Dr. Kathi Enderes and Siobhan Savage on Dynamic Work Design and the Real Work Behind AI Transformation

Siobhan Savage: Hello, everyone!

Siobhan Savage: Welcome, welcome!

Siobhan Savage: Happy Wednesday, folks!

Siobhan Savage: Welcome!

Siobhan Savage: Need to make sure Kathy isn't stuck backstage.

Siobhan Savage: Kathy, I thought we lost you backstage!

Kathi Enderes: I know, I'm like, okay, I need to click a button here, and now I found the button to click. I was like, I don't know how I'm gonna have to solve this one. All good, I'm here now.

Siobhan Savage: I love it, I love it, I love it. Welcome, welcome. Welcome, everyone! We're just gonna give it a couple of seconds to let folks come in, get settled.

Siobhan Savage: Kathy, I am so excited about this session today. I have not done anything but talk about this topic for the last 12 months.

Siobhan Savage: And I feel that there's just so much energy around this in the market at the moment. It's coming from CEOs, it's coming from CFOs, it's coming from CHROs, CIOs, I don't know, like, you must be finding the same thing.

Kathi Enderes: Oh, totally. I think the entire C-suite and everybody in the organization is always looking for, how do I do this thing? Because AI is here to stay, and we need to think about how change… work changes, and job change, and roles change, and activities change, and outcomes change. So, yeah, I'm… I'm thrilled for this, too.

Siobhan Savage: I am, and I'm personally… I'm personally excited because I remember 3 years ago, sitting with you in Vegas.

Siobhan Savage: and talking about this, and I was saying, this is where it's gonna go, I really believe that this is gonna be a shift that we're all gonna see, so to get to go on this journey with you all the way through to the other side has been incredibly exciting, so thank you for your mentorship in this whole space.

Kathi Enderes: Well, thanks for the partnership, Siobhan, and I'm so impressed where you're leading all of this, and how you're kind of helping organizations get their arms around this really important and eerie problem, and how you redesign work. So, it's super exciting.

Siobhan Savage: Well, for those that are dialing in now, and for those that are listening in later, this is going to be the most incredible session with Kathy. So, for those who don't know Kathy, Kathy is the Senior Vice President of Research at the Josh Burson Company. Kathy has been the driving force behind the research, which is called the Redesigning Work for the AI Era.

Siobhan Savage: And what we're going to do today is we're going to split this session up into, you know, a high level… Kathy's going to walk us through, you know, what is everything that they have learned, give a whole pile of context about what customers are saying, you know, right from the actual customers themselves, and then what we're going to do is we're going to try and answer as many questions as possible, and then I'm going to bring you through a session on how you can actually bring this to action within your workforce.

Siobhan Savage: So, I'm Siobhan Savage, I'm one of the founders and also the CEO of Rejig. I am very excited about this topic because we have just honestly seen so many companies at a place where no matter how mature you are as an organization, everyone is wanting to get together.

Siobhan Savage: to really think about how they solve this, because it's a completely unsolved problem. Now, one of the things that we did with Kathy and the team was we got together some of the, you know, most pioneering leaders.

Siobhan Savage: in the space. And what we wanted to do was to go through how their companies are thinking about re-engineering work, what does work look like in this new era, and this research that Kathy's going to walk you through comes right from, you know, talking to these customers, but also talking to, you know, the broader industry as well. So, Kathy, I would love to hand over to you to really take folks through, you know, what is it that you're seeing in the sort of state of business

Siobhan Savage: in 2025.

Kathi Enderes: Yeah, I mean, it's so interesting, because I know everybody thinking about the AI transformation, and we get all these studies and all these insights, of what's happening in the market, so just a few insights that seem to be contradicting, but they're really breaking, pointing to

Kathi Enderes: this need for work redesign. So, 92% of CEOs, that comes from the CEO study that PwC did, earlier this year of 4,000 CEOs around the globe.

Kathi Enderes: said they are investing more in Gen AI and AI overall. And I think that's probably has changed now, that it's probably now closer to 100%. I don't think there's a single organization where the CEO would say, we're not focusing on that. So everybody's focused on that, and everybody's focused on that, not just to

Kathi Enderes: Reduce costs, but really to increase revenue, make the company grow, have better customer products, have better experiences, have better

Kathi Enderes: get into new markets, so that kind of AI transformation is really real.

Kathi Enderes: at the C-suite, but then also it triggers down, of course, to every person in the organization.

Kathi Enderes: At the same time, of course, there's gonna be… there has already been an enormous amount spent. $1 trillion has already been spent by the AI.

Siobhan Savage: Crazy.

Kathi Enderes: infrastructure crazy, right? That's the GDP of Switzerland, so we've spent

Kathi Enderes: They, like, literally the entire GDP of Switzerland, actually more than the GDP of a whole country, just an infrastructure on… for the AI… for having the AI run, of course.

Kathi Enderes: And then there's these studies that come out that say, for example, this study from MIT, and I know there's been a lot of discussion around that, that say only 5% of businesses actually have gotten real impact out of these, all of these investments. So, what that means is.

Kathi Enderes: We're actually very early on in the AI transformation, of course, which is not surprising, right? We're pretty early on on this. I mean, when you think about when

Kathi Enderes: ChatGPT came out, it was exactly 3 years ago now, and so we've… we are trying to learn very quickly, but what this… this research and other research actually shows, it's not because the AI models are not good, they're actually pretty good at what they're trying to do.

Kathi Enderes: But what, researchers here have uncovered, and what we are seeing too, is

Kathi Enderes: what they call a learning gap, that basically people and the companies don't know how to incorporate AI into workflows, into processes.

Kathi Enderes: and how to actually change the work that happens around the AI. So… so that's really at the heart of what we wanted to unpack. Well, how could you, actually, how could you be part of that 5%? And AI, actually.

Kathi Enderes: And the agents are, of course, here, and I was just on a very big webinar last week, where somebody said, well, can you describe what an agent is? Maybe that's a dumb question, and it's not a dumb question at all, and everybody actually defines it a little bit differently for us. We say an AI agent is not something that just gives you an answer, but actually does something for you. Like, they do actual transactions and back-end system. They do… they don't just give you advice.

Kathi Enderes: but they actually do something for you. But when you think about these AI agents, of course, when they do something for you, how does it fit with the things that you do, right? So how does it shape the work that you're doing? And when you're thinking about… I know there's many examples in the HR area, but let's think about

Kathi Enderes: a non-HR area, a marketing person, for example. If the AI agent can create a campaign for you, well, how does it write campaign emails, or find out what you should say in this campaign when the AI agent can do these things for you?

Kathi Enderes: what does it… how does it shape how the marketing person does their own work, right? So that's what we want to unpack here. How can we first prioritize where we should focus on redesigning work and rethinking work?

Kathi Enderes: But then also, how, can we actually elevate what everybody is doing with these AI agents that do all these amazing things for us, and now, soon, we'll work together with each other? And,

Kathi Enderes: So we are really seeing that, in this AI transformation, there's really four stages, and I always ask people, and I'd love to ask everybody on the call too, maybe to put in the chat where you are at in this AI transformation.

Kathi Enderes: That we've seen the AI transformation go in four stages. Stage one is where you're using AI assistance, like ChatGPT, or the Copilot, or any of the Gemini, whatever you have.

Kathi Enderes: for your own personal productivity. So you're using it to make, I don't know, right… Could I jump ahead?

Siobhan Savage: Did I jump ahead in the slide? Is that okay?

Kathi Enderes: No, that's what's good, this was good.

Siobhan Savage: Oh, okay, perfect, sorry, sorry.

Kathi Enderes: Yeah, yeah, we're… we're right here. Yeah, we're right here. I'm just talking about the stage one, so you… you did well, Siobhan, and you're doing this seamlessly. You're reading my…

Siobhan Savage: Agents?

Kathi Enderes: I know, no, but you're doing this really well, and you don't know what I'm gonna say, because I'm thinking about, basically, what I'm saying here, so really kudos to you for moving the slides properly.

Kathi Enderes: So, stage one is basically these AI systems where you're using them for, kind of,

Kathi Enderes: like, personal productivity, and that's great, right? And we've been doing this for the last 3 years. I think most of us have used the AI tools more and more. There was just a study that came out that said, actually, 45% of people are using

Kathi Enderes: for assistive AI, like these, generative AI systems, like ChatGPT, Copilot, every day.

Kathi Enderes: 45% of the population are using them every day, and that is increasing really rapidly.

Kathi Enderes: and then you'll say, well, this is great, but how can I have the AI actually do something for me? That's the agent, that's stage two, where we just talked about how can the AI actually do something for me? But it's still within your own job, basically. It's maybe automating some things. Maybe when you're a recruiter, for example, you use it for interview scheduling or something like that, but your job is still your job. And then there's the huge shift

Kathi Enderes: basically to, when you're saying stage 3, where you have these multifunctional agents that do different things, basically, and tie them together. So you could say, for example, there's gonna be, an interview scheduling agent

Kathi Enderes: on the recruiting side that connects with the pre-screening agent that basically tells them who have we pre-screened and who are the best candidates, and then the interview scheduling agent maybe connects with the,

Kathi Enderes: kind of the onboarding agent. Eventually, you could see, basically, how these agents could connect

Kathi Enderes: more multifunctionally, and maybe the onboarding agent then connects to a learning agent that says, what does the new hire have to learn? So, soon enough, you'll say, well, wait a minute, if these multifunctional agents do so many things of my job.

Kathi Enderes: what's going to be my work? What's going to be my job? What am I going to do? And so you see these cases where you have, for example, the recruiters be more talent advisors to help

Kathi Enderes: Maybe internal people to find a better career in the first 90 days, or something like that, or maybe there are more workforce planners that work with the business to think about where do we

Kathi Enderes: have to go, basically, in the overall… in our overall team structure, and what skills do we need on that team. So, soon enough, basically, when you go from Stage 2 to stage three, you really have to think about how does the work change, and how does the role change, and how do we basically redesign the work? And then stage four is

Kathi Enderes: Full work transformation, where you're basically saying, well.

Kathi Enderes: If we have autonomous agents that basically

Kathi Enderes: Do entire business processes end-to-end.

Kathi Enderes: what are the new roles, what are the new jobs, and how do we oversee and direct these agents? So that's one job, but then how do we do the things that we as humans are best at, right? So how do we then carve the niche, basically, of saying, what should we all do? So, I'm looking, basically, at the… where you all sit, and 1.5 is great, 3 to 4 is amazing, some of you are too

Kathi Enderes: I did this, actually a show of hands in a

Kathi Enderes: in a meeting, in a conference last week in San Francisco, and we had about 150 leaders there in San Francisco, which arguably is the AI capital of the world, right? Because this is San Francisco where, like, all of… much of the invasion is happening. And there were talent leaders, and CHOs, and talent acquisition people, and L&D leaders.

Kathi Enderes: And guess what? Almost everybody was in Stage 2. Even in San Francisco, that was one of the big AI companies, I won't mention who it is, one of the, like, the AI companies that we all know and love, or maybe not love, but we all use.

Kathi Enderes: And even they said with Stage 2 internally, because we're actually building this, but we're not using it internally.

Kathi Enderes: Kind of what.

Siobhan Savage: Yeah.

Kathi Enderes: And it's a really big step here, and a really big differentiator.

Siobhan Savage: Yeah, I do agree with you, Kathy, because what we have seen from, like, just the work that we're doing as well is that I think folks are, you know, at the early stage of AI, just threw it out there and hoped for the best.

Siobhan Savage: and your employees, you go off and do your own thing, and, you know, we're going to hope for massive amounts of productivity gains from this. At the end of the day, the employees are going to create their own version of a process. Any company that wants to increase velocity, increase revenue, do all of those things, you need to have a systematic way of redesigning that at a top level, so that everyone doesn't have a… you know, if you're all in a canoe, and everyone's rowing in the wrong direction.

Siobhan Savage: you're not going to get very far. So I think everyone's kind of went through this AI touristing moment for the last two years, and we've all now come out, and we've woken up a little bit from that hangover, and we're like, oh, okay, so now we need to think about it. So the way that you see this is exactly the way we've seen this being playing out in customers. And what's interesting is your job architecture phase, stage one, because

Siobhan Savage: Like, the job architecture is, like, pretty archaic. I'm… I totally agree with the concept of, like, labeling and orchestrating, you know, a library of ability with the job architecture, but I think that we need to move more into that work architecture and understanding it, you know, at a broader level. But most customers I see are stage one or stage two.

Siobhan Savage: Yeah.

Kathi Enderes: Absolutely.

Siobhan Savage: And that is, like, the most advanced of the advanced, right? So, it's very interesting that what you see is what we see as well.

Kathi Enderes: No, it's absolutely true, and I think it's fair to say also, the reason for that is, of course, AI is new, but then also, I saw a question here, what's the biggest differentiator? I will say that work redesign is a new muscle that organizations don't necessarily have, right? We have all done the, maybe, the job architecture projects and the job architecture consolidation, and we have to do this every few years because the job architecture gets so out

Kathi Enderes: of whack.

Kathi Enderes: of everything. But doing, actually, understanding what the work is and how you constantly redesign this, not something that we've actually really done before, so it's… it's… Yeah. And that's what

Kathi Enderes: is holding companies back. It's not actually the technologists. That's the same finding that this MIT study has. It's not the technology that holds companies back, because these technologies already exist.

Kathi Enderes: But as you said, if you're all… everybody's going in a different direction, how can you make this significant impact, and how can you connect the different walls and pieces? Because I'm not sure

Kathi Enderes: what I should do next, if I'm a recruiter and I only know what I do in recruiting, and I don't know what else I should be doing, how am I going to redesign my job myself? Usually people can't do this themselves, right? They can do a little productivity increases, but

Kathi Enderes: yeah, we really need this kind of guidance and this kind of top-down, looking at this holistically, and then seeing where's the big opportunities, and helping share, kind of, what somebody has been doing, kind of, successfully as well. So that's what we are talking about when we're talking about

Kathi Enderes: dynamic work design, and I know you probably all will get this… this paper from… from Siobhan and team, afterwards to…

Siobhan Savage: Yeah, we'll send it after.

Kathi Enderes: read this? Okay, that's great, yeah.

Siobhan Savage: We'll share it after the…

Siobhan Savage: The other thing, Kathy, that we see as well, that people… it doesn't click automatically, is, like, the job architecture is this, like, once-in-a-moment, like, photograph of who you were as an organization, and everyone's talking about AI removing tasks and impacting jobs, but the part that everyone's forgetting is that AI also introduces new tasks that you've never done before.

Siobhan Savage: So, it's not just the idea that tasks are going away, it's also we're now doing new work. And the second you finish a job architecture.

Siobhan Savage: And they, you know, whoever does it leaves, it's already out of date, you know? It's that rapid of change as well.

Siobhan Savage: well, right? And this is why your dynamic work design research is really important, because it's this living, breathing overview and constantly evolving company, right? And the work that's being done. So, we'll definitely share this after to folks as well.

Kathi Enderes: No, that's great, because here we're just, like, scratching the surface, really, and we have lots of examples and case studies and concepts in there, but really what we're saying here is what we call this dynamic work design. As you said, Giovann, it needs to be constant, it needs to be this kind of

Kathi Enderes: Not just job-based model, but thinking about what outcomes that we're trying to accomplish, and then how can we design a work around these outcomes?

Kathi Enderes: So it's really breaking the mold of saying, oh, we're just looking at basically automating specific things, and really thinking about what's the outcome of a specific business process, and how can we do this completely differently with AI at the table.

Kathi Enderes: And, that's hard to do manually, of course, and that's why tools like what you have, really, Siobhan, with Rejig, are so important, because things are changing so fast. The speed, the scale is all changing much more rapidly, because before, when we did

Kathi Enderes: maybe job redesign or work redesign. We could focus on one process, and we could take a long time to say, okay, we have a new system coming in, how does the workflow change? We've always done this, but now we need tools to do this differently, and

Kathi Enderes: So really thinking about how can we use AI, not just as a tool to automate, but to solve business problems. Where is the biggest opportunity for us

Kathi Enderes: for AI to add value, not just automate things. How can we go beyond just looking at skills? Because now AI tools don't have skills, right? AI agents don't have skills, but they do work. They do tasks, they do activities, they create outcomes, so think about

Kathi Enderes: the skills come after that, after you understand what work needs to be done, what skills do people need to have? And that's a different… I think that's creating that kind of connection between the jobs that you have and the skills that you need

Kathi Enderes: is that work intelligence in the middle of all of this? Why do companies do this? Well, companies do, like, this work design, ask for work design for really four main reasons that we found. And we, by the way, found, when we talked with many of these companies.

Kathi Enderes: all four of them are going on at the same time in most companies, right? Most companies are looking for this productivity, that cost reduction. Of course, we're saying, well, we have too many people, we're spending too much, how can we cut costs? And I know CEOs are always looking for that. I was just talking with a big

Kathi Enderes: technology company, and they said, our CEO said, we have to save 20% in every single department across the board, right? Everybody needs to save 20%, and that's a very drastic mandate. I think you might have different mandates, but most companies have kind of really

Kathi Enderes: top-down mandates to say, where can we cut, where can we save? And that's one thing. Of course, other companies are saying, well, we actually need to grow more than we can hire people around, so we need to kind of up-level, and maybe these, like, semiconductor companies, for example, they need more people than they can ever hire. So, like, how can we use

Kathi Enderes: What can we design to

Kathi Enderes: use AI, basically, to do the work that we can't hire enough people on. So that's kind of another component of that, and you might have that in the same company, having both of these that seem conflicting, but they're really not, because different groups might have different priorities.

Kathi Enderes: How can we create a better customer experience? So how can we create more value for our customers with AI? And that's… many companies are looking for that, too. And the last one has to do with what we just talked about.

Kathi Enderes: This job architecture really being

Kathi Enderes: arcane, and really, like, I don't think anybody, if I asked everybody here on the call who is happy with their job architecture, probably everybody would say, well, no, we're not. Our jobs, as soon as we create a job, it's already obsolete, and nobody does what the jobs say they do, so that kind of cleanup that you have to do, all four of them are probably driving

Kathi Enderes: Wow.

Kathi Enderes: everybody's interested in work design. Do you feel that too, Siobhan?

Siobhan Savage: Yeah.

Kathi Enderes: play out.

Siobhan Savage: So when we first started what we were doing right at the start of this, it was very cost out. Everything was cost, cost cut, everything was about that. What's actually emerged, probably in the last 12 months, is we get, like, a very, consistent piece of feedback. It's, I want to reduce costs.

Siobhan Savage: So, remove out, you know, overhead that we don't need. I want to take away work that we don't need. Second skill for what they look for is they look at, how do you help me make more money?

Siobhan Savage: So look at the things that help, like, us grow and make money. And then the third level is, how do we make our work more enjoyable for our people so that they stay longer? Again, directly linked to cost, because the longer your people stay, the less, you know, knowledge and money you lose and the rehiring of that whole kind of, you know, life cycle. So that's typically what we see. So it's actually very similar to what you're saying. But there's definitely, like, a lot more focus

Siobhan Savage: focus on, like, we don't want this just to be about cost, like, how do you help us make more money through creativity, innovation? Like, what are the tasks that directly link to making more money?

Siobhan Savage: is the more recent one I'm seeing, which is… which is great, because it's not just about removing headcount.

Siobhan Savage: And the other thing I'm seeing a lot is we're going to grow our business by 10% of revenue, let's say, and we don't want to increase headcount. We're not going to let people go, but what we're going to do is we're going to force the business to grow at our normal pace without increasing the normal headcount that we typically would have put in. So that's another one that we're starting to see come from CFO, which is great as well, because it's not like a direct cutting out people out of

Siobhan Savage: of your team. It's just a completely remodeling of how we work, and amplifying the people that you've already got.

Kathi Enderes: No, absolutely. I love that you said that, too, because we hear this a lot, too. I mean, this kind of,

Kathi Enderes: just forcing the productivity to occur without letting people go, right? So we want to grow, and it's always doing more with… I hate, actually, that saying of doing more with less, I think. Yeah, like, exactly, that's, I think, the wrong concept, but I think it's the concept of,

Kathi Enderes: like, putting less effort in and getting higher output, and that's, I think, the right approach, right? So you're just not, like, doing more, more, more, but you're actually prioritizing. You're saying, well, we can actually use all of these AI tools to really elevate all of this.

Kathi Enderes: And for us, this really comes back to how you do work design in a much more business-driven way, and this model on the left-hand side comes from a study that we did a few years ago, actually, on how org design and work design actually works best in a much more agile, much more business-driven way, and we say, basically, always start with understanding the business problem that you have.

Kathi Enderes: And then you're thinking about how you're going to organize things, how you're going to operate things, and what work needs to be done. So always going from, like, what's the real issue that we're trying to solve for? And it might be all four of these that we saw before. It might be another one, right? We might say there's a group that we really want to amplify, a new product they want to bring to the market. How can we do this when we have, for example, constrained resources? As you mentioned just right now, Siobhan.

Kathi Enderes: And, well, the work design is in the middle of all of this, but also from our study, we saw that most companies actually are not

Kathi Enderes: Very good at this, and maybe not surprising, but most companies are very early on on

Kathi Enderes: redesigning work, or designing work around the outcomes that need to be accomplished, so rather than just saying, well.

Kathi Enderes: here's all the steps that we need to do. What is this thing really about? What is this work for? What is this process about? And then seeing, how can we have also employees weigh in and check with them and make sure that it works for them as well? And then really use tools like Rejig and others to understand the work that actually happens at its base. And so you see here, only 2 in 10 actually do that for designing

Kathi Enderes: work around outcomes, 1 in 10 involve employees in any of this work in design, which is concerning, right? Because in the end of the day, at some point, employees will have to work differently, and if they don't adopt this.

Kathi Enderes: It's… it's not gonna happens

Kathi Enderes: And deeply understanding work, of course, really, really important. So that's how we come to this

Kathi Enderes: new organizational muscle, we call it a new organizational muscle, and you see here how we design… define dynamic work design, and how it's different to kind of this episodic kind of job redesign that we've all done, right? We bring a big consulting company in, and they take up a year or a year, and you pay them a lot of money to do all this job architecture work, and

Kathi Enderes: By the time you're done, it's already obsolete, and it's just basically about the jobs, and it's kind of focused on kind of how you do this, hierarchically, versus this continuous, ongoing, like, always looking at where do we need to make tweaks, because the AI is moving so fast every single day, some new AI tool, of course, comes about.

Kathi Enderes: And so, doing this manually is just not going to happen, right? If you think about… and I think it sounds very overwhelming to think about, if we do it the old-fashioned way to, like, do, I think, work surveys, or, like, observe people, how they do work.

Kathi Enderes: How are you gonna keep up with all this?

Siobhan Savage: It also freaks people out. Like, if you go to all of your employees and go, we're looking at AI right now, and we're gonna just sit and watch how you work, or please everyone self-report how you work, you will cause, like, absolute, like, psychological impacts to that workforce where they are petrified.

Siobhan Savage: And also, you'll find a lot of the times you'll not get the true state of affairs, because people will overinflate, or be on best behavior mode, right? It's like, you know, when you're being watched, right? Like…

Siobhan Savage: So, I…

Siobhan Savage: I totally, I totally am, like, anti that way of, like, solving for this. I do agree with you, though, that the employee needs to be directed and shown and kind of shown this kind of new way of working and the new expectations, because it's unfair to expect people to work in a new way without directing them towards what it is with, like, the new outcome. But completely agree with you on

Siobhan Savage: on that whole work design component, and just time and motion studies is, like, super dinosaur days, right? Like, it's like back in the olden days.

Kathi Enderes: Exactly, exactly. And luckily, we have these tools now and these technologies now to do this. And here's one example, and I always love this example. We have a big case study on this, too, that you can, I think, also get from

Kathi Enderes: from, Siobhan and team, where we, were basically WPP, a really, really big marketing services organization, 110,000 employees, right? And they had

Kathi Enderes: 55,000 job titles, and they were looking of… they built their own kind of AI tooling and system, and they were looking why they wouldn't get more impact from it, and then they said, well, it's going to be a little bit hard to understand how it's impacting all these jobs, because we have 55,000 job titles. So, literally, you had, on average.

Kathi Enderes: two people in every job title, so it was impossible to see where's the biggest impact, where's the… where should we redesign this work, how should we see where the biggest impact is of this… these AI tools that we… like, IT built for us, and that we're not getting the biggest impact from.

Kathi Enderes: And so they used… they worked with the regic team and basically said, well, let's first think about how we cluster jobs together and organize jobs in a much more, kind of integrated way, and they eventually got down to just 600 job titles. And now, of course, they have a much better chance of creating these, what they call, job archetypes, where they basically say, here's the job of

Kathi Enderes: maybe a media planner, or a marketing strategist, or whatever their… all of their jobs are, to say, how can they use AI to get better customer outcomes, not just to do things faster, better, cheaper, basically. So… and now they're working even with their customers on

Kathi Enderes: well, how does it impact how they work with customers? And guess what? The customers, of course, are happy with that, because now the customers have less people to… that touch them and that have to work with them, so it eventually impacts their customers as well.

Kathi Enderes: Okay, yeah, let's talk about this work intelligence and how it fits in the middle of the job architecture and the skills, because I know everybody is on the skill-based organization journey, and I know we've been talking for years about how do we get our arms around skills, and how do we map the skills to the jobs, and it never quite spits, right?

Kathi Enderes: all of you are probably working on skill-based organization stuff, and how do we map jobs to skills? And it never quite fits, because we were missing the middle piece of this work intelligence. So if you think about all the skills that people have, and what's in your job architecture.

Kathi Enderes: you're missing, kind of, the middle piece, which we call work intelligence, which is basically, it's like what Ridic, for example, is giving you, but to say.

Kathi Enderes: where's all the roles, not just all the jobs, but where's the different roles that people play? Because usually a person that has a job in your HCM system

Kathi Enderes: plays multiple roles in the organization. Most people don't just have one hat, and they have many different hats that they play in an organization. So, what are all the different roles that people play? What are the goals? What are the outcomes? And then, how do we decompose this in tasks and activities, basically, to say.

Kathi Enderes: how does all of this fit together, and then what skills do they need? So the skills are the most, kind of, granular thing that you can now map once you understand all the tasks that they do, and all the activities that they do.

Kathi Enderes: how… what skills do you need to accomplish these tasks? So, if you're, for example, a recruiter, you do many different… you play many different roles, right? You play the role of a sourcer, and maybe a talent consultant to the business, and all of that. Each of these tasks, the activities, meet a number of different skills, and the reason why it never quite fit was because we were missing the insights on the work.

Siobhan Savage: Yeah.

Siobhan Savage: And I think one of the things that really fascinated me, because, like, I started super early in the skills space, but, like, when you look at supply and demand, we focus so much on the people and the skills of the people, and we never really spent a lot of time thinking about the actual work itself.

Siobhan Savage: And one of the biggest realizations that I had, like, over 3 years ago was, like, like, people have skills.

Siobhan Savage: Jobs have tasks, and then you require a skill to complete tasks.

Siobhan Savage: like, the whole part, the core unit of work that was missing was the actual work itself. The skills were being inferred from a job title, and we all know the job title, job description, job advert data is really terrible. So if we're inferring all of the knowledge about work… of skill based on that, we know that data quality is going to be crappy, right?

Kathi Enderes: That was, like, a.

Siobhan Savage: big realization, and I think before, we were in this kind of democratizing employee retention world, post-COVID was when skills-based really kicked off, and it was because, you know, businesses were genuinely trying to look at pathing and all… we're now in a different world where it's about operational efficiency, it's about excellence, it's about velocity, and that's where your kind of, like, demand

Siobhan Savage: component starts to play, so this is kind of, like, a really good illustration of, like, how the whole thing clicks together, because this is basically how we see it playing out as well within our customers. It's not like skills don't matter anymore, it's both. It's not one.

Kathi Enderes: But you need problems.

Siobhan Savage: Right?

Kathi Enderes: Now you need both, and of course, the AI tools don't have skills, right? They're not people, so they don't have skills, but they do… Correct. They do tasks, they do activities, and they… you can use them to create outcomes, so you need to think about where do they fit here? And I'm not a big proponent, or I actually am very much against using, like.

Kathi Enderes: seeing AI tools as people, because they're not people, right? They are… they're not people, they are tools, and they're doing specific pieces of work. And so that's why you need that middle layer, because now these AI agents are doing some of these tasks and activities, and you can use them for specific outcomes, but you… you wouldn't say that.

Kathi Enderes: like, I don't know, your interview scheduling agent has

Kathi Enderes: skills, right? They're doing actual work.

Siobhan Savage: It's a task. Yeah, it's a task, right?

Kathi Enderes: It's passing.

Siobhan Savage: Becoming…

Kathi Enderes: Yes.

Siobhan Savage: Becoming, Kathy, you know, you know watching, like, vendors, specifically analysts, you focus a lot of your time on, like, looking underneath the hood of what's going on. You've seen this space evolve from that time we were talking in Vegas.

Siobhan Savage: About the whole space, and look at how much it's evolved, right? And how things are happening. Talk us through a little bit of the landscape, and, like, where, who sits where, and, like, how do you see this whole, like, tech vendor space? And be nice!

Kathi Enderes: Oh, of course, of course.

Siobhan Savage: I'm nervous!

Kathi Enderes: No, you see it here, and this is not an exhaustive list, but I will say.

Kathi Enderes: like, most vendors I actually haven't even woken up to this yet. Everybody, like, when you go, for example, to the… to any of the big tech conferences, there was an AHR Tech, of course, there was an Unleashed, everybody says skilled in AI agents, that's on every vendor's booth, whether they're in learning, or whether they're in… in talent acquisition, or any… even the HCM vendors, they're still on the skills thing, and skills are still important, of course.

Kathi Enderes: But where it's really at, and where I think the significant differentiating value comes in, is thinking about this work redesign. And you guys at Regic, of course.

Kathi Enderes: you are really the only one that's making this your primary focus area. There's other vendors that provide data and play in that, but for you, I think you have really seen this opportunity to really dig into this area and say, we want to

Kathi Enderes: make this our entire reason for being, so kudos to you for seeing this much.

Kathi Enderes: I will say we talk with a lot of the big HCM vendors, too, and they haven't even gotten started on that yet. So they're like, wow, really? We should think about that, too? I'm like, yeah, you were kind of late to the skills game, too, of course, because I think the big HCMs, of course, they were a little bit later stage on that skills game. I'm like, it's going to be the same thing, that eventually you'll have to provide something to your

Kathi Enderes: to your constituents and to your customers that works in some way, of course, at least some baseline product, like what they do on the skills side, and better wake up to that. So some of that

Kathi Enderes: Yeah. Maybe we should buy a company, maybe we should build it, like, all of that. But there's… I mean, you see some of the players here, too, but Riji, I think you are really the ones that really capture that and put that flag under your kind of offering and say, this is really ours to take. So, kudos.

Siobhan Savage: Yeah, and I think… I think the thing with the HCMs, the one thing they are doing really well is they're being really open to partnerships. So we're, you know, like, they're being very, like, working with us, with customers around, like, they're very focused on solving that problem for the customer, so I think, like, the Workdays, you know, the SAPs, like, they're very focused on, well, they don't have it, but how do we help

Siobhan Savage: plug the two things to create this beautiful ecosystem for the customer, which is really great, right? I think the other thing that's really interesting about this space, and just one thing to watch for, so, you know, folks are all starting to know that this is a problem. This is not a HR problem, this is, like, a business problem, and there is a lot of activity. One thing just for folks to be mindful of is, like, the quality of the task data that you get, like, you cannot just get it based on job adverts.

Siobhan Savage: If you're getting that type of data, and most of the folks in the market don't have, like, an actual credible data source, so it's really important to make sure that you're asking the right questions to the vendors, because a lot of folks will wrap their product up in doing things and saying they do them, but, you know, you've got to make sure that the quality of the data is going to be really important, because, Kathy, remember the skills harmonization problem? That was hard. Imagine trying to harmonize tasks.

Siobhan Savage: Right?

Kathi Enderes: It's even hot.

Siobhan Savage: Like, you know, we've spent… we've spent, you know, three-plus years, over 40-plus million dollars on building this work, you know? Like, I've got a lot of gray hair. And the reason that we're so into it is because…

Kathi Enderes: Cover the world.

Siobhan Savage: Well, yeah, yeah, it's a good hairdresser. But the whole thing, just for customers that I kind of feel sorry, when you walk onto the floor of one of the conferences, it's very hard to distinguish who actually does what. So I think this is where your role to play is really important, because you help, like, buyers think through, like, actually who does what. So, this has been, like, really, really helpful in terms of, like, setting the scene. One of the things that I was really, keen to go through.

Siobhan Savage: I think the slides have went back to the start, I think.

Kathi Enderes: Oh, no, no, this is… Oh, is this one… Oh, this is your last one? Yeah, this is my… just my wrap-up slide, basically. How you get started, and I think it goes really well with what you're going to talk about, Shubhan, how… how you actually help customers. So, like, just basically saying, what should you do next, because that's always the…

Kathi Enderes: question that we get, like, oh, this solves all great, but how do we get started? I'm like, well, start small, think about this business problem that you're having, not just, like, you can think big picture, too, but a lot of times it's easier to say, here's a specific thing that we want to do, here's a specific business problem, to the point of these four business drivers. Think about what do you want to accomplish, why do you want to do this, and help prioritize, basically, where do you want to

Kathi Enderes: focus your energy on, on work redesign. And then, basically, think about this as a constant thing. Don't think about this as a project, but as an ongoing muscle, basically, that you're gonna have.

Kathi Enderes: Leveraging AI-based work intelligence, like what you're talking about now, Shoban, as well. And then think about how is it going to impact your talent model, because you're going to think differently about how you're going to recruit people, retain people, reskill people, redesign the work itself.

Kathi Enderes: Around those insights that you get from the work intelligence, and then always think about

Kathi Enderes: how can we involve employees here? Because the employees are really key to that, and Siobhan, you called it out too. It can be very frightening to them to think about

Kathi Enderes: Wow, I mean, everybody is already thinking about AIs coming to my… for my job, right, or for the work that I can do well, and all of that, so always keeping the employees in the loop is really, really important from a change perspective.

Siobhan Savage: No, this is incredible, Kathy, and I think what I've seen over the last few years, I've spent a lot of time kind of educating around this space, because, you know, first they think you're crazy, and then people kind of go, oh, this kind of makes sense.

Kathi Enderes: Right.

Siobhan Savage: really, like, this kind of, like, research that you've kind of put together really shows folks that, like, where the market is going, and this is not if it's going to happen, this is already happening. It's whether or not this, you know, folks here are actually prepared for it. And one of the things that, you know, we want to make sure that we're doing, like, is we're all in this together, we're all learning, sharing as much knowledge as possible. This research is really great. The thing that we always get feedback is, like.

Siobhan Savage: to your point, I was like, how do we bring this to life, right? And I think… I think one of the things I'm going to kind of walk us through is really starting to think about

Siobhan Savage: when we're looking at, like, what this new world of data looks like, like, what should that data look like that you need? And we talk about, sort of, you need to build an AI-powered workforce.

Siobhan Savage: you're going to need a new critical infrastructure. You're going to need to redesign, you're going to need to orchestrate, and you're going to have to evolve your workforce, right? And to do that, to Kathy's point, you're going to have to understand your world in a completely different way. So what we were… what we did, a few years ago was… the problem that I had at the time wasn't actually directly correlated to agents, actually. It was… I wanted to be able to match people and jobs together.

Siobhan Savage: And the problem I find is that we only had job advert data as a matching source, and for me, that wasn't enough information, so that's why we really started building out these ontologies. And we describe it as a work ontology. So we look at 25 different industries. We have a model designed for each industry. We don't have, like, this chat GPT output thing. We have, like, deep designed by expert models.

Siobhan Savage: And what you really need to focus on having is, you know, your new architecture will be your role level, how many positions, you're gonna start looking at processes, tasks.

Siobhan Savage: Subtask is really important.

Siobhan Savage: If you don't have the subtask, subtask is actually where the AI is automating at today. So think of the little micro-steps that are happening within a task. So if you're sourcing candidates.

Siobhan Savage: That's a task, right? But if you were to give that task to a candidate, or to a, sorry, to a recruiter, and say, go and build agents, like, they do many little micro-steps within that task, which are called subtasks. So having that data is really, really critical, and then the skill is then triangulated to the task and subtask. And this is important, because if you're taking away, tasks.

Siobhan Savage: Because of agents.

Siobhan Savage: every time you take away tasks, you take away skills. And this is how you're gonna know supply and demand of your org, and what skills are being required, what skills are not being required anymore. So this is how the whole thing plays. The way that we really work with customers, and we're really fortunate we get to work with some of the best customers in the world, and it's really focusing on giving them the source of truth of how their world is working.

Siobhan Savage: Right? And, like, we look at it at an industry level, but then we look at it at a company level.

Siobhan Savage: What we have seen… so I'm gonna kind of show you… we've done this now over and over and over again across multiple different industries.

Siobhan Savage: And we typically have seen this pattern. And, like, imagine you're on… I don't know if you guys had them in America, but you know those, like, spinnies at a playground, and they spin, and you just jump in where you can?

Kathi Enderes: It's a merry-go-round. Yeah, merry-go-round, yeah, yeah, yeah.

Siobhan Savage: So, like, you literally jump in at whatever point, and some of you will be at multiple different parts of this, some of you won't have done anything. All of that is okay, because what you're gonna learn is this new skill to actually build this

Siobhan Savage: work re-engineering journey into your company, and it's never gonna stop, just to be clear, because we are going to be reinventing work for the rest of your career, so this is a skill you all need to learn. And what we have seen successfully play out now, after many goes at this, many trials, many, you know, wins, many losses, like, lots of different, like, ways of testing and trialing, is in order to re-engineer your work for the agents.

Siobhan Savage: You need to, step one, you need to make work visible at a task level. So Kathy's point is bang on. Subtask, task data.

Siobhan Savage: connected back into your job architecture, we call that a work architecture. So it's kind of taking in that old job architecture, evolve it into a work architecture, which means all of your job architecture stuff is really important. It's directly linked to, like, RAM and all of the leveling, and all of that important data, but it also has the work context.

Siobhan Savage: So, give that visibility into your work. Step two, then, is where, you know, we see HR actually playing this really interesting role right now, Kathy. Some are kind of taking a step back and saying, this is not our problem, this is IT's problem, they're in charge of agents.

Siobhan Savage: But what we're seeing is some really awesome companies who are saying, no, like, this is our domain, we should be part of this. IT look after tools, we are responsible for work and workforce. So what we're seeing now is, like, workforce innovation, we're seeing, like, these different teams that are being stood up now, and imagine they're, like, Navy SEALs.

Siobhan Savage: And they're being, you know, put into the business to help them reinvent at an enterprise level, which is very exciting, and it'll be, I think, a really hot role for 2026. So, I'm happy to share with anyone after the fact, if that sounds exciting, what I'm seeing, and the skills that you'll need for that. But where we typically see the business needing help is, tell me where there's waste.

Siobhan Savage: Tell me where there's opportunity, right? Like, actually navigate me to where I should actually go. You're not going to reinvent your whole company in one go. It's not possible. You don't have the cash, you don't have the resources. Some tasks should not be automated, right? Like, let's go after the most valuable task, whether it's going to help us save money or make money.

Siobhan Savage: let's start there, and then what we need to do is then re-engineer your workflow for the agents. So, what agent is actually able to do this work? Most of our customers will not pick one agent. They are multi-agent environments, and what you need to make sure is that, like, what agent is best for what task, and what agent will complete this task with the best hit rate, and how do we re-engineer that new way of working? You don't just stick an agent in and hope that

Siobhan Savage: for the best. You have to change the way that work happens. What is the steps that we're taking out? What new has to be brought in? And then you want to equip your people. Where I see the biggest failure is not connected to the agents.

Siobhan Savage: Agents are not the problem. The problem is that you throw out a new way of working to your people, and then you give them generalized prompt training, and then everyone expects, like, a 100% increase in productivity, and then they wonder why no one has adopted it, right? It's, like, a really interesting thing that we're seeing. You need to equip and amplify your people. Show them their new way of working. If you're expecting that a salesperson books 5 meetings a week.

Siobhan Savage: And with the new ways of working, they should be booking 7. Tell them that. You can't expect them to not know this information, like, what is the outcome? What is the change in work expectation? How does this workflow feel? Help them make sure that they're comfortable doing it. And then step five is really proving that the agents are actually working. You don't want to re-engineer. I don't know if you would have seen in the press, but there was, like, I'll not mention the company

Siobhan Savage: name, but they went out, and they were chest-beating, and they were like, we've…

Siobhan Savage: They've cut thousands of people's jobs because of agents, and they've had to rehire them all.

Siobhan Savage: Right? So, you know, until you're sure this thing bloody well works.

Siobhan Savage: Don't go and make changes that are going to completely impact your people until you're sure that the impact is there, and then start re-engineering your jobs and work, right? Then start looking at those pivots and those reskill moments. So that's where we see, you know, this is kind of like a flywheel, always on, doesn't matter what department you're in, doesn't matter what industry you're in, this is a consistent way of thinking about this.

Siobhan Savage: And for folks who are on this call, like, you need to do this. Like, you need to support the business in doing this, because what they're gonna do is they're gonna focus on one part of this.

Siobhan Savage: And, like, we want companies to be really bold.

Siobhan Savage: We want them to reinvent, like, I'm building a billion dollar company, and I'm going to do it with under 100 people, right? Like, that's my mission, to see if I can do that. But, like, I don't want to impact people on the other side. I want to make sure that I'm doing both at the same time, so really being bold, but also responsible, and making sure that you're actually connecting the flywheel.

Siobhan Savage: The other thing is, if there's folks here that have got businesses in Europe, workers' councils do not like AI. And if you do not have a plan to have the conversation around what you're going to do to make sure that your people are not being left behind, your business will be blocked from deploying AI, and you will not move forward. So it's really important from a compliance and a legislation, but workers' council, that we're also thinking about the whole life cycle.

Siobhan Savage: And CIOs really want to work alongside the CHO's team right now to solve this problem. We're seeing that we're getting bought a lot by the CIO and by the chief AI officers, Kathy. Like, I would say 80% of our new customers are all in that group, and then what we do is we go and knock on HR's door and say, guys, why are you not involved in this project? And they were like, oh, we didn't even know. And I'm like, guys, you need to get together, you cannot be in

Siobhan Savage: silos, like, let me bring you together, and then we get really, like, a great relationship with the customer, because we're looking at it through this whole flywheel.

Kathi Enderes: Yeah, no, it's so fantastic, and I'm so glad you're mentioning that, kind of.

Kathi Enderes: collaboration between IT and the business and HR, because I think for everybody, if you're in HR, this is a huge opportunity to add a ton of value in the thing that matters most to the business right now, right? If you're not involved in this, you're missing out on a big, big opportunity. We get this a lot of times from

Kathi Enderes: CHOs, the forward-looking ones, I'll say, well.

Kathi Enderes: I just inserted myself into the AI transformation. I just have to be head-on-head, like, leading it together with the CIO, because it can't be an IT-driven thing alone, because we've all been there, right? We've all been there when there's something that's purely tech-driven.

Kathi Enderes: Well, CIOs don't have a mandate to look at skills, to look at people, to look at where people are adding value, and all of that, you can't do all of this, like, reskilling, redesigning, recruiting people differently without these insights, so it's a really huge opportunity.

Siobhan Savage: And most of the time with customers, what we see is that when we've started working with HR and give them the work data, and they bring it to the IT and the CIO's team, they're like, oh my god, where did you get this data? We've been really struggling with use cases, and we've spent so much time

Siobhan Savage: Trying to figure out where to go, that we got stuck in status quo, and you're not giving me data that tells me what's happening.

Siobhan Savage: and then where to go, it's like a completely, like… and then HR, like, yeah, that's kind of like our thing, like, it's our job, right? So it's like, I mean, this, I think, will be the biggest career move in 2026. Anyone who's on this call, who's thinking about what your career, future, like, these jobs will be so great next year, because they'll have this kind of, like.

Siobhan Savage: middle ground, kind of, I don't know if you listen, but we hosted a webinar with MasterCard, CHRO, and the Chief AI Officer there, and they described, Kathy, on the webinar, that this is like a COVID moment, where CFO, CIO, CHRO are suddenly, like, working really closely together on a problem that they both… they all need to be involved in, which I thought was really interesting, because you're going to see this kind of new

Siobhan Savage: effector happening across the business, and your CFO cares because they want the money, right?

Siobhan Savage: Also, they're spending millions.

Kathi Enderes: Right? So they say, well, we can't invest in one side and not recoup it somewhere else, and yet only 5% are actually seeing that value, as we saw.

Kathi Enderes: MIT studies, so somewhere… Exactly.

Siobhan Savage: Exactly, and I think… I think the spend on AI versus the not seeing value, plus not knowing where to go, is causing a lot of stress in the system. A lot of the work we're doing right now, our data is getting presented to boards.

Siobhan Savage: Because they have to report on the status, so the CEO is under pressure on this. And I think for folks, you know, practically, if you're thinking about going and running this in your company, or you want to start taking action, is, you know, get, like, a map of your work.

Siobhan Savage: You know, like, get, like, imagine that you have a map of your company, and you can be the navigator that tells people that we're gonna show you what has always been invisible is now visible, and get that map, and get that view, so that you can then be the one that chose the business.

Siobhan Savage: Where is that waste? Where is that opportunity? Can you imagine someone in HR going up to the CFO and saying, I know the tasks that will actually make us more money?

Siobhan Savage: Can you imagine, like, what the impact would be to have that level of conversation, right? And I think, you know, that map is like a GPS, kind of navigating you on where to, like, start taking that action.

Siobhan Savage: And then the complex thing I find… we see Microsoft Copilot is, like, a lot in most companies, because they are already there, and what we see is, like, you know, people are wanting to use ChatGPT, people are wanting to use Copilot. Our view is, like, look at all of the agents that you are pre-approved, allowed to use, make the most of those before you go out and start buying others, because what you're going to find is

Siobhan Savage: If everybody is allowed to go and build their own agents, and you're all building the same agent for the same task, but no one's communicating, you're gonna cost the business an awful lot of money, and it's called shadow AI.

Siobhan Savage: Right? Where it's just everybody has got this, like, scope creep, and it's just a mess. So really helping the business figure out, like, what agents… There's also a debate, Kathy, like, where do the agent logs sit? Like, do HR keep a register of them? Is it in IT? You know, there's, like, this kind of different conversation about where it should sit. So, like, that's gonna be an interesting one that we watch play out as well.

Kathi Enderes: For sure, yeah.

Siobhan Savage: And then I think, like, just really that point around amplifying your people. You know, make sure your people know the expectations about their role. You know, if you are going to have a new way of working and people are not performing to that, have you actually spoke to them about this new workflow? You know, are they knowing that this is really important? That's going to be something that, like, you can't say it's field if we haven't spent time making sure that your people are actually

Siobhan Savage: One, learning the skills that they need just in time to adopt the agents, but also, like, learning that new workflow. So, like, if someone has to do their work in a completely different way, if, like, you have to give them that view of what that's really looking like. And then what you want to then focus on is that impact.

Siobhan Savage: How has work changed?

Siobhan Savage: So when we talk about, like, impact on agents, it's not like, what's the consumption and many people are logging into this thing? Has work actually changed?

Siobhan Savage: did something get better? Did we increase outcomes? Like, that's a real true measurement of actual, like, agent impact as well. So, you know, helping folks be really clear on, like, what is that actual impact, and is it enough?

Siobhan Savage: that we start re-engineering these jobs, and that's the part where I do worry about, is that folks go too soon without, like, watching it play out. I'll give you an example. Like, when we first started really playing hard in agents.

Siobhan Savage: We built, so you know whenever you're booking demos, and, like, salespeople reach out to you and ask you to book a demo, right? We had a whole team of folks that were doing that.

Siobhan Savage: and, you know, cost a lot, a lot of money. And their job was just to go and find folks to go and, like, show our product to. We then completely removed that and built agents, and what we find was it was really great for about 2 months. And then it pissed everybody off. Right? It was just so aggressive, and the language was terrible, it felt like a robot, it, like… so then we pulled the whole thing off.

Siobhan Savage: But… So, like.

Siobhan Savage: you know, I shouldn't have made too much structural change too soon, but I did. And so I think this is a lesson that I've had around this journey, is, like, you gotta wait and see, is this actually effective, and does it work before you go and start to re-engineer. And then, really finally is, like, you need to make sure that your people are being prepared for this. One of the big things that we've seen from customers is that they're really, like.

Siobhan Savage: focused on talking about AI, but the employees are feeling afraid, and a lot of the reason is just we're not communicating, hey guys, that we are going on this journey, but at the same time.

Siobhan Savage: We're looking at, like, what could you pivot into, what are those jobs that are important, and communicating that.

Siobhan Savage: even just the openness to having that type of conversation, and knowing that, like, these jobs will still be really important for our company, these jobs are probably not likely to be things that we're going to invest in in the future, which means that if I'm an employee, I at least have the visibility of, like, what matters to the company, so… and the skilling that's required so that I can drive towards that. So I think that's going to be a really important dimension as well for folks to just make sure that

Siobhan Savage: they're actively evolving their workforce. It's not gonna happen overnight.

Siobhan Savage: Right? Like, the reskilling requirements, this is not like a… it's… it's… AI is definitely having impact into the… into the jobs, right? But it's not necessarily impacting, the actual jobs to the point that we're seeing, you know, massive amounts of changes, Kathy. I don't know about what you're seeing. Like, it's little incremental shifts to task.

Kathi Enderes: Yeah, not yet, not yet. I think most organizations, because of where the technologies are, quite frankly, we're seeing, yeah, we're seeing, like, smaller pieces, which we also see when we look at companies being… most of them are in stage one or stage two of this maturity model. I think that shows also it's mostly still, like, these

Kathi Enderes: within your… your own productivity thing, but things will come, right? Things will come pretty rapidly, and I think if you want, you can either be a winner on this, or maybe I don't want to call it a loser, but maybe a non-winner on this, and if you're not…

Siobhan Savage: I mean, there is gonna be winners and losers, but, like, I don't think you're being harsh. I actually think you're… you're being really honest. I think there… where… where the biggest loss, I think, will be, will be that people waited too long and didn't do stuff.

Kathi Enderes: Yes, yeah.

Siobhan Savage: And they hung out and waited.

Kathi Enderes: fast. Yeah, the tools move so fast, right? AI moves so fast. Every day, something new comes around, and I mean, I always say also, think about the AI we have today. It's the worst AI we'll ever have, right?

Siobhan Savage: I know.

Kathi Enderes: It is, like, it's the worst that we'll ever have, so… and it's getting very, very fast, like, it's getting very, very, fast and rapidly evolving, basically, how… what these AI tools can do. And so, if you are constantly doing this, you're not going to be left behind, but if you're waiting until the AI tools are perfect, you way have waited too long, right? Because they're never going to be perfect.

Kathi Enderes: But soon enough, we're gonna be able to do amazing things, and

Kathi Enderes: And yeah, it's, it's, it's always moving faster, basically.

Siobhan Savage: Yeah, and I think, like, the advantage of going first is the longer the AI has time to train on your model, the stronger the AI is, the harder it is for other folks to catch up, right? And I think, like, most organizations I have seen really focused on, like, what can they build into their offering to get

Siobhan Savage: to their customer. What we're starting to see now is this, like, two-pronged approach, like, what can we do for our customer? But now it's like they're turning their glances into the internal workforce and saying, okay, what are we doing here? Why have this… why have we not shifted the dial here? And I think for folks on this call, like, this is the most exciting opportunity of your career that you will ever have.

Siobhan Savage: This will never happen again. Like, this moment in time, and I know I sound completely crazy.

Siobhan Savage: But this is, like, a once-in-a-generation change to work.

Siobhan Savage: That companies have no idea what they're doing.

Siobhan Savage: There is no, like, even the AI companies are still figuring this stuff out, right? Just like you said at the start.

Siobhan Savage: And I think folks that put their hand up and go, put me in, I'll.

Kathi Enderes: Yes.

Siobhan Savage: Like, let me have a go. Like, these folks are going to have the best career opportunities, and I think the biggest roles of 2026, as I said, is going to be really focused on this kind of work intelligence, work innovation, whatever you want to call it. It'll be about helping the business move from a historic model of people and jobs into this new world of human and agent.

Siobhan Savage: And what is it that we can do to enable that new world? And I think if anyone is interested, you know, after this, like, feel free to, like, send me a DM, and I can send you some information. But also, if you guys want access to some data that you can go, I can give you some of our data for free, like, just take some of our data from our team, and go and bring it to your leaders. We also have it uploaded into… into Galileo as well, so I think, Kathy, like, there's… well.

Siobhan Savage: we'll push the Galileo, tool, because there's a lot… I was actually on a customer call the other day, and I'll not mention the customer, it was a very cool customer, and they were like, oh, we already have seen your data, and I was like, how have you seen my data? And they were like, through Galileo, and I was like, that's actually

Siobhan Savage: That's really cool! And they've been using this with their teams to really start focusing on… and the one other thing I would say to folks on this call, there is a moment in time where you should be customer zero.

Siobhan Savage: Like, don't let this happen to you. I think that HR, right now, even if it's just tiny, and it's a little scope of work in your teams, I think you all have a responsibility to learn how to do this to yourself, to then bring it to the business.

Siobhan Savage: So I think, like, no one… you're not gonna, like, you get to learn openly, there's a whole community of folks that we can connect you to as well that are on this journey together, but I think, like, you need to build the skill for yourself, but also, like, be customer zero. Like, show everybody how to do it, and then bring it to the organization.

Siobhan Savage: And that's how, you know, you become the hero of the story, right, Kathy?

Kathi Enderes: Absolutely. I mean, you mentioned Microsoft, for example. I think they are doing this, or I know they're doing this in HR first, so they're starting with HR, because they're saying, we need to see how this works for us before we can bring it to our clients. And if Microsoft is doing this, who are selling the co-pilot, I mean, that's their business model, is that I think everybody could maybe take a leap from that as well, and

Siobhan Savage: And everyone that we work with, as well, like, we're, like.

Siobhan Savage: even if the business is asking for this, do yourself too. Like, make sure that you're getting this. But, you know, for those, you know, that joined us today, thank you so much. Hopefully you find this information. It's a lot, this is a new world, but we're trying as best as we can to share as much of the learning as possible. Kathy, thank you so much for the expertise, the time, the research. We're going to share, and after this fact.

Siobhan Savage: pack.

Siobhan Savage: with, like, some information. If anyone wants access to some data, happy to share some of our out-of-the-box data so that you can use that and sort of learn. Have an incredible day. For those who are listening on the treadmill, go faster! Like, come on, you can do it!

Kathi Enderes: Thank you so much. Thanks, everybody, for joining us. This was awesome. Thank you.

Siobhan Savage: Daycare.

Kathi Enderes: Appreciate it.