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all lowercase. That's shopify.com

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slash tech. Perhaps

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you or someone you know has exclaimed

0:35

in dismay, AI is

0:37

going to take my job. Critics

0:40

fear the rapid growth of AI could threaten

0:42

jobs or be used for malicious purposes. Another

0:45

concern is could AI take jobs? Let's

0:47

focus now on AI's impact on the

0:49

way we work. One of the biggest

0:51

worries people have about AI is we'll

0:54

take our jobs. It turns

0:56

out some companies are already using or

0:59

considering using generative AI

1:01

internally. According to a Gartner

1:03

survey from June 2023, most

1:06

organizations appear open to adopting new

1:09

HR tech. Only 15% of

1:11

the HR leaders they surveyed said

1:13

they had no plans to add generative

1:16

AI to their HR processes. So

1:18

whether you're an AI optimist or pessimist, generative

1:22

AI is likely coming to your workplace if it's not

1:24

there already. A

1:27

lot of CEOs are going to their teams and saying,

1:30

okay, you work in marketing, how are you

1:32

going to use Gen AI to sell better,

1:34

to operate faster and cheaper? That's

1:36

WSJ reporter Chip Cutter. He

1:39

covers workplace, management, and leadership issues

1:42

for our corporate bureau. Some companies, for

1:44

example, say that they think they can give up doing

1:46

photo shoots for their products. Why

1:48

couldn't you just have various sort of AI

1:50

tools do that for you? In

1:56

The Wall Street Journal, this is the future

1:58

of everything. I'm Charlotte Carter. Today.

2:01

We're looking at our A I

2:03

Hr future, how close it really

2:06

is, and what sorts of generative

2:08

A I resources. Might become

2:10

commonplace for human resources.

2:12

Stay with us. She

2:19

I am on a mission. Y

2:22

C cost fifty four percent of

2:24

black Americans don't have enough savings

2:26

to retire. So in collaboration with

2:28

thick name artists like Wiper, John

2:31

C. I a released paper right

2:33

new music inspiring the new financial

2:35

future with one hundred percent of

2:37

streaming sales going through a nonprofit

2:39

that teaches students how to invest

2:42

stream paper right now and how

2:44

close. The Gap. Ai

2:54

in a chair has been a thing

2:56

for a while, but it's getting more

2:59

widespread. We. Reported in Twenty eighteen

3:01

that nearly all fortune five hundred

3:03

companies were already using some form

3:06

of automation in their hiring processes,

3:08

whether that's robot avatars, interview in

3:10

job candidates, or computers weeding out

3:13

potential employees by scanning keywords and

3:15

resumes. Last. Month Wsj reporter

3:18

tip Cutter went to the

3:20

World Economic Forum in Davos,

3:22

Switzerland. After talking to

3:24

Ceos and company leaders, he

3:26

says that we're likely to

3:28

see lots more Ai and

3:30

human resources very very soon.

3:33

Tip: What has been the pros and cons

3:35

of this kind of a I use in Hr.

3:37

Well, it's helped a lot of companies. Be.

3:39

faster that they but and will do

3:41

more with less for example of your

3:43

screen name millions of resumes a large

3:45

company probably need automation software to help

3:47

you do that or otherwise if he

3:49

really hard for one person to sift

3:51

through all that so that's been around

3:53

for years right of ways to figure

3:55

out the right candidates to interview ways

3:57

to for example look at who might

3:59

be at risk of quitting. Companies

4:01

oftentimes have built like complex software to see

4:03

who's been in a role for a given

4:05

amount of time, who maybe is in need

4:07

of a promotion and hasn't got one. There's

4:09

all these ways that companies can kind of

4:12

get a sense for whether someone's a flight

4:14

risk. So that's been one way that companies

4:16

have used some of this technology in the

4:18

past. But right now, I think we're on

4:20

the cusp of a lot of change. And

4:22

many companies, many CEOs are telling their HR

4:24

chiefs and all their department heads, you need

4:26

to think about sort of how Gen AI

4:28

is going to change how we work. You

4:30

were just in Davos, so I

4:32

want to talk specifically about generative

4:34

AI because it's being more integrated

4:37

into workplaces. What have you been hearing? You

4:39

could not walk down the promenade, the main street

4:41

in Davos, without just seeing one display after the

4:44

next about sort of how AI is going to

4:46

transform corporate America. Every

4:48

interview with CEOs would somehow come back to

4:50

generative AI and how they were trying to

4:52

use it within their companies. Some

4:55

organizations have a real plan. Others are sort

4:57

of earlier in their efforts. But a lot

4:59

of CEOs are going to their teams and

5:01

saying, okay, you work in marketing. How

5:03

are you going to use Gen AI to

5:05

sell better, to operate faster and cheaper? Some

5:08

companies, for example, say that they think they

5:10

can give up doing photo shoots for their products. Why

5:12

couldn't you just have various

5:14

sort of AI tools do that for

5:16

you? But then AI and HR was

5:18

a big discussion as well. So for

5:20

example, one HR chief at a large

5:22

tech company told me that she has

5:24

done an interesting experiment with Gen AI.

5:27

And there was a case in her company

5:29

where a manager and that person's direct report

5:31

were not getting along. And

5:34

so she said with both people's permission,

5:36

can we upload the chat logs between

5:38

them and see what happened?

5:40

Like why aren't they getting along? And

5:42

so they ended up uploading pages and

5:45

pages of logs. And it came up

5:47

with some sort of interesting answers. And

5:49

one was that the employee was asking

5:51

way too many questions. And then the

5:53

manager was getting frustrated by this. And

5:56

the manager also felt that the

5:58

manager wasn't being heard. So

6:00

just having that information, they were able

6:02

to go back to these people and

6:04

they said the ratio actually improved afterwards

6:07

because both sort of knew, okay, this

6:09

is what's bothering the other person. So

6:11

it's almost like AI as corporate psychologists.

6:14

That's wild. That

6:17

to me was the AI example that I

6:19

remembered the most and that stood out to me

6:21

as the most distinctive in a week full

6:23

of conversations on this. But to me, it

6:25

shows that HR chiefs are thinking, could Gen

6:27

AI really change how we operate? And

6:30

if you think about this example, it'd be really

6:32

boring for a person to go through pages and

6:34

pages of logs and try to see, wait a

6:36

minute, why do these people not like each other?

6:39

What's happening there? But a machine can do that pretty

6:41

easily. But who who might have

6:44

an objection to having AI play that

6:46

role in HR? Well, you could see workers being

6:48

frustrated if this is done in a case where

6:50

they don't know about it or they haven't given

6:52

their permission. And if we're using corporate chat tools,

6:54

we've given up our permission like that our company

6:57

is able to sift through that. But

6:59

in this case, everybody knew this this was going

7:01

on. But you could see down the road where

7:03

some might think this feels a little bit like

7:05

Big Brother. What have you been

7:07

seeing in terms of more high level attitudes

7:09

about generative AI in HR? Yeah,

7:12

so many HR executives are optimistic about it.

7:14

They do feel that they want to use

7:16

it to some extent. And it's oftentimes cases

7:19

where companies might roll out, for example, a

7:21

chatbot that helps people say an employee could

7:23

more easily ask, like, how many vacation days

7:25

do I have left? Or

7:27

what is our policy on X? The type

7:29

of questions where someone might go to an

7:31

HR person and ask them, some HR people

7:33

think like, why can't we just build a

7:35

tool that answers them more easily? That's one

7:38

area where a lot of looking uploading policy

7:40

manuals, uploading benefits guides, all of this stuff

7:42

where it just might be easier just to have

7:44

Gen AI sort of be an aid that helps

7:46

employees navigate the company. Overall, HR,

7:48

like a lot of like a lot

7:50

of functions in corporate America is being asked to do more

7:53

with less. So Companies, This is

7:55

this as we're continuing to see sort of

7:57

an era of layoffs and kind of white

7:59

collar job. Cut in particular so it's

8:01

not a H R Teams are and get tons

8:03

of additional people there. She earth the so if

8:05

if you're in an Hr executive you're trying to

8:07

figure out how do we do more with less

8:09

and many are thinking jenny I could be helpful

8:11

here. So. What stands in a

8:14

way. Of there being more generative

8:16

ai in hr in the future. Part

8:18

of it as cost. I mean companies might

8:20

think they'll save money with this eventually, but

8:23

not in a short term. It's gonna cost

8:25

money to set this up and to you

8:27

know that to be able to do that.

8:29

So that's that's one immediate barrier. And the

8:31

others as technical, know how companies are at

8:33

all different stages on decimate. Some executives are

8:36

really immersed in it. Some companies, for example,

8:38

have had rolled out sort of. Mandatory

8:40

Jenny I training where for example by March

8:42

everybody the company has a complete you know

8:44

this multi our course on Jenny Ice. you're

8:46

up to speed on at your experiment with

8:48

it you're thinking about how could him a

8:50

better jobs And then there's a lot of

8:52

other companies that are saying this is still

8:54

hype, it still unproven. We don't want to

8:56

be an early move on Nes, let's hold

8:58

back. So a brew etti depends on the

9:00

attitudes are the executives have towards of technology

9:02

to. Yeah, Ike. I got that

9:05

impression a bit from this Gartner

9:07

survey from this past summer that

9:09

found that only five percent of

9:11

H leaders were reporting that they

9:14

were already implementing generative ai. And.

9:16

Only nine percent of those surveyed.

9:19

We're. Currently conducting generative ai

9:21

pilots which seems low

9:23

to. Me Very love. Is

9:26

this gonna take off soon? Or. You

9:28

keep hearing that while Twenty Twenty Three was the

9:30

year that we experimented with it and Twenty Twenty

9:32

Four as the year that Jenny eyes rolled out

9:34

at scale at companies and and I mean that

9:37

that's what executive some executives are say, but any

9:39

look at numbers like you just eat have shared.

9:41

It's clear a lot of companies are still very

9:43

early on all of us. We

9:48

may be in the early days of generative

9:51

ai been. Used in Hr but according

9:53

to the founder of girls. First,

9:56

the focus of the and innovation

9:58

and mitigating. Rest. Rushmore

10:00

Some johnny tell us about her organizations

10:03

new chat bot and why she's optimistic

10:05

about how Ai will be used in

10:07

the future. look at work and beyond.

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This episode is brought to you by

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Soviet solicit Fisher is is

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Cmu Edu sticker to. Then

10:40

there's. Wsj

10:49

reporter Chip Cutter told us were

10:51

already seen. Chat bots been developed

10:53

to help employees navigate company policy

10:56

and answer questions. Rush Muscle Johnny,

10:58

founder of Girls Who Code, recently

11:00

helped launch a similar chat bots

11:02

to do just that. Her. Goal

11:05

was to reach further than just

11:07

one company. paid leave.ai attack bought

11:09

designed to help people parse the

11:11

patchwork of laws affecting paid family

11:13

leave. New York State went online

11:15

in December of last year. The.

11:17

Tools Lawn Spicer, Johnny's Other

11:19

Organization Mom First in partnership

11:21

with Movie and Craig Newmark

11:23

Philanthropies. When. She first

11:26

came up with the idea for Danny

11:28

Says. She contacted Open A I founder

11:30

and Ceo Sam Altman. She.

11:32

Says he put her in touch with the

11:34

team behind Techy Be T, who provided early

11:36

technical advice and support. The. Connected her

11:38

with Novi, a developer that is part

11:40

of the Open A I Service Alliance

11:42

who developed Paid Leave That Ayaan Open

11:44

A I Tools. The. Goal of paid

11:46

leave that ai. To. Answer questions

11:49

about leave policies in plain

11:51

language. I begin by asking

11:53

restless oh Johnny to take me through

11:55

how the website works. So.

11:57

Let's say you're pregnant and you go to paid leave

11:59

Today I. I am will can you is

12:01

am I eligible? How

12:04

much money? Miles before? And. It's

12:06

gonna give you an action plan when you

12:08

go to the government website. they don't really

12:10

do that for you on in. One of

12:12

the things I love about the site is

12:15

it gives you a bunch of kind of

12:17

easy prompts because maybe don't know what you're

12:19

supposed to ask for. It doesn't just answer

12:21

your questions, it will draft to an email

12:23

to Hr if you need one. It will

12:25

let you email yourself in action plan or

12:28

checklist. You can save it and Senate's yourself

12:30

into to do to work on it. So

12:32

do you see this as a model for

12:34

other kinds of chat bots? Being used in

12:36

Hr. ah it's very interesting. it in a

12:38

because you can tell him pay we did

12:40

a i wears a traffic coming from most

12:42

of the traffic's coming from linked in and

12:44

if you look and see it's like it's

12:46

literally it or professional sending it to to

12:49

one another But I find it super interesting

12:51

that the you talk to each are people

12:53

that are like yeah bring on the chat

12:55

bots. We've. Been operating in a

12:57

state of fear. That. We need

12:59

to move through that. That doesn't mean

13:01

that we should be like there's no

13:03

risk so good you are, but that

13:05

mean so that we should be mitigating

13:07

the risk. But. Since think he

13:10

that the innovation. right? So let's look

13:12

the risks in the face. Hear what are

13:14

the risks? Of creating an hr

13:16

Tough but. I met

13:18

one of them. Is that it's gonna replace. Workers.

13:22

And. So I think that there's a lot

13:24

of fear that like a tool like

13:26

this will make it like you don't

13:28

really need a human. To. Help you

13:30

navigate this process. Good night out chap. Ah, They'll

13:33

do it either. Second thing is it will

13:35

give you the wrong answers. This.

13:37

Is terrifying way when it comes to

13:40

these laws. Momentous. Events

13:42

that you really need to know

13:44

how much money my going to get

13:46

on what I need to save

13:48

for and and sell these risks are

13:51

these Fears are real. Ah

13:53

I'm If you're really feeling them and are in

13:55

a it's funny when I started this project with

13:57

first muscle were hallucinations. Do I have. To hire

13:59

a. Human Yeah, I mean to

14:01

make sure that they're looking through

14:03

all the answers. And as I

14:05

dug in, I learned so much

14:08

right about how this particular use

14:10

case of paid leave the doesn't

14:12

have the same safety risks because

14:14

the wings bottle full limited. This.

14:16

Particular Large Language Model

14:18

or Llm helps mitigate

14:21

potential hallucinations. So. What

14:23

data went into the training of this chap?

14:25

Ah, Are. Ai is trained on

14:27

the New York State paid leave law. I'm

14:29

like like chatty Bt if not pulling from

14:32

the internet and collecting set of data from

14:34

a bunch of different sources. That's when it

14:36

that way, butter and for a sexy make

14:38

things up. To. Do way hallucination

14:41

against how much information you could possibly

14:43

feel. Absolutely listen. I mean like I

14:45

said this is one of the first

14:47

use cases of doing this so I

14:49

did not want set us up to

14:51

fail and so I what's what's interesting

14:53

is because there is like again l

14:55

a limited L am on there's there's

14:57

less safety risk issues so with the

14:59

large language model your training at all

15:01

any to limited to New York state

15:03

law. Yup that helps get rid of

15:05

some or get around some of the

15:07

possible quote. Unquote Hallucination Correct Yeah. But

15:10

then we do have the the risks of like

15:12

is this gonna replace humans the ones that you

15:14

mentioned How do we deal with those things. I

15:18

don't know. I don't know.

15:20

I mean. It's so funny.

15:22

Again, memorize. Spend my life. Teaching

15:25

girls to an avid mom is like wheat

15:27

said they saw learn how to code. you

15:29

know because there there's this perception might have

15:31

the skills that you're really gonna need and

15:33

as artificial intelligence moment his creativity like you

15:35

would actually me to lean more into the

15:37

liberal arts that because. the bottle

15:40

code for you so i think

15:42

that we have to see in

15:44

many ways how this kind of

15:46

plays out but i still worry

15:48

about it in the future i

15:50

mean if we see more chat

15:52

bots replace hr employees that i

15:54

i won't be able to interact

15:56

with a human being during his

15:58

high that's pretty sensitive it It

16:01

feels potentially dehumanizing,

16:03

like I'm just data relating

16:05

with data. It

16:07

might, but what are we going to do? It's here. It's

16:10

here. We're stuck in this

16:12

conversation of like doomsday, and it's

16:14

preventing us from really capturing all

16:16

the opportunities and use cases of

16:18

AI. Every conversation that we're

16:21

engaging on in AI, is it good or is it

16:23

bad? Is it going to destroy us or not? Is

16:25

it going to like replace workers or not? It's here.

16:28

It is here. And so now the question

16:30

is, is how do we use the technology

16:32

in a way to help

16:35

people, to preserve jobs, right?

16:38

To solve COVID, cancer, climate,

16:40

right? To do good things. Not

16:44

having access to paid leave is a

16:46

major driver of women leaving the workforce

16:48

or downshifting. And so like

16:50

if I can actually navigate having time

16:52

off, money in my pocket, the resources

16:54

I need maybe to hire a care

16:57

worker or to get sorted, right?

16:59

I'm going to stay in the workforce. So

17:01

what's the bigger picture here? Where

17:03

do you hope paid leave.ai goes from here?

17:05

We're going to expand. The goal now is

17:08

I want to prove that

17:11

generative AI can actually increase the

17:13

uptake of benefits and figure

17:15

out exactly how to do that. So

17:17

that means I got to have a handful of governors

17:19

that are partners so we could work

17:21

with the Department of Labor to understand what are

17:24

the pain points, is our tool solving for those

17:26

pain points, and now what's the uptake of benefits

17:28

now that we have this tool? I'm

17:31

noticing this trend in how you're talking about

17:33

things. I think one of the things people

17:35

think about AI is like that it is

17:37

the solution in itself. The

17:39

product, the AI, the chatbot is

17:41

the endpoint. You seem to

17:43

be talking about it in that way. You seem to

17:46

be talking about it as this is

17:48

the next step in a journey towards other

17:51

things. Absolutely. I think it's like a

17:54

tool, a friend, a helper. I see

17:56

this from an activist perspective. My sole

17:58

slave. our focus is to get paid

18:00

leave in child care passed. And

18:03

so I'm thinking about all the different ways, all

18:05

tools that we can have to do that. Part

18:09

of why I think this got so much attention, it was unexpected.

18:11

It was an unexpected use

18:13

case of generative AI. And so

18:16

I think as we're looking at what

18:19

are the use cases of generative AI, go

18:22

to the unexpected, go to the most

18:24

vulnerable. Like the next place I want to go

18:26

is like, how am I helping military vets access

18:29

their benefits? How am I helping

18:31

people who need food stamps access their

18:33

benefits? How am I helping people on

18:35

Medicare access their benefits? Who's the most

18:37

vulnerable? And how do we build, you

18:39

know, AIs for

18:42

those communities to

18:44

help them change the

18:46

quality of their lives? That's

18:48

where we need to begin. The

18:51

Future of Everything is a production of

18:53

the Wall Street Journal. This episode was

18:55

produced by me, Charlotte Gartenburg. Thanks for

18:57

listening.