Name: The Human Edge: Succeeding with an AI-Powered Workforce
Uploaded: 2026-08-13T18:03:00.913Z
Duration: 1 h 31 min 12 s
Description: The Human Edge: Succeeding with an AI-Powered Workforce

Transcript for "The Human Edge: Succeeding with an AI-Powered Workforce": Hello, and welcome to this next chapter in the AI Impact series, the human edge succeeding with an AI powered workforce. Now this session is produced by Future b two b and sponsored by Orgview and Fountain. If you are new to the AI Impact series, first of all, welcome. We're very happy to have you here with us today. This series is designed to take a closer look at how AI is shaping our world across industries, throughout our organizations, and into our very work flows. So these discussions give us a chance to explore how AI is shaping, enhancing, evolving places like HR, retail, education, IT, government, security, and more. There are so many facets to these conversations and so many interesting tidbits. You will not want to miss any of them. So be sure you sign up for all of the AI impact series. You can also watch and rewatch and enjoy all of it over again on demand. So lots to stay tuned for, but don't go anywhere just yet because we are about to get started on an absolutely fascinating conversation. Now today, we are taking a deeper look into how AI is rewriting the rules of human resources. We will unpack recent shifts in talent acquisition, look at how organizations are now reimagining employee retention, and elevating the overall employee experience, really redefining what the employee experience even means anymore. Today, our expert presenters will provide actionable insights and strategies to help you effectively partner with AI in order to build a resilient, empowered, future ready workforce. We have a lot to cover today, so let's go over some very quick housekeeping to ensure you get the most out of today's session, and then we'll get things on the road here. Alright. So I wanna start out by giving you a little bit of a tour of your audience console. And first things first, let's meet the tour guide. My name is Jess Steinbach from future b two b, and I'm very excited that I get to be your host for this timely and extremely impactful conversation. And I would like to get to know all of you a little bit better. I see some familiar names already in the chat. So give us a hi. Give us a hello. Let us know how you're doing. Post in that chat tab. Let us know how your day is going. What are you up to? And then as we go through our session today, that chat tab is where you are going to be able to interact with the community that's here with us. So let others know. Something resonating with you, does some things jump out at you, something feels like that has or hasn't worked for you, you have some feedback or thoughts, post that in the chat tab. We want to see all of you interacting there. So give that a kickoff right now. Let us know how you're doing and and, hey, where you're chiming in from. That chat tab is also a great place to reach out to our team. So if you do have any questions about the Console, you need a little bit of a hand today, you can post in that chat window. Now I do wanna say that if you are having any little issues with your console today, a browser refresh is going to clear most of those out. If that browser refresh doesn't work, you can also try enabling light mode. And if that one two punch doesn't hit it for you, give us a shout in the chat tab. Let the team know you're having a little bit of trouble, and we will be there to lend a hand. Alright. Now we started out in the chat tab. Let's take two little bunny hops over to the questions tab. That is where you get to send in all of your insightful questions, all of the cool and interesting questions that you've been rolling around in your head that are burning a hole in your brain. Get them out now. Now is the time. Get those questions in. We are going to answer as many questions as we can today in our session. Anything we aren't able to answer in our conversations or via live chat, we will follow-up with you in email. So stay tuned. Get those questions in, and you will get those answers. I know there's a lot to ask, so make sure you're getting those questions to us. I we took two little bunny hops over, so let's take one on back and head into the docs tab. The docs tab is absolutely chock full of great resources, including ways that you can sign up for more upcoming AI Impact Series sessions. So make sure you explore that docs tab. These resources and opportunities are how you bring this conversation forward after we walk out of the webinar today. So make sure you spend a little bit of time there. I do wanna call out that we do have a $250 Amazon gift card that we will be giving away at the end of our webinar, and we're gonna give that away to someone who is here live, present, maybe a little bit lucky with us today. So make sure you, you keep an eye out for that. That will come at you at the end of our session. If you are not sure if you meet the future b two b terms and conditions, we have them listed for you in that docs tab. So head on over to the docs tab, and you can find those there. The last thing I do wanna make sure that I call out is that we have closed captioning available for you on the audience console. You can enable at that if that would enhance your viewing experience. Say we've got a few different languages there for you. So please do turn on closed captioning, again, on your audience console there if that would help you because you don't want to miss a single moment of the excitement today. And with our housekeeping behind us, I think it's time to get into our keynote address. I am already on the edge of my seat. I'm I'm I'm on the edge of my standing mat because we are kicking things off with an absolutely incredible duo. Our keynote speaker today is Laurie Ruderman, author, speaker, and podcast host of Punk Rock HR. Love that. And Waiting in the Wings, Chat with Lori is the one and only Kanoy Namahoy, editorial director here at Smart Brief and editor of Go Ahead, Call HR. Great name. Alright. Kanoy is going to lead us through our keynote conversation today. I cannot wait to get started. So, Kanoe, I'm gonna stop talking. I'm gonna hand things on over to you. Take it away. My name is Kanoe Namahoy, and I am the editor of Go Ahead, Call HR. And as always, it is wonderful to be here with all of you. So we wanna kick off our event today with a discussion about this human AI partnership. The conversation about around AI has certainly evolved, oh, very, very quickly over the last couple of years, and it's gone from, you know, theoretical to hype cycle, fear cycle, to where we are now, augmentation, where AI is is really the copilot to human effort. And so that's where we wanna that's what we wanna talk about now. We wanna look at how this AI human partnership is redefining how we manage the workforce. Everything from developing recruitment strategies to managing retention and risk to personalizing and scaling learning programs. And here to talk about all of that today is noted author, speaker, workforce expert, and host of her own podcast, Punk Rock HR, Laurie Ruderman. Welcome, Laurie. Thank you so much. I'm really pleased to be here today, and I'm excited to dive into these tough questions and maybe help people put together a playbook and a road map to go forward. It's always good talking with you. You're so practical. So let's just kinda set the stage with for the conversation. Let's kind of do some do a little bit of investigating on this human AI intersection. Many organizations are are trying to navigate the pressure of adopting AI while maintaining that human first culture. You know, from your perspective, how do HR leaders blend these two worlds so they're not just filling positions? They're not just adding bodies to the roster, but they're truly building a workforce that's gonna take them where they need to go. Yeah. Boy, if I had a really simple answer to that, I'd be retired on a beach in Hawaii instead of here today. So I wanna add that. And I also want to add that the expertise I bring today is from over thirty years of working in and around and coaching human resources leaders. So I'm not a technologist, but I am someone who has watched the world of work evolve. Right now, it is very trendy to take a look at this world of AI and to really hone in on the questions that surround how do we make people more productive and more efficient. And I think one of the ways that HR can really add value is to ask better questions, to start to ask why, what is this for, who does it serve. We have not been in the room historically when those questions have been asked. So when we do ask them, when we're like, what is this really all about? What are we trying to get to? We seem like roadblocks, and I'm here to tell you that roadblocks can save us a lot of time and heartache and money. So we currently need HR leaders to be a little bit braver, a little bit bolder, and maybe to slow things down a little bit. And, Kanoi, I know that's controversial to say that. Everybody wants to be the HR professional who says, yes. Yes. Yes. I love technology. I'm here. Let's build the workforce of the future. But the future works best when it's planned thoughtfully. So I wanna stop right there and get your reaction to that. What do you what do you think about that? Well, I think I think you're spot on, and I appreciate the point that you made. You're not a technologist, but you've been doing this for a long time in leadership and trendy. You know, I think that it I think it's very easy for us to get caught in in trendy. You know, as the editor for Go Ahead Call HR, we see a lot of best practice things running around, but we also see a lot of failures. We also see a lot of play face planting. And and I think that there is a balance between doing what you said, being thoughtful and planning, and also taking that risk. And I think that that seems to be in conversations that we have with HR folks, that seems to be the challenge where, you know, you don't wanna be the the barrier. At the same time, what is reasonable risk? Yeah. I love the way you put that. You know, it is really important, I think, for HR leaders today to take that word failure and understand it as an opportunity to learn from other people's mistakes. You know, so often in this world, we wanna go first, which is why over the years, HR has jumped on the bandwagon of chatbots and all sorts of weird things that happened in the twenty tens and early twenty twenties. And we spent a lot of money on HR technology that ultimately went nowhere. So this is where community, whether it's through your newsletter, through this summit, through your local HR chapter of whatever you belong to, can be really important because I promise you there's someone else out there in a leadership role who went first and has horror stories about vendors and implementation and all the things that went wrong. Hear those stories, learn from them, and then spend some time thinking about how you can go second and get it right. This push to go first, I love it. I I go first a lot in my industry, but I am also first to market roadkill sometimes, and nobody nobody wants that. And so going slow to go fast was really good advice in the twentieth century, and I think it's even better advice today. I agree with you. And I think I think where we get stuck sometimes is failure failure has a budget line. Failure failure costs. And it and and when you do fail and it costs a lot, that is that's a reasonable thing for people to be concerned about. And I think that's where a lot of things get but I think that's a good place for you to slow down and look and not slowdown is not stop. And I think that's what you're saying. Slow down, but but it's not stop. It doesn't and it might be slowed down so you can pivot to a better direction. That's right. And I think one of the places where where we're seeing that a lot is is, in our next question, hiring. We're seeing a desire to shift from being reactive, with recruiting to being more proactive. What does that you know, as we're talking through this AI driven lens, what should that transition look like for the recruiter on the ground? What pitfalls do they need to avoid? Yeah. You know, there's a lot of conversations around AI and just in general technology making recruiting more efficient. But I don't know, I've been around for thirty five years and there's no easy way to hire anybody. And I think we're not honest about that. And part of the risk of hiring and getting it right or getting it wrong is just the cost of doing business. And when we try to eliminate that, we're dishonest. First of all, nobody works in one job forever. Everybody watching this today has had multiple jobs where you start in a field, you start in a career, you start in a job title, and things change. You change, and you don't always change at the pace of your organization, and that's okay. It's okay to lose people. It's okay for people to move on. The promise of AI and the promise of a lot of these technologies is to make sure that we're finding the right fit so the time that we do have with this individual pays dividends for us as employers and also for them in terms of skill building and personal development and personal growth. But I am concerned that in trying to automate recruiting, we automate the human out of it. Kind of like the example that many people talk about with radiologists. Right now, radiologists are often assisted by AI to find all sorts of maladies in the human body. But instead of it being more collaborative, the way that the AI technology is sold is that hospitals can get rid of all of their radiologists except for a few, and AI is gonna come in and really just make up the difference. And it turns out that's not happening, and there are errors occurring because there's a lack of human involvement in many of these departments and then we blame the human and we wonder why the implementation has failed. So when I work with HR leaders who are really interested in optimizing recruiting, we fully have to understand that in order to hire humans, we need humans involved. And this promise that we can get rid of the human, that the human is messy and inefficient and biased is kind of wrong because what do you think AI is? It's still inefficient. It's got all of our biases. And I think instead of thinking about a rip out and replace, it's got to be a collaborative process. And the best organizations that are doing that right now are going slow, understanding their culture, understanding their hiring practices, and supplementing that with AI, not revising completely around an automated process. Do you think we rush to some of these automated efforts? I'm hearing some other horror stories. I have not heard the radiology one. That's truly terrifying. As a possible patient, I that's not that's the last thing I wanna see. But do you think that we rush sometimes to to implement something that sounds like a good idea? Oh my gosh. Are we human? Do we like cool things? Yeah. We rush. And in fact, for so long when I was working in human resources back in the 90s and the 2000s and the early 2010s, we had a reputation that we weren't fast enough. And so a new crop of HR and recruitment leaders have popped up and they're like, yeah, Let's go. They're yes, and, and I think it's cool to be yes, and all the time. You don't wanna be no, but, no, but. But in that excitement and that fervor, we still need to use common sense and understand that anybody who is selling us a product is a collaborator and a partner, but also someone who may not be completely and totally transparent about that product. And so, again, this idea of slowing down and really understanding what's in it for the vendor, what's in it for us, what's the vendor's track record on implementation, are there truly case studies, has any research gone into this, or are we just buying marketing hype? You know, we try to use procurement. We try to bring the CFO and all sorts of other stakeholders in, but sometimes we just have to have an honest to goodness conversation with the vendor about where they've succeeded and where they failed as well. We keep going back to failure, Kanoi. Yes. I I think it's I think it's necessary. And I also think that we have to say the hard things right now, which is you have to have the hard conversations. The person who's gonna be running it, the person who's gonna be ultimately responsible and accountable to this thing, you know, finding the right people or not, You you have to have those very difficult conversations, and we're seeing that across the board in so many different, industries. I I know I definitely see it in in education, and it's a very uncomfortable moment. And I think you brought up something really important. Is is one thing to have the CFO in there for because you gotta have that guy involved. He's he's the one that writes the checks. On the other hand, there has to be I think we we're gonna have to shift some things around with the human with the stakeholders involved in some of these difficult conversations, because that's a place where I see things get missed. Yeah. And then you can't always unravel that. No. And then ugh. I know. I think about so many recruiters who don't know that they're getting a new technology stack. Like, they hear rumors, but they don't know until they're assigned to the implementation team. And can you imagine that, like, one day, you know, you've got this job, you're in talent acquisition, you've got a process, you've got a system in place, and the next day you're on a team to, like, kick the tires of something that's already been purchased and help to implement it across the organization, across the enterprise. And you're like, wait. Had somebody asked me six months ago, I could have really been involved in this. And I love the work that so many of my friends and colleagues are doing. There are a lot of former HR professionals who are now part of implementation consulting groups with technology companies where they'll work with a big technology provider to help go through the implementation process with a new HR team and really understand who the key stakeholders are and should be in the room. And it's just a new and emerging way for HR professionals who are looking for work to still work in HR, but also get involved in technology. So the work of doing a thoughtful implementation is like the work of the next decade, and HR professionals are becoming more and more involved in that. That's so fascinating because that's exactly what's happening on the education side. Voices that you that were never involved in some of the purchase decisions are now you can no longer overlook them. If you're the vendor, you can no longer overlook overlook that educator voice, because they are by the time you've you've by the time you've spent a lot of money on something, you have to do it. And now you've got a real problem with your stakeholders who are kicking the tires, and they're and they're bringing up legitimate issues. So Yeah. I think that that I think you've just answered some questions, and I think you've also raised some other really important ones that we that are not so easy to answer. And we have to be ready for that. We do. We do. I think, so I will just say, I really believe that most processes in life benefit from having some really good questions asked, brave questions asked. And I think so often in an economic environment where it's kind of tough, you don't even in HR, you don't know what's gonna happen with your own role, you don't wanna be the person to ask the tough questions. You don't wanna be the person to be the Debbie or the Don Downer. Right? You don't wanna be the person who is always raising the red flag. But there are ways through good storytelling skills, through good go on YouTube and watch a video on how to ask better questions that you can position yourself in a room to slow people down by asking those good questions and really seem like you're adding value. And I promise you, if you raise a flag in the right and constructive way that saves your company money, your job is safe. You're the person they want on that team. You're the person they want around. So so often we worry about, like, raising our hand and being seen, but you could train yourself through the improvement of your communication skills, which can happen on LinkedIn Learning, the University of YouTube, right, get out there, watch a TED Talk, improve the way you ask questions, and I promise you, you're going to be an essential employee and a leader at your organization in good times and in bad. And I think we're also finally at a place even with leadership, executive level leadership, that they truly want that person. They they really do. They want they they don't want the yes, ma'am. They that time seems to they want that person, but they don't they know the cost of just saying yes to everything without reasonable pushback. So they are looking for someone who can ask better questions and add value. I think that's a very important skill and role, and I don't think that's one that we've really emphasized until recently. And and AI has made that an urgency. Absolutely. I have a dear friend who works in corporate human resources, and his leadership team is always coming to him with crazy ideas. Like, they got a friend who's got a new AI tool and sounds great. You know? And my friend is like, yeah, man. Let's do it. Okay. How would that look? And he starts to take them through the system. And what would that look like? And do you know their financials of this organization? And have you met anybody other than the two bros you went to college with? And so he'll go along with it. Like, he'll get caught up in this moment, but he will also, in that enthusiasm and in that conversation, really take the time to break it down so that they can see that their own enthusiasm might be misplaced. And so I think there's all sorts of skills embedded in that with, like, communication, but also relationships, Kanoy. People coming to you and saying, oh, I've got a really great idea. I saw this really cool AI tool is an amazing testament to your leadership in HR, that they think you would know it, that they think you would get it. And the opportunity there is to acknowledge that and say, good. We're we're still friends here. But I also get what can happen when it goes wrong, so let's talk about that. So I just I think it's such a blessing when people come to us with these crazy ideas for AI. We just have to be ready to know what to do with it. Agreed. Agreed. And I I think it I think it's better to to take ideas. Gotta come up with better ways of vetting them too. You know? You don't want you don't want you don't wanna cut off the the creativity. You need those things. And I very rarely see an idea come through, and we take it exactly the way it is and take it to fruition. You know, most ideas come in, and you're gonna change them. You're gonna take a piece from here and a piece from there and a piece from that. And I think that's really the purpose of our conversation today is to get those practical, you know, thought leadership ideas out there. Like, this is what it looks like. You know? You know you've got a a talented and skilled person on your team. So what does that truly look like, and and developing them into this, purchasing conversation, the implementation conversation, help them learn how to make better, ask better questions. Let's get them involved here. And I think I think that is as I think some of the hiring that we're gonna be doing is actually gonna be transferring and shifting around. I think that's I think that's well said. You know, when I think about the future of work, it's so easy to say, oh, it's going to involve AI, and it's going to involve technologies that we don't even know and we don't even understand understand right now. And all of that may be true, but the core of work is relationships. The core of work is communication. And so it's almost irresponsible to have a conversation about the future workforce and the future skill set. I mean, what's it going to evolve? We we don't know at this point. But what we do know is that we need people to slow down, to have critical thinking skills, to know their history, not only the history of their industry, but the history within the company. So I think about the very old school skill of knowledge management right and transferring the historical knowledge of failed projects from one generation to the other. All of that becomes really important but, Konoy is not sexy and it doesn't necessarily have a dashboard. And, it doesn't have a bot that also tells you that hits, that lands, yeah, and feeds your ego. We have got to make a break away from trendy and sexy. Yeah. We we have got to learn where that works and where that is just a shiny bell and whistle that would be better left in the closet. Yeah. But, you know, I do love, AI for a couple of reasons, and I think the conversation around AI, especially within recruitment, is interesting because at the enterprise level at these big companies, they're really hiring at scale. They've got a lot of volume coming in, and I think AI can do some of the very basic work of sorting and also giving more people an opportunity through automated interviews just to say, this is who I am and this is what I believe in and this is why I'm a good fit. And to do that at scale gives more people an opportunity. But on the back end, as we're seeing through many lawsuits, it's really hard to figure out how to evaluate the content that we're getting back. But there's still really great opportunity to think about diversity and inclusion in cool ways in the recruitment process if we could get the algorithms and then the back end processes right. So we haven't quite figured that out now, but I think maybe some of these lawsuits might figure out help us figure out how to do that better in the future. And then for a small business, to be able to really think through the positions I have and to use affordable tools to manage the interview process and the onboarding process so that we can use more of our time to get those people doing the meaningful work that we hired them to do, that's the opportunity there. And, again, we're probably five to ten years from seeing that truly happen at scale in small businesses. I think that's fair. I think we are still in that I mean, when we look at it's 2026. All of this really started to bubble up and become, commonplace conversation in 2022. It's only four years. We've really only been at this at four years, and so Yeah. We are still this is, to me, what happens in the experimentation stage of any, new new technology force. But I that's why I think right here that's why this conversation is super important so we can we know how do you navigate through this successfully so that in five to ten years, you it it you will have something much smarter, much better, sustainable. And that doesn't mean that the from here to the next to the five year marker is a disaster. It's losing money. It's a down economy. It none of that has to be true, but it is it is the point where you're you know, you really are you're getting scraped up quite a bit in this in the next few years, and that's what it means to be the pioneers in the in the in the sea change. That's right. You know, I still have a couple of really important courses up on LinkedIn Learning that I'm surprised are important, and one of those courses is all about how to hire on social media. And I recently heard from someone in the dental industry who watched that video and said, thank you. I was really confused on how to hire people by using Facebook. And I'm like, oh my gosh. You know? I mean, here on the ground, people in dental practices and accounting offices are still going on very basic social media platforms and trying to figure out how to hire a dental hygienist. Right? People still recruit through Craigslist. So the conversation we have about AI at the enterprise level is completely different than 90% of the American workforce, and I think that's also important to consider. While I would love to see a really thoughtful AI playbook in corporate America, that's a dream, but it's even more of a dream for the guy who comes and, you know, does my pest control once a quarter at my house. Right? So it's a completely different world out there in America and all over the globe, and I don't think we talk enough about that. That's really good. That gives me a lot more to think about. Follow-up conversation for another day. Let's move along to to retention and talk about the risks and the gaps, which kinda goes along with what we were talking about. You know, when we talk about retention, data. Everybody loves data. It's the key way to spot flight risks before they become turnover statistics. Talk to me about what data points or indicators you think are most important for HR leaders to be monitoring right now. Yeah. I think the most important thing that you can monitor, in terms of all of this is monitor learning. Because when you're learning, you're growing, and when you're growing, you're thriving. And those are the employees who are most likely to stay and also generate more revenue for your organization and also help lead others onto that righteous path of also learning and growing and thriving in your company. So if you don't have a robust learning program at your organization, you are behind the eight ball. And clearly, there are myriad ways to do this, whether it's augmented through AI or it's just good old fashioned going out and buying something off the shelf, but really thinking about personalized learning so that people are not only learning the skills to do their job today. You know, we have a lot of that with people like getting certifications and remaining compliant, but who are they going to be tomorrow? Who are they going to be in a couple of years? If you're not having those conversations and developing learning pathways for them, I don't know what you're doing in human resources because that is the way that you truly retain people in your organization. There are products you can buy off the shelf. There are things that you can create in human resources. There are a lot of different ways to, like, cut this piece of pie up, but you you gotta have a pie. You gotta have a learning pie in your organization. You know what? It's so funny that you went there because that is actually I wanna get right to the next question because it it you dovetail perfectly. You know, talking about the employee experience and personalized growth. We're seeing a ton of conversations, a ton of coverage about that about that, and the big questions always scale. So more organizations are taking, you know, what you just said very seriously. They wanna create personalized career paths for their workforce, but doing that is resource and it's time intensive. That's just the reality. So how does AI support personalization at scale without compromising that individual touch that is important? Well, I think there are a lot of ways that AI would want to well, AI technology vendors would want to say that they support personalization and also can do it at scale to save you money and save you time. I'm not quite sure we're there. And I certainly don't wanna mention any technology companies or any providers today, but there are those out there who surveil the workforce enough to know what they're doing and how they're spending their time, what their tasks are, who they're meeting with, and then can in conjunction with a leader, can put together a personalized learning path for an employee and then measure whether or not they actually learn something or they just passed a test. That's the promise. That's the promise of this all. I I don't know that it's true, and I also don't know how you do this without having really time consuming, in-depth conversations with people who work for you. Just because someone, you know, is certified Microsoft blah blah blah in their current job today doesn't mean that that's what they want to do tomorrow, and you don't get to know what people want to do and if you have an opportunity for them to do it unless you ask. And so this ties in with the very messy way that we manage people and that we manage their performance and we manage their potential. And I think this idea that we can just automate things, and and certainly we can make this whole process a little bit more efficient, but to automate this through AI completely and remove that time consuming conversation is a mistake, and it's not possible. So, again, Kanoi, there are vendors out there who will say, well, we've got enough information. We know what this person needs to do, and we can also measure on their ability to do it. I'm not so sure that's true. I've read the case studies. I like the idea, but at scale, TBD. I'm not convinced. I agree. And and there's something about having the ability to do something and the want to do something. And I think that's you know, when I have conversations with the folks on that report to me, I it's been it's happened so many times. What do you want to do? And you just get a blank stare, which is which is a fair. I know I've had that blank stare. Like, I'm not really sure. It's a question that can catch you off guard. And so when you're looking at your own workforce, I think there is there are ways that we need to carve out. This is what we need as an organization. This is where we wanna go. This is what we need. This is where you fit. And and I think but I think you do need to have honest conversations about that. And by the way, if you wanna earn a paycheck, you're gonna have to find your own balance between what I want to do and what is necessary to do. And I think I but I think, again, that is not like, I don't know if I want the algorithms to do that. I I do think you get better traction in conversations. Yes. And I think, first and foremost, before we even involve AI and how do we create a personalized learning path and platform for someone, we have to let employees know we're asking you what you might wanna do because we actually wanna hear the answer. And I think a younger version of Lori Ruderman would not have trusted that. If somebody had said to me, what do you wanna do? I'd be like, I wanna pay off my student loans. I I wanna pay my health insurance. I want my husband and I to go on vacation. Right? You know? Like, I had a lot of wants, and I didn't feel it was safe to communicate those very basic needs. I mean, I also, at one point in my life, wanted to be a VP of human resources. Right? But in all of this, I'm not sure I felt like I could trust the company to hear me, that they would listen to me, and that they would care. And I think so much of that, underscores culture, it underscores trust, it underscores vulnerability. And, again, AI can't make your organization better at trust. It can't make it better at communication. Leaders do that. Individuals do that at both the micro and the macro level. So I'm all for a technology platform that can do something, and I I think there are probably I know there are some out there because I've sat through these demos, I've been to their user conferences that can create these personalized experiences. But whether or not they have meaning for the employee and whether or not they pay off in 2030, again, is TBD. And I think that's fair. Especially for where we are, I I think that that's absolutely fair. And to go back to what I said a second ago, there are things I don't want the algorithm to do. I want the I like AI and the copilot. I like the relationship where we are. I like being the pilot. You'd be the copilot, and I think we get, I think we get to where we gotta go in that direction. I'm just gonna keep a careful eye on on my copilot there. Every time you and I talk, Lori, it the time goes so quickly. And I can't believe I already have my last question, but you said something in our last conversation that was super powerful to me. You said the best leaders out there don't motivate. They unleash. And and so as we're finishing up and wrapping up our conversation today, I wanna know how do you recommend we apply that concept in this process of using AI to rewrite the HR playbook? Well, I first of all, thank you for having me today, and thank you for ending with a quote where I sound smarter than I really am. I do believe that all adults are leaders. If you are over the age of 18 and you wake up in the morning and you brush your teeth, inherently, you are a leader. So this idea that, some other person is going to motivate you to do something is just, quite often ridiculous. You motivate yourself, and I think we've seen that play out. As people get older, as they mature, they find their intrinsic motivation, and, hopefully, those of us who have kids or those of you out there who have seen your children develop over the years, you've seen the development of people. You know that eventually, hopefully, things turn out okay. What I wanna say about the world of AI is that AI is a partner and a collaborator to all of this versus the spark. So if you have someone in your workforce who shows a passion and interest for something, and there is a tool out there, a a learning program, a training opportunity that you could hook them up with, jump on that opportunity. If there is an implementation program, if if there's something cool that's happening and you've got somebody on the bench that shows real potential, challenge them to learn and grow. And in fact, do this for yourself, which is really what I want to say. The only way adults learn is by doing. So you're never gonna learn an AI playbook by reading it off the Internet. You've gotta go and have messy, awkward, uncomfortable conversations around your sales cycle and around what's happening in procurement and marketing automation and all of this to understand even how AI would fit into the workforce. You've gotta ask the dumb questions, and you can do that when you build relationships. So I really think that underlying an HR playbook is the same old thing that I said in 1990, in 2000, and 2010. It's all about relationships and building connections of trust and then being allowed the opportunity to really make a difference on something as important as AI. Folks, I can't think of a better way to wrap up this conversation. I that was beautiful. That was perfect. Thank you so much. Well done, Laurie. As as always, well done. Alrighty, everybody. We are just about out of time for this session. Thank you again, Laurie, for making time to be here, for always imparting some, very valuable, thought leadership nuggets for us to take and implement at our own places, for sharing your site, your insights, and your experiences. And thank you to all of you, for joining our keynote. A big thank you to Kenoi and to Lori. What an interesting conversation. A fabulous way to start out our session here. Some really incredible points and and interesting takeaways, to think about for our own teams. Now speaking of being both willing and able, we are willing and able to keep things going in the session today. So we're gonna keep things rolling and get into our next chat. And now it is time to really get into a conversation around this idea of the human edge. This is what we're here to explore today. Let's find out together. What does success look like in an AI enhanced workforce? So to get us started on that, we will be chatting today with Peter Eisenman, head of GTM at Fountain, and Ross Hill, chief customer officer at Orgview. Alright. Now to walk us through this conversation, I'm absolutely thrilled to welcome the one and only Mackenzie Pudasi, my friend and fellow moderator here at Future b two b, who is going to join us to moderate this panel conversation. Peter, Ross, welcome. Mackenzie, I'll hand things on over to you. Okay. Ross, Peter, I'm excited to be with you here today, pick your brains a little around this topic. And I know you've already been introduced, kind of high level, but I'd love to get a a bit of a sense of what you're doing on the day to day and, a little bit of your background as well. So maybe, Ross, we'll we'll start off with you. Yeah. Happy to. Great to meet you. Great to spend time together today. Chief customer officer at Orgview, and I've spent the last twelve years at Orgview helping organizations drive control through their their operating models and then watching that manifest into the detailed design of their organizational structures, primarily looking at the organization through through the lens of activity and work. First, seeing how that translates into supply and demand and ensuring that the, organizational structure, and the organization as a system actually helps you translate strategy into delivered outcomes. I spent the majority of my career at Orview in our professional services business helping deliver and structure work. And then I've moved into the chief customer officer role, which means I have a responsibility over our customer success, account management, professional services, community, and enablement functions. That's me. I'm based in Southern California, and it's great to be here. Pete? Great. Thank you both. Excited to be here today. So, Pete Eisenman. I lead go to market here at Fountain. And, really, what I do is spend most of my days recently working with HR leaders and operations leaders at frontline organizations like UPS, Sweetgreen, CLEAR, talking about what's real in AI and specifically from a frontline talent perspective. You know, we're seeing a lot of hype, what's real, and really what it comes down to is sort of how we see AI moving the needle on hiring cost and retention within large frontline workforces. So I'm I'm really happy to be here to sort of talk more about that, based in San Francisco, and, yeah, looking forward to the conversation today. Fantastic. Gentlemen, I'm excited to dig in here. I I know we're gonna talk about this AI and and how it can bridge the gap here in HR. So for for you, Ross, I wanna open things up and ask about when we talk about the human edge, we have to start with how work is structured, and many organizations lock people into rigid job titles that kind of hide their true capabilities. So how does breaking down jobs into specific tasks help us identify where AI can handle routine tasks so humans can focus on high value, uniquely human work? Yeah. It's a great question. And, honestly, it's the nature of what, frankly, I've been doing for the last over a decade, and the nature of way businesses operate generally. So without the context of AI, I'll just jump into what we believe the organization as a structure actually looks like and as a system actually looks like. And, most of the time, we think about people in boxes and the way those boxes stitched together. So somebody reports to somebody else, reports to somebody else, and we think that is the organization. In reality, that's a small part of it. So people sit in seats. Those sits seats kind of come together in an organizational structure, and you have a taxonomy of roles that might inform how many of those positions you need. Right? That's the left hand side of the page for me. That's purely structural. That's where you'll get job titles or descriptions of jobs, etcetera, and job descriptions that largely are not directly translated into what people do on a daily basis because they become stale. People evolve over time, and there's nuance in the work they deliver. The other side of the organization as a system is skills and activities. And what really matters is the value chain and the work getting done. So people sit in the structure to deliver work. That's the whole point. People have skills in order to effectively deliver work. Without understanding the detail of the work and the worry that work stitches together and translates into outcomes and those outcomes into value, It's irrelevant where people sit, and it's kind of irrelevant what skill set you have. And so I believe that you need to start with what the work looks like. We have, you know, I think, especially in HR and especially in white collar jobs, we have used job title or role as a proxy for what the work is. And a lot of that is due to the fact that the work didn't change that fast historically. Now we've had sprints where work has changed really rapidly. So So industrial revolution as an example a long time ago. But, you know, the the, advent and and the invention of the Internet, the use of email, and then we saw it with COVID. Right? We see it happen periodically. But there tends to be gaps in those major events that drive change, and you kind of spike into those. You make adjustments to your organization. You go into a steady state again. What we're seeing now, especially with AI and disruption into white collar workforce, is that you can no longer make assumptions on what work is getting done or what people are doing, and it's changing so rapidly that you need to be really dynamic in understanding it. And that's where I think breaking those roles down into tasks and then understanding what elements of those tasks are automatable, what are transactional in nature, what are operational in nature might provide a need for oversight, and then what are truly strategic that need the human brain to to engage with. Breaking that down is becoming more and more important. So I believe all of it kind of orients around the organization as a system, and the crux of that system is essentially the value chain of the work that's delivered. So pause there. That's that's the beginning of a response, but hopefully that helps structure some of that. I think that's great, Ross. And, you know, you already gave me some some follow-up questions for you. One, do you think we're sort of hitting a new industrial revolution point here as we shift into AI use? And the second, I think, will take us on another track. So I'm gonna give you that first. Yeah. I I do. And for the first time in a long time, the majority of the impact or unknown exists within, frankly, white collar workforces where it hasn't historically. We think about automation and we think about streamlining delivery of the value chain. We've been doing that constantly in factories forever. That's the nature of of improving, supply chains, and improving, like, assembly lines. We've done that a lot. What we haven't done is looked at the software elements of work and the supporting functions, the go to market functions, you know, marketing, etcetera. We've looked at automation in areas and optimization, but not something that is so kind of intrinsic to the way we do work and so, all encompassing that it requires us to to actually take a step back and look at that workforce. So I think that is a change, and it's uncomfortable because we haven't really played there before. But with, you know, discomfort comes opportunity. And I think there's a huge opportunity here if you can actually bring visibility to what the work looks like, and then be intentional with the way you redesign it. I would also we can get uncomfortable. Or go ahead, Peter. I would also just add in sort of what I think people fear things being taken away from them. But if you if you reorient sort of what's being taken and then what's being added as a result, you know, I think Ross touched on this around we're taking the stuff that maybe you shouldn't be doing and giving you more sort of thoughtful strategic work. Focusing on them that, right, you know, know, especially from a a GTM organization, if we can take away the research of accounts, the prospecting of those accounts, the putting information into Salesforce and have them focus on how do I build sort of deeply consultative relationships with my customers, Like, that's something that, like, I would love to spend more of my time doing, not entering information in Salesforce. And, you know, one of the frameworks that I use internally, and then we can talk a little bit more about Fountain, but sort of this notion and it's nice to hear, sort of it being portrayed in a different way, but, like, the unbundling of a role. Right? What are all the sort of micro components that go into a role and using that as a framework for where you should be sort of attacking from an automation or AI perspective? And, again, AI is another tool to allow you to do more impactful, thoughtful work, but beginning to sort of break down what your role is in sort of these micro tasks as you explained it. And then what should we be automated? What should you actually be spending more time doing? Yeah. No. I think to dig in. When Ross, feel free to pipe on, on this. But, you know, as we unbundle these roles, what what happens? You know, on the ground, do do I get a title change? Do we do away with that? You know, how do we sort of shift in our organizations to to accommodate this? Yeah. I mean, I think they're go ahead. Oh, no. No. Go ahead. I I I think that look. I think there I think we have positions at organizations now that we didn't have six months ago. And so I think the the the nature of work is changing based on the tools and how the adoption curve looks like for these tools. But, yeah, I I think jobs change in what sort of the the job description gets written as. Right? You know, Salesforce hygiene for a sort of a sales rep probably shouldn't be part of the job description anymore or in a sort of a highly functioning so if you go look at, you know, I think job descriptions that have been written over the last ten, fifteen years, My guess is the job descriptions that will be written over the next ten to fifteen years will look a lot different in terms of what the core responsibilities for those roles are. Now I think there's some things that will always be true, but I I think on the on the edges, and it probably falls into the things that most people don't wanna be doing. Right? Like, five years five months ago, we we couldn't imagine somebody's sole job was managing agents. But now we have people at Fountain whose jobs is solely becoming managing a fleet of agents. And so I think just the nature of work and how it's being defined and and and how it's being written is changing pretty fast. And to me, that's exciting. Yeah. I mean, I I think static job descriptions are not useful. Even before AI, I never saw a job description that was accurate. Somebody would walk in with one, you'd show it to the person doing the job, and they say, yeah. I don't do half of this. And that's lack of visibility. That's lack of data and understanding of the job, but it's also that people naturally gravitate to where they're needed and where they believe they have expertise. And so if we look at trying to intentionally change work and be more nimble with deploying resources, redefining jobs, communicating that to the business, making that more visible, you know, at Orgview, we focus on the org as the system and providing data behind that and keeping that alive and dynamic so that we can make those micro changes. What becomes really important is helping people stop doing what they shouldn't do and doing what they should do. And oftentimes, the stuff they wanna stop doing, they actually wanna stop, but they're so ingrained in what they do on a daily basis. It's so comfortable. Getting them to stop is really hard. And what's gonna be really important moving forward is we actually get people to stop the stuff they shouldn't do and move on to the stuff they should and get to give some energy. Change is hard, and change is accelerating. It's going to be difficult to force that change. But once people start to feel what that feels like, which is positive and energetic and and makes you feel like you have high value work, and you're able to nimbly adapt, people will start to gravitate to it. But that initial inertia is gonna be difficult to to manufacture. And to segue us a little bit into sort of how this is showing up in frontline work. Right? You know, fountain fountain's an AI native platform for companies who hire and manage frontline workers. So think, you know, unlike Ross' focus on the white collar, we are very focused on the blue collar. The people driving trucks and in warehouses and behind cash registers and, you know, cleaning hotel rooms. That's our focus. Right? And specifically in retail and QSR quick service restaurant, it was on the store manager to screen applicants. Now that person also has to run a store and make sure that that store is profitable and make sure that they're selling product and make sure that the shelves are stocked and the things are off the floor and in the right position. And screening applicants so they make sure that there's enough people in those stores so there aren't lines and people are getting the right service and, you know, that their those stores are generating revenue. Store managers had to screen candidates all day long or that was the hope. Right? Because for each one of these roles, there are thousands of applicants. Fountain has a product in market that's an AI interviewer where a candidate can talk to a AI voice agent to do the initial round of screening. Now the store manager is only talking to the most highly qualified candidates and those who are coming for the in store interviews versus having to spend the end of their shift before locking up the store to screen candidates. And, you know, at the end of the day, you the last thing you wanna do is deliver a great interview for a candidate. Right? And so by having this AI as part of the process, one, the candidates get a more, sort of equitable interview because they're not some they're not catching somebody at the end of their shift after a long day of putting out a 100 fires. They're having the same interview across every single candidate every single time. And for the the the store manager, the hiring manager at the store, they're not having to do the top of funnel screenings for people who may or may not be qualified, who may or may not show up to day one of work after they get the interview, after they get the offer, and they're only talking to the people who actually can get hired at that store. And so AI in this case is creating both a, you know, good on the candidate side, but also the operator side. And being able to have a single piece of technology that delivers for both as part of the value chain is extremely important and and creates just a better overall hiring culture, a better manager culture, a better candidate culture. So Pete, I love that. And, you know, I think so that's really interesting to think about turn in terms of, k, how does this relieve the the stress and pressure and maybe inefficiencies of a manager that's overworked? How does it create a a pleasant and equitable experience for the candidates? And then once they're working, you know, once you have these frontline workers on their day to day roles, how are HR leaders now using AI to remove or reduce some of these daily operational frictions that they're facing? Yeah. How does that affect retention, engagement, performance? So so we've def we've seen by by putting in things like AI interview, like, the quality of your employees is highly dependent on the quality of your hiring pipeline. Right? They're they're sort of inextricably linked. Right? And so one, being able to screen all candidates, not just some that you can get to, is one way to ensure higher quality. Having more data points versus fewer data points on the candidates allows you to hire for better quality. And so, hopefully, these things are ensuring that you're getting a higher quality, more qualified candidate showing up for day one. Right? We've also started to to leverage some AI voice agents around day one check ins, day 30 check ins, exit interviews. This this isn't typically done because it's a strain it's a strain of resources. Right? Either they don't wanna do it or they they don't have the time, and this goes back to our our previous conversation around unbundling of the role. Part of the store manager's role is to run exit interviews and do day one check ins or day 30 check ins. That's either done to not high quality or not done at all. And so, you know, we have another voice agent that will check-in with a a new employee after their their day one to say, how was day one? Was your manager there to meet you? Did you know where to go? Did your was your uniform ready for you? Was your workspace clean? Did you feel like you had the right training coming into day one? How do you feel about going into day two? And depending on how they answer those sort of, voice interviews, it'll flag what might be risk or not at risk employees. So if you can put up those fires before they start, then you have the ability to retain people longer because you're addressing their needs or concerns much faster than you've been able to do historically or on exit interviews. Exit interviews are great in theory, but never are never done in practice. Right? Or if they're done, the information isn't being used to actually drive change at the organization. But now we can do exit interviews at scale, collect all that feedback, make it actionable, and actually put those things into practice within the store, within the organization to make sure that we understand why people are leaving our company. Right? And then we can go and enact that change. When you have, you know, random phone calls with a bunch of people leaving, it's tough to do anything with that data. And so those become for not. Now we have the ability to do that. And, you know, when it comes to workforce, whether white color, blue color, doesn't really matter. As we start to unpick roles and break them into actions and activities, piecing them together effectively, back together effectively is really important. If you start to take work away, what fills the gap, What replaces it? And making sure we don't create essentially unicorn rules, which, you know, we've gotta be really thoughtful about what we create off the back because there's a different set of skills that's required to do different activities. And if I start to bundle something back together and it's something that nobody can feasibly do, I can't hire for it. I'm gonna continually fail in the the ability to deliver that. So there's a combination of breaking it apart, understanding what's automatable, what's changeable, and then piecing it back together in a way that's actually deliverable and then fits into a structure. And that is essentially like organizational transformation and control. And that's what we wanna see businesses do better and more consistently. Part of the problem is organizations do it as a one off, and it shouldn't be a one off. It should be consistent. You should be constantly looking at the business. You should take data you have. Pete, like you mentioned, if I look at, you know, the employees coming into the business, their sentiment as they leave, what's driving them to leave, I should feed that into the analysis of my transformation, my job architecture, and my job creation so that when I design a structure, I did sign the one that works. So the data that we feed into that system becomes incredibly important. Making that visible becomes incredibly important. Yep. And I would just like Go ahead. Go ahead, Drew. Sorry. I would just say, you know, I think historically AI historically, I'll put in heavy quotes. I think people have leaned on for great at sort of analysis of large amounts of information. Right? But I I think we're now at a place where AI, whether it be agentic platforms or or or the like, are becoming systems of action. Right, where you can you can now do large scale analysis of data to get conclusions and then also act on those, right, in a way that doesn't require human intervention. And I think that this is the pivotal sort of point in adoption and gaining value from these tools is the ability to take that information and now action on that information without human involvement, human oversight, but not human involvement. Right? And to be able to act on data at scale is the is the big unlock that I think we're seeing now. We think if we think about the the breakdown of value chains into individual activities and the way that's deployed, There are two ways that you leverage AI practically. One of them is generic capability. So I use it to help write an email or distill information. It's ad hoc. I've got the ability to use it. That can be better or worse depending on my individual capability and my tacit knowledge of the space and the way I engage with it and the way I iterate through it. So you can get work slop where it creates something that's not overly useful, send it to somebody else. They then use AI to break it down and respond. That's not effective. But that's, like, general usage of these tools. It's not specific. The other way is specific application of tools through a value chain. So what actual things happen, and can I automate the delivery of them or improve the delivery of them using AI? And, Pete, what you talk about is is using your platform to to automate processes to get the outcomes better, more consistently, and faster. And and what we do at Orgview is make sure that we design around that. So if we buy something and we deploy it into the workforce, how does that impact the roles? How does that impact the structure? How do we design around the back of it so that it can be deployed effectively and consistently? And then we redesign the role. We right size the organization. We either take the cost savings, or we take the opportunity to do more strategic work that's net new, we may not have done in the past. And that becomes incredibly important for the workforce that remains, that they have clarity in what they're going to do and what their job looks like in the context of delivering that value chain. Ross, how does how does this actually happen? You know, obviously, I think I think we conceptually can think, k. That's great. You know, we can leverage AI. We can automate this. We can shuffle this around. But if an organization is quite traditional, you know, where where do we go from the old school to this new school? How do we kind of radically transform the workforce? Yeah. Practically, it starts with data. So it starts with understanding the value chain, how does work get done today. Typically, that sits in people's heads. It might be kind of old documented kind of workflows, etcetera. But unless there's a machine involved, typically, it's not overly well documented. So it starts with understanding the way that work flows and gets done. Second, so that's kind of designing that taxonomy of work. Second is the connection from your people to that taxonomy. Who does what? What is their responsibility? Combination of time spent, but also we talk about racy matrixes or rad matrix. What is your responsibility to the work? Who approves it? Who delivers it? Etcetera. Once you pull that kind of network of information together, you can start to look at the work and say, what might we automate, and what new work does that create? So what kind of changes in that flow? And then what is the impact on the the organization? And if I start to look at that and say, we're we're looking to potentially automate about 20% of a role, that impacts these 400 people. We then start to look at the 400 and say, what do we do with that time? And that might be that we are intentional in, you know, consolidating some of that work down into centers of excellence. We take a cost savings or we redeploy. It might require us to do new work that we didn't know we were gonna have to do because we're actually managing those systems, which takes a different skill set. So how do we bring new people into that or reskill the people that are there to effectively deliver? So the data gives us transparency and gives us visibility into the org system and what that value chain looks like. We then need to take action off the back of it, which is redesigning, communicating, and then allowing people to have some ownership over their career. What becomes really important through this type of change is that you don't do it to people. You enable them to have a voice in the process, and that enables you to have effective change management through change. Because if you just tell somebody their job's different, they're not gonna change their behaviors. They need to go on the journey with you. They need to feel like they have ownership of it. They have excitement about the work they're going to do. And that's around the change management, the visibility, and the kind of, collaborative approach to managing change, which is really, really important. You just can't say, I'm taking this away from you. Now do this. You might not understand what they actually like doing, what gets them out of bed in the morning, where they get their energy, or what their skill set is, And they might be demotivated by that. We need to avoid that through the change process. And do the softwares that you guys are using or implementing, you know is is there some evaluation metrics built into them to get a better sense of, you know, hey. What is this individual doing well? What do they like? Are there not like a personality test per se, but a little bit of that sense of, you know, you are doing this on the day to day, but what could we have you doing in sort of discovering some of those skills and abilities? Yeah. I mean, from our perspective, so we take that data that's unique to a business, because everybody's business is to a degree unique, Help kind of create that that, data structure which provides visibility into the work. You can then look at redesigning the work, but you can also pull benchmarks. You can you can start to look at, AI and automation and investment in tools. And you can say, what if we did x? What if we did y? What impact would they have? As we collect the data that sits between the employee or the role and that value chain, you should start also asking questions around what do you enjoy? What do you not enjoy? What fills you up? What do you think is value add? Where is there technology that could support this process that we haven't seen? If we ask those questions, all of a sudden our aperture becomes broader. And that means people have ownership over what that change looks like, but we've also fielded that from the employees who actually do the work on a daily basis. So oftentimes, we'll say we can automate that, and somebody might say, you can automate some of it, but actually, I need to be involved in this because this requires unique task and knowledge that you don't see through my job description or the role. That's really important because you might automate some of that hiring process. But in different geographies and in different areas, you might have a different set of questions that match a different skill set that you get or a different way that you engage with your customer base in geographies, countries, etcetera, that's nuanced. So you wanna, you know, automate what we can and then bring a human to the parts that matter. It's, and, yeah, feel free to add anything. So I wanna flip over to you anyway. Absolutely. So I for our software that we're in market with, you know, our products are are priced on a on a sort of on a, outcome base. So what's the outcome that the customer is getting by using our AgenTic products? Right? In in the case of, you know, exit interviews or or, hiring interviews, the outcome is a screen. Right? And that is one less screen that a, that a store manager is doing, and we price accordingly. Right? We know how much it costs for that store manager to do that screen, and we can work into a savings there. For more of sort of our agentic, sort of infrastructure that sits on top of our our workflow modules, you know, you could think of things like workflow audits. Right? There are customers who have hiring workflows within Fountain. Periodically, they need to do an audit of those workflows. Are they the best optimized? Are they the best given how my hiring looks, on my conversion rates, on my time to hire, things like that? Historically, that's taken them hours to do a workflow audit. Right? Go into the workflow, look at all the elements, dive through, take it themselves, look at a whole bunch of data. With our products now, they can do that in two minutes. Right? And so we're we're driving a lot of savings, on the sort of, operator side. So the HR managers, HR analysts, TA practice, by being able to use Fountain to evaluate their business the way in which the business is configured on Fountain in a much different way than they'd have in the past. And we've we've sort of linked all of that to an outcomes based pricing model, where they are only being charged on the outcome of the work that's being taken place within Fountain. And so as part of our process, you know, in our consultative sale and ongoing relationship with our customers, you know, we're helping them understand where they're taking time, how much time they're spending on these activities, and where we can automate or streamline, you know, through through conversational AI directly layered on top of our product to reduce the amount of times they're spending managing the system or managing the operations or managing the business. And we're seeing a lot of success there and customers really, really leaning into the time savings that they're getting back. So Excellent. And, Pete and Ross, I think you've given us a great overview of, you know, what's possible, what some organizations are already doing, and, you know, strategically speaking, this this is great. Right? It's gonna free up a lot of time, make things more efficient. The data gives us, great insight into sort of where we're at, where we could be. But I wanna bring this back to sort of the the human side, the human edge, and the idea of trust. I know we've we've heard a lot of numbers where job candidates have expressed frustration with AI agents during recruitment. They feel like maybe they're not being seen or being found due due to the filtering process. So, where where do we keep trust in the loop in AI, and how do we give fair screening and make sure, you know, these complaints aren't all valid and we're really doing a good job in in that recruitment process? And maybe, Pete, I'll pitch this to you. Yeah. So in I think people's initial gut reaction is that AI is biased. You know, what we've seen running, you know, half a million AI interviews is that actually humans are biased. And and AI is one of the most equitable ways for everybody to get a fair shot at a job. Right? It's the same interview every single time at any time in the day. And, you know, when we're talking about a frontline population, these are people working night shifts, day shifts, getting out, picking it you know, getting their kid from day care, going home, you know, single parents, and, you know, their life isn't flexible or doesn't have doesn't afford the same flexibility as to be able to have an interview with somebody, right, after work or in the middle of the day. And so the fact that they can take an AI interview at ten at night when they put the kids to bed, it it allows them to actually get an interview with an organization verse first not being able to take an interview in the middle of the day because they're on the Florida retail location. Right? And so this is just one way to sort of increase sort of the equitable nature of the interview by everybody getting the ability to have that interview. Right? And they're not getting it with somebody different in LA than they are in New York or store y to store z. Right? They're getting the same interview every every single time. There's a secondary component to this, you know, making sure that you're on top of and ahead of the regulations. So in in AI screening, New York one forty four is a very important one around impact bias, and we're running, you know, biannual and annual audits both independently as as well as through governing bodies to make sure that there aren't groups being negatively impacted by this. And if they are, how do we adjust the workflows or adjust the base prompts to make sure that, you know, we can we can get out ahead of this and and ensure that this is a bias free way for people to be interviewing. So I think that's sort of the the heart of sort of where the bias is and and how I think what we're seeing actually play out. You know, 75% of the time, candidates are choosing to speak to an AI agent versus a human interview. And that's telling that sort of this adoption curve of, you know, being open to experiencing a different way to get screened, especially, I can take it anytime. I'm gonna get it now, and I'm gonna get the next step right away. It's critical. So then there's the human component. What we're seeing is the less time you're spending screening people, the more time you can spend in the right way, right, with the right candidate at the right place and meeting candidates where they are. Right? So while you might not have a human doing all the top, you know, top of the funnel screens, they're gonna be talking to more people in the store for those in person interviews. Right? And so what we're seeing a bit of a shift in the AI is actually allowing them more time to have human interaction, to insert themselves in the right way, to know where they should be, right, when we're talking about the day one interviews. Right? They know what employee to go talk to because they're getting the right information back. Right? They're not just making, you know, grand statements or not doing anything. They're saying, okay. It looks like employee Jane is struggling. Let me go follow-up with Jane. And so what we see AI is sort of supercharging the human component to it because I have more information. I'm able to be much more specific and targeted on how I use that information and who I go follow-up with. So it's not creating a wall between the sort of the the operations and the candidates or the employees. It's actually telling the operations where to focus their attention to create more human like experiences. I think when when I think about restructuring businesses generally, so you've got the hiring process of getting people externally into the business and into jobs. A big part of changing the orientation of your operating model and your structure is not just what boxes exist, but who sits in what box. So as we redefine roles and redesign them, we gotta figure out what talent then populates the box. And And a part of a transformation program is redesign the structure and then re rearchitect who sits in the box. Big part of that is creating slates of talent. So who do I have in the business now who might be a fit for that job and figure out who actually in the end gets that job. What we believe and the way we use AI in our platform is to accelerate the upfront process. So similarly, let's get a talent pool of the right type of talent. Let's make that visible. Let's use the data we've got effectively. But when it comes time to make the decision on who gets the job, who sits where, that requires a human. And that's hopefully enabling people to spend more time on that part, which is really critical as opposed to the data management pieces upfront, trying to get everything organized and make it visible, it's right there. They can then use their time, their energy, and their brain space on who's the right person for the job, what's the impact on diversity across the organization, equity across the organization as we make changes, as we make cuts. And if we're looking at talent, or performance, where do we redeploy people who might not have a fit now but might have a fit in the future that are high capable high capability individuals but don't have a fit in this role. How do we redeploy so we don't miss people? So AI enables us to make that visible, and make that kind of really clear through the process, but we're still putting a human at the point of making decisions. And there will need to be a human at the point of making human decisions. That's great. Ross, Pete, thanks for, you know, giving us the nuance there. I wasn't aware of all of that, so that's really exciting stuff. And while I wanna shift gears a little here, Ross, I'm gonna ask you this one since you're you're kind of looking at the org and and then strategy. I feel like HR in organizations isn't always, making decisions, for for for how everything should be. That kinda hits the c suite. So how should HR leaders use AI as an opportunity to gain a seat at the table and and to guide some of these strategic workforce decisions and, you know, essentially get into those high level organizational monitoring modeling conversations. Yeah. The ownership, of the business strategy sits with business owners. HR, finance, these are all supporting functions. Their job is to support the delivery from from business leaders of the business strategy. HR is an opportunity with AI and restructuring of work, restructuring of of organizations, and and the push for optimization, and automation to play a role in helping them achieve that strategy, they need to come with an opinion. They need to come with data, and they need to come with the ability to, understand context. And you can't show up at the end. You can't show up with a tell me what you wanna do and I'll do it. It can't be transactional. It can't be operational. You need to move to what is the business strategy and how does that translate down. And the only way to get a seat in that is to show up with value. Show up with data. Show up with a perspective on the business. Challenge the business leader, but be there at the beginning. And what we see with HR is they wait. Too often, they wait. They make it a distributed capability. It becomes soft. It needs to be upfront. It needs to be data driven, and it needs to be strategic in nature. There's a huge opportunity to to have a seat at the table. Now what I will say is the best way to get a seat at the table is start tying yourself to people like finance leaders. So if we look at FP and A, FP and A is about 30% of finance spend. It's the forward looking element of of finance. HR spends about 2% of their money on forward looking HR. It's almost all operational and tactical. So if you can tie yourself to FP and A, if you can tie yourself to the budget, the p and l, and you can say I can help you effectively deliver and align to your financial targets, you have value. If you don't or you expect them to come to you, finance won't come to you. They'll mandate to you. That partnership becomes incredibly important. And too often, we play see these kind of parallel processes that operate in silos, and they can't operate in silos. That's perfect for us. Thank you. Now we are getting close to the end of our time here. So I wanna look at the the bright shiny future as we're reaching the end. And so, Peter, when administrative churn and manual hiring tasks are now stripped away by AI, First, what's left, and what is the blue sky in front of us? What does the human edge look like in practice for HR leaders and recruiters when, when their main responsibility becomes judgment, relationship building, and culture? Yeah. I mean, again, I think to bring this full circle, this goes back to the beginning. Right? This is, you know, how do we start understanding what goes up into these roles, the makeups of these roles, the tasks that these roles are responsible for? How do we unbundle them? How do we understand what needs to be automated and what doesn't? There's so much work to be done. Right? Like, I I can't and we think about this all internally with within my own sales organization or go to market organization. And as we talk to customers about it, there's just so much work to be done. There's so many candidates. There's so many hires to make. There's so many fires to put out. I the the work the work doesn't stop. And, you know, from an engineering perspective, like, features can be be released forever. Right? There's no shortage of requests being made by customers on the feature side of the product. And so, like, the the AI coding tools are wonderful, but it just to, like, to continue to supercharge and empower people to do more is what we're really getting at. You know, if I can have agents doing screens and taking you know, running analyses and acting on those analyses for me in two minutes versus two hours or two days, like, I'll take that time. I'll take that time to to go back to what Ross said around forward thinking activities. What does my org look like over the next two months? How do we support the next 10 stores being, started or or or rolled out? Right? And starting to really thinking about how do I, as an HR leader, impact the business out of just managing the business. And I think these tools empower, you know, the leaders that we work with on a daily basis to do that. Right? To be a lot more thoughtful and proactive around which direction to take the business, because things like analyses are being done and and agents are running in the background and screens are being done. It and the use cases will continue to evolve. Right? And I think, you know, being focused on how do I stay ahead of those and how do I look towards solutions using some of this tooling. I think we get lost in sort of, like, the the great AI is coming. At the end of the day, it's a it's a really powerful set of tools. Right? Like, it's it's, you know, going from, you know, shovel and pickaxe to, you know, a a backhoe or, you know, I'm trying to think of the my my son's favorite, construction. Right? Like, you know, at one point, there were a bunch of people standing around a hole with shovels and pickaxes, and now that can be done in one scoop. And Yeah. It was on the construction industry to adopt new technology, to do more, to build more buildings. Right? They didn't build the same number of buildings. They built more buildings, right, with less effort focused on a different set of of, of tasks. And so I I think beginning to think about these as, you know, a backhoe versus sort of this paradigm shift of the way that work is done, and I don't wanna adopt it because it's gonna come for my job. It's just not the way to look at it. Right? It's Yeah. What do these tools allow me to do moving forward to make a material impact on the business? You know, I I met with 25 interns at IBM last week, and, you know, they're coming out of college. And twenty years ago, it was learning how to use Excel if you're going into, you know, become a financial analyst or something like that. And if you didn't know how to use Excel, you probably weren't getting the job. Now if you don't know how to use Cloud Cowork or, you know, ChatGPT, like, you probably might not get the job. Right? Because what is the adoption of these new tools look like as it pertains to your job on a day to day basis? And I think the people that are leaning in thinking of this as a tool to supercharge their capacity or take away the things that they shouldn't be doing and spend more time on the strategic forward looking things, those are the people who are gonna be really succeed. Those are the HR leaders who are gonna really succeed in this time and through the shift. And the ones who say this is scary technology that I don't understand that's gonna come from my job, those are the people that, unfortunately, I think are gonna get left behind. And A 100%. The faster we can get to a place where, again, tool that supercharges versus scary AI boogie person, like, I I think we'll all be in a better place. Yeah. I think a 100% agree. I mean, every every investment in AI, every investment in automation ultimately raises a workforce question. What roles change? What skills are needed? What do we retrain? What do we retain? How do we deploy our resources more effectively? Where's risk, in it? Those are workforce questions. And so, you know, the the crux of AI investment is and and the effective delivery of AI investment will come down to, did you design your organization effectively to get the most value from it? And the future role of HR is not reporting on workforce. How many heads do I have, etcetera. It's workforce decision making. It's that dynamic, agile organization that can flex. And that's what's really exciting to me about, about AI and the speed at which AI is impacting work is it's forcing organizations to do what I've been trying to get them to do for over a decade, which is look at the work first. Ask the question, you know, like, how is work getting done, what tasks are needed, and then how do we organize our business around that effectively? And I firmly believe the organizations that succeed the most through this process won't be the ones that deploy the most AI. It'll be the ones that understand where they deploy it effectively and the impact of that deployment. It's intentional, it's thoughtful, and it's redesigned effectively. It's really important to use it. It's really important to deploy it. We should understand the impact. And if we create just use AI to use AI, which I see all the time with businesses, which is, oh, have you used it? How many people are using ChatGPT? That is not an effective metric. Yeah. An effective metric is how did it change the work? Were we faster? Were we better? Were we cheaper? And if we were, what did we do with the resource that freed up? And how does that tie to our business strategy? It's a different question than are we using. It's not a good question. And I think this goes back to the previous question you asked, Ross, around how do HR leaders, you know, gain a seat at the table. Right? I I think there's a huge there's obviously a huge human component to this. Change management, fears in the organization, upskilling. And so, like, this isn't a a again, going back to the tool, like, this isn't just a another tool that we're adopting that's being driven by IT. Like, there are sort of more macro considerations here. And that's why I think it's so important for the HR leader to have a seat at this table, have a seat at the AI counselor or whatever it's constructed at the specific organization is because of there's a huge human component to this that I think it's undervalued. Right? And if this is an IT led change, you're probably gonna fail from an adoption perspective. If HR is involved in how this gets rolled out, how this gets adopted, what does the upskilling of our workforce look like, I think you're gonna have a much better survival or sort of, survival chance of getting the technology adopted because we're thinking around sort of what is the intersection of new tooling or new technology and the people within the organization and sort of what's that Venn diagram look like and where do we need to invest our energy. Yeah. Intentionality. Make sure people aren't lost. It's really important. Ross, Pete, I think you've summed this up, perfectly in terms of sort of how to think about this and where people's head should be at. So, I do wanna thank you both for being here. It's been really great to pick your brains, and I think we've had a a great conversation. So thanks for all of your insights and experience. K. Thank you. Thank you. Pleasure to be here. Absolutely. Thanks to Orgview and Fountain for sponsoring today's panel, but also sending their best and brightest. It's been a great conversation. Well, I totally agree, Mackenzie. And thank you so much for leading us through that panel. To echo what Mackenzie said, I I also have to give a big thank you to both of you, Peter and Ross. You've each given us a lot to think about today. Lots of big ideas, important action steps, intentionality. That's a great takeaway. That's a great takeaway in our professional lives, our personal lives, just across the board. Definitely, a big thanks to Orgview and to Fountain for sending their a team. As Mackenzie said, what a day. What a great conversation. Now before we wrap up, a quick reminder to be sure you have signed up for more AI impact series sessions. And on the next one is on August 20, the age of AI agents, driving retail innovation and future growth. This is an important conversation. You won't wanna miss it, so be sure you sign up. Now you will all receive a recording of this session, so you can rewatch any of the segments that you'd like to take in, you wanna spend a little bit more time with. Maybe you wanna do a watch and view party at, at your organization. So, make sure you stay tuned for that on demand version. And we do have one more piece of excitement before you leave us today. We have a $250 Amazon gift card going to our lucky prize winner here live and present with us today, and that is Daniel Langton of California. Daniel Langton of California. Congratulations to you, Daniel. Now we will follow-up with you after we wrap. Alright. Well, with that, on behalf of the teams here at Future b two b, I want to once again thank all of our speakers for being here with us today. A big thanks to Kanoi and Kenzie for guiding our sessions and to Orgview and Fountain for making this webinar possible. And, of course, my biggest thank you, as always, goes to all of you for taking time out of your day to come and join us. Now I hope that you've gotten some interesting ideas, some tips. Maybe you're feeling a little bit more inspired to act with that intentionality. You're getting excited about the possibilities, finding that interaction of willingness and ability within your team, making sure that there's a connection point there. I think AI is is a concept that can feel really huge, a little bit overwhelming, but when we come together for these open ended, expert driven conversations, we can start to make inroads in this wide new, ever growing, ever expanding galaxy of possibilities. So maybe you joined us today. You're just getting started. Maybe you're ramping up or you're evolving. You're enhancing. You're fine tuning. Whatever the case may be, I'm so grateful that we are able to be a part of your AI journey today, and I hope that we get to connect at a webinar again soon. Until then, thank you for joining us, and have an absolutely beautiful end to your day. Thanks all.