Name: From AI Applications to Future Leaders: How Nestlé uses Skill-Validated Hiring to Reveal True Ability
Uploaded: 2026-08-26T15:30:07.762Z
Duration: 1 h 5 s
Description: From AI Applications to Future Leaders: How Nestlé uses Skill-Validated Hiring to Reveal True Ability

Transcript for "From AI Applications to Future Leaders: How Nestlé uses Skill-Validated Hiring to Reveal True Ability": Hi, everyone. We'll give it a few seconds to let everyone join and then get started. Okay. Good morning. Good afternoon. Good evening, wherever you're joining us from. And thank you all for being here for this session, from AI applications to future leaders, how Nestlé uses skills validated hiring to reveal true ability. My name is Kelly Carpenter, and I lead our customer advocacy efforts here at HireVue, And I'm really excited for this conversation today. A few housekeeping items. There is a chat section in the upper right of your screen. I encourage everyone to drop questions in there throughout the conversation. We'll try to get to as many of them at the end as we can, or plan to follow-up afterwards. And we'd love to know where you're joining from. I know we had people register from all across the globe, so feel free to say hi in that chat as well and let us know where you're joining from. And don't worry if you miss something, the recording will be sent out afterwards. You should receive it in your inbox in about twenty four hours. So now, some introductions. I'm thrilled to be joined by Renu Delaney from Nestlé and Christie Brodbeck from our IO team here at HireVue. Renu and Christy, would you like to introduce yourselves and tell us a little bit about you and your roles? Sure. Thank you, Kelly. First and foremost, thank you for everyone joining the webinar. I'm really excited to have this chat today. I am Renu Dalani, and I head up talent acquisition for Nestlé Middle East and North Africa covering 19 countries. And we cover not just talent acquisition, but also youth initiatives and employer branding. And hi, everyone. Christie Brodbeck. Excited to talk through with you today some topics that we're really living and breathing every day, and we know you are too. I've been with Hirevue for about fourteen years. I'm our VP of customer science. I lead our team of IOs who works directly with customers on our science and assessment implementations and ongoing support. Thanks both. Let's go ahead and dive in. I would guess a lot of you on this webinar are wrestling with some version of what we are about to talk about. We all know the rise of AI has really fundamentally changed hiring. Candidates have more tools than ever to optimize their resumes and applications, which make traditional hiring signals like resumes and CVs less reliable than they used to be. And at the same time, candidates are using AI to apply at scale, making application volumes higher than ever. Put those two things together, and it's really difficult for TA leaders to know who has the right skills, who will succeed in the role, and ultimately who will stay in the role. That's why and it's especially difficult, for those in the early career space where a lot of times there's not a lot of work history to draw on and resumes tend to look very similar. That's why skills based hiring and validating skills is something that we're really passionate about here at Hirevue. Traditional workflows like the one you see right here really are based on inference at every stage, inferring skills from a degree, inferring skills from a resume or CV. And for those of you a little less familiar with the HireVue platform, our platform is really built to help you validate skills throughout the hiring journey from the first time you engage with a candidate to the interview and hiring stage. Our platform allows you to screen, assess, interview, and hire the most qualified talent. And I think the really unique thing about HireVue, is really that we've been doing this for a while. We have a ton of post hire data. So we are able to tie that interview and assessment data to real post hire outcomes. We've done I think, actually, now it's more like a 180,000,000, assessments, and we've completed 1,300 validation studies. So we have a lot of confidence in the data that we are providing you. So I'm excited to dig into the conversation with Nestlé today and hear how Nesli has responded to some of the challenges that we just chatted about by leveraging HireVue and leaning more into validating skills for their early career hires. So before we get to that discussion, I am gonna ask, do a quick pulse check and ask one poll. We'll go ahead and launch that now. You can see it up in the upper right by the chat area, and let's see. First poll, what is your biggest hiring challenge that you're facing today? Is it volume, too many applications to review effectively, AI making applications harder to differentiate, identifying candidates with the right skills for the role, delivering a fast and engaging candidate experience, or quality, improving that quality of hire and predicting future performance? We'll give it a second for people to answer, and then go ahead and share the results. Okay. Let's go ahead and share the results. I'm sure a lot of this maps to, some of the challenges we just talked about and some of the challenges that, we are gonna talk through with Reina. So, Reina, as we jump into the discussion, let's start off. Can you share a little bit about the NEST talent program and the roles that you hire for? I would love to. This is by far the closest youth program we have here at Nesli Mena to my heart. Well, let me start by giving you a little bit of an introduction about Ness Talent. Ness Talent is Nestlé MENA flagship youth graduate program. It's a twenty four month accelerated development journey for top talent who graduated this year or last year. The idea is sort of simple. This isn't an internship or a job shadowing. From day one, our graduates join real teams, work on real business challenges, and take on meaningful responsibilities. Of course, with the support of, experienced mentors who are genuinely invested in their growth. Over twenty four months, they move through a series of structured rotations designed to accelerate the learning and immerse themselves fully into the business. So by that so by the end of the journey, they're actually ready to take on an impactful role within the business. We launched our first cohort in September 2024 with 17 trainees across seven functions and five regions within the Middle East and North Africa region. And we're really proud that it's a genuinely diverse group with 65% female representation. Along the way, our graduates take on capstone projects, tackling real business priorities, get direct exposure to senior leaders, and also build a regional network amongst Nestlé. Now in terms of the roles we hire for within Nestlé, they span across, different core functions. And if I were to name them, they are sales and commercial development, marketing, supply chain, finance, technical roles. When I say technical roles, it's production and electrical automation engineering, human resources, and communications. So and I have to say, our second cohort launches in January 2027, and the applications are actually open now as we speak. We are going to welcome 14 trainees for the second cohort across UAE, Saudi Arabia, Egypt, Morocco, Lebanon, and Iraq. Perfect. I love that idea of getting that real life experience kind of on the job. So as you started kind of the nest talent program and were kind of looking at this process, what were those hiring challenge challenges that you were trying to solve for? Amazing. I I would say I wouldn't term it as challenges because, you know, our first cohort is always your pilot cohort. So I I would I would say the learnings that we took away from the first cohort are used to design the second cohort and make it much more stronger and much more impactful. But let me start by saying, I think, something that you just mentioned earlier is the challenges we, as Talent acquisition, are facing across the globe, the volume of applicants, or let's say, the sea of sameness in graduate CVs. For this flagship program of ours like nest nest talent, we attract enormous interest, thousands of applications across MENA. Now I could give you an example where for a single production role in in a location like Egypt, one of our recruiters had over a thousand applicants, while a niche role like electrical and automation engineering had under 200. At a graduate level, CV start to look almost identical. Same degrees, similar GPAs, little work history to differentiate. So the challenge here was really, how do you fairly find the real signal in that noise? So that was our first learning. The second would be AI generated applicants, applications, I would say. Like every employer right now, we're seeing more polished CVs, AI assisted CVs, and applications, which makes it even harder to tell genuine potential apart from the well worded template. So that pushed us to lean less on the CV alone and more on the tools to assess actual aptitude and behavior. Right? The third thing I would say is trying to give every candidate a fair and objective shot. With that volume, human bias and especially recency bias becomes a real risk. And, you know, the best talent may be lost in translation. My guidance to my team was to guard against interviewing someone on, let's say, the fifteenth, and then someone again someone else two weeks later, and only remembering the most recent ones. So we built a process to be structured and consistent across the region, which is fairly hard in a virtual environment, but, you know, we have to make that happen. The fourth area I would say was attracting and encouraging female talent to niche roles like technical and production roles. This was a deliberate priority for us. In markets like Egypt, Saudi Arabia, female representation in engineering and production graduate sits roughly at 20 to 25%, so the pipeline itself is quite thin. We were determined to not let that become an excuse for us, so we made female talent an explicit first screening requirement in those streams. So that's that's, I would say, the learnings we took away. Yeah. I was gonna say I think that's super interesting. You know, I think, I will admit I have been guilty, I feel like, of recency bias. I think when we talk about bias, there's a specific kind of connotation there, but I think it's so. much broader. And I know for me, it's like, did I give somebody a fair shot when I interviewed them at 8AM in the morning on, like, a Tuesday versus someone I maybe interviewed at 3PM on a Friday when, you know, I raped and maybe shut down for the weekend? So I think that's so important to have that consistency throughout the process. Absolutely. I do wanna, I do wanna give you an idea of what we've changed or how we've designed the second cohort and how we try to solve these lessons or incorporate these lessons in the design of the second cohort. I think the first thing is we anchored the funnel in objective skill based assessments rather than just the CV. Every applicant, as you know, goes through the Hirevue game based and video assessment that auto scores and tiers candidates on their abilities and behaviors that they that predict the success. So recruiters are only doing deep screening of the top tier candidates. That's then followed by a Dalani assessment center, which is an in person or virtual assessment center, which then leads to a panel interviews with the business leaders. So that's one part of the the transition that everyone goes through. We also introduced an interview a standardized interview assessment tool. No candidate progresses unless they're scored on it, which keeps evaluation quite consistent and fair across every recruiter and every market. We shifted our principle to assess overall potential and not just academic performance. Looking at, let's say, learning agility or adaptability and mindset rather than just the GPA alone. For female representation, I would say we built into the selection built it into the selection sequence itself. In the technical and production streams, recruiters surface the female candidates first, review their GPAs and the Hirevue scores, and then build a shortlist from there while still holding the quality bar. So driving diversity without compromising on talent. And I would say where the type pipeline was genuinely thin, we went upstream, targeting the specific universities that produce engineering or technical graduates or marketing graduates, which with tailored outreach and single focused application links. So the philosophy is fairly simple, and it ties it all together. And, I would say it's our team's mantra at this point. We hire for skills, select for potential, and build for the future. I really love that mantra. Now I have a question. When you say you have anchored the process, around the skills based assessments versus CVs, Have you completely eliminated the CV screen, or is there still a place for it in the process? Great question. I think, Kelly, it really it really narrows down to the kind of level we are hiring for and the functions we are hiring for. Can we eliminate CVs altogether? Maybe in the future, but not at this stage. I think, where where I just gave an example of the sea of sameness. We. we look we're looking at young talented individuals. How much can they put on their CVs? The differentiation would be maybe certifications and, their their their characteristics, how eager they are. But I think for those kind of roles, it's a it's actually fair to get them to do those online assessment tests to be able to shine out, individually rather than CV screening. But for their for technical roles or more experienced roles, I think we still would have to re refer back to CVs at this stage at least. So you've implemented this skills first kind of recruitment strategy. Why was that so important for Oh, Nestle? sure. Honestly, for me, skills for strategy isn't nice to have anymore. It's actually the need of the hour. The world of work is changing faster than ever. Roles are. changing, business requirements are changing, and AI is actually accelerating all of this. And it's actually a ripple effect. It forces us to change the lens through which we hire. If the work is evolving, then the way we identify the right talent has to evolve with Yeah. if I if I were to give you a little bit of data that backs us up. Right, the World Economic Forum's future jobs report 2025 tells us that employers expect around 39% of workers' core skills to change by 2030. LinkedIn. puts it even higher. They estimate that by 2030, about 70% of the skills used in most of the jobs will shift with AI as its catalyst. And Mackenzie's latest research makes a point, which I actually love. More than 70% of the skills employers look for today are used both in both automatable and non automatable work. So skills don't disappear, but how and where we apply them changes completely. And that's exactly why we at Nestlé Manna are moving to skills first hiring. When AI can take over the mundane, repetitive, time consuming tasks, we we have to be really clear about the skills and competencies that actually drive success in a role. So relooking at job descriptions, reimagining this. So for us, it's about hiring skills and behaviors and not just a CV. I think you've mentioned a few of them earlier, but when you're thinking about building the workforce, that can adapt and change to this kind of evolving world moving forward, What are the skills that you're kinda prioritizing, and why are those qualities so important to Nestlé? Sure. That's interesting. Now, unfortunately, it's not one size fits all. Yep. Every role and every function has their own set of competencies and skills required for the success of that role. But overall, within Nestlé Manor and I think the wider Nestlé, we assess collaboration, speed, learning, and courage. And the other norms, which, of course, I think it's it's, it's a word bank now that we all use, which is resilience, agility, cultural fit. But I think for us, what matters most is IQ, but EQ is equally important. Yeah. So once you've defined those skills that you were looking for in candidates, how did you approach kind of redesigning your hiring journey to address that? It, I have to say it hasn't been an easy journey in, making that change, within with a such a large organization like, Nestlé. and then a larger, a large part of that being Nestlé Middle East and North Africa, it was not easy to manage the change and the movement. I would call it a movement because you are changing the mindset of people, Yes. of people hiring, not just the recruiters. Right? The business leaders, the hiring managers, and so on. So it was a well thought through structured approach. We've partnered with the business. First, you have to understand the business requirements. You have to understand the functions, lens of what has changed with AI in their function. To pull pull out the required skills and competencies and then devise the right selection methods. You know, if we talk about innovation, what has changed in talent acquisition selection methods? We we see we still have interviews. We still have assessments. We still have case studies. So we have to look outside of the box, and we have to think wider in the selection methods to assess the set competencies because it is, as I said, it's not one Yeah. size So what are some of the, fits solutions that you implemented all. in that process? The Hirevue solutions that we implemented were, the the game based assessment and the video based assessments. And we put that actually at the top of the funnel. Nice. So thinking about this process, I know one of the core learnings was ensuring people got a fair shot. How do you ensure every qualified candidate gets an fair opportunity to demonstrate their skills in this new process. Amazing. That I love that question. I think I'll refer to what you just, mentioned earlier. You've mentioned a point that, you know, you always think whether you're doing the right thing if you're interviewing someone at seven in the morning or three in the afternoon. Right? Different. experiences, different level of energies, different level of, moods, and so on. To be honest, you cannot expect to use different selection methods for different candidates applying for the same role. We start by putting all candidates to the assessment online assessment test that which I said, which was the top of the funnel, focusing on behavioral and cognitive cognitive capability. And then structured assessments measuring skills linked to the job performance. I think what I want to emphasize here is the consistency. Consistency will enable reducing bias, which in turn would be giving everyone the same opportunity to showcase and shine. Nice. Now we know there's always resistance to change, and you talked a little bit about working closely with the business your business partners and really understanding the business needs. But how have your recruiters responded to this news process? How have candidates And, specifically, kind of the use of AI in the process. Great. I I think I would say the the reaction has been split, but I think it's a healthy split, and I'll explain why. Let me start by my recruiters, my team. Recruiters were were cautious at first and then, because I okay. Let me sorry. Let me rephrase this. The concern of my recruiters or any recruiters with AI was always the same one. Is this replacing me? What shifted it was showing them where it actually lands. Now we can't take out the human element in human resources, and TA is the first point of contact in human resources. So we need to know exactly where technology, where AI, where automation sits in this whole process. When AI takes the scheduling, the screening volume, and the note taking, Recruiters get their week back to do actually. the job that they are trained for. That is headhunting, the right judgments of the candidates, and the relationship building. Most most importantly, stakeholder management. Once they felt that in their own diaries, skepticism turned into demand in my team at least. I remember, when I first, introduced Copilot note taking in video interviews, the words used were, this is gonna change my entire world because I'm not going to be focusing on writing down what I'm hearing. I'm gonna be focusing on probing the candidate, getting to know the candidate whilst Copilot is doing our note taking. You know? So that that's for my recruiters in the team. Now candidates are more mixed, and rightly so. They accept AI when it makes the process faster or more consistent. Now where they lose the trust is when they feel like a decision is being made about them by a machine with no explanation. So. our position is fairly simple. Be be transparent about where AI is used. Keep the human accountable for every final decision, and never let a candidate receive an outcome nobody can explain. So that's that's what I would feel. Yeah. I agree. I love the way oh, right, Renu. I was gonna say, I I love the way you talked about it from both the recruiter and the candidate's perspective of these are justified concerns. Right? This is something new. And in HR, I'll be often in TA, there are new things. We go through different phases. There are different tools. Each one comes with their own concerns. And I think starting from that position like you did of thinking this is reasonable. And, also, I love how you started by talking to your stakeholders because I think particularly with recruiters and hiring managers, a lot of times when you ask them about their pain points, that makes it really easy to connect. Your pain points. Actually, these are the things we're solving. You know, all of these things that don't feel like a good use of your time, a good use of your skills, that's what we're taking care of. But having that foundation and making sure that we're considered it in our process can make that so much smoother. And just thinking about that in the design of our tools themselves. So candidates, we we understand that there's some hesitation when they don't know exactly how that AI works, but we can tell them it's being used. We can give them the option of do you wanna opt in or do you wanna opt out. We can make sure that the content they're taking is really job relevant, and they understand how it's used in the decision making process. So So they still feel like I'm getting the opportunity to demonstrate my skills, and this company is showing that they're really thoughtful and also progressive and probably think about hiring. I couldn't agree more, Christie. I just wanna make one some one simple point on this. You know, Nestlé has obviously, we we with with AI coming in full force, we we have to be cautious and we have to use AI responsibly. And being fair is one part of that. Our AI committee is is strongly positioned to ensure that any AI tool used within the organization doesn't give you a decision regarding another human. It's more about giving you an overview of what what the candidate has, what the candidate can display, the behavioral aspects, but it does not give you a decision of whether to hire the candidate or not. I think that's really important. I think that's fundamentally something that we at Hirevue believe in, and I think it's important too to share with the candidates that an assessment is one more data point and one more opportunity for them to be seen. I think especially with the volume of resumes and applications that TA leaders are having to sift through, It's almost you know, the candidates have kind of helped create this problem using AI to kind of mass apply. And so this is really one more opportunity to help them be seen and be, you know, picked from that stack of resumes and applications. Because, Kelly, very, very good point made here because it's it's basically an AI assisted CV talking to an AI screening tool. I mean, where is the human element in all of this? Yes. Yeah. I love that. So I know, I'd love to talk a little bit about outcomes. I know you've achieved a 90% conversion from your first graduate cohort into full time roles, which I think is very impressive. What other outcomes have you seen because of these changes? Sure. I think, because of these changes, I think, I I would like to focus a little bit on more on the the outcomes that help the business as well. Through the 90% the 90% conversion rate from Nestlé Talent, the first cohort, we were able to have cross functional talent pipelines. So Nestlé Talent has created a pipeline of early career talent across multiple functions, helping build a broader bench strength across the business rather than just in one specific area. And I think that's the USP of NEST talent. I would say it also has helped an accelerated readiness. We all know, there are thousands of top applicants, top graduates that go through immense, you know, I would say selection methods or, application methods or constant applying to different roles. But they're always given your your you're known as entry level or they're they're known as entry level roles that they apply to. Right? Here, with Nestlé Talent, through the structured rotations, exposure to different teams, and real business challenges, it's an accelerated journey to really understand the business and not just end up into an entry level role. And I I would say most importantly, it it built a stronger internal pipeline that the the youth of today are the future leaders of Nestle through Nestlé programs like Nestlé Talent or our Nestlé turnips and job shadowing and so many more youth activities that we have. Nice. I was gonna say this is just such a great kind of transformation story, and I'm sure a lot of people in the audience can relate to some of the challenges you've encountered. I would love to ask the audience one more kind of poll question. Just getting a sense of where you are on your skills based kind of hiring journey. Where is your organization? Are you still primarily using, resumes and CVs? Are you starting to introduce some skills based approaches, assessing for skills in some roles or certain programs? Is it embedded throughout your hiring process, or would you consider yourselves advanced in really measuring skills against performance outcomes? Give people a second to answer. I'm interested in the results of this fall. Yes. Let's go ahead and show those. Okay. It seems like pretty split between, primarily using CVs and resume. Oh, that one's growing. Yeah. I feel like. jumping around. But, yes, a lot. I think still primarily using CVs and resumes, and I think that speaks to, you know, how embedded that process is, in the hiring process. Yeah. Great. Well, I'd love to switch gears a little bit. I I think it's always great to get the perspective, kind of practitioner perspective as well as kind of the science and IO perspective. So we'd love to hear a little bit from Christie. I know we, talk a lot about skills based hiring, but from your perspective, you know, we've been hearing for years that the resume is a poor predictor of success in a role. We know a ton of people are still using resumes, but why do you think organizations are finally starting to maybe move a little bit more away from the resume as the primary hiring signal? Yeah. It's a great question, and I think a lot of it goes back to also some of the things that Renu been pointing out, as that Nestlé has encountered as they go through, especially as AI has come onboard a little bit more. What we've seen is that employers, they've been aware for a long time of a lot of the the challenges and drawbacks that come from basing decisions on CVs. And like you mentioned, Kelly, research shows this is actually pretty much the worst predictor of performance that you can use. But and but, you know, I think there is also this inertia factor as well. But Renu mentioned a lot of the things that we're seeing struggle with, which is you're seeing this very curated view of the candidate, when they submit that CV. You know, they're of course, they're incentivized to really optimize that view on themselves as they're going through the process, especially now that they know these huge volumes are out there and other candidates are using AI. CDs also make it really difficult to compare apples to apples, and ensure that there is a structured and fair evaluation process. Everyone's giving you different information, and so you don't even have the same information to compare. But then it also really introduces those biases that Renu mentioned earlier, which is we're humans. Even if we're trying our best to be very fair, that's hard even when we do have consistent information, and CVs just make that worse. And even if we could trust the data, what they do tell us is really very surface level information, things like titles, degrees, the companies they work for, how long they work for those companies, but they really don't tell us about how well that candidate performs. You know, what we've seen in our research is years of experience is not very highly correlated to how well someone performs. You can do a job pretty poorly for a long time, or those skills just might not transfer to the new role that you're applying to. And a CV can't directly measure those things that organizations actually need, those underlying skills like judgment and adaptability. Before the widespread usage of AI, what we saw is that many companies and, you know, still many companies continue to rely on CVs because they're rooted in tradition, and they seem like a really easy way to get a lot of information by the customer or sorry, by the candidate quickly. But with the widespread use of AI, what's happened is these drawbacks of CVs are just continuing to stack up. The the sea of sameness that, that Renu mentioned, the really high volume in the world of work and hiring are just evolving more rapidly than a CV can keep up with. And, also, what we've seen is that AI has highlighted a lot of the inherent issues with CVs that we've known about for a while, but it's just really exacerbating them. And the way that we like to think about this is, you know, AI, it's not going to solve weak signals. It's going to amplify them. And the way we've seen this play out is, again, Renu mentioned. Right? So you you start to see this influx of high volume candidates using AI, and a lot of companies have looked at the solution of, well, then we'll use AI too. It'll help us sort through really quickly. It'll help us make some sense of this data, but then quickly realize that AI alone is not gonna solve that when the underlying signal is still weak. Technology can't make up for bad data. These bad bad signals just get amplified. So, you know, we're seeing in the data even we just collected here. We're not at the depth of the resume or the CV quite yet. AO companies are still using it, and there are a lot of practical reasons that they're doing that. I think what we are seeing is the end of the CV as a trusted tool and as sufficient evidence to make a hiring decision. And, really, what we're seeing with our customers is that this hiring question is shifting from just these basics, you know, where have you worked, what have you done, how long to what can you do, and how do we know what you can do and just focus on direct evidence? And that's what we see in a lot of the customers who approach us. They want to work efficiently, but they know that making bad decisions quickly is not gonna help them. And the more synthetic the CV and the application become, the more authentic the assessment and the other parts of our process we need to become to make up for that. So when you think about skills, how can TA leaders identify the right skills for the role that they're hiring for and ensure kind of success at the organization? I think a key part of that is just in how you frame the question, which is we start with the skills. Yeah. And, you you know, it's it's really starting there, and this is represented in what we're showing right here, which is Hirevue flow of how we get to really a role driven assessment. And to identify these skills that predict success, it's also really important to take that step back, think about how we're going to define success, and think about it more broadly. The most common misses that we see in organizations are not starting with the end in mind, meaning that they may approach skills like a job description, which is sort of the the the other, document we are calling out on our last slide, which is it's describing what you see. It's designing or it's describing how things have always been done, but not necessarily evaluating. Do these behaviors and skills actually lead to success? Are they aligned with the company and also how the roles and work is evolving? But once we know what good looks like, once we've taken that step back, you know, we understand what are the key outcomes, what are the metrics that are most important to us around quality, production, retention, advancement, then we can really determine what differentiates those top performers from their peers. Things like, you know, key responsibilities, their experiences, and the underlying skills, which, of course, is our our theme here too. Those are what really are going to drive the performance is getting that foundation right. I'll speak a little bit to our process around that a little more in our next slide. But all of those skills do serve as a foundation for determining our approach to assessment. So at Hirevue, we then link that foundation to our vast content library that assesses experience, expertise, job knowledge, behavioral competencies, individual attributes. And there should always be this direct mapping of the assessment content to your critical skills. We don't measure anything that's not important, and we don't wanna leave out the most critical skills either. And having this really comprehensive and varied library lets us create these role specific assessments and measure those skills in a really robust way and produce decision tools that measure skills in a way that then directly links to performance. And it's really about starting that with that foundation. So let me go into a little bit more of with the next couple slides of what this looks like at Hirevue and how we think about it. First I mentioned is just let's start by identifying the skills and the foundation. And this is a work work we've been doing for 10 leads at Hirevue, and a really important part of our process to start with is just we wanna understand your job better. And a lot of that is understand the context, but really fundamentally understand what those skills are that drive success. And we do this different ways. You know? On the left, you can see the way we've approached this for decades. You know? We're continually iterating this, but the fundamentals are the same. So for our customers who we really make sense to dive into those roles more, No. We're speed isn't the primary thing that we're we're shooting for. It's high impact job. It's a high volume job. We really wanna make sure we're gathering that data. We can talk through the focus groups, interviews, and those really help us get at the future. But, also, looking at job documentation, doing structured questionnaires where we can gather a lot of qualitative and quantitative data to help us drive those decisions and, what skills are most important. And then as we've evolved, the other thing we really think about is, you know, we are both a science and a technology company, and so how are we combining those two things is the question we're always asking. And some of our new tools like assessment builder are a good example of that. We can take those decades of research, and our Hirevue AI that we've been developing, and we can combine those to automate a lot of that to incorporate our learnings, incorporate on that data, market data, to allow customers to input their own context and give them immediate results that help them get a lot of those skills. So we still of course, it's the the same problem is always we've gotta put good data in there. But getting that good data and having those problems, you can really quickly use technology too, in a science heavy way, look at the skills that are gonna drive success. Last thing I'll point out here is, as we're thinking about this is because we have that job analysis, the learning process that really lays out the key skills, the decision making that we're then making is more around that. We measure those skills, and you can see here a few examples. If HireVue has this arsenal of validating assessment content and products, so we can really design the assessment experience around several requirements without losing our ability to accurately and directly measure those skills. So we can design for, you know, period experience, length. You know? Does a customer wanna use AI or not? Do they wanna do an interview format? Do they wanna do an assessment format? What we can do is think about because each of these measure a lot of different skills, we have overlap. It gives us a lot of options for how we balance to maximize our ability to measure those skills and predict outcomes and ultimately narrow down that broad library to a role specific assessment. So, Christie, when you think about how organizations can implement AI responsibly, what should they be looking for? What should they kind of ask for when assessing that different vendors? Yeah. And I'll I'll start with some background or just a quick snapshot of of how we thought about this traditionally at HireVue, which is we we start with the decision, not the technology. So we always want to think about, let's the you know, defined you know, speed is easy. Right? What's hard is getting it right. And so what we do is we we build these guardrails. We test accuracy. We look for consistency. We think about fairness before and after launch. And as an employer, I you can take that same lens. Right? Automating is easy. Lots of companies can do that. Lots of products and tools can automate. But can you do that in a way that actually helps you make better decisions? Making bad decisions fast, you know, again, kind of our mantra. Right? That doesn't help you. That just gets you in a bad place faster. So as you're thinking about vendors, the first thing I would say is just just think bigger. You know, hold your vendors to a high standard. We can do more than automate. Make sure that quality is part of that picture too. Yeah. The other thing that I'd say is, you know, there are a few principles, again, that that we think about internally at HireVue, and we spend a lot of time thinking about how are we integrating all these pieces into the work that we do. It's research that comes together before something goes out. It's the way we're maintaining. It's the way that we're implementing. But a few a few key principles to keep in mind. One is start with the decision, not the technology. Right? Back to our last, slide there's find the problem, what's your intended benefit and role that AI will play before you start evaluating products. Two, match the scrutiny to the consequence. So the more influence AI has in an outcome and the more important those outcomes, the stronger the evidence and the safeguards and the oversight must be. Hiring decision is very important. It has a big impact to not just on the company and their overall ROI. Right? We all know the the big impacts, financially and culturally of making a good or bad hire, and it also has a really big impact on that candidate. We owe it to that candidate to make a good decision to use good data, so we really should put very high standards in place for that AI. We also need to demand proof. Right? Goes along with that. Ask how was it tested? What was tested? What were the results? What changed as a result of that? And I won't go through this small checklist, but, you know, a lot of the different aspects too of just how are they using that? It's not enough to understand the algorithm, but it's also how did they build that algorithm, what safeguards do they have in place, when will they not score, how does a human come into the loop, make sure that you're comfortable with that. Right? I think everyone speaking here is kind of aligned with the idea that humans should be in the loop. But the more you think about taking humans out of the loop, the more critical it becomes that you need to be supremely confident in the data that you have. The last thing I'll mention here, because I I think sometimes this piece gets mixed, is when we're looking for a vendor or a company is thinking about a vendor, it's looking at their AI. Right? So, hopefully, we get past is this thing fast when we look at the quality. But even then, we don't wanna stop them because a lot of the success of the AI or any hiring tool is in the how. So we've hit some of these before. Right? We need to make sure when we're implementing it, you can't just throw an algorithm at a job and expect it to work well. It needs to be particular to that job. It needs to be grounded in what are the skills that are most important. We need to link those skills to the assessment content, how we're scoring the assessment, the overall strategy of that assessment. If someone says, oh, we have a great universal assessment or we don't really need to understand your job. You can pick that menu. That's a big red flag. We need to be more nuanced than that if you want nuanced results. The other thing is just consider how it's used. We've had some of these before too. You know, candidate experience is hugely important. We we owe that to our candidates. It's a reflection of the brands or the companies that are using it. We wanna make sure that it's really transparent and fair to them. It feels job relevant. And we also wanna think about a lot of the other aspects too of just what are the guardrails, what's the process, the how how you use it is just as important, and also the maintenance of it. You don't just roll it out. Right? Like any. assessment tool, you don't just dust it off and say it looks great. Renu made a great point that jobs are always evolving, and that's coming even faster now. So we need to not only check that what the way we think something is gonna form is how it's actually performing, but we need to think about how that's evolving over time and incorporate that. So, you know, my kind of summary here would be an effective partnership with an AI vendor or an AI tool. Really, it should allow you to benefit from that work and due diligence they've already put in, but also comfort the nuances and the evolution of your role. Both those things in a thoughtful implementation really need to be in place before you can get the full benefits. I love that. Before we get to a couple of questions, I'd love to end with maybe one last question for both of you. What advice would you give other TA leaders who are thinking about embarking on this skills based hiring journey and leveraging AI more in the hiring process? Maybe ring yeah. Sure. Christie, would you wanna go or should I? You you kick us off, Renu. I will take you back on on you. It seems like we're pretty aligned here. Lovely. Well, my advice here would be stop treating skill based hiring and AI as two different agendas. One is actually a precondition of the other. That's my my view. Most organizations today want to buy AI for TA before defining what good actually looks like. If your job description, let's say, is is still a wish list of qualifications, then AI is just screening against the wrong criteria much faster. So basically, you're in industrializing your own bias. So that's why we started with hiring packs, created, the hiring packs for critical roles, define the skills, define this what a successful profile looks like, the evidence that could prove the skills at the the successful profile, a structured interview guide, a scorecard that everyone uses. So it's the unglamorous work. It's the no technology involved, you know, but a machine can only assess against a standard you have actually written down. So creating a hiring pack would be the beginning of using skill based assessments within the hiring structure. And I I really feel that standard matters more than ever because every CV in the inbox is now polished as we've discussed, and it's polished by AI. The CV has basically stopped being a signal of success or a signal that you should be interviewing this candidate or not. Once you've identified what good looks like, then you should be ruthless with the placement of AI. Automate what's what's high volume, what's high repetition, low judgment, and and any other tasks that really take away from judgment. So, basically, protect the human where stakes are human, like final judgment, motivation, values, negotiation, early talent conversations, or even adverse decisions, let's say. So that would be my overall, advice at this stage. But I also want to add the honest deadline for this to happen was yesterday. We are actually I feel you moving at a slower pace. Okay. But but, again, I I do wanna say moving fast is not buying fast. Pick one process, baseline it, pilot it, prove it, and then scale it. So technology is not the hard part here. The discipline is. That's that's what I would look at it as. I love that. Yeah. Yes to everything Renu said. And, you know, I I think one of the great things to keep in mind is AI is relatively new in the in the span of a TA. And it's a very powerful tool, but it doesn't change the fundamentals of hiring. It makes thoughtfulness more important because AI can supercharge mistakes in many ways that may not be immediately obvious. And like any other new technology, take time to be an important consumer of that technology. Your AI should never feel like a black box. It shouldn't help amplify the good foundation that you've laid down in a really transparent way, and be wary of potential partners who who make big claims without evidence or treat that evidence as part of that black box. We were the first assessment vendor with an AI explainability statement because we think that transparency is really key to trust for all of the stakeholder holders that are using our AI. And we look at science and rigor as something to automate and continue to make more intelligent based on data and using AI rather than secondary to speed. So anyone who tells you who can't have both, it is really missing the point of AI and everything that it can do. HireVue's been using AI for many years before it checked GBT because we start with science and evidence, and we use AI to make that more efficient and effective rather than driving with technology and kind of hoping it works or, you know, taking those shortcuts to get there. And, you know, we mentioned this earlier, but the quality of your decisions is really crucial to your organization. So take advantage of AI's efficiency, but continue to demand the evidence behind your tools because that really is more important than ever. I'd love. that. I do wanna add one thing here, Christie. Absolutely amazing points there. And and I think one thing I I keep reminding my team is that the minute you start using AI as a replacement of thought, that's where your downfall starts. Absolutely. You know? AI has to be used responsibly and not as a decision making factor or a replacement for thought. Exactly. Well, I think I'm gonna try to get to just one question because it's it's, I think we had a couple questions around this. But for Renu, how did candidates respond to moving the assess the first stage of the process or kind of that initial, initial phase? So, Kelly, that's actually a great question. Reality is I think, candidates today are well aware of the shift that has happened with AI. They're well aware that whilst they're using AI to create their CVs, organizations are also going to be, having a rigorous process in place to to ensure that the right talent gets through the process through the funnel. Reality is it has only amplified, the applications. To give you an example, our first our second cohort launches in January, and the applications are open now for Nestlé talent. We have already got over 14,000 applications. Back. 14,000 applications for 14 roles. And that, shows you that each and every, candidate of those 14,000 applications has gone through the initial funnel of the HireVue test. That means they're quite receptive, to be honest. This is a positive this is a positive, let's say, reaction to to changing, the placement of the online assessment test. Yeah. That's great to hear. Well, I think we might have to wrap things up there. If we didn't get to your question, we will try to follow-up in email afterwards. I wanna thank everyone for joining us today. Renu, Christie, I think this was a great discussion. I'm sure it's been really informative for those on the call, and I'm sure they've learned a lot about how to implement a skills based hiring strategy and the importance of implementing AI, in a responsible fashion. So thank you both for the conversation. I really enjoyed it. And for you those of you, on the line, if you wanna learn more about HireVue or some of the products we discussed today. If you're a customer, please reach out to your account team. If you're not, please follow-up, and we will be in touch. Thank you so much, everyone, for joining. Thank you, everyone. Thank you, everyone. Thank you, Kelly. Thank you, Christie. It was a lovely conversation. Perfect. You enjoyed it.