be obviously ready to

[00:27:06] hit the ground running faster than potentially external one would be.

[00:27:09] Yes. Over to you, search for the final question. Well, extremely interesting conversation,

[00:27:16] Chano, I love the perspective that you bring. You've been in this industry for a fairly long time

[00:27:22] things that we don't see being the co-CEO of a large enterprise in this industry,

[00:27:27] take out your crystal ball. What can you predict for 2024 that is going to be a big disrupter

[00:27:35] in this space? I think it's an easy one potential. We're already in 2024, right? And I guess being

[00:27:42] in this industry for long, is there some unfortunate angle bit older than I would like to be, but okay.

[00:27:47] Look, we're all there. So I would say it's clearly generative AI, right?

[00:27:52] We've talked a little bit about AI, of course, susceptibility is generative AI and I think

[00:27:57] everyone has been trying and familiarized with that one. So if you look for example,

[00:28:03] in a full case, we'll talk about co-pilot because everybody's understanding us where

[00:28:07] we're a co-pilot is. But if you think about going beyond AI, and going where you have

[00:28:13] the generative AI capabilities of a chat TPT, but then you merge that one with your

[00:28:20] internal knowledge database, like you as an employee, better understanding, okay, if I'm going to be

[00:28:26] asking for a leave, what is the policy in this company? I'm not only telling me the policy,

[00:28:31] but asking and requesting them on an automated way, or okay, if I'm doing this job, we should be

[00:28:37] my potential options for the future and you merge the public information coming from the

[00:28:41] generative AI to the internal information on ways and experiences, some promotions in the past that

[00:28:47] taking place in the company, giving you good advice on which you should be doing and enriching that

[00:28:52] one. That is really powerful. So you think about this co-pilot are the new way of browsing.

[00:28:58] You would say the new way of interacting going forward, and of course, there would be typing,

[00:29:03] but on a conversational way, typing all these tools, but that's the way we're interacting,

[00:29:08] and then again, the machines didn't reach me off during internal kind of public information to

[00:29:13] chat TPT with all the information you have during internal sources, during internal policies and

[00:29:19] procedures, during internal compliance and reaching that one, or okay, what does that mean is

[00:29:24] actually from something that is a bit more generic, that it could be a provided chat TPT,

[00:29:29] on general TPT on generative AI to really what it means in a company like AFO with a generative AI.

[00:29:33] In turn, about this particular process can be done or what you should be expecting or even applying

[00:29:38] on more than forward with it. So that's what I think is going to be the inception, what we're going

[00:29:43] to be starting and seeing many of our customers using, and it is really exciting again from the

[00:29:49] productivity increases that is going to derive. So to recap, the robots are taking over and that's

[00:29:56] a good explanation. The robots are really helping us to do a lot of automation and a lot of manual tasks,

[00:30:02] and they're helping us to do our job better. There is no jobs substitute for

[00:30:06] consciousness and something that is substitute for great human judgment today. I don't know

[00:30:13] in 20 years from now, I don't think it's in five or 10 years from now. For the social interaction,

[00:30:18] for that care, we do have as a human for understandings and all the aspects that are more human-based,

[00:30:24] and human-tacked today. I don't think that there is a substitute for that. And honestly,

[00:30:29] I'm not the best predictor, but I don't think it's in the next 5 to 10 years. I don't know in

[00:30:34] 30 years from now. I guess we will see our kids will be deep in the workforce by then.

[00:30:40] Their world is gonna be different than ours. So, you know, this was an amazing conversation. I really

[00:30:46] appreciate you joining us. Thank you for watching. This is a great podcast. It's amazing being

[00:30:51] with Shelley and you here, Shergs. Thank you. Have a great host. I really appreciate it.

[00:30:56] Shenho, before we leave, though, if anyone wants to get a hold of you, what is the best way?

[00:31:00] And if anyone wants to get a demo reach out to 8 for what is the best way as well?

[00:31:06] I guess it's yes and in me, and email. That's the best way, right? I'm

[00:31:10] channeled 8 full dot AI. So, that's pretty easy. I don't know how perfect. And 8 full dot AI is

[00:31:18] The always not the AI there since 2016, right? Yeah. Because right now, as you said before,

[00:31:23] I see companies that you know, that are going through that transition, you know, to name, right?

[00:31:27] But it was always there, always there. Thank you so much for joining us. It was a pleasure.

[00:31:32] Thank you so much for having me. Thank you for making the time. Thank you,

[00:31:43] Shelley, let's face it, texting candidates is the easiest way to hear quicker today.

[00:31:50] But your cell phone doesn't connect to your ATS. You're sharing your personal number with strangers.

[00:31:55] It's pretty scary, right, Shelley? And it's not even legally compliant.

[00:32:00] This is where our friends at RecText come in. They've created simple yet powerful text recruiting

[00:32:06] software that works with your ATS plus it's designed by recruiters for recruiters. So, you know,

[00:32:13] it works. To learn more and book a demo, visit, www r-e-c-t-x-t.com, mention the recruitment

[00:32:22] flex, and get 10% off annual plans. Do you love news about LinkedIn, indeed, Google, and just

[00:32:29] about every other recruitment tech company out there? Hell yeah. I'm Chad. I'm cheese.

[00:32:34] Where the Chad and cheese podcast. All the latest recruiting news and insights are on our show.

[00:32:40] Tripping in Snark and Attitude. Subscribe today wherever you listen to your podcasts.