Ep 50: Adaptive Workforce Strategies and Responsible AI to Optimize Human Potential with Nuno Goncalves

[00:00:09] Hi everyone, welcome to another episode of Elevate Your AIQ. Today I'm joined by Nuno Goncalves, Head of Workforce Strategy at REJIG, to explore the profound changes AI is driving in workforce strategy and the future of work. Nuno has an amazing background across disciplines, industries, geographies, and cultures. In our conversation, we unpack the critical role of human-centric approaches in navigating the rise of AI and automation, and discuss how organizations can leverage AI responsibly to adapt to the evolving landscape of skills.

[00:00:39] Nuno shares his insights on the transformative potential of work ontologies, the importance of trust and transparency in fostering a thriving workplace culture, and why upscaling and rescaling are key to staying ahead in our increasingly AI-powered world of work. He also shares some personal stories about what he has witnessed firsthand and what he's hearing from talent leaders across the globe. If you've been thinking about the intersection of AI and skills-based workforce strategies, there's plenty of insight from Nuno in this wide-ranging conversation.

[00:01:08] As always, thanks for listening. Let's dive in. Hello, everyone. Welcome back to another episode of Elevate Your AIQ. I am your host, Bob Pulver. With me today, I have the pleasure of speaking with Nuno Goncalves from REJIG. How you doing, Nuno? Awesome. It's been an awesome week so far, Rob.

[00:01:26] Excellent. Excellent. Thank you so much for being here. I know a fair amount about REJIG. I've been following their trajectory and growth for quite a while. Really fascinated with some of the work that you guys have been doing, especially of late around work ontologies and really digging into what skills-based organizations really mean and how AI plays into that.

[00:01:50] I know you've been advocates and have gone through the painful process of being independently audited for your AI technology. All feathers in your cap as far as I'm concerned.

[00:02:05] But anyway, I thought I would take a moment just to have you give a proper introduction for yourself and want to hear a little bit about your background. I know you've been all over the world in different organizations and different countries. And I want to hear about why you joined REJIG.

[00:02:22] Thank you. Thank you, Bob. And you're absolutely right. So one, REJIG is, from my perspective, a company that is changing the industry. And that's one of the reasons why I ultimately decided to join REJIG. But we'll get into it in a little bit. So you asked me for some introductions.

[00:02:41] I have been on my life and it's been 25 years or so in corporate roles. I was born in Africa, raised in Portugal, started to work in HR when I was in Portugal and had the pleasure, not by design, but by opportunity to actually go and work in different industries and in different companies and multiple latitudes and places.

[00:03:07] Worked in Europe for many years and in Belgium and France, worked in the US where I am right now. But work also in South America, in Brazil, which was a phenomenal experience as well. So my roles and as I was growing my career in HR, I became a chief learning officer. It was somehow where the trajectory was taking me. At this time, I was in the pharmaceutical industry.

[00:03:32] I then kind of evolved to a chief talent officer, where I think was one of the most exciting jobs at the time and still in my career, where you actually bring together different disciplines of HR from a talent acquisition, learning and development from a talent management perspective. And it was where I started to really broaden my views of how HR needs to work differently as well.

[00:04:01] Right. So kind of in an integrated way and not in the silos that we always have in the past decades and starting to understand how can we better support our businesses. And instead of creating a learning portfolio, talent management, succession planning, but actually create a strategy that is a build by borrow strategy that is transversal to the business and so on and so forth.

[00:04:24] So great, great time there. COVID hit. I ended up joining another company, a multinational company in consumer goods with Mars. Family owned company, a family owned company, phenomenal values, great company as well, where I was leading the strategy for their corporate university. And we all ended up creating also with this appetite that I have of transforming and understanding the business needs and understanding how, what does that translate to HR?

[00:04:49] We ended up building somehow of a sort of a sort of a team that was trying, that was called strategic capabilities, trying to understand what HR needs to do to support the most strategic initiatives in the business, which was again, great people and hopefully, and I know that they were doing, continue to do great work there.

[00:05:10] So ended up joining Nike. Nike is a phenomenal brand, iconic brand, going through some rough patches from an economic perspective as well, but a great company. And as I said, a great brand and hopefully a company that will be able to turn it around because we need a Nike in the world as well.

[00:05:33] So left Nike beginning of summer last year and really wanted to start understanding because we all see these Gen AIs and the Gen Tech AI and everything. And my instinct was that ultimately the way that we did and that I did HR in the past 25 years would be very different than the way that we will need to do HR in the next 20 years or so that I'll be likely working.

[00:05:58] So started digging in on to the great founders and speaking with VCs and so on on what are the great companies coming in, the nascent companies that are native Gen AI that ultimately will be transforming the way that we do HR and that we do HR in the world as well.

[00:06:18] So Rijig was an obvious conversation starter because everything that we, that Rijig is for, the history, the courage, the pivoting, the directions that Rijig is going. So we ended up, they ended up asking me, you know, do you want to help us build the US market? And I said, and I said, 100%.

[00:06:40] So here I am trying to hopefully help the industry get to a place where they can do and they can take the place and they can do what HR needs to do, which is ultimately anticipate the needs of the business, but also making sure and being very bold.

[00:06:57] You'll hear me talk about being very bold in the way that you do work, but also being very responsible in the way that you reskill and upskill your employees to be able to be in a, have a sustainable career, have jobs, and that ultimately will exist in the future as well. So that's been my trajectory so far where I'm here at Rijig and hoping to shape and reshape the industry as we go.

[00:07:23] Wow. Amazing background. And I can see why you joined. I can see why Siobhan wanted you on the team. There's so much that sounds like you've been able to absorb, not just from the different, you know, hats that you've worn, but understanding that, you know, variability in different, different cultures, different obstacles and challenges, you know, across different industries, which I know we're going to get into when we talk about, you know, some of the work ontology work and some of the blueprints that you guys have developed.

[00:07:52] And just for anyone who doesn't know what Rijig is, could you just explain your role as head of workforce strategy and what Rijig is sort of elevator pitch? Yes, a hundred percent. So Rijig, and I'll tell you a little bit of the story that's probably even better because Rijig has been shaping in the past four and a half years. Rijig started to be a company that was very focused as any others on trying to understand what would be the skills of the future. The World Economic Forum was at that time also saying, okay, AI is coming and so on.

[00:08:22] And as many other companies, five years ago, Rijig also wanted to be a company that solves for that problem and answers that question. So Rijig started as any other companies with technology and building a skills cloud and building a talent marketplace and a career path and technology that ultimately allows people to select and understand what would be their career and evolution and the upskilling and skills that they will need in the future.

[00:08:46] So two and a half years ago, then Siobhan and the co-founders and the team there said, listen, we don't have good enough data. We don't have good enough data to be able to actually understand what are the skills of the future and therefore what are the jobs of the future. It was when, and this is the courage that I love, it's the courage that I love from Siobhan and the team there, is that they said, listen, we're not going on the right path. We need to create a deep understanding of the language of work first.

[00:09:14] Skills are incredibly important, but work is what it's changing. Skills are not being automated. Work is being automated. So for us to understand what will be the impact on skills, we first need to understand the impact on work, right? It was a beautiful incident and insight from my perspective. So we ended up developing and building these 23 industry-specific ontologies. And an ontology, we'll go to this, but it's somehow imagine an ecosystem, right?

[00:09:38] A system that ultimately has all the details of work, of the jobs, the different jobs by industry, the work, the tasks. We call it tasks. We go to very, very fine detail on tasks, but also what are the skills that are associated to the task. So the reason I'm telling you that, and explaining this is that not everybody knows what an ontology is and how that works. We can go a little bit more, but that has been a big and important shift for RIDGIC.

[00:10:05] We do a lot of work intelligence and data for decision-making. And we also have our technology and our talent marketplace and our career pathing that helps organizations ultimately drive the employees to the right roles and the roles that will be sustainable. And ultimately understand what are the skills that they will need also in the future.

[00:10:27] So those are two things that I haven't seen in many different places or in any other places, at least not with the level of quality that RIDGIC has today. Yeah, no, that's a great sort of perspective. And I think gives people sort of a framework to sort of understand what the mission is. I mean, we hear a lot about skills-based hiring, skills-based organizations. Where do I start? Do I have to, you know, catalog everything, you know, first? Can I do it on the fly?

[00:10:53] How might this integrate with, oh, I just implemented an internal talent marketplace. How does this work? Or, you know, tying it back to some of your earlier, you know, experiences in learning. Like, what does this mean? How can I effectively, you know, once I identify where there might be some skills gaps, you know, how do I know, you know, maybe who I can upskill or reskill to, you know, fill those gaps? Do I need to hire? Can I move people around? There's just so many questions that people think about.

[00:11:22] And so, I guess one of the things that I found really helpful is that I did get a chance to attend some of your, you know, master classes that you did last year that really got into at an industry level. Here are some things that we're seeing, right? Like, this is how to think about some of the skills. And you talked about it before that, you know, AI and automation isn't, it's not about the skills. We're talking about the tasks and the work that actually has to be done.

[00:11:52] And if you use that as your sort of pivot point, then you can look around in a sort of network analysis or like a sort of a graphical kind of way. Like, what are the skills that could be needed to execute that task? And what other tasks would those skills allow me to do or whatever? Because you've got to start thinking about how roles get redefined. You have to because it's a survival question right now.

[00:12:18] So, with Gen.ai, and it's not only with Gen.ai, but there's, in the past 15 years, in particular, every single company that I went to, they were in a transformation mode, right? Because everybody's trying to understand, you know, after the kind of this industrial revolution and that you do things at scale and all of that, that you have global footprints that everybody now has and all of that. What is the next big thing that ultimately will give you the advantage towards the others, right?

[00:12:43] Yes, you can go in IP and you can, you know, pharmaceuticals, you can have your drugs, you can, you can, you have your, your, your, your devices, you can have. So, it can go from there, but the biggest, biggest differentiator right now will also be the speed to market, for example, will be how fast you're able to go to market from a research perspective, but also from a, from a go-to-market commercial perspective as well.

[00:13:05] And the reality is that, you know, since with Gen.ai, you now have something that ultimately will be, will allow you to accelerate every single thing that you do. And it's not only Gen.ai, robotics is also very much, and even, even if, if it's probably on a second layer, we've been talking with agro-business companies and, and the level of automation, the level of robotics that they are having there with, with robots going and, you know, through their fields, trying to understand if the soil has the right conditions.

[00:13:30] If there's no kind of pests, if there's no, it's, it's, it's, it's just very, it's unbelievable. I never thought that we would be doing farming like that, right? Right. So, the reality is that it's, it's not only Gen.ai, it's not only robotics, it's everything is changing. There is no single company that says, you know, I'm okay, and I'll be okay for the next two or three years. So, we are in a very fast change.

[00:13:52] So, this differentiation and how effective you'll be able to be, how much duplication you'll be able, simplification you'll bring, and duplication you'll be able to retrieve from, from your, everything that you do. How faster, how, how much you are able to, to automate parts of the processes. How will you be able to bring people there will be, will be, will be the key differentiator.

[00:14:14] So, let's not forget that, and, and, and for me, it's, it's always still very mind-blowing is that we, we always counted on humans to do work. And then we said, let's have machines, right? Industrial Revolution. And then we now have, for the first time in many, many, many years, we have a new actor in this workforce ecosystem, which is this digital worker. And this, this digital worker, people say, oh, they will take work. Yes, they will.

[00:14:43] And we need to understand what will they take, how fast will they take, and ultimately, how can we reshape everything else so that we give the right careers to people? And then we, people are able to be, you know, to, to, to thrive and have their, deliver their talent as, as, as they have been so far. It's a massive transformation. And, and I think, you know, Siobhan and Rigi extends, and one of the things that we say, it's trademarked.

[00:15:06] We want to make sure that we have a world with zero wasted potential, a world where companies, and we've been seeing some of them lately, that just lay off people and, and then, and then recruit them back again. And then lay them off, they lay them off after, which ultimately, it's not the human thing to do either. So how can you, we give HR, the business, the leaders, the visibility of what the transformation needs to be?

[00:15:31] What will be the changes around the work that they do today, and the work that they will do tomorrow? And that correlation with the tasks and the skills, and then what are the skills that you have today and the skills that you need tomorrow will be phenomenal, will be one of your biggest competitive advantages for the future. Yeah, absolutely. Well said. I think that you're striking an important balance, I guess, is one way to put it, like between like that human centricity.

[00:15:58] There's plenty of other work to be done that's probably not getting done, or there's these net new opportunities to go after different markets or launch new products and services, maybe a new line of business, other things that people could be doing.

[00:16:14] And, you know, being more customer facing and building relationships, innovating, there's all these things that people could be doing if they weren't, in some ways, sort of mired in things that are somewhat, you know, routine and robotic.

[00:16:31] And obviously, I don't mean to discount some of the more advanced, you know, and latest capabilities with some of the AI tools that we're seeing, because it's doing some things that are surprising people in terms of how it's able to exhibit some level of reasoning, maybe not human brain, you know, reasoning, but certainly it's taking on, you know, more things.

[00:16:51] And you also hit on an important point in your agricultural example, where this isn't just about, I mean, headlines everywhere about AI and specifically generative AI, as you mentioned, but you've got to look at the intersection of trends as well. And certainly, you know, robotics and AI is one of those very important intersections for a lot of industries, whether you're in agriculture and farming or you're, you know, in, you know, warehouse and, you know, supply chain and things like that.

[00:17:21] So these are disruptive things, not necessarily the individual trend, although that could be disruptive as well, but certainly not as disruptive as, you know, the intersection of some of these, these trends, you know, industrial internet of things with robotics and with AI. I mean, it's, it's amazing. We saw some of those examples coming out of the consumer electronics show, you know, very recently where we've seen some very lifelike humanoid robots doing some incredible things. So it's, it's scary and overwhelming for a lot of people.

[00:17:50] I mean, I get it. It is. And I think it's, you know, for me, I always describe these feelings of butterflies in the stomach. It's like, it's like the scary and excitement, you know, a couple of weeks ago, I was seeing someone and I was reading something and, and someone was, was, was saying that it will be the, for the first time ever, we will have something that is more intelligent than the humans in the world. And that's, that's very scary. That's very scary, Bob.

[00:18:16] And, and, but on the other side, one, it's inevitable because there's, there's no way to stop it. And the question is, how can we make sure that ultimately we make the best out of it? So it's inevitable. And I think, you know, through the millions of years and, you know, nature has, has given us some, some, some things that are gifts, right? So this is a gift. Let's try to, to make the best that we can to make sure that we, that we create a better world, that we create a more equal world.

[00:18:44] And, and, and at the same time that I'm talking about this, it's, we also seeing, you know, cause it's not only technology and work, it's also politics and social and, and all of that. I get it. Right. So hopefully, and that's what I'm trying to do is that, you know, that we're trying to do is that step-by-step where at least we start to create something that is, that will allow us to, to accelerate, to be better, to be more performing, to, to evolve science faster, to, to do faster, to produce faster.

[00:19:12] And with the hopes of bringing more and better lives to people. And that's, that's what we ultimately need to, and want to, to achieve as well. Yeah, absolutely. So of course that brings me to one of the topics I wanted to ask you about and drill into it's around responsible and human centric AI. And, and to your point, this, this is about not just, you know, decisions about people's livelihoods and being fair and equitable and bias mitigation.

[00:19:38] I mean, it's, you know, that stretches to all kinds of other decisions and how people are creating content. And, you know, you mentioned social media. So without going too far down that rabbit hole, I mean, you know, if you could just talk a little bit about like how you think about that human centricity in terms of making decisions, because just pointing back to, to what you mentioned before, it's like, well, companies.

[00:20:05] Hi, I'm George LaRock, and I'm looking forward to exploring the critical trends, shaping the future of work and technology with you over on the WorkTech podcast. Now this podcast is a little different. I bring together industry leaders, innovators, and investors, and we go deep into market intelligence that matters to HR pros and tech providers alike. So give the WorkTech podcast a listen here on the WorkDefined Podcast Network, and please subscribe if you like it. See you there.

[00:21:04] Yes, of course. For the company to grow, you need to make more money and shave costs or whatever. But at the same time, you know, you can't execute your business or technical strategy without the right talent, right? And you want them to be loyal and tenures are shrinking. Engagement is still, you know, abysmal at a lot of organizations. So when you talk to your clients and prospects, I mean, how do they view that, you know, trying to strike that balance? Can I give you a personal view first? Of course.

[00:21:34] A few years back after COVID, I was working with the Josh Burson company. And I always stay very close to them because they're dear to my heart. They do really important research. And given my background, I typically am part of those conversations as well. And one of the things that we did, we did an initiative called The Big Reset. We were bringing kind of leaders in different parts of different organizations and so on. And we ended up building with a good friend of mine, one of the chief of officers that I admire the most in the world.

[00:22:03] We ended up building a report on human-centered leadership. And there is one thing that, for me, I think it's a myth that we need to demystify. Human and business-centric are not polar opposites. Being human and being business-centric are not polar opposites. You can be business-centric and ultimately be very human.

[00:22:27] What I think, my personal perspective, is that your top priorities cannot be only your shareholder value. It's the balance. And Mars had this beautiful value called mutuality. And you cannot ask for someone to be loyal, for someone to be present, for someone to be performant, and not treat them with respect that they are due.

[00:22:53] And what I'm talking about, respect, is not taking up kind of bad performance. It's actually being able to show them transparently where they are, what the company stands for, what are we doing, how the strategy is evolving, why is it evolving, investing in people, being transparent, investing in people so that people can continue to thrive where they are. Now, does this mean that you'll never have to let go of anyone? No.

[00:23:23] But even letting go people, you can be human, right? You can explain, you can rationalize, you can ultimately be able to follow and support that person through the process of transition as well. So I love to demystify that you can only be one or the other. You actually, I think, the best business decisions are those that, for me, have a significant portion of human centricity.

[00:23:48] Because the reality is that if you're not being good with people, then you're going to create a culture that you don't want, a culture that will never be performing. A culture of fear that will never be able to be as innovative as any other. So how do you strike that balance so that you can actually be one and the other? Business centric with a human centric perspective. I think we can deal with those two dimensions at the same time as well.

[00:24:12] So a lot of our customers and a lot of the people, and even earlier this week, yesterday I was in New York and I was with a big company. And most of these companies are trying to understand what needs to be done. Because the reality is that, and especially HR, there are different levels of maturity, right?

[00:24:36] And HR is typically the organization that is focused more on people and even on work, even if HR doesn't necessarily like that. But we are all trying to understand what are the best decisions for the organizations. And how will we make sure that ultimately we have the work and the people that we will need in the future? So I guess, yes, we are a lot of most of the organizations and most of us are really good people. And we want to make sure that we do the right thing for the organization and for the people that are in the organization.

[00:25:05] But most of the times we don't know. We don't have data that is strong enough for us to make a decision that we can stand behind one way or the other. And, well, I don't know what your experience has been, but a lot of my experience is that a lot of the most critical decisions in HR are made based on opinions and hunches and perspectives that are very, very biased.

[00:25:26] So how can we help strengthen those decisions so that people can have better informed data and ultimately, hopefully, at least make some decisions with more confidence and hopefully better decisions for the organization that includes also their people as well? Yeah, absolutely.

[00:25:43] Well, you're sort of alluding to some of the other areas that I think companies need to work on, not just in HR, but more broadly, just in terms of data informed decisions, right? And data and analytics maturity that goes into a lot of these important decisions, especially decisions about people and their career trajectories and their performance and things like that.

[00:26:10] So let's make sure we're looking at clear evidence. And let's make sure we understand what data we have available, where there might be some signals from maybe data that comes from a different source. And how do we aggregate that to get a more comprehensive picture of someone's potential and where their real skill gaps are and understanding skills adjacencies?

[00:26:39] And I know you know all of this, but I think it's really important. And when it comes to AI adoption, which people are sort of struggling with right now, that data and analytics maturity gives you an advantage in terms of not just adoption in terms of implementation, but I mean actually trusting the output of some of these AI tools. I mean, that all goes back to have you cleaned up your data?

[00:27:07] Do you understand the provenance of that data? Do you understand and can you trust those inputs? And some of this ties into what I like to think about in the responsibility I context, which is, are you being responsible and ethical by design? And that starts with understanding the data that went in to train the models. Which has been one of the premises that the premises that the Rijig has, right?

[00:27:31] And ultimately what we did, we started to, you know, Rijig hired workforce strategies, people that were part of, you know, that are experts in the industry. And ultimately ended up building these 23 different industry-specific ontologies with people that ultimately understand the work as well. What we wanted to do was to have a foundation of really strong and quality data that we can stand behind from, right?

[00:27:55] So, but I've also been in many other organizations that their technology stack, their data stack is so complex that ultimately you can't. You can have numbers. You probably don't have data and you certainly don't have necessarily insights either, right? So that's the thing that we were trying to contract. We're trying to make sure that we bring the data that ultimately, and by the way, we do that not in months, not in years, right?

[00:28:23] But we do that with our ontologies, and that's what powers us to do that in weeks one way or the other. So that's the intention. So that then, of course, you're making decisions based on data that is unbiased and based on good quality data as well. Otherwise, then you're just making a very strong point of view based on things that are not necessarily the best sources of information as well. So 100%. And that's where the ontologies, I think, are one of the biggest.

[00:28:48] There's other players that say that they have ontologies, but the reality is that the question is what is the quality of the data inside? And Riji has by far audited, yes, from my perspective, by far the best data on work and best data on skills as well. Excellent. So I know you guys have done a lot of work going deep into these different industries, these 23 different industry sort of lenses with these different ontologies.

[00:29:18] I guess I was curious about looking at it from a different dimension given all of your vast global work experience. I mean, do you see there any particular like differences or similarities from a geographic or cultural perspective that you've witnessed in terms of how people are thinking about, you know, the work that you're doing? There are always.

[00:29:42] And I think there's if we look back in the probably the past 15, 20 years, a lot of the trends starts typically in North America. And then Europe is typically more regulated. So it takes a little bit more time, but eventually gets there. And I think and this is this is my belief that ultimately South America is for me. And it's it's it's it's a it's a geography that ultimately continues to follow some of the leads as well and following also the lead of the US.

[00:30:11] So a lot of what I see, I see and bridging is is a company that started in Australia. There's a there was there has been a lot of very interesting kind of startups companies coming from there with especially with the AI and everything. So I'm starting to see bubbling a lot of the a lot of a lot of a lot of the expertise in different parts of the world. The US continues to be the biggest market from an economical perspective.

[00:30:39] So you'll see you'll see the penetration of new things and and new technologies and new approaches much faster in the US than in many other part of the country of the of the world. But this doesn't mean because there has there is really good innovation in Europe. There's really good innovation in in in Asia. There's there's really good innovation also in South America, South America, less visible, maybe. But there is there's really good innovation there from a penetration perspective.

[00:31:09] I think there's number one continues to be US and North America. And that's why you you somehow believe or see when when when when a startup starts to become bigger, it becomes bigger in the US. And it's when you start seeing the old but that's new. But yeah, but new but it started in Sydney, for example. Right. So that's that's what I've been seeing so so far in the different geographies that I've that I've worked on.

[00:31:31] And I think one of the things I guess related to responsible, but more more broadly, just in terms of how people are embracing the technology. You know, you mentioned all these different sort of hotspots of of innovation and people are progressing on different trajectories and different different speeds. But there's also that balance that people need to strike. Everything's about balance. Right. Right. But just balancing, you know, responsibility with with innovation.

[00:31:58] I mean, sometimes, you know, legislation or even proposed legislation and, you know, being risk averse versus, you know, leaning towards, you know, a thousand little thousand flowers bloom and see see what happens or whatever. I mean, I imagine because legislation is such a sort of patchwork across the globe that, you know, that could influence, you know, the speed or, you know, the sort of risk profile of different organizations. Yeah.

[00:32:25] Listen, I think it's so it's different rules and different different rules of engagement as well. Right. So and that's where it's interesting. Right. I think I mentioned this to you. I was born in Africa and I was raised in a small country in Europe.

[00:32:41] And the reality is that companies and innovators that are coming from some less privileged places, it just takes them much more time to actually be able to come to market with something exactly because of those regulations. Now, should we lower the levels of regulations or not? It's a different debate.

[00:33:02] But the reality is that right now, if you're if you're an innovator in Portugal or innovator in the US, your chances of actually bringing something to market are exponentially higher in the US, not only because the regulations are lower, but because there's also more money here. But but but if we cannot change the money kind of factor from a speed perspective and a regulation perspective, it's it's much slower in other parts of the world as well.

[00:33:32] So it's it that's something that we haven't been able to equalize that access to or the ability to get to to companies that ultimately are as fast as some of the others that are in less regulated places as well. So maybe something for the future. I'm not not not too confident. Yeah, but but maybe something for the future. Yeah, I am.

[00:33:54] I am very interested in how legislation in the US evolves over the next, you know, even six months. Yeah. So we'll we'll see. It's another podcast, Bob. It's another podcast. Yeah, exactly. Exactly. One of the things that I wanted to circle back on when it comes to skills, I know Rejig has spent a lot of time and I think Siobhan specifically has some affiliation with the World Economic Forum.

[00:34:24] As you know, they just came out with their new jobs report for 2025. And everyone's all my LinkedIn feed is now filled with people giving their hot take on on some of that. But, you know, some of what I saw was, you know, novel and and very interesting. Some of it was relatively consistent in the sense that, you know, even as AI is maturing and evolving and at a speed we can't necessarily, you know, predict.

[00:34:50] But, you know, they did a reasonable job trying to do so. But one of the things that, you know, stood out to me over the last couple of years is, you know, what are the most in demand skills that are expected, you know, through, you know, 2030? I think this report talks about and to no one's surprise that's been paying attention to a lot of this. You know, those top skills that are in demand are not technical skills at all.

[00:35:20] Right. They're human, you know, cognitive leadership management kinds of skills. So I guess I'm curious about like how, you know, or how you personally and how, you know, Rejig and maybe how your clients think about that in the context of, you know, building their sort of skills ontology and just thinking about that as they use this for workforce planning and lots of other things.

[00:35:48] I agree with you. So there's a focus and a continuous focus that is not new and has been like that maybe the past seven years on the human skills, whatever they call it, the skills that are kind of that only human can ultimately have. So the ones that we will not be replaced by, got it. Okay. I think there's from my perspective, there's one that's not new.

[00:36:16] Second, I think what we will have to do is to probably change a little bit the way that we think as well. So one of the premises and all our education system somehow tells us that we need to think linear, right? Even math, right? The way that we teach math, it means that we need to think linear. And the reality is that now with AI, you don't need to think linear. You need to think systemic. So it's a big shift one way or the other.

[00:36:45] So we continue to see, and one of the things that we do at RIDGIG, we try to, we estimate and we infer what are the tasks that are more exposed to automation, right? And if you have production tasks, they are typically kind of technology-based. There's a high level of exposure to automation. Now, if you start having more management tasks, the exposure is lower. If you start to go more to leadership tasks, the exposure is lower, right?

[00:37:15] So if you start to go more kind of innovation, it's the exposure is lower as well. So we do a lot of that work on, because not only we showed the work at the task level, but we also start making sure that you understand what are the tasks that ultimately will be less human and that will be automated and given to agents and AI agents and AI workers versus those that will stay. So it's not new, and we already have that embedded on our ontology.

[00:37:44] So with every single deal and client that we go to, we provide them with this level of visibility. Now, the question is, for example, we can say, listen, you have 10 jobs or 10 roles in one function or one subfamily. It doesn't matter, right? And you can have, oh, this is, you know, this role is 30% of those tasks are exposed to automation. This role is 70%. So what are you going to do, right?

[00:38:10] So when you start seeing all of this, it can even start to be overwhelming because there's the potential of automation is across the board. So what we do is ultimately we go through with them and say, okay, listen, what are you ready to automate? Because in some cases, even automation requires some investment as well. So what are you ready to automate? So we actually guide them through the process because you cannot be non-automated to everything is automated in one go. So we guide them through those tasks.

[00:38:38] We guide them through that process to understand, okay, yes, we're ready to automate parts of supply chain. We're not ready to automate part of our commercial. And then that will feed also the speed of evolution of the skills that you'll need as well. So I guess what I'm trying to tell you is that, one, not new, so it reinforces what we've been saying. Second, not every company will do kind of zero to 100 in one go. They will have to make their decisions on what are the areas that they will be evolving in most likely the most critical areas.

[00:39:08] But the reality is that gives you a little bit of time to be able to become the worker that you need to be in the future. You know, Josh has just released one of his reports and he starts talking about in 2025 about this super worker. And what is this super worker is one that maintains a differentiation from the digital worker. So that knows that that part is safe between brackets.

[00:39:35] But also another one which ultimately allows these people to understand how to deal with AI and how to leverage AI to become faster, better, more efficient, with better insights, making better decisions, and so on and so forth. That for me is the most critical thing, is to actually help people shift from a linear perspective to more of a system view and understanding what can be done with AI.

[00:39:58] And yes, to excelling on everything that is critical thinking, on everything that is innovation, on everything that is around reading cues with people in management and conflict management and so on and so forth. So yeah, I think it's even more time for us, for humans to become more humans, which I think is bad. I think it's good. I think it's a good thing. I do think it's a good thing.

[00:40:19] You reminded me that I did just came across Justice Bersons, the rise of the super worker, I believe the report was called. So yeah, I'm looking forward to digging into that. I had just commented, I think, on one of Glenn Cathy's. Leading a growing business, it's like building a plane while flying it. Team building, decision making, and scaling all at once. At CPO Playbook, we get it.

[00:40:48] That's why our podcast, ranked in the top 10% worldwide, tackles the toughest leadership challenges with insights to help you lead smarter and grow faster. Tune into the CPO Playbook podcast because leadership doesn't come with a manual, but we're pretty close. Close the other day, because he was one of the guys that shared about the World Economic Forum report. And I said, oh, so basically, don't outsource your critical thinking.

[00:41:17] Continue to wear that systems thinking hat, apply your critical thinking, analytical thinking, gain leadership skills, and all that. But you also need to figure out how to augment yourself with AI by increasing your AI literacy. No one's asking you to become a developer and go too deep on the technical side. But you need to understand what it can do and how it helps you.

[00:41:45] It's there to augment what you do. Hopefully, it's not there to replace you. But if it can do some of the things that you don't like doing or that are very time-consuming or are somewhat routine,

[00:42:00] if you can train some of those agents to think like you and have some powers of deduction and help you go through a lot of the documentation you have to go through, manage your inbox, whatever it is, how can you make yourself more efficient and effective? And that puts you in an ideal marketable position because you just don't want to fall below wherever someone draws a threshold or a red line.

[00:42:30] You just don't want to fall below it. I think we all get up in the morning, Bob, and wanting to be the best version of ourselves, right? I've never heard of someone saying, you know, I'm going to be the lowest version of myself today just because, right? So we want to be the best version of ourselves. But sometimes we have fear. Sometimes we have emotions. All of that, right? And the reality is that some people will be early adopters and say, okay, kind of more fearless and others less.

[00:42:57] The key here is I think there's, you know, and we've been talking a lot in the past, especially during COVID and after COVID, around the social contract, right? The social contract between kind of the organizations and the institutions and people, right? And it goes back to mutuality. So we as leaders and people that have, you know, some visibility of the strategy and can see and have some foresight are estimating that ultimately this is an evolution is going to happen.

[00:43:23] Then you have the leaders of the organizations that say, okay, how will we apply this to our organization so that we can do better in the future as well? So the key thing here is that we now need to understand that, yes, we have all these people. And then we need to, one, create visibility. But we need to give them the confidence that we're in this together, right? That we're in this together.

[00:43:49] That it's not about necessarily you losing the job or a company, you know, firing, you know, 5% of their workforce as it has been announced this year or this week. But it's the trust to say, you know, listen, it is evolving and we're in this together. So, and let me give you a visibility of what are the jobs that ultimately are more sustainable and the ones that I believe will exist in the future. Do you want to go there? What are we going to do? Let's engage in a conversation. And then if you want to go there, what's your upskilling?

[00:44:18] What's the reskilling that I need to do? What are the transferable skills that you have? And then we put up a path so that you can transfer and, you know, and continue to build and be the best version of yourself as well. So that for me is one of the, it's critical. And I think there's not a lot of trust to your point.

[00:44:39] You've been talking about engagement that has, and it's not a lot of, there's not a lot of trust on many companies out there because what we've been seeing is kind of this decision making that is not explained or contextualized. This is this kind of hiring and firing, not understanding the impact that every single time that you fire one person, there's a ripple effect of like maybe 10 people one way or the other. So if you're firing 10 people, then it's 100 people.

[00:45:06] If you're firing 100, you see, it's exponential. So let's make sure that we try to understand what the future is ahead of us, which HR has been terrible in doing. I think it is feasible now. I know it is feasible. I've seen it. Once we understand what the future is looking like, can we bring people along, really bringing people along? And some people will not want to come. I understand that. But we need to try. We need to give it a try.

[00:45:36] We need to try to bring them one way or the other. And yes, we all are then owners of our own decisions. But we need to make that effort so that people that want to thrive can thrive and have the conditions to thrive in an environment that is safe and an environment that ultimately invests on the continuous subscaling and subscaling of people. That last point is where my mind went.

[00:46:27] We need to understand their role. We need to understand their role that they currently had or that they could have as it relates to that because they're just too many sort of degrees removed. Or that was the perception that they were just this isn't something that they sell, build, anything. So you had to bring it to life.

[00:46:47] You had to build some digestible information for non-technical audiences that really talked about what these cognitive services, we called them at the time, but these sort of AI powered APIs could do as you plug them into your processes and your software solutions and things like that. And just for people to get that education, it unleashed so much ideation and innovation.

[00:47:17] There were tens of thousands of novel ideas of people combining these different APIs to understand, well, look at this would work internally. This would work for my clients in industrial sector. This could work for retail or whatever. Tens of thousands of ideas. So you've got to be willing to experiment. You've got to be able to listen to great ideas wherever they come from.

[00:47:42] And you've got to sort of think a few steps ahead to say, what would be possible if everyone sort of elevated their own knowledge and their own game about what we could do as a company and do that together? And so I think there's incredible opportunity to unleash that potential. I mean, you talk about your Rejects tagline, which I love is zero wasted potential.

[00:48:11] I mean, that's how you start to move in that direction. Because right now, without going deep in skills, intelligence and skills-based organizations, without understanding the trajectory of some of the AI and automation that you could deploy, then you will never be able to get someone at their full sort of cognitive capacity and creativity and all of these things.

[00:48:38] But it starts with some of that and showing them just what else is possible. Because otherwise, it's just these unknowns that people aren't aware of their own potential. My own personal evolution is bringing me to a place where I think, one, we need to equip the organization to be able to have the data and all the conditions for good decisions.

[00:49:02] The psychologist in me says there's another dimension, which is a culture without fear, right? Because the reality is that most of your organizations, when you start talking, in some organizations, when you start talking, okay, oh, listen, your job will change. And here's people are starting to say, oh, my God. Right? Oh, my God. Right? And then they disconnect because they're always trying to understand, okay, what's going to happen to me? What's going to happen to me? And so on and so forth. So I think this trust on, yes, it is changing, it is evolving, but we got you.

[00:49:31] We're going to go together. This is a second piece, right? So because then you need the good decisions, but then you need the people to actually help you implement those decisions as well. So you're absolutely right. It's even more complex than the data. The data is already very complex as well.

[00:49:49] And earlier today, I was with an analyst team, and they were saying, you know, listen, it seems that HR, given, you know, we were at the end of the conversation, I was telling everything about what we do and how do we bring together and create somehow of a work operating system. So the details of work so that we can understand, okay, if we shift this, what's going to happen and so on.

[00:50:08] And they say, listen, then what you're saying is that ultimately in the past decades, HR has been trying to focus on skills in different parts of the organization and comp and from a job architecture perspective and then talent acquisition, talent management, L&D, leadership development, all of them in parallel and not actually being fed by something that is common, right?

[00:50:29] So if comp already has a very different vocabulary than talent acquisition, give them two different data sets and they will never understand each other, right? So how do we bring that together from a data perspective so that then people can start and say, okay, but hold on, but this makes a difference because we're talking about different levels of seniority in one roles in the job architecture, but hold on. But that then is reflected also on the skills that I need to hire and that is also reflected on the proficiency, right?

[00:50:59] On the skills that I need to develop. Yeah. So it's that common language will be critically important. So I guess what I'm saying is that common language, that common around work, around skills that will power a skills-based organization, but also a culture that will allow people to understand and be transparent, understand where we are, where they need to go and how to get there. Yeah.

[00:51:52] But for themselves and understand when good ideas come to them to be able to recognize those and see the opportunity. Totally switching gears with you, Nina. I was curious just from your personal perspective, are there any particular generative AI tools that you find particularly interesting? Anything that blew you away in a good way or a bad way, in a concerning way? So, Bob, can I tell you a story?

[00:52:22] Then I'm just a few weeks back, I was connecting with a very good friend of mine that was a C-level executive at Airbnb. And then he went to work through another company and he's been working and building kind of agents and agencies of agents, AI agents as well.

[00:52:42] And he was trying to solve other issues and he's trying to focus more on procurement and more on making sure that companies actually make good buying decisions and so on and so forth. So how to do that. So and then he said, listen, Nuno, what's the most complicated thing that you guys are dealing with? And I said, listen, we've been at the skills-based organization for at least a decade, if not more. And we cannot in HR figure this out.

[00:53:06] Now, I think we are because I had, you know, I had Rijigan, I know where we were going. But he said, I told him this is the most complex. And I explained and we spent like 20 minutes talking about why the complication and all of that. And then, you know, we parted our ways and they said, you know, great to connect and all of that. And that same afternoon, if you believe it or not, he sent me and he said, here's the agents that I created for you to actually tackle the skills-based organization.

[00:53:33] And he said, listen, I created an agent that is actually an expert and a specialist in OD and workforce strategy, for example. I created one that has more of an expertise, more on recruiting. I created another one that has more expertise in organization. And he said, you know what? They're talking to each other. And I said, oh, my God. Right. So and he showed me a video of he making a question and then the agents actually interacting and building on each other, which is phenomenal. Right.

[00:54:02] It's like the agents say, oh, my, you know, here's a response from an OD perspective. And the TA agent saying, you know, this is really good. This blows my mind. This, you know, even for me that somehow, you know, I'm not having a linear thinking anymore. I'm seeing more systemic. But the reality is that when you start having agencies and not only agency isolated and working in an isolated way, which is great and supercharges you,

[00:54:28] the power of actually bringing different agents together and to solve issues and help you solve issues is mind blowing for me. So I'm still I'm still trying to put my, you know, my head around it as well. Yeah, no, I've seen some examples of that. I've seen some firsthand. I'm actually in a boot camp right now trying to learn how to do that. Build a one agent that has my sort of brand voice, as they call it. Like, does it give it writing samples or whatever?

[00:54:57] Can it write like me and sort of think like me? But then to your point, other ones that sort of take that and know, you know, where to maybe start those conversations or how to execute other types of tasks or whatever. But yeah, we're giving new sort of a new variation to the definition of an agency, right? That's authentic workflows. We're going to be really, really impactful. And I know it's going to make some people nervous as well.

[00:55:27] But it goes back to whether you're stringing agents together in a workflow or you're just trying to, you know, upskill or reskill yourself within your organization. This all goes back to responsible design and thinking about, you know, human centricity and fairness and making sure you're evaluating those agents and those workflows very, very carefully. So it's a lot. It's a lot to get your head around, as you said.

[00:55:58] You know, I want to thank you so much for spending so much time with me. This was really insightful. And I know my audience is going to have a lot to think about after this episode. So thank you again. Bob, it's been a pleasure. And anything that I can do to help us evolve and clarify the better. But it's been an absolute pleasure. Thank you so much for the great conversation. It was a great hour. Excellent. Likewise. Thank you again, Nuno.

[00:56:27] And thank you, everyone, for listening. We will see you next time.