HR Strategy 2025: Workforce Planning in an AI-Powered Era

Post Date:
Jun 4, 2025
5
minute read
Original Event Date:
HR Strategy 2025: Workforce Planning in an AI-Powered Era

HR Strategy 2025: Workforce Planning in an AI-Powered Era

Session Recap

AI is no longer an emerging trend—it's a present force reshaping how organizations plan, manage, and grow their workforce. In this forward-looking webcast hosted by Built By People and Achieve Engagement, HR and innovation leaders came together to explore how AI and people analytics are enabling new strategies for capability building and agile workforce planning.

As organizations face increasing talent scarcity and demand for adaptability, the conversation has moved beyond headcount. This session emphasized how HR can use AI not just to solve tactical problems, but to lead with strategic foresight, aligning people planning with business growth in real time.

Key Takeaways and Insights

  • AI is a Core Strategic Tool
    CHROs are using AI to inform critical decisions—from workforce modeling to talent development—enabling proactive responses to change.
  • The Shift from Headcount to Capability
    Success in 2025 will depend on how well organizations identify, develop, and deploy capabilities, not just roles.
  • People Analytics Drives Strategic Foresight
    Data is powering predictive insights that help HR anticipate needs, uncover gaps, and plan for future capability requirements.
  • Real-World AI Use Cases Are Emerging
    The session highlighted practical examples of AI in action:
    • Attrition risk prediction
    • Internal mobility optimization
    • Personalized learning journeys
    • Intelligent skills assessments
  • Collaboration Fuels Innovation
    By bringing together HR and innovation leaders from across communities, the session showcased the value of collective insight in navigating transformation.

Topics Explored

  • Aligning workforce strategy with fast-changing business objectives
  • The evolving role of CHROs in a technology-enabled enterprise
  • How to embed AI and analytics into HR decision-making
  • Creating agile organizational models fit for the future

Final Thoughts and Next Steps

The path forward for HR is clear: lead with data, build with agility, and plan for capability—not just capacity. As AI continues to reshape the talent landscape, HR teams must evolve into strategic workforce architects—leveraging technology to anticipate change and drive growth.

Next Steps for HR Leaders:

  • Assess AI opportunities within your current workforce planning process
  • Shift focus to capability building, not just staffing
  • Integrate workforce analytics with broader business strategy
  • Engage peer communities like Built By People and Achieve Engagement to stay current, collaborative, and connected

Click here to read the full program transcript

WEBVTT Let's jump into this. First off, I'm really excited to share that this is a collaborative session between two separate communities within this space. That's something I think at Achieve Engagement we're really excited to do. I'm excited to welcome Dave DeAngelo, director of Community, , at Built by People. They also lead an incredible podcasts, , called Built by People Podcast. we'll share the link for that in the chat in a second. But Dave, really excited to develop this new collaboration between our networks and bring some of these insights and thought leadership to, to both of our communities. it's great to see you and appreciate you being here with us. Zach, thanks for the invite. And Zach. Zach and I met through another community, which was transform. And, , , I really admired the work that ZEC is driving, and it was clear to me from the beginning that we had to find a way, , to come together and, and partner on something like this. And I can't think of a hotter topic to do this over than workforce planning and, and AI powered era. , thanks for this and, , I'm excited to get us started. And, , , for those in the audience, , as Zach mentioned, , I am the host of the

Built by People podcast, where we elevate the stories and insights of the world's top HR leaders. , we put out about 50 episodes a month across Spotify, apple, and Amazon. , Zach will share a link to that. I hope you're, , listening into that and continue to participate in great, , networking and learning opportunities like the one that Zeck helped put together today. Exactly. It is, definitely check it out after this session. Right? And there's tons of amazing episodes as Dave shared. , incredible leaders taking this, the podcast stage with them on that front. But that being said, I'm excited to dig into this discussion with you, Dave. , this is a very much a, a relevant conversation for all of us as we try to move our organizations, our HR departments, and even our people strategies into the future. , that being said, should we start this off, Dave, and jump into it? Let's do it. Yeah. And, , we're gonna be doing like two fireside chats here. It's a, it's kind of a unique approach that we're taking today, and I'm excited to welcome to the stage Josh Newman, , who I met through Cal at Transform because of a, a LinkedIn post that went out and Cal was like, you gotta talk to this

guy, Josh. 'cause if there's one guy who knows workforce planning and how to kind of integrate that with ai, it's Josh. Josh was also featured on, , the Josh person podcast. , , I think that's gonna be shared with everyone after or toward the end of this call. , Josh, thanks much for joining us today. I wanna dive into the, the seven questions I'm gonna be asking you in the 20 minutes we have, , together. And the first question that I wanted to ask you is, what are CEOs expecting from leaders regarding AI integration in workforce planning? First of all, thanks for, , thanks for having me, Zach and Dave, and Glad Cal connected us, , after that LinkedIn post. a quick two seconds on, on WPP and, and my, my role within that. And, , it'll help contextualize the commentary from here on. , I work for WPP. It is the leading advertising and marketing, , holding company. , we consist of media, creative pr, commerce, production agencies. We're about 110,000 people across a hundred countries. , my role within that is leading people strategy and employee experience that consists of employee listening, people marketing and communications, all things future of work, which really lately the past

two years has been understanding the impacts of AI on the workforce. , to step back from the question. now, now with the context on WPP and my role, this all started when, , our board of directors was asking our HR team, , my, my boss, our C-C-H-R-O and myself, what is the future shape of our workforce, right? We have 110,000 individual humans that comprise, , a number of, of roles, , quite a varied amount of roles. And, , our instinct to answer that question was to pull the microscope out because we can't make any estimates, , using broad brush strokes. , the only way we thought we'd be able to predict, , or guide us to a new future with new sets of tools and capabilities within the organization would be to unpack the work that every individual is doing. Now, it sounds like a tall task, but to understand the impacts of ai, we were fortunate enough to have at our fingertips AI tools to do that and accelerate that. , creative industries, , particularly advertising industries, do not do the best job at capturing process. There is some magic that happens to produce truly creative game changing business growing work. , there's a bit of pride that comes with the magic behind the

process, which means there's very little documentation for a lot of processes. , some of our media type businesses do have documentation, but , others do not. , again, our first thought was let's unpack who we have, what they're doing, and really understand the work architecture. This wasn't exactly the question that our CEO and the board were asking, but we knew in order to answer their question around what is the future shape size of the workforce? What is the hierarchy gonna look like? Are we gonna have more or less junior, mid and senior people and we do today? , that was the only way to get to it, right? I'll, I'll, I'll pause there. I can keep going. , but, , the instinct is to reframe the initial ask, which, , with all of the media, , around ai, of course, that question is going to come to the people team, but it's up to us. And, and what we did was reframe the question to something much more granular to begin unpacking that. Josh, building on that thread, how do you see AI changing the definition of strategic hr, especially in the last year? I think it depends the starting point of where HR sits in your organization, , there's obviously spectrum of organizations that

leverage their HR functions extremely strategically to make business decisions. And then there's HR functions that are more compliance based. And I wouldn't say there's any right or wrong. , I certainly get more jazzed by the former, , than the latter. And I think what, what AI and really generative AI is what, what we're talking about. 'cause that is what instilled this new wave of, of questions, , and ideas. , what it's given us permission to do is be at the table in a way that, , we might not have been asked to in the past. Right? The only way to understand how, , the workforce is gonna change is by understanding the impacts of new technology on workflows, how work, how those workflows can be delivered against by teams that are probably structured differently, which means individual humans need new skills or different skills, or different skill combinations. , we're getting a lot of really call it, , traditional HR questions pointed to our team, , in a way that we haven't in the past. Right? There, leaders are more interested in talking about talent, talent management, , skills, capabilities, , in, in a way that, that I find hasn't, hasn't been the case, , as fervently in the

past. Josh, where is AI creating tangible value in workforce planning? And is there anything that you consider to be a bit overhyped in this space at the moment? There's a lot that's overhyped. , , I think what's what's overhyped is that, , AI will instantly unlock all sorts of capacity across all, all sorts of roles. , in, in the research that we've done, , which included shrinking the complexity of our job architecture from about 55,000 individual unique job titles to about 600 archetypes, , is that there, there is such a spread depending on one, the types of tasks and work that an individual contributes, and also the technology that's available. because people have all of the latest large language models at their fingertips, one does not mean they'll not, they know how to use it. And even if they do, it might be adding time to their process rather than taking time away because they're using it on higher value work. , from my own experience, I know that I go into avenues of exploration and research that I otherwise wouldn't have in my natural language conversations with, , LLMs that I use on a daily basis. that, , that that I think is the overhype, that it will instantly unlock a

type of capacity that will then, , reduce head count and we can drive that to the bottom line. I think that's bs. , and, , , I think you gotta get into the details to figure out where that value is. your first question on value, I think value needs to be looked at holistically. , we're, we're lucky, and I feel fortunate, again, to be pulled into conversations with our commercial team, right? In, in media and advertising, we're basically professional services. the bulk of our revenue comes from time and materials where we, we sell our people's time and their expertise. And if clients are expecting, , rightfully , they're, it's a, it's a good question to ask if, if a widget takes 20% less time to create, , we sh we want to pay you 20% less. we're, we're thinking through new commercial models that are win-wins for both us and our clients. We are pulled into those conversations from a talent perspective. Capacity unlocked time savings, if you will. Productivity increase is a value that you can derive from certain types of technologies if applied, with pinpoint precision on tasks. But you're not gonna give someone a tool and say, oh, over overhaul your work. , the best, , the best

examples of this I've seen are from talking to people who represent that 2.0 of the job that they're already in. They have naturally organically evolved how they drive, , delivery and outcomes of the work they've been doing already. it's about asking them, Hey, how are you using the tools differently? And then setting that as the minimum standard for all of their peers in those same type, same archetypes. Josh, what are some of the new AI driven demands that you're seeing being placed on people teams? , especially in mid-market pressures? I, I would imagine that most HR teams like ourselves are being asked to, , find efficiencies. , and again, there's some validity there, but efficiency for the sake of efficiency will never drive growth efficiency. In our case, the way we think about it, efficiency for the sake of effectiveness is the only way we can balance out the bottom line and growing the top line, right? And, and I've used this phrase a few times, capacity unlock. If we figure out, if we predict capacity unlock based on certain roles by augmenting and speeding up certain tasks within those roles, what we can do is go to business leaders and say, Hey, we expect this role to

have a 15% capacity unlock or productivity potential. , now it's your decision. Do you wanna have less people in those roles delivering the same amount? If you plan on growing, you should have more people delivering more. Do you wanna spend some of that time on upskilling? Do you wanna, do you want to apply some of that time to wellbeing initiatives, right? there's optionality that business leaders have when you're thinking about that efficiency and the same exact thing applies to, to HR teams. Josh, are there any ethical concerns that you see arising when integrating ai, , within workforce planning processes? Absolutely. , , I, I think it's something that, it's something that we keep, , at the forefront. We work very closely with our legal and compliance team and our data privacy team. I know not every organization, , has functions like that. I think we can, , leverage, , you can, you can leverage tools out there, especially LLM tools to understand some of the latest, , regulations around data privacy. , we are confident that we will never be using AI to make hiring decisions, promotion decisions and things like that. , , take something like performance management. , the

traditional way to do that, , back, it's been around for quite some time. You would use the nine box. , there are more advanced methods, but you can use generative AI and certain AI technologies, specifically purpose for performance to understand where your areas of opportunity are opportunity from there. But you're gonna want to use, , that unbiased, , human eye on, on making those decisions. Josh, how should AI powered people analytics influence daily HR decision making? In your mind? How should AI power people analytics and data-driven decision making? , and I think it's kind of one and the same. If we talk to our people analytics teams, , they're at the forefront of, of AI capabilities and have been for the last 10 years. , if we're talking specifically about generative ai, , I'll give you one example on the people listening front. , as we roll out things like engagement surveys, we're training managers and leaders who get access to the results on how to leverage generative AI to interpret the results, to support with action planning. , and you can very simply build, whether you have internal tools within your organization or, , free or premium tools like Chachi, BT Plus or

Claude, you can fairly easily build an agent to put parameters around those recommendations. , you can maintain the kind of responses and, and limit the ethical concerns. Josh, as, as we wrap up this, , fireside chat around AI driven workforce trends, I have one more question for you before I bring Z up on stage. And that is, , how else do you see AI reshaping some macro level workforce planning strategies? I think it, , I I, all of the conversation around AI makes me, , compare it to the most recently probably equally hyped, , technology, which was laughably, , the metaverse. , I am not comparing the two in terms of provenance or sticking power. I think, , generative AI and AI is clearly here to stay, , and transform everything. What I think it does is it further enables HR teams to do what they have always strip strived, strived to do, , which is ensuring that the right skills are in the right place to do the right job at the right time. , and the, the more we understand the architecture of work, the more we can, , support strategic workforce planning and ensure that those initial, , outputs of HR are held true. Awesome. that wraps up our first fireside chat. I'm now gonna bring

on, , Zach who's going to lead the second fireside chat with Taylor. And then we'll all come together as a group toward the end, , to, to discuss, , some other things. I'm gonna hand things over and, , both me and Josh will go on mute and take up our video. Now. Thanks for the rapid fire talk soon. Great kickoff fellas, everyone attending. Let's give 'em some appreciation for warming up this virtual stage for Taylor And I, , that was awesome. I'm excited to kind of continue that build off of it, maybe even repeat or provide some different perspectives on some of those things. Taylor, welcome VP of talent strategy and success at touring. You are all warmed up from a couple sessions today, what's going on? Taylor, good to have you here and, , appreciate you taking some time with us. Thank you for having me. And I have my cup of coffee, that's keeping me going, but I'm always living the dream, looking forward to the chat here. Love it. Nothing, , AI can replace our coffee for these things at this point. Not yet. Not yet. I tried the AI, or at least the robotic coffee and s o's airport, not the same. , if there's one insight for all of you, stay away from, from that coffee, but, ,

yeah, Taylor excited to keep this going. Obviously I think as, as Josh expanded on, like it gets really excited when we're able to really understand our workforce job architecture, the, the mapping out of our talent and the needs from our talent and, and do a lot of things that we've always wanted to do. yeah, one would love to hear more, like maybe introduce yourself a little bit of who you are, the type of work you're doing. But I would love to start off with like as people, leaders or CHROs and the people attending, like what are some shifts in our mindset and the way we should view and embrace AI for workforce planning and going forward with our strategies? A big question to begin with and we'll kick off with an intro here. my name's Taylor Bradley, head of Talent Strategy and Success at Turing. For those unfamiliar with Turing, we are an AI infrastructure company. We have two main services. one is a GI advancement where our teams work with researchers and collaborate with them at leading AI labs, if you would think of chat, GPT, Gemini, Claude to improve their models. And then on the turn intelligence side, that's where we leverage that expertise on training those frontier

models and helping those organizations release all those updates to all of you is we leverage that expertise to help you identify and implement practical AI solutions in your own workflows. And we've had quite a range. There's a lot that I do internally on the HR team. One case that I found really interesting that I learned that we did is worked with a pretty prominent clothing manufacturer to replace, , their human models and onsite photo shoots, all digital and AI now. And that was mind blowing to me to, to see that demo. there's a lot we can unpack there, Zach. , thinking about that example too, 'cause it's like, it's hard for me sometimes even to grasp at should we be looking at this from a workforce planning standpoint as a AI model that we invest into to execute a certain job? Or is this a human that we need to hire and train? Sometimes it's a blend of both. What was maybe even that client that you worked with, like what are some of the mindset shifts we need to have that we can start to even include those type of questions in our workforce train, like planning, like how, what are some of the shifts you see there? Broadly speaking, with all of our clients and internally, the

first thing that we encourage organizations to do is understand the roles that they have. And this comes back to job mapping. It's a skillset that has been getting a little dusty in hr, but really understand what task each job is doing. Because our roles are not one single thing that we do. If we did that or if that was our role, that would be very easy to replace with ai. But our roles are a combination of hundreds of different tasks that have to come together in our brain matter to lead to a certain outcome. And what we do is on my team, look at those tasks for a people operations specialist, for a talent development partner, and not set out immediately to replace those roles with ai. I think that's a misguided approach, but instead go what part, what task of those individual roles are suited to be AI managed either through automation or augmentation, and then go step by step at chipping away, freeing up that human to work on additional task. And when it comes to my internal org, I lead a team of around 40 human HR professionals, but also on my org chart, I have two ai, FTE that report into me. One is a talent development partner and the other is a people operations specialist. ,

how cool is that? Is that your, like two full-time AI agents reporting to you? That sounds wild to me. Like, , how did you start to uncover those tasks? Like how do you determine like what are questions are you asking yourself or can you encourage even our audience to start asking themselves about their own roles or their team's roles to figure out which one of those they can start to augment with AI to free up that capacity? Yeah, there's a nice infographic that we made very simple, and you, you all can see it on my LinkedIn right now, is it highlights a role and then there's little dots that represent each task. First you have to map all those out. What are the gray on this infographic? If you look at it, what are the gray human-centric tasks, right? And then having an understanding as what are the strengths of large language models of ai and how can you take those individual tasks and convert them from the gray human-centric dots to blue AI managed ones on the infographic and having an appreciation. This is where we see with some of our clients sometimes of what AI is currently capable of, , versus what people think it's capable of can be a chasm that's not even manageable to

cross. the tangible use cases that we help our clients to identify are ones where they can build momentum behind and more importantly, either go to their shareholders or their executives or most people on this call their CFO and say, here was a use case that we beta tested, we designed a proof of concept, it led to this and improvement and get tangible results associated with it. We want additional investment to accelerate that and to scale that use case. That's where we really do a lot of work at, , with our clients to be able to do that. And I see some people in the chat, I'll drop a link in here too they can follow along on that infographic. Yeah, I was gonna ask for that myself. Like even thinking about my own role, like where, where are those dots for me? And , I think that's another important point that you hit on. I think there's also that, that chasm where we have this idea of what AI in large language models can do, and then there's additional potential what it can do that if we're not educated or aware of that it's hard for us to even support this next evolution of our workforce to build out some of those AI agents that report to you. Right? That's right. And a common, it

was at CN BBC's CEO summit in Arizona. And that always affords me the unique opportunity to connect with CEOs to some of the world's largest companies to get an understanding of how they're a applying ai. And last year when I went to the event, I saw this big disconnect of what I ended up breaking down is short term versus long term getting to orbit versus getting to Mars. I used a SpaceX analogy, which I realized may not, , resonate with everyone, , given the recent climate, but going with me on this journey is we had many CEOs that were fixated on, we need to get to Mars and we're gonna launch this huge AI program. It's gonna take multiple years to implement. That's, you do need to be thinking about those long-term initiatives, but if you don't even get to orbit with your workforce of having them leverage the LLMs that are existing today or embedding in the workflows that's going to be dead on arrival, you're never gonna get to Mars in this analogy. And that's one area that we're pretty diligent with our clients of. Do they have a realistic expectation of where they are today and a realistic expectation of where they want to go to make sure that it's a good fit for us. Because if

they have an unrealistic expectation that for Star Wars fans, they're gonna immediately have C3 PO walking around their office office, that may not be a realistic outcome in the near future one day though. Yeah, yeah. It's funny 'cause , obviously May 4th was, was around the corner and I watched, , star Wars again. I was like, oh my gosh, is this, am I gonna have that someday? , yeah, fingers crossed. Hopefully you, you help us build that out. But, , I wanna back up to Dorothy's question too, and there's a few others I want to cover, but since we're on the topic for your department as , , could you tell us more about these AI agents that report to you and give some examples of those two employees and what they do? Absolutely. on the talent development side, we made a use case in 2024 at this point, a little over a year ago where we wanted to rapidly scale our asynchronous learning modules for our, , employees or for our team members around the globe. And what you learn is when you're on the frontier of helping train large language models with open AI or Gemini, is that the technology rapidly changes, therefore the processes continuously change. And a general industry standard is to

create a technical asynchronous interactive learning module can take anywhere from two to five weeks. We set out to disrupt that and started to train a proof of concept. before we invested a ton of money, we went to chat. GBT used that as our r and d lab and effectively create it in the aggregate. What an instructional designer would do with raw input from a engineering leader, engineering leader would record a th 30 minute video. They would put it through the custom GPT and the output would be anything that we would need to immediately create an asynchronous learning module that could be from quizzes to how information is positioned. And that was all trained. We helped do the fine tuning on adult learning practices, best practices in instructional design, English as a second language best practices, because many of our team members are not based, , we're or do have English as a second language. And that output was astonishing. we took that two to five weeks and dropped it down to about three to six hours from the moment that an engineering leader would have an idea of what they wanted to train on. Three to six hour later we'd have a fully built out module ready to go. And that

blew me away at that time. Now we literally have that down to within two hours that we get the raw input and we generate that output. And that has not replaced the talent development members of my team. I have three and we support an organization of around, if , fluctuates based on the types of work we do, but anywhere from six to 7,000 people that I'm supporting, which is three talent development partners. And that has been the exponential gain that we've had on that. I'll take a breath here though, before we get into the people ops to see if you have any feedback or questions on that. , that's incredibly exciting and I'm sure for everyone listening who has done curriculum design content development for l and d and facilitated learning, like how much time and hours goes into that. Like I'm sure we would all love to have something that could take a raw video and convert that into the deliverables you need to upskill your workforce on something that's happening right then, right? And you think about how fast demands shift in the environment where we need to upskill people quickly to be able to deliver to the new demands and needs of whatever that might be. Like that's the type of

turnaround you're, you're seeing here. And and one of the questions that came in was like, what does upskilling look like as it looks to ai? as you think about those three team members that maybe traditionally were doing that, that curriculum development and designing the worksheets and all those things, what was the the, the change management and upskilling like for those three individuals now that you have an AI agent operating in that way? We first faced the same challenges that many leaders in this call are facing as a fear around it. We're an AI infrastructure company, yet some folks on the team were nervous on am I training my replacement? getting them to understand the upside and showing them more importantly how they could achieve a significant productivity gains through A LLM usage. Custom GPT really started to get them on board. The biggest shift that I've noticed is the creativity that's unlocking for these team members. These team members are generally at the merging part of their career. They do not have advanced degrees. Two of the three as first time they've ever worked in talent development. They were internal transfers to the team, but they're putting out PhD level

work because of the LLMs and the use cases that we've designed. now, instead of going through and doing tedious things and attempting to try to understand the technology, or I'm sorry, the SME content behind it, that's all being handled by AI with a final human in the loop checkoff by our SME, right? But they get to work on creative things. And an example of that is one of them said, Hey, why don't we put the senior engineering leader out of a job of facilitating the session? And I was like, what do you mean by that? That's when we started to have one of them go out on their own and they developed an ai, , lifelike indistinguishably lifelike AI synthetic voice that would replace the facilitator entirely and rewrite the script to be high quality voiceover script. that way when we need to go change things, you edit the script and click publish. You don't have to go do any reshoots. If the SME wanted something said slightly different, no reshoots, no editing, change the script, click publish. And that was because the talent development partner had time to go think of what else can we do? What other gray dot can I turn to a blue AI manage one? And that's also unlocked a tremendous

amount of productivity for us. And the SMEs love it because if there is anything that they want to change, it's very light lift. It's a matter of minutes to go back and change the content. Ah, it's exciting and it sounds like you're helping a few people's even leadership proposals right now, like Alison and Sharon in the chat with. That's awesome. And, , yeah, I was, I was curious of maybe lessons learned as you shared, like as the models change, that also changes how you need to update and refresh and train the model differently for the tasks that you're doing. And Sue, you asked a great question now. I was even thinking about like the risks involved to this, where we hear about these hallucinations that are happening, , or these hallucination problems. , obviously it sounds like you have that quality control checkpoint as a part of the process. , but yeah, what, yeah, what's your thoughts on some of that and what are some of maybe like yeah, the lessons learned where I know some of the people in the chat that are like, , I want to get on this journey. How can I maybe learn from Taylor's mistakes and issues that he had I don't repeat those? , plenty need to go around. And the HR

business partners, when I first started getting them to use LLMs, a very important guideline that I put in place is it needs to compliment, not replace your voice and expertise. And what I encourage them, and I shared with them some highly publicized use cases of lawyers, , putting forward things to the courts of made up cases that were hallucinated, I don't want you to be in that position. Right? And it's important that you always remain the human in the loop. And even if you look at the graphic that I shared earlier, even in the getting to Mars, there's still a, there's still a human at the center of it, even if they're managing teams of LLMs or AI agents. we have to have that quality control check. And the second piece on the talent development side is recognizing when you are letting AI write too much. And some of the feedback that we got immediately when we implemented, , some, , AI assistance on the people operations side is the people operations team didn't like it. Not because it was helping them speed up their task, but because what was being written was clearly written by a robot. And the exciting part for me was, Hey, that's a linguistics problem. That's a matter of

training it on linguistics. I asked the team share with me some emails where you feel like you were the hemming way of writing a good email back to someone. What does that look like? Then we can take those examples from the humans and apply that into the linguistics training of the LLM itself to say, mimic this. And once we got the team involved and they understood that that's something that they can find, tune out of the system, they were able to then embrace it. It was, oh no, this is exactly writing it how I would've written it, or even better than I would've written it. And that's when you really start to get the team involved. Yeah. Gosh, it's cool and , really exciting to hear kind of how ahead you are with this, and then obviously serving and supporting other companies. that being said, let's keep the conversation going, but I wanna bring the full group to kind of continue unpacking this a little bit more. , let's maybe as a, as an audience, can we give Taylor some appreciation though for, for unpacking kind of that journey to, to Mars far? , that was cool. Josh, Dave, welcome back. Let me bring you up, , how cool of a discussion this has been far, and as we kind of open it

up to the group, I'll shoot it over to you, Josh, start, like, anything pop into your head as we talked about, like this journey to Mars, but how do we get in orbit first? Yeah, I, I love that analogy, Taylor. , , I I I've heard in some round tables and some conversations with, with leaders, , this tension or this back and forth between do we invest and then what, what, what's the value realization look like? , or do we kind of wait and see? , I I, I don't think wait and see is an approach that will win any game, business or otherwise. , I think you gotta start, even if you start small, start very practical figure out, again, I'm going back to the same few points, but someone told me, say one thing and repeat it, , to get the message through. understand the workflows, understand the work architecture, pick out the stuff, tasks that people don't like doing and that doesn't add value. , and start figuring out how to, how to accelerate that. And Taylor had some wonderful examples there. Zach, this is my, this is my calling now, isn't it? , jump in. Yeah. Otherwise, I, I kind of have a follow up for, for Taylor. Yeah, yeah, Yeah. , I've Been, I've been thinking about Taylor's, ,

answers, , 'cause Taylor, your company at its core is very AI driven. , a lot of companies don't have that luxury of being, , like AI native or, or AI first. I, I wanted to ask a question to both Taylor and Josh on how can we start getting HR leaders to prioritize being AI first or AI driven in the context of workforce, , planning, especially with all the other competing demands, and what are some, some, some tactical and practical insights you might share to help them take that first step if they're getting started? Yeah, happy to cover that. The, the first part about Touring's journey or my part with touring is we 10 x headcount in the past year and a half, maybe a little less than a year . And that led to a lot of demands that totally obliterated any kind of, of HR processor system that we had in the organization. And what ended up being a joke at first ended up becoming our mantra I shared with our CEO. He asked how you would get to scale the team this way. And I said, I'm very AI our way out of it. And that ended up being the mantra of the only way that we were gonna be able to scale is if every team member in HR started to use AI to get that proficiency. But not every

organization's going through hypergrowth like that. it may feel like, oh, this doesn't connect with us. And I wanna go back to something that Josh said, 'cause this is the most common complaint I hear from CEOs when we discuss it with them. I went out and I got all of our organization chat, GPT or Gemini licenses, and no one's using it, right? That is the biggest missed opportunity of getting folks comfortable with ai. It's like the least threatening entry into utilizing ai. And what we encourage team members to do is encourage your managers to speak to their teams and start using AI daily in conversations, getting people familiar with help. Help me rewrite this email it sounds less harsh or is more constructive. Have a look at this performance feedback I'm going to give to someone. What coaching tips can you give to me as a manager? May not be always right, but it's encouraging team members to really go explore in their own individual workflows how they can start to leverage AI to be more productive. And that applies to every one of our organizations. those are the two things that come to mind on that front. I forgot the initial question, , Dave, but Taylor, to build on that, , I,

I talk to people often who are, , not in a situation that you're in with 10 x growth in a year and a half, but people who are often, , wondering why we don't have budget for additional headcount. They wanna hire, hire someone, they need some, , junior support and forth. And, , my my go-to is similar, the framing is slightly different in the sense that, , your average LLM treat, treat it like the smartest intern you've ever had, right? You're always going to check an intern's work, you're going to make sure that they're on message, on task, on whatever it may be, but they're gonna generate some things that you're like, holy s**t. Like, I didn't realize I, I didn't, I hadn't thought about that. And it'll take you down a different path, right? like writing, writing the email is I think good. The, the three things, , within that intern umbrella I think about are search, research and content, right? Use it for search, , play around with it for search versus Google. Play around with it for research because you can ask it to cite references and sources, , most of the models anyway. And then content, , no matter what kind of content you're building, and we're all content creators one way

or or another. We're all communicators and storytellers one way or another use it for, , to ensure you're not starting on a blank page. And the, the misconception that prompting is hard. I hate it that somewhere along the time timeline, someone added engineer prompt, right? Prompting is super easy. And, and I gave the example Dave, I think when you and I were talking, is I'm teaching my 3-year-old daughter how to prompt. And how we do that is every night she's learned that if she gives me an animal, an activity and a location, Chachi PT will create a custom coloring book page for her that we print out. And she gets, we've also branched into, we're creating a little Pixar movie with vo Google's new VO where she's a character in it. And I'm a character in it, and we're making a soundtrack for it. But the one thing that I I I claim as an hallucination is I have hair, but every time it generates me is a prompt. It, it, it's always bald. that's, that's the one thing that we still gotta fine tune that one. You are next level, Taylor. I will say, , my toughest mornings with my five and 3-year-old, I turn on the chat function on chat GBT, and I'm like, , Stella, Julian, what are you into

right now? Dinosaurs and princesses. Sounds good. , chat, GBT Stella wants to hear a story about di princesses and Julian dinosaurs. Tell it a five minute story, make sure to ask it five questions in those five minutes and build on what they share. And that's, , and then I can go get dressed. I love that. for some of you listening with kids and you're looking to open up some capacity in your mornings, like you already got some use cases here. And, and I, I feel like you answered this, and Melissa asked this in the q and a as , like, how can humans gain the practice and experience and some of these more entry level skills that, , to outsource to ai? And it sounds like in many ways it's like getting in the game and getting in the arena and starting to think about scenarios and, and it's, it's, it's, as you do that almost your imagination and your perspective on what's possible expands through the usage of it. And Taylor, what, even as a company, when you're going into organizations to enable 'em in this way, is there certain things you're doing to like guide their people to become more enabled and, and part of this transformation? Yeah. The methodology that we share with non-

technical folks, again, I'm not an engineer, is the idea methodology is we want every person in your organization to be able to identify design and experiment and action and AI use case in their workflows. And the beauty of it is, I'm partial to chat GPT though Gemini is certainly growing on me with a 2.5 pro release is those are your r and d labs. You don't need to write a single line of code to create a custom GPT that will enable you to have the fine tuning. You want to really test something. And if it's an enterprise license, which is important for governance because it's not gonna train on your data, is that you can use that as a sandbox to safely test ideas. And that enables you to then go again to your executives or your CFOs and say, Hey, I did this, Josh, I did this. Here was my results. If we invest, it's gonna add more fuel to the fire. Right? That's a much more powerful positioning than, Hey Josh, I I wanna do this thing. We haven't tested it. It looks pretty cool either. They've heard that before on every SaaS tool that we've ever requested, right? this is a, a great enablement and it really democratizes your ability to create some really cool use cases. And I'll end

with my talent development manager. She messaged me last Wednesday, it was, and she coded an app that helped automate a specific notification process when they were onboarding candidates. She coded herself, she's never coded in her life, and she used Codex to help create this tool. And then I of course said, this is amazing, it's working, which is awesome, but let's go have engineering and it review it to make sure that it's right. And they looked at her GitHub repo on, , our platform and they were like, this is great. This is perfect. There's no other things that we would change. she did that by natural language prompting. , getting, , executive teams really bought into this. It's, it takes time, it takes trust, it takes evidence, , building a use case for how this is going to drive performance results, efficiencies in the business. I'd love to get both of your perspectives on what you've done as a p in a people function to build executive trust, , when deploying AI in the context of workforce planning or even something as generic as the HR function. Yeah, I can jump in first. , , at the end of the day, with the tools that are at all of our fingertips, we no longer have to go. And

Taylor, you were broadly making this point. We no longer have to go and say, here's what I think's gonna happen. We can go to our leaders, whether you're asking for budget or permission or whatever you typically go to your leaders for and say, here are the results of the test that I did in my garage, , my proverbial garage. , it, again, it, it accelerates if it gets you that step ahead. , you're going with proof points, Taylor, you'd add to that? Yeah, I always joke, yeah, proof not promise. And especially when it comes to ai, like if we had that approach, probably going back to Josh with like metaverse and some of these other things may have not have expanded in the way that it did, but certainly there's, there's proof to be had and I think my executive team, it's a little unfair comparison because we're leading the AI charge, right? , , in fact there's probably some use cases where I've had to say, let's slow down, let's figure it out. Let's, let's work through some of these things first before implementing. at times it's raining in rather than getting buy-in on my execs. Yeah, I got, I would jump into, oh, go ahead Josh. No, go ahead Josh. Yeah, I was gonna jump to one of the

questions in, in the chat. , it was posted by anonymous. , talk to me like I'm five sounds like the start of a prompt. Talk to me like I'm five, what are the basic steps I can take to begin building a capacity model or hiring plan aside from looking at total budget? , , one, one of the things that we recently were asked to do, you're, you're, you're talking about the basics of workforce planning. We were asked to think about the new, , the three, five years from now, what is the shape, structure, , and skillset of the workforce look like based on the new technology coming in. we, we were being basically asked to do workforce planning 1 0 1, but with, , the tech first component. Now, I wouldn't propose to do it that way, but the four steps or the four nesting dolls that I think about within that, and maybe this beyond five-year-old, , but you can transcribe and translate using one of the tools we've mentioned here. , first is understand the outcomes and deliverables you are trying to get out there in the world, whether that's, , physical production or some knowledge management, whatever, whatever the outcomes or deliverables you deliver to clients or customers. From there, you need

to figure out the types of teams that need to deliver it. What makes up teams. Teams are made of up of individuals in roles. Those roles are occupied by humans with skills, right? you start with the outcomes you're trying to drive, and ultimately you get skills. And if you have enough of that, and that's like the work architecture I mentioned earlier, you have enough to start building out, , the beginnings of a workforce model or workforce planning model And a, , five-year-old language. Taylor, you would add on that one or I feel like yeah, that hit on the nail though. The, the no, that, that hit the nail on the head. The thing that I would add is reiterating what Josh said at the beginning, that sounds like a good prompt, right? Popped In. Cha it in. I did that while, while we were talking. I was trying to find the, the, I was, I was gonna put that in. I couldn't find the question to copy it. Yeah, I'll, this is a great question that Melissa re-added in the, the q and A as . And I think this has come up a few times and some of my conversations with the network, and someone sent me a meme the other day of, , I don't know if you've seen the movie Idiocracy and kind of the future

that, , humanity could potentially go to and, and she shares like employees entering the workforce for more entry level roles, right? They're coming in with entry level skill sets and, , a lot of times to get to a more advanced skill set, right, you need to practice and build those, those more entry level skills like writing, like to become a better writer, you need to write a lot. , and I think about even the scenario, Taylor, you added of, , your, your teammate created this app, , using prompts, but you had a more advanced engineer review it and see if it's a good quality, but for that person to be an advanced engineer, they had to go through maybe stages of of development, right? I think there's a fear maybe of, of how do we make sure that the entry level people that are com entering the workforce, , are continuing developing skill sets, especially if AI is starting to replace some of those tasks on the front end. Like what does that, I guess maybe what does the future, how does that scenario play out or what are some thoughts around kind of that potential reality we're going into? One of the questions I got recently from a new hire HR generalist on our team was what skills can

I learn over the next year that are gonna be most valuable? And I said, the number one skill you can learn as an HR professional right now is prompting, is understanding how it can work. Because now a new hire isn't inherently restricted by the years of experience they have compared to the output that they can generate. And the example I gave earlier around our talent development partners, some of them have never, never been in instruction and design and they're putting out PhD level work. Now, there still is likely needs to be a human in the loop that does have that experience that can monitor that quality. That's important to do. You don't want to find yourself as one of those lawyers that's putting out stuff that's not true or accurate. That said there, if I was in the emerging part of my career, or if I was unemployed and getting outta university, aside from looking for a job, I'd spend all my time on deep learning.ai or go to open AI's free content and learning prompting, learning how to do this that I can specialize in the field that I want to get into. And hey, if you're into video games, you could potentially make one, like there's nothing holding you back from creating a

portfolio right now that will make you stand out even in a sense of unemployment. And generally speaking, I, I hear this anecdote that unemployment at the earlier age of, , new hires coming into the workforce, that that part of that workforce, if you look at Jolts data, has historically been had higher unemployment than those that are later on in their careers. that not necessarily a new problem, I think it's a, going back to an old anecdote, but promptings the key. , we have one minute left here. , maybe we go around the horn quick and I'd love some closing thoughts, especially as you think about, , majority of people are attending here today are, are in similar shoes as yours. Maybe they're trying to get the organization involved or even their own department. Yeah. Any closing words of wisdom you would provide for, for some of these groups trying to get into orbit as we, as we talk about? I would say, , oh, did he freeze? Oh, he's back up, Back. Sorry about that. The one thing that I, one piece of advice I would give any HR professional right now and really ever, , is understand your business. Understand how your business makes money, understand the commercial models and

understand the work that the people closest to the revenue are making. Because regardless of AI or otherwise, if you understand the problems they're solving, you'll be able to figure out how to apply whatever the latest technology is to go and support that problem solving. I, I would add that as HR leaders, we, we must shift from a headcount driven mindset to one that's capability centric, and AI is going to be a core part of our strategy, but that shouldn't come in a pendulum swing to everything else that we need to drive for the business. And exactly what Josh said, if you're tying this back to the business, if you're tying your use cases back to the business, you will continue to be successful HR leader. It's amazing. , everyone joining today, can we give a final round of applause and appreciation to Taylor and Josh for, for bringing their expertise and their experiences to this? Yeah, this was amazing. , huge shout out also to Dave and built by people. , Dave, this was awesome to collaborate with you on, on bringing our communities together and bringing this thought leadership to our, both of our networks. , yeah, Dave, this has been a blast and any parting words on your end?

Thanks. Thanks all for attending and showing up. And thanks to Taylor and Josh for, , taking our, our calls on, on LinkedIn to participate in this. This is, this was awesome and we'd love to do it again sometime. Absolutely. Thanks for having us. Thank you all. for everyone else, we shared their LinkedIns, feel free to reach out to them. Maybe you'll get them on, on one of your sessions as . , we shared built by people's podcast as in the chat, make sure you check out those episodes. Check out Josh's and Josh Burson's episode that they did together as , the SHRM codes and everything in else in there. But thank you to all of you for joining too. Taking time outta your day to level up your own skillset, your expertise, sharpening your craft to build a better world of work and, and bring these companies into the future. , as, as a, a leader of this community, it, it warms my heart and, and makes it worthwhile what we do. thanks for joining today. that said, that wraps us up and, , yeah, have a great rest of your afternoons, everyone. Take Care.

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