Skills Shift—Thriving With AI

Welcome to the Future of Work podcast with Berkeley Extension and EDGE in Tech at the University of California, focused on expanding diversity and gender equity in tech. EDGE in Tech is part of the Innovation Hub at CITRIS, the Center for IT Research in the Interest of Society, and the Banatao Institute. UC Berkeley Extension is the continuing education arm of the University of California at Berkeley.

In today’s fast-shifting workplace, the rules of success are being rewritten. HR leaders and executives are looking for skill sets that blend adaptability, agility and the ability to work along AI, not against it. We’ll explore the skills that are on the rise and the ones fading away. What skills do you need to fortify your career? And how do you demonstrate that you have those skills? From practical, project-based work to redesigning of roles, let’s look under the hood of the modern workplace so we can see exactly what’s driving the change and how you can stay in the driver’s seat.

To get this behind-the-scenes look at what companies are prioritizing, we’re delighted to welcome Kate Bravery. Kate is a senior partner and global leader of the talent advisory at Mercer, where she is the lead author of Mercer’s Annual Global Talent Trends Study. Having lived and worked in Asia, Australia. the U.S. and Europe, Kate has a global perspective on people strategy, talent management and leadership development.

As a corporate psychologist, Kate has a keen interest in humans at work, and co-authored the book Work Different 10 Truths to Winning in the People Age. She has partnered with the World Economic Forum on the Future of Work and AI, and helps clients with a talent advantage. Welcome, Kate.

Kate Bravery: Hi, Jill. Thanks for having me.

Jill Finlayson: Well, we’ve been hearing a lot about the speed of change. And the change doesn’t just affect employees. It’s affecting how companies operate. Can you tell us a little bit about your role working with companies? And what would you say is keeping them up at night?

Kate Bravery: Oh, I love that question. There’s a quote, I’m going to forget it at the moment, which is, it’s only ever going to be going as fast as it is today. And it’s just going to get faster. And I think we see great opportunities with that speed. But we also see some great impact on our people, not just their jobs, but also their well being.

My role at Mercer is to help our consultants bring the right advisory solutions to our clients. And of course, that’s rapidly changing when you start thinking about, how do we assess talent coming into the business? How do we monitor their engagement levels? How do we create compelling career paths and jobs?

That all looks wildly different to what it does just a couple of years ago. So we’re trying to stay ahead of the curve on what’s relevant, how to take the best of the products and solutions that are out there, and not forget that at the end of the day, work fulfills a vital function in society, not just for businesses, but for individuals.

You asked what’s keeping executives up at night. And I would probably say the number one this year has been productivity or agility. And it probably depends where you are in the world. I think you know I’ve just come back from a project in Australia. And in Australia, they really, at the moment, do have a bit of a productivity slump. They’ve been comparing themselves against the US.

Whereas in the US, it’s less around productivity, which was the dialogue last year, and more about agility. How can we get our top talent into where they can demonstrate the most value, or have the most impact as quickly as possible? How can we pivot, and pivot again, as the business climate changes around us? So that’s definitely the number one. It’s productivity or agility, maybe depending where you are.

The second big one this year has been cost containment. So I think everybody is being challenged to do more with less. I think sometimes, we need to do less with less. But that’s even harder. And then the third one is everybody’s saying, are we grasping the opportunities of the age? Are we doing enough with AI? Are we coming up with innovative ideas that we can commercialize? And how well are we doing on AI adoption within our own businesses? I’d say those are the pretty meaty ones.

Jill Finlayson: They are. They’re sizable. And when you talk about integrating AI, what are they seeing as use cases? And is it actually replacing employees? What is it doing to the companies and the workforce?

Kate Bravery: Well, I love the fact that you cut straight to the chase there. Will AI steal our jobs? And there’s that phrase that I think has been bandied around the last couple of years, which is AI won’t steal your job, but someone who can use AI better than you will. And I think that really has played out. And I think people do understand it.

Now there’s some fantastic work done by ours and other authors at how much percentage of jobs will be declining, how much percentage will be changing. The World Economic Forum has a great future of jobs report. You can use ChatGPT to find out the latest data on that. But I think the reality is there definitely are some jobs which are declining. And there’s definitely some populations being impacted more than others.

And I’m going to call it as it is. I think we’re certainly seeing some of those graduate roles certainly being impacted maybe more than we had anticipated at a faster rate. And there’s certain functions like marketing that I think really got burrowed into last year.

But I think the broader piece is every role is going to be changed in some way by AI. So the conversation for me is less about what jobs are disappearing, but more, what’s the trajectory of that job? How is your role evolving? How is the industry evolving? And how can you stay relevant? A couple of examples. I think AI is definitely helping with that productivity question. So that concern that is number one for many of our executives.

A lot of people are using AI to just speed up how they do day-to-day tasks, whether that is creating a job description, or improving your email communication, or brainstorming how you’re going to pitch to your boss for investment for something. I think that’s only just the tip of the iceberg, because I’m also seeing that there’s some things that are real productivity sappers that AI is helping with.

Just last week, I was talking to a client who has totally transformed their performance management system using AI. Performance management system is not necessarily the most-loved talent process. Managers say, oh my god. It’s exhausting. I don’t know if we get the value out of it. Employees often lead quite deflated from it. And executives are saying, oh my gosh, this is a big sap of time. Why are we doing this in the way we do it? But it does fulfill a vital function. And it does link to rewards, and differentiating talent, and all that good stuff that we need.

But this company had started to use an AI coach. And that AI coach knows the business strategy, knows the functional strategy, knows you, and your personal goals. And it’s sort of saying, this is what I think you should put in your goal system. And the individual just has to sort of till a touch, or edit it. And then it’s sort of regularly nudging people to say, hey, how are you working on that? Or I saw that you recently have been doing a project on that. That’s great. That could go against your goals. Should I put it into Workday for you?

And I really like that because that’s quite exciting. That’s the type of sort of productivity aid I want. I think the other one I’m seeing, which I’m probably more excited about, is improving prediction. So can we predict how well someone coming into our organization will fit within a team, or what type of job they’re going to be most successful at, or what two or three skills, if they added them to the skills they’ve already got, makes them more valuable over the future time, or helps them earn more money, or a bigger career, or whatever it is they’re interested in?

I think that’s really interesting. And a lot of that prediction is underlying this move towards skills-powered talent and reward processes. So really understanding the skills we have today, the skills we need, and then matching people. I think that’s fascinating.

And then the last one is personalization. I think AI is allowing us to say, Jill, if we know what gets you out of bed in the morning, I can make my rewards more appealing to you. Instead of maybe giving you a pay increase, I could invest in some education that you dearly wanted to do. Or I know that you’ve been recently searching for something, and the solutions we’ve got haven’t quite met your need. Let me think a bit differently about that.

Now that we can kind of track what you’re asking for, we can find out what your opinions are on different things, and we know what makes you you, we can be a lot more nuanced. Now that’s scary, but it’s also quite exciting as well, if we get it right.

Jill Finlayson: I was going to say, some of this sounds very good, and some of it sounds a little bit concerning. The things that I like that you brought up are this idea that AI can help nudge people toward goals. It might help open their eyes to opportunities or careers that they might not have thought of. That’s pretty exciting. Certainly, personalization and understanding kind of what makes people motivated is an interesting topic. But this idea of looking at potential, how does the AI really know what I’m capable of, and what I’m looking forward to doing?

Kate Bravery: Absolutely. I share your concerns, can I just say? As a psychologist that spent their whole career in the assessment space, I have a fair bit of skepticism around some of this. And certainly, at Mercer, we’re going very gingerly into the role of, how can AI help with understanding people, and also help with critical decisions such as who we promote, who we hire, who we recommend for plum jobs. And I think that’s the right course of action.

But I do think that it is happening. And we should be very open and honest that a lot of what the AI is using is what we’ve put out there. So if we sit back, and we don’t put it into the internal career system, or talent marketplace, what we’re interested in, it’s going to make some inferences that might be inaccurate. If we don’t keep our LinkedIn up to date, and yet our system, or a recruiter’s system, is scraping that to infer what skills we may have, and whether we’re a fit for a job, we might be overlooked.

So there’s also a piece about we’ve got to pivot. And there are quite a few companies now that are using AI-driven interview techniques. And they are different to being interviewed by a human. So if we’re not regularly asking ChatGPT, can you interview me on this topic, tell me how I would rate against competencies for this job, and we’re not honing those skills of being succinct, linking into what they’re asking, we could inadvertently fall behind.

So yes. It’s great that companies are being cautious about it. But I also think, there’s a lot we can do to make sure that we shine the best, and we are visible to those AI systems that are looking for the right next talent.

Jill Finlayson: This is a really critical two sides to the coin. One is, how are we putting the information out there about what our interests are, what our skills are? The flip side is, what are the companies putting out there as desirable skills, as desirable traits? I mean, the algorithms are just automating in many ways what is happening. But those inputs on both sides are really key.

So you’ve had this opportunity to be on the inside. Many of us are only on the outside. So when you’re inside the company, and you’re looking at what are they looking for? And who in this day and age is getting promoted?

Kate Bravery: Actually, before I answer that, I might make a comment on what you just said there. It is automating a lot of what was being done before. And that is where a lot of the bias is creeping in, because let’s be honest, humans weren’t great at bias-less interviewing. So I also sometimes think we hold AI even to a higher standard. And we should. So what happens behind closed doors in those kind of talent reviews?

Look, there’s been a lot of debate in the literature about, should we have numbers 1 to 4, 1, to 5, should we use talent nine boxes, where we put our people on a 2 by 2, one y-axis being performance, x-axis being potential, et cetera? And although I think philosophically, most people agree, people are a lot more nuanced than being boxed. The reality is it’s still a very popular practice. So let’s just break that down a little bit.

What people are looking for is the people they want to promote are those that are high performing today with potential. So what does that mean for the people on the call? High performing is against often the goals that are in the system. And I see so many people in my organization that are committed, driven, work long hours, go above and beyond. But when it comes to the end of the year, they haven’t quite met their metrics because they’ve been available. They’ve done other things. They’ve been good citizens.

So I do think when you’ve got machines and data driving a lot of the decision making, be really, really clear what goals are being set. And be pretty protective of making progress on them first. Because often in those meetings, it’s the data that comes through on that performance angle. On the potential side, that again, is often driven by the leadership competency model in the organization, or the skills framework, or the values and beliefs that the organization holds.

So if you’re new to an organization, make sure you’ve got your hands on that, whether it’s across the organization, or it’s for your level. Because sometimes, those four or five leadership behaviors, or skills, are the ones that you’re going to be rated against. Now I think pretty much every company I know at the moment has, and you mentioned it right at the beginning there, adaptability and agility. So if you’re not showing learning agility, whether they’ve got a competency model or not, that’s probably going to be a concern.

And of course, the growing one is comfort with digital tools, whether it’s digital leadership at the more senior level, or whether it’s openness to using generative AI at more junior levels, that is definitely being discussed.

The only other thing I would say about who actually gets promoted, I would probably say there is more homogeneity in those that get onto those high-potential programs, or are in high-potential pools to be promoted. And they often are the people who are more vocal, more articulate, active, visible, great in interviews. And some of these AI processes actually are going to change that, because they are going to really surface who has the skills? Who has the right mindset to do well?

And we’ve been talking to a lot of clients around, how do you uncover not just the hypos, but the shypos, the people who are more shy, who maybe don’t perform so well in a traditional video where they’re reading cues off the human interviewer? So I’ve actually got some hope that we’ll start to see more diversity in our ranks as AI drives some of that.

Jill Finlayson: And you bring up a good point, this idea of, how do we surface the talent if it doesn’t show up in what has been traditional ways? So if you are more of an introvert, you might be better at observation, better at focus, better at delivering on a task. So maybe these AI systems will surface that.

It does concern me that people who are a good team player, who are willing to pitch in, who are willing to be collaborative, why is that not being captured in these metrics? Why are we not measuring for collaboration and team contribution?

Kate Bravery: I think it’s going to grow in importance. I really do. I think as we start to understand what machines bring to the table, and what we need from humans, it is going to be those human skills around inspirational leadership, around collaboration, around being able to sell an idea, being able to empathize with others. I think they are coming to the fore. And we do have progressive companies that really are putting a premium on collaboration.

I also do think that some of the data that can now be collected is coming from peers, rather than managers. In the old world, where we’re very hierarchical, having your manager do all your performance review made a lot of sense. I really think for many roles today, your manager does not have eyes or sight on what you’re actually like to work with. And that means that if we just rely on their view, the people who manage upwards, who tend maybe not to be the most collaborative, or spending time coaching and developing their team win out.

But we are seeing now, as people embrace 360-degree feedbacks, or they are taking team samples, you just finished a project with someone, would you work with Jill again? Just very simple things are beginning to make a difference. We’ve also got kudos boards now, which can feed into that. Or we’ve got ways of saying, go to Jill. She’s an expert in presenting. But I agree with you on the extrovert introvert piece. We’ve talked for years about extroverts often coming off better in interviews, sometimes because they actually just say more so they get more ticks on a competency model.

We’re working with AI to say it’s not about the words people say. It’s about their understanding of them. And so I think we can actually have an AI coach when we’re interviewing to mitigate some of that risk. But I do see that risk also coming through into internal talent processes. So today, we’re often asking people, what other jobs could your skills make you suitable for? And again, we see that a bit of a gender split. We see males often being more comfortable to put themselves forward for a job where they don’t have all the skills yet, but they know they’ll develop the skills on the job.

And we need more of that mindset across the board, and people who are more introverted, who are less confident. And certainly, we see a bit more of that in the female population. They’re not doing that. And the whole world learn, and then apply your learning, and then retire is gone. So we do have to be thinking about, we’ve got to put ourselves forward to our opportunities that scare the living daylights out of us. Otherwise, we’re not learning at the pace of change.

Jill Finlayson: So when we’re looking at these talent reviews, can you get into the skills that they’re actually looking for? We kind of got in a sense of who personality type gets promoted, and what data can lead to that promotion. But what are they actually filtering for?

Kate Bravery: Look, every company is different. But if you were to challenge me on what are maybe the top four or five, at the moment, AI literacy and data fluency is absolutely top of that. And that’s not just coding, but knowing how to write the right prompts, ask the right questions, work with AI tools, understand the difference between the different types of AI tools that are emerging, and what they can do, and how good quality data can drive decisions.

I think that is definitely the hottest topic everyone’s discussing. The other one was what we’ve already talked about. I think people are getting rated on adaptability and learning agility.

Jill Finlayson: How do you rate somebody on adaptability? That just doesn’t seem like something that’s a quantifiable metric.

Kate Bravery: I think it’s a tricky one. I actually see, I see some good attempts to measure it. Look, nothing’s perfect. Humans aren’t also perfect. There’s a company that we work with, Infosys in India. So they are a tech consulting firm. And they’re using AI to first nudge individuals if they haven’t acquired new skills in the last 12 months, or they haven’t taken up a new opportunity to build skills. And I quite like that because that’s factual. That’s about movement, and then nudging the individual before they nudge the manager.

That to me feels quite solid. Other companies that have been saying, we look at how many times people have been on our edX program, or our internal, whatever their internal learning system. And you and I both know, people learn in lots of different ways. And so I certainly would hate to be measured by how many times I have downloaded learning because that’s not how I learn.

Having real data is sometimes a great counterbalance to the subjectivity we bring when we’re making these decisions. But sometimes, having the wrong data has unintended consequences. So I think we have to be aware of that.

Jill Finlayson: I think you’re right. I mean, people could try to game the system. Oh, look. I’m opting in. Oh look, I’m downloading. But are they actually learning? Are they actually curious?

Kate Bravery: You know what? I love all the things you just mentioned there. We’ve just done a bit of research around, who is actually leaning into generative AI tools? And the two top ones were people who were naturally curious, and people who are collaborative. People who collaborate with other people, like diverse perspectives, are more likely to collaborate with machines. That makes perfect sense to me.

And so I do think as we bring in more talent science, and we start to understand that, and we don’t just use real data of who actually ended up using these tools, or who actually ended up taking up a new opportunity. Or who’s been putting their hand up for an internal gig. That will start to feed back into the machine, and the insights, and we’ll get better and better.

But it also needs to feed back to the human as well, because gone are the days where if you’re really slick in front of your manager, you can convince them of anything. And I think that’s a good thing. But we’re in this period of transition and change.

Jill Finlayson: Well, it’s interesting because I work with a lot of people who are more of the startup mindset, and even in companies, intrapreneurs. And one of the things that they say is it’s not about being persuasive. It’s about piloting, testing, getting data, and letting the data speak for the opportunity, rather than you just trying to persuade people that this is a good opportunity.

Kate Bravery: Yeah, and we see that in the world of HR, which is what I’m more familiar with. It used to be you would run a pilot, and then you kind of went on this sort of charm offensive, convincing everyone else it was a good thing to scale, as opposed to letting the data. If the data is compelling enough, and there’s cost savings for the business for doing it, people will flock to you.

That, to me, is one of the skills we need. We need people who can tell a story with data. And that is another skill that I’m hearing people are looking at. Can this person sell an idea to me based on data? Not giving me all the data, but give me what’s relevant to make a commercial decision. And that’s definitely growing.

Jill Finlayson: I love that focus on relevance, and perhaps conciseness, and curation. What is the insight that this data is giving me, not just what is the data?

Kate Bravery: A lot of the skills that you have naturally, Jill. But you’re absolutely spot on. There was one other that came up in our research that I thought was also quite interesting. And this was actually people with a global mindset. And it’s interesting because there’s such a big debate at the moment about deglobalization, and what does that mean. And I actually think we’re a little bit more fractured. But we’re more connected than ever before.

Actually, in your very first job, you might need a global mindset. And so many managers today might have some of their team in a global capacity center in India, or Warsaw. They might now need to pivot where their customer bases are, or pivot their suppliers. We’re living in a world where business fundamentals are changing rapidly. And so what actually came up in our research was when we’ve got more multicultural teams, that actually can lead to not just new ideas and diversity of thought, but actually help with execution.

And in fact, there was an article last week. I think it was the summer MIT Sloan edition. And they just came off a longitudinal study. I think, an eight-year study. And they found that when there was a good mix of domestic leadership with international leadership on the leadership team of Fortune 500 companies, they outperformed in profit. And so I think that’s fascinating.

And the challenge, though, is how do you build that? Because less people are traveling around. We’ve got less influx of migrants coming in. And we’ve all got quite inward focused over the last couple of years. So that’s going to be a really interesting one to grapple. But what I was saying is it’s something that companies are beginning to look for. So if you’ve got some international exposure, if you’ve been on an MBA program, and you’ve been working with a multicultural cohort, bring that to the fore. That’s really valued.

Jill Finlayson: Absolutely. And whether our current climate is less friendly than it has been in the past, the facts are the facts. And that’s that we have a very diverse world. The borders are permeable. You’re working with distributed teams. You’re working with people from all different backgrounds. And having those kind of skills to be able to be a good partner, and to understand differences, and be able to work respectfully across borders, I think, is a really key thing.

It kind of brings me back to this question of how we validate skills. So you’ve talked about they’re looking for AI literacy and data fluency. This adaptability storytelling, influencing, because AI isn’t terribly good at that yet. Cross-cultural collaboration.

So the question is if they’re trying to validate these skills, you’ve mentioned a couple of things along the way. But maybe you can sort of put this together now. What are companies doing? What tools are they using? What data are they scraping to judge our skills and potential?

Kate Bravery: There’s a whole range of different ways that companies are approaching it. And they, of course, have different levels of validity. So we’ve been talking a lot about inferred skills, which is sort of scraping skills from data that’s already available. And that could be data you’ve got on LinkedIn, or data in social media. We can actually learn a lot about you by your digital footprint.

It can tell us a lot about your personality, but it can also enable the AI to say, given the companies you’ve worked in previously and the jobs you hold, we infer you’re likely to have these skills because other people that have had that similar career have those skills. It’s not perfect, but my gosh, it’s pretty good. And that kind of data is great for looking at maybe matching someone for a potential job.

So it was a kind of an early sift. But that technique of sort of scraping to inferred skills that used to only be used for sort of talent acquisition is now being used internally in talent management systems, or talent marketplaces, which is quite interesting. So you’ve got internal companies using external data to influence it.

They also might be scraping other data, such as projects you’ve been on, courses that you’ve done, and sort of inferring from that the skills that you have. So that’s sort of right down the left-hand side.

Jill Finlayson: How do I know what my company is scraping?

Kate Bravery: Great question. Depending where you are in the world, you have different rights to ask about that, and to understand what information is being held on you, and also the right for that information to no longer be held. But I think it’s the absolute the right question to be asking. A lot of the systems, and the partners that we work with at Mercer, have mechanisms for full transparency for individuals, and full opportunities for them to vet and validate what’s being said at them from a skills basis.

And that, I think, is really important. What we are finding, though, Jill, is sometimes, people are not bothering. And I think that’s really important for us to sort of lean in with these systems as they sort of learn and get better, because that digital — we’re getting into almost the world of digital twins here. But that digital twin of you is forming a personality. And people are beginning to have perceptions of them, and opinions on them. And you want to make sure that you’re being represented well. So I do think that’s an important question to be asking.

Jill Finlayson: Let’s talk about that a little bit more. I got a couple questions on this scraping data. So if I’ve been a VP at a small company of five people, and somebody else has been at a VP of a company that has 500 or 5,000 people, are we making assumptions that just that title, VP, means something?

Kate Bravery: No, I actually think that might have been the reality five or six years ago. The companies that are now doing this have got such huge databases, and data that they have trained their systems on, that they really are making differences. Saying that, some of the technical skills, when you work in a small firm, you often get to do a wider a range of things, and actually start to build your competence in many different areas.

But there are other areas, such as maybe navigating politics, or managing a large team, or working on a large M&A, that you just aren’t going to get that sort of experience. So I think actually, the system is very nuanced. But I think there’s a learning point in here because historically, I think people entering the workforce says, I want to work for a brand. Or I want to have the title. And when skills, not jobs, is the true currency of work today, I think we should be taking any opportunity, any opportunity that is going to get more skills on our skills passport.

Because actually, you can get a lot of skills sometimes quicker in a small firm, or maybe doing some volunteer work, or even in parallel to educating. And these need to be surfaced because this is just as valid as the more traditional routes. And when skills are being used to predict who is suitable for a job or an opportunity, as opposed to job titles and company positions, or the quality of the education institute you went to, it does bypass some of the things that you were just mentioning.

Jill Finlayson: Well, this is why I think that transparency, where the data is being scraped, is so important. Because if I know where you’re getting your data, I can go and update it. I can change it. I can say, look. I’m less focused on marketing. I’m more focused on strategy. But if I don’t know where you’re getting your data, I don’t know that I can influence the perception.

Kate Bravery: Jill, I think we are living in a world where there is more data out there than we’re ever going to be able to have control over. And so I would love to be able to have that sort of precision. But I think there is a lot of curation that is happening from scraped data. And often, it’s getting its validity by going to multiple sources.

One thing I would say, though, in the job market, LinkedIn globally is a really key source. And you can go in there and start to play around with what skills you want to be promoted, and what skills you don’t want. You can start to be very targeted in the type of language you want from people when they’re giving you references.

So it is all set up for you to have quite a lot of influence. But there’s probably other job sites that you might have been active in, or information that you proactively given to a recruiter or employer over the years, as you have applied for roles.

I think today, if you are submitting a resume, a cover letter, putting something on LinkedIn, if you’re not running it through some ChatGPT or another generative AI model and saying, what does this say about me, if you read this, what would you say are the top skills? What would I be suitable for? Or I’m going for this particular job. These are the people who’ve had these jobs before, have had these backgrounds. How do I stack up?

If you’re not kind of using AI as a coach, yes, you could end up thinking you’ve got the right messaging out there, but not. The great thing about now compared to two years ago is the same tools are in our hands. So we actually can find out a lot about ourselves. Gone are the days of I Googled myself. We can actually find what other people are reading from us by the things that we are aware of.

Jill Finlayson: That’s great. OK, so scraping data is one way that they’re identifying our skills. What else are they doing?

Kate Bravery: We’re seeing a lot of that when people do future of work redesign. So how could the work be done differently in the future? Once we have really thought intentionally about what tasks could be done by machines, what tasks could be done by humans, we might then reconfigure the job. Sometimes, we’re reconfiguring the job because there isn’t enough talent out there. And we need to make the job more narrow so people can do more of it. Or the talent is too expensive. So we need to have more junior people doing the job, or parts of the job.

But once that redesign is done, then we can map it to the skills that map to those tasks. And then people can self rate. So we’re seeing a lot of that. Now that self rating sometimes has all the competencies and skills, and then a 1 to 5 proficiency rating. Pretty good data. Really good for closing the gap. If you’re in a role today that maybe is declining in prevalence, and you want to move into a role that is increasing, it can give you a really good steer on, what are the two or three extra skills I need to actually accelerate that journey?

And we can do that in enterprise level to guide training plans and development interventions. That’s the greatest skills. Sometimes, it includes more than just the manager. And then, of course, you get into the more validated and demonstrated skills. And this is, I think, where the science of psychometrics and business simulations come into play. And that gets me very excited.

On the one hand, I actually think some of the cognitive assessments are very relevant for the age that we’re in. I think the ability to think critically is actually growing in importance as we work with machines. And that’s the type of skill that people at universities really do hone, and develop. And sometimes, I think it’s undervalued. But I’m definitely seeing that as a big differentiator today.

You can also have a look at people who are naturally inclined to innovate, high-divergent thinkers, good at transformative thinking, et cetera. And that’s great, because if you want to put a diverse team together, knowing that information can really help. And then, of course, we have the ability to give people a job preview. This is what the challenges might be in the next role, or the role you’re being considered for. And we can run a really engaging, all-immersive business simulation.

And then individuals can kind of put themselves into that scenario, and answer questions about how they might handle different situations. I get very excited about that because given the shelf life of skills, technical skills certainly, is getting shorter and shorter, measuring, as we have for years, people’s coding ability, or their technical skills, or their domain knowledge, is useful. But it’s only as useful as we are today because the challenges we’re facing as leaders now are net-new challenges. They’re complicated challenges. They’ve got lots of things interconnecting.

And therefore, we need leaders who can handle paradoxes. They need to be good at strategy formation and strategy execution, managing tasks, and managing people. They also need to be able to bring a broad mindset. So we can measure, what’s the breadth of someone’s commercial thinking, or their strategic thinking, or their risk awareness, particularly when we’ve got cybersecurity risks, and AI governance issues to manage?

That, to me, is more useful when people are going into jobs they’ve never done before. And they’re going into jobs they’ve never done before because we’re facing challenges we’ve never faced before. So this starts moving beyond, what skills do you have that could be deployed in a new area? to what mindset do you bring to net new problems? And that’s for me really sort of on the cutting edge of where a lot of that sort of predictive science is going today.

Jill Finlayson: Kate, there’s so much to continue to talk about. And I think this is a good spot to stop for this episode. We’re excited to have you back next month to continue this incredibly interesting conversation. So for our listeners, stay tuned to hear more from Kate. In the meantime, please share with friends and colleagues who may be interested in taking this future of work journey with us. And make sure to check out extension.berkeley.edu to find a variety of courses and certificates to help you thrive in this new working landscape.

And to see what’s coming up at EDGE in Tech, go ahead and visit edge.berkeley.edu. Thanks so much for listening, and I’ll be back next month to continue our future of work journey. The Future of Work podcast is hosted by Jill Finlayson, produced by Sarah Benzuly, and edited by Brandon Gregory and Matt DiPietro.