SAP’s Josh Gosliner & Reejig’s Amy Wilson on Using AI to Reengineer Roles and Skills

Josh Gosliner: Right, right.

Siobhan Savage: Hello! Hello! Technical issues, Josh, are you there?

Josh Gosliner: I'm here.

Siobhan Savage: So you get a whole pile of AI product. People on a call, and no one enters the way.

Josh Gosliner: However, I'm just here for a skills. If inference in zoom.

Siobhan Savage: Hello, everyone! Sorry for our technical incompetence.

Amy Wilson: All right. Hi, thank you so much for joining us, everyone. I'm Amy Wilson. You might remember me.

I was the former head of product at SAP Successfactors, and I am currently the product advisor for Reejig. So I'm just so delighted that this webinar came together, bringing my past and my current together, but also an opportunity to really help Successfactors. Customers thrive in this new world, which was my passion for many years, working with Successfactors, customers, and continuing to do so. I do believe wholeheartedly that Reejigs work.

Intelligence is the missing ingredient that we all need to unlock the power of SAPs. talent, intelligence hub. So that is what we will be talking about today. And we're going to get into the nitty gritty.

We're going to see a demo of Reejig's work, intelligence. We're going to better understand how how this integration works, how the whole thing comes together, and it is an exciting new category of work, intelligence, and it is an exciting new partnership between SAP Successfactors and Reejig, and I am just so honored to be joined by 2 of my favorite people. and we're going to just show you all sorts of things.

So if you do have questions, please put them into the chat. We will have plenty of time to address them. All right, so my speakers today are first, st Siobhan Savage. Siobhan is the CEO and co-founder of Reejig.

She has over 20 years as a workforce strategist, which I don't understand, because I think she started at the age of 5. She's seen the cost of wasted potential firsthand through her workforce strategy work. And she has built Reejig to give companies true visibility into their people so they can re-engineer work and responsibly transform for the AI era. Siobhan is one of the most visionary people I have ever met, and I'm so excited that you all get to hear from her.

Josh Gosseliner is Vp of product strategy at SAP Successfactors, my former colleague, and he is charged with defining the long term vision and strategy for product developments and partnerships. He has a knack for synthesizing the what and getting to the why and all with such positivity and verve. Josh, it is such a pleasure to have you join us today. And so I thought, we just go ahead and dive in and have you walk us through some of the workforce challenges that you're seeing SAP customers encounter all right over to you.

Josh Gosliner: Well, I 1st I want to contribute to the gush fest and say, Amy Wilson, an absolute legend in the HCM. Space, and certainly somebody that I look up to quite a bit myself. And, Siobhan, one of the things that really strikes me about the conversations that we've had together is just,, the way in which you think about this space. one to 2 steps ahead of where everyone else is.

So I'm super excited to talk about work, design, re-engineering work and all of the above. I think one of the things that really strikes me about some of the conversations that we have with our customers is that they're all over the place.. Everybody has ambitions around skills. but where people are today certainly is not necessarily aligned with, where they want to be and and what we've seen is that we have this real dichotomy within the the Successfactors customer base, where you have organizations whose ambitions have not yet caught up to where they are in terms of an execution standpoint. And then you also have some of our customers who've gone out bought a bunch of software, but don't yet have the data to actually make that software really work for them. And so I think one of the things, especially as we get into this topic of work design is really thinking about.

how can organizations be more intentional about what it is that they want to accomplish with skills so they can actually execute upon them. But really, what, what I wanted to talk about and and what's on this slide is really looking at the different levels of maturity or or different thought processes that our customers have around skills. And this really is a spectrum that goes from the skills implied to the skills based. I will be fully honest and just say that I'm a little bit of a skills based skeptic.

I think this notion that we're gonna all move to a world in which there's no job roles. And we just all have skills. And we move through the world into different things that we do based upon skills that we have is probably a little bit far fetched. But for sure what our customers are thinking about and and what we're thinking about in success factors is, how do we enable organizations to be more intelligent about skills and how they leverage skills within the organization?

And I'll talk about that a little bit more on the next slide in terms of what that means in real world implications. But really understanding how involved organizations are with skills. The data that they are making available to infer skills is really important. So if you start with the skills implied,, this is where really, I would say most of our customers are today.

They're very much living in that world of being job Centric. And really the majority of data that they have, or they think about in terms of skills really comes from a CV resume. And it's really all about a manager being able to understand the history and the capabilities of their employees and putting them in the right position to be successful when you start getting into a skills included organization.. This is where organizations. And I would say, certainly, this applies more to particularly technical organizations., I would think a lot about pharmaceutical life sciences, organizations in which credentials are highly important, really understanding specific technical skills and making sure that that's an important part of people's jobs.

When you get into skills led. Now, we're starting to think about what are the data sources that organizations have? And how do we bring skills out of those data sources to really better infer skills and give us a better sense of who are the people that we have? What are the skills that they have?

And what does that mean for how we deploy those people within the organization. And,, I said, before, I think the skills based organization or the fully skills based organization is still a little bit of science fiction at this point. But the notion that we can get to an entirely skills operating model is is still a little bit far out. But the idea that we really want to fully understand the skills that people have make intelligent decisions from a workforce planning perspective and operational perspective around skills, I think, is certainly an ambition that every organization should have in one way, shape or form.

Just moving on to the next slide.

Amy Wilson: Hey? Before we before we do that? Josh? Just just in terms of looking at these different maturity levels.

Siobhan, are are you seeing the same thing when you talk to organizations. or are you seeing a different spectrum of maturity?

Siobhan Savage: I think I agree. I agree with most part of what Josh said, what we're seeing and and and I think most customers, or folks supporting customers on this call will probably have felt a huge shift in priority. So, and and the direct correlation between,, a couple of years ago we were really focused on our people and understanding who they were, and finding meaningful work and opportunity for them and passing. And it was all about retention, retention, retention, keep our people grow them, I mean.

The party stopped. And and it's switching on the the lights in a nightclub and everyone went. Well, actually, we don't really care so much about that right now, we're fighting for survival and and what basically that did was it created a real hard handbrake shift on that component. And that's not to say that companies don't care about people and people's skills.

I think what happened was they 1st needed to understand., efficiency, productivity, and all of these things became a much, much bigger component. And back to Josh's point, it's, who are our people? What are they doing?

Amy Wilson: So so no matter no matter where people were on the skills journey, everyone's starting at at the 1st step now, anyway.

Siobhan Savage: Yeah. And I think the other, the other thing, just to call out as well, it would be a bit crazy for us not to mention this, we've gone from skills based org being the focus to now, how do we build an AI part workforce because our Ceos are caring about that. So that's that's universally in every country around the world, every customer, everyone is now driving, and that is because of the efficiency and productivity and velocity focus that the Ceos are really caring about right now, which means that it completely changes this whole conversation around skills. And this is why this partnership is so beautiful.

Because if you bring the 2 levels of expertise together, how do we solve? For the good times and the bad times. How do we make sure that we know where people? How do we make sure that we're understanding the work so that we can,?

Look at that productivity and velocity. But I think to Josh's point on the maturity cycle this is a data problem. This is, how do we know who our people is? How do our people are?

How do we know what the work is? And there is customers to Josh's point that went out and bought 6 different systems and didn't have any data in it. And it doesn't tell a story. And one of the biggest problems that we see, which is why we love the approach of the talent, intelligence, hub is that if there's no one common language of skill, how are you hiring, and how are you mobilizing in different languages doesn't make any sense.

So there is definitely a different shift in parties which has led to a different conversation than in skills and data. And everybody is at a completely different maturity cycle. And I can talk a little bit later on on what we're seeing in the market around that. But I do agree with Josh on.

Folks are at all different stages of what good looks, and I also do agree with Josh on. I don't believe there's a world where skills are going to be., the jobs don't exist. As much as I believe in tasks, and I believe in skills, I still do believe that we will have one foot semi in that world with a flexibility around movement of work. The worker.

I think it's around task, but not skill. And I do think you will always have folks clustering around squads, job titles. I. So I do agree with Josh on that.

Amy Wilson: Okay. Cool. Alright.

Josh Gosliner: I mean that honestly, that's the perfect transition, because I couldn't agree more. And I think of one of the things that I think of, Siobhan. You're pointing out. There is this transition from a covid and post covid paradigm, of which, employee experience was everything, and retaining our people was everything, and making them feel amazing and have all these opportunities, is the most important thing to really.

We're moving into this AI paradigm, where the balance of power is really shifting back to organizations. And, when you, when you think about this employee experience paradigm, it was all about, how do we create internal mobility? How do we create opportunities for employees? And I think that remains important to be clear.

But, what you see, on the right side of the screen, here in talent, development was really the way in which a lot of organizations have, I would say, gone from skills, curious to skills involved. And, really thinking about,. how do we make informed decisions at the employee and manager level on who we hire? How do we upskill?

How do we provide that employee experience all these things you see on the slide here? And and really, I think, where we're moving towards in the era of AI is that we're going to be getting into massive workforce transformation. And I think we don't fully understand what this is going to look, and I would assume that the visionaries of Amy and Siobhan have a better idea of this than I do, but really thinking about the thing we know for sure is that AI is going to be disruptive in one way, shape or form. And so what that means is that organizations that make this transition faster are going to be more successful, and and their ability to bring people along for this journey is going to be incredibly important.

So I think that gets into the 2 main jobs that you see on the screen here is, how do we understand the way in which we want to redeploy people in a in an AI world to make sure that they are performing jobs and tasks that are really aligned to the capabilities that humans have, and getting them away from the things that they shouldn't be doing, because they're not good uses of their time when we do have generative AI and and agentic AI tooling. So for sure work, design is an important part of this, because you need to understand the things that your people should be doing and the things that they shouldn't be doing, and that goes directly into strategic workforce planning. And I would say one of the things that's probably unique to SAP is our ability to bring in data from across the enterprise, the idea that you can understand not only the people you have the skills that they have. But being able to understand, what's the demand for labor?

Where does that exist? How does that relate to our supply chain data. How does that relate to our customer data that is something where organizations are going to be so much better informed about the demand for labor. And really then, being able to look at, how do we optimize the workforce?

Because,, there's, of course, knowledge workers where you're going to look at a much more permanent way of thinking about deploying people. But there's also just workforce optimization that's more of a day to day frontline worker view of things. And and really, the next step in this is once you've made data informed decisions about how you want to redeploy people. One of the things that organizations are going to need is really this notion of organizational modeling getting into?

How do we actually reshape the workforce? Because I would say, one of the things I expect to see is that co-location of skills is going to be much more important as we move into this world of which, the knowledge work, is going to change. making sure that you're bringing together the right knowledge in the right places and getting work done face to face is going to become more important because that day to day grunt work. That AI is going to be much better at doing is is something that you don't need to be co-located for.

Amy Wilson: And so.

Josh Gosliner: And.

Amy Wilson: Josh, just just to interject for a second. So.

Josh Gosliner: Yeah.

Amy Wilson: . Last year you introduced this open skills ecosystem, which we can see down on the bottom right? And that was really meant to create,, single languages of skills to to bring forward the talent development, use cases that you have over on the right hand side, right? And of course, Reejig is is one of those skills sources, one of those skills ontologies that can be leveraged.

But what we're talking about here today is much bigger than that. And and you were starting to get into this, the whole workforce transformation piece. And so I think I think it's really important to to recognize that that what we're talking about here is is how Reejig fits into that right? And so does create a skills ontology.

But that is really a byproduct of the work intelligence. That is what I would consider to be the tip of the spear into that workforce transformation.

Josh Gosliner: Yeah, I, mean, look.

Amy Wilson: Insight.

Josh Gosliner: For sure. And and and I would say one of the things that, if you look at all of these partners that we have that we've opened up the Talent intelligence, hub to. It's they are bringing different things to the table. and certainly, Reejig and work intelligence and being able to get into work design that directly feeds to that strategic workforce.

Planning is, that's why we're on this call today is because I think. That's a unique ingredient that Reejig brings to the table here, and that should inform that top down view. But as I was alluding to at the beginning. you can't change the workforce unless you're bringing people along for this journey.

And so part of this is, of course, defining what people should be doing and then empowering them to upskill and reskill to do the things that they should do, understanding the transferable skills that they have. They're gonna make them successful in those new roles that you create. So part of this, of course, is for sure, the unique components that each of these partners bring. But it's also making sure that at the end of the day an organization has a single view of skills, because, you see, some of the partners down there are learning or in recruiting.

You don't want to have one set of skills that you look at for making hiring decisions and a different set of skills that you look at in terms of upskilling and reskilling in a marketplace or leveraging an lms or an lxp. What you want to do is you want to have one set of skills that you leverage across all of the the talent development lifecycles so that you're making informed decisions on a single set of data.

Amy Wilson: Absolutely great. Alright, so. So, Josh, I I think this. This makes complete sense in terms of how how SAP is able to start with the the workforce planning, and then bring that forward into talent execution.

So, planning all the way through execution and and really understanding how important this planning piece is. Now, in order to do the work, design the work, re-engineering, and how that's necessary to better understand the skills that we can predict that are needed for our talent and which ones aren't right. And so I think at this point. why don't we?

We move over to Siobhan and understand a little bit around her, thinking right in terms of understanding. getting a sense of how customers have changed their needs, and and what Reejig brings to the table. Sound? Good.

Okay? So, Siobhan, I'm gonna stop sharing and turn it over to you.

Siobhan Savage: Yeah, cool. One of the things that, I think Josh and I are complete. Sink in for for me, we are all moving towards this once in a generation. Change to work.

That that's that's happening the train has left the station. That's what's happening. What we believe is that you want to help customers on one side boldly reinvent your workforce we can have opinions that we don't. The fact that AI is coming in.

And we can have opinions that, we're worried about people's jobs. But the reality is this is happening. Ceos have a mandate. They're doing it.

And our role, all collectively here, is to have really strong leadership in navigating our, whether it's our customers or whether it's our companies to be really bold, so boldly reinvent this workforce, but at the same time be really responsible, and make sure that we don't leave people behind. So at Reejig our mission has always to be,, create a world with 0 waste of potential in people, in business and society. My expertise is in workforce optimization. So I specialize in really understanding how to make a machine hum?

How do we get this whole org moving at a rapid pace so that we're high performing, and we're hitting our objectives. But in the same hand, the other part of my brain thinks about. If I do that job really, really well, I'm going to impact a whole pile of people, and I don't want to do that. And I know most customers don't want to do that either.

So the way that we come to this conversation with customers is that we're saying, Okay, let's go. Let's help you reinvent your workforce. And one of the main things that blew my mind. And and just for context, if anyone doesn't know,, we started the business 5 years ago, very focused on skills.

we were. People have skills, jobs have skills. Everything is skills. Everything was skills, skills skills.

And what we find was that when we started to look at actually moving work to workers at that time employees. What we find was that the matching algorithms weren't super amazing because we didn't have enough context of understanding what was actually happening in work. and that led us down a rabbit hole to go hold on a second. Let's day 0.

Think this through. And what we ended up finding was, people have skills. absolutely. People have skills, jobs don't have skills, jobs have tasks.

And you do things in jobs. Right? So people do, whether you want to call it the jobs to be done, the tasks, whatever jobs actually are, a collection of things to be done, a collection of tasks. And where skills come into this is, you require skills to complete those.

So what we found was, we're missing a whole piece of information here that is limiting us from understanding what's actually happening in this organization. And at the time this was Pre AI,, chat Gpt hype cycle stuff. this was years ago when I 1st met Amy. This was very focused on, how do we understand?

So we can give a better data set to our customer on the skills and the skills should be triangulated to the task. So the way that we started to to build we took our skills model that we built, and we chucked it in the bin. We spent over 40 million dollars building that. So it wasn't a little thing.

It was a very long process of us over that period, and what we ended up building was what we call a work ontology. and this is where we look at what is actually happening within an organization, what is all of the work that's being done? And what are the skills required to complete that work? And that became our new model.

And we started doing that over 3 years ago, because we wanted to make sure that our customers had a good understanding and an example with , one of our big customers in the Uk. Was on one side of our business. We found that they were recruiting in a very different way, and how they were learning versus how they were mobilizing. So they had all of this different thing, and their job descriptions were different from their how they do their position, descriptions versus how they do capabilities versus how they do learning, and what we did was went back to that customer and said, Hey, listen!

We need to create this common language. And if we don't create this common language, what you're going to find is the whole thing is not going to work. So this work ontology at the time was born from a place of How do I help customers solve for creating that one common language, what we didn't realize. And this is where it got really lucky was that we didn't realize that when we looked at our little playbook of people have skills, jobs have tasks, tasks, need skills.

AI does not automate skills, AI automates, tasks. So what we realized was that we're sitting on this gold mine of data that is giving a full view of what is all of the work that's happening in the company. And if you talk to your CEO. They're talking about tasks.

If you talk to AI companies, they're talking about tasks. If you listen and follow any of the AI conversation. Everything is talking about tasks and processes and workflows. And that is the common unit of work that is really important right now in this new era.

So we really believe in the skills mindset, but at a connection point to the people, and when it connects back into the task. But we don't. We don't come from the approach where it's 1 or the other. We think it's both.

We look at skills and tasks. And this is why this partnership is so exciting, because no one else in the world has the data that we have. We're sitting on the world's 1st ever work on tologies, 23 major industries, every single role, every task, every subtask, every skill, every action. all of the skills, are triangulated to those tasks.

Which means that if you are looking at, what does Josh do if he's a product. Vp within Successfactors. We know with a level of certainty that this data actually is really highly accurate. So we can tell you about the people, and we can tell you about the work itself.

So this is really where we came on this journey. And what's really fortunate is that it's hot. We've been busy, and I think that bringing together this view where, we completely are aligned with the success factors. Governance you need to have the shell that governs the whole thing.

You can't have everybody off doing their own thing, because if you have that, that becomes a complete mess. So we're completely aligned with, how do we create a really strong house that will make sure that this common language of work is looked after. And we're not allowed random stuff just getting pushed in and out. We have this walled system that'll allow for this data to populate.

And this really starts to come off the work operating system. So that's a little bit of context of where we were and where we got to. What we're seeing now in market is is consistent everywhere we have the same conversations with customers over and over and over again. They have cost pressure, they need to reinvent a lot of Hr people are actually taking it upon themselves to reinvent themselves before it happens to them which I love.

They're taking that whole thing into their own hands because they want to be customer 0 so that they can then bring this expertise to the business. and a lot of it stems from help me think about how to build this AI powered workforce. How do we redesign work for this new era of work. How do we look at our job architecture?

It's built for the 19 eighties. All of the job architecture stuff that the consultants are doing is so out of date in this new era that it's snapping a piece of chewing gum that hasn't been touched right? It is so terrible for this era of you need in this new world. If you imagine that every time you bring in AI you take away a task.

But you also introduce a new task that you've never had before. So if you go and do a project where you create a job architecture and you create a static skills taxonomy, and you have it on a spreadsheet. By the time you actually save that file in your drive, that thing's already out of date. Right?

So it is extremely important for you to use the technology that you've got to keep this whole thing live and dynamic. Because if you're thinking about transformation, AI is not going to be we do one team. And then we're done. This is going to be a continuous evolution of work itself., the maturity of AI today will keep 10 x in over the next couple of years.

And what you're going to do is you're going to keep going through the business and continuously evolving, which means you need data and a system that's going to keep everything constantly dynamic and evolving with a level of governance and structure around it to make sure that you've got one view of what's actually happening within your organization at a people level, but also at that work and job level. So big cost pressures want to be AI first, st a lot of Hr folks. I'm loving right now, because maybe 12 months ago, when I came to market talking about this,, mostly in the Us. It was, what?

Very much. So a change now in okay, this is coming from our CEO. This is,, non-negotiable. We've went from taking 12 months to sell a contract to our average sales.

Cycle is 5 weeks now. So that shows you the urgency on, how much this is a problem right now. Sorry.

Amy Wilson: Okay. So, Siobhan, you you talk a lot about the the rigidity and archaicness of job architecture, and that the new thing is. Oh, no, no! The new thing is work, I mean I I don't think anyone would disagree with that.

I mean, how many, how many customers do you have, Josh, that are,, in the middle of a multi year, job, architecture, refresh or.

Siobhan Savage: Run away!

Amy Wilson: It's a month.

Siobhan Savage: What a way!

Amy Wilson: They. They've have it on the the back burner because they haven't gotten to it. Or,,, what what are you seeing with with regards to job, architecture.

Josh Gosliner: Yeah, I mean that there's a reason that some of the partners you saw on on the previous slide are in that world of that consultative job architecture, creation, but is because they don't have anything. And and I think part of this is this. This just stems from a just a general lack of sophistication. Because we've we've been in this world where the I what Siobhan I what you said around this super stale piece of gum from the 19 eighties is,, the the notion that, we, why do we have job descriptions.

We have job descriptions because it is a shorthand to tell you what somebody is doing. And and, in an organization say, SAP, I mean, there are thousands and thousands of product managers at SAP that are gonna have the same job description. And and really what that. what that doesn't get into is the different types of work that they're actually doing.

And it doesn't get into the the different experience and the different skills that they've brought to the table. And one of the things that I really love about getting into this notion of work, design, and work intelligence is and and really being skills informed is that you can get to a place where, as an organization is making massive transformation, you can actually think about what are the transferable skills. And in a world in which you're creating all these new roles. some people are going to be better suited for those roles of the future than others.

And and it means that,,, you could have a bunch of people who are in the same role today, and they may diverge in terms of the thing that they're better optimized to do in the future. And so getting to the heart of the tasks. Getting to the heart of the skills really means that you can be so much more intelligent about the way in which you deploy your people. Moving forward.

Amy Wilson: Great. Thank you so much. Yeah, go ahead.

Siobhan Savage: Of the things. One of the things, Josh, I'd love to show because the way that we talk and that you've been talking, and I've been talking is that it's task and skill. It's not one is right and one is wrong. It's actually you need both.

And I know there's a whole pile of weaponizing this in the market right now that it's anti skills and protest. Just to be clear, it's both you need to have both, I am not going to get quoted for saying skills are not required. Skills are super important. And I want to show Amy, is it okay, if I can hijack this a little bit and just maybe show how that presents in real life.

Amy Wilson: Yeah, because I think that.

Siobhan Savage: I think.

Amy Wilson: Correct.

Siobhan Savage: I think there's customers that sorry. Go ahead, Josh.

Josh Gosliner: I I just wanted to do a a in intermediate hijacking, just to say,, one of the reasons a job. I I we shouldn't say a job. Architecture is totally outdated, just because, one of the things that I would say, a lot of our customers are still grappling with is, they're not even at the point where they understand the basics of what they have. And so for a lot of them getting to that foundation of even a job.

Architecture is an important 1st step to getting to where they need to be. And so, just going back to some of the things I talked about earlier is the spectrum of of maturity that customers have is so great. I think one of the things is we're talking about the customers that are further down that journey and a greater sophistication. But that's not to say that, if you don't have a job architecture, you shouldn't develop one you should have a sense of the jobs.

You should have a sense of the skills you should.

Amy Wilson: Knew that.

Josh Gosliner: In order to take it in, in order to take that next step.

Amy Wilson: Yes, and you can leapfrog directly to a workaro. Correct,, I think, is.

Siobhan Savage: Yeah. And that's the the way that I would describe, and the way I describe it to customers, especially customers who go to me, hey? I don't even have a job architecture. We have no idea about everything.

We've got. Thousands upon. Thousands of individual jobs have been put onto a system, and we don't track anything. Our view is, do what you're starting at?

The best place? Because actually, what we can do is we can create you a work architecture and imagine that the work architecture is the old days where we think about it structurally, at a hierarchical level salary, banding the job leveling the job description. That stuff is still super important. Just to be clear.

That's the stuff that keeps the banks out of trouble. It keeps the governments out of trouble. They have compliance and regulatory things in place that require job architectures to be there right. But there is a way of to Amy's point.

It's then bridging them to the future which is then bringing it to that work architecture level where you have the job, and you map it at a job level. But you still include the task and the skill and all of those key components for redesigning. So what we say to customers. That's okay.

We start with customers who have nothing. We start with customers who have a job architecture that was done 2 years ago. or they've just done a project with a consultant where it's completed, and they're starting to think, oh, we've actually missed this whole task thing. So either way,, I think I think that whole view of the New World is going to require critical infrastructure for you to,, really build for this AI part workforce.

And and we think of it more as a as a work architecture than a job architecture. To give you an example.

Amy Wilson: I just have to say something. Be before you dive in here, Siobhan. So a a work architecture is a beautiful painting that's well constructed and and is vibrant and a job. Architecture is the hideous curtain sitting behind Josh right now.

So.

Josh Gosliner: I mean I I'm just. the the hotel staff is super upset with what you just said. Not so just for everybody. This is not my house.

Siobhan Savage: But I think I think.

Amy Wilson: Alright. Let's let's see the demo.

Siobhan Savage: Typically typically job architectures sit in a spreadsheet in a file somewhere in a customer's world where it's it's it's just art of day. But we're all here to talk about building this new world of work. So the new world of work requires a new infrastructure to enable us to become AI part. Everyone is agreement on that.

On this call. Everyone we talk to a market has the same view of. We need this dynamic model. The way that we bridge the gap.

So what I'm going to do is I'm going to take you on a little tour, and just for disclaimer purposes. All of this is demo data. So there's no real humans harmed in the process of showcasing this. I just want to be super clear, because I know this is being recorded.

And what I want to do is I want to bring you on a harborside cruise of how we would think about solving this problem. So one, you hear customers talking about not having a job architecture or having an out of date job architecture or anything connected to that conversation. I'm going to show you a new way to solve that problem which keeps you safe for the compliance, but also brings you that one step closer to this new world. Then what I'm going to do is I'm going to take you through a journey of.

I'm in the CEO role. I'm I'm sitting with the AI transformation hat on. How do I think about transforming my workforce with AI? And then how do I look at the impact that that will have on my workforce.

And how do I think about pivoting and reengineering work? And then finally, I'm gonna give you a little sneak peek of what's about to be launched next week, which is where we look at the we've we've been doing this for,, the last 6 months with some customers. How we think about agent orchestration. And how do we think about when we're moving work to worker?

What does that look in terms of orchestrating humans to work or agents to work. So that's a little bit about what we're going to do. I'm super open, for if anyone wants to come off, Mike, and ask questions. Live, or Amy, keep an eye on the chat because I can't see anything.

I can slow right down because I get a bit giddy whenever I talk about this. So what we're going to talk about 1st is customers. They don't have a job architecture. They've no idea of the hierarchy of work.

They need to understand what is happening within their work. They need to have a clear view of each role. Now, the one magical thing that we're doing is we're giving you an enterprise view of what is happening within your company. So what we do is we take the older world job architecture.

So you look at job families, rules, tasks. And then what we are able to then look at is we then take this role. Typically, you would see. And these are just,, demo numbers.

But the grading, how many positions are in that the banding level, and what we're able to then look at is what is all of the work that is then being done within this quality assurance technician role. So this is where Reejig has used the Reejig work ontology. So we're never going to a customer and saying, Tell us all the tasks that your people do. We already know, with a high level of accuracy.

We're sitting at a 90% validation. Right? So we go to a customer. We say, we already know what you're doing because we take some of your work data and your job data, and we bring it together with our data set so that we come to you within a couple of weeks and say, we know everything about your work.

What we want you to do is just to validate that. And we're sitting at that 90% validation proof for every single task. The secret to remember after this call, if there's 1 thing you take away is that people have skills. But jobs have tasks that require skills.

So this is where for every task we are now creating this triangulation between the task and the skill. And this is what is really important. Because if I take this task away. I'm going to impact a whole pile of skills that exist within this job.

And it's going to be really important for us to know every one of our tasks at a skill level so that we can start to look at that. What we're then able to then look at is, we know, all of our tasks. This is the data, then, that gets so we've got a highly validated set of data. We know the customer trusted the data, and then this is the skill data that then gets pushed back into the talent intelligence hub.

So this is where our Api is currently feeding in and out of Josh's system to make sure that we are getting that skill language that's actually triangulated back into the talent, intelligence, hub. So this is a really important thing to note is that as our system work changes, we're telling talent, intelligence, Hub, hey? Things are changing over here that you should know so that that can be accepted into the governance structure. And you can make sure that that data is going in and out of those systems as well.

What we're then able to give you is,, a high level of information on the AI potential index. The effort level that's predicted for each task how much the AI will likely impact because of AI maturity, what the savings, what the R's will be, and whether or not this task has been roadmap for any form of transformation. So the user here is typically someone in the Hr team. Now, if we were to go and have that use?

Case of my CEO is asking me, where is the best place to think about? AI adoption? Where do we think the best places are. You cannot innovate and reinvent your organization overnight, all in the one go.

You need to,, phase this what the work ontology then allows you to do is to be able to search by all of your tasks. So here is a list of every task that is happening within your company. And this is telling you which tasks have the highest potential for automation. We can tell you the AI impact.

And we can tell you the actual savings, and we can roadmap these tasks when we trigger this to be roadmapped. What this does is it tells the Hr team that change is coming in that rule. Now, what typically in the AI transformation teams will see is they go. Okay.

Here's the task we want to do. What Reejig has then is our work orchestrator and our agent orchestrator, which is then basically telling you that for every single task, so we know that this customer uses Microsoft, we know that they're using copilot studio. Let's say what Reejig is, then gonna do is tell you, based on every single task. Here is actually AI agents that are available for you to deploy right now out of the box.

So we're not just telling you what task has the highest potential. We're telling you where to take action. And then we're telling you, Hi, so what's out of the box? What could you actually configure?

So this is a normal user that can configure things, and then at a level 3. This is where you would go into the copilot studio and design that agent. And we're giving you simply the recipe in which to do that. Now, the thing that's going to be really important for customers is that when we're looking at this, what we've really focused on is we don't want the AI transformation happening separate to the people team.

Because if you're out building AI and you're telling the Hr team that you're doing this. Jobs are changing constantly, and if we cannot tell you how things are changing, what then happens is your job. Architecture isn't kept up to date. Your skills aren't kept up to date.

You don't know what your people are doing. So, for example, this role. 64% of this rule will eventually be automated in a normal customer world. That would mean that when we look at that original rule for the quality technic, the quality assurance technician, 64% of this rule over time will be automated.

And what we want to make sure that the customers have empowerment and data to do is that back again. I'm in my workforce strategy, old role. I want to. Then look at the reengineering agent and the reengineering agent and Reejig is where we're basically telling you here is actually what's gonna happen with this role.

So here is the tasks that will be automated. Remember that every time you bring in an agent. You're also going to add new new tasks as well. So we're going to tell you what's gonna be impacted.

And then we're going to tell you actually the emerging tasks, because this is really important for reskilling and making sure that your people are equipped and amplified to be able to do this new part of their work as well. So we tell you about the emerging skills, the impacted skills. And then what we're also going to be focused on is if we're then looking at, you've got 100 people in this role. 64% of that rule is no longer going to be required.

That typically would mean a customer would just cut those people. And what we're basically saying is, no, you can reinvent them into something else and pivot them into a role back to Josh's point where skill adjacency becomes really important. So what we're then looking at is, how do we then start to think about what rules are other rules that require similar skills and have task adjacency as well, because we see a lot of correlation not only within the skill but also within the task itself. So what Reejig is then doing is telling you.

Here's essentially how you Reejig the careers of your of your people on these paths, and what essentially that's doing is giving you that view to really think about, how do I pivot folks into other directions? So we've got the agents that are helping you with reinvention. We've got the agents that are focused on making the right agent orchestration. So, recommending some customers have salesforce.

They've Microsoft. They've service now they'll have SAP agents. Reejig has this ecosystem and marketplace essentially, of all the agents where we're recommending here is the best task. And then we're making sure that you don't leave people behind.

Now, the final thing which.

Amy Wilson: On before you move on to the next bit. I just have a couple of questions in from the chat, so the 1st one I think you you actually answered already. But I think it's worth putting an extra point on. And that was, how do you balance focusing AI development with with employee engagement and employees needs, and so just to recap.

And then you can clarify to recap. The really, the point is that by understanding what the roadmap of AI developments are, and the re-engineering that is going to happen, and having a plan for that allows you to better skill your people and better to prepare your people for the future. Is that? Is that how you would answer.

Siobhan Savage: I mean, yes, but I would also say the way that we're doing our learning strategies right now is a joke. people are just picking things out of I don't know where, and deciding that these are our future capabilities, that we're going to train our people up in with no context to what actually the business requires. So this one really frustrates me because we spend millions of dollars. We waste people's time by getting them to learn things that are irrelevant.

What we should be focused on is, how do we help the organization reinvent? How do we make sure that people are amplified and able to reinvent themselves. And also the company has a responsibility to make sure that if they're making these changes that they're telling them, hey, this is the things we do really care about in the future because we've analyzed it. We know.

And here are the skills that are going to be really important to our organization. And here's the learning to help you. So that's one dimension of how I would respond. The other thing I would tell you as well is, I really care about.

me as an individual, I would say. I'm pretty obsessed with the amplification of the individual. How do I help you? Amplify yourself.

and that is with the use of AI. And I can tell you, at an individual level that if you're in your role. Here are ways that you can 10 x yourself, so that you can do whatever the hell you want in terms of better quality tasks to get home for dinner in time with your family, there's these 2 dimensions of amplification and rescaling to protect your career and make sure that you've got a house to live in, and that you're not out of work. But there's this other side where we don't talk a lot about it which is really focused on.

How do I then show the employee that if they want to really 10 x. And amplify themselves, that here are the tasks that they should think about. Here are the agents that could help them. So I think it.

It looks at the golden side and the shadow, because I think most of the time we're talking about. the world where the robots take over and everyone's out of work. But there's a part in between. I think that we need to, as when we're thinking about our talent strategies is, how do we look at both the risk side and making sure that we are looking at human and agent together and knowing that.

But the biggest problem that customers are making right now, and the biggest advice that is getting given to market to be clear and has been is that they're picking random jobs or capabilities, that they think with no evidence of what the impact will be. And the 1st thing you need to understand is, where is our company today? Where are we going? What will the impact to work be?

What does that then mean in terms of supply and demand? How do I then make sure I've got the gaps filled so that we know what to skill our people in, whether it's for reskilling or upskilling same concept. And that's where really it's focused on. Everyone is then learning for the right things.

I spoke to a customer not so long ago. They basically did this massive multi multi 1 million dollar project where they had a whole pile of folks scale up into these jobs. All of these employees are probably working 2 jobs right? And then they had to do mandatory learning to reskill into these other things.

Turns out. They all spent 18 months of their lives doing all of this learning. And then the company turned around and said, Oh, by the way, we don't need to do that anymore. Because we got that wrong.

Do how impactful that is for an individual who's probably surviving? And so this is why I get really annoyed about it, because I'm, use the data, understand the impact, know where to reinvent, make good decisions on where you deploy capital and where you drive your employees to, because you can't get that stuff wrong you can't waste people's time. So a lot of the data that we give is this high level work intelligence of seeing the work, finding the waste, reinventing, evolving your workforce. So we categorize, and our agents are looking at high level.

Where is their low value? Work trapped? Where is their opportunities to unlock ours? Where is reinvention even possible?

And if so, what does that mean to evolve our workforce to whoever asked that question, how do we then think about the disruption of skill? What does that then mean? And then that becomes the strategic data that informs those decisions hopefully that helps.

Amy Wilson: So now I have a A a quick comment from Kyle says, I really love how blunt you are, Siobhan.

Siobhan Savage: It's just super annoying, though, people are making these decisions on people's careers without even checking the data, it's just sorry, Kyle, you got me going.

Amy Wilson: And then no, I think he was serious, and then he has a question as well. So he was wondering about the add to roadmap. That sounded really interesting. What's under the hood there?

What change management will be required for the organization? And what's the governance around it?

Siobhan Savage: Yeah, before I answer the question, I think there's a really important thing that we all as a collective, whether you're supporting customers on the success factor side, or whether you are an actual talent leader. We all have a collective responsibility to make sure that we are making best friends with whoever is in charge of the AI reinvention of the company. There is a team right now within your company who is responsible for this reinvention. Go and find them today and make best friends with them, because that will tell you what your talent strategy is going to be.

You cannot do the things in separate. We have to be a collective. So that's why you're seeing companies start with the chief work officer, and the Chro is now becoming,, AI leaders, because the 2 things cannot be separate, and if one is sitting over there. So if you have AI teams reinventing and roadmapping without telling the Hr team.

We're going to reskill people in the wrong things. We're not going to focus in keeping our job architectures up to date. We won't actually know what the requirement is for our people. So these things have to be best buddies.

To answer Kyle's question as well, there's there's a couple of things on change management one. What we typically seen at the start of our journey was when we said, Hey, folks, here's the opportunity for you to reinvent. But we didn't show them how to take action. There's a rabbit in the headlights moment.

So that was one of the biggest moments that we realized was, we're empowering with all the data. But then, taking that jump for them is too much. We've got to actually give the the individual or the user or the strategist. A view of basically here is the agent to use and to deploy in the context of your org.

So that was why we've deployed the agent orchestrator, because that helps them. Then take the action where it will get tricky. And this is a consistent, no matter what industry, whether it's the best tech companies in the world who are leading AI, or whether this is,, early stage companies starting to think about it is, if your employees don't make these changes. Then all of the investment into the AI goes out the window.

So this is why you need to have a collective view as an AI strategy and the people strategy because you need to bring the 2 things together, because if you spend millions upon millions of dollars bringing in AI. But no one uses it did the whole thing work or not. Right? So there is a view of we need to know a roadmap for what we're reinventing so that we can not only enable the agent strategy, but you can also enable the change strategy to make sure that people are enabling to use this thing because we have seen where we've deployed agents, and if only 10% of the population use it.

But they've got a 10 x experience. And they,, deal with way. More customer queries than anyone that's not amplified is that valuable? Because only 10% used it so very much so this is a, not a technology.

This is an actual, Josh was saying, workforce transformation. It's a whole end to end game. But I would definitely say,, if you're not best friends or find out who is in charge of this in your company, or if you're on the success factor side. You need to go find those people because they're the people that are.

Gonna tell you what your people strategy will be looking for this next wave as well.

Amy Wilson: Awesome. Thank you so much. Well, we are almost out of time. I wanted to give Josh and Siobhan just a quick opportunity to say any last comments.

And in the background here, while they're thinking about their last comments, is a link to learn more about Reejig's work, intelligence to connect with one of Reejig's workforce strategists to dig in even further than where we went today. So, Josh, any any final thoughts before we conclude.

Josh Gosliner: Yeah, look, I mean, I I think, ultimately, what Siobhan's talking about in terms of this redesign. It's happening,. And and I think the main thing is that we have to take a people centric approach to this, because if we don't I ultimately fear there's gonna be a lot of bean counters out there who simply say, Great AI,, let's use blunt instruments to simply slash budgets as opposed to being thoughtful about the way in which we do this. And that is clearly not going to be the best outcome for people.

And it's definitely not going to be the best outcome for the business. So I think being thoughtful about this is really going to be critical.

Amy Wilson: Love it. Yeah. And Siobhan.

Siobhan Savage: Amen. Amen. To that I completely agree. I think, that we all collectively have a responsibility to make sure that that doesn't happen.

So whatever side of the table you sit on in this call, that we all should be enabling the reinvention. But we need to make sure that we don't leave people behind. And I think for for us,, we're super excited about this partnership because the skill and the expertise that success factor has as an organization, and the customers that you guys are working with, and where you have access that we can bring,, both talent, intelligence, and work intelligence together to make sure that this is,, our customers are set up to be doing the right thing, I think, is incredibly important. So, Josh, a massive thank you you to you for your leadership in really making this whole thing happen.

I'm really excited to to see how this goes.

Amy Wilson: Pleasure. Yeah, thank you so much to both of you. Siobhan, and josh and thank you, so much to the audience and for everyone who asked questions and and got the conversation going really appreciate it.

Siobhan Savage: I'm sorry to consultants if you watch this.

Amy Wilson: They'll have plenty to do.

Siobhan Savage: Hi alright! Thank you. Good!

Amy Wilson: Bye-bye.

Siobhan Savage: Bye, I'm gonna go backstage.