Episode Transcript
[00:00:00] Speaker A: Foreign.
[00:00:05] Speaker B: Thank you, Josh, for joining me today to talk about WPP and the future of work and job redesign and all sorts of things that we're going to get into about AI. Tell us about your role at WPP and tell us about WPP for a few minutes because it's probably good to just start with the company and the business situation.
[00:00:23] Speaker A: Well, first of all, thanks for having me. I've been a fan of yours and your work, I'd say, for nearly 20 years now when I started in human capital consulting and first learned what HR is. So thank you for being a voice inside my journey one way or another for quite a while. So I'm excited to be here, starting with wpp. WPP is the world's largest creative transformation company. What does that mean? Well, at the smallest scale, the micro scale, you can think about more than half of the world's advertising expenditure going through wpp.
At a macro scale, we help the world's biggest brands stay relevant through storytelling, marketing, media and so forth. We have about $14 billion in revenue. We're just over 100,000 employees and we're in over 100 countries. So the scale is quite large there. In terms of my role at wpp, I am the global head of people strategy and employee experience.
Because employee experience in particular means something different at every organization that I've ever worked at or peers with similar titles that I speak to. Just for a bit of definition, what employee experience means for WPP is bucketed in a few different capabilities, one of which is employee listening, another of which is people marketing and communications. Being a marketing organization, we believe that the best way to change behavior, change minds, change perception, inclusive of our clients, customers, is internally applying that same thesis to ourselves. And then like I said around people strategy, I spent a lot of time working on the big question around the future of work. What will be the future size, shape, skillset capability set of WPP in the future? And with marketing and advertising being some of the tip of the spear fear of the impacts of artificial intelligence, but really this latest iteration of generative AI, it's a great place to be. I feel really fortunate to be able to think about the types of things we get to think about here.
[00:02:31] Speaker B: Yeah, you're definitely involved in some of the most exciting new technologies and business issues and cultural issues around the world. Now, before we get into sort of the project that we're going to talk about the company, as I understand the company, it's a company of companies that has been acquiring many companies. Tell us a little bit about the organization and sort of the culture of how the company works.
[00:02:52] Speaker A: Yeah. So the company's been around for quite some time. It was founder led up until about six years ago. And if you think about the advertising holding company structure, it was started by the founder of wpp.
And basically what he did was make advertising companies an investable asset. And he did that by buying up as many as he could. And you know, if you think, if you think about advertising organizations, especially smaller agencies, if they lost their biggest client, they go out of business. Right. So if you have tens and hundreds, if not thousands of small agencies, you can even out P and L sheets much more easily and sell that to investors. So that's the concept behind the holding company. Over the past six years, we have gone much more from a holding company to a version of an operating company. There are moments in the organization where we want our employees to know, realize and appreciate that they work for WPE. Even though 96% of our workforce works for one of our six main agencies, those agencies are creative, PR, media, commerce related and so forth. So within those agencies there's a multitude of other smaller agencies. So we're kind of a Russian nesting dollar of organizations. And we have a fairly small, you can call IT center or hq, which I and my colleagues sit. Right. So thinking about WPP holistically across the network.
[00:04:24] Speaker B: So the. So the company's organized into these six sort of functional agencies and then within those agencies there are other industry oriented or client oriented organizations. Is that how it works either?
[00:04:37] Speaker A: In many cases, that's how it works. And the complexity exists in all of the major holding companies in the sense that, you know, as a client is acquired, there might be instances where a client wants resources from multiple agencies within the holding company within wpp. So we'll spin up a legal entity with a new name so that we can financially manipulate.
[00:05:01] Speaker B: I see.
[00:05:02] Speaker A: You know, manipulate is the wrong. But financially support the client deliverables. Right. Instead of having our companies pay each other for ask for resources and asset.
[00:05:11] Speaker B: Development, each company has a P and L and like a general manager or a CEO or some sort of a P and L owner.
[00:05:17] Speaker A: That's correct. There's a CEO of each of the major agencies.
Yeah.
[00:05:21] Speaker B: Okay, so it's a very interesting organization structure. I want to ask you about your, your employee experience role across all that because it sounds very complex, just that part of it. But there's also this other project going on. So why don't you maybe talk about the second project, which is the job redesign or job Simplification project or whatever you may call it.
[00:05:42] Speaker A: Yeah. So we're using the term workforce intelligence.
[00:05:45] Speaker B: Okay.
[00:05:46] Speaker A: Around this work. And really the impetus for all of this was our board of directors coming to us and our people team and saying what's the future shape of the organization? How many people are we going to have in the future? Now the instinct that we had was to say that's the wrong question to be asking. Right. Because we can, it's the same as asking how long is a piece of string. If we have more work to do, it might mean we have more people. If we have the same work, same amount of work to do from clients, we might have fewer people or we might not. If you think about everywhere we have implemented loads of new technology, those are the pockets of our business that have grown in headcount the fastest. So the, the size of the organization and searching strictly for efficiencies to us was the wrong way to approach the question. Which is okay, because the question was being asked and we took that as permission to say, you know, let's step back, let's think about one who we have, what skills they have, what kind of work they do and how that should look like as we implement a new AI driven marketing operating system that we call WPP open. So that was the impetus for the entire project.
I can go on to more details unless any of that sparks some more.
[00:06:57] Speaker B: No, I mean, so I think most companies that I talk to are being asked or being told to simplify the organization for one of several reasons. Reduce the costs, implement AI and we want to see the benefits of AI or we're not scaling fast enough and we can't keep hiring people at this rate.
[00:07:16] Speaker A: Yeah.
[00:07:16] Speaker B: So I'm sure any and all of those probably apply here.
[00:07:20] Speaker A: It's certainly an amalgamation. And another way to, to think about it, another way we, we thought about approaching it was to look at WPP and you know, the situation that we face holistically. One, clients want simplicity and they want our integrated services. Our largest clients are growing faster than our other clients and, and they want access to the best WPP has to offer regardless of our internal complexity. Another reality that we have to face is that the majority of our revenues come from time and materials. So what we would call the AI discount clients asking for, you know, to pay less for the same amount of work is very real. So therefore we have to think about, and the teams have been thinking about how commercial models needed, need to evolve to match that. If commercial models are evolving because clients are asking to Pay less. If we are still in a time and materials situation and clients want integrated, more simplified way to work with wpp, it's only obvious that workforce transformation is inevitable. So this is a workforce transformation challenge and really a talent management challenge. A new way to think about talent management or talent or AI is the fuel that is sparking a new type of conversation. Because I've never really had in depth conversations with P and L owners or business leaders where they get super jazzed about, you know, the ins and outs of talent management, about the operations of hr. That's not the way they think and talk. But if they have an eye towards efficiency and improved effectiveness, we can put all of this together by developing some of the work we've developed. And I can talk more about in order to begin to answer this. This is going to be a long term project to catch up to our present rather than, you know, talking about the future of work. All of the tools that we need to be better, to do better, to do more efficient type of work are available today and they're only getting better.
[00:09:25] Speaker B: Okay, so you have this mandate to look at the future workforce and in the process of getting that off the ground, you realize you have to look at productivity improvement and value to customers because the customers are expecting you to charge them less if you implement AI, which I, which makes perfect sense to me.
So how did you bite off this elephant? I mean this is a hundred thousand person company with hundreds of job roles. I would imagine, I don't even can't imagine. And as I understand it, you don't even have one big HCM system. Or maybe you do, maybe you don't. I where did you start and what process did you use?
[00:10:02] Speaker A: So we took, maybe unintentionally, but we took a leapfrog approach because like you said, we do not have one live HTM system across our entire workforce. That's for another conversation. What we did was we partnered with almost borrowed resources from our incredible data team Enterprise, our enterprise data group. And we built a homemade large language model. And what that model did, we trained the model on our own job architecture, which includes, we found out by doing some of this analysis we have 50,000 unique job titles within this Excel spreadsheet of 110,000 employees.
[00:10:41] Speaker B: So there's an average of two people per job.
[00:10:44] Speaker A: Some of that is based on translations and so forth. But still what the LLM was able to do for us. So we trained it on our job architecture. We also trained it on a list of about 150 column generic AI related or AI adjacent technologies such as natural language processing, native generation, process automation, RPO and so forth. So we, we had, we had the tech training in the model, we had our own job architecture training in the model. What we then asked the LLM to do was to clean up the jobs. We got down to 6,000 clean job titles, much more manageable than 50,000.
And then what it's, Hang on a sec.
[00:11:23] Speaker B: Yeah, just talk about how you did that. So you've, so you've got this LLM because I've actually tried this with Galileo and I'm curious how you did it. So you end up putting 50,000 individual job titles, levels, descriptions in there, is that correct? Yeah, and then the system, somebody programs the system, find the similarities and group it come down, reduce it by a factor of 10, something like that.
[00:11:47] Speaker A: Well the, the ask was not to reduce it by a factor of anything. The ask was to take our job families, our job family groups and then look within the roles of those job family groups and see where there might be similarities, do the translations and you know where you might have, you might have someone who is a senior director of media planning, you might also have someone who is a senior director comma, media planner. They're probably doing the same work.
[00:12:16] Speaker B: Did it do that using the job descriptions or just the job titles?
[00:12:19] Speaker A: The job titles. So the job descriptions were generated by the model. So once we had a clean set of now 6,000 job titles, we generated job descriptions for all of those unique titles. We don't need to see this, this is all happening in the back end of the model. What we asked it to do from those job descriptions was to identify the top 15 tasks for each one of those titles, classify those tasks, identify the skills related to those tasks, assign one of the 150 AI adjacent technologies to each task and estimate the time savings from tool to task. And it gave us an estimation, it gave us an estimation about, I think it was the range was about between two 20, 20 to 25% quote capacity unlock, end quote. That's what that, that was the term.
[00:13:13] Speaker B: Exactly what I was going to guess the number would be. That's pretty amazing.
[00:13:16] Speaker A: And, and we could, we could dig in. So we, we, we built a whole dashboard in Power BI to look and explore this data.
It was, you know, very interesting. But the biggest question we got from business leaders was is this real? Like, is this valid? Let's look at, let's look at the tasks. We did some task validation for a few roles and it passed the sniff test. What it didn't do was one pull in our actual access to tooling that our technology team has built for client delivery services. And it didn't pull in any data from the outside about roles and jobs. So we decided to partner with an organization called Rejig, who little did we know, because we were doing this kind of in the skunk works environment, they have a whole business around this or version of it. And so since we launched that homegrown LLM, we spent you know, not a lot of time. In a very short amount of time, we spent time with Rejig building out an equivalent of this that we are between 85 and 95% confident that there is accuracy there. And the tasks, the task mapping goes much deeper. The tool to task mapping is much more real because it's based on. We've trained Reject's model on our tooling and it gives us some incredible prioritization insights into how and who we need to upskill reskill within the workforce.
[00:14:49] Speaker B: Okay. That this is just a spectacular project you're doing. Okay. So you used your own tool to get to the 6,000 jobs, tried to come up with a task for job, validated that against Rejig. But Rejig, by the way, just so people don't know who, if they don't know who they are, does this using AI across the whole job market. Right. So they're, they're getting data from hundreds and hundreds and hundreds of companies for similar job titles. So that, so they can kind of almost in a sense benchmark every one of these jobs. Now you have a database of, I guess it's still 6,000 or maybe it's less.
[00:15:23] Speaker A: We've, we've mapped 600 roles that actually cover 84,000 of our employees. So the total will be much less than 6,000 once we get to a hundred, you know, the full, full mapping. So we've mapped with rejig, 600 roles that cover 84,000 employees. So the final number, once the full organization is mapped will be much less than 6,000.
[00:15:45] Speaker B: Wow. So you went from 100,000 employees with 50,000 jobs to 85,000 or so employees with 600 jobs.
[00:15:52] Speaker A: That's correct.
[00:15:53] Speaker B: And then you had to sort of throw this in front of all the business leaders and say, hey, what do you guys think we're doing A few. How do you validate that they're actually going to be able to do this?
[00:16:02] Speaker A: So do. This is a, is a broad term. Because what this is entirely varies based on the business challenges that each of our agencies are Trying to solve for. So for one of our agencies, a design company that, a design company that they, they just finished their quarterly review, they're hitting their numbers. It's great. What they found is there is opportunity for improvement around margin. Why? Because their teams are spending more time than allocated in the scopes to specific work. So therefore. Right. Not as efficient as intended. I don't know about possible, but intended. So we're applying this data based on the roles that they're telling us are guilty of the most overspend of time. So we're going back and we're mapping how the roles that are most guilty of the overspend can become more efficient. In this case because they're solving for margin.
[00:17:02] Speaker B: So you're in a sense you're testing out this new smaller job architecture for productivity on this business unit in particular roles.
[00:17:10] Speaker A: It's job architecture. Yes, but it's also the level of granularity that the task mapping plus the tasks tied to the tools available is providing us. We can say, you know, a senior associate level designer should be working in this way according to our spreadsheet, which of course doesn't always match reality. And that's okay. Using the tools per task that we've identified here, how are they working currently? And then we can look at the delta and say is there room for improvement? Even if we are overestimating the efficiency play now, not all of the problems we're solving for are an efficiency play because the greatest use of these tools is an effectiveness play. That's how we get to growth. We don't get to growth through efficiency in, in our mind anyway. And what we're doing there is a bit more future focused with a few organizations within wpp. And we are thinking about this concept, about capability combinations. Right? You think about for some types of work, especially high volume work, you have copywriters and art directors doing, you know, doing the creative ideation, doing the creative work around producing ads. In some cases, when combined with the AI tools we have available, you might only need, you might need fewer individuals doing that type of work, but they do need to know what good and great looks like. So we're calling this concept M shaped Workers.
It's an evolution of the T shaped worker. And my, my, my colleague who has a PhD in org, org analytics.
Org network analytics, Laura Weiss, she's brilliant around this, this is her thought leadership. But we're at, we're, we're applying this concept of what M shaped workers can be, should be and how we start scaling them and we have some active pilots.
[00:19:03] Speaker B: The idea of M that you have multiple peaks of, you're not just a single specialist, you're specialists in multiple things.
[00:19:10] Speaker A: It can be looked at through individuals and through teams, how teams deliver. So teams can be M shaped, but also individuals can be M shaped. So it requires certainly systems thinking, that horizontal integrator who.
The individuals who will be orchestrating the agentic future that we are building. Right. So they are going to need to know how to use the tools, how to integrate the workflows that used to exist into these new workflows. And they're going to need to know what good looks like across two or more special areas of specialism.
But I mean, the other side of that is they also need these human skills, you know, to figure out how to best interact with clients in a, in a more strategic way.
[00:19:53] Speaker B: So I have a sort of a big, big question. That's something I've thought about for many, many years. And that is, I think in most of the companies I've worked with, including ours, there seems to be a productivity acceleration that happens when somebody is very, very good at what they do. There are what I would call hyper performers in certain jobs that are 10 times better at that job than everybody else. I mean, literally 10 times better. And other people can kind of reach up to that level over time, but some, some will reach that level of performance and some will not. And I would imagine in the creative agency, where there's a lot of creative stuff, there's a lot of mechanical stuff too. I'm sure there are people that are just gurus at certain things. Where does that fit into this? Or do you or does that not count?
[00:20:41] Speaker A: It absolutely counts. I think those are the individuals that have redesigned their roles and will continue to redesign their roles as technology continues to get better. I think the latest, the saying that we hear over and over again about generative AI in particular, just today ChatGPT released O3, which is incredible. You know, this is the worst it's ever going to be. And if individuals have optimized their role for what today looks like, those are going to be the champions and ambassadors who are going to reinvent for what tomorrow looks like on a continual basis. So the job of talent functions more broadly are to capture and identify and capture the workflows of these archetypes or.
[00:21:24] Speaker B: What'D you call, superworkers, whatever you call them. Yeah.
[00:21:28] Speaker A: And set that as the set.
Set that. And incentivize peers and others in those similar roles as the minimum standard of how work needs to happen and that that applies much more to mechanical type work.
[00:21:42] Speaker B: So, so in your process of working now, now you're working with one business unit or a whole bunch of business units on this implementation or how what's the scope of the people that are working with you on this?
[00:21:53] Speaker A: We're working with nearly every part of our business, some in small scale to solve small scale problems, high impact but small scale problems, and some to impact organizational structure.
[00:22:05] Speaker B: So in terms of operationally how you do this are you does is the process of come you come up with these new roles and responsibilities and tasks and systems and skills, do you then look at people and refit them back into the new roles? How do you implement these changes in structure in the operation?
[00:22:23] Speaker A: So this is where we come back to the concept of the M shaped worker. Because we believe that if we look two to three steps out and that might be a few years down the line, everyone or many roles will require this type of capability. So if we think about that as grounding, how do we help people have multiple areas of specialism in order to deliver higher value work? One, we're going to need to unlock capacity. So that is the efficiency play, but it's in service of higher, higher value. And then we do a lot of the traditional great HR work that tends to go underappreciated. Right. How we set stretch assignments and how we incentivize and reward for that, how we do rotational programs, mentorship for those archetypes of superworkers who are already doing great work. So some of the traditional HR work still has very much place to play here.
[00:23:21] Speaker B: Yeah, I mean, I think in some ways, I mean this is such an incredible story because what you're really doing is taking all of that really hard work and maybe you call it traditional or high value HR work and now you're pinpointing it towards exactly the right roles, the right people, the right skills, as opposed to sort of sprinkling it out there and assuming everybody's going to get better at everything they're doing? Right?
[00:23:42] Speaker A: That's exactly right. And so I'll, I'll give you another, you can call it hypothesis of ours or piece of thought leadership and how it applies. So as we are implementing WPP open across the enterprise and across our clients, it is organically re engineering workflows. So workflows that look that went one way in the past no longer go that way. They no longer the levers are completely different. So as we re engineer workflows by implementing technology, teams need to reshape in order to pull the new levers, who sits on teams, Individual roles, individuals that occupy roles. Those roles need to be redesigned. And how do you redesign roles? One, you unlock capacity, but then you need to upskill and reskill individuals to occupy successfully those roles that are part of teams that are working within these re engineering workflows. And to your point on directing, in some cases budget, but efforts more broadly, we can look at the insights that we've generated through the workforce intelligence project and say your L and D dollars are best spent first in this role for this client, because this will be the impact.
So did you discover, just out of.
[00:24:57] Speaker B: Curiosity, did you discover any extraordinarily important pivotal roles that you didn't realize existed that were. Because when you ration it down like that, you probably see a lot of things that were invisible in the 50,000 job titles.
[00:25:11] Speaker A: Doing this work has opened the door to have conversations with functional leaders and operators, people with hands on keyboards that traditionally we wouldn't be having for better or worse. So we're having conversations with individuals who are saying, hey, you know, our job architecture says we have X,000 media planners. I'm categorized as a media planner. I'm client facing and I am, I'm really an account person. Let me look at the tasks of the account person in the media planner and tell you what feels right. Wait, I do a combination of both of these. For us, that's a, that's a light bulb that goes off to say, okay, one. As we figure out what our, you know, future job architecture looks like, we're also identifying individuals who are these M shaped workers. So it comes full circle. And then we talk to these individuals about the work that they don't like to be doing.
[00:26:03] Speaker B: Right.
[00:26:03] Speaker A: Low value to them and they know best. And we can help facilitate the transition to optimize.
[00:26:11] Speaker B: I think I get this. So a media planner that's also an account person is an M shaped worker because one of their bumps is the media planner bump and the other bump is the account management bump. Is that correct?
[00:26:21] Speaker A: That's correct.
[00:26:21] Speaker B: So they're kind of doing two jobs, but they don't realize that they might be done by two different people or maybe it's better to do them as one person. But you get to have those conversations and figure that out now.
[00:26:30] Speaker A: Exactly. And in pockets of the organization, it might be someone who is only focused on, you know, manipulating the spreadsheets that decide how many client dollars go towards an ad placement in Disney plus versus the Wall Street Journal. Yeah, but you also might have that individual in another market for another one of our clients, interfacing with the client themselves.
Say, what is your strategy? How do we do this?
[00:26:57] Speaker B: So what it sounds like is this work intelligence new system you have. And this is an ongoing thing, right. With rejig. It's, it's, it's kind of a living thing.
[00:27:05] Speaker A: Absolutely.
[00:27:05] Speaker B: Allows you guys as consultants to go work with these business areas and help each one of them optimize different areas. Is that the way this works? Or is this isn't a corporate wide mandate, thou shalt use this model everywhere.
[00:27:19] Speaker A: Or is it, it's not a corporate mandate. Tend to shy away from corporate mandates just because of how we're structured. So there's a lot of influence work that has to happen here. But again, as we speak to leaders and those with hands on keyboards, everyone sees the value.
[00:27:37] Speaker B: Even if their eyes probably open up and say aha. I get. They're probably all thinking, wow, never thought about it this way.
[00:27:43] Speaker A: The way, the way I'd put it is this is more workforce data than we've ever had, more than we believe our competitors have, and more than our clients have on themselves.
Right. I'll give you, I'll give you another anecdote or another example of where this is almost being monetized or is being monetized for us as wpp. So we're working with a client, one of our largest clients. They're asking us what the agency partnership of the future looks like. What is the working relationship between client team and client? Clients don't necessarily want to have super heavy marketing teams. They're overhead. Right. So we can help by optimizing both our own team, but also in some cases, helping our clients transform their marketing workforce. And that gets us into rooms where with types of clients, individuals in roles on the client side that we haven't, we don't have that many conversations with like people leads and talent leads. Typically when we're in the room talking about HR talent, it's related to L and D. We do a lot of L and D offerings for our clients to upskill their marketing teams. So we can do that, but we can also, and we can also help optimize the kinds of work that you.
[00:28:55] Speaker B: Can, you can, you know, we used to do this at Deloitte. You can teach them how to work better with you so that their organization matches your organization for the optimal mix.
[00:29:03] Speaker A: And I don't want to sell the team short in the sense that we have been doing that for quite some time. This gives us additional Ammunition to have more intelligent conversations, more specific conversations.
[00:29:16] Speaker B: So let's take a step, up a little level so we can wrap up a little. So this is a business driven work intelligence process, right?
That's what we call it. It involves getting to know virtually all the jobs in the company in a sense to figure out where the big hard hitting M shaped jobs are and where the high value jobs are. Skills rolls out of that, learning and development rolls out of that. You have to know the business very well in order to accomplish this. Obviously you had to have a C level mandate to do this project. This wasn't just like for one of the general managers, this came from the top. Sounds like. Yeah. And the ROI has been, what have you, what kind of benefits have you seen so far?
[00:29:57] Speaker A: So we, we have hundreds of pilots at varying scales testing our new AI tools as well as integrating some of this workforce intelligence into those tests, those pilots. And what we're finding is that or what we're testing anyway. We're looking at the delta between what we think we can do in terms of capacity savings and moving people into new jobs or higher value jobs versus the reality of introducing technology and just having it happen organically. And that will tell us one, is the data right or do we need to do a better job of training? You know, we'll find combinations of both. We are finding value in certain pockets. So within, within a few of our large job families we are seeing capacity unlock. There are specific tools that are rolling out that, that are not workforce intelligence related but specific tools that are rolling out related to, let's say media planning, where something that used to take two weeks takes two hours. And that's not uncommon.
You know, when you talk about any AI tools. So do we have concrete proof points of us expanding or shrinking the workforce? I don't think that's necessarily the goal of it. But what we the ROI for us and for our executives and our board is clarity. We're providing more clarity into the optionality of what the various futures could look like depending on one, how we commercially structure ourselves and our partnerships and two, how we want to operate as a business.
[00:31:33] Speaker B: Fascinating. I don't know if Deloitte is like this anymore, but when I left Deloitte six or seven or eight years ago, they needed this so badly because there was so much activity. It was all activity, project based staffing. I need somebody to do X. Do you know anybody who knows how to do this? Yes, I do. Oh, let's go find them. Oh, they're on a busy on Another project. Well, can you do this? Can you do two projects at a time? Right.
[00:31:55] Speaker A: I have a former colleague who likes to say about resource planning and it applies to most professional services organizations. Availability is not a skill set. Right.
And some of this work, that's exactly.
[00:32:09] Speaker B: The way it is. Well, or there's somebody that you like and you always use them because you know them well.
[00:32:14] Speaker A: And, and, and there's another. I mean there's a whole nother conversation we can have around, around the concept of resourcing and how it happens, how do, how to optimize that for skills based in the reality of how organizations are today. So for example, the spectrum that I probably have in a slide somewhere is currently what you're describing is network based resourcing. I know you, you're in my network, we worked well together. I'm going to put Josh on this project. The opposite side of that is gig based resourcing. Everyone, full skill awareness. I know how you feel about skills based organizations and the evolution that you've gone on in terms of your feelings about that. I don't know if that's possible in certainly our organization. I also don't think culturally it's the right fit.
[00:33:00] Speaker B: It works, it works for large construction firms, things like that, you know, where there's very repeatable projects that makes sense, you know.
[00:33:08] Speaker A: Yeah.
[00:33:09] Speaker B: For what you guys do. I could see it could be possibly. Anyway, this is. So one more question and then we'll wrap up. So rejig and the talent or workforce intelligence infrastructure. Do you see this as a ongoing critical muscle and sort of marketplace of solutions that companies need to invest in or do you see this as something we just needed to do once and then when we're done with it we'll go back to doing things the way.
[00:33:31] Speaker A: We always did for us or for other organizations?
[00:33:33] Speaker B: For you. Well, for you and what you think others could learn from you.
[00:33:37] Speaker A: So I think the initiation of the work to learn not only who you have because some organizations have their people data in great form, but who you have in the context of the tools that are becoming and are currently available to employees is something that every organization should initiate. And that in itself really can be a one time activity. Where it's not a one time activity and where it needs to be an ongoing piece of work is the idea that work will continuously be re engineered. Workflows will continuously re engineer as tooling changes. But again, this is not just a, a technology play, this is a talent management play, if you want to use that term to move into the future, into future proof organizations. So as people evolve into new these M shaped roles, we. What is the next stage of that? It's not going to stop. Right? We, right. The future of work. We've been talking about that for, for 10 plus years tier and it's cheesy to say that at this point.
[00:34:38] Speaker B: Yeah, I mean that's, that's my reaction is I, I feel like this is a muscle that can be used over and over and over again in different situations as things change in the company or new technologies come along or you get into new business areas or new services.
[00:34:50] Speaker A: I think if you think about the concept of staying continuously relevant, which is another way of saying future proofing, you can structure your entire HR team to support that. From a talent management perspective, from an incentive design perspective, from an L and D perspective, from a culture perspective, all of those functional areas that in many organizations remain siloed, interconnected but siloed can be pointed towards the same objective which is helping organizations stay continuously relevant. Employee experience is a perfect example. Rather than employee experience design, journey mapping and all of that which is great to do and necessary for organizations to understand their employees journey rather than having the spotlight on solely employee engagement, it should be on business enablement or in addition to employee engagement. It's about business enablement, not engagement for the sake of engagement. That's great but we also need to continuously find ways to, to stay relevant to our clients and for other organizations or customers.
[00:35:53] Speaker B: No, no question about it. In fact the word enablement is coming up a lot in the reframing of learning and development and the reframing of employee experience. I'm really glad you brought that up. That's, that's kind of the new to me framing of a lot of these ideas.
[00:36:06] Speaker A: You've, you've spent the last, I don't know, 44 minutes asking me questions. I have a question for you.
[00:36:13] Speaker B: Sure.
[00:36:13] Speaker A: Who are you talking to that is doing this best is operationalizing this?
[00:36:17] Speaker B: No, nobody yet. I mean the situation in most companies that I talk to is it's very clear that the organization could be more efficient that there, that there's opportunities to apply AI, that multifunction agents could streamline all sorts of cross functional business workflows. But there are companies are. If they don't really have a workflow orientation already in their business and some companies do, but a lot of them don't and they've been just hiring and hiring, hiring and coming up with new job titles willy nilly they need a project to get themselves going they need a problem. And you know, where the problem tends to start. We were just talking to somebody about this the other day is sales. It's really easy to say we have too many salespeople and we're not generating enough revenue. Can we reorganize these guys? And I've been through like 15 different sales reorganizations in different companies and they're always the same but, but if we can do it in a more intelligent way and think about the skills and the activities, not just the, you know, it's going to be product sales, it's going to be customer, you know, by industry or it's going to be by company size. I think everybody's trying to figure this out. That's why I wanted to talk to you because Rejig is sitting in the middle of this enormous problem in companies. And it's pretty clear to me when I look at AI like things like the one you guys are building, they're going to be cross functional systems. So the old job architecture probably isn't going to be around forever. There'll be new, there'll be new jobs created because the AI crosses what used to be different functional areas. The company that I just interviewed recently was Standard Charter and I'm in the middle of publishing her podcast and the chro of Standard Charter Tanuj. Seven or eight years ago they did a business analysis of Standard Charter to remind themselves what business they're in.
And they basically decided that they're only in two businesses. High wealth management for very high income wealth management, portfolio management business and global money management for multi country large global businesses. There's a million other things you can do in banking. And she said, seven years ago we sat down and figured out what are the jobs we need to win in the high net worth management business versus the global money management business. And then years later they started to look at skills and AI and other things. So I think this in some ways is a fundamental thing companies need to do regardless of AI.
[00:38:37] Speaker A: Absolutely right.
[00:38:38] Speaker B: I mean I think what happens is people, companies just get lazy and they hire and hire. They delegate a lot of hiring to a lot of managers and they end up with a lot of jobs and some really good stuff happens and some really inefficient stuff happens and nobody sees it until somebody comes along with a job like yours and says how can we make the whole company more productive and more scalable and more high value? So I think it's cultural too. I think there are some companies that are very process oriented by nature. Phone companies are like that. Construction companies are like that. They can't afford for things not to work because either the building will fall down or the phone system will fail. In your case, if one of your ads doesn't perform very well, you know, well, okay, we got kind of an unhappy customer, but we can fix it.
[00:39:22] Speaker A: The standard charter example resonates quite a bit when I think about types of businesses that we're in. Maybe it's not exactly equivalent, but I think about high volume and high value. Right. And there's different ways to apply workforce intelligence to solving for high volume work versus high value work. At the end of the day, I wanted to start with this point, but I'll, I'll lead us towards the end with it. As technology is democratized, as generative AI is democratized and people have the skills to use it, the only differentiator between competitors in an industry is the human potential that they unlock with it. Right. So it's not about a pure play efficiency. It's about giving people the best experience at work in order to do great work for clients and customers.
[00:40:10] Speaker B: Bravo. I couldn't have said it better. And I think right now people think the AI is the solution to every problem. It's going to unlock new people opportunities for everybody. Okay. Josh, this is fantastic. Thank you so much for sharing.
[00:40:22] Speaker A: Thank you.
Sa.
