Welcome to Meet the Leader, the podcast

where top leaders share how they're

tackling the world's toughest

challenges. In today's episode, the

latest in our Chief People Officer

series, we talk about navigating

upskilling's messy middle and what is

needed to fix the skill mismatch.

Subscribe to Meet the Leader on Apple,

Spotify, and wherever you get your

favorite podcast so you don't miss an

episode, and don't forget to rate and

review us. I'm Linda Lacina from the

World Economic Forum. And this is Meet

the Leader.

>> So, the worst case scenario that we want

to avoid is having workers who do not

have the skills that are needed for the

jobs of today and for the future.

>> What is one of the biggest problems

facing the labor market today?

If you were going to say a talent

shortage, you would be wrong. According

to our recent Chief People Officer

Outlook, one of the biggest constraints

is the skill mismatch.

Organizations are struggling matching

talent to roles, not struggling with

supply itself, which means many

companies we surveyed are leaning

[music] on skills-based hiring. Maria

Flynn understands this. She is the CEO

of Jobs for the Future. Jobs for the

Future is a nonprofit in the US that's

working to reform workforce and

education systems to ensure that every

worker and every learner can advance

economically. It also has one [music]

great big goal, 75 million quality jobs

by 2033. That's doubling the number of

quality jobs that workers are in today.

That will take tackling the skills

mismatch. She'll talk to us more about

that mismatch and also what needs to be

in place for a quality job, and what's

working right now when it comes to AI

adoption. Stay till the end for my

little special game of red light, green

light. That's the game where she tells

me which hot new work trend will

actually go forward. But for now, she

will start us off on a hot take, the

trend that no one is talking enough

about, the system.

>> Our education and workforce systems were

really built for a different era and

they are not equipped to be keeping pace

with the changes that we are seeing in

the labor market driven by artificial

intelligence and advancements in

technology overall. So, we are really

kind of building for a future on a

system that was built for the past and

we need to solve that problem.

>> What why is this so important to fix?

What's the worst-case scenario we want

to avoid?

>> So, the worst-case scenario that we want

to avoid is having workers who do not

have the skills that are needed for the

jobs of today and for the future. And

the issue is that our education systems,

our public workforce systems, often they

are not agile enough to be making the

changes that are needed to really be

keeping pace with what employers are

asking for. So, it really we run the

risk of kind of divorcing kind of supply

and demand because the systems aren't

agile enough to keep pace and to kind of

be moving forward together. And really

the solves for this are a combination of

policy change, practice changes, and

also mindset changes. So, how can we

really be building and investing in

future-forward systems in this moment of

rapid change?

>> And what does the world look like maybe

in 10 years if we're able to get some of

that in place?

>> 10 years from now, if we are able to

have our systems, you know, in a agile

fashion, really with the investments

that we need, we will be seeing workers

have the ability to access quality jobs

more readily, that we will see them

being able to enhance their skills kind

of throughout their lifetime in a true

lifelong learning model, not like a

traditional one-and-done approach where

you go to school and then you go to

work. They will have a much more

seamless system of learning and earning

that can kind of ebb and flow throughout

a person's lifetime and throughout their

career.

>> What is a quality job?

>> So, a quality job, we believe, has five

essential elements. And we established

the American Job Quality Study last year

as a way of really having a common way

for everyone to talk about job quality

and to really think about the five

dimensions of job quality. And really

briefly, the first is, you know,

probably what you would think of first,

is pay and benefits. But beyond that,

the second is really looking at the

ability for a worker to advance in their

career. The third is ensuring workers

have agency in their work, so have the

ability to have a voice in decisions

within the workplace. That they have a

safe and respectful workforce. Um, that

they have a safe experience at work. And

then finally, that they have access and

some control over their schedule. We

realize that for every individual, like,

the right mix of those dimensions may

vary. And the mix of those dimensions

might vary across their career. There

might be times where schedule is more

important to you, and there might be

times where salary is more important.

But these dimensions really let us take

a kind of a 3D approach of looking at

job quality across sectors and across

levels of occupations.

>> And sort of in practice, what have you

guys been seeing that works to sort of

make those things possible, put that in

place? Can you give us a couple examples

of what you guys are are making happen?

>> Yeah, an example of a best practice that

we are seeing is around the worker voice

dimension of job quality. And we have

some playbooks and some toolkits that

employers can use to really embed worker

voice kind of in their company. But the

one pain point that we're seeing is many

workers, over 60%, are saying that they

wish they had more say in how

technology, including artificial

intelligence, is being deployed within

their company. So, that's kind of an

easy thing that employers can do to

really look to see how can they have an

authentic channel for worker input and

worker feedback as they are rolling out

AI adoption throughout their

organization. And so I think that both,

you know, increases worker satisfaction.

They feel they have a say in how that's

happening. It also, you know, we think

we'll see more uptake in the use of AI

like within those companies. And that

type of satisfaction can, you know,

really result in stronger retention and

less turnover within the company.

>> And are there maybe other tactics that

people are putting in place that you

guys are seeing a really impactful

before and after?

>> Yeah, so you know, I think scheduling is

a is another piece. We found that only

about 25% of workers right now have what

we would consider to be kind of quality

scheduling policies at their work. And

so this again can look different for

different people. Some people actually

value greater flexibility where, you

know, if they have um young children at

home and they need that flexibility

around hours. Other workers really want

kind of regular schedules, right? And

really find it unsatisfactory when their

schedule changes at the last minute. So

again, it really depends on the person

and kind of what is working for them at

that point in their life. But we see

very strong ties between

high quality scheduling practices and

high quality satisfaction with the job

and again stronger retention rates.

>> Leaders who are uh listening to this or

watching this, what is something that

they should be doing that maybe they

haven't considered?

>> So I think it's easy things. I think a

lot of times when people talk about job

quality, they automatically go to issues

around pay, which of course are

critical. But there are a lot of

practices that employers can put into

place that don't cost a lot of money,

but can really enhance worker

satisfaction and worker experience

overall. So, things like ensuring that

employees can have a voice, that they

have a say in different workplace

policies. Also, making sure that there's

transparency around things like safety

and scheduling and what those policies

are. And also really looking to see how

can we be offering advancement

opportunities for workers, particularly

in this age of AI when we're seeing such

disruption, particularly at early career

levels. The employers should really be

thinking about what skills they need in

different occupations, how that might be

shifting with AI adoption, and how we

can continue to be investing in workers

and helping them move up the career

ladder.

>> Uh Jeff, for the future you guys did a

recent study on AI. Can you tell me a

little bit about it and uh sort of what

surprised you?

>> Yes, so we released our recent findings

earlier this year, March 2026. And it's

a follow-on to a study to a survey that

we released last year. And one thing

that surprised me is

a shift in the percent of people who

feel that AI is doing more harm than

good. So,

the first year we saw generally people

felt it was doing more good than harm,

and that reversed in this past year. So,

to me it's surprising that as AI is

advancing that folks are becoming, at

least in our survey findings, more

skeptical of what that means.

We're also seeing a lot of almost splits

down the middle in terms of how

individuals and workers are experiencing

AI. Where we see almost half and half

workers who feel they're going to need

new skills and workers who don't feel

they're going to need new skills.

Workers who feel they are more connected

than before or less connected. And so, a

lot of that is really telling me that

we're very much in the messy middle of

AI implementation

and you know, it's kind of the early

innings of a long game, but I think it's

also telling us that we need to really

get this right or else these, you know,

pendulums can be swinging in ways that

we don't want to see.

>> You talked about how the AI adoption is

hitting different workers in different

ways. Can you talk a little bit about

that and the big gaps that could be made

even even bigger?

>> Yeah, so one thing at Jobs for the

Future, we are really invested in

equitable economic advancement for all.

And when we think about our North Star

and quality jobs, there are four big

buckets of workers that we really

prioritize. First are workers without

four-year college degrees, women,

workers of color, and workers who have

been impacted by the criminal justice

system. So when we look at the impact of

AI, we're particularly looking at the

impact that it is having or not having

on those population groups. And one

group that really stood out in our

recent findings was women. I think both

in terms of we are not seeing as many

women as men really engaging in AI. We

are seeing women kind of express more

skepticism or fear about AI. And that

gender gap, you know, is concerning to

us and I know that's an issue that

there's been a lot of conversations with

a lot of the technology firms and

even at the UN around how do we start to

address kind of the gender gap in AI

both in terms of usage and in terms of

leadership in the AI space.

>> Some of these gender gaps in technology

and also with the sciences have been in

place [clears throat] for a long time,

you know,

is there other learnings that we can

take that we can finally maybe

accelerate? What can we do now to really

make a change?

>> So a great point because we have seen

kind of inequities in STEM fields for

for decades and we we do know some

things that work. So one across the

board is intentionality. So, really

designing pathways and programs with a

diverse kind of worker set in mind. So,

really be designing for that inclusion

instead of exclusion. So, that can mean

really thinking about how we structure

gateway courses like within colleges.

How do we make those more inclusive

instead of a screening out mechanism,

really a pulling in mechanism. So, also

looking at things like cohort-based

learning. We know that learners like to

learn alongside folks that they can

really relate to. So, you know, we've

seen great outcomes with, you know,

all-female cohorts or first-gen cohorts

or really thinking about how that

learning kind of cohort can be

structured. Also, you know, I think just

in general, you know, a lot of people

say, you know, you have to be able to

see it to be it. So, they think the more

that we can really highlight the

profiles of women in tech and women who

are leading in the AI space, the more we

will be really encouraging young women

and girls to kind of be considering that

as a career path. So, it really it needs

to start early in terms of career

awareness and career exploration in, you

know, middle schools and high schools,

but really kind of that policy and

practice of inclusion kind of throughout

undergraduate and also once in the

workplace, I think are really critical.

You know, during the the boot camp era,

we saw programs like Girls Who Code and

other kind of gender-specific programs

do a good job in terms of having women

complete and get employed, but then we

saw a lot of retention issues once the

individual was placed in employment. So,

I think really thinking about what does

inclusion and belonging look like

post-placement, post-employment is a

important piece of this picture as well.

>> Um, in that messy middle and helping

people will it, what's something that

maybe a worker might not be doing right

now, may not have been be thinking

about, but they really should. They

should do it right away, do it in

tomorrow's meeting, or whatever. What's

one thing they should be doing right

now?

>> They should be open to AI. They should

be taking steps to learn as much as they

can, to really think about how can it be

making their job, their work day easier.

Just in my own experience, I have two

kids, one who is 13 and one who is 21,

and I find that you know,

they are not getting kind of exposed to

AI in ways that I wish that they were,

kind of either middle school or in

undergraduate. And so, I think in this

messy middle, really I think individuals

need to be thinking about how can they

be gaining these skills, building

awareness, because this is more and more

going to be a reality on the job site,

um increasingly, um over the next months

and years.

>> You have said that employers will need

to be educators, not just around AI

tools, but also on people skills, that

people-to-people collaboration. So, I

have to ask you, you know, we as as long

as humanity has been in existence, this

has been an an area of improvement. What

can we do now to make this work,

finally?

>> Yeah, so I think, many researchers are

saying, and at JFF we definitely agree,

that human skills aren't going to kind

of go out of fashion, like the the

humanity in our roles and in our work

life are going to be what is key kind of

to success in the age of AI. Um it's

something that has really been a through

line across the 30-plus years I've

worked in the workforce area, like

dating back to the early '90s, there was

a federal commission called the

Secretary's Commission on Achieving

Necessary Skills, and that was really

outlining skills like communication and

problem-solving and teamwork that they

felt were really kind of be the going to

be the key skills of the future. I think

if we reran that report today, it would

say pretty much the same thing. Um I

think where we fall short is that we do

not really

teach those skills in a very specific

way. I think making it clearer, you

know, even in elementary school and high

school, you know, we are having you do

this project with a group of people

because we want you to learn how to work

with others. We want you to be able to

communicate. We want you to be able to

learn across differences. Making it

clearer, you know, like

what we are teaching and why and how

that applies to someone's future career,

I think is is something easy that we

could be doing. And there's also been, I

think, kind of uneven assessment of

those skills. And so really how do

employers think about assessing

someone's kind of durable skills or

human skills as part of their kind of

advancement mechanism. So I think this

is where we can kind of all agree that

these skills are the right skills, but

how we best embed them in our learning,

really make them kind of stand out when

we're doing talent assessments, is where

we have a lot of room to grow.

>> What works? What are some tactics that

maybe your organization has helped put

into place where you're like, "Yes, this

is a way forward."

>> Yeah, so you know, we are big proponents

of skills-based talent practices. And

there's been a lot of work and writing

around skills-based hiring. So hiring on

the basis of skills that people have

versus the degrees that they do or don't

have. I think we're still in the early

days of that, but I think it has really

promising approaches to kind of

diversifying the workforce and really

helping workers find the workers that

they need at any given time. We would

really advocate to go beyond that and to

really be thinking about skills-first

practices throughout the talent life

cycle. So meaning how can we be

advancing folks in the workforce based

on the skills that they have. How do we

help managers kind of shift their

mindset to kind of really base their

assessments on skills and giving them

the framework to do that. So, there are

a lot of great tech platforms out there.

There's one that we invested in our

venture arm, JFF Ventures, called

AdeptID, for example, that can really

help employers like put a common

language in place and a common protocol

for really identifying the skills that

are most important for any occupation

and how you're assessing your workers

against those skills. So, it kind of

goes beyond just kind of a worker kind

of self-reporting what they're good at,

which is important and a piece of the

puzzle, but how do you make this kind of

a more rigorous part of your talent

practices throughout.

>> Yeah, which kind of brings us to our

Chief People Officers Outlook, which is

a big survey that the World Economic

Forum has done with the Chief People

Officer community that we have. And one

of the big themes of that report, big

hard realities that we have to face is

that it says there's not necessarily a

talent shortage, but there's a skills

mismatch, right? And so, right in this

AI era, why is fixing this so so

critical? If we don't do this now, what

happens in 10 years?

>> I think this is a critical issue right

now because to me the issues of AI and

the issues of skills go hand in hand on

a number of levels. One, I think that

the advancements in AI are going to

change the skills needed for almost

every occupation. So, there's going to

be a shift in skills that we see across

the board, increase in some, a decrease

in others, maybe new skills all together

that kind of come on the scene.

But we also, I think given how quickly

these changes are coming, it's also

going to make it more important for

employers to be thinking about kind of

skills as the currency of their

workforce. And to instead thinking that,

oh well, we have these MBAs or we have

these, you know, policy folks with

graduate degrees. It's really going to

really come down to the nuance of

what specific skills are needed, how we

test for those, how we assess those, how

we can train folks in them quickly. Cuz

I think as we're seeing, you know, many

people are going to need to be reskilled

or upskilled, but I think this era of

reskilling and upskilling is going to

look very different than what we saw,

for example, after the 2008 financial

collapse, right? This is going to be

less about someone going back to get a

new credential over the course of 6 or

12 or 18 months. It's going to be much

more about kind of rapid learning of new

skills, often through kind of new

technology. So, really then also

thinking about how does AI and

AI-enhanced learning help us kind of

articulate the skills that are needed

and help us access kind of the

information we need to enhance those

skills. So, AI and skills, I think are

going to be have a very strong interplay

across a number of different axes.

>> Skills-based hiring, there's been growth

and some some improvements in some

areas, but how do we really scale it?

What do you think we really need?

>> Where we see um kind of some of the

hold-ups around skills-based hiring. So,

first I would say some of the good

things. We are seeing, for example, over

the majority of states have removed

degree requirements from state jobs. So,

that's a great example of policy changes

that can help get to kind of adoption at

scale. But where we're seeing some of

the roadblocks is really around kind of

culture, mindset, you know, of hiring

managers in particular. And if you look

at some surveys, almost all CEOs will

say they're in in favor of it, but then

when you get down to kind of hiring

managers, there's less positive

reception, I think, to the idea. So,

it's really, I think, again, comes back

to culture change. How do we show that

this is a practice that can kind of help

across the board? It can help the

worker. It can help the business. It can

help regional economies. But, it's

really, I think, that culture change,

which is where we need to focus in order

to make this scalable. And also, I

think, looking at this type of change in

parallel to what's happening within

workplaces around AI. So, I think being

able to look at both of these things in

tandem, I think, can also help kind of

accelerate adoption.

>> Given all this, straight young grads are

going to be entering the workforce very,

very soon, in a few months. What is the

most important advice that no one is

giving them? What do you want them to

know to drive home? What should they be

doing?

>> My oldest daughter is graduating in May,

so I'm I am living this in in real time

and talking with her and a lot of her

her friends. I would say two things

really come to mind. One is where you

start is not going to be where you

finish, right? So, it's like

understanding that, even in the best of

times, that first job is likely not

going to be your dream job. And I even

say to my daughter, you know, I I wasn't

a CEO coming out out of college, you

know, I was an entry-level government

worker working with, you know, five

other women in Philadelphia, you know,

it was not a glamorous lifestyle. But,

wherever you start, really be sure that

you are learning from that experience,

both in terms of really identifying the

skills that you're learning, identifying

what you like and don't like about that

experience, um and how that can help you

guide to your next thing. So, it's

really understanding that nothing is

forever, right? Your first job is not

going to be the job you have forever.

And but yet, every job, including that

first job, can really give you a wealth

of information and experience and skills

that you can build upon.

>> We're going to do a fun rapid response

game here. A little game I like to call

red light, green light. I give a special

future of work trend, and Maria will

hold up a red card and a green card,

letting me know which trend she thinks

will move forward. Red card being stop,

green card being go.

First trend on the docket, we'll finally

get a four-day work week.

Let the record show Maria is holding a

red card. Tell us why.

>> Given how long it takes for any

workplace reform to take hold and to

scale, I don't think that a four-day

work week is one that we are going to

see adopted at scale anytime soon. I

don't think that the

um

that the conditions are right for that,

basically.

>> The white-collar job is over, and we

should all become plumbers.

>> So, I think skilled trades jobs are

critically important, and we need many

more workers to become interested in

those roles and take them on.

But, I don't think that that means that

we all need to be going down that path.

I think there are going to continue to

be a rich mix of jobs that are out

there. They may not be the exact jobs

that we see today, but there are still

going to be high-quality jobs across the

kind of white and blue-collar spectrum.

And I think what's going to be important

is that we have robust career navigation

systems that can help workers really

make the choices that really are best

for them.

>> Specialists, not generalists, will be

the kings in the AI era. I Let the

record show we have a green card. She

said yes, specialists will have the

advantage. Tell us why.

>> I think AI is going to make it easier

for everyone to be generalists in some

way. I think that baseline information

will be more accessible. You won't have

to kind of go and take a college course

on, you know, something. You will be

able to kind of get that information and

kind of become an expert on your own in

a much generic and kind of like ongoing

way. I think it's folks who are going to

be able to kind of use that baseline of

general knowledge and augment that with

specialized skills and information and

kind of analytics that are going to be

kind of the most in the future.

>> The four-year degree is obsolete. Yes or

no?

>> Not yet. I think that four-year degrees

are going to continue to be important,

but I think number one, we're going to

see more openness to skills-based hiring

and kind of looking at hiring in new and

different ways. I also think that

four-year universities are going to have

to be adapting how they are preparing

young people for the world of work.

They're going to have to be embracing

more experiential learning and

work-based learning opportunities and to

be embedding kind of that career

navigation and career awareness more

into their core curriculums. I think

four-year degrees will remain. They will

be important, but they're going to have

to look different and they're going to

be one of many high-quality options that

will be available to learners and

workers.

>> Liberal arts majors will finally step

into their power. Red or green?

You have a red card. Tell us why.

>> I am a liberal arts major. Again, I

think liberal arts majors are critical

and they are those majors are great ways

of learning kind of a lot of those human

kind of fundamental skills, but I think

unless we can also be equipping those

majors at the same time with strong

knowledge of the labor market, really

helping them see how to apply those

skills in real-world settings, they're

going to continue to be a little too

traditional and ivory towerish and not

as agile and real world as we need in

today's economy.

>> And your last question, we will finally

fix the skills mismatch.

I love it. You have a green card. Tell

us why.

>> I think we are going to finally fix the

skills mismatch because advances in AI

are going to force us to. So, I think in

some ways advancements in AI and how

fast it is moving is going to make it

critically imperative that we really

get on top of our game and kind of solve

these mismatch issues when it comes to

kind of supply and demand and the skills

that connect those two key features of

the labor market.

>> That was Maria Flynn. Thanks so much to

her and thanks so much to you for

listening. If you know someone wondering

what's ahead for AI and skills, send

them this episode. If you really want to

help them, let them know that this is

part of a series on HR leaders digging

into the biggest trends they're facing.

I mentioned our chief people officer's

outlook. I will have a link in our show

notes. Find a transcript of this episode

as well as transcripts from my

colleague's podcast Radio Davos at

wef.ch/podcasts.

This episode of Meet the Leader was

produced and presented by me with Jerry

Johansson as editor, Ees Schaftner as

studio engineer in New York, and Gareth

Nolan driving studio production. That's

it for now. I'm Linda Lacina from the

World Economic Forum.

Have a great day.