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A podcast that explores how AI is transforming careers, businesses, and industries. Hosts Greg Boone and Erica Rooney deliver real-world use cases and actionable AI strategies to help professionals stay ahead of the curve.
What happens is if we don't have a
leader that's saying, you know what?
I tried this thing and it didn't work.
I couldn't figure it out.
I gotta go back and try it again,
then you don't feel comfortable
as an employee doing that.
You don't wanna tell your manager that
you made a mistake or went down a whole
road and have to come all the way back.
If they've never done that, or if they've
never shared that they've done that.
'cause we all have, right?
And so we want that vulnerability and, and
transparency and honesty from our leaders.
AI isn't the future.
It's now.
And whether you're in hr, sales,
operations, or leadership.
The choices you make today will determine
whether you thrive or get left behind.
Welcome to ai, voice or victim.
I'm Greg Boone, marketing
executive and AI series.
And I'm Erica Rooney, author,
speaker, and gender equality advocate.
And I'm AI curious and we are here
to cut through the noise and show you
how to leverage AI in your career,
your business, and your brand.
In every episode, we will break down real
world use cases and give you AI driven
strategies that you can apply immediately
ready to stay ahead of the curve.
Let's jump in.
Today's guest is a powerhouse of empathy,
advocacy, and inclusive leadership.
Jackie Ferguson is the co-founder of
the Diversity Movement, a bestselling
author and host of one of the top
diversity podcasts in the world.
She has spent her career creating
conversations that change
hearts and minds, and today.
She is here to help us explore how AI can
be a tool for inclusion, not exclusion.
This episode of AI Voice or victim is all
about bridging DEI and tech, and Jackie
is the perfect person for this journey.
Jackie, welcome to the podcast.
Thank you.
I'm so glad to be here.
Oh my gosh.
It's incredible to have you here.
And I wanna know, you've built this
career around inclusive communication.
Mm-hmm.
How do you see AI playing a role either
in expanding or constraining that mission?
Yeah.
It's interesting because
AI is just taking over.
How we work, right?
It's, it's integrated into everything.
I think that with regard to inclusion,
you have to think about some of the
concerns that people have with ai, which
is bias and adoption, um, of specific
demographics being lower than others.
Um, but I think that what's important is.
Thinking about how it can level the
playing field in lots of different areas.
The ways that people work, the ways
that people are learning, the ways
that, um, people do their jobs and how
that can be, uh, expanded and right,
because for example, I'm a writer,
but I can write significantly faster.
Or use my chat to help write and then
edit, um, which allows me to do my
work faster and, and then get into
more things, learn different things.
And so I, I think that AI is such a great
tool and everyone should be working to
adopt it and incorporate it into the work.
Hmm.
I love it.
I kind of wanna rewind it and take
a step back because we have got a
wide range of AI listeners, right?
Yes.
All the way from the ai
anxious to the AI serious.
Right.
I wanna talk a little bit more about
the bias that, that we're hearing about.
'cause I don't think everybody
really understands what that means.
So can you tell me what
kind of bias are you seeing?
What should we be concerned about and
what do leaders need to be thinking about?
So there's a couple of steps, right?
And using ai.
One is you create your prompt
and then you get this output.
The very important part is you
have to look at it and edit
it from a human perspective.
Generative AI and large language
models are pulling information from
what's already on the internet and then
inputs of the people that are using it.
Um, so there are a few things there.
One, depending on the information that's.
Provided right where they're
pulling, where AI is pulling from,
you're, you can get a biased view.
For example, one of the things that,
that I read and, and used as an
example a lot is if you put in, you
know, give me the top philosophers.
Right.
Sometimes what you'll get
are Western philosophers.
You won't get Eastern philosophers, right?
So very important philosophers
like Confucius are excluded.
So you wanna make sure that in your
prompts, you're creating a level
playing field and and eliminating
bias there by saying global, right?
Just being deliberate about your
language and the words that you use
to create that prompt so that you get
a broader view, um, in the output.
And then you have to look at it
and say, you know, if, if that were
the prompt, and that's what I got.
Hmm.
These are all from
Western tradition, right?
So then you wanna adjust your prompts.
So you have to be the human.
Intervention is extremely important
in how you use AI to prevent bias.
The other thing, um, and we can talk,
this is a whole different topic, but
just bringing this into the conversation
is one of the things that we're
seeing in McKinsey, uh, did a study
that shows that women are adopting
AI at 25% less of a rate than met.
So if it's majority men that are creating
those inputs, right, and creating the
prompts, then what happens is, you know,
you are not getting that input from women.
The perspective from women and then large
language models, again, are developed
and improved by those inputs in part.
And so if you're not having the same rate
of women as men, creating those inputs.
You don't get, um, you
know, the outputs that are.
Level and even and inclusive.
Yeah.
This is super interesting because in
a previous episode we were talking
about the importance of prompt
engineering and how that plays into
everything, but we were also talking
about how women aren't adopting ai.
Yeah.
And we were looking at it more through
the lens back then of like, okay.
Women, you're gonna be not getting
the jobs that all the men are
getting, et cetera, et cetera.
But we didn't touch on this lens
of, okay, now all the information
that we're all gonna be getting.
Yeah.
Out of ai.
Even the women who are using it.
That's right.
It's gonna be all skewed towards the men.
That's exactly right.
And we don't want that.
Right.
So that's one of the things that's
identified that is a concern
that, you know, as with all
technology, it's gonna evolve.
So we want those inputs.
To be able to evolve it
in a more inclusive way.
It's a real issue that women are
not adopting it at the same rate.
McKinsey also did a study that showed
that 92% of companies are investing
in ai, reskilling, upskilling, um,
and training for their employees.
And so if women don't participate
in that at the same rate.
It affects both them as individuals and
how they're able to grow their career.
It affects businesses and, and,
you know, the outputs and the
productivity of the organization.
But you know, it also affects the, the AI
algorithms and the large language models
and, and how they're able to develop.
Um, so we, we definitely want more
women participating and sharing and.
Also, you know, when you get those
outputs, then what happens a lot of times
is we go back in and we tweak what, what
the answer is to get a better answer.
Right?
And so we need women doing that
with their perspectives, with their
experience, um, with their needs, with
their habits, and so that we get a, a
better, um, a better technology overall.
I wanna touch on a few
things you say here, right?
I think the, um, the,
the, the innate bias.
The systems themselves.
Like people have to understand, like, you
called up, you made a great point, right?
It's global in nature
and how it was trained.
It was trained on, you know, everyone.
Sure.
Not just the folks that are in your town
or in your state or in your country.
Right?
Right.
And so it is because humans have a bias,
whether it's conscious or unconscious.
So now the machine has been trained on it.
It is, you know, folks say,
well, the machine is biased.
Well, it's, it's biased
because humans are biased.
Correct.
Right.
And so that's how it was built.
And so I think thing one is making sure
people understand fully that it's trained
on a, a kind of a global view, right?
And not all parts are equal.
Not every country, you know, has the same
laws and rules and things, to your point.
So it's, it was an excellent call out.
I think the, again, on ai, voice of
victim, we're talking a lot about, you
know, if you, we need to have more women
voices, so we have less women victims.
Right?
Right.
As, as a part of what, what
we're describing here today.
And then to the concept of what
you're describing, if we have less
women that are actually using it,
there's the concept of reinforced
learning from an AI perspective.
So kind of what you were hinting
at is there's the things that it
was trained on and then it's the
things that IT is actively learning.
And so if it was trained on bias things
and then it realizes or rec, not that
they realizes if the reality is that
less women are actually adopting and
using it, then they reinforce learning
from the men or from the folks that are.
Speaking in a way, or using it in
a way that's detrimental to women
will then also compound the issue
of potentially becoming a victim.
Right.
So, you know, you need the women
to be the human in the loop.
You need a broader set of folks, and
not just women, but just in general.
Mm-hmm.
Whether
it's, you know, there's gonna be
age discrimination, there's gonna
be all these types of things.
So if you don't have a
broader demographic that's.
Actively engaging at the same rate.
I could see how you're
gonna, um, outpace that.
So I appreciate you
providing that context.
Absolutely.
And one thing just to, to level
set is the word bias, right?
Because so many words in our
society right now are being
weaponized or, and negative, right?
But bias is all of us have bias.
We are.
Skewed based on our experiences, our,
the people around us in our, you know,
family, our friends, our work environment.
Um, and so, and because
our brain processes so much
information so quickly, we.
Put things in categories very fast.
And so all of us have bias.
It's not a a negative, the outcomes from
bias are negative, but it's not negative
in that people are intentionally biased.
Right.
Necessarily some people, but not all.
Um, but you still have to
mitigate that in how your.
Writing, how you're using ai, um, the
outputs of AI so that you're helping to
train this, like very large technology
to be better and to be more even
and balanced, um, among all people.
I always wonder like, why do we think
women are so hesitant to lean into ai?
You know, I've never been that person.
Yeah.
So like I've played around with it, but
I know plenty of women that just aren't
touching it and I don't understand why.
What, what's your perspective?
There's a couple of reasons, and I
think this was also in a McKinsey study.
McKinsey's done actually a few
studies, um, along with Boston
Consulting Group in Deloitte on this,
and there are a couple of reasons.
One.
Women feel that it might not be as
ethical, right, to use AI versus, you
know, I can do this work on my own, right?
And and don't understand the
implications of yes, for sure
you can do it on your own.
I can write a thing on my own.
I have.
Many times, but if I can do that
same thing and 15% of the time,
can I do it the rest of my time?
How can I make more of an impact?
So that's one.
The second thing is what women
in the workplace experience very
often is when they make mistakes,
the the impacts are harsher.
And that's certainly.
The case for black women in the workplace.
And so they're hesitant to use it because
they're not quite sure how, what if they
make a mistake, what if the output's
not exactly right and they, they're.
Hesitant to make mistakes because of
their experiences and the experiences of
others that they've seen in the workplace.
And so new things sometimes are tough
to adopt because they don't, they are,
you know, good at their job as is.
They don't wanna make mistakes.
They don't wanna try a new thing
that could cause them to, um,
you know, lose their job to be
written up, whatever the thing is.
And so, unfortunately, because
of that, uh, imbalance, right
in our corporate world, um.
Women are just judged more harshly
and they're afraid of that.
And so, especially
the fact that AI hasn't even been
gamed out all the way, and they're
like, I'm not gonna touch that.
Right.
And you said something
that got my wheels turning.
And that was the fact that men are
promoted based off their potential.
Women are promoted based
off their performance.
That's right.
And so how will women be promoted for
something that we can't yet perform on?
Exactly.
And that's just gonna be interesting
as it paves the way forward for
future AI roles and how those are
being put into companies, so, right.
I don't know.
Greg Boone, what's your thoughts on that?
Well, I mean, I think first
and foremost, you gotta have a
lot more men that are allies.
Right.
In general, it's not,
it's not just about ai.
Right.
I was at the, uh, uh, just recently
at Women's leadership, uh, conference,
uh, here in, in, in Raleigh.
And I, I was taking a picture,
you know, and the person that was
running a camera says to me, she
said, look at you being an ally.
Like I wasn't really
thinking about it that way.
Mm-hmm.
I was just here to support.
Yeah, the team, right?
We had multiple folks that wanted to join.
I wanted to see what the
conference was about.
So first and foremost, I think.
You gotta have more leaders that
are ally to women in general.
Right.
Just like most underrepresented
groups have to have some
form of an ally Absolutely.
To really make, uh, significant gains.
Right.
So that's thing one.
I think thing two is, and, and
you write about this and in one
of your recent articles you talk
about you have to have more ai.
Leaders, you have to have more CEOs
and C-suite folks from the top down
that are using it, empowering folks,
giving them, you know, pathways to
actually, you know, grow within, uh,
their organizations using these tools.
Right.
So one of the things that I will say to
the team quite a bit is like, Hey, you
know, and I say this, you need to be AI
curious because I'm AI serious, right?
Yeah.
But I'm also saying it's
okay if you make mistakes.
It's okay if certain things like you gotta
give them enough air cover, you know,
to fail fast and to learn and explore.
Right.
I think, um, one of the other things
I talk quite a bit about is we have to
also move from this abstract concept of.
What AI actually is, right?
Everyone on this show today, you
know, maybe half of the listeners
may have already had some, uh,
interaction with ai, but they
don't really know what it's right.
And, you know, my co-host here is
helping me work on different analogies.
But what I always say to folks,
it's, it's that moment, like when
Henry Ford was talking about the car.
And he says that if I would've
asked people what they wanted,
they would've said faster horses.
Mm-hmm.
Right.
Not because they didn't necessarily,
you know, want to have a car.
They had no idea what a car was.
Right.
Right.
They thought this crazy guy
was talking to them about this
box that I was gonna sit in.
It was gonna go way faster than a horse.
Right,
right.
It'd be much more
convenient, blah, blah, blah.
So now we're talking about going
from horses to space shuttles.
Mm-hmm.
And people seem to be very
surprised at the adoption level.
Isn't great.
They have no idea what
you're talking about.
Right,
right.
And AI can mean so many different
things in so many different situations.
Right.
What you're talking about is more
conversational in nature, using more
of a text interface, if you will.
Right.
And bringing this back to bias,
even in image generation, right.
Generative AI is multimodal.
So meaning it will generate.
You know, it can take in, uh, text
input, it can take in, uh, image,
it can spit out text, it can spit
out images, it can spit out video.
Right.
It's the same fundamental
concept of generating content.
Right.
But even within image generation,
I was having a moment where I kept
trying to, I was trying to just
create an image of an executive.
I said, gimme a, a c, a, A, a, confident
CEO in their, in their mid fifties.
Hmm.
Time and time again, it was the
same white male that kept coming
up to the point where I had to
force it and say, give me a woman.
Mm-hmm.
Of color.
Right?
Blah, blah, blah.
Right.
And so I think it's helpful for one,
for folks to put a face on it and
understand what we're talking about.
We're not talking about
robots coming in necessarily.
Take your job.
What we're talking about is having
a PhD. In your pocket on your
phone, democratize intelligence,
the ability you've referenced,
uh, McKinsey and BCG and others.
You can create McKinsey like, you
know, reports, McKinsey like right
information to then learn faster,
being able to reinforce the things
that you want to do with your career.
So there's a lot of different things, but.
I think in general, going back to
the original point of the question,
is you gotta have more men that are
allies and you have to have more
executives that allow folks to lean in.
Absolutely, Greg.
And you know, the thing with
leadership is leaders have to, one,
create a communication plan on ai.
That's very important.
Why are we doing this?
How is it gonna help me?
Right?
Why is it good for the company?
The second thing is training, right?
So as you're thinking
about how you roll it out.
So providing training and
then practice is the other.
Right?
So Greg, you talked about making
it okay for people to fail and the
problem in the workplace generally,
and not just with women, but they're
afraid to make a mistake because.
As a Gen Xer, right?
When I went into the workplace,
your leader had all the answers.
They never made a mistake.
They were in a good mood every
day or a terrible one, right?
But that was their, their stick, right?
They weren't a whole
person in the workplace.
They didn't make mistakes.
And so what happens is if we don't have
a leader that's saying, you know what?
I tried this thing and it didn't work.
I couldn't figure it out.
I gotta go back and try it again.
Then you don't feel comfortable
as an employee doing that.
You don't wanna tell your manager that
you made a mistake or went down a whole
road and have to come all the way back.
If they've never done that, or if they've
never shared that they've done that.
'cause we all have, right?
And so we want that vulnerability and,
and transparency and honesty from our
leaders, especially when it comes to
change, what I call change leadership.
Some people call it
change management, but.
Change with people can't really be
managed, but it can be led through, right?
And so leaders have to one, be
human, be vulnerable, try a thing.
Hey, I've been using this
for this amount of time.
It was a little clunky at first.
I've gotten better.
Here's, here are the
classes that I've taken.
Here are the ways that
I've used it, and just.
Create that connection and openness and
comfort with their employees to be able
to say, okay, I am gonna try this thing.
And if I mess up, or if it's not good, or
if someone can say, yeah, AI wrote that.
Right,
right, right.
It's
okay.
Bring it back.
Let, let's work on the editing.
And I think whether it's AI or any
new thing in the workplace, I think
that's what's important to employees,
to feel comfortable and know that
they have the right leader that's
gonna be behind them and say, it's
okay if you mess up, if you try it.
Let's work on it.
Let's practice.
And I think that's one of the most
important things with adopting
a, a new thing, whatever that is.
One of the things that I would imagine
work would work really well if you
had a very apprehensive team, is set
them down and get them in an exercise
where you were all practicing together.
Mm-hmm.
You can fail together, right?
Like make it so that it's
not a business critical.
This is your one big client
communication and you're putting
it through AI for the first time.
No, let's, right, let's not do that,
but let's sit down and just get people
comfortable and say, Hey, why don't
we take 10 of our documents, throw
'em in here and say, Hey, can you.
Streamline this.
Mm-hmm.
Or tell me what I'm missing.
Right.
You know, when I wrote my book, one of
the things somebody suggested to me was
to take your outline and run it through
and say, give me a five star Amazon review
and then give me a one star Amazon review.
Mm. So that you could see, what pieces
would they pick out as something that
could be really great that you wanna
make sure you double tap on, or like,
okay, you got a one star because maybe
you gave too many real life examples.
Yeah.
Who knows, right?
But it's a safe way to
start playing with it.
So if you're a leader, start thinking of
ways that you can bring people together in
that small group and fail in AI together.
Absolutely.
Right?
Yeah.
Now one of the issues though, is
right now we have space to do that.
But as the technology grows and
advances and more organizations
are using that on a regular basis,
then you're gonna be behind.
So you need to get into it now.
You need to practice and play with
it with lower stakes right now,
because the stakes are going up.
Month, over month, over month, and
you're gonna be behind, and then
there's gonna be a different pressure.
So you need to get in and have
fun with it and play with it and
work problems now because you need
to be good at it a year from now.
So, so what Jackie just said is
you need to be a voice right now so
you don't end up becoming a victim.
Exactly.
Hey, look, tie it in there.
But look, I'm gonna, I'm gonna
pull it back into this because.
Snarky women exist, right?
There are women.
Oh, don't look at me kicking who they, I
have no idea what you're talking about.
We climb, right?
Mm-hmm.
They kick while we climb.
And I remember when I first started
using AI and I was using it for all my
social media posts, I would hear all
this chitter chatter in the background.
Oh, you can tell what's AI and what's not.
And I wanted to be like,
like, yes, it's ai.
And guess what?
I was able to cook a full ass meal with
my family because I used ai, right?
And you know, a lot of those are
the laggards, the people who don't
wanna lean into it and adopt it.
So like, what do we do
about those snarky women?
You know, they're gonna figure
it out.
This is like the AI and the empathy piece.
I'm pulling from you here, right?
They're, I would say nothing.
Because they're gonna figure out
that the time that they're taking
to, I'll use a, a very clear example.
So I did for the diversity movement,
a, um, micro videos platform.
So we did 600 micro videos, which
required, um, you know, identifying
topics, script writing, editing.
Now you bring people in, you
record their video, you edit it.
It's a long process.
Took us a very long time and
was very expensive to do.
I imagine I could have saved 90% of
my money and time using AI for that.
Oh God, it makes me cringe.
It's painful.
That really does 90%.
And then what could I have done
with the rest of that time?
I could have started a
whole other business.
So those naysayers are, are going
to, they're, they're going to
get quieter and quieter because
business is just gonna change.
And the expectation of what you can do
in that eight hour day is gonna change,
especially if there's, you know, heavy
admin heavy process that can be automated.
It's the people that can get
more done in that eight hours
that are gonna be valuable.
And especially in an economy that's
a little bit shaky right now.
They want the people
that are most valuable.
Provide the, the most
productivity for them.
Yeah.
I
mean, one of the things I, I tell
folks all the time, I say by the end
of 2025, it's more likely than not
that most hiring managers in the US at
least are gonna ask you what AI tools
are you using to be more productive.
Absolutely.
And the idea that you're gonna say,
I'm using none, you're basically
just raising your hand and saying,
Hey, I'm gonna be the least
productive employee you've ever met.
That's right.
Right.
Please hire me.
Mm-hmm.
And what do you
think is gonna right and
what is gonna happen?
And again, you know, I think.
One of the things I wanna take
a step back on, like how do you
get folks to be more curious?
I do love the idea.
I think you write about
this around AI literacy.
Like I try my best not
to use change management.
'cause people don't like
to be changed or converted.
Right?
Right.
And so I would say AI awareness,
AI adoption, AI literacy.
Right.
Because what's not happening, again,
taking a step back to the abstract.
A lot of companies are excited
about using AI to have process
improvements, productivity gains, right?
And they're saying,
Hey, we're gonna use ai.
We're gonna be this much better.
We're gonna drop things this much faster.
But what they're not understanding is
that 90% of their workforce, all they hear
when they is, you're gonna replace me.
You've not taken the time.
You're talking about a use case
downstream, and then you're highly
surprised that upstream you're having
so many people roll against you.
The thing I remind folks all the time,
I've been in digital transformation
and, and kind of a change management,
if you will, for a long time in
consulting and for the last two decades.
And I tell folks all the time, it's like
this is the first technology in my career.
That is touching every single employee.
That's right.
A lot of times when you're doing a digital
transformation, it's contained to an
IT group or a marketing group, right?
And so you're only having to focus on one
or two potential saboteurs or naysayers.
Now you're trying to transform
your entire organization.
You're leaving it to the
IT department, right?
To do this thing.
That's more of a people and culture change
or shift, not just a technology shift.
And people are highly surprised of
the lack of adoption or the progress
they're making, like the thing that's
underneath some of these, uh, um,
surveys and things they talk about.
90% of companies are doing AI or
80% of this or developing use cases.
All right.
But is that one person in the company?
I. Or everyone, like no one's quantifying.
Are we talking about 2% of your
co company is digging into this?
You're just asking one
person are they using?
Yeah, and and I would argue
because of FOMO and everything
else, who's gonna say no?
Who are the 8% that said
they're not doing it?
That's true.
I don't know, but I
would like to meet them.
I really would.
Interesting.
But since we were just talking
about snarky things, I do think
it's a good time to introduce
our favorite game to the podcast.
Okay.
Which is called The Last Chat.
We're doing last chat.
Let's do last chat.
All right.
Already we're doing last chat.
Last chat is where everyone involved
has to pull out their phone and
read about their last chat, GPT or
Gemini Prompt and what they put in.
What
was the last thing you looked for?
And they're gonna have to
explain themselves.
So
we're getting AI serious now, so I have
to, on my shades, have to themselves.
Ai serious shades.
Yeah, we gotta put, now
we're getting serious.
I know.
Mine.
No mind.
You gotta pull out the phone
because now we just think
you're making up stuff.
Okay.
Right.
This is not one of those trust exercises.
You will know that I did not make this up.
Well, you know, as we get into
this, one of those things, right?
When you talk about snarky and folks
saying, well, I know this person used, uh,
you know, chat GPT or, or this and that.
The way I, I think about that sometimes
is like, are you saying I was dumb?
Like, is is your take on that?
I could never say
anything that articulate.
Is that what you're saying to me?
That's how I take it.
I don't know.
Look,
so, let's see.
I'm going last.
Is that okay?
All
right.
Yeah, yeah.
All right.
Jackie, you
wanna go first?
Yes.
It was the, um, introduction that
I made to Greg to my product lead.
Very good.
Check.
Done.
Wait,
wait, wait, wait, wait.
I, you didn't
take the time.
Nope, she shouldn't.
She should write
that.
Very compelling.
Now you know how many times
I reread that email because
it was just so thoughtful.
I read it at least 10 times.
Oh, which I'm also lying about right
now.
I put my, I know, I put my
notes in and there you go.
Write me an email.
I
may or may not have used AI
to help reply to the email.
Right.
I love that.
So I passed it back in kind.
Well, I use it all the time.
I've been on it a lot this
morning, and my latest one said.
My cohost always uses the same
analogy that if you ask people if
they needed a, a, uh, faster horseback
in the day, they couldn't imagine
a car, you know, that whole thing?
He said it.
I think on every single episode, could you
just read your chat?
And I said, uh, what's a new analogy
that will give him the same result?
And it says, I love that you're looking
to update the faster horse analogy.
It's iconic, but definitely
due for fresh twist.
And then it gave me some examples.
Oh, so that I didn't particularly love.
So I said, those are lame.
Give me some better ones.
And then I haven't gotten that part yet.
So is this your digital twin,
Cheryl, or is this this?
No, this is just straight up chat.
Okay.
I do have a digital twin,
but it But it's learned about you?
Yes.
And so the snarkiness didn't come from
the chat, it came from you directly?
No, it came me channeling the chat.
Okay.
So you reinforced the learning
got got, it's upgrading your
typewriter instead of
inventing Google Docs.
All right.
So, okay, we'll, we'll,
we'll work on that.
Mine is a little bit deeper.
Okay, let's go.
Let's go deep.
Uh, so mine was list out the AI
adoption rates in percentages for men
and women knowledge workers in the us.
Breakdown by industry,
geography, role and title.
Put in a table for ease of comparison.
List any detailed comments per row
in the last column of the table.
And so I use deep research.
This is chat, GPT-4 Oh.
Uh, then it asks a couple of
clarifying questions about which
specific technologies or tools.
You're talking generative
machine learning.
It asks about geography.
It did ask me again about whether this
for us, even though I just told it, it was
for the us so we're not gonna judge there.
But this was deep research.
This wasn't a fast response.
This thing reasoned for 16 minutes.
It was searching the internet,
it was doing the consolidation.
It was, you know, refining, making
sure that it came back with, you
know, and that's one of the things,
there's the fast things that can
come back, but then there is, where
I'm using it most is deep research.
I'm trying to understand McKenzie
level, B, c, G level, Deloitte level.
How should I really be
thinking about this?
Okay, I got a question.
A AI curious over here, right?
I have heard that perplexity.
Is better for the deep research that
it's more accurate than Chad GBD four.
Now, again, I don't know Right.
These are just words on the streets.
I think
the, uh, I mean, so to
to that point, right?
Perplexity was one of the
first large language models to
be connected to the internet.
Yes.
Claude recently announced that, uh,
or philanthropic, Claude recently
announced that they're connected.
Now they're, uh, to the internet and folks
are, you know, uh, excited about that.
When chat CPT first came
out, it was trained on.
It wasn't connected to their internet.
And then the partnership with
Microsoft being and all that
gave you that accessibility.
Gemini out the gate was tied to it, right?
Because it's a Google product.
So obviously that made sense.
Now, having said that, first of all,
Google had launched, uh, deep research
for folks like me that were like, uh.
Premium, uh, subscribers back in December.
So deep research as a function
has been around for a while.
Right now
what I would say is people
are getting more familiar with
different large language models.
So some folks will like perplexity,
some folks will like Claude.
'cause they said it.
I've, I've heard it just gets them right.
It feels more personal.
Right.
And that's one of the weird
phenomenon that people are starting
to, people are starting to gravitate
to different ones because they're
feeling a certain vibe, right?
I don't think that there's any data
that says that perplexity versus
Chad, CBT versus Gemini from a deep
research is any better than the other.
I think that what people also
will gravitate to is how is
the, the experience, how is it
presenting back the information?
Right?
What level of detail and chain of
thought are is being documented, so I
understand how it came to its conclusion.
One of the big knocks early
days was it was a black box.
Right?
And so you had no clue what it did.
It just spit out an answer.
Right?
Right.
I prefer also the the deep research,
because by design it's intended to
be thoughtful and to think about it.
Now you can hack that too and just
tell it, Hey, think deeply about this.
You can use the.
The faster ones, and what it'll
do is it will actually think
through what it's doing, so.
Mm-hmm.
I don't know, maybe some people
just like perplexity, right?
I mean, look, I don't know either.
I'm on the curious side of things,
so I'm just playing around.
But what I do think is it's
gonna be very controversial.
Like in the future, it's gonna
be like a PC or a MacBook.
Yes.
You know what I mean?
That makes sense.
Like people are gonna be like, Uhuh, Nope.
I'm all in over here.
Mm-hmm.
Or I'm all in over there.
Because the more you work with it,
the more familiar it becomes with
you, becomes like your bestie, right?
Like.
That's, there's a reason why you like
hanging out with her than other people.
Right?
And it's 'cause she knows
you and she gets you right.
So, so
what I would say, so two things on that
point, just going back to an adoption
standpoint, if we're talking about at a,
at a company level, what I will see is
that a lot more folks, so for example,
we're a Google Workspace, uh, company.
So we already have Geminis already tied
into our email, our Google Docs, right?
Uh, there's just so many things,
so it's gonna be natural.
The same thing for folks that
have Microsoft and use copilot.
If you're a Microsoft shop, it
is gonna be more natural, it's
gonna be more safeguarded, right?
Sure.
So using it in that environment.
Will make a lot of sense.
I think the thing that I'm urging
folks to do, especially those
that are becoming more curious, it
don't try to go play around with
10 different large language models.
Right?
Go pick one or two.
Right?
There's, they're quickly reaching
a point where there's a lot
of commonality across them.
The other part of it is
to, to Erica's point.
If you're using, and I've run
into this multiple times, chat,
GBT knows me a lot better.
Gemini knows me a lot better
because I use those more.
Mm-hmm.
But then I still will
use perplexity and claw.
But the problem is they don't know me.
That's right.
Right.
And so you spread yourself too thin.
Now.
You didn't get the benefit of it
actually understanding what you look
for, your writing style, your voice.
Right.
And so it's actually not beneficial.
And then over time it's, it's
getting easier and easier to
create software and use technology.
Right.
There may end up being a dozen
different large language models.
There may be thousands upon thousands
of AI apps that you can use.
Correct.
And then you get that, uh, you
know, that analysis paralysis
or the paradox of choice.
Yep.
You know,
situation.
So I would say start small.
Just choose one or two.
Let it get to know you,
you get to know it.
Right.
And then play around with it.
They also all have different
models to do different things.
Right.
And so I. I like, uh, some of
the image generation stuff.
There's some cool things.
It's not, one of the things I did to
help demystify AI was not just talk about
the text version of the things I showed.
Using Suno and making a song.
Still kinda same type of
experiment, uh, experience.
You're prompting it use, so to
make a video, you're prompting it.
It's creating something, using
lovable to create a. Website or
an app, you're still prompting it.
The concepts are the same.
Across generated ai, it's the
applications and how you use, it's
so sorry to be long-winded, but No, I
wanted to give greater context to that.
Absolutely.
Well, all that being said, we always
love for our listeners to have something
tangible they can walk away with.
Mm-hmm.
So we love to hear what
is your perspective?
What is one action that someone
should take in the next 24 hours?
That's all you
got, 24 hours.
24 hours to
further their knowledge, their
expertise, their experience with ai.
I would just say go in and practice.
Give a new prompt.
Give a personal prompt.
And, and just try something new.
Try something you haven't done.
If you use it to write emails, like I
just said, use it for something else.
Use it to help you think through
a problem or a a new process
and see what it comes up with.
I
love that.
Incredible.
Jackie, thank you so much.
Thank you.
It's good to be here.
Thanks for joining us
on AI Voice or Victim.
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