June 5, 2025 • 43 mins
Charlene Li, author and analyst, joins us this episode to discuss proven strategies for safely and successfully rolling out AI solutions to employees within an organization. She also compares the roles of generative AI and AI agents in the workplace.
[0:00] Introduction
- Welcome, Charlene!
- Today’s Topic: Proven Strategies for Integrating AI in the Workplace
[5:43] What are the biggest challenges HR faces with moving Generative AI into the workplace?
- Developing a strategic, rolling 18 month plan for AI implementation
- How HR can avoid stumbling through an AI rollout with a proper governance plan
[15:15] How can HR teams avoid stumbling with AI initiatives?
- Building a minimum viable team to champion the AI integration strategy
- The importance of training everyone in the organization
[31:25] How does generative AI compare to AI agents?
- Managing a team of people and AI agents
- Understanding how AI agents are designed and how to best prompt them
[42:47] Closing
- Thanks for listening!
Quick Quote
“Yes, humans need to be in the loop, but the real power of AI is when you take the human out of the loop because you trust it.”
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Announcer (00:03):
The world of business is more complex than
ever. The world of humanresources and compensation is
also getting more complex.
Welcome to the HR Data Labspodcast, your direct source for
the latest trends from expertsinside and outside the world of
human resources. Listen as weexplore the impact that
compensation strategy, data andpeople analytics can have on
your organization. This podcastis sponsored by Salary.com Your
(00:26):
source for data technology andconsulting for compensation and
beyond. Now here are your hosts,David Turetsky and Dwight Brown.
David Turetsky (00:38):
Hello and welcome to the HR Data Labs
podcast. I'm your host. DavidTuretsky, alongside my co host,
best friend, partner @Salary.com Dwight Brown. Dwight
Brown. How are you?
Dwight Brown (00:48):
I am Well, David, it's a beautiful, sunny day, and
we're probably going to be inmoving into summer soon.
David Turetsky (00:58):
Well, that's funny, because it's 41 degrees
here and raining inMassachusetts, exact opposite.
Yeah,
Dwight Brown (01:04):
Yeah thanks Dwight for rubbing that in right?
David Turetsky (01:07):
Yeah that's all right, but it is sunny here. Do
you know why it's sunny here?
Tell me, because we have withus. Charlene Li, Charlene, how
are you?
Charlene Li (01:16):
Great. So glad to be here.
David Turetsky (01:18):
You are our sunshine today, our only
sunshine. And for those of youwho are following along at home,
yes, it makes me happy whenskies are gray to be on the HR
Data Labs podcast. So Charlene,why don't you tell us a little
bit about yourself? Sure,
Charlene Li (01:35):
I'm a long time author and analyst. I have
written six books, New YorkTimes bestseller and really
focus on,
David Turetsky (01:43):
wait, wait,you can't just gloss over
Dwight Brown (01:46):
Breeze over
David Turetsky (01:47):
you just like, breeze over New York Times
bestsellers. Yeah, you know,everybody's got this. Everybody
does this. Well, you know, blah,blah, blah. I've written lots of
books, but I haven't gotten aNew York Times bestseller yet.
Charlene,
Charlene Li (01:58):
well, it's fun, you know, it's, it's, you get one.
It's fine. All you need is one.
David Turetsky (02:04):
Okay, all right, well, you know, it's like a
Grammy or an Emmy or a Oscar,right?
Charlene Li (02:09):
It's, it's great.
It's great to have that and thebut by focus, I keep writing
about disruptive technologies,and how do you create disruptive
innovation and disrupt yourselfand your organizations, and
there just keeps to be new stuffto write about and to talk
about. So I been in this spaceabout three decades now, and
continue to have new things. AndI'm working on my seventh book
(02:32):
out, winning with AI the 90 dayblueprint for success. So that
should be working out later thisspring.
David Turetsky (02:42):
Outstanding.
Dwight Brown (02:43):
David and I are big techies and big into how we
can integrate that into everyaspect of the workplace in some
way or another.
Charlene Li (02:53):
yeah and we'll talk about this. But I always believe
in looking at this, at all thesedigital disruptions and
transformations, thattransformation is never about
the technology, and it isalways, always about the people.
And we make the mistake byfocusing on the technologies and
not about how people are goingto adopt it, how to use it, how
(03:15):
it's going to transform them.
And so that's why the vastmajority of these efforts fail.
So it keeps coming back toleadership and culture. And I'm
a techie, geeky person that'sout there. Yeah, love the tech.
Dwight Brown (03:30):
We can love that
David Turetsky (03:32):
Geeks!
Charlene Li (03:33):
But, if you don't also include the people, yeah,
in your effort, managing, yourthinking, it will fail
David Turetsky (03:40):
absolutely
Dwight Brown (03:41):
makes sense.
David Turetsky (03:41):
We will get into that. But first, what's one fun
thing that no one knows aboutCharlene Li?
Charlene Li (03:47):
I train cats for fun.
David Turetsky (03:49):
You what
Dwight Brown (03:50):
What?
Charlene Li (03:51):
So
David Turetsky (03:52):
YOU TRAIN CATS?
Charlene Li (03:54):
Other people like, teach old dogs new tricks. I'm
playing a completely differentgame. I am training my cat how
to de tricks? Yeah!
Dwight Brown (04:02):
okay, so clue me in here. What kind of tricks
people?
David Turetsky (04:06):
By the way, you're talking to two dog people
Dwight Brown (04:08):
yeah, with dogs that never listen to us, but
Charlene Li (04:12):
who's snoozing over there, it can do about a dozen
tricks. So he can give me highfives both paws. He can turn
around in circles. He'll liedown, which is really hard for
him to do. He will, like, kindof do this funny Boppy thing
where he just kind of bobs up onmy hand. He'll put his chin on
the table. He'll come and sit onmy lap when I call. But the best
(04:33):
trick is he will jump over myarm and through a hoop in my
hold a hoop, and he'll jumpthrough it.
Dwight Brown (04:39):
Get out, really?
David Turetsky (04:41):
But let me ask.
Let me ask anything useful, likesitting on the toilet and going
potty in the toilet,
Charlene Li (04:48):
He'll pee in the sink.
Dwight Brown (04:51):
Okay, listen, because you wanted to, or No, I
mean one or two way it.
Charlene Li (05:00):
Think when his little box is not, oh, I have a
little robot too as well.
David Turetsky (05:05):
So oh, okay all right, there you go. Yeah. Well,
I'm not going to get as graphicfor my next question as I as
everybody who's listening mightimagine, but what we should do
is now transition to our topicso we don't get into trouble
because we have no explicitratings on any of our episodes,
Dwight Brown (05:22):
And we're a little punchy,
David Turetsky (05:23):
Yes, as you can tell, as you can tell. So
Charlene, our topic for today ishow generative AI will transform
in HR in the workplace. Andwe've had lots of conversations
about this, so we are extremelyfascinated.
So Charlene, our first questionis, really, what are the biggest
(05:47):
challenges HR face withgenerative AI and moving that
into the workplace?
Unknown (05:53):
I think the biggest one is that we don't have a lot of
good strategies that tie AI toeither HR specific goals or even
your business, your strategygoals as an organization,
typically the AI strategy I'mputting quotes up here is a list
(06:14):
of use cases. Use Cases are nota strategy. And more
importantly, what I'm finding isthat HR simply isn't at the
table in the strategydiscussions. They are either
shut out because technologiststake over and say, This is a
technology issue. We don't needHR in here, or HR says, I don't
(06:35):
need to be involved in this.
This is technology. It doesn'tinvolve people. And as we
started at the top of thispodcast, it's always about the
people. And so what I find isthat if you treat AI as a
technology versus atransformational force, you're
looking at it the wrong way. Ifyou look at it as an enterprise
technology that's going to berolled out in the traditional
(06:57):
way it's you're going to fail,because that is way too long,
way too slow of an impact tohave. The space is changing
literally every day, and you'vegot to get a strategy out there
that immediately drives impactand value against your top
strategic goals. I
David Turetsky (07:16):
think one of the worries that a lot of us have in
HR is the current strategy andthe current thinking is, reduce
cost, reduce headcount, andwe'll you know, that's it.
That's a strategy forimplementing AI and HR, but it's
not a good one, and it'scertainly not one thought
through with those use cases.
Charlene Li (07:35):
Well, I think you're missing the
transformative value of AI andespecially in the context of HR,
because if you only look at itas efficiency and productivity,
and those can be verytransformative, you're missing
the ways that you can engagewith people that's both your
customers and your employees,and completely different ways. I
mean, think about training andLeadership and Development, the
(07:59):
scale and the speed of which youcan create customized,
personalized development foreach person based on their
learning styles and theirdevelopment plan is incredibly
exponential in terms of theimpact. So this goes beyond just
saving a little bit ofautomation about generating
training scripts. That'sthinking very small. I'm
(08:21):
thinking, you can think big,start small and then scale fast
with AI. The problem is thatwe're not thinking big. We're
thinking very small. And so as aresult, we don't take these big
swings. We don't start with ourbiggest strategic goals and say,
how, what are the biggestproblems we have in addressing
(08:42):
these big strategic outcomes.
And unless you think about itfrom a strategy perspective,
thinking big, you're going tostart small and stay small.
Dwight Brown (08:52):
Do you think part of it is people just don't
understand AI and what thecapabilities are?
Charlene Li (08:59):
Think they don't understand their strategy.
David Turetsky (09:01):
Well, yeah, I mean, that's, that's a problem
we've always seen in HR. Thestrategy has always been
administration. The strategy hasalways been keep the lights on
and then fill in gaps, like thestory of the little Dutch Boy,
you know, putting their fingerin the dike. It's always been a
just try and be everything toeverybody. And what you're
(09:21):
saying is think moretransformatively about how we
can offer better, morepersonalized service that
doesn't scale with people, itscales with the available
technology. Is that correct?
Charlene Li (09:32):
Right? Well, again, this is a very interesting time
where you can scale and growwithout adding new people,
without a huge amount moreexpense in a fraction of the
time. You've never had thisopportunity before. It is
completely scale leveldifference in the way we
operate. And so you do have tostep back and be more strategic.
(09:54):
And this is the transformationthat HR has been going through
for the past 15 years. I. Wewere so bogged down by the
administrative aspects ofrecruiting people, engaging
people, retaining people, again,just trans, just like all that
administrative aspects of it.
And now technology can remove alot of that administrative
overhead and allow you to thinkmuch more strategically. But we
(10:17):
haven't trained ourselves as HRprofessionals on how to even ask
strategic questions.
David Turetsky (10:25):
Well, so to Dwight's point before, though,
isn't that? The bigger problemis we need more help building
that strategy. We can't reallygo to anybody else inside the
organization. They're going tobe like, Are you shitting me?
Pardon my French. You didn'tknow how to develop a strategy?
Why are you coming to us now?
This should have been done yearsago. You know what I mean?
Charlene Li (10:46):
Yeah. Again, better to acknowledge it that, yeah. So
let's go do it there and say, Idon't have a strategy. And
again, a strategy is fairlystraightforward. A strategy is
just a set of integrated choicesthat you make to achieve your
business objectives and to win.
(11:06):
So align them against youroverall core strategy as a
business. And here's the thing,most businesses cannot tell you
what their strategy is. So it'snot just HR, it has a problem.
It's the entire executive teamand the board. The hardest part
about AI and strategy, it causesyou to make you focus on what is
most important to us, integratedChoices, choices, what we will
(11:27):
do when we won't do achieve ourgoals. If you are not aligned on
what those goals are, you can'teven begin to make decisions.
Which is why we default to theefficiency and productivity,
because that's within mypurview. I can look at these
small, little problems. I don'thave to deal with alignment and
harmony and making sure we'reall on the same page.
(11:51):
Fundamental problem with justoverall strategy inside of our
organizations.
David Turetsky (11:54):
So one of the things that I wanted to ask was
a lot of us did a lot of us doin the HR world, build a strat
plan. You know, every year wehave a strategic plan for what
we're going to try andaccomplish next year. I don't
think what you're talking aboutis developing anything broader
than that. You're just saying,Is there a way of looking at
that strategic plan and beingable to accomplish that a
(12:16):
different way, more efficiently,without having to throw money at
it without having to thrownecessarily people at it. I
mean, you might have to throwsome money at it to begin with,
but you're gonna get a muchbetter expectation out of it if
you're using potentially Gen AIinstead of just powering through
it with people.
Charlene Li (12:37):
Yeah, again, I think that's a very efficiency
and productivity right? To lookat things and frankly, that
strategy plan for the next 12months, it's an execution plan
and a budget, right? And as youget closer to the end of those
12 months, your plan shrinks andshrinks and shrinks. What I
really advocate is a strategyplan that helps you achieve your
(12:59):
overall business objectives asan organization. So it's longer
term, long term, whatever thatlooks like. It can think about
three to five year horizon, butI encourage people to think
about it as 18 months, sixquarters, and each quarter
you're laying out what is theimpact and the value we are
going to deliver every singlequarter. And this is more than
(13:21):
just administrative things. Whatis the change and the impact
you're going to create in orderto help us achieve our long term
goals? So if you are creatingthat change, then how can AI
help you accelerate that changeor achieve that change? Because
there are a lot of barriers inthe way to make that
transformation happen. And thekey is, at each at the end of
(13:43):
each quarter, you're evaluatinghow far you've come. You're
making adjustments to the nextfive quarters of what the impact
is going to be, and then add onanother quarter. It's a rolling
18 month plan, and you shouldn'thave to redo it every single
quarter. It's an extension ofwhat that plan looks like, and
it's far enough in the futurethat it is actually strategic.
Dwight Brown (14:05):
So, the strategic plan you're talking about is
that a strategic plan specificto AI, or are you saying look at
your business strategy and workbackwards from there and figure
out where AI can help youachieve that strategy,
Charlene Li (14:23):
Absolutely, because the your C suite and your board
won't care unless it helps youcreate your business it helps
you achieve your businessobjectives. Creates competitive
advantage. They won't care. It'slike, yeah, go to go deal with
it in your size, yourdepartment. But if you really
want to have an impact and totransform the organization, then
(14:43):
help us achieve our big,audacious goals that we have set
for ourselves, identify thebiggest problems that lie in the
way of us achieving it. What areour biggest challenges, and help
us understand how HR and AI aregoing to help us get. To those
goals, better, faster, cheaper,
Announcer (15:04):
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Salary.com. Now back to theshow...
David Turetsky (15:14):
Charlene. One of the things that troubles me
though about adding AI into themix is that HR typically
stumbles when it comes to theseinitiatives anyways, but because
AI is still in its infancy, itstill has its training wheels on
in in reality, we also don'thave these private clouds or
(15:36):
these private instances yetenough in organizations that
will enable us to have thesetypes of strategies well thought
through, without it leaking toothers by saying, Hey, tell me
some examples of other companiesthat have gone and improved
their HR strategy this way. Andall of a sudden, chatgpt just
spits out our company name. Oh,well, salary.com did, by the
(15:59):
way, salary.com just an exampleof a company name. Should it
just? Probably just a companyname. But you know what I'm
saying is, to me, there are somany barriers from the
technology being nascent. Isthere in your mind as you're
thinking through this and asyou're espousing this, is there
(16:20):
still a problem of this kind ofgoing up the maturity curve yet.
Or are we? Are we ready for thisnow?
Charlene Li (16:26):
Well, first of all, one of the first things we
encourage people to do is to getyour a core team of people
trained on how to use AI in asafe and secure way. And that's
you can do that simply with chatGPT teams, if you put that in
place automatically, allsettings to feed the model, any
of your data is off, justautomatically. It's all private,
(16:49):
then it's 100% private. Okay, sothat's the system I use. You
know, can you trust them? Well,if they can't do this securely,
then the whole entire businessmodel is is put but trust them.
And then the second thing is,you want to put in place the
governance models forresponsible and ethical AI. You
(17:10):
build what I call it an AI trustpyramid of safety, security,
fairness, all those things, allthe way up to transparency. Then
make sure everyone understandsthis is what responsible and
ethical AI looks like. Itdoesn't have to be complicated.
It's built on the foundations ofexisting data policies and
security policies you have inthe company. And then third most
important is to give peopleaccess broader across the entire
(17:32):
organization. Right there areoff the shelf solutions you can
get from your existing cloudproviders that can just turn it
on and it's completely lockedoff. I talked to a global
organization. They have 65,000people, and they run AI for
everybody for a couple $100,000a year. Yeah, we're talking like
(17:54):
this is like a small littlesludge budget within the IT
department.
David Turetsky (17:59):
Sure. I guess I'm a little more concerned
about those people, each ofthose employees who have not
heard about that yet, go to1min.ai which is a off the shelf
aggregator of of lots ofdifferent Gen AI models, and you
buy, literally, can buy alifetime license for $69 and
(18:21):
have access to a bunch ofcredits per month, but that is
completely public. I mean, youcan set all the settings to be
private, but Charlene, the thingI'm worried about is that that
thing you mentioned aboutrolling it out to all your
employees, if you don't do thatand you haven't done it yet,
believe me, each one of youremployees has created their own
(18:42):
private little cloud. They havenot made those privacy settings
private, and all of the thingsthat they're asking about Gen
AI, Gen AI in your company arebeing not necessarily broadcast,
but they're being absorbed.
Charlene Li (18:55):
This is why it's so important to lock it down right,
and the more you can catch itwith your IP nets. And anytime
they go to these things, theyget redirected to your
authorized, safe and secure waythat you're locking down that
technology. It's logging things,but it's removing anything from
that log within an hour. Sonothing is keyed in secure. It's
(19:18):
all software compliant. So eventhe transmission lines are
secure, everything is lockeddown. So even if somebody
inadvertently uploads somethinginto your private cloud, it
David Turetsky (19:28):
What about copilot or Apple intelligence or
doesn't stay there.
Gemini or whatever it's calledthese days? What about the ones
that are more ubiquitouslyavailable within the
applications we use every day,either our phones or Microsoft
Excel has or Word has co pilotbuilt in.
Unknown (19:46):
Yeah, those are all locked down again. It's in
agreements that you have, if youhave copilot, if you have any of
the Microsoft tools, again, onyour systems, those are locked
down so they're in the MSA soyou can be secure by that. So
you want to work with yourvendors that they understand
you're responsible in AI ethicsand guidelines. So it's like
(20:07):
your entire ecosystem is on thesame page. Want to make sure
that all the vendors areapplying and abiding by your
safety and security privacypractices. You do that anyways.
You do that with AI.
David Turetsky (20:19):
Well, they may not have had that done yet after
listening to this, hopefullythey will.
Charlene Li (20:25):
The thing here is that safe and secure access to
AI and that you have you canproceed with it with confidence,
with trust, is vital to your useof AI, and if you cannot have
that as a foundational buildingblock, then you cannot go
forward. We are very, very keen.
My co author and I are very keenon making AI readily available,
(20:55):
expose it as much as possible.
So because once you have thatsafe and secure platform, you
can blow the doors off of this.
And the CIO we were talking tosaid, I don't care if they use
it, if they use it, if theydon't, I really don't care. And
we're just going to make thislike electricity. It's going to
be ubiquitous, and they can haveit, and I don't have to worry
about ROI because it's so cheap.
But I know that there's impact,because if I try to take it away
(21:19):
from them, they're like, over mydead code, fingers will you
again? I he can't measure thevalue, but in an organization of
665,000 people globally, tospend $300,000 a year on AI for
your biggest safe access toeverybody, nobody cares about
(21:42):
how much it costs, all thosequestions around ROI, just go
out the window, and then you canfocus on, what can we do with
this when everyone has access mygoodness of things that come
out? And then it's a focus ofsaying, efficiency,
productivity, go, go, do it.
Just no brainer. Just do thesethings, right? We don't really
need to really think too muchabout them. If it's helping you
(22:05):
do your job better, just do it.
Stay within these guidelinesthat we have and go for it. But
what really has to happen is weneed to know strategically, how
are we going to use this acrossour business so that it drives
the biggest bang for the buck tohelp us achieve our switching
goals. That is a strategyquestion that your C suite and
(22:27):
your board level should betalking about, and HR needs to
be a part of that mix, becauseyou don't get that level of
transformation unless you'rethinking about the people aspect
of things.
Dwight Brown (22:36):
So does it start with HR developing a strategy
and move up the chain? Or how doyou see this working best?
Charlene Li (22:45):
There's a minimally viable team, minimum MVT, of
somebody who's thinkingstrategically, the highest level
strategy person you can get inthe organization. You get
somebody from HR who can thinkabout people and their adoption
and use of these things. Youhave somebody who is thinking
from the digital and technologyside. It's not your traditional
(23:05):
technology department, it'ssomebody in digital and then you
get somebody who understands thecustomer, who has that
perspective. It could besomebody in marketing and
product in the commercial side,but somebody who can encourage
the team to be customer centric,whoever that customer is, and so
that's a very small team. Andbuy in from your legal team to
(23:29):
like, Hey, we're going to abideby all these rules, but outside
of that, and inside with thoseguardrails, we will stay within
that, but they don't belong inthe room, because they're the
departments of No, they're notthe ones who think deeply and
imagine what the work could looklike, and if they promise to
never say no, the minute theysay no, they get
Dwight Brown (23:51):
terms and conditions that you have them
signed before they can walk inthe door. You know,
Charlene Li (23:55):
Like, do you really want to be a part of this
conversation? Yeah, exactlycareful what you wish for. But
you know, we're not going toimplement anything until we run
it by you anyways. So you don'tneed to be here to say no to
something that we haven'tfigured out how to do yet. So by
having this really small teamthat is going to be very
strategy based, you have awinning you have a fighting
(24:18):
chance to really focus on thebig problems that you can solve
with AI.
David Turetsky (24:23):
One of the things that I worry about, or
that I'm encouraged by, is, ifthis tool is so ubiquitous and
everybody's got access to it,the one thing that we're going
to need to do, I think youmentioned this the beginning, is
train people, is to give themguidelines, is to tell them how
to write a prompt or to whattools will you use? How will you
(24:44):
use it? You know which ones arethe right tools for you, even if
it's just one, even if there'sone product that they use,
making sure they don't go toother products don't use their
iPhone, those are all importantthings to make sure they
understand. And and just sendingemail won't work. So training is
going to need to be key here,and making sure the trainers
(25:08):
spend some time with that team,the MDT, to be able to roll out
the appropriate level ofcommunications, change
management, whatever it is,webinar, because everybody wants
to try and use it. Everybody iswaiting for this to, like, come
out so they can go, Oh, I'mgoing to put an email in it to
see if I can make it better.
(25:29):
Okay. Is that the right use ofyour time? That's what this
training should answer as well.
Charlene Li (25:33):
Yeah, I do believe people need to have a
foundational level ofunderstanding, and yet they may
have it right content for themin the beginning, and I really
believe in training the top twolevels of your leadership as
soon as possible. These are thepeople who understand the
strategy of the organization, orat least they should, but they
(25:53):
are more likely to thinkstrategically about the use of
this, this tools. So the morethey can transform their own
work with AI, the more likely tosee the transformational power
and potential of AI for theorganization, but they have to
experience it themselves. I wasrecently at an HR conference,
(26:15):
and we did a poll to say, howmany of you, how often do you
use these things, and mostpeople were using it a couple
times a month, a couple times aweek. And 6% of them said, I
couldn't live without it. Just6% about 500 people, just six
that's 6% Wow, yeah. So Iencourage, first of all, for
(26:38):
those people to raise theirhand, stand up, so other people
could see them and go go talk tothe rest of the conference. But
that's where you need to be.
Maybe not at that level, but youneed to be using it to do the
work that you do every day. Sothere's a huge hump to get over
again. Trust is a big part ofit. Example, prompts. I do
believe in having promptlibraries and the also, I just
(27:00):
really train people to do threethings, to create content, to do
research and to use it as athought partner. And the higher
up you are in the organization,the ability to just use it as a
thought partner, as a soundingboard, becomes more and more
important. So habit reviewsomething, give it a problem
that you're wrestling with, giveit a situation with a an
(27:24):
employee, a tough conversation.
You have to come up and ask itto help you do a script or do a
role play. And what I encouragethem to do is ask to add this
one line at the end of anyprompt that they write. Because
most people, we're not very goodat writing prompts. So you ask
the AI to prompt you by askingthis question, you add at the
(27:45):
end, before you start ask me anyclarifying questions you may
have interesting.
Dwight Brown (27:51):
I could really use that one.
Charlene Li (27:53):
Yes. What happened the table? The AI then starts
asking you all the questionsthat it needs to make to do that
task that you've given it.
David Turetsky (28:04):
Why Charlene?
Why wouldn't that be natural?
Because when we haveconversation, we're doing it
right now we if I don'tunderstand something, I'm asking
you, you know reality, I'masking you so I can clarify and
understand, so I can then thenext thing coming out of my
mouth, which usually should besomewhat smart, but usually
(28:25):
isn't, especially in thisconversation, would be a better
output. Wouldn't normally thatbe the kind of...
Dwight Brown (28:33):
But the system, the system doesn't know what the
system doesn't know, I think,is, the
Charlene Li (28:38):
problem is we're treating AI like a Google
search. We put it in and we getan answer. We don't like it. We
put in another question and tryto get another search in. AI is
intelligent. It has a tonintelligent. And so you can
treat it almost like a person.
And so I treat it like aresearch assistant. So I'm like,
(28:58):
here's a task, I want you to godo it. And what do you say to a
person? Do you just give themthe task and then, like, shoo
them away to go do it, push abutton, tap and go out the door.
You go, Are you clear about myinstructions? You have any
questions for me? That's whatyou would normally do. So treat
the AI like a really smartassistant who needs more
clarifications. And the mostnatural thing you would do is
(29:18):
like, here's what I want you todo. Got any questions for me?
David Turetsky (29:23):
But that goes back to your point before, of
being able to understand how tomake prompts appropriately.
Dwight Brown (29:30):
Prompt engineering, yeah,
David Turetsky (29:32):
Yeah. Prompt engineering classes
Dwight Brown (29:35):
on,
David Turetsky (29:36):
yeah,
Charlene Li (29:37):
yeah. It's again I usually will include an example
that says these are the keycomponents to include in here.
Here's your role. Like your roleis to be a business copywriter
who's an expert on HRcommunications. Here's the
instructions I need you to writea 10 minute script for a
(29:57):
presentation I'm doing a collegerecruiting event. Here's some
context. This is who theaudience is. There are a bunch
of engineering students in thisclub, and they're curious about
what it's like to work at yourcompany. And here's some
information about the company,about how we have college
internships, how what earlycareers look like at the
company. And then very clearoutput. Give me a 10 minute
(30:21):
script divided to three sectionsthat I can do and, you know, go
to the races and then ask me anyclarifying questions you can
have. But even giving peoplethat level of prompt, and by the
way, all these engines haveprompting FAQs in them, and
they're getting more and moresophisticated, but at a basic
(30:41):
level, these are the kind ofcore things you want to include
in a prompt. And we also havethese beautiful things called AI
agents coming along now, becauseprompting is
David Turetsky (30:52):
now we're going to make a left hand turn and
talk about agents. Well, that'sa bigger one.
Hey, are you listening to thisand thinking to yourself, Man, I
wish I could talk to David aboutthis. Well, you're in luck. We
have a special offer forlisteners of the HR Data Labs
podcast, a free half hour callwith me about any of the topics
(31:13):
we cover on the podcast orwhatever is on your mind. Go to
salary.com/hrdlconsulting toschedule your FREE 30 minute
call today.
And Charlene, I know we've gonea little off script, but, but
let's now talk about AI agents,because that is kind of the next
we were talking about from GenAI, which is more of a to your
(31:37):
point before a research partner.
And now let's talk about agentswhich are, think of them as your
administrative assistant, right,or administrative partner,
right?
Charlene Li (31:46):
Think about them as peers. Or again, one of the
things I just did was completeda course on, how do you manage a
team made of a people and AIagents like, how do you think
about this? It's no longer thispassive technology. You have a
bunch of things that has to bedone your team to create some
(32:07):
output and value. You havepeople who can do it. You have
AI agents who can do it, and youhave a combination of the hybrid
of the two of them that could doit. So how do you figure out
which is the right combinationto use? And the reality is,
you're going to have toconstantly adjust for it,
because these agents are goingto do a lot of that automation.
They, in combination with aperson, would do a lot of this
(32:30):
prompting for you. Basically,agents are, think of them as
orchestrators. They will takeand understand a particular task
you want done, and it'll go andlook for the right information,
the right data. It'll feed it tothe AI engine, the generative AI
engine. It'll come back evaluatethe quality of it, and if it's
(32:50):
not up to snuff with thequality, it'll send it back and
say, do it over again. And thenfinally, give you an answer. So
all those steps we've had to doin the past ourselves, manually
with generative AI agents makeit a lot easier, and they can
learn over time. What does goodquality answers, what do they
look like? And just improve andlearn alongside humans on a
(33:13):
team.
David Turetsky (33:14):
But there has to be a step for humans in there to
check it, because we've heard,and I've especially we've had
some guests on the podcast,Dwight, and I've said, Well, you
know, AI lies. It doesn't reallyunderstand crap up. So don't we
have to have a check in therealongside the agents to make
sure that the agents are beingchecked?
Charlene Li (33:36):
Yes, that's what these agents do. And frankly,
that that description of itlying is putting some sort of
judgment on what AI is doing.
You could think about it asmaking things up, lying again,
but treating it as a Googlesearch engine, that's not what
it's really good for. If youwant an answer, go to Google
search if you're looking forsomebody who can be creative,
(33:59):
right? When you're writingsomething, you wanted to
understand your style, you wantto understand the facts, and you
can tell it to be very careful,don't make things up. This is
very important to my career, andit won't so there are ways,
again, you think about it as anover eager intern who wants to
make you happy. They will doeverything possible to make you
(34:20):
happy, including make things upuntil you tell them exactly, so
that, again, they needinstructions. And what the AI
agents do, it gives them verydetailed instructions. Go and
look only at this data for thisparticular problem, and it
understands your problem evenlike are you asking about just
how many vacation days we have,or are we looking at and
(34:43):
evaluating what makes a greatemployee and understanding
something beyond what's inresume to understand that full
person? Those are two verydifferent activities, completely
different types of data. Youwant to be looking at different
reasoning. So you wouldapproach. It the same way. And
that's what these agents do.
They understand your request,route it the same way. And then
(35:05):
has these, what I call judging,or critic AI, that sits on top
of it and says, Is that a goodanswer or no? And so that critic
is trained by humans to say,Yeah, act on my behalf. Get rid
of all those things that don'tmatch what was quality, send it
back, and then train thefoundational engines to be
(35:28):
better, because that's, in theend, how you train and over your
intern to be a great employee.
Same thing with agents, samething with AI. You've got to
train it.
Dwight Brown (35:39):
Now I'm just dying to go back to chat GPT and start
figuring out the fun tools thatI can try.
David Turetsky (35:46):
Well, yeah, and I think Dwight, that's the
problem. Is you have to actuallybe able to exercise these
muscles to learn how it all fitstogether. And then how does the
agent fit in the use casesyou're talking about, on top of
it, if it was the strategy,then, to me, one of the best
things about the AI agents wouldbe to help notify you or inform
(36:11):
you when decisions have beenmade or situations have occurred
that seem like they could betrending you away from your
strategic goals. And I mean notto tell tale on my peers or on
my my friends in my group, butthen to show me the examples of
(36:32):
where this is so that I can gocorrect it.
Charlene Li (36:35):
Well, I know some people are starting to use these
agents as true co pilots andtrue leaders alongside them,
because there's too muchhappening. There are too many
reports to look at. I have toomany team members and employees,
especially the higher up you goin organization. So they're
using AI to build thosenotifications for them to say, I
(36:57):
will routinely look for thesethings. Look for these things.
If anything is out of order.
Give me those exceptions, andgive me recommendations on
actions I can take. And thenover time, you just go like,
Hey, I trust you, because youkeep recommending the right
actions. Just take them. Just doit. And that's how AI you begin
to automate, is that it'sshowing its work, it's giving
you the logic. And over time,you're like, you don't need to
(37:19):
ask me anymore. Just go do it.
And the things that you begin totrust AI to do comes through
uses. You just don't say, Yeah,I trust that you as a vendor,
can do all these things. Go forit, just turn it on. No, that's
not that's never the way itworks, right, right? You
wouldn't do that people. Whywould you do that with tech? So,
yes, humans need to be in theloop, but the willpower of AI is
(37:42):
when you take the human out ofthe loop, because you trust it,
and you trust it to do the jobit's been meant to do, and you
trusted that it could probablydo it better than a human could.
And that's where things getreally interesting, because it
calls into question thisexistential issue of, what does
it mean to be human? Because ifAI can do all these things, then
(38:06):
what are we? Who are we? If AIcan do all these things,
Dwight Brown (38:10):
the million dollar question, yeah,
Charlene Li (38:12):
and AI still needs us ask the questions. It needs
us to imagine the future. Itcan't do those things.
David Turetsky (38:21):
I think Charlene, this goes back to when
we started using computers andthe same. I mean, back in the
early 90s, late 80s, we werestill in the working world. We
hadn't really fully bought intocomputers in the workplace yet,
but it started to get into manyjobs. You can't even imagine
them not using computers today.
(38:45):
And they there were somethoughts of, well, you know,
this is going to take the placeof so many jobs. Well, yeah, it
did. How many administrativeassistants do we see? How many
receptionists or secretaries ormail what do they? Call them?
The mail room. We don't see mailrooms anymore, really. You know,
you have an inbox at the frontdesk, and people kind of pick
their stuff up, but there's alot more capability and
(39:07):
functionality we have nowbecause we use computers. And I
think it's the same paradigm,isn't it? It's, it's kind of,
we're at the nascent stage.
We're back to the 80s and 90s,trying to figure out how this is
going to improve what we do, notnecessarily just replace us, but
how it will improve what we do.
Charlene Li (39:27):
Yeah, there's a saying that's been out there for
a while that it's not AI that'sgoing to replace you. It's
somebody using AI who will
David Turetsky (39:34):
Yeah!
Charlene Li (39:35):
So learn how to use AI, and the people who most
strenuously object to AI, myfirst question to them is, have
you used it?
David Turetsky (39:45):
Yeah,
Dwight Brown (39:45):
right.
Charlene Li (39:46):
And havent, I'm like, if you have then I can
have a rational discussion withyou. But if you have it, then
let's get you on it to seereally what it does. And I
understand these are bigconcerns, and it's. Gary, and
it's AI, it's Skynet coming totake us all over and everything.
I get it, but the best way tounderstand it is to use it, and
(40:09):
then you can understand whatit's capable of doing and also
what it's not capable of doing,and understanding the technology
can strip away a lot of thefear. And I'm not saying that AI
is fantastic, automatically, agreat thing. I'm optimistic
about its uses, but it requirespeople to put it against those
good uses. And in the absence ofthat positive, optimistic view
(40:31):
of AI, we have the people whoare using for nefarious reasons.
And the only way to overcome thenegative effects of AI is we use
it for good. So we need morepeople using it for good. So if
you're worried about the impactof AI, then find ways to
mitigate the bad and make sureit's being used for good.
David Turetsky (40:49):
And I think that's the mic drop moment right
there, Charlene, so we're gonnaleave it at that. There's so
much more we could cover. And Ifeel Dwight. I don't know what
you think, but we could talk toyou about this all day.
Dwight Brown (40:59):
Oh, for sure, yeah.
David Turetsky (41:02):
I mean, there's so many things that we can go
into on this Charlene, right?
Dwight Brown (41:06):
You can see the wheels turn in our heads right
now
David Turetsky (41:09):
Dwight and I are both thinking, you know, we
should go back and I've gotmine, my, as I said, I have
1min.ai over here, and it's up.
And it's normally up on a dailybasis. And I do ask a questions.
But you know, to your point, Iam at my nascent stages of being
able to be a prompt developer.
Because, right, I don't get ityet. I gotta get I've got to do
(41:29):
training on it.
Dwight Brown (41:31):
Yeah, I mean, you go to Coursera or edX, and
they're all kinds of promptengineering courses out there. I
remember the first time I sawthat. I'm like, what?!?
Charlene Li (41:43):
Yeah. And the thing is, I encourage you to take
those courses, but literally, golook at open AI's guide to
prompt engineering. They'll layout like these are the six
things to make better prompts.
Cloud has Gemini has errors.
Read through those, and you'regood to go. And if you want more
than go and look at the otherthings. But it's pretty basic,
(42:04):
and it's kind of like we knowhow to optimize for Google
search. We had to learn thatover time. You have to do AI. I
do believe that little questionat the end you train you like
this is how to ask me. This isyou need to give to me, and
instead of trying to write theperfect prompt, prepare yourself
to have a really goodconversation with AI.
David Turetsky (42:26):
So just so we're clear, especially for the for
the transcription, what was thatone question
Charlene Li (42:34):
Before you start, ask me any clarifying questions
you may have.
David Turetsky (42:47):
Charlene, thank you for being on the HR data
labs podcast.
Charlene Li (42:50):
It was fun.
David Turetsky (42:51):
And Dwight, thank you. Thank
Dwight Brown (42:52):
you. The wheels are turning.
David Turetsky (42:56):
Smoke coming out of both of our ears. See the
video because you had all kindsof ideas at work. So thank you
very much for listening. Takecare and stay safe.
Announcer (43:08):
That was the HR Data Labs podcast. If you liked the
episode, please subscribe. Andif you know anyone that might
like to hear it, please send ittheir way. Thank you for joining
us this week, and stay tuned forour next episode, stay safe.
Y
The world of business is more complex than
ever. The world of humanresources and compensation is
also getting more complex.
Welcome to the HR Data Labspodcast, your direct source for
the latest trends from expertsinside and outside the world of
human resources. Listen as weexplore the impact that
compensation strategy, data andpeople analytics can have on
your organization. This podcastis sponsored by Salary.com Your
(00:26):
source for data technology andconsulting for compensation and
beyond. Now here are your hosts,David Turetsky and Dwight Brown.
David Turetsky (00:38):
Hello and welcome to the HR Data Labs
podcast. I'm your host. DavidTuretsky, alongside my co host,
best friend, partner @Salary.com Dwight Brown. Dwight
Brown. How are you?
Dwight Brown (00:48):
I am Well, David, it's a beautiful, sunny day, and
we're probably going to be inmoving into summer soon.
David Turetsky (00:58):
Well, that's funny, because it's 41 degrees
here and raining inMassachusetts, exact opposite.
Yeah,
Dwight Brown (01:04):
Yeah thanks Dwight for rubbing that in right?
David Turetsky (01:07):
Yeah that's all right, but it is sunny here. Do
you know why it's sunny here?
Tell me, because we have withus. Charlene Li, Charlene, how
are you?
Charlene Li (01:16):
Great. So glad to be here.
David Turetsky (01:18):
You are our sunshine today, our only
sunshine. And for those of youwho are following along at home,
yes, it makes me happy whenskies are gray to be on the HR
Data Labs podcast. So Charlene,why don't you tell us a little
bit about yourself? Sure,
Charlene Li (01:35):
I'm a long time author and analyst. I have
written six books, New YorkTimes bestseller and really
focus on,
David Turetsky (01:43):
wait, wait,you can't just gloss over
Dwight Brown (01:46):
Breeze over
David Turetsky (01:47):
you just like, breeze over New York Times
bestsellers. Yeah, you know,everybody's got this. Everybody
does this. Well, you know, blah,blah, blah. I've written lots of
books, but I haven't gotten aNew York Times bestseller yet.
Charlene,
Charlene Li (01:58):
well, it's fun, you know, it's, it's, you get one.
It's fine. All you need is one.
David Turetsky (02:04):
Okay, all right, well, you know, it's like a
Grammy or an Emmy or a Oscar,right?
Charlene Li (02:09):
It's, it's great.
It's great to have that and thebut by focus, I keep writing
about disruptive technologies,and how do you create disruptive
innovation and disrupt yourselfand your organizations, and
there just keeps to be new stuffto write about and to talk
about. So I been in this spaceabout three decades now, and
continue to have new things. AndI'm working on my seventh book
(02:32):
out, winning with AI the 90 dayblueprint for success. So that
should be working out later thisspring.
David Turetsky (02:42):
Outstanding.
Dwight Brown (02:43):
David and I are big techies and big into how we
can integrate that into everyaspect of the workplace in some
way or another.
Charlene Li (02:53):
yeah and we'll talk about this. But I always believe
in looking at this, at all thesedigital disruptions and
transformations, thattransformation is never about
the technology, and it isalways, always about the people.
And we make the mistake byfocusing on the technologies and
not about how people are goingto adopt it, how to use it, how
(03:15):
it's going to transform them.
And so that's why the vastmajority of these efforts fail.
So it keeps coming back toleadership and culture. And I'm
a techie, geeky person that'sout there. Yeah, love the tech.
Dwight Brown (03:30):
We can love that
David Turetsky (03:32):
Geeks!
Charlene Li (03:33):
But, if you don't also include the people, yeah,
in your effort, managing, yourthinking, it will fail
David Turetsky (03:40):
absolutely
Dwight Brown (03:41):
makes sense.
David Turetsky (03:41):
We will get into that. But first, what's one fun
thing that no one knows aboutCharlene Li?
Charlene Li (03:47):
I train cats for fun.
David Turetsky (03:49):
You what
Dwight Brown (03:50):
What?
Charlene Li (03:51):
So
David Turetsky (03:52):
YOU TRAIN CATS?
Charlene Li (03:54):
Other people like, teach old dogs new tricks. I'm
playing a completely differentgame. I am training my cat how
to de tricks? Yeah!
Dwight Brown (04:02):
okay, so clue me in here. What kind of tricks
people?
David Turetsky (04:06):
By the way, you're talking to two dog people
Dwight Brown (04:08):
yeah, with dogs that never listen to us, but
Charlene Li (04:12):
who's snoozing over there, it can do about a dozen
tricks. So he can give me highfives both paws. He can turn
around in circles. He'll liedown, which is really hard for
him to do. He will, like, kindof do this funny Boppy thing
where he just kind of bobs up onmy hand. He'll put his chin on
the table. He'll come and sit onmy lap when I call. But the best
(04:33):
trick is he will jump over myarm and through a hoop in my
hold a hoop, and he'll jumpthrough it.
Dwight Brown (04:39):
Get out, really?
David Turetsky (04:41):
But let me ask.
Let me ask anything useful, likesitting on the toilet and going
potty in the toilet,
Charlene Li (04:48):
He'll pee in the sink.
Dwight Brown (04:51):
Okay, listen, because you wanted to, or No, I
mean one or two way it.
Charlene Li (05:00):
Think when his little box is not, oh, I have a
little robot too as well.
David Turetsky (05:05):
So oh, okay all right, there you go. Yeah. Well,
I'm not going to get as graphicfor my next question as I as
everybody who's listening mightimagine, but what we should do
is now transition to our topicso we don't get into trouble
because we have no explicitratings on any of our episodes,
Dwight Brown (05:22):
And we're a little punchy,
David Turetsky (05:23):
Yes, as you can tell, as you can tell. So
Charlene, our topic for today ishow generative AI will transform
in HR in the workplace. Andwe've had lots of conversations
about this, so we are extremelyfascinated.
So Charlene, our first questionis, really, what are the biggest
(05:47):
challenges HR face withgenerative AI and moving that
into the workplace?
Unknown (05:53):
I think the biggest one is that we don't have a lot of
good strategies that tie AI toeither HR specific goals or even
your business, your strategygoals as an organization,
typically the AI strategy I'mputting quotes up here is a list
(06:14):
of use cases. Use Cases are nota strategy. And more
importantly, what I'm finding isthat HR simply isn't at the
table in the strategydiscussions. They are either
shut out because technologiststake over and say, This is a
technology issue. We don't needHR in here, or HR says, I don't
(06:35):
need to be involved in this.
This is technology. It doesn'tinvolve people. And as we
started at the top of thispodcast, it's always about the
people. And so what I find isthat if you treat AI as a
technology versus atransformational force, you're
looking at it the wrong way. Ifyou look at it as an enterprise
technology that's going to berolled out in the traditional
(06:57):
way it's you're going to fail,because that is way too long,
way too slow of an impact tohave. The space is changing
literally every day, and you'vegot to get a strategy out there
that immediately drives impactand value against your top
strategic goals. I
David Turetsky (07:16):
think one of the worries that a lot of us have in
HR is the current strategy andthe current thinking is, reduce
cost, reduce headcount, andwe'll you know, that's it.
That's a strategy forimplementing AI and HR, but it's
not a good one, and it'scertainly not one thought
through with those use cases.
Charlene Li (07:35):
Well, I think you're missing the
transformative value of AI andespecially in the context of HR,
because if you only look at itas efficiency and productivity,
and those can be verytransformative, you're missing
the ways that you can engagewith people that's both your
customers and your employees,and completely different ways. I
mean, think about training andLeadership and Development, the
(07:59):
scale and the speed of which youcan create customized,
personalized development foreach person based on their
learning styles and theirdevelopment plan is incredibly
exponential in terms of theimpact. So this goes beyond just
saving a little bit ofautomation about generating
training scripts. That'sthinking very small. I'm
(08:21):
thinking, you can think big,start small and then scale fast
with AI. The problem is thatwe're not thinking big. We're
thinking very small. And so as aresult, we don't take these big
swings. We don't start with ourbiggest strategic goals and say,
how, what are the biggestproblems we have in addressing
(08:42):
these big strategic outcomes.
And unless you think about itfrom a strategy perspective,
thinking big, you're going tostart small and stay small.
Dwight Brown (08:52):
Do you think part of it is people just don't
understand AI and what thecapabilities are?
Charlene Li (08:59):
Think they don't understand their strategy.
David Turetsky (09:01):
Well, yeah, I mean, that's, that's a problem
we've always seen in HR. Thestrategy has always been
administration. The strategy hasalways been keep the lights on
and then fill in gaps, like thestory of the little Dutch Boy,
you know, putting their fingerin the dike. It's always been a
just try and be everything toeverybody. And what you're
(09:21):
saying is think moretransformatively about how we
can offer better, morepersonalized service that
doesn't scale with people, itscales with the available
technology. Is that correct?
Charlene Li (09:32):
Right? Well, again, this is a very interesting time
where you can scale and growwithout adding new people,
without a huge amount moreexpense in a fraction of the
time. You've never had thisopportunity before. It is
completely scale leveldifference in the way we
operate. And so you do have tostep back and be more strategic.
(09:54):
And this is the transformationthat HR has been going through
for the past 15 years. I. Wewere so bogged down by the
administrative aspects ofrecruiting people, engaging
people, retaining people, again,just trans, just like all that
administrative aspects of it.
And now technology can remove alot of that administrative
overhead and allow you to thinkmuch more strategically. But we
(10:17):
haven't trained ourselves as HRprofessionals on how to even ask
strategic questions.
David Turetsky (10:25):
Well, so to Dwight's point before, though,
isn't that? The bigger problemis we need more help building
that strategy. We can't reallygo to anybody else inside the
organization. They're going tobe like, Are you shitting me?
Pardon my French. You didn'tknow how to develop a strategy?
Why are you coming to us now?
This should have been done yearsago. You know what I mean?
Charlene Li (10:46):
Yeah. Again, better to acknowledge it that, yeah. So
let's go do it there and say, Idon't have a strategy. And
again, a strategy is fairlystraightforward. A strategy is
just a set of integrated choicesthat you make to achieve your
business objectives and to win.
(11:06):
So align them against youroverall core strategy as a
business. And here's the thing,most businesses cannot tell you
what their strategy is. So it'snot just HR, it has a problem.
It's the entire executive teamand the board. The hardest part
about AI and strategy, it causesyou to make you focus on what is
most important to us, integratedChoices, choices, what we will
(11:27):
do when we won't do achieve ourgoals. If you are not aligned on
what those goals are, you can'teven begin to make decisions.
Which is why we default to theefficiency and productivity,
because that's within mypurview. I can look at these
small, little problems. I don'thave to deal with alignment and
harmony and making sure we'reall on the same page.
(11:51):
Fundamental problem with justoverall strategy inside of our
organizations.
David Turetsky (11:54):
So one of the things that I wanted to ask was
a lot of us did a lot of us doin the HR world, build a strat
plan. You know, every year wehave a strategic plan for what
we're going to try andaccomplish next year. I don't
think what you're talking aboutis developing anything broader
than that. You're just saying,Is there a way of looking at
that strategic plan and beingable to accomplish that a
(12:16):
different way, more efficiently,without having to throw money at
it without having to thrownecessarily people at it. I
mean, you might have to throwsome money at it to begin with,
but you're gonna get a muchbetter expectation out of it if
you're using potentially Gen AIinstead of just powering through
it with people.
Charlene Li (12:37):
Yeah, again, I think that's a very efficiency
and productivity right? To lookat things and frankly, that
strategy plan for the next 12months, it's an execution plan
and a budget, right? And as youget closer to the end of those
12 months, your plan shrinks andshrinks and shrinks. What I
really advocate is a strategyplan that helps you achieve your
(12:59):
overall business objectives asan organization. So it's longer
term, long term, whatever thatlooks like. It can think about
three to five year horizon, butI encourage people to think
about it as 18 months, sixquarters, and each quarter
you're laying out what is theimpact and the value we are
going to deliver every singlequarter. And this is more than
(13:21):
just administrative things. Whatis the change and the impact
you're going to create in orderto help us achieve our long term
goals? So if you are creatingthat change, then how can AI
help you accelerate that changeor achieve that change? Because
there are a lot of barriers inthe way to make that
transformation happen. And thekey is, at each at the end of
(13:43):
each quarter, you're evaluatinghow far you've come. You're
making adjustments to the nextfive quarters of what the impact
is going to be, and then add onanother quarter. It's a rolling
18 month plan, and you shouldn'thave to redo it every single
quarter. It's an extension ofwhat that plan looks like, and
it's far enough in the futurethat it is actually strategic.
Dwight Brown (14:05):
So, the strategic plan you're talking about is
that a strategic plan specificto AI, or are you saying look at
your business strategy and workbackwards from there and figure
out where AI can help youachieve that strategy,
Charlene Li (14:23):
Absolutely, because the your C suite and your board
won't care unless it helps youcreate your business it helps
you achieve your businessobjectives. Creates competitive
advantage. They won't care. It'slike, yeah, go to go deal with
it in your size, yourdepartment. But if you really
want to have an impact and totransform the organization, then
(14:43):
help us achieve our big,audacious goals that we have set
for ourselves, identify thebiggest problems that lie in the
way of us achieving it. What areour biggest challenges, and help
us understand how HR and AI aregoing to help us get. To those
goals, better, faster, cheaper,
Announcer (15:04):
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show by clicking subscribe. Thispodcast is made possible by
Salary.com. Now back to theshow...
David Turetsky (15:14):
Charlene. One of the things that troubles me
though about adding AI into themix is that HR typically
stumbles when it comes to theseinitiatives anyways, but because
AI is still in its infancy, itstill has its training wheels on
in in reality, we also don'thave these private clouds or
(15:36):
these private instances yetenough in organizations that
will enable us to have thesetypes of strategies well thought
through, without it leaking toothers by saying, Hey, tell me
some examples of other companiesthat have gone and improved
their HR strategy this way. Andall of a sudden, chatgpt just
spits out our company name. Oh,well, salary.com did, by the
(15:59):
way, salary.com just an exampleof a company name. Should it
just? Probably just a companyname. But you know what I'm
saying is, to me, there are somany barriers from the
technology being nascent. Isthere in your mind as you're
thinking through this and asyou're espousing this, is there
(16:20):
still a problem of this kind ofgoing up the maturity curve yet.
Or are we? Are we ready for thisnow?
Charlene Li (16:26):
Well, first of all, one of the first things we
encourage people to do is to getyour a core team of people
trained on how to use AI in asafe and secure way. And that's
you can do that simply with chatGPT teams, if you put that in
place automatically, allsettings to feed the model, any
of your data is off, justautomatically. It's all private,
(16:49):
then it's 100% private. Okay, sothat's the system I use. You
know, can you trust them? Well,if they can't do this securely,
then the whole entire businessmodel is is put but trust them.
And then the second thing is,you want to put in place the
governance models forresponsible and ethical AI. You
(17:10):
build what I call it an AI trustpyramid of safety, security,
fairness, all those things, allthe way up to transparency. Then
make sure everyone understandsthis is what responsible and
ethical AI looks like. Itdoesn't have to be complicated.
It's built on the foundations ofexisting data policies and
security policies you have inthe company. And then third most
important is to give peopleaccess broader across the entire
(17:32):
organization. Right there areoff the shelf solutions you can
get from your existing cloudproviders that can just turn it
on and it's completely lockedoff. I talked to a global
organization. They have 65,000people, and they run AI for
everybody for a couple $100,000a year. Yeah, we're talking like
(17:54):
this is like a small littlesludge budget within the IT
department.
David Turetsky (17:59):
Sure. I guess I'm a little more concerned
about those people, each ofthose employees who have not
heard about that yet, go to1min.ai which is a off the shelf
aggregator of of lots ofdifferent Gen AI models, and you
buy, literally, can buy alifetime license for $69 and
(18:21):
have access to a bunch ofcredits per month, but that is
completely public. I mean, youcan set all the settings to be
private, but Charlene, the thingI'm worried about is that that
thing you mentioned aboutrolling it out to all your
employees, if you don't do thatand you haven't done it yet,
believe me, each one of youremployees has created their own
(18:42):
private little cloud. They havenot made those privacy settings
private, and all of the thingsthat they're asking about Gen
AI, Gen AI in your company arebeing not necessarily broadcast,
but they're being absorbed.
Charlene Li (18:55):
This is why it's so important to lock it down right,
and the more you can catch itwith your IP nets. And anytime
they go to these things, theyget redirected to your
authorized, safe and secure waythat you're locking down that
technology. It's logging things,but it's removing anything from
that log within an hour. Sonothing is keyed in secure. It's
(19:18):
all software compliant. So eventhe transmission lines are
secure, everything is lockeddown. So even if somebody
inadvertently uploads somethinginto your private cloud, it
David Turetsky (19:28):
What about copilot or Apple intelligence or
doesn't stay there.
Gemini or whatever it's calledthese days? What about the ones
that are more ubiquitouslyavailable within the
applications we use every day,either our phones or Microsoft
Excel has or Word has co pilotbuilt in.
Unknown (19:46):
Yeah, those are all locked down again. It's in
agreements that you have, if youhave copilot, if you have any of
the Microsoft tools, again, onyour systems, those are locked
down so they're in the MSA soyou can be secure by that. So
you want to work with yourvendors that they understand
you're responsible in AI ethicsand guidelines. So it's like
(20:07):
your entire ecosystem is on thesame page. Want to make sure
that all the vendors areapplying and abiding by your
safety and security privacypractices. You do that anyways.
You do that with AI.
David Turetsky (20:19):
Well, they may not have had that done yet after
listening to this, hopefullythey will.
Charlene Li (20:25):
The thing here is that safe and secure access to
AI and that you have you canproceed with it with confidence,
with trust, is vital to your useof AI, and if you cannot have
that as a foundational buildingblock, then you cannot go
forward. We are very, very keen.
My co author and I are very keenon making AI readily available,
(20:55):
expose it as much as possible.
So because once you have thatsafe and secure platform, you
can blow the doors off of this.
And the CIO we were talking tosaid, I don't care if they use
it, if they use it, if theydon't, I really don't care. And
we're just going to make thislike electricity. It's going to
be ubiquitous, and they can haveit, and I don't have to worry
about ROI because it's so cheap.
But I know that there's impact,because if I try to take it away
(21:19):
from them, they're like, over mydead code, fingers will you
again? I he can't measure thevalue, but in an organization of
665,000 people globally, tospend $300,000 a year on AI for
your biggest safe access toeverybody, nobody cares about
(21:42):
how much it costs, all thosequestions around ROI, just go
out the window, and then you canfocus on, what can we do with
this when everyone has access mygoodness of things that come
out? And then it's a focus ofsaying, efficiency,
productivity, go, go, do it.
Just no brainer. Just do thesethings, right? We don't really
need to really think too muchabout them. If it's helping you
(22:05):
do your job better, just do it.
Stay within these guidelinesthat we have and go for it. But
what really has to happen is weneed to know strategically, how
are we going to use this acrossour business so that it drives
the biggest bang for the buck tohelp us achieve our switching
goals. That is a strategyquestion that your C suite and
(22:27):
your board level should betalking about, and HR needs to
be a part of that mix, becauseyou don't get that level of
transformation unless you'rethinking about the people aspect
of things.
Dwight Brown (22:36):
So does it start with HR developing a strategy
and move up the chain? Or how doyou see this working best?
Charlene Li (22:45):
There's a minimally viable team, minimum MVT, of
somebody who's thinkingstrategically, the highest level
strategy person you can get inthe organization. You get
somebody from HR who can thinkabout people and their adoption
and use of these things. Youhave somebody who is thinking
from the digital and technologyside. It's not your traditional
(23:05):
technology department, it'ssomebody in digital and then you
get somebody who understands thecustomer, who has that
perspective. It could besomebody in marketing and
product in the commercial side,but somebody who can encourage
the team to be customer centric,whoever that customer is, and so
that's a very small team. Andbuy in from your legal team to
(23:29):
like, Hey, we're going to abideby all these rules, but outside
of that, and inside with thoseguardrails, we will stay within
that, but they don't belong inthe room, because they're the
departments of No, they're notthe ones who think deeply and
imagine what the work could looklike, and if they promise to
never say no, the minute theysay no, they get
Dwight Brown (23:51):
terms and conditions that you have them
signed before they can walk inthe door. You know,
Charlene Li (23:55):
Like, do you really want to be a part of this
conversation? Yeah, exactlycareful what you wish for. But
you know, we're not going toimplement anything until we run
it by you anyways. So you don'tneed to be here to say no to
something that we haven'tfigured out how to do yet. So by
having this really small teamthat is going to be very
strategy based, you have awinning you have a fighting
(24:18):
chance to really focus on thebig problems that you can solve
with AI.
David Turetsky (24:23):
One of the things that I worry about, or
that I'm encouraged by, is, ifthis tool is so ubiquitous and
everybody's got access to it,the one thing that we're going
to need to do, I think youmentioned this the beginning, is
train people, is to give themguidelines, is to tell them how
to write a prompt or to whattools will you use? How will you
(24:44):
use it? You know which ones arethe right tools for you, even if
it's just one, even if there'sone product that they use,
making sure they don't go toother products don't use their
iPhone, those are all importantthings to make sure they
understand. And and just sendingemail won't work. So training is
going to need to be key here,and making sure the trainers
(25:08):
spend some time with that team,the MDT, to be able to roll out
the appropriate level ofcommunications, change
management, whatever it is,webinar, because everybody wants
to try and use it. Everybody iswaiting for this to, like, come
out so they can go, Oh, I'mgoing to put an email in it to
see if I can make it better.
(25:29):
Okay. Is that the right use ofyour time? That's what this
training should answer as well.
Charlene Li (25:33):
Yeah, I do believe people need to have a
foundational level ofunderstanding, and yet they may
have it right content for themin the beginning, and I really
believe in training the top twolevels of your leadership as
soon as possible. These are thepeople who understand the
strategy of the organization, orat least they should, but they
(25:53):
are more likely to thinkstrategically about the use of
this, this tools. So the morethey can transform their own
work with AI, the more likely tosee the transformational power
and potential of AI for theorganization, but they have to
experience it themselves. I wasrecently at an HR conference,
(26:15):
and we did a poll to say, howmany of you, how often do you
use these things, and mostpeople were using it a couple
times a month, a couple times aweek. And 6% of them said, I
couldn't live without it. Just6% about 500 people, just six
that's 6% Wow, yeah. So Iencourage, first of all, for
(26:38):
those people to raise theirhand, stand up, so other people
could see them and go go talk tothe rest of the conference. But
that's where you need to be.
Maybe not at that level, but youneed to be using it to do the
work that you do every day. Sothere's a huge hump to get over
again. Trust is a big part ofit. Example, prompts. I do
believe in having promptlibraries and the also, I just
(27:00):
really train people to do threethings, to create content, to do
research and to use it as athought partner. And the higher
up you are in the organization,the ability to just use it as a
thought partner, as a soundingboard, becomes more and more
important. So habit reviewsomething, give it a problem
that you're wrestling with, giveit a situation with a an
(27:24):
employee, a tough conversation.
You have to come up and ask itto help you do a script or do a
role play. And what I encouragethem to do is ask to add this
one line at the end of anyprompt that they write. Because
most people, we're not very goodat writing prompts. So you ask
the AI to prompt you by askingthis question, you add at the
(27:45):
end, before you start ask me anyclarifying questions you may
have interesting.
Dwight Brown (27:51):
I could really use that one.
Charlene Li (27:53):
Yes. What happened the table? The AI then starts
asking you all the questionsthat it needs to make to do that
task that you've given it.
David Turetsky (28:04):
Why Charlene?
Why wouldn't that be natural?
Because when we haveconversation, we're doing it
right now we if I don'tunderstand something, I'm asking
you, you know reality, I'masking you so I can clarify and
understand, so I can then thenext thing coming out of my
mouth, which usually should besomewhat smart, but usually
(28:25):
isn't, especially in thisconversation, would be a better
output. Wouldn't normally thatbe the kind of...
Dwight Brown (28:33):
But the system, the system doesn't know what the
system doesn't know, I think,is, the
Charlene Li (28:38):
problem is we're treating AI like a Google
search. We put it in and we getan answer. We don't like it. We
put in another question and tryto get another search in. AI is
intelligent. It has a tonintelligent. And so you can
treat it almost like a person.
And so I treat it like aresearch assistant. So I'm like,
(28:58):
here's a task, I want you to godo it. And what do you say to a
person? Do you just give themthe task and then, like, shoo
them away to go do it, push abutton, tap and go out the door.
You go, Are you clear about myinstructions? You have any
questions for me? That's whatyou would normally do. So treat
the AI like a really smartassistant who needs more
clarifications. And the mostnatural thing you would do is
(29:18):
like, here's what I want you todo. Got any questions for me?
David Turetsky (29:23):
But that goes back to your point before, of
being able to understand how tomake prompts appropriately.
Dwight Brown (29:30):
Prompt engineering, yeah,
David Turetsky (29:32):
Yeah. Prompt engineering classes
Dwight Brown (29:35):
on,
David Turetsky (29:36):
yeah,
Charlene Li (29:37):
yeah. It's again I usually will include an example
that says these are the keycomponents to include in here.
Here's your role. Like your roleis to be a business copywriter
who's an expert on HRcommunications. Here's the
instructions I need you to writea 10 minute script for a
(29:57):
presentation I'm doing a collegerecruiting event. Here's some
context. This is who theaudience is. There are a bunch
of engineering students in thisclub, and they're curious about
what it's like to work at yourcompany. And here's some
information about the company,about how we have college
internships, how what earlycareers look like at the
company. And then very clearoutput. Give me a 10 minute
(30:21):
script divided to three sectionsthat I can do and, you know, go
to the races and then ask me anyclarifying questions you can
have. But even giving peoplethat level of prompt, and by the
way, all these engines haveprompting FAQs in them, and
they're getting more and moresophisticated, but at a basic
(30:41):
level, these are the kind ofcore things you want to include
in a prompt. And we also havethese beautiful things called AI
agents coming along now, becauseprompting is
David Turetsky (30:52):
now we're going to make a left hand turn and
talk about agents. Well, that'sa bigger one.
Hey, are you listening to thisand thinking to yourself, Man, I
wish I could talk to David aboutthis. Well, you're in luck. We
have a special offer forlisteners of the HR Data Labs
podcast, a free half hour callwith me about any of the topics
(31:13):
we cover on the podcast orwhatever is on your mind. Go to
salary.com/hrdlconsulting toschedule your FREE 30 minute
call today.
And Charlene, I know we've gonea little off script, but, but
let's now talk about AI agents,because that is kind of the next
we were talking about from GenAI, which is more of a to your
(31:37):
point before a research partner.
And now let's talk about agentswhich are, think of them as your
administrative assistant, right,or administrative partner,
right?
Charlene Li (31:46):
Think about them as peers. Or again, one of the
things I just did was completeda course on, how do you manage a
team made of a people and AIagents like, how do you think
about this? It's no longer thispassive technology. You have a
bunch of things that has to bedone your team to create some
(32:07):
output and value. You havepeople who can do it. You have
AI agents who can do it, and youhave a combination of the hybrid
of the two of them that could doit. So how do you figure out
which is the right combinationto use? And the reality is,
you're going to have toconstantly adjust for it,
because these agents are goingto do a lot of that automation.
They, in combination with aperson, would do a lot of this
(32:30):
prompting for you. Basically,agents are, think of them as
orchestrators. They will takeand understand a particular task
you want done, and it'll go andlook for the right information,
the right data. It'll feed it tothe AI engine, the generative AI
engine. It'll come back evaluatethe quality of it, and if it's
(32:50):
not up to snuff with thequality, it'll send it back and
say, do it over again. And thenfinally, give you an answer. So
all those steps we've had to doin the past ourselves, manually
with generative AI agents makeit a lot easier, and they can
learn over time. What does goodquality answers, what do they
look like? And just improve andlearn alongside humans on a
(33:13):
team.
David Turetsky (33:14):
But there has to be a step for humans in there to
check it, because we've heard,and I've especially we've had
some guests on the podcast,Dwight, and I've said, Well, you
know, AI lies. It doesn't reallyunderstand crap up. So don't we
have to have a check in therealongside the agents to make
sure that the agents are beingchecked?
Charlene Li (33:36):
Yes, that's what these agents do. And frankly,
that that description of itlying is putting some sort of
judgment on what AI is doing.
You could think about it asmaking things up, lying again,
but treating it as a Googlesearch engine, that's not what
it's really good for. If youwant an answer, go to Google
search if you're looking forsomebody who can be creative,
(33:59):
right? When you're writingsomething, you wanted to
understand your style, you wantto understand the facts, and you
can tell it to be very careful,don't make things up. This is
very important to my career, andit won't so there are ways,
again, you think about it as anover eager intern who wants to
make you happy. They will doeverything possible to make you
(34:20):
happy, including make things upuntil you tell them exactly, so
that, again, they needinstructions. And what the AI
agents do, it gives them verydetailed instructions. Go and
look only at this data for thisparticular problem, and it
understands your problem evenlike are you asking about just
how many vacation days we have,or are we looking at and
(34:43):
evaluating what makes a greatemployee and understanding
something beyond what's inresume to understand that full
person? Those are two verydifferent activities, completely
different types of data. Youwant to be looking at different
reasoning. So you wouldapproach. It the same way. And
that's what these agents do.
They understand your request,route it the same way. And then
(35:05):
has these, what I call judging,or critic AI, that sits on top
of it and says, Is that a goodanswer or no? And so that critic
is trained by humans to say,Yeah, act on my behalf. Get rid
of all those things that don'tmatch what was quality, send it
back, and then train thefoundational engines to be
(35:28):
better, because that's, in theend, how you train and over your
intern to be a great employee.
Same thing with agents, samething with AI. You've got to
train it.
Dwight Brown (35:39):
Now I'm just dying to go back to chat GPT and start
figuring out the fun tools thatI can try.
David Turetsky (35:46):
Well, yeah, and I think Dwight, that's the
problem. Is you have to actuallybe able to exercise these
muscles to learn how it all fitstogether. And then how does the
agent fit in the use casesyou're talking about, on top of
it, if it was the strategy,then, to me, one of the best
things about the AI agents wouldbe to help notify you or inform
(36:11):
you when decisions have beenmade or situations have occurred
that seem like they could betrending you away from your
strategic goals. And I mean notto tell tale on my peers or on
my my friends in my group, butthen to show me the examples of
(36:32):
where this is so that I can gocorrect it.
Charlene Li (36:35):
Well, I know some people are starting to use these
agents as true co pilots andtrue leaders alongside them,
because there's too muchhappening. There are too many
reports to look at. I have toomany team members and employees,
especially the higher up you goin organization. So they're
using AI to build thosenotifications for them to say, I
(36:57):
will routinely look for thesethings. Look for these things.
If anything is out of order.
Give me those exceptions, andgive me recommendations on
actions I can take. And thenover time, you just go like,
Hey, I trust you, because youkeep recommending the right
actions. Just take them. Just doit. And that's how AI you begin
to automate, is that it'sshowing its work, it's giving
you the logic. And over time,you're like, you don't need to
(37:19):
ask me anymore. Just go do it.
And the things that you begin totrust AI to do comes through
uses. You just don't say, Yeah,I trust that you as a vendor,
can do all these things. Go forit, just turn it on. No, that's
not that's never the way itworks, right, right? You
wouldn't do that people. Whywould you do that with tech? So,
yes, humans need to be in theloop, but the willpower of AI is
(37:42):
when you take the human out ofthe loop, because you trust it,
and you trust it to do the jobit's been meant to do, and you
trusted that it could probablydo it better than a human could.
And that's where things getreally interesting, because it
calls into question thisexistential issue of, what does
it mean to be human? Because ifAI can do all these things, then
(38:06):
what are we? Who are we? If AIcan do all these things,
Dwight Brown (38:10):
the million dollar question, yeah,
Charlene Li (38:12):
and AI still needs us ask the questions. It needs
us to imagine the future. Itcan't do those things.
David Turetsky (38:21):
I think Charlene, this goes back to when
we started using computers andthe same. I mean, back in the
early 90s, late 80s, we werestill in the working world. We
hadn't really fully bought intocomputers in the workplace yet,
but it started to get into manyjobs. You can't even imagine
them not using computers today.
(38:45):
And they there were somethoughts of, well, you know,
this is going to take the placeof so many jobs. Well, yeah, it
did. How many administrativeassistants do we see? How many
receptionists or secretaries ormail what do they? Call them?
The mail room. We don't see mailrooms anymore, really. You know,
you have an inbox at the frontdesk, and people kind of pick
their stuff up, but there's alot more capability and
(39:07):
functionality we have nowbecause we use computers. And I
think it's the same paradigm,isn't it? It's, it's kind of,
we're at the nascent stage.
We're back to the 80s and 90s,trying to figure out how this is
going to improve what we do, notnecessarily just replace us, but
how it will improve what we do.
Charlene Li (39:27):
Yeah, there's a saying that's been out there for
a while that it's not AI that'sgoing to replace you. It's
somebody using AI who will
David Turetsky (39:34):
Yeah!
Charlene Li (39:35):
So learn how to use AI, and the people who most
strenuously object to AI, myfirst question to them is, have
you used it?
David Turetsky (39:45):
Yeah,
Dwight Brown (39:45):
right.
Charlene Li (39:46):
And havent, I'm like, if you have then I can
have a rational discussion withyou. But if you have it, then
let's get you on it to seereally what it does. And I
understand these are bigconcerns, and it's. Gary, and
it's AI, it's Skynet coming totake us all over and everything.
I get it, but the best way tounderstand it is to use it, and
(40:09):
then you can understand whatit's capable of doing and also
what it's not capable of doing,and understanding the technology
can strip away a lot of thefear. And I'm not saying that AI
is fantastic, automatically, agreat thing. I'm optimistic
about its uses, but it requirespeople to put it against those
good uses. And in the absence ofthat positive, optimistic view
(40:31):
of AI, we have the people whoare using for nefarious reasons.
And the only way to overcome thenegative effects of AI is we use
it for good. So we need morepeople using it for good. So if
you're worried about the impactof AI, then find ways to
mitigate the bad and make sureit's being used for good.
David Turetsky (40:49):
And I think that's the mic drop moment right
there, Charlene, so we're gonnaleave it at that. There's so
much more we could cover. And Ifeel Dwight. I don't know what
you think, but we could talk toyou about this all day.
Dwight Brown (40:59):
Oh, for sure, yeah.
David Turetsky (41:02):
I mean, there's so many things that we can go
into on this Charlene, right?
Dwight Brown (41:06):
You can see the wheels turn in our heads right
now
David Turetsky (41:09):
Dwight and I are both thinking, you know, we
should go back and I've gotmine, my, as I said, I have
1min.ai over here, and it's up.
And it's normally up on a dailybasis. And I do ask a questions.
But you know, to your point, Iam at my nascent stages of being
able to be a prompt developer.
Because, right, I don't get ityet. I gotta get I've got to do
(41:29):
training on it.
Dwight Brown (41:31):
Yeah, I mean, you go to Coursera or edX, and
they're all kinds of promptengineering courses out there. I
remember the first time I sawthat. I'm like, what?!?
Charlene Li (41:43):
Yeah. And the thing is, I encourage you to take
those courses, but literally, golook at open AI's guide to
prompt engineering. They'll layout like these are the six
things to make better prompts.
Cloud has Gemini has errors.
Read through those, and you'regood to go. And if you want more
than go and look at the otherthings. But it's pretty basic,
(42:04):
and it's kind of like we knowhow to optimize for Google
search. We had to learn thatover time. You have to do AI. I
do believe that little questionat the end you train you like
this is how to ask me. This isyou need to give to me, and
instead of trying to write theperfect prompt, prepare yourself
to have a really goodconversation with AI.
David Turetsky (42:26):
So just so we're clear, especially for the for
the transcription, what was thatone question
Charlene Li (42:34):
Before you start, ask me any clarifying questions
you may have.
David Turetsky (42:47):
Charlene, thank you for being on the HR data
labs podcast.
Charlene Li (42:50):
It was fun.
David Turetsky (42:51):
And Dwight, thank you. Thank
Dwight Brown (42:52):
you. The wheels are turning.
David Turetsky (42:56):
Smoke coming out of both of our ears. See the
video because you had all kindsof ideas at work. So thank you
very much for listening. Takecare and stay safe.
Announcer (43:08):
That was the HR Data Labs podcast. If you liked the
episode, please subscribe. Andif you know anyone that might
like to hear it, please send ittheir way. Thank you for joining
us this week, and stay tuned forour next episode, stay safe.
Y
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