Siobhan Savage: Excited about this conversation, Josh.
Josh Bersin: Oh yeah. Lots of stuff to talk about. Big topic.
Siobhan Savage: It is a big topic.
Louisa Grundy: So we've got the chat rolling already, but, if everyone can post in where you are, where you're dialing in from, let's get some chatter going. Wanna use the chat as much as possible, throughout today's session.
Siobhan Savage: Welcome everyone.
Louisa Grundy: I'm dialing in from New York City, all the way from Sydney.
All right. We've got Boston dialing in New Jersey. Amsterdam. Amsterdam. Ooh, that's fun. We do. We've got Indiana love it.
Austin
Josh Bersin: and Spain with electricity.
That helps. Otherwise, I couldn't imagine he'd be here.
Siobhan Savage: London.
Josh Bersin: Okay. I recognize a lot of names.
Siobhan Savage: Who do I'm gonna probably DM them at the same time.
Louisa Grundy: Okay. We'll just keep it another, just until two pass and then we will, we'll kick off.
Well that's quite, very quickly. Hit over two parts. Okay. Hello everyone. And welcome. My name is Louisa Grundy. I'm the VP of marketing, here at Rejig. And thank you for joining us on this hot topic of work intelligence redesign or be left behind. I'm very pleased to be joined by two.
Fabulous. Panelists here. So firstly, Josh Bersin. Thank you for joining us. Most people know who you are, but globally recognized industry analyst and thought leader, founder of The Josh Bersin Company, and someone who's been at the forefront of every major shift in the way we work. So, welcome Josh, and thank you for being here.
Josh Bersin: Thank you, Louisa. Great to be here.
Louisa Grundy: Of course we have Siobhan Savage as well, our fabulous CEO and co - founder of Rejig. Shahan herself is a pioneering work intelligence, and whose bold vision for the future of work is helping organizations reimagine what's possible. So we are in for a hot discussion, today.
Before I kick into the panelists, though, I wanna set the scene a little bit, for everyone. There is no doubt we're standing at this very pivotal moment right now in the world of work. So for years we've been talking about transformation, whether it's digital, the internet, we all know what transformation is, but there's something different happening right now with ai.
Why? It's because the way that we organize work today, it was built for a much slower and more predictable world. And today that old system is reaching its limits. AI has opened up a new frontier where every job. In every industry can be reimagined. And we have the opportunity to turn employees into super workers to unlock new levels of productivity, creativity, and speed.
but there is a gap. And that is that 92% of CEOs are investing in ai, yet only 23% of them believe their workforce can adapt. And a mere 7% of those actually are creating revenue for it. So the reality is we cannot p patch the old systems anymore, and we do fundamentally need to reinvent and redesign how work is managed and delivered, which is exactly why I've got Sivan and Josh, on the call here today.
So we're gonna talk about what is broken, what are the bold and smartest leaders doing differently in this new world of work? And how do we build this new, infrastructure that's gonna allow us to really succeed as AI powered workforces?. So to dive in, let's talk about this big topic of transformation and why it's hitting so differently with AI right now.
So, what has really changed, and for every leader tuning in today, what's the real pressure they can't afford to ignore right now with the AI transformation? Josh, I'm gonna go to you first on this one.
Josh Bersin: Well, I would say there's two things that have collided at the same time, maybe three that are affecting every company that I meet with.
The first is because of the last two or three years of excitement about ai, the level of expectations on the return on investment of AI is extraordinarily high. So the 92% of CEOs that comes from the P - W - C - C - E - O survey, and if you read through it, every single one of those CEOs, and that was 2, 700 CEOs, believes that AI is going to improve productivity and scale leaning towards productivity.
Productivity also means in many people's minds, downsizing or rightsizing of the staff of the organization. So we have this belief system hitting the top of the company that this new technology, which I'm hearing about from every vendor on the planet, and we're getting hundreds of billions of dollars invested in the infrastructure behind it, is my secret to staying ahead.
That's number one. Number two, in the reaction to that, those of us that are at the working levels in companies or employee levels of companies are saying. Okay, what does that mean for me? What about my job? What about my role? What about my career? What about my future? And just this week, I was mentioning this in another web house today.
Barack Obama who gave a speech was one of the most, he looked more worried than I've seen him in a long time, that maybe AI is going to eliminate a significant number of the jobs on the white, in the white collar workforce. And honestly, that's true. We are going, those of us that do white collar work, we are going to experience the situation that blue collar workers have been through for the last 20 or 30 years with machines.
So the job we have as implementers and HR people is how do we redesign the company, the organization, the teams, the work itself, so that the AI can deliver on what the CEO and the CFO wants. And take care of and leverage the skills of the human beings. And we'll talk about what that means in a minute.
This is a very big topic. This is not the same as buying SAP or Workday or whatever it is, turning it on and hoping that everybody becomes more productive. This is a very different dynamic. And the third thing I'll just say really quick, you guys, is the rate of change of this technology is so high that what you think AI could do last month, it does more next month.
So in the middle of all of this, the expectations and the promises by the vendors keep going up. So we have no choice but to be part of this. And it's really in a sense, maybe the biggest big business reinvention period that you're gonna live through in your career. So it's an exciting time.
At the same time, it's a little bit intimidating.
Louisa Grundy: Yeah, I couldn't agree more. Josh. I think one thing that I know we've been talking a lot about Siobhan as well is around the weight that sits on the leader's shoulders. Not even the weight though. It's more the opportunity that these leaders actually have.
It's a once in a generation opportunity for them to really reinvent how work's being done. So definitely is that, the opportunity side as well as, obviously, the side for some employees, but Shahan, can you build on that for me? What are you, from your conversations with executives, what are you hearing about the pressures that leaders simply can't afford to ignore right now?
Siobhan Savage: I think Josh and I might be sitting in the same rooms because we're hearing the same thing. We're
Louisa Grundy: going to d, we're all talking to similar people.
Siobhan Savage: , I definitely, agree there is pressure that I have never seen before at a CEO level from shareholders to perform in a way where velocity, productivity, efficiency is all improving.
And AI has come at the most perfect time when you think about it because, for something to really take off, there has to be a critical event. And you've got a critical event right now where CEOs are prepared to throw things at it, to make it work. Which is great if you're in the AI space.
But what we are seeing across the board is where there has been AI and strategy in place. It's not working out very well. It's because there's this approach to AI that's happening that isn't, it's one, not at an enterprise level. It's more BYO ai. Everyone's getting to have a crack on their own and just make their own thing.
And it doesn't really give much efficiency. If anything, that approach slows down the workforce because, if you are getting folks creating one version of a process themselves, the whole process and the machine of how big companies work actually starts to break because people are just sticking nails in the side of the, in the side of the ship, right?
So this is, that's one big thing that I'm seeing once I start to dip down the other. And that's caused by the way, from the AI companies and the CEO driving this pressure into the business. Everyone adopt ai, let's go people. And then everyone goes, okay, the CEO said do it. Let's do it.
And they all go out and buy things and then they slow everything down even more. So we're actually causing the opposite effect of what we're actually wanting to create when it comes to velocity by allowing that strategy. The second big thing I would say is this is a once in a generation change to work.
You will never experience anything this ever again. It will never happen this again. And you all on this call are once in a generation leaders who will somehow have to figure out what the hell we're all doing and how we do this. Therefore, there creates a pressure on everyone in this room to quickly upskill themselves, to become an AI expert, to suddenly understand and pivot thinking from the way we used to build our workforce in terms of jobs to now this new way of unstructured jobs into tasks.
And there is a lot of pressure and I'm seeing two mixes of folks in terms of in market. You've got the folks that are aware that they are important and that they need to solve this and they're trying to get help and trying to figure out what to do. And then we have on the other side, people that think that AI is not gonna be a problem for another three years.
So, everyone on this call, this conversation is a really important conversation because the reality will be that this critical event, if you do not upskill yourself and you do not enable the CEO, if I was a CEO, I would find someone else who could. So there's a moment right now where you have to prepare the organization and help yourself by making sure that the workforce is actually building high velocity productivity.
And also to Josh's point, making sure that we don't leave folks behind because that is the other side and the shadow side of this whole story. So that's what I'm seeing in market. I think it's both a bold opportunity, but also we gotta be super responsible at the same time.
Josh Bersin: Yeah. Thanks Mike. Any comment?
Louisa, just to add to that about the vendor space. So, I was with a bunch of companies last week in Europe. I would say, unfortunately for those of you on the webcast, I. You are going to get bombarded by vendors, Microsoft, Workday, Oracle, OpenAI, et cetera. And just as Siobhan said, they're all gonna sell you promises of how wonderful this is gonna be and how much money you're gonna save.
But unfortunately, the bulk. Of the effort is not buying the products and turning them on, teaching 'em how to do prompts. That's a piece of it, but it's really applying it to the engineering process yourself. That's why really rejig is here. I'll give you just one quick story. I was with a very large bank meeting with a very senior executive there.
She's a business transformation executive, and she said, my job is to look at all these AI ideas that are sprinkling around little fairy dust and figure out which are the few huge ones we need to invest in first, because there's a SWAT team that goes around and does these things.
And she said the, it turns out the business process that is consuming the most resources in our company and the most number of people is account opening. Account opening. This is a bank that does business in 130 countries. She said account opening turns out is the most bureaucratic, inconsistent, maybe un unfortunately unproductive process.
We have. So we're gonna start there now. I think that's a brilliant process you went through. I think 99% of companies are not going through that process. They're just throwing this stuff out there and hoping people are gonna figure out what to do with it. And that's what Rejig is all about, is preventing you from maybe wasting too much time in that process.
Louisa Grundy: Thanks, Josh. Let's get into, I wanted, before we go into more what's broken and how we fix it as well. Josh, I'd love for you to kinda share with everyone. AI is a transformation journey and there's a really practical model, your teams or you put together that speaks to work.
Josh Bersin: Sure. You wanna go through the four stages? Yeah. Do you guys have a, I think you guys have a picture, right?
Louisa Grundy: I do. I'm gonna share it up here. Yeah, so
Josh Bersin: let me share what we've, and this is really what we've discovered. We didn't invent this, but this is after a lot of discussions with many people.
So we're all going through, including by the way, the vendor market. Four steps here. The first is I got my hands on chat, GBT, I got Galileo, I got Claude, whatever it is, I'm gonna use it to take the current job I'm in and do it a little bit better. I'm gonna upload some stuff. I'm gonna ask you some questions.
It's gonna generate some job descriptions for me. Maybe I'll use it to schedule my meetings. This is the Microsoft Co - pilot out of the box, turn it on and look at all the cool stuff it can do to make your job easier. You can get 10, 15, 20% per personal productivity out of this. And maybe if you're a well organized company, you can share best practices and you can come up with common tools on how to do that.
Second level is you've been using the AI for a while, and again, maybe it's an off the shelf product or maybe it's a more specialized one ours. And you start to realize, now that I've been using this, I'm going to automate part of my work. I'm a software engineer, I'm gonna let it write code for me.
I'm a marketing person. I know I'm gonna let it, I'm gonna teach it how to write copy for me. It's gonna write headlines for me, or it's gonna analyze spreadsheets for me, or employee engagement data. And at level two, you have become, in a sense, an automated worker now where you have an automation tool to give you a greater level of productivity and you're getting repeatable use cases now that the company does for you.
One of the companies I met with last week. It has their own GPT, system they built and they encourage employees in that company to create little mini apps, little mini GPTs, and share them. So that company, it's actually a big financial services company. They have a library of GPTs that people are building for each other to do level two stuff.
So that's great. And I would say most companies are at level one or level two, level three. You say to yourself, okay, same company, or same job, same, role, what I'm doing in my job. Maybe I'm a recruiter, is related to this other guy sitting next to me, or gal that's a sourcer. And then there's somebody after me who's the hiring manager or the onboarding person.
Well, it turns out that in order for me to do a good job of maybe interview development and assessment of candidates, I'd to know how they're doing the sourcing. Because some of the people they're getting don't seem to be the right people. So I want my AI to talk to the one before me. And by the way, once I go through the assessment process and the screening in this selection, I would to take my AI to transfer that data over to the next person who's gonna do onboarding because they would know what this person needs in particular because of their background.
So now we have what I would call a multifunctional AI or agent that can do three things. That is a huge transformation in the business of recruiting now, because you have common data across all three. And by the way, it goes beyond that. It goes into performance management and leadership development, everything else.
If you just think this through and you end up with an AI agent and can do multiple things, and now you have to scratch your head and say. Do I need a sourcer? Do I need a recruiting scheduler? Do I need the person that does the, benchmarking of salaries? Do I need this? Do I need, and all of a sudden we're into a re - engineering process, and that's where the massive a hundred percent, 200%, 300% returns come from.
And once that agent, multifunctional agent stuff is in place, and it's just barely beginning by the way, to come outta the vendor world, now we can start automating things at level four. And the system can say, gee, the last five people we hired from this pool of sourcing, turns out six months later, they all quit.
So let's go back and stop sourcing from there. And by the way, I don't need to ask anybody for permission because the AI is smart enough to know this. I'm gonna tweak the sourcing to make the hiring better. And you can see this going on all over the company in all sorts of multifunctional, functional ways.
So that's the reason. And of course, on the right side of this picture, you're - engineering what people do. On the left side of the picture, you're automating what they're currently doing. So, what we have to do, and this is what the conversation I was having with this company in Europe. Is figure out where we can find opportunities on the right side of this picture sooner rather than later.
And that's where Reig comes in as opposed to being stuck on the left where everybody's building their own little Excel macros and hopeful, hopefully we're getting some performance improvements. I think the stuff on the right, the re - engineering is a hundred times higher return over time on the way we run our companies.
And so that's why, we're so excited about what's going on at Rejig because they basically built a tool set that makes this possible and relatively easy, as we re - engineer the way our companies operate.
Louisa Grundy: Thanks, Josh. I think this really opens the conversation up nicely with, when we think about each of these four stages here, it is fundamentally breaking the way that we have traditionally run our workforces.
So I wanna now talk to what is breaking or what is on the brink of breaking or already has collapsed, with this, that this new AI era that's coming in and get ready for the twist. But I'm gonna, you first, mine's all warmed up. Now. She's ready to go. I'm, where do we go? Oh, I wanna go to you firstly on, you've said many times that the old job architecture is done.
It's dead in the water. Why, in your opinion, is it no longer fit for purpose? And if we're throwing it out, what exactly should companies be building instead?
Siobhan Savage: , forget AI for a second. It was already broken. Even pre ai. Whoever designed that was designing it for a completely dinosaur era. The traditional jaw market Archite was Frederick
Josh Bersin: Taylor in the 18 hundreds, and it was part of the automation of factories in the steel industry, by the way.
That was
Siobhan Savage: the time in motion stuff, right? Exactly. That's where it came from. That's where we have a clipboard and, yeah, sure. Listen, even pre ai, we were all pulling our hair out and I'm, my background pre - reg was a workforce strategist, right? So I was trying to operate really large organizations with a spreadsheet that tried to correlate what was the actual hierarchy of work and what people were doing, and it was terrible and it went out to date.
The second you paid a million bucks to complete the project over a 12 month period, and then it was already out of date. So that was already a problem. You now bring in AI and. The real ai, you've got, the way Josh has described it as really perfect because, there's a lot of marketing where we're talking about agents taking everything and this being incredible.
The reality is right now, today, that work is being automated only at a sub - task level. So there is no agent that's taking it from the whole process and the whole task. And then it's looking at it from a subtask perspective, it's looking at assisting. So there is no real full on places that take it completely, but it's coming.
it's absolutely going to get to a place. And I think we're, this is a next year thing in my opinion. Just I believe that we're getting there, but we're still not gonna get to that full nirvana until next year. But the opportunity that we see right now is even at a subtask level, if you imagine that one, let's say one job.
Has anywhere between 20 to 22 tasks, high level tasks. And then within each of those tasks there's probably five to eight subtasks. So micro steps that people take within that task. So the way that Josh was explaining it, each one of those is a recipe for an agent to take over and come in and assist.
So how the hell does a job architecture one tell you what that actually looks? So that you understand? Because the thing that we were talking at the start of the call, people want to adopt ai, everyone wants to do this. The problem is they don't know where to start. And in order to know where to start, you need to understand work at a task level.
TA work and jobs are not skills. People have skills, jobs and work have tasks require skills to complete those tasks. So in order for you to understand where the opportunity exists for the organization to reinvent, you need to have task level data. Write down to subtask now, and then next year you'll start to go on the journey of, today I'm looking at assist, augment.
Next year I'm gonna go into full replacement or re - engineering that completely out of our jobs. So the bold reinvention moment requires a completely different architecture. If you'd imagine you are building now, everyone in this call should be building an AI part workforce.
So your CEOs, I looked at pretty much every major shareholder report in every major company around the world. We built an agent actually to do it, and we analyze pretty much everything all your CEOs were saying, and all of them are wanting to go towards this direction. So in order to do that, you need a critical infrastructure.
That will enable this new way of working. And if you plug in the job architecture to this, you basically break the whole system of how to operate and actually design for this world. So really understanding your work at a task level, but also for every task that you come in and augment or you put in an agent for.
If you imagine that recruiter example, 20 tasks, and let's say Josh keeps chipping away and taking out tasks week by week. So every time you go to interview folks and you haven't taken out those tasks, that job is reinventing every single week. And then you're hiring people in with the wrong expectations.
You're bringing them in, they're gonna actually be doing 40% capacity, less than what you actually thought you needed because no one told you these tasks had been changing. And then what ends up happening is you have a situation where you've got this low performing team because the capacity adjustment hasn't happened.
And the re - engineering of that job itself actually hasn't been evolved. So the job architecture,. Chuck it out. You have to actually start again. And sometimes you have to go to day zero thinking and reinvent your organization in a way, if you want to actually be successful in this new world, you need critical infrastructure Absolutely.
Of ram and compliance and risk and, to support you in your workers' councils, in your unions. But there is a way of doing this that isn't going to lock you back into that dinosaur age because you'll not be successful doing this. So I really get a bit feisty about this one because I'm looking at the quality of the work that's actually being put to a lot of companies as well.
And I'm, how did you pay that amount of money for that? It's already outta date. It doesn't make any sense. So, yeah. Yeah.
Josh Bersin: I'll, can I add to this idea of the job architecture? Okay. So we have all these jobs. There's some, let me just take a step back. There's two, as Siobhan said, there's maybe two big ways to do this and lemme talk about the second one in a little more detail.
The first is you use the Elon Musk approach and you go to, as you call it, day zero, he calls it first principles, and what he does is he starts cutting stuff until everything breaks. So he just goes all the way down to the bare metal and says, we don't need any of these people doing any of this stuff.
Let's get back to the core and then add, as soon as something breaks, we'll add something back in. By the way, this is the way SpaceX became 10 times more cost effective than NASA is. He created rockets and he kept taking stuff out of them until, they were lighter and lighter, and eventually they blew up.
He said, oops, add that piece of metal back in. Sorry, we took out too much. But sure enough, he ended up with a rocket that was, I. A hundred times more cost effective than NASA had ever built before. Now, some companies can do that if you have the mandate, this company I was talking to earlier, they are gonna strip down the account opening process, and they are gonna build it up from the base because she's a very senior executive and she has the mandate to do this.
You may or may not have that. The second approach, of course, is you look at the jobs you have and you find out, and we're gonna publish this case study for one particular company, that we have a hundred thousand employees and 65, 000 jobs. In other words, every job is unique to one person. Hm. Which is insane, if you think about it and then you go through and you say, okay, let's turn on Rejig.
Figure out that actually we don't have 60, 000 jobs. We only have 3000 jobs. They all look the same because everybody created different names for them. And we go back and re - engineer from the bottom in a more rational way. And I think honestly, 90% of you are gonna do the second approach, but 10% of the time you're gonna get to do the first approach.
And as these agents get smarter, the agents will be designed in a way that you can look at the agent and say, okay, if we implement this agent, we're gonna have to do all of this reengineering to be successful with it. And it'll be a little more out of the box, but we're not there yet. But I agree with you, Shaban.
We're gonna have to let go of a lot of sacrificial, religious ideas on what people's jobs are. And by the way, of course, that gets into this issue of. Well, I'm at this level, well, I'm making this much money. Will I have this many people reporting to me? I'm getting a bonus for this. Who's responsible for that?
Right? There's a million cultural things that get in the middle of this. But for those of you that are in hr, and I think most of you probably are, this is your job. We have to get through this issue of people hanging onto their job title. With, for the Fear of Death, I'm not letting go of my job because I'm the one everybody needs.
Right. To get to this point. And, I do believe from the stories I'm seeing that is the way going forward. And by the way, part of this, the reason we use the word super worker. Is so that you can communicate to all of us. By the way, we're all getting disrupted, including me, that we're all gonna be okay.
We're not gonna get fired. We're not gonna end up out on the street looking for things to do. There's gonna be new jobs created, new roles, higher level jobs, and I think a lot of the new jobs are gonna be better paying jobs, frankly, and more interesting as this process takes place. And that is one of the parts of this process for you as an HR person is thinking through that whole change strategy.
Siobhan Savage: Yeah. Here's the thing as well,. All the big companies that we're working in right now, the HR is not the driver of this transformation, right? It's coming from CEO and it's either being driven by the chief AI officer or the chief transformation officer. They're the two typical CIO sits in around it.
But that is typically who is driving this strategy And here's the moment of opportunity that this team you all have here as a team because they have no idea. They have none. And the funny thing is, all these calls I'm on and I'm chatting and going to meeting all these CEOs, I'm, so tell me where are you deploying ai?
And they're, well, we're trying it in engineering. We're trying it in product. And I said, so you've basically in your own team? And they were, yeah. And I'm, but did actually, that's only gonna unlock. 15% of the opportunity that you have. Did in supply chain that there is an 82% unlock for your org right now?
And they have no idea. 'cause they don't understand work. It's not their job, it's not their expertise. So what you actually need to do is this is your moment to really help drive the strategy. So all of you get off this call, find out who, don't leave it right now, but get off the call after, find out who is driving your AI strategy.
Go to them and say, how can I help you? How can I help you? Because in order for you to deploy this AI strategy and for it to work, we've got access to all of the job and work data. Can
Josh Bersin: I point something out? Siobhan? So one of the problems I think we face and we're all dealing with this, is there really hasn't been a job title other than the job you had perhaps Siobhan to do this.
We have. We have compensation and benefits specialists, we have business partners. We have org design org development, which is a little bit more training than it is work design. So there has to be a new role created to do what you guys do. Honestly, Siobhan, this idea of work design is, done by line leaders.
Siobhan Savage: Mm - hmm.
Josh Bersin: One function at a time. I talked to the CHO about this at IBMA year or two ago, and I said, how do you guys, do org design when you wanna do productivity projects? She goes, we don't have a process. We expect a business leader to do it themselves.
Siobhan Savage: Yeah. They're
Josh Bersin: supposed to figure it out.
Well, how many business execs know how to do this?
Siobhan Savage: Yeah.
Josh Bersin: None. Correct. They just graduate with the team they have and then they hire the best people they can find and they hope that things work better. So, there's a discipline here that we're all gonna have to get better at, which is understanding these tasks and roles and act activities so that we can, basically go start at the basics and redesign the way things get done.
I said, maybe two or three years from now, these agents will be so good, we just turn them on and they'll find everything. But
Siobhan Savage: yeah, they'll just find the humans are gonna get in the way.
Josh Bersin: , we're gonna be in the way, so.
Siobhan Savage: Yeah, exactly. The other thing as well, in these, in, if you put yourself in the shoes of the chief AI officer and the CIO, they have went and bet.
Millions of dollars. I,
Josh Bersin: what is a Chief AI officer? Does a Chief AI officer, it's every business process in the company. They don't think,
Siobhan Savage: no, they're techies. They're in charge of, they're in charge of the AI strategy though. So someone needs to be at an enterprise level in charge of your AI strategy.
Right. And they're under a lot of pressure right now because they've been doing these little pilots that aren't working out very well and they've been trying, genuinely, most of them are excited. It's a new creation of rule for them too. It's a new opportunity and they're getting a whole lot of pressure from the CEO.
Now, where's the ROI? You've just spent X amount of millions of dollars on technology and we got nothing to show for why. And that's because, and it's not because of the technology, it's because how they've used it or they don't know. And this is where everyone on this group, this is the opportunity. 'cause Josh is right.
if I was in my previous career, if I wasn't doing rejig, I would be pitching to the business right now. I'd send a, an email to the CEO saying, I. Make me your whatever chief work officer. I will look after all of the deployment of your internal employees. I'll look after my flex work strategy and I'll look after all agents, and you're gonna pay me a really big salary for that.
I'll, I
Josh Bersin: think, let me ask you a question, Shaban, because, I don't talk to Chief AI officers rarely if that, if at all. But I do know when I talk to big senior level execs, they know their companies so well. They know where the low hanging fruit is. They know where the process gaps are. Where do you think, how can we help people decide where to focus?
And now I obviously, Nuno did this for a living. He's on the line here, but, I don't think it's always clear who's responsible for finding the killer app here for ai. Yeah.
Siobhan Savage: the low hanging fruit. We've done a little bit of an experiment to prove customers wrong a little bit in our sales processes.
So where they thought the low hanging fruit and the best ROI for the business was never the right place to deploy. One, it wasn't worth enough money. Two, most likely the vendors had just done a really good job of pitching it and making them fall in love. But that was a low hanging fruit. So they've got a really good en, enablement from a vendor trying to sell them something.
And the third thing is the maturity of the AI matters. If you are going and pitching to solve a task and the ai, and it's not because the AI is not good, it's just some levels are just not ready yet, then you are gonna actually go after the wrong thing. And the whole thing feels, what actually needs to happen is you need to deeply understand the work that's happening in your org at a task and a subtask level.
You need then work intelligence to tell you based on the work that's being done in my company, based on the AI maturity today in the market, based on the cost per task. Where was the highest potential for me to start? Yeah,
Josh Bersin: I would guess that in most companies, this is what I talked with about this bank, is that the number one application area is the customer facing roles.
She told me the wealth managers was a no brainer. It was the first place we went and they built the, this whole platform for wealth managers or account managers. Then you probably look at customer service,
Siobhan Savage: finance, big one. Yeah, big one with finance. I think
Josh Bersin: things that are, that have direct relationship to revenue or customer retention or growth.
But again, you, someone has to be at the right level to make those decisions., there's a big insurance company we know of that has built a custom AI for claims management. Obviously it's a massive cross - disciplinary area of a, of an insurance company is dealing with all these claims and the various processes that come in there.
Before you rush around, to those of you on the podcast and buy something and do a bunch of experiments or maybe while you do that at the same time, I think it's really good to have a series of discussions about where are the biggest opportunities first, and then you can bring Rejig to help you focus on those areas as opposed to try to do everything at the same time.
Siobhan Savage: Yeah, our whole view would be to tell you where is the best place to then take action. But I think as well, I think it was, Jan and the channel said, chief Skills Officer Jan, I disagree. It's not a chief skills Officer. Skills is only one tiny part of this conversation.
skills is people have skills but actually work doesn't have skills. Work has tasks. So if you go down the road,
Josh Bersin: Jen is a chief skills officer, so be nice to him.
Siobhan Savage: Yeah. But it's, but it's not the, if you are thinking about this being a revenue productivity generating role, it's actually a commercial role.
It's not actually a skills role. And it's actually, it would probably be a peer to the chief people officer.
Josh Bersin: , if you look at the professional services, there's gonna be, I think there's gonna be a lot of people with a job Nuno had that are actually transformational executives that are looking at business, re - engineering execs to help do this.
Yeah. Because this is such a existential future here,
Siobhan Savage: but the one fact, regardless of whether you look at titles or whatever, you still need to understand what is all the work at a task in a subtask level. You need to then make that decision of based on the intelligence, and that's the reinvention, the really bold part of the strategy.
But then you get to the responsible side and it's, okay, now I've told you your shopping list of where to deploy your agents. I've given you the script that tells you how to build them and the recipe to do it. You're gonna go and do that, and then I need to know what the impact is, and I'm gonna tell you to your workforce, this is gonna mean X, Y, and Z.
Because then what you are gonna do is you're gonna bring that over, whether it's to your CHRO, to your CEO, your chief learning officer, and you're gonna say, we now know in the next 18 months this is about to change. So all those learning strategies that you guys have all been just making up with not a lot of data, this is actually what you focus your energy on.
I make a comment, this becomes it. Comment,
Josh Bersin: unlearning John. Somebody just asked a question about learning. So while this business re - engineering job, re - engineering stuff is going on in these agents, there's also a re - engineering of how people learn going on. And we're gonna talk about this a lot in the next couple of weeks.
But to answering John's question, I think one of the things that we're also dragging around some history that we're dragging with us over the years is this old idea that if I have a fixed job. I can learn the job step by step in a fairly linear, predictable way.
The first six months I learned this, and then the next six months I learned that the next six months I learned this, and then the next six, and then two years from now, I'll be really good for the job, and then three years from now I can get promoted to the next level of this job, right? We start blowing up all these jobs and destroying them and reorganizing them.
That doesn't work anymore. Now we have to start teaching people or enabling them on what to do in a much more real time basis. And I think one of the peripheral or maybe parallel paths to this AI reengineering that's going on is how are you going to enable people to do these new jobs more quickly?
Mm - hmm. And I to, I'm framing this up. We have a big paper coming out in May. I think we're gonna go into a role of more enablement and less job training because if we decompose the job architecture, all those curriculum and training and levels and things that we had in the l and d department are also gonna have to read, be redone.
Siobhan Savage: Yeah, and I'm loving. K's comment. Yes, Kandy, go for it. Go for the job. This is your moment. There's so many folks as well, that. This has becoming such an exciting topic because there's opportunity for folks to think about this in a completely different way. And, the one thing that I would caution everyone on is if you're being sold, stick a large language model, a chat GPT front end onto a job architecture.
Old job architecture run a mile, because that itself is just gonna cause even more chaos. You actually need to boldly reinvent it. You need to actually step back for a second and look at your org and think about the redesign of that. Yeah. Need debate
Josh Bersin: from Jan William here. He's gonna argue that it's Chief Skills Officer Still a good idea.
I'll, yeah. You can book another
Siobhan Savage: webinar for that one. I, but do what? I think the thing that'll be really important is for folks listening is that we, this is where we are, we're thinking about, we have to reinvent our workforce, and in order to do that, you need a new critical infrastructure.
We would describe it as a work architecture. Because it looks at work in a completely different way. Tasks, sub tasks, the skills required to complete the task, the action. We look at all of the movement that's how we describe it at Rejig. And then the work ontology is the language of work itself. So it's that the way we talked about having that one common language that connects the business team and the HR teams together on one language.
That's how we think about it.
Louisa Grundy: Shahan, a few questions have come up in the chat, right? I, a few questions come around on how do I actually start from a task on the task and a subtask based approach. I really talked about. Can you just more explicitly share, how would a leader.
start on that journey.
Siobhan Savage: So, we started out very skill centric when we first started, back five years ago. And what we find was that actually to my point, people have skills, but jobs don't have skills. And there was this massive missing gap in the market. And this was pre che, EBT, large language model movement.
I was already seeing that we were missing this whole context of work. So what we have built is 23 different industry specific models. So think of every major industry we know I have Google Maps, the internet JI Maps work. And what we have done is we've paraded these foundational models that basically have that universe of work for each industry.
And we look at it from a rule, from a task, from a sub - task, from an action. We basically connect the whole dots. And then what we do is we come to you. 'cause the best companies in the world I get to work for, and they do not have great work data because it's not something that was required before. So what we do is we make an assumption that you guys don't have any good data and we come to you and we give you our data.
and Josh, you've seen our data. You've, you really tested us. We give not a handful of skills. We give an incredible amount of information. No, and in fact,
Josh Bersin: can I make a little plug for you guys? So I've talked to a lot of Rejig customers and I dunno how you guys do it, but so what they basically can do is show you what people actually are doing, even though it's not written down in their job descriptions because of the AI data source that they've been able to collect over many companies.
So I agree. It's an amazing tool set for all this re - engineering that has to take place
Siobhan Savage: cost me over $40 million. Make it back. It took a long time and a lot of money, but it's you're just gonna have to
Josh Bersin: charge enough and you'll get it back.
Siobhan Savage: Yeah. And we've got some, cus we got some customers on the call now too, which is great.
but the whole thing is we are gonna assume that you don't understand your work. 'cause that's never something that you've had to look at. You've always had job descriptions and position descriptions and that's it. What Rejig does is it comes in and we give you your own ontology and then we harmonize it to your own data.
So some folks have started out on a skills journey. We take that data, we ingest, we harmonize 'cause it helps us learn more. We then look at job ads, job descriptions, your old architecture, and then what we do is we basically create you your own unique work ontology. And then this is the language of work that is shared between the HR team and the business.
And what we have found, because we've invested so much time into getting workforce strategists to review this and make sure it's good, you've got this foundational data set, which is what we never had before. When we had skills ontologies, we had this big AI model that would just pump out stuff. Whereas now we are sure that we've got really good data.
We typically are sitting between 85 to 97% approval rating. So when HR take this to the business goes, yes, that's what we do. And I'm, that's because we've got this data and no one else in the world has this data. We, you would have to spend a lot of time, a lot of money building this.
And we iped it because it was really critical data. What that data allows you to do then is to do all the stuff that Josh is talking about. So you see that maturity model that requires data for decision making support. You wanna be driving your car and have someone whispering in your ear what is the next best move for you to take?
That's, imagine the way Rejig is positioning with the data that we're helping you make good and fair decisions on what to reinvent and what to re - engineer or what we call rejigging, right? For the Americans, rejigging is. And Google it, you'll see. But the whole idea of this is to give you that data.
And that was a big shift for us in direction because we, there's just, I can't solve this problem if I don't have this data. And we haven't seen a customer yet that has any of this data. Their job architecture's outta date, their position descriptions are terrible. Their job adverts are marketing adverts and they're all, a million of them are different even though it's the same job.
So that's how we've got into this mess in the first place. How many did you say Josh? There was one company that had thousands of the same position, which was the same job essentially. That's how you get there. You
Josh Bersin: know, in some sense, by the way, I have noticed one of the places where this works sometimes gets done is when l and d comes into a problem area, they have to decompose what people are doing to figure out what to train them.
So some of the expertise on how to do this is actually an l and d, so that those may be some folks that graduate to these other roles. Chabon and these work. Re - engineer. Yeah. Opportunities. Funny,
Siobhan Savage: funny enough, we're hosting a big community thing and there's a lot of learning leaders. They're reinventing themselves.
They're trying to figure out how to do it. And on the learning side as well, it's the content
Josh Bersin: development stuff that they used to do is getting done by AI now Exactly. To do
Siobhan Savage: neither the chief work officer that job's open, apparently openly. But the other thing as well, from a learning perspective, so the other component that folks don't remember when they're doing agent deployment.
So you see your work, where to take action, where to build the agent. You see, whenever you build an agent, you remove a task out of that job, but you also introduce a new task that no one has ever had to do before. So not only think of the Jenga blocks, you take 'em on in and you're pulling one out, and that's how the whole thing is getting built.
So see, if you're a learning leader, you actually need to have this data as well, because you need to understand what AI is coming into my business. Where is the task being removed, but urgently. You need to support the AI strategy to make sure that people are re - skilled. I have seen so many deployments of agents right now where the productivity improvement would be a three x to five x of their output, but only 20% of the population of that cohort actually adopt it because they actually need to be skilled up on how to even use the agent in the first place.
So there's, I'm calling it this, just on time learning. You need to have that rapidly going across your business. And then the learning strategy and the chief learning officers that are really caring about the impact of workforce, instead of them picking jobs that they think are critical capabilities for the future, what they're actually doing is they're looking at where is the jobs that are gonna be impacted today?
Where are the jobs that we know are gonna be super required because we're building the superpower workforce and the super worker environment, and then drive those people to those paths. Because what you're gonna do then is you're gonna make sure that you've got the right talent for the organization, that people are not being made redundant.
Because the other thing that's gonna happen is CEOs are gonna be under a tremendous amount of pressure very soon. From the general population and the general workforce, that this is not just the generative AI thing. I'm seeing customers that are building digital factories. So think about little robots walking around building things in factories, which no longer requires humans at all.
they're these, what they're calling dark factories, which is essentially a handful of people that will run this, what used to have thousands of people in them. So you're gonna have not just this problem at a desk worker, but you're also gonna have at a desk list worker. And what that essentially is gonna cause is some major societal issue for sure.
I really do believe that we're coming close to a part where people are now gonna start holding leaders to account on this type of stuff in the ways that we don't wanna imagine. Yeah.
Josh Bersin: That's a little bit why I made that comment about Barack Obama. I think another dynamic that is gonna impact everybody who's listening to this webcast is if you wait and don't jump into this with both feet.
The business counterparts that maybe aren't talking to you are gonna do it anyway because some head of sales, or head of manufacturing or head of IT or whatever, is gonna find some tool. They're gonna get enamored with it and they're gonna bring it in. They're gonna say, all right you guys, I don't need half you guys.
I'm buying this new system. So having rejig and using your own tools in HR or wherever you happen to sit to do some, business re - engineering will get you in front of that situation where they don't wait. 'cause I think the pressure to downsize and become more productive is coming from the top.
so, it's gonna get more urgent. Over time.
Siobhan Savage: Yep. I agree. And Bob was asking a really good question, and not to scare everybody in the next level of this, but right now we're looking at task and subtask adoption, right? The agents and the AI is typically at a task and subtask level. When you get to autonomous agent, you then start taking on full end - to - end process.
So think of, the top down where you understand the role and the task. That's how most folks are thinking about this right now. But then once you get to that level of maturity, then you gotta understand the end - to - end process and the pipes that plumb the whole workforce and the whole organization.
And that's how the process and improvement really starts to evolve because you can take something end to end. Most customers are not there yet. Mo most customers are very much so at that sub - task level. But I think we're starting to see now, you'll see Microsoft and others really lots of releases in the last couple of weeks, which are incredible.
the research agent alone that co - pilot released is just incredible because it's actually, I can see now that displaces a lot of the actual tasks that we're seeing. Not even just at a subtask level, but at the full task, which means we're getting closer and closer now to these becoming actual workers within our organization, not tools becoming full workers.
And that's the impact that you're gonna start to see. So it is moving rapidly and I think that's why folks probably need to stay super close to this narrative and tracking and understanding what does that mean?
Louisa Grundy: So we, I'm conscious we're getting close to the end of time 'cause the discussion has been, how did that happen?
I know. Amazing. Today, before I go to the q and a, so please, anyone in the audience, please get your, any more questions, throw them in the chat here. Siobhan and Josh, I'd love to get your take on, we've talked a lot about some of the, success stories that are happening and what leaders should be doing.
But if you were to summarize each, what does it take, what should lead, what's a bold move the leaders in this call should be doing right now to prepare their workforces, for the AI powered workforce? I'd love to hear your thoughts. So maybe Josh, I'll hand over to you first. Okay.
Josh Bersin: Well, I think there's two steps. One is educate yourself on what these AI tools and systems can do. Honestly, I think most of the people I talk to in hr, I. Are underestimating the power of these tools. And I don't mean just watch a video from Microsoft. Literally go to some of these shows, talk to some of these vendors, talk to us and see what the possibilities are.
And it's gonna open your mind to, the amount of automation and the amount of cross process work you can do. The second is, working with Rejig or with us or both of us, figure out where you should prioritize. I think the experimentation where everybody does a little bit of, their own thing is great because we have to build general literacy on AI everywhere in the company and people have to be, un intimidated by it.
But that doesn't mean you're gonna get the high ROI. So the second thing is what we call falling in love with the problem. What is the problem you want the AI to solve? Not, here's a bunch of ai, let's see where we can apply it. And I think that's. A little bit of a business discussion of you and your other senior leaders about where in our company are there things we could be doing better that would exponentially improve our performance, our competitiveness, our customer service, whatever it may be, and get yourself focused in those areas.
those are the two things that I see, for the next year. And the market's gonna move really fast, and if you're not doing the first piece, you're not gonna be keeping up. Hmm. And by the way, I was at a conference last week and unfortunately it was an l and d conference. Every single I. Booth looked the same.
All the vendors are saying the same thing.
Siobhan Savage: , is that me? So,
Josh Bersin: , you're gonna have to just get your hands on this stuff or dig in with some, prototypes to see what's real and what's going in the direction that your company wants to go. And that's why the second piece is also so important.
Siobhan Savage: Yeah. And what Josh, is saying is so true. Everyone is selling stuff. What I would do is I would actually ask the vendor for proof and ask for some data. So how we've been able to grow so fast and so quickly is that we don't even sell 'em stuff. We're just, here, have a look and you tell us and go and see if someone else can do that.
'cause I know for a fact that no one can do what we can do and they always come back. So test your vendors, literally have evidence of their ability to deliver, because you're gonna be right at the front of this. And if you don't have good quality data, and I've been on the other side of not having good quality data when I was at the start of my journey, that's why I built this.
I think that's a really good thing. Just ask, show me evidence of this and what it looks so that you can build confidence between you and your partner. And I think that becomes a really important thing, what Josh is saying. I think as well, to Louise's point, there's probably two things that I would say are critical.
really important. You need to have this new work architecture. You need the new critical infrastructure. So start taking one food, keep one food in the past with your old job architecture, which links to your RAM and all of that good stuff, but bring one. Put into the future. So sit in the area that you're looking at, that ability that I understand and it can keep the company safe at a compliance level for my jobs and all the good stuff, but I know my tasks, I know all of the architecture of this new world of work.
So I'd say do that. The other thing I'm noodling on quite a bit at the moment, and it's causing me a little bit of problems watching it happen because I've now, I'm probably one of the only vendors that are actually end to end with customers now from helping them build these agents and watch what happens at the output.
And what we're seeing is the individual employees, there's amplification of the employee that needs to happen. There's a couple of things that are, no, if no one adopts these things while it's at a subtask agent level. Then this doesn't work when it's full autonomous, different. 'cause you can completely move the human out of the road.
But right now we want everyone to be on board, right? So there is a process that you need to go through and it's not change management, it's a completely reset on thinking about, actually, if we say a rule is going to give you an unlock and removes 50% capacity, resetting expectations with those individuals so that they're, they know that's happening.
And the expectation requirement, because of enabling this ai, they need the right learning to enable them to adjust in time, be able to use and deploy and operate, with the agents as well. And there's a communication thing. A lot of people are pretending right now at CEO level and in the news that they're not planning to do this and it's rubbish.
They're 100% doing this. They're saying, oh no, we want humans and robots together to be in love and everything to be great while they're actually looking at ways of reduction of force. And planning for that. So that, I think there needs to be some level of acceptance that's actually, we are going down that road.
But what, we've invested over the years so much money and time into marketplaces and all these other things that can enable the reskilling of your people because you've already been smart enough to do that before. So I think there is definitely a communication acknowledgement that, an honesty factor one more thing,
Josh Bersin: ?
Mm - hmm. I think there's another trend here that we haven't seen for a while back to, going back to my earlier days in it. I think a lot of the really high value AI solutions are going to be built by you, not a vendor. A lot of the companies I've been talking to now are finding that the off the shelf tools are not.
Tweaked enough or customized enough. Or configured enough for their needs. So you're actually gonna be in, interestingly enough, probably doing a little bit of product management with the IT department on how to make the agents do what needs to be done to fulfill on this destiny of improving this process, which is great.
that's the way it used to be years ago when I worked for IBM where we all built our own stuff. Because a lot of these tools have developer tools in them, so you don't have to use it out of the box. And if the agent isn't doing exactly what you want it to do, or if the people are getting in the way or whatever, we can change that.
So this is a, not just an implementation process, this is an implementation and design process.
Siobhan Savage: Mm - hmm.
Josh Bersin: Of doing ai, which I think is great. You couldn't do that with most ERPs. You just implemented it and configured it.
Siobhan Savage: Yep. And you're completely right because there's three levels of competencies.
In an individual for ai, there is the user. So they take something outta the box and are just given it. There is the makers and the makers are no code workflows that they can go and make their own things. And then there's the builders. So they're designing and developing agents themselves that's the competency level of an individual at an AI perspective that we model into our intelligence.
And it's exactly what you're saying, Josh. It's that whole world now where I ch on Savage with no technology background can build, actual full agents that talk to an agent orchestrator that do all my shopping and, get my kids to bed. That's all existing now. What?
And it's that level of competency requirement and it's gonna be really interesting. Another thing the l and d folks should get on top is how do we understand the level of competency within their teams to understand what's their actual ability to complete those things as well.
Louisa Grundy: Alright. We are, I hate to say it, but we are a minute from time.
but I think it's very clear from the chat that has been buzzing the entire webinar here that this, I know
Siobhan Savage: I'm trying to keep up
Louisa Grundy: seriously that this is a hot topic. It's not industry specific topic. It's happening across, near a really traditional industries, right through to your technology space as well.
So, we know that reinvention is not optional. It's essential. It's a responsibility that sits on the shoulders of all the leaders, who are on this call. So, with that, thank you very much Josh and Siobhan for, and a really engaging discussion. And thank you to everyone who joined us today and, we hope to see you again soon in another webinar.
Siobhan Savage: Thanks everyone. Okay guys. Demi, if you've got any other questions, dear you folks.
Louisa Grundy: Hi, you.
Siobhan Savage: I couldn't keep up with that chat to be honest.
Louisa Grundy: Can, there we go.
