Episode 268: The CHRO Framework for AI: Culture Determines AI Outcomes Not Spend (with Paul Rubenstein)
What if HR is still thinking too small about AI?
In this episode of the Digital HR Leaders podcast, David Green is joined by Paul, Chief Evangelist and Talent Strategist at Visier, to explore why traditional transformation approaches may no longer be fit for purpose, and what HR needs to do differently to keep pace.
Join them, as they tackle some of the biggest questions facing the function today:
Is the current framing of AI in HR too narrow?
Does HR need to operate more like finance to remain relevant?
What does it take to move from pilots to real, enterprise-wide impact?
And as AI becomes embedded across the business, does people analytics become less visible… or more critical than ever?
This episode is sponsored by Visier.
Visier 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 Visier Workforce AI to navigate the biggest workforce shifts of our time. Move from knowing to doing, faster.
See it in action at visier.com
Also, make sure to read and explore Visier’s latest research on strategic workforce planning in the AI era.
Read more from Paul Rubenstein:
This episode of the Digital HR Leaders podcast is brought to you by Visier.
[0:00:08] David Green: Who would have thought, even two years ago, that much of the conversations 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 mostly 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 Garr 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 of 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 Talent Strategist at Visier.
In this episode, Paul outlines a framework for CHROs on AI comprising of three arcs. We discuss why the third arc on culture is the most important. Paul is someone who's been thinking very deeply about this, and today I'm excited to discuss what it really means for HR to operate with the kind of rigour and business influence this era demands, and how AI amplifies the importance of people analytics 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, since you were last here.
[0:02:03] Paul Rubenstein: No!
[0:02:04] David Green: Yeah, really. September 2023. I had to check it, though, I must admit. But September '23. I think it was around the time that Visier just introduced Vee, actually. A lot's happened since then. I know you've moved roles in Visier as well. And you've been at Visier, I think, for nine years now. So, tell us about your new role, Paul, and maybe your background as well that you bring into Visier, because I know we're going to be going back into your background for our conversation, I think.
[0:02:31] Paul Rubenstein: Sure. So, you know, if we haven't met before, I'm Paul, I'm a recovering consultant. I spent a lot of time diving deep into the questions of what should HR look like, right? How do you create a fit-for-mission HR function? And 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 you unpack business strategy in a way that is granular and specific enough that you can write great talent strategy. And that led me to the third act of my career, because if you're going to measure talent strategy, you start to act like the CFO, right? Why is the CFO awesome? Unpacks business strategy into a financial strategy and creates a rhythm of numbers, and everybody understands exactly where they are on that plan. Third act of my career was 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.
If you think about the first time I 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 changed my career. And I've 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. There's a gap. You and I commiserate with so many analytics people like, "Oh my God, 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 of their work? Why is the people data held behind these iron walls of HR?" 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 of months ago, I've 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, this is not another labour arbitrage moment. This is a moment where you can change the arc of how a company executes. And I just see so many possibilities in it. I'm excited. I feel like 12 years old again, hopefully wiser this time.
[0:05:44] David Green: Probably a little wiser. You should be by now. It's actually, listening to what you were saying there, Paul, around how 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.
[0:06:06] Paul Rubenstein: Absolutely, and that's what it comes down to. 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. They were getting good at digitally describing what a strategy should look like or what the key indicators should look like. They were creating this sort of set of alerts and thresholds within all of the data that they were bringing together. And I saw some organisations starting to get there in a gap to plan sort of way. And at the same time, To quote Mark Berry from Inari, "Workforce planning is like the Rodney Dangerfield of HR. It doesn't get any respect". Like, for how many years have we been talking about workforce planning and companies have been struggling with it, and the technology has taken a while, right? So, a lot of people are still using spreadsheets.
But AI is a catalyst for people to say, "Oh, I've got to replan work, 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 a dimension, but skills. Which of these skills 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 organisational structure look like? And by the way, my workforce plan might not just be people, it might be people and agents". So, there's this whole new, "Wow!" wake up for everybody in HR to say, "Workforce planning is …" I was talking to one of our customers, a tech customer, and they were like, "Paul, our annual workforce plan, we're replanning it twice a quarter". I'm like, "Oh my God, that's amazing". He goes, "Yeah". I'm like, "Well, why do you call it an annual plan?" "I don't know", he's like, "Sense of humour?" But even the words we use haven't caught up.
[0:08:47] David Green: And obviously, as you said, Paul, and obviously we talk quite a lot and I know you're particularly thoughtful about where HR 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, or maybe it's a topic that you're not hearing that should be getting more attention than it's getting?
[0:09:08] Paul Rubenstein: 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 is a big thing I observe about HR and people analytics and HR tech, etc, within this, not quite sure what their either permission or obligation is to play in the redesign of work. And the redesign of work is compulsory for an organisation that wants to really outperform the competition, but also deliver a return on investment in all of the AI tech they're spending on. 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 who already fit, they get fitter. The people who are more inclined to drive their own personal productivity and have the psychological safety to self-disrupt their job, those are the people who are coming up with cool stuff.
But it is AI re-engineering existing work processes for the most part. And they're disconnected work processes, they're individual work processes. (A) Proving the ROI on it is hard, It's like trying to prove the ROI on email; (B) a lot of people will now expect that this is part of their tools. It's like, if you're a chef, you're not going to work in a kitchen with a junky oven, right? 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, what is required for somebody to selflessly train an agent, because it's a selfless act, right? You have to have the incentive systems that are there, you have to have a manager and culture that supports it; that changed. And I think the second thing is, it's showing where friction comes into play in AI. I hear all the time, the new complaint is, "Why is it so hard for me to connect my model to the data I need?" It's like, "How do I get the tools I need to work?" Or, "What do you mean you're limiting my tokens?" Have you heard, places are 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 that system has limited context, and it's reinforcing the silos of HR.
[0:12:45] David Green: Yeah, we don't need more silos, do we?
[0:12:48] Paul Rubenstein: It's magnified. I mean, everybody has their own system, the comp system, the ATS, the learning system, your policy system, your ticketing system, your HRS, your analytics system. They don't talk to each other. So, it's amplifying it, because the expectation is you type in or it's like, "How do I register for that course in the LMS?" Well, the actual policy for registering it might be in the policy section, not the LMS, and we didn't think of those things.
[0:13:21] David Green: This episode of the Digital HR Leaders podcast is sponsored by Visier. When top talent leaves and skills gaps appear, how do you find your way? Visier 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 Visier Workforce AI to navigate the biggest workforce shifts of our time. Move from knowing to doing faster. See it in action at visier.com/demo.
How do companies get from just gym membership or Little AI to Big AI? What do they need to do?
[0:14:33] Paul Rubenstein: Let's first dive into Big AI. So, if Little AI to me is this gym-membership phase and this prompt-box phase, where it disconnected technologies, Big AI means you are going to do the hard work, right? The first phase of Big AI to me is the first principled reengineering of work, how do I go back and deconstruct jobs, understand 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 doing it or it's part of our business model to have people doing it, and how do I re-engineer all that? And that, I can unlock cost leadership there. And when I start to do that, I can then rethink the workforce. I can liberate the capacity that you can't and redeploy capacity that you can't in the gym-membership phase because it's so distributed and it's the tiny parts of everyone's job. And so, I think that the key to that is going to be a new cycle for design planning and operating the workforce, right? 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. So, 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 an everyday decision? Because when I go in to make a small decision on a hire, 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. But 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 mean, I want to see this world like Minority Report, where Tom Cruise has got to go find one thing out and he can see everything is there at his fingertips.
AI makes it easier for us to interrogate strategy and understand how my small moment may relate to a larger picture. So, gym membership, prompt boxes, Little AI; re-engineering 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. And I think there are two cases here for how HR can both help the organisation transform the workforce and transform HR. And I think I look at it as, I was always taught, when you're coming up with big investments in corporations, try to find one bowling ball that will take out many pins. And 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.
So, connect that for everybody on the call who's not the head of HR. Walk into your Head of HR's office and recognise 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-Joneses 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. So, there's two hands to this. 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 centre of strategically executing on the business plan. Companies that do this right will run over companies that don't.
[0:20:09] David Green: I want to take a short break from this episode to introduce the Insight222 People Analytics Program, designed for senior leaders to connect, grow, and lead in the evolving world of people analytics. The programme 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 insight222.com/program and join our group of global leaders.
One of the challenges, I guess, with people analytics over the years or people science over the years is too much of it is rear-view mirror, looking at what's happened. Now, that's helpful. But what you just outlined there is what do we need to do forward to execute on the business strategy, etc, what's the right mix of people and agents, etc? And that sounds definitely a lot more like marketing.
[0:21:44] Paul Rubenstein: But why? Why was it -- but I'm going to defend all that rear-view 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 and you work in HR, you spend half your time fighting just to get the data right. 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 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 that the company needs, all the way back through the insight to the data origination, and then say, "That chain must follow". It's very inside-out, like the rest of HR, than outside-in. The CFO will say, "These are the measures that are important to understand for 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 this data".
RevOps is a great example where 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? 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 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. We've seen that at Visier with the success of Vee. It completely bends the curve of adoption, 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 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&L, not in HR efficiency dashboards or measures of the work that got done, and I think that's critical.
[0:24:31] David Green: And what would be your guidance to HR leaders or CHROs listening, people analytics leaders or professionals listening, how do you get your organisation, or CHRO maybe, to move from function metrics, and we all know that there are multiple function metrics in 100-page slide decks still around on that, to business outcomes?
[0:24:53] Paul Rubenstein: Yeah, so 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, crafts measures that help everybody understand how they might keep pace with the market in the plan. I think it requires new thinking on the CHRO on what do they want to understand about AI and change and 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. And I think the first one, David, is what I'll call 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 may have customer churn, you have reputational risk, all these different things if you are too early an adopter of certain agent technologies, because it is moving so fast and it is so new.
But let's look at the opposite. If you move too slow, you don't become a cost leader. People will outmanoeuvre you, and I'm not talking about a little bit of a cost leadership, right? 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 is astronomical. It doesn't make sense if we think of what it took to create Oracle and SAP and Workday, etc, right? It's mind boggling. I was in a room with a bunch of people the other day and I was like, "Hey, what was the last technology transformation you went through and how long did it take?" I was like, "Six months?", they laughed, "12 months?" 18 months, last time they put in Workday or change their ATS or something. But the cycles of available technology change. It's coming out weekly, monthly, we see these new models and what they could be.
So, this first is, how do I keep pace with the efficient frontier of the human-agent mix? The second one is a financial one, so 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 labour savings or am I able to redeploy capacity effectively? There's two paths, right, because I have introduced agents on that curve. And again, this is where if you only automate existing business processes and you don't liberate capacity in new ways and re-engineer work, you're going to fall behind. And so, it's interesting to see some people have already taken the money, or they've AI-washed existing challenges, Block, Meta. I always think the best time to disrupt your labour cost is when your things are good, and when you're not doing it in dire straits. Some people have a three-year plan at which they have to return something. It's like being in a private equity fund or something.
So, this cost thing and the CHRO 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 digitise it in an agent, that's a selfless act. So, we have to have people who are willing to dance with the robots. What we don't want is people who are sabotaging the robots. And history has a lot of lessons on this. When we look at Toyota versus GM and Ford in the early days of robotics, the word 'saboteur', throwing a shoe into the machine. Some people sabotaged the robots, while other people created the conditions that showed people how to selflessly give up work because they had psychological safety. Their incentive programmes were right, they trained 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 the 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 the culture that unlocks the competitive advantage that AI offers us.
[0:30:35] David Green: I'd love to hear your thoughts, 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 the first question. The second one is, who is doing what you've just outlined well at the moment? And I I'm guessing it's probably a fairly small number of organisations. 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 evolve?
[0:31:05] Paul Rubenstein: It's huge. And so, this is where I see a lot of progress. And it's impolite to name names, but I'm actually just going to name a sector, because it's weird, David. Pharma. I love what our pharma customers are doing and some applied pharma and some 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, "We'll probably be more successful, we don't have to go acquire the talent or compete for it in this area". This is what I mean about people data science and people sciences being an equal contributor, not always following the business; you can drive the business.
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. So, "Hey, Heads of HR, I think the two biggest candidates for HR employee of the year 2026 are going to be the MCP". That's something a Head of HR is like, "What is that?" 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 coaching agents are incredibly popular. Jeez, that coaching agent is operating off of a methodology that is one-size-fits-all.
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 score's bottomed out, they haven't taken vacation in a couple of days, and the person that they report to has just quit", whatever that context might be, that's all available now. And so, we have a couple of customers who are putting in an orchestration layer. So, think of it as the modern version of the HR service delivery model. But we need to see beyond that, and get to a connected intelligence, and that's where the MCP server allows, and your vision for how analytics powers the other systems. 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 engineer has been trying to create some sort of an data warehouse, data lake, call it 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, govern it in a way so it operates securely from 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 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 just 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". And so, now you're flipping it where HR isn't organised 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 other HR service delivery model. Again, I've described in deep, I've got pictures and diagrams of course, because I'm a 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, if not thinking about it, is beginning to lay the foundations for it.
[0:36:30] David Green: People analytics, there's some talk out there that more AI, people analytics starts to disappear. Personally, I think it's the opposite and our research at Insight222 backs this up. Actually, AI is an opportunity to amplify people analytics. Love to hear your view and what you're seeing with your customers as well, in terms of does people analytics potentially have a bigger role, a different role perhaps, but a bigger role?
[0:36:55] Paul Rubenstein: 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 datasets without being an expert in analytics, the people analytics function extends its reach. By the way, the purpose of analytics functions, investing in them, what I was taught a long time ago, the purpose of people analytics is not to answer questions, it's to get everyone to ask better questions. And a guy named Mark Sullivan taught me that a long time ago. And so, the more people you can engage in question-asking, and the metaphor has changed, right? Visier was founded to start with the question, not the data. AI allows us to amplify that philosophy. So, when we see customers take on Vee, 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, 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 -- I used to hear this, "There's a lot of room in the world for analytic centres of gravity". I think a lot of people are used to the financial, the P&L is the centre of gravity, and let's build all the way we look at the world around that, right? Some people take a look at Salesforce data and say, "The customer is the centre of that gravity", people bringing all that data and the person is the unit. The centre of the unit of analysis is just as important. And when you think about all three, it's the Venn diagram. And so, the space in which the people analytics leaders operate to understand that their central unit of analysis is as important as the data it intersects with, that that is distant, all of those systems demand people data, well understood people data. You have that expertise, you are the decoder ring. You have a responsibility, you have 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.
[0:40:08] David Green: Brilliant. And you've talked a little bit about how Vee is helping organisations to amplify the use of data. Where else is Visier placing its bets right now? And how is the platform designed to support CHROs navigating everything that's going on at the moment?
[0:40:26] Paul Rubenstein: Yeah. We are all in 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, rethink 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. 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.
[0:41:24] David Green: 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 it 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 talked about balancing pace and risk. How can HR move fast with AI without losing trust, fairness and governance?
[0:41:45] Paul Rubenstein: I think there's a couple of mind-shifts that have happened, right? I think first of all, the centre of gravity for HR, I'll say it again, it's got to be business outcomes over function metrics. Classic function metrics is how many lawsuits 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 recognise there's an inherent trust in writing in natural language and getting something back in natural language, an almost deceiving trust. And there's a responsibility incumbent on enterprise leaders to make sure not only that the AI has quality data, but it has full context data, because that's where I think people get in trouble.
The history of governing data, the default is, if it's too sensitive or somebody might misuse it, and we are so informed by edge cases, restrict it, don't let it out in the wild, keep it 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 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 how 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.
At the speed that we are going to need to disseminate data 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 casual gathering of data, we understand our relationship with it and the responsible use. You're recording this conversation. This conversation will be digitised.
[0:44:28] David Green: I hope we're recording, otherwise it's going to be lost!
[0:44:32] Paul Rubenstein: Okay. But how many of us have note-takers now? I'm futzing around with home automation. I saw an ad for something that was, you put it in your house and it's a camera in your own house and it learns 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 so that we understand the cost? All this can happen casually through our digital footprint that we leave, which is incredibly rich and fine-grained. 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 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 that doesn't mean always laying off. That 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 must take a leadership role in that.
[0:45:58] David Green: Very good. Paul, we could carry on for a lot more.
[0:46:02] Paul Rubenstein: I have a question for you.
[0:46:03] David Green: Go on, fire away.
[0:46:05] Paul Rubenstein: What is the number one breed of dog that every human should own?
[0:46:09] David Green: That's going to be a standard poodle, isn't it, really?
[0:46:12] Paul Rubenstein: We are going to get so much hate mail on this one! "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
[0:46:26] David Green: A standard 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 when this episode comes out, actually in a few days, it's People Analytics World. I also believe it's Visier Outsmart next week as well.
[0:46:41] Paul Rubenstein: We will be -- yes. Greetings to all the customers. I'll see you in Palm Springs, a joyous place on earth.
[0:46:49] David Green: It really is the perfect episode for this particular week. A huge people analytics 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 fascinating conversation. Where can people find you? I'm going to give you a help here: on LinkedIn. And how can they learn more about all the great work that you and Visier are doing?
[0:47:15] Paul Rubenstein: Find me on LinkedIn and please go to www.visier.com and you'll see amazing work and amazing publications from myself, Dr Andrea Derler, 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. And, 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 analytics community.
[0:47:57] David Green: And amen to that. Paul, thank you very much for being a guest on the show again.
[0:48:03] Paul Rubenstein: Take care.
[0:48:05] David Green: Thanks again to Paul for joining me today and for the Visier team for sponsoring this series and this episode. For me, it was really an inspirational conversation on 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 bi-weekly newsletter at myHRfuture.com to get the latest thinking on HR, people analytics, and everything shaping our field.
Right, that's it for today. Thanks for listening, and we'll be back next week with another episode of the Digital HR Leaders podcast. Until then, take care and stay well.