Bill Pelster and Siobhan Savage on AI’s Role in the Future of Work

Siobhan Savage: Hello, hello, how are you?

Bill Pelster: Hey, everyone. Happy Thursday!

Siobhan Savage: Is it Thursday? It is.

Bill Pelster: It is the first week of September, you can see the holidays on the horizon, so… It's coming quick.

Siobhan Savage: It is crazy, isn't it? It's September. My kids went back to school today, Bill. Thank God.

Bill Pelster: Crazy. Crazy, but also exciting, right?, to see all the kids off.

Siobhan Savage: I know my daughter started in middle school today, so we've never… I never had a locker before in our family, so, that's been a treat, is to get a locker.

Bill Pelster: Well, hopefully you got some good photos of day one, and all the excitement.

Siobhan Savage: We did, we did. It was a bit of a rush to go between two schools. Welcome, everyone! Have any folks have had to do the rush to get everyone off to school today? It's one of those days, so thanks for joining us. We'll just give it a minute or so.

Bill Pelster: Yeah, but there's a real excitement in the air when you start seeing all of our schools up here in the Seattle area started going back last week, and so all the school buses are out, and kids.

Siobhan Savage: I know.

Bill Pelster: Else, it's… it's an energy, you can feel it.

Siobhan Savage: I agree. I agree. I think it's, it's finding out as well. I find out, one of the folks we know, who's one of the very known HR folks, her kid goes to the same school as my kid, so that was… that was exciting.

Bill Pelster: Very cool.

Siobhan Savage: We're just gonna give it one more minute, just to allow folks to just arrive in. Everyone, get yourself settled, get a cup of tea, cup of coffee. Whatever you need to get the… get it all… get the energy going. I'm just looking in the chat to see where everyone is dialing in from. Milan… we've got someone here from Milan, that's cool.

Bill Pelster: One of my favorite cities.

Siobhan Savage: We've got San Antonio, San Jose… Now I am gonna get us kicked off, and we're gonna start, and hopefully we've got this recorded as well, so we can make sure that folks are gonna get the replay, after the fact. So, first, a huge welcome to you, Bill. We're so grateful to have you here. For those that don't know, which would be crazy, Bill is the co-founder of the Josh Bersin Company, and is one of the leading experts and voices in this space. We've been doing an incredible amount of researching this new topic. It's an ever-evolving, fast-moving space, so we thought we would get Bill here today to really give you an overview of what they're seeing in the market, what they're hearing from customers, what The research is starting to tell us.

We're hopefully going to give you a super dynamic session, which will involve,, deep research, some really great ways that you can think about solving this problem, and then show you a practical way of how to bring this to life with our tool… two tools being brought together. So, Bill, welcome!

Bill Pelster: Siobhan, thank you very much, really appreciate it, and you are correct, this is one of the hottest topics out there. Whether you look at our research or anybody else's research, every single organization is trying to figure out what is AI actually impacting our organization? What should we do, and how should we think about doing this? In fact, I think if folks know the Josh Bersin Company, we're probably having 50 to 100 conversations a week with companies. And the number one question out there from managers and executives is, how is AI going to change the jobs?

Siobhan Savage: And when we take a look at it, there's an instinctive answer to go right away to job architecture, but that's interesting and partially correct.

Bill Pelster: But the better answer is AI is not coming for your job, they're coming for activities and tasks. Agree. And so understanding the underlying of what is actually being done inside of a job is the most important thing, and that's setting up the framework here. And Siobhan, this is where,, we found each other between the Bursin Company and Reejig. Is your ability to look at the task level against all these jobs and skills and provide a perspective back to the organization very quickly is just incredibly powerful. And for those of you that know a little bit of my background, I came out of,, 25 years professional services, primarily Deloitte.

And if we were to do this type of,, consulting job, this would be 6 months, people, maybe 10 folks in a conference room, thousands of sticky notes, everybody on Excel spreadsheets and maybe databases, trying to map and figure this out. And what took months to get to a baseline analysis and the old legacy model You're doing it in days and weeks. And then, in real time, once you've done that, and I was absolutely blown away when I saw it for the first time, the ability then to model the entire organization and do that what-if analysis on the spot,, ultimately the company's got to decide what to do with that analysis, but you have the analysis right there on the spot.

And so, in that context, could you actually bring up the next slide, please? Because I'm going to help frame what I think we're hearing out there, and what's going on. So, one of the things that the Bersin team is really, really good at, and Josh was a pioneer on this, was,, taking these really incredibly complex topics. And being able to boil them down into a simple graphic that is easy to understand, and you can have a conversation with a variety of people in the business, not just with other strategic workforce planning or HR professionals. And early in the year, we inadvertently created, I think, the single most powerful slide that we've seen all year long, and people just love it, once I do a little bit of the voiceover.

And so, what we tried to map out was what was really going on from an AI perspective. And if you notice, there are 4 stages here, and on the left side, you've got stages 1 and 2, and on the right side, you've got stages 3 and 4. On stages 1 and 2, what is fundamentally happening here is humans are using AI to be slightly more productive. It could be 10%, it could be 15%, it could be 50% more productive, but the underlying job stays the same. And all you're doing is using AI to,, do something better, but you're still doing the legacy thing the exact same way. By the way, and I'll throw this out, because I think this is really important in Stages 1 and 2, and I come from an HR background, so I feel comfortable saying this, HR folks are really good at trying to optimize things that may not need to exist anymore.

And in Level 1 and 2 with AI, we have an opportunity to actually look on the right-hand side, which is… we can ask that question, does this actually have to still exist in the way that we've always done it? Does AI allow us to redesign the work? So on the left-hand side, you're making the existing job slightly more productive. On the right-hand side, you're reimagining what the work could actually be, and that's where the power is. And by the way, on the left-hand side, humans are using agents to be more productive. On the right-hand side, humans are educating agents who have infinite capacity.

And so that's where you start getting this mental shift as to what's really going on here. And so. what do we hear in the marketplace? Because there is a lot of noise between this is what teams are doing on the ground, yet CEOs, CFOs, CTOs are really, really frustrated that things are not moving fast enough. Here is our proposition, and Siobhan, I would love,, your reflection on this. I think the fundamental thing is the CEO and the executive team is on the right-hand side, and they want the organization to reimagine work, and the rest of us are on the left-hand side saying, hey, I can write an email 10% faster.

Any thoughts on that, Siobhan?

Siobhan Savage: I mean, so many, one, completely agree, every single CEO wants to become an AI-powered workforce. The lens that they're looking at it from is they've got cost pressures, they've got a board shouting at them to,, bring the cost down, focus on realigning some capital for innovation programs, or for them to build out more innovation and technology into their own business, and that money has to come from somewhere. So there is,, a lot of shift around the market, and,, it also doesn't help that a lot of the AI companies are hyping,, there's billions of dollars being spent on marketing right now, so a lot of the money and a lot of the time and a lot of the energy that's coming to the CEO is very much so you are behind.

So what's happening is the CEO thinks that they're behind, and is driving down to their teams. that why have we not done this? And that created, Bill, this … everybody went out and threw AI out there, and you had a load of BYO AI, you have a whole pile of shadow AI, you have all of your employees being allowed to go out and build their own versions of processes in companies right now, and ultimately, there's this disconnect between reality of this actually,, changing work and unlocking the capacity and the Rs that we would expect the CEO wants to see. Versus the reality, and I think a lot of folks are treating it a chatbot rather than an agent that could be, right?

And I think there is this complete waste of potential. right now, and to your point, you've got the CIO, the CEO over here, saying, this is where we need to be, and then you've got the CFO somewhere in the middle going, we're spending all this money on AI. where is the return? And then you've got the reality of the business hasn't really changed much. So, that's what we are seeing for sure.

Bill Pelster: Yeah, and then what comes out of all these conversations, this is where we're going to get into a little bit more of the Reejig conversation, or why it's so important, is on the left-hand side, people automatically gravitate to what is comfortable and known, which is job architecture. So. I have to tell you, I have not heard a job architecture conversation,, over the last 10 years, maybe once or twice a year it came up, right? In the last 12 months, everybody is running around looking at job architecture, because they're trying to figure out what are the skills associated with jobs, how do we actually think about this?

And again, that's necessary work, but if you think about it in an industrial age model, and we're in a post-industrial age model. The whole purpose of the job architecture was really to help total rewards with the levels, the banding, structure in the organization, pay equity, all those really important things, but it doesn't answer the question of what are the activities that are actually being done. Because an agent is not coming with a skill, they're coming with a task or a series of tasks that have been strung together. And so, this is where,, Reejig really comes into the equation, is, it's great to have the job architecture, and we can help accelerate your process around that, but the fundamental question, and by the way, as imperfect as all of this is, it gets you to a really crisp answer. on,, what is happening from the enterprise level, from an activity and task perspective.

And you can still go in and tweak, and you can add and subtract, and say, we're slightly different. By the way, 25 years of consulting, you're going to have an infinite number of those conversations. But it should not distract from the ability to get that 80-90% view of the organization. in a matter of hours, days, to be able to model this. And so, as you go from left to right around job architecture, then it … and these are words that we're still playing with from a research perspective, because we're trying to… this is such a new approach. that we're all learning together, and luckily, with Regent, there's a host of clients that are on this journey, and we're working very closely with them to understand,, what's working and how best to address,, the business and all this analysis.

But we're going from,,, a job architecture where we're,, organizing the jobs and trying to,, just clean up our organization, to that work intelligence of,, what are we actually doing as an organization? And then when we understand the work we're doing, suddenly we can slip on the other side of that dotted line, and now we're in stage 3 and 4 activities. And that is really around understanding the work, and maybe we redesign the work, and maybe we actually just fundamentally transform what the work actually is. And where we're seeing this really come into play, and if this is an HR audience, I'll just give a couple of HR examples. , if you use AI-first learning, or you are really heavily invested in AI in your talent acquisition process.

Your learning people and recruiting people are not doing the same jobs they used to do before. They have completely reinvented the way that they are doing their jobs, and we can go into much more detail on that when I give you the demo. But the opportunity here is to really reimagine the work One other caution, and I'm going to put my change hat on, Siobhan, and please, I would love your comments on this, but in every audience I talk to around the world, and it literally is global on this topic. when something… this is from a change perspective, when something brand new comes in to the playing field and to our toolset, we immediately try to put it in our legacy mindset model.

And we try to force it into,, okay, maybe this is the way we've always done onboarding, and so now I can do it 10% quicker, as opposed to. maybe I don't need to do onboarding anymore. Maybe the agent can do it for me, right? Maybe there are tools out there that allow the humans in this classical model of, let's bring people in and gather documents and hand out the benefits booklet and get you logged into your IT solution or whatever it is. Maybe that entire legacy process can go away and be completely reimagined with the agents that are out there. This is the power of the conversation that we're having today, and I know you are in the thick of these conversations every day.

Siobhan Savage: Yeah, one of the things that I say, and I probably sound a little bit of a wrecking ball when I say this, but,, you cannot build this new world on a legacy… system, right? So,, you… you can't.,, our old world was designed for,, the olden days,?, with jobs and people in jobs who owned a job, and they stayed for long periods of time within a job. this new world is a world where you have humans and agents, and,, the one thing that I would say, and I probably sound a little bit anti-job architecture when I say this,, that had its purpose in terms of creating some structure in a chaotic environment, but that job architecture If you think about it, Bill, every time you bring in an agent, You take away tasks. or even just parts of a task.

But also, when you bring in the agent, you also introduce a new task that you've never done before. So, paying for a one-time stamp of your data. to give you a job architecture, or,, even just to do a one-time stamp of what is the work that's happening in your company. You see, by the time we finish that project, that's already out of date.

Bill Pelster: Yep. And I think one of the things you've been learning,, working alongside our customers is that the first step, exactly the way you've described this, is that the maturity cycle is that they first want to know what work is actually happening within their organization.

Siobhan Savage: And then as they add and remove tasks,, they start to see what's coming in, and the supply and demand shifts, and then what is being allocated more to your employees versus your agents. And then you get into that next level of 3 and 4, which is where you actually have to start re-engineering work itself. And that's where this ongoing dynamic data becomes,, the superpower for decision making, and a lot of our customers,, it's the first step is starting with that data, but then… As work changes. keeping that up to date as well. And one of the things I would also say as well is there is some parts of the job architecture that won't go away., it's,, we're not moving to a task-based only workforce straight away.

That's a long time away. We're still paying people at grades,, people are still in jobs, so you have to find a path to marrying the two things together, where you're one foot in the past, where you've got that compliance side of your data, with also making sure you've got the data to really start reinventing your workforce. And that's the stuff that,, we've been talking deeply about with customers, is they're going on the journey, and everyone's learning together.

Bill Pelster: No, absolutely. And I know we've got a lot more to actually show and demonstrate here, and so really, when we talk about,, work design at scale, there are a lot of folks that play in this space here, right? But really understanding, understanding the task analysis that's associated with this. is critically important. So again, this is us just trying to map out for everybody against the stages that we've seen out there, ways to think about this. And again, one of the reasons we partnered with, with Reejig is when we started looking at this. probably the most comprehensive set and the deepest thinking on all the different tasks and activities out there, so there's a universal lexicon that can be applied from an industry vertical as you do the analysis.

And so, just really wanted to put this out there and happy to have deeper conversations on any of this. But,, the key here, Siobhan, is,, this trusted source, though. And, I'm gonna… I'm gonna give you just a really quick stat that I found last week, before you can jump into this, and that is… I don't know if people are familiar with visual capitalists, but they do a really great job of taking all these different topics and creating beautiful visual diagrams, and they went in and analyzed all the current LLMs out there, so thank the ChatGPTs, thank the Anthropics, and so forth. And they said, what… what did they learn on?

And 40% of the knowledge of the LLMs across the most popular ones out there starts with Reddit. And so, do you really want Reddit to be your source? And then the next 26% is a scraping of Wikipedia, so 66% of the underlying knowledge comes from Reddit and Wikipedia, which any college student would tell you that you could never, ever, ever use as a source. And then the rest is YouTube and TripAdvisor and Home… actually, Home Depot, 4.6% for some reason. But it's just fascinating that there is this.

Siobhan Savage: Thanks.

Bill Pelster: of information. But trust is really important, and there's a combination of what you bring to the table, and then I think what the Bursin brand… but let me turn it over to you around Reejig and the source of truth.

Siobhan Savage: It's really interesting that you say that as well, because there's so much information, right, about everything in the world, but we have no idea what anyone's doing at work. So,, this… it's crazy, right? I know the job titles of my people. I have a rough idea of what people are doing, but if you ask me. what people are doing at work, and to be able to list that out, we have no idea. And that was really where we got started 3 years ago. I mean, we started really early in the skills space, and what we started to find was that the more that we moved towards,, actual, what I would be describing as more workforce optimization, which is my expertise, in order to understand that you need to deeply understand the work.

And then a couple of months later, you start to see the large language model moving in the chat GPT moment, and what we really started to realize in Reejig,, right back at that early days, was our bet was that every CEO will want to become an AI-powered workforce. But the problem that will happen is exactly what we're seeing right now. It's bloody chaos. No one knows what they're doing, no one knows where to go, what to automate, they have no idea what the impacts to work will be. So what we really focused on was creating… I mean, we describe this as,, the how the world is working. So we have the biggest, most comprehensive set of data that we've spent over $40 million building over the last few years., this is,, a crazy large amount of data.

And what we've got is every single major industry has its own unique ontology, and that's really important, because when you're starting from data. Imagine that we're going into,, some of our customers, a Micron or a WPP., these folks don't have this data, and they don't have,, a year to go and track all of this data and interview everyone, so what we do is we show up and we give the customer, here is what we know about you based on the industry that you operate in. And we give them pretty much every,, connected to every rule, every process, every task, every subtask. And where it gets really cool, Bill, is,, there's this last mile of the data that,, it's never going to be perfect, you said.

We are promising customers,, 80% accuracy from the outside in, which is enough to get you started. And then when you harmonize that to the customer's data, we're getting right up between 90% and 97% approval rating of this This is then harmonized live to the customer's data. What that essentially does is it gives you the source of truth. for how to start and where to think. And one of the things that's going to be really important, we're in this once… , once in a generation really changed to work, this is one of the most important times that we all as leaders will have in our career. And as we go about this, we want to make sure that we're being really bold and helping reinvent our companies with AI, but we want to make sure that we're not leaving our people behind.

So it becomes really important to have this understanding of actually what is the work and how we're doing it. Now, one of the things that's been really cool about our partnership is,, we bring this at an enterprise-wide. to customers. But Bill, what you ended up doing was saying,, why can't we put this into the hands of everybody at an individual level to really start bringing them on the journey? And you guys have been helping the community rise for as long as… how many years have you guys been in business, doing,, your practice, right? So,, when you came to us and said,, this is something that would really help the community, start them on their journey,, we were super excited to partner up with you. to make sure that we could bring our data to your customers and to make sure that we could essentially democratize access, where customers could start Part of this for everyone on this call is going to be getting yourself comfortable with building this new muscle. where you're shifting your own mindset, and,, feel really excited about,, where we're going to take folks and really start to bring them in. to giving them this source of truth, where you can start to really take folks on their transformation.

So, I would love to hand over the reins to you to really start giving folks a little bit of a,, explanation, show and tell. And while Bill's getting this ready, I'll give you guys a little bit of,, context underneath the hood of what's happened. So, we have this single source of truth of what we call the work ontology. which we have then automated and connected live into the Galileo tool. Bill, before you jump into detail, can you give us,, a… for those that might not have come across Galileo, can you give us,, a quick… what is it, what does it do, what's the purpose?

Bill Pelster: Yeah, no, and by the way,, our little,, Bersin company here, we've been on the exact same journey that every organization is going through, so let me just give you 2 minutes of context of,, what we have been on and how we got here. And so, I think many of, if you follow Josh Bersin,, industry-leading analyst in HR,, well over 30 years, published thousands of articles and research papers, and We were sitting on this mountain of data and research reports and articles and videos and podcasts. You can imagine how much information was there. And one of the challenges we always had, no matter how intently, we tried to do knowledge management and make it available through tagging and searching.

It was really hard because… to unlock the library, because there was just such a volume of information there. So, 2 years ago, when ChatGPT came out. we said, hmm, this is really intriguing. I wonder if this could actually unlock our entire library for our members. And so we started playing with ChatGPT, and I have to tell you, for every 10 questions we asked it once we loaded our library and parts of our library into it. Eight of the answers that came back were absolutely amazing, and we were just floored by what it could do, and then we would get these two wonky answers. And it's, that's the Reddit answer, and I cannot put my name against the Reddit or the Wikipedia answer.

And by the way, we never ever talked about that, right? And so we were sitting here scratching our head a little bit, trying to figure out what to do, and we ended up finding this small little company out of Sweden that allowed us to implement an idea that we had, and that is, what if we could contain the search to a trusted library. So content that had the Bursin stamp of approval, and that gave full traceability to where it got the answer from, and you could actually take this forward, and with confidence, know that it's authoritative. You may not agree with the answer, but you're gonna see the source of the answer, you're gonna see the research piece, the article, the video, the conversation, where that came up.

And so, in the context of doing that. We launched something called Galileo, and Galileo is really for the entire HR profession, but we've also found it's for managers. Because not every manager has a full-time HR business partner sitting next to them, and they still have to do HRE stuff, whether it's job descriptions, or analysis of the work, or,, do a PIP for somebody, you pick it, the ability to do that. And so, we created Galileo as a trusted source, not a place that goes into Reddit or Wikipedia for answers. And then along the way, when we found a very limited number of organizations Reejig, where we looked at the data, we talked with the customers, this is real, right?

And it's, well, would you want to be a trusted content partner and share some of your content here so people can understand how to do this analysis, because it's so important? And so, in August, we merged your HR vertical into Galileo, so all the tasks, activities, all the stuff we've been talking about from an HR perspective was loaded into Galileo in August, and we did that as part of our Venus release. And, it has been absolutely fantastic, because two very powerful things have happened. So, Siobhan, one, it made your information exponentially better, because now it's in the context of all this other research that is out there, so it's not just the straight analysis, but it's in the context of that.

And it made all of our research smarter, because now we knew exactly what activities and tasks all these HR people were doing. And so, I've got several of them that I went ahead and ran earlier. I didn't want to sit here and run the prompt live, because…

Siobhan Savage: It's that awkward where you can't spell it correctly.

Bill Pelster: Yeah.

Siobhan Savage: That happens to me all the time on a demo.

Bill Pelster: Yeah, number one, you don't want to watch Bill type, and number two. If you actually watch it, it does take about 3 to 5 minutes, because we use the large reasoning models, and what's fascinating is to get to this very simple question or prompt, analyze how the role of the HR business partner will potentially change with the rollout of AI across an organization. detail the work tasks and activities and link to AI automation opportunities. So that's at the broadest level, right? It went through 179 documents in the Bersin Library, which included the Reejig documents. It synthesized those 179 in less than 3 minutes, and it came back, and one, it started using our research to go ahead and frame what the role, from a traditional research perspective, what is the HR business partner?

Then it starts pulling in the Reejig data here that says what is the AI automation potential by HR business partner task category, and so the Reejig data says, hey, one way to think about HR business partners is they have some production activities they do, they've got some advisory work they do, they do some compliance and risk, and client and stakeholder management. By the way, you may not fundamentally agree with these, but it's an organizing principle that starts off the process. And so, then it goes down and says, hey, listen. From the production, what we mean is, hey, they partner with business leaders to translate objectives and HR strategies.

By the way, using the Reejig data, 75% automation capability. Lead workforce planning and talent acquisition, 70%, resolve complex employee relation issues, develop HR programs and policies. It defines what the advisory tasks, compliant tasks, on and on and on. And I'm going to pause here for a second, because In a matter of 3 to 5 minutes, you just had a view into a major part of the HR organization, the HR business partner, and a way to mentally organize the model and see the potential for automation, so that's,, step one in this process. Before I go any further, because it gave us a lot more information here.

Siobhan, any thoughts or comments?

Siobhan Savage: The only one thing I would say here is when you're looking at this, you see the 71% AI automation potential, right? What that is essentially saying, so if you imagine that each rule has a task, and within a task can be 5, 6, 7 subtasks. And parts of… not always is the full task being… removed with AI. It's actually at the subtask where you see most of the automation. So if you're partnering with business leaders to translate objectives,, there is a lot that you can do to amplify yourself in that moment with AI. So, writing the strategies, creating the documentation, so it's not taking out the human part, it's actually it goes after the parts at the subtask level., you would typically do multiple different steps When it comes to those different levels.

So, when you're looking at this, keep in mind all of the underneath-the-hood data that comes up with this translation, because it's not,, sometimes people look and go, so you're saying 70% of my job is gone? It's actually,, at the… at the subtask level, and this is the opportunity for folks that access this data. When you're really looking at it, Bill, what I really coach folks around is,. where are the areas that you could really amplify yourself by using AI?, versus the looking at it and going, oh my god,, X percent of my job is going. It's, where can I unlock,, high-value tasks and subtasks using AI?

And this really gives you that starting conversation. Where you're now flexing that new muscle that you're starting to build. Before you take over the whole org and start helping them, you need to know yourself, how to think about this data, right?

Bill Pelster: Yeah, and, Siobhan, you're bringing up a very interesting point, and I'm going to show another prompt here in a second, but think about… , you as a user, you now have an expert sitting next to you in the form of Galileo with all this data where you can start asking a very high-level question to start framing it, and you can go deeper and deeper and deeper and deeper and deeper, and I'll show you here in a second how you continue to drill down. All the way to the point where at the very end, you could say, hey, listen, at the subtask level for this area of an HR business partner production task, let's say, build me a 60-day implementation plan, and how should I manage stakeholders in a complex environment?

And which would be the milestones, and so it's translating all this really good information into something that you can actually take action on. But let me go down a little bit further, because one of the really cool things that Reejig brings to the table in all of this is understanding at the task and subtask level, and if I'm misrepresenting this, Siobhan, please tell me,, what are the AI solutions out there, right? It's great to say that it's open to automation, but let's keep,, pulling on that thread. Which ones are bespoke and which ones are pre-built? So how do you think about the automation piece?

And then I'll give you my view on how we prompt it here.

Siobhan Savage: Yeah, I mean, I think… I think there is the one explaining how the decision is made and the recommendation, but then how to take the action. So, what I would say is that,. we don't want you to go off and start having to buy millions of dollars worth of AI tools, right? So when we're looking at this, typically in a customer's environment, we already know that they use Copilot, or whatever they're using, right? So we know the technology stack that exists. What Reejig is essentially doing is telling you, for each one of these tasks, or each subtask. What is the thing that I could take right now out of the box, that's already pre-deployed, let's say in a co-pilot studio, or let's say it's something that I can configure?

So, stitching one or two tools together, or what's the third level of build? And that's where you really want to design and build your own. And this is where, when we look at this, we go… and in the HR, I mean, we're really blessed in the sense that there's a lot around the HR space that has already been pre-built. So, anyone who's,, on this call, there's a lot that you can do with existing technology that's there that maybe you just haven't thought about using. But then, because you deal with really sensitive data, quite a lot of tools need to be custom, because it's hosting people data, so that could be a scenario where you build your own internal, tooling.

So that's the one way to think about it. And then, really,, it's about Which are the tasks that unlock the most bang for buck? You cannot automate everything. So, what Bill's really showing you is the prioritization of, if I take away this task, what is the dollar value connection of unlocked back to the business, whether that's capacity or whether that's just it causes the most pain or costs the most money? And that's where we really say,, stop spraying and praying AI just for,, little use cases., this is information that you would… how much would you charge Bill for this type of consulting advice back in the olden days, right?,, when you think about it, right?

Bill Pelster: Yep, they would be very… they would be $300,000 to $500,000 for just a small part of the organization. It could be multi-million for,, a much larger analysis, and now you're doing it in real time, and you keep prompting against the underlying data set, which is amazing. And just to pick up on the thread there, Siobhan, what you're talking about there. Let's start going a little bit deeper into the specific AI automation opportunities, right? So, strategic advisory enhancement. And by the way, this is, again, we're showing you the full,, the source of the data, so you could actually click into the data and actually see the source data set if you wanted to,, play with that yourself.

But, again. The system is having a conversation with you, where you and continue to do some analysis in a really interesting way that would have taken days, hours, and months before to make that happen, right? Yeah. Then… Go ahead.

Siobhan Savage: No, no, one of the things I was gonna say was that I just imagined myself being back in my old job, and before I started Reejig, I was obviously in HR too, right? And I remember there's an opportunity for… if you want to be right at the front of the table right now, and being valuable, typically the CEO or the CIO or the chief AI officer is asking for use cases. Right now, for them to test. going in here and analyzing the HR team and coming up with your use cases to go as a team back to the business to say that this is the area that if we were to go after these tasks with this technology that we know we already use, would unlocks X amount of millions of dollars and X amount of thousands of hours, you could do this literally on this bill.

That's,, what's crazy, right?, you could actually go back and give that, and then that really starts you on your journey for how do you be customer zero internally and reinvent yourself as a team, which is what everyone's being expected to do.

Bill Pelster: Absolutely, and if you're taking a look at,, what are the key transformations… and by the way, it's giving you a high level, this is,, step one in the process, and it's giving you the source, and it's blending the fact that,, we talk about AI elevating roles, we've published stuff on… with Reejig and on the new HR business partner, we've got stuff on what it means to be a strategic advisor, what does it mean to be a pace setter in a super worker era, so it's synthesizing 179 documents, and come back with a saying, hey, listen, by the way, at a very high level, and let's go ahead and build this out in more detail, here's how you should maybe think about implementation, right?

Maybe start with high-impact areas, focus on tasks with 70 plus percent plus automation potential,, maybe invest in some bespoke solutions, and it's giving you a way to approach that. And you can continue having a conversation of saying, let's go deeper with the high impact areas, articulate them, let's create a plan and make that happen. I'm going to show you that here in a second. But I'll show you one other key thing. So, in this threaded conversation, I said, okay, I'm just curious, what skills are essential for HR business partners today? So we asked the job architecture conversation. And it, again, it rolled… pulled in all the Reejig data, all the analysis that we do on research, and you may disagree with this, but man, this is a better starting point than 99% of HR organizations to describe what is the role of an HR business partner, and they said, listen, think about it as general skills, human interpersonal skills, and technology skills.

And here's a graphic that you can export,, to help frame that. And so, the combination of a trusted AI Galileo, with trusted content that is powering the engine for it to make the decision, so you've got the data, we've got the research, it comes together better than peanut butter and chocolate to really help People model through this process in a…

Siobhan Savage: said.

Bill Pelster: in a really real-time manner. Let me show you just another view here.

Siobhan Savage: Before you jump to that, Bill, there's a question here from Cynthia I just want to make sure we answer before we jump in. So, thanks, Cynthia, for the question. So, Cynthia's asking, how do we translate from subtask AI automation potential? to the actual role level to prioritize where to start. So, Cynthia, that's a really good question. I'm gonna take a crack at it, if it's okay, Bill. So… by our model,, right now you're getting a beautifully presented, answer in the front end of Galileo. What's happening in the background is you've got your job architecture, traditional, how you describe jobs, and we layer that in with pretty much all of the task data.

And then under… typically, you can see anywhere between 15 to 25, 30 core tasks. But remember, underneath the sub… the task is all of these subtasks, and then actions. So, if you think about, if you think about,, from a recruitment perspective, and you are researching candidates, that is the high-level task, but then all of the things that you do to do that, so you click on LinkedIn, you run a Boolean search, you then click from here, you then reach out, and you create a shortlist. So there's all these,, sub… we call them subtasks underneath. And then there is all the action, so each time you click and go from one place to another, that's another micro-step in the process.

So we go down to that level. Now, what we're doing is, in that recruitment, research, and researching candidates, that example, you're not taking out 100% of that task. As much as the technology in the market is great, you still require several human-in-the-loop moments. So what we would be saying is,, maybe 60% of those subtasks could be taken away, and what you're essentially doing is re-engineering, then, that task. And then what we would be looking at, Cynthia, is essentially, out of all of the tasks in this recruiter role. what is the actual total percentage of AI impact? And it could be you keep all of the main tasks the same, but they've completely changed, because there's an agent, and then you've also added in new tasks that you haven't had before.

So it's typically looked at it from the rule level. We've got two,, levels of looking. We look at rules, and we look at process. And each one of those has the tasks, the subtasks, and the action, which then triangulates into that AI impact, which is then the data that Bill is now showing you at that advisory. This is … Bill, I would describe this as more an advisor. Essentially, it's taking all of the data, pulling it all together, triangulated it in a way that makes sense for everyone to understand. And then nudging you to take that action of,, what's possible. That's why I the way our data is being used in this moment.

So hopefully, Cynthia, that answers your question. Happy to,, talk after the session as well.

Bill Pelster: Yeah, can't add any more to what you just said there, Siobhan. And again,, what I really encourage folks here, In the course of a couple of hours. just interacting with both Galileo and the Reejig data, you're able to get a depth of understanding that it would have taken a consulting project months to come back with a 40-page PowerPoint to make that happen. That's really powerful. And so I want to show you another one here, because this is where it gets interesting from a different dimension, and it shows another angle of why the Reejig data is powerful in these scenarios. So, the beginning one is similar to what I just did previously with the HR business partner, but we made it crisper and more contextual.

Analyze the typical compensation and benefits organization for the key tasks, subtasks, work, and activities. And so, I'm not going to drag you through the mud here, but you can see how,, it says, hey, you may be organized different, but using the Reejig data as the… a universal way of thinking about this, there's really five career levels. You may label them something different, and by the way, you could load your career levels in here, and Galileo would use that and apply that, and obviously,. tailor it to whatever data you provided around your organization. And again, it's using all the Reejig data here, so you can go right to the source documents to go ahead and find them.

It's saying, hey, listen,, for a comp and bend, it's got 7 different core task categories, out there. There's ways to think about,, the product… it's defining what each one of them is, and so it's giving you all the background contextual stuff, similar to what we just did with the HR business partner. But here is where it gets a little different and interesting, and it uses that same data, now with some additional prompting, it's coming back with another perspective around the AI automation. So, based on the above. what tasks can AI help automate, summarize, and show percent of time saved by automating?

So it's now helping you go step by step by step. In the… if you agree with the upfront framing, right, it's now taking apart that at a much deeper level, and saying, task by task, and potentially sub-task, what are the AI automation potentials? And so, again, you may agree with it or disagree with it, but it's giving you a framework in less than a few minutes that would have taken a long time to really do. It gives you a little bit of a gap analysis, some things you could use for a business case. And here is where it gets really, really fascinating. And Siobhan, when… and Sarah actually created this prompt, and so I give full kudos to Sarah on this one.

She said, hey, listen, what if I tell you my technology stack? Right? I'm not giving anything proprietary, but,, provide examples of pre-built agents for each type of task. Our technology includes Workday, ServiceNow, and Microsoft 365. And so now, what it is doing, it is doing, in the context of of the technology that it understands that you have, because Reejig has this data and is constantly updating it, it's saying, hey, listen, you want to have the task of prepare and deliver total reward statements? Well, guess what? Workday Reporting and Analytics Agent. ServiceNow, now assists document generation agent.

So, it's helping you think through where we went from a conceptual conversation about the big picture. to, let's get down to, now, this is my technology, not technology I can… by the way, you could also do technology off the shelf, but let me tell you what I have in-house, because it's easier to do it with in-house technology, versus going out and buying new technology, because that's a whole new process. And all that analysis that you just previously did, it is now mapping one-to-one to the agent that it thinks best could handle that. Again, you may not have configured it in your tech environment the entire way, but there's an aha moment here, because one of the things we do on the Bersin side quite a bit is we're asked to do tech stack reviews.

And whenever we do an HR tech stack review, invariably, there's a lot more capability that's been purchased that has not been turned on and has not been utilized. And so suddenly, what seemed an impossible scenario of,, how do I analyze the organization, how do I think about agents? You're ending up with a very detailed list of, hey, 80% plus, 75% plus, because remember. the recommendation was focus on 70% and above, and so it's given you what it thinks are the high-value opportunities, and the technology in your environment that makes it available, and then you can… and then the last thing was create an implementation plan, and how to do that, and where to prioritize.

And so, it's your partner in crime here as you're working…

Siobhan Savage: It's, literally, it's killed a whole industry. It is, it is.

Bill Pelster: and… and,, prepare a short pilot plan for the market benchmarking and survey it now. So, the final task that we created was,, let's look at one particular task. and figure out exactly how we want to actually organize that. So,, it's given the potential for the investment, the expected ROI, the risk level, and the technology. And again, you may tweak this, but you went from zero to a lot of information,, in 7 minutes, right? Yep.

Siobhan Savage: Agree.

Bill Pelster: I don't know.

Siobhan Savage: I agree.

Bill Pelster: We've seen this,, show up in our system, Siobhan, but what's your thoughts on all this?

Siobhan Savage: I mean, I feel so proud. Of the decision that we made to put a whole pile of effort into building the common language of work.,, for us, that was,. , hard. It was very difficult and cost me a lot of money, so to now see how this can be given to access to everybody,, typically, Bill, we sell to enterprise, and we sell,, enterprise-wide contracts. What we've been able to do is take our product and actually, by partnering with you, give this access to everybody. Right? And I think that's the thing that I look at this and I go, isn't it really cool that,, that's happened? And I think,,. you can see the potential here.

Most of the… most of the thing you will see is that… and I spend a lot of my time coaching,, executive level on,, the reality of,, hey, here's how to think about solving this problem. this here is where it's taken it from,, that moment of,,, how do,, what?, how do I think about solving this problem?, what steps can I take, and then how do I take action? And then, by the time you finish this process at an individual level, you guys all should be doing this on your teams right now. If you are not,, guys, you have to. You have to be the voice of reason within the business, and you don't have to go out and purchase a whole enterprise license from us to get started.

Start here, start testing, get yourself familiar. And I think that's where it becomes,, that starting point. And then,, Bill, when you get to enterprise-wide,, you've seen our products, you've talked to our customer,, that's when your muscle is ready and you guys feel confident enough, that's when you start to think about becoming the transformation squad internally, where you helped guide the CIO, the CFO, the chief AI officer. And the one thing that you said that I think people need to think about as well, start with the stuff that's already available internally. , part of where you'll get shut down is if you get shiny tool syndrome, and you have to go out and buy new things, everyone's gonna tell you no, because,, hundreds of products are being trying to be bought right now.

The likes of Copilot at Microsoft and others, they have incredible technology that's already deployed in most organizations that we see., I would say majority of them, it's already available, it's just no one knows where to go, how to use it. This is the thing that you can very quickly go from where is the opportunity to how to deploy to what tool, and let's go. So, yeah, I feel super proud, Bill.

Bill Pelster: Yeah, no, and, if I take a step back and I put my consulting hat on, and so many conversations we have every single week with companies around the world. They're… they're all stuck in their analysis. Right? And the ability to pick an area that is very precise, that you're comfortable with, with the technology that, and be able, in a matter of an hour, create a presentation that you could take to the executive team and say, listen, we actually looked at this part of our business. We set some thresholds around 80% or 85% automation capability. We believe these are the opportunities, this is our tech stack, and we believe this is what it would take for us to implement this, and here is the return on investment, and we could have a pilot in 6 weeks, and here are the steps to the pilot.

Siobhan Savage: Yep.

Bill Pelster: That is… that is.

Siobhan Savage: Yeah, it's…

Bill Pelster: allows you to get started. And when you do one, then you do three, and then you do 50, and then suddenly you're transforming the entire organization. And I do want to.

Siobhan Savage: I know.

Bill Pelster: as powerful as Galileo is for for individuals to go in and play with this. What's really cool, and I'll give a shout out to the… what I've seen when I've been on your system, is the ability to model against the entire organization in real time simultaneously. That is… so the whole what-if analysis of what if we pull this out against these job functions and redesign this. In a matter of seconds, you see what the dollar savings potential are, what the opportunity is, and so if I'm a strategic workforce planning person, or I'm in charge of the digital or the AI transformation in a company, if we're not using tools this to figure out what's going on, how are you making decisions?, I don't know… I really don't know.

You're listening to vendors,, pitch a product when you should be modeling an organization in real time. Siobhan, I know there was a lot of questions out there.

Siobhan Savage: There is, there is, so maybe we stop sharing screens, and let's jump in to open the floor up to folks, because I think there's… there's been some awesome questions. I mean, if anyone wants to put their hand up and just jump in, feel free, but put it in the chat. So, we've got, a question from Chris. If we subscribe to Galileo, do we get the Reejig content, or do we need to have an agreement in place with Reejig? Bill, I'll let you take that, because it's your tool.

Bill Pelster: Yeah, we've, so we've loaded the HR data in there, right? And so if you want all the other industry verticals, that may come in the future, so Siobhan, you and I will talk about this. But right now, as a proof of concept, we've loaded all the HR data, because our primary audience is HR-focused, and then we'll look at next steps in future releases.

Siobhan Savage: Yeah, perfect. And Meg Baer, has said, deciding where to start is hard. Meg, I couldn't… I couldn't agree with you more, and I think this is that starting point of… , us now becoming our own change agents, and really starting to figure out how we help advise the organization, and it gives you that starting point into… into really getting going, which is… which is really exciting to see.

Bill Pelster: Hey, Siobhan, can I just throw this out there? Because I think one of the things that I'm… by the way, full empathy about where do we start and getting started, my number one advice is get started. Even if it's something small, and if I go back into the origin story of,, how we actually got here, we stubbed… we on the person's side, when we were experimenting with ChatGPT, you learn a lot by experimenting, and you stub your toe, and you make some mistakes, and it's, why is it doing that? And in that process, you suddenly accelerate through. And so, if you're waiting for perfection, we're, by the way, in conjunction with Reejig, and we're trying to, and many customers.

We're putting together research and a roadmap and a playbook on how to think about this. But that's… that's gonna come out shortly, and anybody who wants to just raise your hand and we'll get it out to you. But this… this market is moving so fast, you've got to get on that train, because every move that the LLMs and the agents and the different vendors are making, it's not a linear step change, it is an exponential step change every single time. So the longer you wait, the gap and the amount of distance you have to cover gets exponentially bigger, right? So just get in there and start doing it.

Siobhan Savage: I agree. I think the thing that I talk to customers about a lot is,, you're going on a,, re-engineering journey of your organization, right? And you, one, have to understand what is everyone doing, then you gotta get,, a GPS. So think of it a work map that tells you where to start. And then you have your GPS, which is giving you directions of where to go and what steps to take, which is a mini version of what we're doing here. And then it's the, okay, well, what agent do I use? And then deploy that agent, and then the next conversation that folks are going to have is,, well, what ROI does this agent bring?, what's the unlock?

So,, don't just deploy tools without checking,, what's the value of this tool being in place? Because people will ask you that, and you want to make sure that you can quantify it and say, hey, that we're using this tool, and it's actually freed up 3 hours a day of 100 people, that quantifies into X, because you see those types of stories? That's the conversation CIO wants to hear right now, because they're trying to find… areas where AI is truly transforming, so I think when you go and you start deploying from this data, just track where the value comes from. And then the final part, Bill, which is where we have exploded in terms of our growth as a product, is,, is one, the task being removed.

But then the new tasks, this is not,, a one-time thing. You're gonna be in transformation for the rest of your careers, because as Bill said, the AI is evolving so rapidly, and that there's constantly new technology advancements, that you're going to be re-engineering your work and your tasks,, for a long time, because that's just going to be this forever shift. So, my suggestion to folks,, if you're a leader's on the call of teams,,, start setting that expectation that's who you are now,?, you don't just stop and let it go. You're going to have to keep moving forward. And,, once you put in an agent to re-engineer, the next thing the agent will get better.

Take out the whole process,?, and then who are all the dependencies? So I think that becomes, that whole view of moving forward. Hello, Barry Prost.

Bill Pelster: That's one of my very first bosses on the call. Oh, there you go. Hey,, Siobhan, I'm going to tie this back to a bigger piece of research that we did over the last several years, independent of this topic, but directly impacts it. And we were doing industry verticals, so we were looking at healthcare and pharmaceutical and CPG and retail and manufacturing, on and on. And as part of our methodology, we would look at the top 10%, and we had very strict criteria of what it meant to be top 10%, and the other 90%. The top 10% all had one thing in common across every single industry, and we published that in our pace setter research, and it actually showed up in the prompts here.

They are comfortable with change.

Siobhan Savage: They are comfortable with experimentation. They are internally more agile than everybody else in their industry, right? And so, if we think about the era that we are in.

Bill Pelster: The number one characteristic for survival, growth, and thriving is this high degree of comfort with change and internal agility as the world changes around us.

Siobhan Savage: Yeah, agree. And it's. test it, try it, kill it. If it doesn't… if it doesn't give you the ROI, because I think… I don't know if everyone's seen the click-baity MIT study that came out that said that,, all AI projects are failing, and no one… and that's got… made its way now into the boardroom, so everyone's semi-freaking out. One of the things I think is really important is you've got to quantify this stuff., it can't be,, a little vanilla nice-to-have project, it's. okay, I've went and done this now. How do I actually track how the work has changed? And a lot of the AI companies, they talk, Bill, about consumption., these amount of people are using my product, so what?

No one cares. How has it actually changed work? what has it unlocked our business to be able to do? And you folks talk about,, that super, super human superpower workforce., tell us a little bit about that, because I think that's the… that's what the CEO was asking for right now, right? So,, what's your… how do you guys frame that in your research? Because it's really interesting.

Bill Pelster: Yeah, so I mean, it… and it goes back to that four levels of maturity that we talked about with AI automation, right? And what we are really advocating is,, the humans are working with agents to create infinite capacity. And as we… and then sometimes it starts with productivity improvements and getting comfortable with the AI, but then suddenly there is that switch where… I'll give you the example again, I'll share some data points on Galileo. We are a small organization, we're just 35 people and 10 contractors, right? Since we launched Galileo a year ago, we've had over 200,000 sessions on there.

Siobhan Savage: People are interacting, asking a question that they used to try to get 30 minutes on a phone call with us to be able to ask us. They're now having… able to do this.

Bill Pelster: And so, essentially, we have multiplied our 35 people almost infinitely, and we're now seeing upwards of,, 7,000 sessions a week. with people going in there and asking questions. The other thing around the ROI, and I think this is important, we were up at… Josh and myself were up at Microsoft a few weeks ago just talking about a bunch of stuff with Copilot, and we had some clients who called in who are Galileo and Copilot users, and on the HR team, every single one of them said, when we go to the executives, we use the Galileo response. Because you can use it, because we can tell them it is a trusted source, and we.

Siobhan Savage: Yep.

Bill Pelster: that trusted source. And so, when you saw the ROI calculation, you can drill down on it, you can play with it, you can manipulate it, you can model it, but it's coming from a trusted source., if you would have hired us as a consultant to come in and do the analysis, we just did it in minutes, as opposed to weeks that it used to take us to do that.

Siobhan Savage: No, it's such an incredibly exciting time. I mean, never has there been more an important time for what we are doing as HR leaders as well, right?, I feel super,, grateful that we get to be in this moment right now as leaders, where people actually need some guidance right now, because the CIO,, they specialize in the technology and the tool. They don't specialize in,, the work and the outcome, and the HR team are the closest place to that, so anyone that has done,, resource management, mobility,, when you think about job architecture and capabilities. this is,, the closest adjacency that you can think of.

That's why,, we're good at it, because we started our early careers in,, that world, and I think,, anyone who's on this call, this is really giving you, I suppose, the permission to start playing and testing and really building that muscle in your teams to then go and find…, we had a… we had MasterCard's ex-CHRO and CIO chief AI Officer. on a webinar last week, and what we were talking about was,. when does HR put their hand up to be part of this bill? Because they want to help, and everyone wants to be part of it, and they just don't know what the role is to play. And Joanne was saying that what you should be doing is,, get some data, go to the chief AI officer or the CIO, and say that this is where we see the greatest opportunity exists for us right now, today, using the tools that you've already supplied us with as a business, and it's going to give us this amount of expected output come. we're gonna try this, because what they're looking for is, given all of the confusion around AI right now, and is it valuable, is it not,, the click-baity thing that happens, this is actually evidence of,, transformation, and that is where you start building up that reputation and that muscle.

The other thing that I think is really important that we cover off as well is We've talked a lot about AI, and we talked a lot about using it and reinventing your workforce. The other part that I think is really important is that if you then start impacting and really getting good at bringing in AI, you're going to impact your people. So, what the other part of the data tells you is,, well, what will be the impact to the job, so that you can think about, how do I,, pivot, folks?, what are the actual skills that my people need to even use the AI? Because that's the other part of the puzzle. If you roll out the AI and no one knows what the hell to do, does it work?

No, because they might not know how to use it. So, this is where it gives you that full life cycle of,, I know how to reinvent I know what agent, I know the impact, and I know how to amplify my people and make sure we don't leave anyone behind. And I think that's where, Bill, I think it's… the two things coming together. I wouldn't have said peanut butter and chocolate, that's an interesting one. That would have been more of the jelly, but anyway. But I think the two things coming together really is that starter. for everyone on this call, and I mean, whether it's Bill or myself or the team,, feel free, if anyone's,, got any questions that,, we haven't answered,, feel free to jump in now and ask us any questions.

If not, what we can do is we can make sure that we come back to any of the questions, whether through DMs or through our team. So,, we've got one minute to go, so I want to make sure if there's any questions that anyone wants to ask,, please feel free to throw it in the chat.

Bill Pelster: And while that's going on, Siobhan, maybe one closing thought. If I think back on my legacy consulting career, we always talked about innovation occurred at the margin, and then you brought it back at the edge, and you brought it back to the core. AI changes everything. The innovation is coming at the core, right? Yeah. And that's what the CEOs and CFOs and CIOs are asking for.

Siobhan Savage: I agree, I agree. All right, Bill, we're gonna wrap it up. Thank you to everyone for joining. This can be available on download after recording has been released. Bill, thank you so much. Thank you to everyone for joining. Everybody, get started. No excuses now. You don't have to buy a full enterprise software service to do this after what Bill showed you. So, thank you!

Bill Pelster: All right, take care, everyone. Pleasure. Bye-bye.