Paradigm CEO on Governing AI Before It Governs You

Speaker A: If you're an HR leader, you're likely under pressure to adopt AI while employees are asking a more fundamental can I trust it? M. That question becomes urgent when AI influences hiring, performance, pay or career opportunities. Today, Max Wessel is joined by Joelle Emerson, co founder and CEO of Paradigm, to explore the governance of intent. How leaders define what they want AI to optimize for before allowing it to act. They discuss why culture is shifting from a periodic evaluation to an always on development engine. How to navigate the AI context crisis, and why human judgment, presence and relationships must remain the ultimate owners of critical talent decisions.

Speaker B: I want to start with like one big question which is you've gone through this unique transformation at Paradigm. You started as a civil rights attorney and, and you moved into driving a, uh, company around this problem of culture. I would love to understand, as AI collides with culture, as you've gone through this journey, if you could talk a little bit more about how leaders are going through this change and what you've drawn upon from your time consulting and driving that as a trusted advisor all the way through delivering technology to support the change.

Speaker C: Yeah, you know, one of the reasons that I, uh, left my job as a civil rights lawyer is I felt like I was here hearing every company in the world. It felt like talk about how important their people were to them, but then treating people and talent and culture as sort of orthogonal to business success. And what we know is that, or what I believe is that people, talent, culture, these are actually the most critical levers you can pull if you want to build a healthy business. And so our challenge when we started paradigm 12 years ago was how do we help organizations understand that and then build for it? How do we help organizations cultivate talent density? How do we help organizations create the conditions for people to perform at their best? And so we spent many, many years hands on as consultants in organizations, analyzing data from all of their systems, designing interventions to equip managers to manage more effectively, to help talent systems produce better outcomes. And the good news is we actually found there are some really consistent themes that can work and that can work in all sorts of different types of organizations. But we also found that this work was hard, it was time consuming. You know, people's data is spread across all these different systems. Leaders often don't understand sort of what it takes to unlock performance. And so what we've been really excited about recently is what we can do with AI, how we can sort of equip leaders to better, more quickly, more comprehensively understand their workforce, understand the connections between talent and culture and the outcomes leaders care about most. So they can make decisions that, uh, just like, help them do whatever it is they're trying to do as a business far more effectively.

Speaker B: So you used to do this in like, a very episodic way, I imagine you said, takes time hard. Like how, uh, how, how do you think about the shift as AI allows that to be more always on. What's the sort of changing role of a talent leader? And, and how do they leverage that as superpower as opposed to something that might be overwhelming?

Speaker C: So I think what has historically been the case for a lot of chief people officers, talent leaders, is they've been operating in a very responsive way. Head of a MIA business comes to them and says, I'm noticing that I'm losing great people. What's going on? They then go to their people analytics team to try to pull data from workday and maybe sentiment data over here and performance data that might live over there and exit interviews that might be in a PDF file somewhere. They then try to collate that. They get back some insights two weeks later. They then pull members of their team to try to make sense of those insights. Um, maybe a few weeks later, they give an insight or perspective back to this person that asked for it. Meanwhile, they've lost three more people and sentiment is declining. And then the question comes back, well, what do we do about it? And then that's like a new project. And then they maybe come hire us to come help with that. We are trying to use AI to collapse all of that down so you can quickly move from, you know, first of all, having a question about your business to being able to solve it. But also, I mean, one of the things that, you know, an effective agent can do is proactively surface these insights for you. So you're not waiting. If I'm a chief talent officer, I'm not waiting for someone to come ask me what's going on in their business. I'm actually identifying a trend before too many people leave. I'm actually saying, hey, based on attrition patterns among high performers over the last year, we actually see people that are at risk of attrition on your team right now. And here's what we want to go do about it. And so I think it sort of turns the role from being, um, reactive, sort of endeavoring to be data driven, but never really being data driven, to being proactive, connecting strategy to business outcomes and always doing that in a way that's informed by data without necessarily having to have the deep technical expertise that it's often required to pull these sources of data together to make meaning of them.

Speaker B: There's like so many threads I want to pull on here. We talk about at workday that your business system should effectively be a partner. It should be proactive as well. It should anticipate, advise and act on your behalf. And then there's a whole set of questions around how the process then should change and how folks get ready for it. I know you talk to people teams all the time across a variety of different companies that are at different stages of that transformation. I'd be interested in. When you think about working with a proactive system, when you think about agents being always on, what do you think that means for the future of the people team?

Speaker C: Yeah, I think there are a lot of implications. I think the most ambitious, I think is just that the future of the people team is a team that is a true partner in driving organizational strategy and organizational transformation and can sit side by side with a CIO who's figuring out how should AI reshape our workforce. The people team is not reacting to that. They're coming with a point of view that's informed by data. I think the long term implication is this very strategic team that can, um, play a much more central role in business strategy. But I think all of the parts that go underneath that one is I think, um, the people team really needs to think about how do we define what roles are held by humans, what decisions are owned by humans, where human judgment is critical, where human accountability is applied, and what we're actually comfortable with an agent going and doing. And that's going to depend on the agent itself being trustworthy and having the right guardrails and having the right evaluation criteria for all of the things that it's doing. Um, so kind of figuring that out. And then also I think a big implication for the people team is how does the people team sort of champion this change across the organization? Because I continue to see in the organizations we work with that AI adoption is, yes, a technical question, but most organizations aren't really struggling with whether they have the right foundation model. Um, they're struggling with getting people to change the way that they work and doing that at scale, getting people to rethink workflows, getting people to trust and buy in to the change the organization is trying to drive. And that is such a human problem and one that I think the people team, if it is itself AI native and building these skills, um, is so well suited to handle. There are A bunch of tactical questions that I think will come up of like, is the people team of the future one of all generalists or is it one of all specialists? And I've heard people debate this on both ends. I don't actually know that there has to be one right answer to the people team will look like this. And here's the org structure. I actually think that answer hasn't been written yet. And really what we know is that the people teams of the future have to be AI native, they have to be partners to the business, and they have to be sort of like leading the narrative of why this organizational change is happening, what it's going to mean for you, and why you should be so excited about it.

Speaker B: Um, plus one. Also, you just described a, ah, world where the people team is showing up as a partner driving the change. I think you started by saying, hey, we're organizational strategy partners. And the reality is most organizations, at least in my experience, have approached the first era of AI implementation as an efficiency challenge.

Speaker A: Right.

Speaker B: How can I take a process and squeeze incremental 20% of savings out? And that's a very different thing like, than redesigning a workflow as you described. Uh, uh, how do you think about, uh, that journey and equipping the people team with the necessary knowledge to sort of come and partner and uh, provide their business counterparts a sense of what things can look like in the future?

Speaker C: I think if you're approaching AI as an efficiency play wherever you sit in the business, that is one of the most unambitious and unmotivating ways, um, to think about what this technology can do for your organization. And so it's no surprise that I was sitting down recently, um, on a podcast that I host. So, uh, it's fun to be on this side of the table. And I was interviewing the chief people officer of. Right. And they're Jevin. Jevin. I was interviewing Jevin, um, and he told me that they had just run a study where they found that 40% of employees are actively sabotaging their company's AI implementation efforts because they feel like you are asking me to train a model to take my job or my coworker's job. And so this whole idea of efficiency, um, it's no surprise that people are, we're running into these trust issues. You're seeing these, like this kind of Sabbath saboteur behavior. Um, and I think that, and you know, we're also seeing a lot of tech companies are doing this kind of massive downsizing and sort of blaming it on AI um, and how unambitious to say that the best thing we can do with this new technology, this revolutionary technology that is, you know, on the precipice of being smarter than humans is like cut budgets. Instead, I think the people team can really be telling a story of, you know, AI as first of all, from a human standpoint, giving you leverage. Like, how do we remove all the low judgment work from your plate so that you can do the things that only humans can do. Like that. How exciting is that? I don't have to do, I don't have to wrangle spreadsheets. I can go have conversations that need a human. I can go make strategic decisions. I can go, um, you know, collaborate and be creative, the things that we want to be doing as humans. And then also telling the story of like, what does this allow our company to achieve? We can deliver three times more. We can do things that were previously not possible at our headcount to deliver for our customers. Now efficiency is going to be a byproduct of all of that. Of course we are going to get more efficient, but it's not about cutting people and costs to do the same. It should be about roughly keeping people in cost. Some, um, roles might change and responsibilities might shift, but so that we can do more. And I've actually been blown away in whatever the negative version of that is. Negatively surprised, but by how few companies seem to be championing like that ambitious, telling that ambitious story. And I think we wouldn't see all of this fear if we were hearing more of that story.

Speaker B: Um, I love that the level of ambition sort of gates our focus for A.I. um, sometimes I've described this as the Star Trek versus Star wars problem.

Speaker C: I'm not quite a big enough nerd to understand that.

Speaker B: Yeah, well, tell me what that means. In Star wars, everybody's a scavenger on a rim planet and the empire dominates the ecosystem. And in Star Trek, everybody's an explorer figuring out how to use all of this incredible technology to go do amazingly ambitious things. And if we look at AI in this world of we can do whatever we want, there's never been this time where, there's never been a time in human history where humans haven't elevated their level of ambition with technology. And I think, I love that if you apply that to the people team and how they engage with their business partners, we can get them to do so much more.

Speaker C: Yeah. And the work. Okay, so now I get it. So Star Trek is what we want to be.

Speaker B: Yeah, we want to be Star Trek.

Speaker C: Star Trek. Um, and I think like the thing that I think people teams can maybe uniquely tell the story about is how much more human work can become. I think so much of what we are doing now that AI can take on for us is the uh, drudgery. It's the stuff that people don't like doing. And, and so when we have less of that in our jobs, it opens up, people think about AI coming into the workforce and making everything more robotic, removing collaboration and humanity. And I actually think it should be the opposite. Right. It's removing all of the things that take us away from each other so that we can work together and we don't have to be like doing as much work on our computers. We can come together and collaborate around these insights that ah, an agent found for us and figure out how to take action on them. And I, I've heard so few companies and people teams talk in that way and that's what gets me so excited and optimistic about AI. And I think every company and every people team really has a unique role to play in telling that side of the story of what does work become when agents play a bigger role in our workforce? And why does that make your life better and more exciting and your job more fun?

Speaker B: I love that. In the spirit of kind of diving in and putting you on the spot with a practical example. I mean I'd love in the context of Surface, for instance, how are you seeing that transform your customers ability to kind of go focus on the human things?

Speaker C: Yeah. Um, well, I'll share one example that comes to mind from a recent customer of this is a global retailer and they knew that they had an attrition problem in their field, but they didn't know much about what was going on, what was driving it, what to do about it. That previously would have been like a six month consulting partnership with us. They would have come to us, we would have gone and interviewed district managers, we would have of course looked at attrition data, we would have maybe run like focus groups of people who were leaving or looked at certainly exit interviews and we would have tried to understand what was going on. Then we would have gone and tried to design solutions instead. In this case they typed I think a very simple prompt into Surface, something like, you know, tell us what is driving our attrition and what we can do about it. Surface went and looked at their HCM data. Um, it went and it looked at point of sale data, it looked at performance data to understand more about regrettable versus non regrettable attrition Interestingly, side note, we just ran a workforce study at Paradigm and we found that 50% of companies don't track regrettable versus non regrettable attrition.

Speaker B: Interesting.

Speaker C: So like, you just know people are leaving. You don't know if it's a good thing or a bad thing or if you even care. So anyway, performance data can be one thing that's really helpful for telling you whether the people who are leaving are the people you really want to be.

Speaker B: Keeping workday can help with any of your customers who, uh, you know how

Speaker C: to help people track this. If you have workday, you can track this. It's very easy to do. Um, so Surface looks at all these things. This is happening in minutes. And it immediately tells the company that attrition is concentrated in a few districts. It's concentrated primarily among first year, um, high performers. There's like a high performer attrition problem. And it diagnoses based on this additional context layer we've built in onboarding the customer, um, a couple of key drivers, uh, lack of visibility about the role and what it entails in the hiring process. The hiring process really doesn't get into what are you going to be doing in this job? And then manager under enablement. And we can see that where those things are different, we don't see these attrition patterns. And so Surface then starts to draft a recommended, uh, retention sprint. And it gives, it creates a manager toolkit that we recommend training managers on in these districts specifically. And then it recommends changes to the hiring process that give more visibility on, um, what people can expect in their roles. And so now I, as a person sitting in this, uh, organization can go do those things. I can go start working with managers, getting them bought into this, equipping them to understand their jobs. I can go start, I can give our recruiting team updated materials and I can start training them about how to have better candidate conversations. And that's the end goal. That's what we want humans doing. We want humans driving the change. We want humans and we also want them like looking at that output, making sure it makes sense to them. In this case it did. But we want humans to be able to as quickly as possible, go solve problems rather than spending their time in spreadsheets or debating what the problem might be. Um, and so that, like, to me that was just this great example of what a people team can go do when they're not spending all their time wrangling data and you know, or hiring us and paying a lot more money than we charge. For Surface as consultants to go like interview every district manager, just they didn't need to do that.

Speaker B: I, um, love that. I also love that you snuck in something very specific, which is you talked about, hey, we built this context ahead of, you know, implementing Surface. Um, we, you know, we obviously see AI popping up everywhere. One thing that we don't talk enough about with people organizations is how do you build context for AI to be most effective? Love to get your thoughts on that and sort of where we are in the cycle and the importance of creating sort of people and human specific context for people teams.

Speaker C: I have so many thoughts. I think this context and when I say context, I would actually break that down into a couple of different things. Context can be deeper. Organizational context, richer information about the unwritten rules of your company, how decisions actually get made, norms, institutional knowledge, leadership priorities, leadership like personalities, like all of these things that we as consultants have learned over the last 12 years and that you certainly know at workday really shape talent outcomes. So that's one type of context. Another type of context is like the judgment, the expertise to understand what to do in different situations. So, you know, a, uh, foundation model has access to all human knowledge, but what it doesn't, what it's not so good at, not just in the people context, but in any domain, is the application of that knowledge to different use cases. Because it's never done that. And that's not like written in the corpus of all human knowledge is how does this apply to a company of this many people with these leadership personalities that is facing these governance challenges? AI doesn't know that. And then the third kind of context is sort of like, what's everyone else doing? Um, benchmarking and general purpose. LLMs have access to whatever is publicly available on the Internet, but if it's not publicly available on the Internet, they don't know about it. And so that's with Surface, those are the three types of contexts that we really focus on powering our agent with. So as part of onboarding to our platform, we not only connect in to all your sources of data, so we're pulling your workday data, but if you've got, in some other system, you've got data on your return to office success because you have people badging in and out. And maybe you use a third system for your point of sale data. Um, if you're a retailer and maybe you've got another place where you're running surveys and people have these diffuse HR tech stacks and technology outside of the people function, that's still relevant for the insights we need. So we connect into all that. That's sort of table stakes. You can feed that to a general purpose LLM. We then want you to provide us sort of like documents, anything that's documented your leadership competencies, your HR handbook with all your policies and benefits. That's also kind of table stakes. The piece where I think a lot of our expertise becomes relevant is we have something called our Talent Practices Inventory where we're asking you questions about all the stuff that's not written down anywhere. The stuff that if you went to go prompt an LLM to ask why attrition is happening, you wouldn't even think to prompt it with because you don't know what's relevant to that question. We know it's going to probably be relevant to some question you might have in the future, so that's why we're asking you about it. But you don't know which of the hundreds of things that you could feed an LLM, um, matter here. So you're never going to prompt that agent with that detail. And then the second piece is just application. So we're giving our agent all this context from having worked with 2000 plus companies over 12 years of under what circumstances? If a company sees that its interviewing practices not actually predictive of success in a role, we know that another company that has the same interviewing practices as this company is seeing success in the predictive nature of those interviews. Why is that? Well, we've seen this pattern time and time again and so we have a good amount of like, you know, frameworks and um, you know, uh, interventions that we've tried. And under what circumstances have they worked? Our agent has that context as well of the work that it actually looks, what it actually looks like to do something. The stuff that you would have that people have always hired consultants for. Like, why do you hire a consultant even if you're really smart? It's because you've worked at five companies maybe and consultants have done something similar with hundreds or thousands and then like benchmarks. One of the things we're really interested in doing with Surface is asking companies about all those things in the Talent Practices Inventory that aren't part of any publicly available data set. So it's not just like representation numbers or policies you might publish on your website. And it's like how structured are your interviewing practices in each part of the business? That's not on Google anywhere. I can't find that out. Or what are your mental health benefits? You haven't published that specifically Online. But we've got a lot of customers that want to have a barometer on where they fall on those things. So the more context you can give an LLM, um, the better it will be at the purpose you're using it for. And this is why I'm a big believer there's this whole debate online right now of is the future going to be owned by, you know, OpenAI and Anthropic only and Google or whatever foundation model you use, is it all going to be these horizontal models? Because they're so, so smart. And I would stake my bet on purpose built software. I think if you are working in a law firm, you want software from a team that is going to sleep every night and waking up every morning thinking only about your problem and building an agent that is excellent at your problem. And I think the same is true for hr. And um, that's who we want to be for people teams.

Speaker B: So when, when you look at that sort of horizontal versus vertical play and I think this is important for people teams holistically because you know, we have customers with hundreds of thousands of employees and they have diverse people teams who are, to your point, cataloging, you know, across a uh, multi hundred thousand person organization, cataloging insights in one part of the globe versus another or one business unit versus another. Most of that context is lost, right? It goes poof because it's held in the head of one individual or it's discussed in a team meeting amongst your people, business partners. But I really like this concept of part of the job of setting up the AI for hr, of asking the right questions to organize context for reuse. And so I'm sensing that that's like a very, when you say vertical AI system, you're really thinking, and I think we would say the same like this is about the LLM, it's about the prompt, it's about the instructions and it's also that whole system setup for how you ensure that you engineer the right

Speaker C: context, the structure of your data and the context that's flowing into it from your organization. And perhaps another piece that's similar to kind of prompt, as you said, what are the skills that the agent is equipped with? Because anyone that's using AI enough, you go through this sort of cycle with AI of you start using it and it can do anything and you're really optimistic and suddenly you're like, I could be a marketer, I could be a sales leader, I can do anything with this. It can do anything. Then you use it enough and you're like actually no, there's a reason we have a marketing team and a sales team and an engineering team. And there's a difference between prototyping something and really being an expert at it. And so I actually find the people that have used AI the most are uh, right now maybe sort of the least optimistic that it can just out of the box do anything for them, that they can just hack together in a weekend, their new CRM or whatever it might be. And so I really believe in like these agents have to be given all that context and underneath it, and that's a huge part of it, but also like the specific skills for the task at hand. Because if you're an agent that's like generally good at data science and we see some of these models fable and stuff, really good at this stuff that ah, doesn't necessarily mean they're great at people analytics because it's a different kind of set of questions and way of thinking about data and visualizing data. And so what we find is in each of these kind of domains, people analytics, talent management, learning, we're having to also equip the agent with skills that are specific to the role for it to be best in class at that rather than just like 8 out of 10 at everything in the world. We want it to be a 10 out of 10 at the things the people team needs.

Speaker B: So how do you make um, all of this? We just spent a lot of time talking about sort of the underlying technology, the change, how this uh, is going to unfold in the role of the people team. A big part of it is understanding some of the technology being familiar and maybe not being afraid of it. Right. How do you make this not scary to somebody who may be new to, you know, considering themselves a, you know, an armchair technologist? Like this has never been part of my job before.

Speaker C: Yes. And it's never been easier to make it part of your job like that is. The exciting thing about this moment is I think if you wanted to understand how software products were built in the past, the barrier to entry was in my experience, very, very high. It was very hard to understand how these products are built, to understand um, coding and to learn how to code. And all these things um, I think were very challenging for non technical people. I think there are two things that make this moment the moment for the generalist for someone that's non technical. I think one is, um, things are changing so fast and that's actually a good thing because what that means is it actually doesn't matter if you were an expert at which model was best at what six weeks ago, that's like maybe no longer relevant. And everything is changing so fast that dive in today like wherever we are and you can actually get up to speed very, very quickly. The other amazing thing, maybe there's three. I said two, maybe there's three. There are so many people on the Internet at some of these, you know, um, at the foundation model, companies that are producing content all the time to teach you how to use their tools. So if you're motivated, it's extremely learnable without being technical. And then the third is you can get um, feedback immediately. So I remember like 10 years ago or something, maybe it was eight years ago. We were starting to build software at Paradigm and I was like, I guess I should learn how to code so I can understand and talk to our engineering team. And so I downloaded some app and the first thing I could do for a while was like little games that didn't produce anything that I cared about. It was so unmotivating to me, so demotivating. But now if you want to build something, um, and you want to go use Claude code or you want to use VZR or lovable or whatever, you can prototype something that looks like the thing you want to build immediately. The motivation is there to then keep learning and going deeper and tweaking because you're seeing what is coming at the end of the tunnel and how you're going to build towards it. Now of course you should still have engineers build any production software but I think to me as ah, someone that is non technical but is running a software company now, it has never been easier to learn and to sort of the gap between um, someone who's highly technical and someone who's not at all technical and at least understanding what it is you're trying to do with your product and how you're building it. I think that's shrinking for anyone that's sort of motivated to jump in and

Speaker B: learn, um, how, you know, in this process, um, you mentioned maybe folks don't want to run their own production software and they need engineers. Um, how have you experienced that in the wild? Because I've seen a lot of folks who kind of first glance, they say, great, I want to build this and I'm going, our people team is now our people technology team.

Speaker C: Yeah, I say I'll see you in a few months, good luck. Um, and I don't mean that in a dismissive way, but truly every company that we've seen, the companies that we saw that were Most out front on saying they were going to build all their people, team's own technology are the ones that are now on the other side of that. They've tried it, they have seen what works and what doesn't, and they now are looking to use existing vendors to solve their um, challenges. And I think part of what happens AI makes it almost impossibly alluring to not try because you can build a really nice prototype that works on a few use cases in a weekend and that's, it's never been possible before. Try it. The problem is, and the reason software has existed throughout, you know, the history of its, um, of like our lives is because it actually is like extremely inefficient for every single company that has a shared problem structure to be solving, to be addressing that problem structure internally. Um, it's like reinventing the wheel in every single company. And what you lose when you're building something internally is the benefit of a vendor who has seen your problem before you run into it, who has seen edge cases because they're doing this at scale. So you're not going to have to solve all your own bugs and all your own issues. You're not going to have to figure out all of the data security and trust. You're not going to be validating outputs of an agent yourself. This is all going to be happening at scale and you're going to be the beneficiary of it. Um, I think it's again, makes a lot of sense to try prototyping stuff. And I actually think the fact that teams are doing that should push all of us as vendors to be better and make sure that what we can deliver is better than anything they can build internally. But I think if you're really, really excited around building all your own software internally, two things I would say to think about is where are you on your technology team's priority list, um, and who's going to maintain this after it's built? Because I think building something is one thing, maintaining it in production and all of what that entails is a very different thing.

Speaker B: As somebody who maintains a lot of software, I can attest to that fact, um, and also think that there's a huge amount of value to what you said at the beginning, which is, hey, it is impossibly kind of uh, alluring to try and everybody should, in part because it feels like the trying aspect of it also teaches you enough to be dangerous in terms of expectation. Like, how do you see that with the customers that you've engaged with and the People teams that are sort of most out there. Are they like prototype typing nonstop? Um, just from a uh, learning perspective.

Speaker C: Yeah. I see people teams that are frequently doing like, sort of like hackathons and buildathons and I don't think their goals are always like we're going to build software in this that we then maintain forever. It's just to see what's possible and it sparks ideas, things that they then may come to us and ask us to build if it's really, really cool. And that's great for us. We love customers that are um, trying things that are so specific to their use case that we can learn from them and then maybe we can scale that to all of our customers. Um, I also think it is helping them understand their underlying data better sometimes to actually understand our people. Analytics team has told us for two years that this analysis is really hard to run. Let's go try and build a software that does that and then either we'll see if it is really hard or if there was some structural barrier to, in the way we collect or manage our data that we actually can just go solve now that we understand it. So I think it sort of breaks down the barriers where I think historically, um, on people teams, each person has been a holder of specific knowledge that other people don't have. And so I need um, the person on my learning team to go build our courses because I don't know how to do scorm files or I need our people analysts to go to this. I don't know how to do those things. And I actually think if we all just understand the barriers and the challenges and how to do this stuff better, I think we'll work faster and more effectively. And I don't think it means those vertical areas of expertise are irrelevant. I think it just means like we can free learning experts up to like not be like doing course authoring all day long and to be thinking more about like how do we build really great learning rather than like how do we tactically like move these tiles in this very old learning software.

Speaker B: Goes back to your earlier point of, uh, there's something that is deeply strategic around business partnership and outcomes that folks can actually focus on instead of the sort of tactical or mundane work that they're otherwise caught up in.

Speaker C: Yeah, um,

Speaker B: you, you know, when, when you experiment, you were talking about sort of learning and seeing what's, what's there, what's possible. Um, you know, it seems like there's also some aspect of that that is, you know, the prototyping the engaging with external content, you get to sort of pick your eyes up off of what might be internal and start looking outside. How have you seen, um, influen teams in this unique moment coming from other industries?

Speaker C: Man, that's a really good question. And I'm not sure I've seen a ton of that. Like, I don't know that I've seen people teams be as, um, Like, I think the focus has been very internal on their own industry, their own company, their own team. I actually haven't seen as much like cross pollination of really creative ideas across these teams. And I don't know if I'm just not seeing that. Are you seeing that?

Speaker B: Well, ah, I may not be seeing. I'm not seeing it enough. Um, and it's interesting because I juxtapose it to software teams. And software teams are always scanning the horizon for what's happening in the marketing infrastructure space or what's happening in industrial verticalized software. And how am I taking those lessons over? And it's interesting, as you say, like, hey, you can pick your eyes up and you can kind of prototype and play with these things. Because I imagine there's also something about an HR community where we can do a better job of sort of looking outside maybe and looking at counterparts in other business functions elsewhere and other industries and starting to try to incorporate lessons because the industry just seems like it's moving so fast.

Speaker C: Yeah, I think that's a really good point. I think we should do a better job of that. And I think it's especially important when things are moving fast, but especially hard, like to like, I gotta just like do my thing. I can't like, pay attention to this noise. But I think you're right. I don't think the people function has been as good at that historically. And it's probably something that we all, myself included, should be much better at.

Speaker B: All Right, so we're taking a note that we're gonna figure out how to go tackle that one together.

Speaker C: Okay, I'm in.

Speaker B: Joelle, that was amazing. Thank you so much for joining us today.

Speaker C: Thanks for having me.

Speaker B: If you enjoyed today's conversation, ah, please subscribe to the Future of Work podcast wherever you get your shows. Until next time.