Welcome everyone, to this webinar. It's ten Pacific, one eastern sharp. I know the, little headline there saying starting promptly at at ten AM, but we will give folks about another minute. I see participants starting to trickle in. So bear with us here. We'll start in in a minute as soon as I see the participant count start to just level up a little bit. Less than a minute here, and we'll get going. I see folks joining in. Welcome. Welcome. Appreciate you all taking the time to join us. Super excited about the session we have today. I do see the participant count ramping up pretty fast, so appreciate you all bearing with us those that were already waiting in advance and those joining in. We'll start in a in a moment here. I realized I didn't have any hold music queued up, but consider yourself lucky. My taste in music can be jarring to many. So we'll start here in in another twenty seconds or so. Again, appreciate you all joining. We're about to get going. Alright, folks. In the interest of time, let's get this thing going. Really excited to bring you some insights fresh off of a week that our CTO spent in Davos. No surprise given who we are as criteria that we're focusing a lot on the future of work and the impact that AI is having on it. I do wanna, emphasize this isn't a product webinar. This is really a perspective session. You know, what we heard in Davos suggests that, you know, the the future of work is quietly sort of crossed a threshold. Maybe hiring hasn't caught up yet, but, you know, we've got some really interesting perspective, and and but we feel there's some real actionable takeaways, that hopefully will be useful to everybody here. Some housekeeping, there should be a q and a tab on your panel, at the bottom or at the top depending on your your layout. If you use that to put in your questions, they don't get lost in the chat. While Chris and I are speaking, we won't act we'll try our best, but we won't actively be able to respond to the chat. If you put it in the q and a section, then we'll be able to go and and take a look at it. Additionally, you know, once, this session's over, there will be time for q and a. And, again, then if the chat's a little quieter, feel free to put it in there. But, you know, the q and a section makes it so that we definitely will be able to get to every answer. Alright. So today's session is hosted by, you know, a short partnership with our CTO, Chris Dayton. He's come fresh from his participation in Davos where he was invited to attend based on a lot of his thought leadership and his work in defining what the future work looks like and the impact of AI and work. And we're gonna focus a lot on the takeaways that he had from there and a lot of what he learned while he was at Davos. I am gonna be your emcee. I'm the CMO at Criteria, and I'm gonna jump right into things. For those who are familiar with us, this is a little reminder for those who may be unfamiliar with us. We focus really on elevating the talent and potential in every person, and our fundamental point of view is that companies need to go beyond the resume and really think about how they reveal the real potential behind every candidate. We use a combination of science that you can trust and ethical AI you can explain. That seems very relevant right now when AI seems to be creeping up in every facet of work. It certainly dovetails a lot into what we're gonna talk about, like, today. And so we're super excited to take a lot of historically what we know as well as what we're learning about the impact of AI and work. So today, we've kinda gotten through the introductions. We'll start with a little bit of context setting. We've been kinda championing this idea of work four point o. I'll explain that just in a moment here, because we feel like this is not just a technology shift. There is a real shift in how we define work, and there is some context for this. So I'll touch on that a little bit. And then we're gonna jump right in, and and I'm gonna get a chance to to interview, Chris. And, we've sort of distilled a lot of this into some, you know, core themes that emerged from the discussions there that we think will be very relevant to this audience, and then some of the takeaways, that Chris had from there, and then we'll have a chance to do some q and a. A moment to advance my screen here that seems to have frozen. Alright. So I think it is interesting to just zoom out a little bit and recognize that while what's happening today might feel like it's very new, certainly, every vendor out there that has any AI based solution is telling you that this is, something we've never experienced before. But, actually, we've been through multiple work eras. We had the industrial revolution. We had sort of the corporate era where we were introduced to corporate structures and titles and hierarchies. You know, the digital revolution with the Internet and and how we engaged. Each one of those did have a very profound impact in how we thought of work, the type of labor, the type of skills, and just work itself. So while this may feel very different, it should also be a bit familiar. It's just these changes don't come very often. You know, if you look back in the past century, you know, the pace at which the change happens maybe accelerates with every era. But there is some precedent for this, and we can draw upon that and see, you know, what the impact of this will have. But, certainly, you know, what we're experiencing right now isn't just a technology shift. While AI may be very tech focused, it's impacting us in a far more profound way. And so it does feel relevant to say, hey. We we are in a new era of work, what we're calling work four point o, and it's probably best summated in a AI augmented shift. This isn't just about, you know, AI in itself. It's about the impact of AI in the workplace. And so when we think about organizational structures, measurement of output, you know, what are our approaches to hiring? How do we gauge talent? You know, all of these are sort of being reimagined and rethunk. And so it's that context that dovetails really well actually into what we're facing right now, a lot of the discussions that were a big topic at Davos. And that's why we felt like fresh off of that event, this would be, you know, really compelling session. And here's the interesting thing is, like, we we went from maybe viscerally understanding that there'd be an impact to really feeling that impact I felt in in twenty twenty five, and the data brought that to the forefront. A massive surge in applicants. We'll be very curious if folks do jump in on the chat to say if the if you know, it does vary by industry. But one of the things that AI has let us do is really apply en masse in a very easy way where a lot of the effort required to find a job to apply to it. A lot of the manual barriers were completely shattered. You see, you know, some LinkedIn data that shows eleven thousand applications a minute, a massive surge in applicant volume. Compounded by that was also an issue with the candidate authenticity. It's very easy now to doctor up a resume, to tailor your resume, to personalize it. Everybody's probably always embellished their resume a little bit, but the the extent to which you can embellish and the automation made it to a point where it started to break systems that allowed to gauge, like, is this really a qualified candidate or not? Which really did a disservice to truly qualified, like, candidates. And we started to see that skills do, like, shift, you know, in as technology shifts in advance, this is the way we engage with it does shift. And so we see a massive impact in, you know, the the skills people want, the skills people need. It's not that the talent isn't out there. Our means to gauge them, to determine who's the right candidate, and even whether the person is the right one in, like, the candidate funnel is the right one does impact our, like, business. And so the problem isn't that there's so many applicants. The problem is it's much harder to find the truth in these applicants. And that's been a very visceral impact, but that's not the only impact that AI is having. It's also in having us think about what this means about organizational structures moving forward. And that's where, I think, Davos comes in. It's easy to dismiss it just as, you know, a talking shop for world leaders. There's a lot of the Davos headlines, but then there's a lot of very grounded conversations that take place. And I think what makes Davos really valuable is that global alignment. There aren't too many opportunities where heads of state, CEOs, labor leaders, technologists all come together to discuss, you know, a common topic. And, you know, in this case, there was something very, very sort of relevant that's very pervasive, the impact of AI on the global economy. And so that dialogue and those very practical discussions are what we're distilling and and bringing to you. So I think Chris said he averaged around three hours of sleep for the week, and that is because there's just a lot that gets packed into the week at at at Davos. There's panels. There's networking events. There's conversations in in hallways. We sort of picked Chris's brain and distilled the hundreds of conversations he had into six core themes and takeaways that we felt would be super relevant for this audience. And I'm gonna get right into it and, start picking Chris' brain here. Great. Well, thanks everyone for having me join. Jim, why don't we kick off? Yeah. Chris, so, I'll start at the very beginning. You know, you attended Davos, you know, this year. It's a very pivotal moment. A lot of folks left there feeling like this year was a little different from prior conversations about work and technology. Maybe people always have felt that way, but it did feel like this year because of just how pervasive and global the impact of AI is could have been quite different. What do you think made it different this year? That's a good question. I think, Jim, the convening power this year, you know, we we talked, about, you know, six of the global g seven being present together in one area. And just for those of you who aren't as familiar, Davos is pretty much five blocks of real estate where everybody is immediately close together. So the convening power this year was high even compared to prior Davos standards, and I think you could feel that really immediately. That means generally that more can get done, more, you know, cross functional worldly conversations can occur. I mean, when that many senior leaders are in one place, those conversations you have tend to move very quickly from ideas to real ways on how it can be applied. Another element this year is that the the World Economic Forum is under new leadership after more than five decades. So this is the first year, twenty twenty six, where there's been a a brand new leader running the World Economic Forum. And and I think there was a really noticeable reset in how issues were being framed. The World Economic Forum is famous for aspirational statements, and, you know, I think this year, it was way more tactile in how it felt as a focus on what is actually under strain right now. So in my conversations with, you know, whether it's government, CEOs, or institutional leaders, my conversations weren't as much about what is going to happen in the future. I think we're in this mix right now with how AI is transforming the world as we know it, and those conversations were really about what is already happening inside organizations today. And all of my discussions really centered around work skills, hiring, and trust, which I know we're gonna talk a lot about today. So, you know, AI in particular was really no longer discussed as kind of an innovation or an experiment. It was really accepted amongst everyone that this is happening, and we're in the thick of it. And it was discussed, around, like, how AI is economic and national infrastructure. And I think as AI becomes infrastructure, workforce capability stops being a future HR topic and really becomes a present day risk for our organizations and our teams. So I'm gonna double click there a little bit, Chris. As as a marketer, we're always prone to a little bit of hyperbole. I feel like it's in our, like, DNA. And there's a lot of talk about the future of work, and people talking about, you know, the future of work is already here. You've been saying the same thing as well. What did you hear at Davos that you felt really reinforced that idea? Look. What reinforced it for me was how little time leaders spent talking about kind of pilots and experimentation. I think there was an emphasis that AI is already embedded in workflows, in hiring, in productivity, and in decision making. And, again, the question is no longer should we adopt AI. The questions we were talking about were how do we control it, how do we govern it, and how do we maintain trust as that scales within our organizations and around the world. And what became very clear is that work has already changed. I think we talk a lot about the future of work, and I think Davos framed for me that work has already changed. I think there's different degrees of change that we're all going through, but many of the systems that we are using today to evaluate people have not changed in alignment with how our workforce is already changing. So hiring today is still very much, in a lot of cases, anchored to resumes, static role descriptions, and credentials that were designed for really a slower, more predictable world. And and I liked your slide, Jam, with work three dot o. And I think that work three dot o is is more predictable and a bit slower than the pace of change we're gonna see in in Work four dot o. So why does that matter? I think for talent leaders and our colleagues in HR, that creates a growing disconnect. And the wider that gap gets, the harder it is to confidently say we are hiring for how work is actually done today. So, again, the takeaway here is the work has already changed, and the hiring systems just haven't caught up completely yet. So we're we're looking forward to being part of that solution. I think what's really interesting here, Chris, is, you know, here in at least in in the US, we tend to think that we're at the forefront of a lot of, like, tech. And when you see a common, you know, a conversation happen across world leaders and, you know, across companies that are being represented across world, that's when maybe you feel like, okay. This this is a little bit little bit more than maybe hype. Like, this is being felt. This impact of AI and work is a global phenomenon. It's just not just isolated to, some of the the stronger economies as it were. That's right. I think the takeaway here is completely accurate. So we need to treat AI in a way that, you know, doesn't focus on, you know, we need to stop treating AI like work transformation as kind of a road map problem and something that's coming from the future, and we really need to recognize that it's here today. And I think that's a good message for for all the folks that are joining us here today. So moving over to that, there's a lot of anxiety, understandably, on AI because anytime and and this is going back to the whole work four point o, work two point o. So anytime there's a change of this magnitude, the first thing you need think about is what impact does this have on my job. So, you know, what was that you know, what kind of, you know, jobs will AI replace? Was that a dominant concern in in the in the conversations at Davos? Yeah. I think, I think this is gonna surprise many. AI replacing jobs was not a big part of the discussion. In fact, kind of the opposite is is the center of the discussion. So we weren't talking as much about job loss. Now don't get me wrong. There are certain roles that will be more susceptible to, automation, and, you know, there there's going to be some loss in some of these micro verticals. But, you know, the the concept of creating human leverage and redeploying skill and talent augmented by AI is a big part of the discussion. If we just zoom out to the macro, we talked a lot about how work gets reorganized when AI shows up at scale. Again, not from the lens of job loss. So it's about reorganization, changing the way we work, recognizing that. In fact, the World Economic Forum has been really clear that by twenty thirty, AI and automation are expected to create more jobs than they eliminate on a net basis. And I think that framing is super important because it moves all of our conversations away from fear and moves us a lot closer to system and infrastructure design. I think that's a super, super important framing. And I think one example that came up often was in health care, particularly around radiology, early narratives around AI coming into radiology said that AI would replace radiologists. What's actually happening, and there's lots of data released on this, is is very different. AI is being used to draft reports, handle documentation, take care of kind of the precursory tasks, prediction tasks that AI is really good at. And what's happening is radiologists are spending less time on kind of repetitive reporting and repetitive tasks, and they're getting insights faster. And what that does is that means they can focus more on complex interpretation of those radiology outputs and patient care. And what we're actually seeing is the opposite. There have been more radiologists hired since that AI supplementation has occurred. So, again, that's not what you would expect. And, really, the same dynamic is emerging across all different job families, you know, even in nursing and other areas of of medical where AI assisted charting is reducing the amount of time nurses will spend doing those lower value tasks, and they're releveraged into these higher, you know, value tasks, and they're helping more patients. You know, revenue for hospitals is increasing, and many systems are hiring more clinicians, not fewer. So, again, the real concern in Davos was not about jobs disappearing. It was whether organizations are redesigning work so that humans are applied where they add the most value. Chris, I think that's a a a fascinating sort of microcosm of, you know, do you want efficiency versus output? And instead of looking at it like, yeah, I can do I can read two hundred x rays with half the number of radiologists, the real question is, hey. Can you go from two hundred to five hundred x rays, and continue to add more? In general, we don't have enough coverage. There's there's largely more things that need to be done than people to do them, and this is where maybe you know, when you talk about human leverage, what do you think that really means in practice for organizations? I think that's great. I think we have some work to do on some of these definitional terms, you know, around the world. But to me, human leverage is about being intentional about where humans create the most value. So the reality is machines are very good at scale. Machines are very good at repetition. Machines are very good at pattern recognition. I think where I reflect a lot is that humans create value through judgment. We create value through creativity. We create value through ethical reasoning and the ability to learn while the whole complex system is changing. That's where human value really shines. And what I heard repeatedly was that organizations are are redesigning roles around outcomes and decisions and not just task lists. So they're asking where human judgment really matters, and then they're protecting and elevating that work as opposed to the the other tasks. And I think from a talent perspective, this means learning velocity and adaptability are becoming more important than any single technical skill. And I I really mean that when I say that. I think, over decades of hiring, even I've been guilty of this, you affix on someone's innate ability to do something technical or specific. And this new economy we're describing is that from a talent perspective, something like learning velocity and adaptability are more important than any single technical skill that your talent will have. So, again, I think human leverage is about where humans must lead, not where machines can help. I think from a hiring, like, lens, it's such an important, like, point because there is so much pressure around efficiency. But, you know, the real gains come when you increase your output. And, you know, I would say any any company, if given a choice, would favor increased growth as a lever versus cost savings. The growth sometimes just seems, you know, harder to attain. But when you start thinking about applying AI the right way and getting more out of the human capital you have, that's where it really that's to me, that is where it goes away from from being anxious about it into into human leverage. What was your takeaway from from this general topic? Is it I think it is very top of mind for folks. I think the framing that we need to continue to encourage around our teams and particularly with many of you HR leaders on the on the the webinar today. The framing and reassurance that humans are amplified by AI, is a theme that is accepted by these world leaders, president Endavos. And I think that organizations that understand that concept will pull ahead from those who don't very quickly. Perfect segue into the next topic. So, you know, let let let's talk a little bit about what humans amplified by AI means for hiring, like, right now. How does that reality change and how organizations to think about hiring today versus five years from now? Look. I think it changes the risk profile of hire. And what I mean by that is in AI enabled environments, the cost of a bad hire compounds so much faster than it used to. I mean, a bad hire was still expensive. We know that. Right? There's lots of data that supports that. Anybody who's built a team or organization already feels that pain. A weak hire does not just struggle individually in this AI enabled environment. They slow teams in aggregate. They might degrade decisions, and they'll introduce noise into systems that now operate at scale. So the amplification of a bad hire is gonna count compound just that much faster than it used to, and I think that's a a real core philosophy of mine as I I think about building teams in this AI future. And I think that's why precision is overtaking speed. I think leaders are becoming less comfortable filling roles quickly if they do not trust the talent signals they're using. And an example would be scenarios where maybe resume first screening, you know, resume first screening is under real pressure, especially as we talked at the top of this, webinar jam. As AI generated applications are increasing, that noise is increasing with it. And for talent leaders, hiring is becoming less about throughput and more about signal quality. So I think we're still hiring. Like, the future is five years away. I think we need to, you know, we need to really think about that and and resolve that together. And I I think, you know, you you you touched on a point around, you know, the the cost of a bad hire being more amplified. That probably has been true every time a major shift like this happened. Right? When we went from the industrial to the corporate era, you you anytime this change is more resistance, and resistance means resistance can also be contagious in a in a bad way. And so when you start thinking about the cost, you know, of of an incorrect hire, I think even more relevant when we're going through the kind of a shift we are right now. What do you think the shift implies for how companies should define and identify top talent going forward? I think, you know, top talent as a definition is being redefined in real time. I think what I think is top talent today is very different than what I thought top talent was even six months ago. Here are the trends that I'm seeing. I think pedigree and credentials still matter. They definitely don't matter as much as they once did. They're no longer sufficient. And I think we would argue that they haven't been sufficient for a very long time, you know, in our criteria philosophies that I, of course, completely believe in. What differentiates top performers now is their ability to adapt, work alongside AI, and make sound decisions under this kind of pretext of ambiguity. So as as there's a little bit more ambiguity, it's gonna lean into the requirement for people to critically think a bit more, be more adaptable, and I think that is a core part of top top talent. And I think that's being redefined in real time. You know, I think these these discussions at Davos are often kinda, like, six to twelve months in the future, like, kind of the discussions we're having around things like quantum computing, regulation, and AI, those things, like, being on the ground, these bites, you know, may sound crazy to you right now or may sound, you know, insurmountable in your organization, but these are kind of inclinations or they're inklings of of where where we're headed in in six to twelve months. So organizations that continue to heavily rely on static signals, I predict, are, you know, selecting for the past, and those that invest in measuring real capability are gonna be the ones who are positioning themselves for what comes next. So, you know, top talent isn't who looks qualified. It's who can compound value as the system is changing. So the takeaway here, summarize, is moving from more, we've always felt like capabilities, if they're measurable, are stronger than just credentials in of themselves. But one of the real changes here is, you know, AI has also simplified and and made a lot of more complex things accessible. You talked about not relying and us having a bias towards relying on provable sort of, quote, unquote, hard skills. But one of the fascinating things for me with AI, especially generative AI, is the increased usability. You see, access to things like coding and and natural language based ability to code. All of a sudden, the the world of coders has gone from just programmers with computer science degrees to almost anybody, which means suddenly your your credentials mean mean less. Do you see I mean and I can only imagine a lot of these complex things get that much more simplified with AI. Is that sort of the was that part of the narrative there around, like, needing to think more about capabilities because AI is also just making so many more complex things that much more accessible? I think that's right. I think we have to be specific with our messaging that not all hard skills will be replaced with AI. Like, there are a big part of the discussion was, you know, plumbers and electricians as we build these data centers. Like, AI is not gonna turn the wrench for us at this moment. Right? So I think there's just a real important understand that has to be had of certain hard skills, abilities that, you know, are durable and, aren't going to be displaced by AI. And, I think we we need to not forget that. Right? Especially in in verticals like software, or SaaS, like, we are very much, like, writing a lot of code, and, you know, there are a lot of organizations out there in manufacturing and otherwise that have a very different distribution of skill. And I think that's something to really, really remember while we have these discussions. So I think a topic that always is top of mind anytime there's there's a work type of shift is is just trust in general is a constraint. And you mentioned coming up this coming up repeatedly, just trust coming up repeatedly in conversations. I think one thinks about the corporate world as being very, you know, material driven, which in a time of change will naturally anytime there's anxiety, there's trust issues. You know? Is that concern showing up more clearly, or or, you know, what what was your takeaway from the conversations you had there? Look. I think when we were debating three years ago whether or not AI would change the world the way we think it, you know, did at the time and now how it has, I think we were getting over that original hurdle of, is this even a real thing? Now that we're over that hurdle, we're past it. Again, like I said, that didn't really come up in Davos. It's it's here. We're operating with it. Now we're trying to figure out how to, you know, maximize it and see how it's changing the world. You know, because we're in this stage now, trust isn't the soft issue anymore. It's viewed as an operational constraint. If we can't trust these systems, if we can't trust the way AI is evaluating or helping us augment our human abilities, then it will not maximize for our workplace. And so, you know, trust is showing up at the inner intersection of hiring, AI, and human judgment. Like, those are three kind of concentric circles that come together for me. You know, in Davos, leaders expressed declining confidence in hiring signals in aggregate because of this material shift, and candidates are unsure how they're being evaluated. I think that's also important. And employees are unclear about how decisions are being made in some scenarios. And, you know, these AI generated resumes and interviews are eroding authenticity and challenging that trust pretty fast. And I think whenever there's an issue with trust, you know, trust issues scale quickly. They become really big problems really fast. That's that's my observation. And once confidence drops, adoption slows, and then skepticism will spread. And I think that would be really bad for our transition to work four dot o. So, you know, I think our our takeaway here is that this is another reason that hiring has to move from credentials to capability, and trust comes from signals people believe are fair, relevant, and they have to be clearly tied to real work. So this is, this is definitely something that's top of mind, you know, even as, you know, you you think about building up a a team. There's a lot of initiatives that that leaders have around wanting more AI fluency, more AI adoption, and everybody is is is meant to have some sort of AI initiative because it almost feels like an imperative. It feels a little backwards to me because the focus should be on the outcomes you wanna achieve with AI as an enabler. And I think a bit of mistrust gets created when, you're forced to just focus on attack versus an outcome. So when leaders at every level, whether you're leading an organization, a business unit, a a country, how do you balance this push for rapid innovation with maintaining workforce stability and trust? I think what I see is that organizations navigating this well is that they're designing trust into the system. They are clear about where AI is used and where humans decide. And they're transparent about decision logic, meaning how are their systems making decisions if they are powered by AI even if they don't expose expose the technology itself. And I think that's an interesting distinction. I think there's a certain level of intellectual property being created around the world right now with these AI systems, and I think that being transparent about decision logic doesn't necessarily mean you have to expose the technology itself, but there are very meaningful and mindful ways that you can build trust in that system. Employees do not need to understand, you know, the model. They need to understand the logic and where there is human accountability in that system. So as leaders balance rapid innovation with maintaining workforce stability and trust, don't assume that you need to teach your employees every single thing about the model that is operating under the hood in the technology, but definitely expect that they need to understand the logic and where the human accountability is in that process. So trust has to be designed into the system. And I think right now, sometimes we find ourselves with a lot of these AI solutions hoping for trust afterward, after it's been built or designed, and that is not a position we wanna find ourselves in. I think this is one of those hard to quantify, but super important points around just being intentional about trust being baked in in a prerequisite. And and it comes with just being cognizant around the magnitude of of the shift. And and that's why just thinking through, like, hey. This is not just a tech, but the whole framing for me at least of a a work three point o to four point o, it gives it the gravitas that's necessary to understand that this is not just people asking their teams to adopt a new way of working or a new tool, but this is inherently creating a certain amount of anxiety that requires a trust based model. And the idea of human as decision makers, no matter what level of role you're at, there's always a decision has to be made that I don't think we wanna completely hand over autonomy to. And so, if we don't think of trust as a prerequisite, I think that that's where things can start to go awry. So super important, takeaway, I think. And I guess to that level, like, what do you think organizations are getting wrong right now as they try to modernize sort of work work and hiring? That's a tough one. I think the biggest mistake is modernizing tools faster than decision making. Many organizations are automating assumptions that were already outdated, and I think that's where I would strategically flag, you know, how we think about our evolution in this process. When those automated assumptions that were already outdated make it into a system, they get embedded into this powerful workflow, and they become harder to see, and they become harder to challenge. And I think, ultimately, what we have to remember is that technology does not correct judgment. It can amplify it. So if you're automating the wrong assumptions based on work three dot o, you're not rethinking and redesigning your roles for work four dot o. You're then gonna you you run the risk of these big powerful AI tools coming in, and you're then automating these outdated assumptions, and that's gonna put you farther behind if you don't pay attention to that. It'll amplify, you know, the the the incorrect judgment in that scenario. So just remember that automation doesn't fix those bad assumptions. It'll actually scale them, and I think a lot of people forget that. So bringing it from, you know, leaders very broadly to if you're advising head of HR today, what needs to change first in hiring and and their overall view on talent? I think it's it's definitely a challenge, but the first shift is treating hiring and development as one continuous system. And you need to move away from static roles toward skills and capability. We talk about human leverage, and we talk about human potential. And we need to rebuild those internal mobility pathways. I understand that'll take time, but as certain hard skills are a bit more commoditized as AI comes together with the workforce, that's going to increase the opportunity for internal mobility pathways in an organization and improving the quality of the signals used so that HR leaders and CHROs and their teams can then make decisions, people decisions with those quality signals is, you know, something we just have to continue to jump on and improve. And one of the things I think is fascinating is there's a world of data now that can be measured that is relevant to somebody's likelihood to perform in a specific job role and measure that human potential. And it's it's a lot of unstructured data, and the great news is we now have technology that can harness those unstructured data signals and give us, you know, quality of talent signals that we've never had access to before. And I know that's something we're truly excited about at Criteria and and how we're we're working to harness those. So, again, before adding more technology, make sure the decision logic itself is sound, and I think all of that will be how we we will see the hiring and talent systems change as we we move forward. But but also stay open minded. I think that's really important because what I'm telling you today is not the same talk track I had three day three months ago. I think one of the things that's super interesting, Chris, is the idea that, you know, you you love the idea of first principles thinking. People that sometimes have no background, can see things from a completely different, like, lens. But the way we've hired, the way we've skilled, the way we put people into certain lanes, it it it tends to be more of the exception than the rule. And one of the nice things about AI is a bit of an equalizer because it sort of simplifies, you know, your your access to some of these more complex tools. Means there's a world where it should encourage and and amplify more first principles thinking if you have those kind of people in seats. So you're less constrained by tools, and you just put more of a lens into who you have in your system versus people who can actually use the tools. And I think those you know, your takeaway there was around, you know, organizations right now will always gravitate towards the tech. And I think there's, you know, a required focus on who you're actually bringing in into this new, like, workforce. Yeah. I think we we need to make sure we fix the decision system before layering on more technology. I think that's the the takeaway there. Alright. So as we look ahead, what should leaders keep front and center as work continues to evolve? You just mentioned that even in the last three months, your your thinking has shifted. Now you've come fresh from a week of very consolidated thinking around around this topic. What do you think is, like, the biggest thing people should think about? Workforce strategy is now a business strategy. All of us wanna grow our organizations. We wanna grow our revenues. We wanna execute. We we want growth, growth, growth. I would say that a growth strategy cannot be a growth strategy without a workforce or talent strategy. Those two are now hand in hand. And, again, that's because the future of work that we describe is already operating now. Human capacity is really the limiting factor in that perspective, and talent decisions now will compound faster than ever before. So trust, learning, and leadership are becoming infrastructure, and that should be top of mind. Workforce strategy is business strategy. If you wanted the HR leaders in the audience to sort of have a takeaway from your Davos experience, what would it be? I think there's strong consensus that a good move right now is to focus on hiring for capacity to grow into the future, that human potential element, compared to hiring for fit, which I know a lot of us are doing today. So I think instead of asking, can this, you know, person do the job? I think there's a strong element of asking what could this person become here or, you know, how can their potential really drive value for our organizations? I think that that, surprisingly, I think Davos has a reputation of, you know, certain aspirational thinking and then on the other side, super in the weeds tactical thinking. I do think that is a, you know, a mindset that was really well received and adopted by some of the leaders of the largest governments and organizations in the world. So what's your what's your takeaway, from this before we we jump into the q and a? I think that's a great way to put it there on the screen. I think we're not just focused on predicting roles. It's about unlocking that human potential we've described today and doing it at scale. That's really important. I mean, if we have to spend, you know, ten times the amount of time with every person to understand that human potential, it will not scale. We will not build the best teams in the world. That's why technology and and solutions that we're working really hard to do ethically and responsibly at Criteria are pivotal for this transformation of workforce. Awesome. Well, I'll I'll I'll try to sum it all up here, Chris. Let you have some final, like, thoughts. Audience, again, the q and a tab is available to you. Go ahead and type your q and a in there. If you're having a hard time finding it, go ahead and type questions in in the chat. There's a few in there already that I'll I'll jump into already, but please please feel free to add to it. You know, I think if we if we take, you know, away the one thing is to to think about this with the lens of optimism. I think, you know, if we if we look at the methods we've been using, and the traditional static credentials, it it starts to expose the quality and quantity of of hiring that, you know, both AI creates an opportunity, but it also creates some headwinds in terms of being able to find the right skills and the right talent. There are better ways out there to assess your capabilities. And the nice thing is, like, AI, I think, opens us to having folks think about, you know, adapting their skills in different, like, ways. So I feel like the talent pool actually expands, but you just get to think about new ways, to hire. And so we think about smarter, more scalable practices that you can use to actually uncover true potential, not looking at, like, what you've done in the past, but really getting a feel and a prediction for what a person can be in the future, then you're already a step ahead of the game. And it's an exciting opportunity, I think, in HR to really have an outsized role in in shaping, like, the future of a company's, like, growth. The one thing about, you know, technology like AI is it tends to be a great equalizer. Access to tech has never been, you know, easier across, like, the board, which means, you know, the the talent that you have in your organization is gonna be your biggest, like, you know, leverage. So it's easy. Like, any of these shifts we've been through, work one point o, two point o, three point o, they all came with anxiety. They all came with a little, like, shift, but the labor force always expanded in each one of them. The people that were able to be hired, the economy got more, like, global. And so with this shift as well, we're going through the natural period of anxiety. But I think one of the takeaways, great reinforcement from what you heard as well in Davos, Chris, is that this should actually be a time of of great optimism. I'll let you give us some some some final thoughts, and then we'll jump into some of the questions that have come in. I'm excited to get to the questions. Love to hear firsthand. So I think my final thoughts are, again, we can get the policy right, and we can absolutely get the technology right, but it still comes down to people. And while technology will shape what's possible, human attitude and aptitude will decide what actually happens with that policy and technology. So that's that's good. Let's go into some questions. Alright. So one of the questions here is based on what you heard at Davos, how can early career professionals stay relevant as AI adoption and even layoffs shape teams? One of the perspectives I share quite a bit is that early career relevance is no longer about role security. This might be a little bit of a hot take, but early career relevance today isn't about role security. It's about learning faster than the system is changing. So, again, focusing on that learning velocity, the people who stay relevant, particularly those in early career, are the ones who can work alongside AI, learn quickly, be able to add and inject their judgment and context and follow through into the AI context. And I think if you can show adaptability and real problem solving, you stay valuable even as roles may change in ways that we frankly don't know yet. And related to that, and I think, probably near and dear to your heart as a technologist as well, Chris, could the same AI used in the hiring process be used to give candidates feedback on where they're lacking, why they've been rejected, so that they can turn those into learning opportunities? The answer is technically yes, and it should be. And I know at Criteria, we care as much about our candidate satisfaction and experience as we do our customers wanting to use the product to build the best teams. And I think the reality is, yes, the same AI can be used to evaluate candidates, but also help prepare them for the world. And I think there is a consortium of companies emphasizing on applying AI to candidate readiness and workforce development. We, of course, also play in the post hire space with our developed by criteria product. So, you know, I I just I think that turning hiring from this black box into a learning loop, is gonna help improve the trust we talked about today. It's gonna help improve candidate experience. And, ultimately, that means long term talent quality for all of us. So, you know, the key is that humans still own the logic and accountability. And, yes, I think it can be used on both sides. I think that's a a really a really important, you know, comment. Ai shouldn't just decide who, you know, gets the job. It should help people understand how to get better, and I think that aligns with our philosophy on having the best, candidate experience. I think one of the nice things about, you know, the dynamic nature of generative AI is our ability to give that sort of feedback real time versus the lag that the the current's very transactional hiring process, like, dictates. It's just not very conducive to that feedback. So like you mentioned, the tools are there. You know, the tech is there. The knowledge is there. It's a combination of adopting a hiring mindset that takes into account, you know, other things versus hard skills and then being able to use some of the tech to give that feedback real real time. And I will say a a somewhat shameless plug, but in the next month, our candidate experience report is coming out where thousands of candidates that we survey give their feedback. So for those interested, you know, you can drop a you know, just put Davos in the chat, and we'll make sure that you're on the list to receive that. Otherwise, just come back to the Criteria website, and you'll see links to to get that report soon. I'm personally very eager to see how much it's changed given how much happened in the last twelve months. You mentioned, you know, and this question has has come in as, I think, very, very relevant and and timely. You talked about the power of humans working alongside AI and that very much becoming, you know, a reality. Have you yourself seen some encouraging examples of where organizations are really leaning into that, rather than just focusing on, you know, reducing roles? I I think so. I think but just to be clear, there are there's probably as many examples that I would think are bad examples of how to do this. I think that things that I've seen work really well is, you know, just to lean on some of the framing we had today, you know, having a real direct conversation with your all staff, your global teams, and say that AI is about human leverage and augmentation and not just for bottom line, you know, changes to margin for organizations. I think that having that open and honest dialogue and be meaningful about it, I think, having an AI first culture takes time to build, but you don't build it if you don't, you know, work in increments. Right? So I know at Criteria, we we like to have, you know, global AI office hours that, you know, are focused on the practicality and the skills, needed to interact with AI and, you know, opportunities to augment our existing roles across the business. We have a scorecard across departments in the organization that, give us insight into, you know, how aggressive we're being with adoption. We have open dialogue. Not all AI use cases pan out or are effective. Some some don't work, and we have to be open and honest about that. I think all of that is critical, and those are some of the areas that, you know, I think it really works. I think I've also and I I see this question as well in the chat. You know? Did I meet, with any business leaders who are already doing, you know, these six point points right? Did they mention any practices that they've adopted? I think it's a really good question. I think bottom line is, yes, I I met a few leaders who I felt were good examples, in all of those categories. And I think what stood out in those discussions of people who were doing it well, and these are some, you know, some, like, mid market companies, some kind of multinational enterprises I'm talking about right now. You know, it was not flashy AI use. I think when I when I hear certain organizations like Flash AI, it tends to mean that when you peel back the the layers of the onion there, you know, you start to cry a bit. And I think it stood out that it wasn't just the flashy AI use. It was really the discipline and how they design work and their talent decisions to be successful. That was a big takeaway for me. And leaders who are doing this well, I think some of the common practices that that I remember were, you know, they rely less on resumes and more on job relevant proof early in hiring. You know, they redesigned roles around outcome and judgment and not just the task lists. You know, they treat learning as part of the job, not just like a separate program. I think back to our comment about static credentials. You know, if you go through college and you learn once, you know, that's a static credential. We need to really invest in infrastructure around continuous learning. You know? And these leaders are also explicit about where AI is used and where humans decide, and I think that builds trust and makes them more successful. And then, of course, they pilot changes in those small ways. They measure outcomes, and they're willing to be honest about what will scale, what will work, and what didn't work. So I think the pattern amongst those leaders I noticed was, like, this pattern of intentionality. They're not just chasing the flashy tools. They're designing systems in real time that meet the needs of their organization. So I think those are behaviors we all need to adopt. Bad news always tends to travel faster, than good news, and so I think the the headlines get dominated by by layoffs that sometimes are even incorrectly attributed to to AI. I think it becomes a convenient, trope, but the most AI forward companies, the AI native companies, all the LLM companies, they're growing like crazy. They're hiring, like crazy. And so I think it it is is important to to recognize that, you know, if if handled well and if adopted correctly, this should be, you know, a a great amplifier. We obviously have a very strong point of view at Bing criteria on how to measure a human potential, capabilities, personality, some of the tried and true ways to always predict a successful hire. You know, we really thank you for for joining us today. If you wanna keep hearing more from us, we've got for those of you who might be in the Bay Area, San Francisco area at the end of Feb, we're doing an exact dinner focused on the future of work with our partners at at Greenhouse in UKG. There'll be more information. Contact us, and we can we can pass that on if you're interested in it. We also have a webinar coming up on the death of the resume as we know it at the end of, like, Feb. So we'll be sharing details on that soon. And then we've got a few events we're gonna be at in March. For those of you who are in the Atlanta area, we're at an event, focused on Chris and I are both gonna be there. It's very relevant to this topic, HR in the age of AI. In Philadelphia, our partners, Phenom, are having their I'm Phenom conference. And for those who happen to be in in Vegas, Unleash is a large HR conference. We're gonna be there at all of them. And if you happen to be in any of those locations, we'd love to see you there. Really wanna thank everyone for attending. Thank you, Chris. I know you're probably still jet lagged and catching up on sleep, but this has been, super relevant. You you're all gonna get a recording of this webinar. If you're interested, we've got your comments in the chat, so we'll make sure you're top of the list for that candidate experience report. Again, wanna thank everyone for their time today and for attending.