Episode Transcript
[00:00:03] Speaker A: The organizations that win here in this transformation aren't going to be the ones that have the most AI, the most tools. It's going to be the ones that have the workforce that's best prepared to use it.
[00:00:15] Speaker B: Hi, this is Josh Burson. Welcome to the what Works podcast where Josh Burson company analysts talk with innovative HR and business leaders about what's really working in talent, technology and the future of work.
Hi everyone. I'm Kathy Andreis, senior Vice President of research and global industry analyst at the Josh Ferson Company and I'm excited to speak with Sarah Gutierrez, the Chief Science Officer at shl, about how companies can assess and increase AI capabilities.
Sarah, it's so great to have you here. Thanks for joining us.
[00:00:51] Speaker A: Thanks Kathy. It's a pleasure to be speaking with you today.
[00:00:54] Speaker B: Get started, tell our listeners a little bit about yourself, your role and about shl.
[00:01:00] Speaker A: Absolutely. So starting with shl, we provide trusted talent intelligence that empowers organizations to make confident data driven people decision.
We've been doing this for over 45 years now, really looking to combine that behavioral science with the analytics and AI to give leaders clear, objective insights into skills and potential.
My role as Chief Science Officer is to work with our industrial organizational psychologists, our research scientists and AI experts to really understand how we can measure skills, potential and performance in ways that are both scientifically rigorous and practical for organizations to use.
[00:01:38] Speaker B: What an important role for a company like SHL that's based on science and research and the data quality that you get from your deep dive assessments of the skills and the capabilities and the behaviors that for that people have in all settings. Using it for recruiting but then also for internal assessments across the board.
[00:01:58] Speaker A: Yeah, absolutely. So a lot of talent acquisition usage and then more and more so as the world changes here, we're seeing a lot more mobility opportunities for folks to look at what skills do we currently have in our org and how do we move those folks to the right roles as we move forward?
[00:02:14] Speaker B: Yeah, there is such an important use case obviously, and especially the internal mobility. I was just talking with a big client of ours in the Middle east actually and they were saying we don't have those capabilities for this AI transformation. We were talking about what capabilities and skills and roles you might need in the future and most organizations feel they don't have that. So how can you assess where people are already at and then move them into the right direction? Develop them of course as well?
[00:02:41] Speaker A: Yeah, absolutely. We're looking at some of those key requirements that we're seeing change as AI is coming into the fold here. And there's some common themes that you touched on the first one there, and that is that this workforce transformation is just accelerating beyond what anybody could really be thinking. Reskilling is not anything new to what HR professionals are doing and thinking about, but it's just happening so much more quickly because we're seeing these roles evolve faster than ever and job architectures are trying to keep up. And it's leaving Chros and other HR professionals really asking just basic practical questions like what skills do we have today and where are the gaps? And how can we quickly get to a point where we can effectively redeploy that talent? It's a big conversation with us and the customers that we serve every day.
[00:03:30] Speaker B: I know there's all these different ways of assessing, of course, the skills that people have. And having something research based and science based is really so important and probably more important than ever as things change so quickly with AI. Do you see that too?
[00:03:45] Speaker A: Yeah, absolutely. I will always stand behind the science and the objective measurement. That's what we do. It's core and critical to our mission here at shl. One of the misconceptions that we see coming through some of those other client conversations is that, well, AI should be making these talent selection or talent promotion decisions easier, not harder.
But really what we're seeing is this tension where very true that AI can provide additional insights or information, but on the flip side, it's impacting their application volumes. They are seeing candidates using AI to generate resumes. So you've got this real signal to noise problem where a lot of candidates actually look equally strong on paper. And so that's where organizations are turning back to some of these more objective assessments to try to differentiate the talent that they're seeing.
[00:04:35] Speaker B: Just basically hiring people that you know or that you, that you basically have worked with in the past. And that makes you of course, overlook a lot of talented people that wouldn't have that opportunity otherwise because maybe they're new to that area, maybe they're changing careers, or maybe they just don't have the relationships in your company.
It also helps make your hiring decision and your mobility decisions much more defensible and much less biased.
[00:05:04] Speaker A: Yeah, absolutely.
[00:05:05] Speaker B: How do you see this change in the whole AI transformation? Have you seen the last three years since AI has been really the talk of every CEO and every Chro on a daily basis? Have the customer requirements changed in terms of speed, quality, what they're looking for, how they're using what you're providing?
[00:05:24] Speaker A: Yeah, absolutely. One of the main themes and really important for the conversation we're having is this realization that AI talent is no longer a nice to have. So as they're looking at the skills that we would typically map to the job and do a job analysis, they're also saying beyond the job today, what are those AI related skills that they will need to have to take us into the future?
And organizations that we're talking to are starting to realize that having AI tools, whether you're using cloud or Copilot or so many third party apps that can help in different workflows, just having the tools isn't the same thing as necessarily having a workforce that can use them effectively. So many cases the technology is going to stay ahead of the people, but you really as an organization need to focus on your people to make sure that they're ready to pick up those skills and those tools so that they can do a better job, more effective, more productive job in their day to day.
[00:06:18] Speaker B: It's so important. And just defining what these skills are is challenging because you might think of the obvious more technical skills, but really using AI is not just a technical thing, it's really more than technology overall.
[00:06:32] Speaker A: It is. And one of the ways that we've been describing this has resonated pretty well with our customers, so I'll share it with you is when we think of the skills that a person would bring to the table.
Our analogy is that there's a tree. So a person has their skills tree and at the top of their tree they're going to have these leaves that would represent some of these technical skills you just mentioned. And the problem with trees is that every season the leaves will fall off and new leaves will need to grow. And so with the advent and the use of AI, those that cycle is happening exponentially more quickly. So if you're trying to train or hire based on the skills of those leaves that are continually falling away, you're never going to keep up.
[00:07:10] Speaker B: Yeah.
[00:07:10] Speaker A: So in our tree we've got some semi durable. Those may be those AI processes that they may need to put in place. They'll still change maybe a little bit more slowly. But where we're thinking about how we can help organizations identify the right talent to really work in these AI enabled environments is the roots of the tree. And those roots are those durable, transferable human skills. Things like adaptability, critical thinking, learning orientation, things that I could take from my role today into a role that I go into tomorrow. But they really ground me. And by modeling on those human skills, we can really look to predict who's going to succeed in this AI enabled world we're all in.
[00:07:51] Speaker B: We see the same thing actually happening in that microcosm. If you want where we're seeing the, what we call power skills or the human skills that you mentioned here being becoming more and more important because more functional and technical skills getting and they're still important but they're getting more and more AI augmented and then also as you mentioned too, they're getting more and more obsolete rather quickly. So how you did certain things in, I don't know, recruiting or talent mobility or careers, some of these skills are actually getting updated rather quickly. And the old way of doing it might not be actually the way that you're doing it. For example, when you enabling with AI as well. So even the processes and the workflows will change as well.
Yeah.
[00:08:37] Speaker A: So it's grounding in this behavioral model these durable skills really gives that organization a place to start that isn't changing day to day. So instead of trying to chase every new tool or capability that's out there, they can really focus on that foundation and really look to enable people on the skills that will help them evolve as the technology evolves.
[00:08:56] Speaker B: And we've been talking about the concept of the super worker in that context and I know you've, we've had many discussions about that and I think that's something that resonated with you and your clients as well. And I'll just mention how we came up with that concept of the super worker. And when we put it into the market it was really well received because we started talking about that as soon as AI basically came to the table and everybody thought is a technology tool that's taking our jobs and making workers obsolete. And I think the perspective that we've taken is the opposite really. It's an empowerment tool. It's an enablement tool. It's a tool that actually if used right, every worker from the front line to the executives to managers of leaders, everything basically around the entire company can become the super worker that uses AI in order to make themselves more productive to do much of the work that they are really uniquely qualified for that type of license work that healthcare is calling it and, and really do not have to do all the things that actually AI is better to for because AI and people have complementary skills.
We don't have the same skills. And so that's how we're thinking about the super worker. How have you seen your customers apply that mindset?
[00:10:16] Speaker A: Yes. So it's been pretty Exciting. So obviously we worked with your team and your company alongside the Super Worker research. We really wanted to get to a point that we could help organizations scale this problem. It's one thing to identify what are the key skills, but then being able to actually measure that and gain some insights about what's the bench strength against. This profile gives organizations almost an area to start because it seems so big and broad when you. Before you can define what. This is probably one example that I would share. It was really exciting for us. It was one of the first organizations to deploy our AI readiness model that was based on the Super Worker. And they're a media and telecommunications organization. And they, they were coming to us knowing they had to bring more AI into the business. They knew that there was going to be significant productivity gains if they did this, but it's a really big transformation. They wanted to do everything they could to de risk it, both in terms of growth for their people, but making sure also that they were approaching this in a fair and transparent way.
So we partnered with them and they utilized our AI readiness model across the organization. And what was really exciting is as we were presenting back the results from those assessments that were given and we summarized those for them at the organizational level, they just told us this fits exactly what we think. And we thought, but now we have some data to go behind it. And they found that the enthusiasm for AI was really high.
So people understood the direction of the organization, they understood why AI could help them in their roles. But when it came to actually applying AI into those new workflows, there were some gaps in the capabilities of the team and having conversations with our subject matter experts. We identified things around using the right guardrails, applying judgment when you're getting outputs from AI, and then really setting boundaries. Those were areas that the team really needed to upskill. And what that did was allowed them to create a really actionable targeted development plan for their groups. And then now they've got something that they can track along the way as well, their progress over time.
[00:12:20] Speaker B: And that's probably what's happening in many companies, I'd imagine. It's usually not so much the enthusiasm for AI that's lacking, but how do we actually do this? What are some of these kind of behaviors or skills beyond the technical ones, or maybe also the technical ones that you see in this Super Worker profile?
[00:12:41] Speaker A: We've created a model that's really four overarching factors with eight skills that sit underneath. And so we start with our AI literacy factor, which breaks into the ability to understand AI and the ability to apply AI. So that's really moving beyond the buzzwords and kind of getting us to a point where AI can provide value while at the same time staying in guardrails and doing so ethically and responsibly.
Our second factor is analytical thinking. Thinks critically and reimagines solutions are both really key components of that.
You've got to have the ability to critically think about what's coming out of the AI and understand is it hallucinating? Is this relevant? We shouldn't as humans just put blind trust into these tools. They're fantastic, but we're not there yet. So we need folks who can critically think and at the same time we need people who can look at problems differently and look at opportunities for innovation. So rather than thinking about how AI works for me today, it's almost taking a step back and looking at the process overall to see how could I apply some different tools to maybe reimagine how this works in a totally different way that could be much more efficient.
The third factor is continuous learning. Individuals who embrace new technology and have that trailblazer mindset, being able to stay resilient even when the path forward isn't clear.
And then finally the AI promotion. So this is somebody who champions AI. Those skills are really important as well as the ability to share their knowledge because that's what allows the person to scale this across, not just themselves or teams, but organization wide. So you need those people to be a part of your organization as well.
[00:14:18] Speaker B: I hadn't really thought of that. But for the company to scale that super worker profile, you meet people that can't just do it for themselves, but they're also going to share with their colleague, with their peers, share their learnings, but they also share what they learned from what didn't work. I think that's probably, probably as important to saying, hey, I tried this and that didn't work, but then I thought about it differently and then that did work. So people can also see what that mindset looks like of continuous learning and thinking about problems in different ways. We think about that, thinking about things in different ways and reimagining new ways of doing things like that innovation, where you really see that similarly to how, for example, when the self driving car was invented, it wasn't making the driver more profound, efficient or more efficient.
The whole purpose of the car is not to make the driver more efficient or support the driver like the power steering and the lane control and all the things that we put in before we had the autonomous cars to make the driver more efficient. And then we thought about it. Oh, maybe the point of the car is actually not about the driver at all. It's about the passenger getting from point A to B. So that was a total mindset shift. It's not about the driver, it's about the passenger. And that now allows us of course to have cars like zoo cars that don't even look like cars. They look like a living row as you feel like that they don't have a swimming wheel and nothing. So that kind of mindset is really important. And I learn a lot from my kids, they're teenagers, about using AI because I just don't have that kind of sometimes don't feel I have the capacity to think beyond what I'm thinking it can do. And they just use it for everything. Do you see that correlation between experience and kind of that mindset of innovation or trying new things?
[00:16:08] Speaker A: Yeah, absolutely. So we were, we're very lucky to have such a plethora of data at shl. So once we created our model, we were able to apply that to a large data set against our global skills assessment, which allows us to measure those four factors and eight skills that I just described. And across the million people that we were able to explore. We actually dug into job level because we did think maybe there would be some differences there. And one of the misconceptions is that graduates out there, like young kids, maybe that that are just coming up and out of school, they must be using AI constantly at university or even in lower level high schools. So surely they will be naturally AI ready. But our data actually told a nuanced story on that. So yes, graduates tend to be higher across the board, but they still had some capability gaps as well.
So particularly around the application of AI using that responsibly, interpreting the outputs following guardrails, it appeared that they really did know how to use the tools, but not necessarily how to use them well.
So it's really easy to say that early career AI native talent is going to be great and out of the box ready to go, but there is still some opportunity there to grow their skill set as well, to make them a well rounded user of AI.
[00:17:28] Speaker B: So I'd imagine sharing between people that are more experienced that probably think about that, those guardrails more and how to apply AI in a business context which a new graduate wouldn't have and that kind of combination and maybe a reverse mentoring and also mentoring obviously from the more experienced professionals might be a really
[00:17:47] Speaker A: good way of sharing that Professionals though, when we looked at their scores across the board just for their job level, we saw that they actually did have really strong AI literacy, a clear sense of how AI could fit into the work and the decision making, particularly for the organizational goal.
So more consistent in applying the AI responsibly. But they maybe didn't have what you were saying earlier, the skill around reimagining solutions.
[00:18:12] Speaker B: What gets in the way of a company applying this super worker profile?
[00:18:19] Speaker A: Ah, it's such a good question. Really. One of the biggest barriers we see is when organizations approach this purely as that technological shift, when it should be this people transformation first. One of the things I hear a lot here at SHL is everybody has AI, but not everybody is AI ready. And that's what we're finding through these conversations we're having with organizations that although they have a lot of tools and access to tools, they don't have the workforce that trusts in the tools, knows how to use them, or even feels comfortable kind of being evaluated in the context of AI readiness.
And from our research, that stems from trust. So trust is a real issue.
We did a survey last fall, I believe, and it was about a thousand HR professionals, and we found that 70% of the employees working at these organizations did not fully trust that their employer knew how to use AI responsibly.
So if you're going to introduce something like an AI readiness profile or an assessment that goes along with it, it's really not surprising that there might be some hesitation there.
[00:19:21] Speaker B: Yeah, the trust is such a huge thing. And we've seen this over and over in our research too. It drives great employee experience. Of course, it drives like employee engagement, but then also even business results all depend on trust in the organization and the organization trusting the employees too. And we always say trust goes both ways. As organizations want their people to trust them, they also need to trust employees that they're actually going to use these tools effectively and within the guardrails of the frameworks that they put out. Once companies actually looking into what are these broader skills of employees, then they are already asking the right questions, basically investigating the right questions. And then they can adopt that super worker profile basically across the board, because then they have something to tie to. And then also see how can we up level our capabilities, our skills, and how do we develop all of this? Yep.
[00:20:17] Speaker A: And I think it is sort of that development that helps build the trust too, because you can frame this assessment or the evaluation of this, these critical skills, not as judgment, but instead a tool for growth. So employees if they believe that, okay, I'm not going to be eliminated because I didn't score well, but instead I'm actually going to get some development plans out of this and some ways and to ensure that I'm ready for the future. You're likely to get better partnership across the board from management to your employees.
[00:20:45] Speaker B: That's a really good point. So how you frame it and how you position that assessment is critically important, of course, because yeah, people don't like to be obviously judged, but everybody wants to for like evaluation purposes or they say you use it for my performance review or something like that, it's probably not going to be all that well received, but it's positioning more or we want to help build that workforce of the future for us and we need to know where you're at in order to support you get into that right direction where we need to see you grow. It's a different mindset.
[00:21:17] Speaker A: Yeah, I always come back to this across when we use AI, even going into the depths of assessment and AI transparency is the big word and I think it applies here too. So if you as an employer, you can be transparent, that's going to go a long way.
[00:21:30] Speaker B: It's such a useful and such a meaningful service that you're providing to companies. What all the studies about AI show is actually not about when you don't get value out of AI. It's not because of tools, it's because of the capabilities and the skills and the mindsets of the people that need to use them. We're beyond the point where AI is just this tinker toy that was fun and you ask funny questions about, I don't know when Madonna was born or how do I plan my trip to Thailand or something like that, which can we do too. But I think now we're at the point, especially with the agentic AI tools that really can transform the entire business, but we need the people to in order to actually make that happen.
[00:22:11] Speaker A: It's.
[00:22:12] Speaker B: In the end of the day, it's a people transformation.
And business transformation is not just a tech implementation.
[00:22:19] Speaker A: Definitely, absolutely, completely agree with that.
[00:22:21] Speaker B: What lessons learned did you have from doing this research on the super work?
[00:22:25] Speaker A: The organizations that win here in this transformation aren't going to be the ones that have the most AI, the most tools. It's going to be the ones that have the workforce that's best prepared to use it. So that would be the best advice is really come back to that people transformation first and treat this AI readiness concept, the super worker concept as a developable capability, not necessarily a fixed trait. So don't use this as a filter and then say oh, you didn't meet our threshold and move those people out of opportunities. But actually use it as a guide for reskilling and then mobility or even leadership development because that's where it becomes powerful. When you're using this as a tool to develop your either candidates as they come in or your employees if they're already in the org.
[00:23:11] Speaker B: Really fantastic points and I think so valuable. And that will in turn also create more trust. We talked about how important trust is and people will actually enjoy that to be made future ready for their current company and potentially for the other companies too. Helping them build their career in a way and making them more valuable for this organization and potentially for other organizations. They take their talents of course with them. So it's something that gets, not just gets benefit, not just for the organization, but for every single person as well.
What's next for you in this journey, for you personally and for SHL in terms of AI readiness, AI transformation and the super worker concept or protocol that you've done?
[00:23:56] Speaker A: There's really two areas that we're looking to head next and one is where we're going to focus on AI leadership. We've done a lot of work to define what effective leadership looks like specifically in an AI enabled environment.
Not just from a conceptual standpoint, but something that's grounded in data as well.
And what we've seen there are that leaders who are going to succeed aren't going to be the ones that are the most technical, but are going to be those that can integrate human judgment with AI input, lead those hybrid human AI teams. Because we're going to start to see more AI as employees potentially coming in. And what does a leader look like if you're going to be managing that and then making decisions in environments where the answer isn't always clear. So we're really excited about that model, really trying to understand how organizations can select leaders today, but also within their pipeline, ensure that they're building up the right skill set as well so that they've got those AI ready leaders when they need them.
And then the second piece would be pushing forward on what we consider next generation of assessment. So there's a lot of noise as I mentioned, coming in through the selection systems of our current position. And so we need to start to think about what does selection look like as AI continues to come into every aspect of the cycle, both from the hiring and selection, but also the day to day lives of people who are doing work. So we're really exploring more simulation based interactive assessments that are going to put these individuals in real life job scenarios, putting them alongside AI tools so that they can interpret the outputs and make decisions. And the goal there would be really to have a proficiency based view of capability that can work in combination with our behavioral model.
[00:25:43] Speaker B: Both of these are so valuable. A ready leader, of course, is what every organization is looking at as well, in combination of course, with their workforce of the future, the leader of the future, if you want. And then how do assessments get upgraded or changed, basically for using the AI as a tool, basically to reinvent that. So both of them are fantastic things that you have coming up. So I'll be watching and cheering you on and of course we'll maybe want to hear from you as we go along in another podcast. So thank you so much for joining us, Sarah. It was great to have you.
[00:26:21] Speaker A: Thank you so much for having me. This was a very fun conversation.
[00:26:24] Speaker B: What a great conversation. With Sarah Gutierrez, Chief Science Officer at shl. We explored how companies can assess and enhance their AI capabilities, the importance of developing a workforce ready for AI transformation, and the concept of the super worker.
Remember, it's not just about having the right tools, but preparing your people to use them effectively.
Thanks for listening to the what Works podcast. Until next time, keep exploring what works in your world.
