The CHRO Framework for AI: Culture Determines AI Outcomes Not Spend

This episode of the Digital HR Leaders podcast is brought to you by Visia. Who would have thought even two years ago that much of the conversation about the role of HR were going to be about integrating AI agents into the workforce. And yet here we are. But what I have observed over the past year or so is that much of the dialogue around AI and HR still feels anchored in rolling out and giving employees access to tools with the emphasis most of the on efficiency, automation and experimentation. And while none of that is wrong, I can't help but wonder whether it's too small a frame for what is actually happening. Industry analyst Stacia Garh refers to this as "Little AI" and my guest today likens it to gym membership. Because this shouldn't be about doing the same things with new tools, big AI, as Stacia also refers to, is about a fundamental shift in how work gets done, restructuring tasks and workflows, rethinking how decisions are made and how organisations create value. And the pace that shift is starting to expose the limits of many of the transformation approaches HR has relied on for years. Which is why today I'm absolutely delighted to welcome back to the show Paul Rubenstein, Chief Evangelist and Tamil Strategist at Visia. In this episode, Paul outlines a framework for CHROs on AI comprising of three arcs. We discuss what a third arc on culture is the most important. Paul with someone who has been thinking very deeply about this. And today I'm excited to discuss what it really means for HR to operate with a kind of rigor and business influence these era demands. And how AI amplifies the importance of P-Planetix and shortens the time from insight to value. So let's get started with an introduction from Paul. Paul, welcome back to the digital HR leaders podcast. Been nearly three years, I think, for two or a half years. Yeah, really three years. September, but 2023, I had to check it though. I must admit, but September, 23, I think it was around the time that Visia just introduced V, actually. A lot's happened since then. I know you've moved roles in Visia as well. And you've been at Visia, I think, for nine years now. So tell us about your new role, Paul, and maybe your background as well that you bring into Visia because I know we're going to be going back into your background for our conversation, I think. Sure. So, you know, if we haven't met before on Paul, I'm a recovering consultant. I spent a lot of time diving deep into the questions of what should HR look like. How do you create a fit for mission HR function? That was probably the first third of my career. The second third though became, well, what is fit for what mission? And I dove deep into the space of how do you unpack business strategy in a way that is granular and specific enough that you can write great talent strategy, right? And that led me to the third act of my career because if you're going to measure talent strategy, you know, you start to act like the CFL, right? Why is the CFO awesome, unpax business strategy into a financial strategy and creates a rhythm of numbers and everybody understands exactly where they are on that plan, right? Third act of my career is like, well, how do you help everyone understand where they are on that plan? And that led me to the amazing world of people analytics. And if you think about the first time actually met you was in New York at one of the early days of the people analytics meetups when we used to get like, I don't know, like 25 people or something like that. And I became obsessed with that and so much so that I joined Visier and you know, changed my career. And I'd been chief HR officer here. I've been chief customer officer here. And now we're in the age of AI. And when I look at it, God, we've spent the last 12 years, I think in a critical mass of thinking around analytics and focused on insights. And the thing that has always frustrated me was how you turn those insights into impact. Right? There's a gap. You talk, you and I commiserate with so many analytics people like, oh my God, I, these patterns, these amazing insights. Why aren't people consuming them and acting on them? Why don't they respect the science? Why isn't it in the flow? Their work. Why is the people data held behind these iron walls of HR, right? AI changes that. AI makes it so much easier for everybody to understand those insights and distribute them in ways that we can finally break through and turn those insights into impact, which was the point of all that work in the first place. So a couple months ago, I'm like, been set loose on the market to help everybody understand what's really changed and how can HR unlock that magic? Because man, what a world we live in. This is not just another technology, right? This is not another labor arbitrage moment. This is a moment where you can change the arc of how a company executes. I just see so many possibilities in it. I'm excited. I feel like 12 years old again. Hopefully, why is it this time? Probably. Probably a little wiser. You should be, should be by the help. So you think, listen to what you were saying there, Paul, around how do you get insights to decisions to outcomes and impact with your CFO lens on. Insights are great, but they're not actually adding any value unless someone acts on those insights and drives an outcome and you measure that outcome as well. Absolutely. I mean, and that sort of comes down to, I'm like, I think a lot of the work that interested me the most during the past eight years of analytics has been when people were creating a rhythm, right? They were getting good at digitally describing what a strategy should look like or what the key indicators were, should look like. They were creating this sort of set of alerts and thresholds within all of the data that they were bringing together. They could, you know, they were some organizations starting to get there in a gap to plan sort of wife. And at the same time, to quote Mark Barry from an Ari, you know, workforce planning is like the Rodney Dangerfield of HR. It doesn't get any respect, right? And like for how many years that we've been talking about workforce planning and companies who've been struggling with it and the technology's taken a while, right? So a lot of people are still using spreadsheets. But you know, AI is a catalyst for people to say, ooh, I've, I've got to replan work, right? And it's not just FTE and headcount. This has got to be incredibly strategic. I've got to answer hard questions about, okay, yeah, sure location, as it dimension, but skills, right? Which of these skills are going to, do I have? What do I need? And what is the rate at which AI is going to impact those skills? And then, oh, if I'm going to leverage this, my workforce is going to have to have some different composition. And so what do my spans and layers and organizational structure look like? And by the way, my workforce plan might not just be people, it might be people in agents. So there's this whole new, wow, wake up for everybody in HR to say workforce planning. And I was talking to one of our customers, tech customer, and they were like, you know, hall, where our annual workforce plan, we're replanting it twice a quarter. I'm like, oh my God, that's amazing. He goes, yeah, like, well, what do you call it an annual plan? I don't know, he's like sense of humor. But the, but even the words we use haven't caught up. And obviously, you two said, well, obviously we talk, we talk quite a lot. And I know you're particularly thoughtful about where HR needs to, needs to head to. And you spend a lot of time with customers as well. What's the topic that you keep hearing that you think deserves more attention than it's getting? Well, maybe it's the topic that you're not hearing that should be getting more attention than it's getting. You know, I think there are a couple of things there that aren't getting the attention that it needs. And ultimately, I think that culture is going to determine AI outcomes and not spend. And there's a big thing I observe about HR and people analytics and HR tech, et cetera, within this, not quite sure what their either permission or obligation is to play in the redesign of work. Right? So, and the redesign of work is compulsory for an organization that wants to really outperform the competition, but also deliver a return on investment. investment in all of the AI tech they're spending on, right? Like, it's funny, I think a lot of HR functions don't know where they are. I think a lot of people are still in the gym membership phase of AI. There's a lot of optionality in it, right? We put it out there and what I found is the fit. Those people already fit, they get fitter. The people who are more inclined to drive their own personal productivity and have the psychological safety that, you know, to self-destruct their job, right? Those are the people who are coming up with cool stuff, right? But it is AI re-engineering existing work processes for the most part. And they're disconnected work processes. They're individual work processes, right? A, proving the ROI on it is hard. It's like trying to prove the ROI on email. A, a lot of people will now expect that this is part of their tools. It's like, you know, if you're a chef, you're not going to work in a kitchen with a junky oven, right? You know, it's like the increasing expectation of what are the tools I have to bring to work. It's also illuminating a lot of the culture challenges that I think we're going to face. Number one, you know, what is required for somebody to selflessly train an agent because it's a selfless act, right? You know, you have to have the cyclist, you have to have the incentive systems that are there. You have to have a manager and culture that supports it, right? That changed. And I think the second thing is it's showing where friction comes into play in AI. I hear all the time, you know, like the new complaint is, why is it so hard for me to connect my model to the data I need? Hey, it's like, how do I get the tools I need to work? Or what do you mean you're limiting my tokens, right? Have you heard like the place they're giving token allowances because they're like, oh, no, don't use it too much. So I think as we understand this from a cultural perspective, part of how you address this will be how talent sorts and selects different employers. And so I think there's a lot to be learned in this gym membership phase. The second phase is really interesting because it's also technology driven rather than business model driven. And this is what I call the proliferation of prompt boxes. Each of these individual prompts, prompt boxes is tied to a system and it's re, and that system has limited context and it's reinforcing the silos of HR. And we don't need both silos doing. It's magnified. I mean, you think about how many like everybody has their own system, the comp system, the ATS, the learning system, the, your policy system, your ticketing system, your HRIS, your analytic system, they don't talk to each other. So it's amplifying it because the expectation is you type in, it's like, how do I register for that course in the LMS or the actual policy for registering it might be in the, on policy section, not the LMS and we didn't think of those. This episode of the Digital HR Leaders podcast is sponsored by Visia. When top talent leaves and skills gaps appear, how do you find your way? Visia Workforce AI is your GPS for workforce decisions. Spot attrition risk, uncover pay gaps, measure leadership impact, and track skills shortages before they slow growth, then act, align talent to real business outcomes. Across industries, HR and business leaders are using Visia Workforce AI to navigate the biggest workforce shifts of our time. Move from knowing to doing faster. See it in action at visia.com/demo. How do companies get from just gym membership or little AI to to big AI? What do they need to do? Let's first dive into big AI. If little AI to me is this gym membership phase and this prop box phase, where it disconnected technologies, big AI means you are going to do the hard work. The first phase of big AI to me is the first principled reengineering of work. How do I go back and deconstruct jobs? At what rate parts of those jobs are going to be available to agents, which parts of those jobs need to remain human, either because we don't want the technology to do part of our business model to have people doing it. How do I reengineer all that? When I start to do that and I can unlock cost leadership there. When I start to do that, I can then rethink the workforce. I can liberate the capacity that you can't and deploy capacity that you can't in the gym membership phase because it's so distributed. It's like the tiny parts of everyone's job. I think that the key to that is going to be a new cycle for design, planning and operating the workforce. I think that's huge. The fourth phase, and that's the one that excites me the most, isn't about cost leadership. It's about how we use AI as a management system and become a strategy execution leader. How can we now do the magical of taking the context and awareness of many things and the strategic headspace that the C suite is in and collapse the distance from them and in everyday decision? When I go in to make a small decision on a higher, what kind of person I should hire, where I should hire them, should I automate that job, etc. That everyday decision is most likely running on inertia. When I can interrupt that inertia by having a conversation with somebody who's not in the room all the time, either an HR business partner or a very senior leader, I can begin to extend my reach and collapse the distance between that everyday decision and that central strategy by what the manager encounters. I want to see this world like a minority report where Tom Cruise has that, he's got to go find one thing out and he can see everything is there at his fingertips. An AI makes it easier for us to interrogate strategy and understand how my small moment may relate to a larger picture. So, Jim membership, prompt boxes, little AI, reengineering work and management system, big AI. In those two big AI moments, I think it becomes really important that we are all operating off of one AI-driven AI-enabled workforce intelligence layer. I think there are two cases here for how HR can both help the organization transform the workforce and transform HR. I think I always always thought when you're coming up with big investments in corporations, try to find one bowling ball that will take out many pins. I think workforce intelligence as an investment handles that because, and I think this is especially important for anyone in the analytics community, they are workforce intelligence. AI takes everything we've done for the last 20 plus years of people's data science and makes it truly intelligent at scale to both power AI and be driven by AI. And so connect that for everybody on the call who's like, you know, not the head of HR. If you walk into your head of HR's office and recognize this, with their left hand, the head of HR is being asked by the CEO, how do we transform the workforce? How do we turn around and understand the competitive advantage that we can get from AI or just the keep up with the Jones's deployment of AI? How does that transform the workforce? And with the right hand, transform HR to both take advantage of AI and be fit for mission in transforming the workforce, right? So there's two hands to this, right? And now you start to see the elements when you understand that AI makes that whole thing go faster and more consumable. It picks up the signals. It helps us do planning and trade-offs in multiple dimensions. Humans are good at one or two. The machine helps us do it in five or six or seven, right? That's the beauty of it. So now we've created a rhythm for HR that doesn't just put HR in a, how do I manage cost and put butts in seats motion? It actually puts it at the center of strategically executing. on the business plan. Great, you know, companies that do this right will run over companies that don't. I want to take a short break from this episode to introduce the Insight 2-2-2 P1Listics Program. Designed for senior leaders to connect, grow, and lead in the evolving world of P1Listics. The program brings together top HR professionals with extensive experience from global companies, offering a unique platform to expand your influence, gain invaluable industry insight, and tackle real-world business challenges. As a member, you'll gain access to over 40 in-person and virtual events a year. Advisory sessions with seasoned practitioners, as well as insights, ideas, and learning to stay up to date with best practices and new thinking. Every connection made brings new possibilities to elevate your impact and drive meaningful change. To learn more, head over to Insight2-2-2.com, board/program, and join our group of global leaders. One of the challenges, I guess, with P1Listics over the years, or people's sites over the years, is too much of it is rearview mirror looking at what's happened. Now, that's helpful. But what you just outlined there is, what do we need to do? What do we need to do forward to execute on the business strategy, etc. It was the right mix of people and agents, etc. And that sounds a little bit more, definitely a lot more, more like marketing. But why? Why was it. Yeah, but I'm going to defend all that rearview mirror stuff for a second, okay? Because I often get frustrated with it. But I have a lot of empathy for it. Like, if you are given a data set, right? And you work in HR. You know, you spend half your time fighting just to get the data right. You then you spend another part of your time trying to get the HR business partners to pay attention to you. And then the rest of it, you know, you do some really interesting good science work. But what don't you have time for? You don't have time to get out of HR. Walk in the shoes of a manager work backwards from the impact you that the company needs all the way back through the insight to the data origination and then say that chain must follow, right? It's very inside out like the rest of HR, then outside in, right? The CFO will say, these are the measures that are important to understand from my business, not be constrained to the measures they have and make sense of them. They will go back and say, change the way we record record this data. Revops is a great example where, you know, if people want to understand what drives their business, they're constantly tinkering with the measures and with the core record systems. And how many times are they adding new fields and changing what they have in Salesforce, etc. To really understand what will help everyone make a better decision or give a high sensitivity indication if we are progressing on the path we need to, right? And so it's really important to get that outside in by the way. I think that part of it also is we've been hindered by charts and graphs and the love language of a lot of analytics is charts and graphs. A lot of people don't want to read charts and graphs and, you know, and they don't want to know how to manipulate it and they don't want to keep asking you to look at it a different way. AI changes that like we've seen that at Visier with the success of the right. It completely bends the curve of adoption right. It, you know, it takes away the fear. It reaches the C suite, but you also have to be willing to let it go. If you love something, let it go. But I think the the last thing I'll mention is the head of HR. They are obsessed with function metrics. We have to be obsessed with business outcomes. We have to work backwards from the notion that HR's value is ultimately measured on the P and L, not in HR efficiency dash boards or measures of the work that got done. And I think that's critical. What would be your guidance to HR leaders or CHROs listening P-analysics leaders or professionals listening? How do you get your organization or your CHRO maybe to move from function metrics and we all know that there are multiple function metrics and 100-plage side decks still around on that two business outcomes? Yeah, so like the CFO spends a lot of time on what are they going to tell the market, right? And what is the outcome they want to see in the market? And then, you know, crafts measures that help everybody understand how they might keep pace with the market and the plant. I think it requires new thinking on the CHRO on what are the what is the dot what do they want to understand about AI and change in competitive position and how are they keeping pace? So I like to think of it in three like my CHRO AI framework is three arcs that every CHRO should be keeping pace with, right? And I think the first one David is what all called the efficient frontier of the human robot mix or the human agent mix. If you automate too fast and bring on agents too fast, you introduce too much risk into the system and you know, you you may have customer churn, you reputational risk, all these different things if you are too early an adopter of certain agent technologies, right? Because it is moving so fast and it is so new. Let's look at the opposite. If you move too slow, you don't become a cost leader. People will outmaneuver you and I'm not talking about a little bit of a cost leadership, right? You know, I heard a story the other day of a five person team that is rebuilding an ERP from scratch. And the pace at which they are building, you know, is astronomical, right? It doesn't make sense if we think what it took to create Oracle and SAP and Workday, etc. It's mind boggling. I was in a room with a bunch of people the other day. I was like, hey, what was the last technology transformation you went through and how long did it take, right? I was like six months, they laughed. Twelve months, 18 months, right? Last time they put in Workday or changed their ATS or something. But the cycles of available technology change, right? You know, is, you know, it's coming out weekly monthly. We see these models and what they could be. So this first is how do what it how do I keep pace with the efficient frontier of the human agent mix? The second one is a financial one. So how do I am I keeping pace with a return on investment for what either I've invested or what the market expects my financials to look like as a result of AI being available in the market, even if I didn't deploy it. Or I spent money on it and made a commitment to it. Did I did it happen and that has to do with am I able to unlock labor savings or am I able to redeploy capacity effectively? There's two paths, right? Because I have introduced agents on that curve, right? And again, this is where if you only automate existing business processes and you don't liberate capacity in new ways and reengineer work, you're going to fall behind. And so like it's interesting to see some people have already taken the money or they've a I washed existing challenges, you know, block meta. You know, I always think you know, the best time to disrupt your labor cost is when your things are good, right? Yeah. So and we know not doing it at Dyerstreet. Some people have a three year plan at which they have to return on something that's like being in a private equity fund or something. So like this cost thing and so the C H arrow understanding first, the rate at which the jobs will change and the agents will appear. The second is the financial return, but the third one is I think the most important one. I'm going to call that the humanity index culture determines AI outcomes not spend the small moments of taking things that were not written down that now become a business process, something you know, how you communicate, how things work, institutional knowledge and digitize it in an agent that's a selfless act. So we have to have people who are willing to dance with the robots, right? What we don't want is people who are sabotaging the robots, right? And history has a lot of lessons on this when we look at Toyota, right versus GM and Ford in the early days of robotics. The word saboteur throwing a shoe into the machine, right? Like some people sabotage the robots while other people created the conditions where that showed people how to selflessly give up work because they had psychological safety there in their incentive programs were right. They train the managers in the right way. So I think the first part as we consider a humanity index is are you creating the culture incentives and rewards for people to go through an AI transformation. So efficient frontier of the human robot mix the financial return and the humanity index I do believe HR has to design plan and operate at the speed of AI and rethink The core of its operating rhythm and double down. It's been pretty good at operating It's got to be amazing at designing and planning workforce, right? I do think it has to do those things But I also think it has to have a really strong vision of what is it going to take to create a culture that unlocks the competitive advantage that AI offers us Love to hear your thoughts about because it's a topic that drives a lot of interest What's your thoughts about the HR operating model for the future? So that's that's the first question. The second one is who is doing what you've just outlined well at the moment And I'm guessing it's probably a fairly small number of organizations if it's some Visier customers you can talk about fantastic And then third thing we'll talk a little bit about people analytics, but let's start with HR operator. It's quite a big question, but how do you see the HR operating model? It's huge, you know, then and so this is where I see so I see a lot of progress, right? And you know, I it's implied to name names But I'm actually just gonna I'm gonna name a sector because it's weird David pharma I Love what our pharma customers are doing and some applied pharma and some, you know, technology companies that are around that space And then a couple of our tech customers on the west coast and even some east coast financial they're all really leaning into this workforce intelligence layer to power intelligence service delivery by putting a lot of emphasis in the space of skill and task one of the most advanced things I've seen is let's infer tasks from skills skills to jobs looking at both the external data and the internal data and helping use that to make decisions on the rate at which we should acquire agent technology and then the reverse I see people doing product planning based on the skills They have available to them like hey, we have a critical mass of this type of science You could probably go, you know, we're probably be more successful I don't have to go acquire the talent or compete for it in this area like like this is what I mean about You know our people data science and people sciences being of an equal contributor not always following the business You can drive the business right and that leads you into this notion of I'm already watching Driven by pain right now not as much as opportunity people start to embrace The orchestrator and the mcp technology right so hey heads of HR I think you know the two biggest candidates for HR employee of the year a 2026 are going to be the mcp like that's something ahead of HR was like what is that right? You know Model context protocol servers what this allows you to do is to take that workforce intelligence layer and broadcast it To all the other agents making each other agent smarter so imagine if you have a learning agent or a coaching agents are incredibly popular Jeez that coaching agent is operating off of a methodology that you know is one size fits all right, but now if I can take that Coach and I can give it the context that an HR business partner would give a human coach to say hey This person's had five managers in three years their engagement scores, you know bottomed out right They haven't taken vacation in a couple of days and the person that they report to just clear Whatever that context might be that's all available now right and so we have a couple of customers who are putting in an orchestration there so think of it as the modern version of the HR service delivery model But what we need to see beyond that right and get to a connected intelligence and that's where the mcp server Allows and and your vision for how analytics powers the other systems right and by the way, it's the reverse of what you've been doing for a long time Every HR leader in HR every data um engineer has been trying to create some sort of a data warehouse data Lay call out what you want where we take lots of systems and bring it all together This is the reverse. This is where I take everything that I've gotten together and create standards metrics and govern it Govern it in a way not to restrict it with government in a way so it operates securely from system system to system And inform each of those agents so they become smarter and by the way this breaks down the silos of HR And it allows us to overcome and give integrated smart advice to lots of people All of that has been built and the economics of that have been built around low cost service for those people who engage HR When you flip this around and you start to digitally describe the desired outcome of the workforce in your intelligence layer you can now alert people in the surface of their work To say hey you have an opportunity We can help you in HR or there are resources available to you to think about right and so now you're flipping it where HR isn't organized around who around Dealing with triaging what is incoming it's broadcasting at a scale that the HR business partners couldn't What are the opportunities and the intelligence service delivery helps them navigate it with all the wisdom that you wish they had That they don't always have time to have top of mind make sense and that's a whole another HR service delivery model Again, I've described it in deep. I got pictures and diagrams of course because we're recovering consultant But I think it's a really exciting time and by the way all the consulting firms are out there building this And every single head of HR is if not talking about it You know, if not thinking about it is beginning to lay the foundation sports P-Pyno-Tix there's there's some talk out there that more AI P-Pyno-Tix starts to disappear Personally, I think it's the opposite and our researcher inside 22 back sees up Actually, AI is an opportunity to amplify people and listings. I love to hear your view And what you're seeing with with your customers as well in terms of does P-Pyno-Tix have a potentially have a bigger role a different role perhaps but a bigger role I 100% agree with you David It amplifies the ability for people analytics To have impact because it makes it easy to interrogate and understand Data sets without being an expert in analytics the people analytics functions extends its reach By the way the purpose of analytics functions investing in them where I was taught a long time ago isn't to the purpose of people analytics is not to answer questions To get everyone to ask better questions And I got named Mark Sullivan taught me that a long time ago and And so the more people you can engage in question asking and the metaphor has changed right this year was founded To start with the question right not the data AI allows us to amplify that philosophy so when we see customers take on V They are pushing this out to managers to employees to senior executives Which requires a psychological shift For most of HR in that the shepherding of the data isn't where you get your value It's responding to somebody that sees the data but again The people who understand how the data is originated how the metrics were calculated what you should do with it are good at the advanced pattern recognition right my god the more People analytics leaders can spend time on really things that are hard to see with the naked eye and testing hypotheses and doing a b testing and really advancing us into A level of people data of people science that Approaches what marketing is doing the more we can do that The function becomes that much more critical and the reliance on You know, I used to hear this uh, there's a lot of room in the world For analytic centers of gravity I think a lot of people are Used to the finance and financial the pnl is the center of gravity. Let's build all the way we look at the world around that right some people Take a look at sales force data and say the customer is the center of that gravity right people Bringing all that data and and the the person is the unit right the center of the unit of analysis is just as important And when you think about all three it's the bend diagram right and so the Space in which the people analytics leaders Operate to understand their central unit of analysis is as important as the data it intersects with it that is distant All of those systems demand people data well understood people data you have that expertise you are the decoder rank your responsibility of an obligation To set the data in motion Not just as a data set but by meaningful metrics and context that all those other AIs can operate with responsibly. >> You've talked a little bit about how V's helping organization to amplify the use of data. Where else is Vizier placing its bets right now and how is the platform designed to support CHROs navigating everything that's going on? >> Yeah, yeah. We are all in unintelligent on this workforce intelligence, AI-powered, AI-driven, AI-capable workforce intelligence. Because I do think it both powers, I think it powers the two biggest bets that HR should make. Number one, we think the service delivery model into this concept of an intelligent service delivery model. But number two, matching the left-hand obligation and responsibility of the CHRO, which is to design, plan, and operate the workforce at the speed of AI. Right? So everybody who's designing single-live data set, everybody who's planning one single-live data set, everyone who's operating one source of truth. And it's not the old system of record, workday, or the financial source of truth. It's a much richer source of truth around metrics, performance, and the combination of data. And we've got to the last question, Paul, it's the question of the series. And you've talked about this a little bit, but maybe allows you to maybe provide a bit of a summary, I guess. How can HR move fast? Or maybe that's the right flow? We feel like balancing pace and risk. How can HR move fast with AI without losing trust, fairness, and governance? >> I think there's a couple of mindships that have happened, right? I think, first of all, the center of gravity for HR, I'll say it again, it's got to be business outcomes over function metrics. A classic function metrics is how many wall suits did we avoid? Interesting metric, right? Not how many risks did we take business outcomes? We want people to make good decisions on AI. People are still responsible for those decisions, but we want to serve them up. Let's recognize, there's an inherent trust in writing in natural language and getting something back in natural language, and almost deceiving trust. And there's a responsibility, incumbent on enterprise leaders to make sure that not only that the AI has quality data, but it has full context data, right? Because that's where I think people get in trouble. The history of governing data, the default is if it's too sensitive or we're not sure, or somebody might misuse it, and we are so informed by edge cases, right? Restrict it. Don't let it out in the while. Keep it in the within HR. When in reality, we're entering a world where we want the full content, we want people to make rich decisions that understand the totality of humanity at work. And if those decisions and the interrogation of those of the concepts around those decisions, and if your coach is digital, and if your coach is AI, and it doesn't know what HR knows, you're not going to get good decisions. So, you know, how a HR thinks about risk and their place in the universe as default to zero risk or to how do we take smart risks? And that comes back to culture. Culture determines AI outcomes not spend. And so what kind of a culture do we help people understand? They are still responsible for outcomes of the agents. They are responsible for the use of data. They are responsible for these. There is at the speed that we are going to need to disseminate data, to make it, to make the AI useful and rich, we're going to have to have a new comfort layer at the speed at which we generate data and give it up. We're going to have to have a culture where observational, we're casual gathering of data, we understand our relationship with it and the responsible use. You're recording this conversation. This conversation will be digitized. I hope we recall this. We do. Let's get a bit of a look. Okay. But like how many of us have note takers now? Right. How many, you know, I'm I'm fudzing around with home automation. I saw an ad for something that was if you put it in your house and it's a camera in your own house and it'll learn your patterns of how you move around in the house and it will train your AI for the lights and music for you. I'm like here we are. How many of us hate timekeeping at work or recording, you know, so that we understand the cost. All this can happen casually through our digital footprint that we leave, which is incredibly rich and fine grain. The relationship with that data, how we use it can only be governed so much by rules. It has to be governed by principles and good human choices. How we set up the culture and incentive systems to do that is a critical function for HR and that's where I come back, you know, down to like people are going to have to selflessly train the agents. People are going to have to learn to manage with them, not fear them. People are going to have to learn to disrupt their own jobs if we're going to unleash the capacity that is available. And if we're and that doesn't mean always laying off, there also means re-imagining jobs that are even more humane than they were before. That's the societal benefit of any sort of technology. HR has a must take a leadership role in that. Very good. Po, we could carry on for for a long while. I have a question for you. Go on. By the way, what is the number one breed of dog that every human should own? That's going to be a stand of pudo, isn't it really? Because we're going to get so much hate mail on this one, right? You know, no, no, no, a poodle. Come on, too fancy. For everyone in the audience, if you want to be better at people analytics, go get a new dog, a standard poodle. A stand of poodle. That's the big ones, everyone. Not the small ones. So, yeah, Paul, it's always a pleasure to speak to you. I know I know when this episode comes out, actually in a few days, it's P-Pyland6 World. I also believe it's busy or else smart next week as well. So it's we will be yes. Reading store the customers, I'll see you in Palm Springs. A joyous place on earth where we're going to have yeah, it really is the perfect episode for this particular week. You know, a huge P-Pyland6 event taking place in London and one taking place in Palm Springs as well. So those of you attending both enjoy and learn. Paul, also as ever, a really fast thing conversation, where can people find you? I'm going to give you a help, help here on LinkedIn. And how can they learn more about what the great work that you and Vizier are doing? Find me on LinkedIn and please go to www.Vizier.com and you'll see amazing work and amazing publications for myself, Dr. Andrea Duraler, Ian Cook, and all kinds of amazing thought leaders and our customers too. Thank you to our customers, thank you to our people analytics community. It's been an amazing journey that we've taken. Man, I am so excited as we unlock the next generation of what to do with people science and move from all the rich insights to real impact and unlock the potential of the HR function and unlock the potential of the people and works community. Amen to that. Paul, thank you very much for being a guest on the show again. Take care. Thanks again to Paul for joining me today and for the Vizier team for sponsoring this series and this episode. For me, it was really an inspirational conversation how AI can amplify the value that HR functions and people analytics can deliver. For those of you listening, I'm curious, what stood out for you the most from today's episode? I'd love to hear your thoughts, so please head over to LinkedIn, find my post about this conversation and let me know what resonated with you. I always read the comments and love learning about the different perspectives in the field and if this conversation got you thinking, please subscribe to the podcast and share it with a colleague or friend who might benefit from hearing it too. It really does help us bring more of these conversations to HR professionals across the world. For those who would like to stay in the loop with what we're working on at Insight222, follow us on LinkedIn or head to Insight222.com. You can also sign up for our biweekly newsletter at myhrufuture.com to get the latest thinking on HR peep, analytics and everything shaping our field. Right, that's it for today. Thanks a listening and we'll be back next week with another episode of the Digital HR Leaders podcast. Until then, take care and stay well. [BLANK_AUDIO]