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[VIDEO PLAYBACK]
- We brought you
into the cloud--
- Software is
totally superfluous.
- --led you through
mobile, through social.
We pioneered
predictive AI for CRM.
Now we enter a
new era together,
where we unlock
experiences that were
once the stuff of dreams.
And while AI has forever
changed our world,
we need to ask more
of and build trust
into every experience,
making sure no one gets
left behind.
All of this so we can
connect with customers
in a whole new way.
Say hello to the
new Salesforce.
- That's one small
step for man.
- Reimagined for
an AI-first world,
with trust at its core.
Calling all trailblazers.
Join us because business
will never be the same.
[END PLAYBACK]
SPEAKER 1:
Salesforce Chairman
and CEO Marc Benioff.
MARC BENIOFF: Thank you.
[APPLAUSE]
All right, all right.
All right, well, good
afternoon, everybody.
How are you doing?
[APPLAUSE, CHEERING]
Great.
Fantastic.
Isn't this a
beautiful room?
We've done so many
incredible events
in this room
over two decades.
We're so happy
to be back here
to talk to you about
artificial intelligence,
one of the most
important technologies
of our lifetime.
Well, I think
artificial intelligence
and generative AI,
well, maybe it's
the most important
technology
of any lifetime.
And we're going to talk
about that today, what's
happening with it,
what's happening
with all of you.
And we're so excited
to have all of you.
We're going to do our
best to educate you to,
to inspire you, and to
motivate you, and even
entertain you.
But before we can do
any of those things,
do you know what
we have to do?
We have to thank you.
We have to thank each
and every one of you
for everything that you
do for us every day.
We're so grateful for
what you do for us
and thank you very much.
And thank you for
being here today.
Thank you for your great
support of our company.
Thank you for your
continued relationship
with us.
And we're so
grateful to each
and every one of you.
I want to also thank one
of our customers, who's
inspired me so very
deeply in generative AI.
And I thought it would be
appropriate to have them
here.
So Vasilis, will you
just stand up and be--
please welcome our friend
from Italy, Vasilis,
who's here from Gucci.
[APPLAUSE]
He's with Cloud 9.
And I think a lot
of folks don't
know too much about Gucci
9 and what you're doing.
And I just want
to thank you
so much for inspiring us.
I would say you are
customer case number 0
for us for generative AI.
And anyway, just give
us some inspiration.
You've come so far
to be with here.
And tell us what
you've been doing.
VASILIS
DIMITROPOULOS: 1, 2.
Marc, thank you so much
for the huge sponsorship
and support
that we have and
the amazing partnership.
Thanks to your team.
Thank to Sylvia from the
sales department, Clara.
We started this journey in
uncharted waters one year
and a half ago, trying
to see how with AI
we can augment
the capabilities
of our advisors because
in Gucci, our mantra
is the human touch
powered by technology.
So we started testing how
we can "Guccify" the tone
of voice and to--
"Guccify," hey,
isn't it obvious?
MARC BENIOFF: That suit
is very "Guccified."
VASILIS DIMITROPOULOS:
We should do something
for September here.
MARC BENIOFF: Help me.
VASILIS DIMITROPOULOS:
And very importantly--
MARC BENIOFF:
Please "Guccifiy" me
for September.
For Dreamforce, I need
to be "Guccified."
VASILIS DIMITROPOULOS:
Florence should be
working tomorrow on this.
MARC BENIOFF: Thank you.
[LAUGHTER]
And very importantly,
how we can test it.
Gucci 9 are our
client services
that we have
across the world
powered by Salesforce,
where we brought beauty
in our client services.
The aesthetics of
our stores are there.
Our advisors create
unique relationships,
craft relationship
with our clients.
So we tested how AI
can augment this.
We measured it.
We saw positive results.
And the journey
just starts.
We are the business
case 0 in AI, at least
in the luxury
arena, I hope.
Thank you so much, Mark.
MARC BENIOFF: Well,
thank you so much.
And I want to
thank you so much.
And I'll tell you,
while you're standing,
I want to just ask my
whole AI research team--
we have a few of
members here--
to stand up.
If you're with
our AI researchers
and engineering team,
will you just stand up.
We have Syliva and
Simon over there.
VASILIS DIMITROPOULOS:
Thank you, Sylvia.
MARC BENIOFF:
Steven's over here.
Very good.
Thank you.
working very
closely with Gucci.
Thank you.
Well, we're so happy
to have been doing
this incredible work.
And for a year
and a half, we've
been using our
generative AI with Gucci.
And what we saw this
incredible thing happen.
And what happened
was, is we're
working with the call
center and the customer
service group, and
it's very powerful.
And we saw that
the service agents
can do so much more.
They become augmented.
They begin to have
more capabilities.
Where they were in the
service before, now
they can have marketing.
Now they can have sales.
Now they can
have commerce.
They have more
capabilities.
And I think
maybe this is one
of the great promises
of generative AI,
to augment our
capabilities.
So I want to thank you
so much for giving us
that vision of what
the possibilities are
and the tremendous work
that our AI team has done
with you, bringing
our own LLMs,
bringing third-party
LLMs, open source LLMs,
and finding this
incredible combination
using Salesforce's
apps, but also
your data and your
incredible employees
to achieve this result.
And I guess if we could
take everyone, not just
here in this room,
but also everyone
online and to do the same
thing that's happened
with Gucci, it would be a
tremendous accomplishment
for productivity and
also for the promise
of augmented and
generative capabilities
so that we start to
get the next generation
of artificial
intelligence.
Please thank our
good friend here.
Thank you so much.
[APPLAUSE]
We have so many exciting
things to talk about.
And we have a great
group of speakers here.
And I'd like to
invite them up.
We have Clara Shi, who's
our CEO of Salesforce AI.
So Clara, would you come
up here and be with us
up here on our panel,
Coming from the curtain.
Patrick Stokes, the VP
of product and industries
marketing.
Cathy Baxter, our
principal architect
of ethical AI.
And the most probably
important thing
we're doing with AI is
trusted and responsible
AI.
Srini, our president and
chief engineering officer
works directly
for me Thank you.
Srini has done an
incredible job making
all of this happen.
And also Julie
Sweet, who's
the chief executive
officer of Accenture,
is also joining us today.
So please welcome all
of them, would you?
[APPLAUSE]
Well, Salesforce has been
on an incredible journey,
and these leaders have
been bringing us forward.
And of course,
all of our 70,000
Salesforce employees
all over the world
are doing so much to
take all of our customer
relationships
forward, especially
with this incredible
new technology
that you're all
going to see.
And it's probably
best represented
by this amazing
slide right here,
where you can really see
the tremendous growth
of Salesforce now
over 24 years.
And none of that
would be possible
without my co-founder
Parker Harris, who's
standing right here, I
just had breakfast with.
Stand up, Parker,
would you?
[APPLAUSE]
And I think that when
Parker and I started
this company 24
years ago, we never
anticipated that we'd
get here today quite
like this and to
see this growth
but also to see this
incredible quarter we
just had with these
incredible numbers,
but also to see how the
company has received
so many other accolades.
So thank you, Parker,
for everything
that you're doing.
Really appreciate it.
[APPLAUSE]
Probably the best thing
that Parker and I did,
which was we put 1% of
our equity profit and time
into a 501(c)(3)
nonprofit 24 years ago.
It was very
easy at the time
because there was
no equity profit
or employees or anything.
It's a joke that
still works,
which is kind of amazing.
But it really paid
out as a business
is the greatest
platform for change.
We've been able to give
back more than $621
million in grants to
local nonprofits and NGOs.
[APPLAUSE]
Thank you.
8.1 million hours
of volunteerism
has been accomplished by
our employees and 54,000
nonprofits and
NGOs running
for free on our service.
Probably the greatest
thing that we've achieved
is not just
doing it ourself.
But we've inspired
17,000 other companies
to join us in
our 1-1-1 model.
And that is
incredible for us.
So I want to
thank all of them.
And I think we have a few
nonprofits and NGOs here,
including Maya and Amber
from Marcy and Robin Hood
Foundation.
And if you're with
a nonprofit or NGO,
would you just stand
up and be recognized?
Because we love to do
that in our program.
So right over
here, here, here.
Very good.
Thank you.
[APPLAUSE]
Thank you for
everything that you're
doing for us every day.
At Salesforce, it's
always comes back
to our core values, how
are we operationalizing
those values
into our company
and our community.
That's about trust.
It's about
customer success.
It's about innovation.
It's about equality.
It's about sustainability.
Today, we're going to talk
a lot about trust, which
is a critical part
of what we're going
to do with generative AI.
And when we talk
about the augmentation
of the human
beings to give them
even more capability
or more productivity,
it's going to have to be
done with a lot of trust.
And we're going to talk
a lot about innovation,
which is why I'm so
happy that Srini is here
with his team and
why we're here
with so many of
our engineers
to really show you
what we're doing
and what we're
about to release
over the next few days
to all of our customers.
You're going to see the
beginning of our June
release and our July
release, which are filled
with these technologies.
And as we get
to Salesforce
Dreamforce conference
in September,
I believe that
you're going
to see our entire
product line transform,
completely change
to advance,
to get to another level.
And it's going
to be extremely
exciting to see this
capability happen
so fast.
Now, probably one of
the fastest things
that we've seen
really happen
is the growth of this
incredible new generative
AI.
But what I'd like
to show you today
is this idea of where
our AI capabilities have
gone.
And for those
of you who have
been with us
since 2014, you
saw us first
launch Einstein.
That really came
out of where we all
had an existential
freakout at Salesforce
around 2010.
That's when we started to
think this AI revolution
was going to happen.
And we had to get going.
We started to acquire a
lot of amazing companies
and bring a lot of
incredible minds
into the company.
And we built this amazing
platform called Einstein.
And that was very
much the beginning
of our AI journey.
Today is another
critical step on it.
But I'd really like
Srini to walk us
through what we've been
doing since we first
introduced Einstein.
So Srini.
SRINI TALLAPRAGADA:
Thank you.
OK, this is good.
So I think Einstein
is AI for CRM.
We've been pioneering
AI for CRM since 2014.
And today, Einstein does
about a trillion trials
predictions every week.
So what does that mean,
trillion predictions?
If you are a sales
cloud customer,
if you use our lead
scoring app, Einstein
lead scoring, you can
convert your leads
3x faster.
Just in Thanksgiving week,
if you are a commerce
cloud customers, we
generate more than 50
billion AI product
recommendations.
And customers see a
300 million GMV uplift.
Now, to do this, we
had to first invest
a lot in hiring
world-class talent.
We acquired some
great companies
like MetaMind,
RelateIQ, PredictionIO,
and coupled it with a
lot of organic hiring.
So a world-class AI
team, both researchers,
data scientists, data
engineers, and all--
invested a lot.
That's one thing
we had to do.
We also had to solve a lot
of fundamental problems
because one of the things
that you want to do
in AI, especially in the
predictive case, was--
as you're doing
machine learning,
we cannot see the data.
All the data
scientists want
to see a lot of data.
And we hired all of them.
They say, Srini, give me
access to all your data.
I said, no, you cannot.
So what we had
to do is we had
to invent new
techniques like
autofeature engineering,
autofeature selection,
automodel selection.
And we open source
a lot of these
but invent new
technologies.
Some of the other
things as we
learned-- as
we are doing it
where as we
deeply invested
with our customers, new
use cases started coming.
This is when we started
investing in LLMs.
And some of the
earlier papers
that we have invested
like decaNLP and all,
we're suggesting
how prompting
could be a new technology
that we could invent.
And then what we
did is we said,
if this is where
the world is going,
let's invest deeply in
these LLM technologies.
And we learned
very interestingly
that some of
these LLMs are
autoregressive techniques.
And then we said,
we could-- really
interestingly,
we could use it
for protein generations.
And then we publish a
lot of protein generation
papers in Nature
magazine and all.
So we started seeing
this early one.
So what we want to do--
in the predictive
API, what we did is
we brought all
these technologies
for our customers.
So we absorb, abstract
all of that technology
so that you can
get business wins.
What we are going to
do is in the new world
of generative
AI, we still have
to solve the trust
problems, security
problems, scale problems,
privacy, ethics.
We'll solve all of
that and bring you--
and that's what we
are excited about--
to bring you
to the future.
And that's what
today is about.
Thank you, Marc.
MARC BENIOFF: Very good.
All right, thank
you so much, Srini.
[APPLAUSE]
I want to-- I want to just
stay on this slide for 1
minute.
And I'll tell you, I
don't think a customer
interaction or story
or meeting today
starts or ends
without this idea
of what's really happening
with generative AI.
And the story usually
goes like this.
I know about these new
large language models.
Many of these
models are amazing.
A lot of us have used
ChatGPT and this GPT-4
model.
Also, a lot of
the models that
have been created by our
own engineering teams
are amazing.
And many of the start-ups
who are in this room
are also building
these amazing models.
And so these CEOs
are so inspired
or CIOS that
we're talking to.
And they're
like, what we're
going to do is we're
going to start working
with these models.
And we're going
to take all
of our corporate data.
We're going to put
it into the LLM.
And then all of
a sudden, we're
going to have instant
intelligent company.
And it's not
quite like that.
It's not quite like that.
Now, the reason
why they think that
is, they already know how
these other more public
models are working.
They take these huge
vacuum cleaners.
And they're just vacuuming
up all of the data
off the internet
that they can get.
And you see that.
So they're all
just taking--
if there's publicly
available data or just
data that's out there
off of an internet
that can be
scraped, they're
taking it and
training their models
which as much data as
they can bring down.
And then they're
taking that data.
And then they're
turning on their LLM.
And whatever comes
out of it is great.
And if there is this
concept of hallucination
or if the LLMs
basically starts to lie,
well, that's not really
their responsibility.
Their responsibility
is to give you
the best case they can
with their generative
artificial intelligence.
That's not exactly
the world of trust
that we live in.
And recently, I was
with one of our largest
customers right
here in New York,
very large bank.
And the CEO made
it very clear
that they want to use
LLMs to become much more
productive in
mortgages and account
service and all
the capabilities
that a bank will do.
So can they do that?
Can they just take all of
their account information
and all of the
history of all
the accounts
and everything,
just put it into an LLM?
Well, I don't think
that's going to work out
very well when it comes to
the regulated industries
in the way that data
works in large companies.
That's why, really,
the onus becomes on us
to give our customers
this next generation
of generative AI.
And that's what you're
going to see today.
Because as I
mentioned earlier--
and this is going to
ground our presentation
today--
trust is our number one
value at Salesforce.
We saw that when
Parker and I first
designed the
product, which
was way back in 1999.
We came up with something
called the sharing model.
And that idea was
that every person
in the company who
gets Salesforce access,
they also get access
around what data they can
see or not see or
use because we know,
especially for a bank,
every bank executive
cannot see what every
other bank executive can
see.
Or in health care, what
one health care executive
can see, another health
care executive can see.
We all understand the
enterprise sharing model,
don't we?
We all understand what
that means in a company.
And we also
understand that a lot
of us in
enterprise IT come
from the relational model.
And the relational model
is the rows and columns
model of data where
it's security is down
to the cell level.
Not only where readers
don't block writers,
but the idea that cells
can be locked by one
particular user so that
cell cannot be seen
by another user.
That is not the kind
of generative AI
that we're all
experiencing, is it?
Because the way these
large language models
work is they're taking
down all that data
that they grab.
And then they're
amalgamating
and tokenizing
that data and then
using their algorithms
then to generate
their intelligence.
So that idea of
cell-based security
is not in the current
generative model.
That's the breakthrough
that we're really
going to try to show you
today, where we really
are leveraging
starting back in 2016,
where we came up with
our first trust model
for predictive.
Now, Parker and I were
joking about this today
because I always called
it anonymous predictions.
Because the idea
is that when
our systems, when our
applications, when
our platform looks at
all of your data and then
uses machine
intelligence or machine
learning or deep learning,
which are the three
primary artificial
intelligence
techniques that
really existed
before the current
generative AI techniques,
we don't look
at your data.
We're able to provide
you those predictions
and that AI capability
without actually looking
inside the data-- by just
keeping it anonymous.
And now, with generative
AI, what we're able to do
is we're able to take
the same technology
and the same idea to
create what we call a GPT
Trust Layer, which
you're going to see today
for the first time and
we're about to roll out
to all of our customers.
So they have the ability
to use generative AI
without sacrificing their
data privacy and data
security.
This is critical for
each and every one
of our customers
all over the world.
Every transaction and
every conversation
that Salesforce begins and
ends with the word trust.
So we understand
that very well.
And there's one
other critical part
of all of this.
It's not just
about trusted AI
and delivering
the technology
to the right person
at the right time.
But it's also about
responsibility.
As we're all
going to learn,
because we're now on
a societal AI journey,
there is going to be a
lot about responsibility
in this technology.
We've all seen the movies.
And we've all seen where
this can go, haven't we?
We all have
these crazy ideas
in our head of
what could happen.
There's many different
possible scenarios.
So that's why responsible
AI use is so critical
and why I'm so
excited that we have
an incredible AI
ethics team that
has been in place
at Salesforce
for many years.
And Kathy Baxter
is here, who
wrote an incredible
article that
got published in HBR this
week on ethics and AI.
But I asked her to
speak on our panel.
And, Kathy, would you
just ground us right now
in responsibility
of AI before we
begin our technical
journey today?
KATHY BAXTER:
Thank you so much.
MARC BENIOFF: Thank you.
KATHY BAXTER: In 2019, we
published our trusted AI
principles.
And the very first
principle is responsible.
We believe that
at the core,
our responsibility
is to ensure
that we are protecting
human rights as well
as the data that all
of you, our customers,
entrust us with.
And this first
line, your data
is not our product, that
is a key differentiator.
Your data is
not our product.
At the beginning
of this year,
as we began delving even
more into generative AI,
we recognized
that we needed
more specific guidelines
than the principles that
have been driving
all of our work.
And so we created
these five guidelines
that are specific to
not just generative
AI but generative AI in
an enterprise environment.
First and foremost,
it has to be accurate.
You're making
business decisions
based on the content
that's being generated.
So it must be accurate.
It also has to be safe.
We need to assess it
for bias and toxicity.
It needs to be
transparent.
We are ensuring that the
data provenance of all
of the data that
trains our models,
it's fully
consented, and we
are transparent whenever
content is AI generated.
We believe that AI
needs to be empowering.
So keeping humans in
control, giving them
the tools that
they need to know,
is this content useful?
Is it accurate?
And then finally,
sustainability
is one of our core values.
And so when we are
building models,
we are going to
ensure that they
are right sized.
It's not about
creating the biggest
model possible.
It's creating the
right size model
that is going to
be most accurate
for our customers but also
takes into consideration
the carbon and
water footprint
necessary to train them.
Thank you all very much.
MARC BENIOFF: Very good.
Thank you very much.
[APPLAUSE]
We're so lucky
to have Kathy
and also our
incredible ethics team.
And guiding
us, I think, is
going to be a huge
burden for them
as we start to
move forward
very rapidly in
this new AI journey
where we understand
this is going
to be a critical part
of every single thing
that we do.
In the world that
we live in and in
these industries
that we participate
in like banking or health
care or media or so many
of the industries that are
here in this room with us
and all watching us
all over the world,
they like to know exactly
where their data is.
And they want to
know where that is
and audit that place.
And that is not how
generative AI works.
Generative AI works with
this kind of expansion
from the deep learning
principles, where
deep learning have these
amazing neural networks
and all had these
amazing insights.
And now, the
networks just got
expanded and expanded.
And as the
network expanded,
all of a sudden, the
ability of the models
expanded as well.
But as those expansions
happen, the weights
and how they're
moving all are
shifting and changing.
And that is
really the burden
of our AI team over here.
They have to really
be able to use
these next-generation
models
but have that capability
to deliver a trusted
experience to
our customer.
We're already the
number one AI CRM.
We already know that.
All of our
customers are doing
a trillion transactions
a week using Einstein.
There's no company that's
even close to what we're
doing in the customer
relationship management
area with artificial
intelligence.
We understand
the burden there
for must be on
us as we're going
to take this forward.
We're already
trying to do all
these incredible things--
to maximize return
on investment
for our customers, to
deliver them the fastest
time to value, to
innovate the fastest
with low-code or no-code
capabilities, but now,
giving them the trusted
productivity that they're
demanding with generative
artificial intelligence,
how will they augment the
employees' productivity
just as we heard from
our friends at Gucci.
Now, our taste of
that has really
started to accelerate
earlier last year when
we introduced
our customers
to this incredible
new product
that we have
called Data Cloud.
And Data Cloud has become
our fastest-growing cloud
ever.
And one of the reasons
why this is becoming such
an important cloud
for our customers
is as every customer
is preparing
for generative AI, they
must get their data
together.
They must organize and
prepare their data.
So creating a data
cloud is so important.
But the problem for a
lot of our customers
is that they might be
creating data clouds
but with teams or
with technologies
that are outside of the
Salesforce ecosystem.
And that's why we extended
our Salesforce core
platform with this product
that was intelligent,
real time, automated,
and hyperscale.
We introduced it at
Dreamforce last year.
It's taken off
like a rocket ship.
And we've seen this
incredible capability
now.
We're already delivering
30 trillion transactions
per month with Data Cloud.
And we're
importing, I think,
about 12 trillion
components of data
already on an fully
annualized basis.
It's incredible what
our customers are doing.
I mean, one of the amazing
stories that we have--
we work so closely
now with Ford
and these next-generation
component cars
that they're
building and trucks.
I have one of these
new Lightnings.
They're amazing.
It's just dripping
telemetry.
And it drips data because
of all the technology
and all the capability.
But it needs a
receptacle for that data.
It needs the ability to
have all the data so then
Ford can provide the
next-generation sales
experience.
I just bought a second
truck, actually,
because it was so amazing.
A service experience,
a marketing experience,
all of those things
are then generated out
of the Data Cloud.
So the Data Cloud
sets the stage
in the beginning for every
customer's AI journey.
And then our
primary vision,
the vision that
Parker and I set
up with 24
years ago, we're
ready then to
begin connecting
with our customers
in a whole new way.
This idea that we
want to then provide
that Customer 360
experience from sales
to service to marketing
to commerce to Tableau
to Slack to all
the capabilities
of Salesforce on the
360 but then augmented
by generative AI.
We already know
generative AI
is changing everything.
We already can see
the huge growth.
How many people here have
already used ChatGPT?
Raise your hands.
Everybody.
So I didn't have to
call for hands, did I?
It's the fastest-growing
consumer product
of all time.
We all know the story.
And it's an
amazing experience.
But we've all seen
the limits as well.
And we all know
that all the data
that we're putting in
there as we sit there
at home and play things
or whatever, well, it's
getting stored.
And you're training
that model.
And that's how it works.
And if you want to
understand that,
talk to some of
the AI experts
in the room today.
And they'll explain to you
why that consumer model
is so powerful for
OpenAI, of course.
But does that work
in our enterprise?
Does that give us that
trusted experience
in our enterprise?
Is it going to give us
trusted productivity?
We want the augmentation.
We want that
next-generation
capability for
our enterprise.
But what about the trust?
How are we going
to store that data
when what the LLMs
desire is to take as much
of that data and then put
it into its weights as
needed.
But is that
what we're going
to allow it to do with all
of our enterprise data?
Will we be able to
preserve our sharing
model?
Will we be able to
preserve our security
model, our privacy model?
Will we be able
to lock down
for each and
every customer
and each and every
employee what they need?
That's why we already
know that every CIO needs
an AI strategy now.
We've been talking about
that for over a decade.
And we have the
best team when it
comes to enterprise AI.
There's no question.
But when we look
at these new models
that everyone's
going to roll out,
we know one thing.
And that is, there's
a pretty big gap.
And it takes place
in every conversation
I have with every CEO.
It's this AI trust gap.
When we talk
about privacy,
when we talk about
hallucinations, when
we talk about
data control,
when we talk about
bias, when we talk
about toxicity,
which are these
are technical terms,
actually, in AI.
They're not
societal terms.
These are actual
technical explanations
of things that
are happening
inside these models
that we realize,
that over here, we
have this desire
to rapidly move
forward, to have
more productivity.
But over here, we have
this need for trust.
And what we
hope to do today
is to close that gap.
This is a critical time.
That's why I
mentioned, we already
have delivered that first
generation of our Trust
Layer with our
predictive model in 2016
when we rolled out
Einstein, wear it
across that bridge.
And every customer who's
in this room and online
who uses Einstein
has already
crossed that bridge.
We never had to
say to anyone,
oh, yes, we had to
look at all your data
to make a prediction.
No, we didn't
have to do that.
And we also know
that we have
to apply it to all
of our applications,
not just some of them.
And we also need
to then say,
we're going to
take all of this
to be not only be number
one AI CRM in predictions
but number one
AI CRM when it
comes to this incredible
generative AI capability.
So that is why we're
introducing today
our AI Cloud--
trusted enterprise
AI, built for CRM,
built for Salesforce,
built for our customers,
implementing these
key technologies that
are so critical for us
using our Einstein GPT
Trust Layer, which is
about to get rolled out
for all of our
customers worldwide,
allowing all of
our applications
to deploy that
capability, and augmenting
our own capabilities to
be the number one AI CRM.
To help us understand
exactly how this works,
I would like to introduce
you to Patrick Stokes.
And, Patrick, welcome.
Please give me a hand.
PATRICK STOKES:
Thank you very much.
[APPLAUSE]
All right, thanks, Marc.
Thanks, everybody.
So we're going to
answer and bring to life
the most
important question
of the day, which is, how
do I trust generative AI?
Maybe more
importantly, how do I
leverage all of the
productivity gains
that we can get
from generative AI
without giving away all
of our company's data?
Now, Marc talked a
little bit about this.
We understand how
it works today.
We put our data
into databases.
And databases have
this inherent concept
of location.
You put the data
into a database.
You're specifying
what database
it goes to, what table,
what row, what field,
et cetera.
And within that
location, we
can put access
controls on top.
We can specify
who's allowed
to pull the data out of
that particular location.
And on top of
that, we can build
all of the security, all
of the data governance,
all of the access
controls that we
need to keep our
data safe across all
of the employees
that we have
and define how that data
ultimately gets used.
But large language
models are
completely different
because they don't really
store data.
Instead, they learn data.
And learned data is
very, very different.
If I asked you all in the
room what an apple is,
you can probably
immediately tell me
what an apple is.
But if I ask you to tell
me where in your brain
is the knowledge about
what an apple is,
nobody would be able
to tell me that.
A large language
model works very much
the same way.
The reason you know
what an apple is
is because over
time, you've
come to identify
certain properties
about the apple.
You know that an
Apple is around.
You know that it
grows on trees.
You know that it's red.
But sometimes it's green.
And all of these
properties combined
give you the knowledge
of what an apple is.
But in that store--
excuse me, in that
learned environment,
we can't put those same
types of access controls
on top.
We can't control how
the data comes out
of the large
language model, how
the recall comes out.
So that's the problem that
we're trying to solve.
And how do we solve it?
Well, it all starts
with a prompt.
And a prompt is
a word you're
going to hear a
whole hell of a lot
over the next couple
of years and months.
A prompt, you've all
done this probably
if you've used ChatGPT.
A prompt it's
just a question.
It's the question
that you're
going to ask the
large language model.
So let's consider
things in the context
of a business here.
Let's pretend I'm an
investment manager
at a bank.
And I want to write an
email inviting my client
to discuss our
investment services.
Fairly simple question.
And from this fairly
simple question,
I am going to get an
answer or a generation.
That is two things
at the same time.
First, it's amazing.
It's incredible
what this was
able to generate
with very, very
little information.
This is something
that I can take.
I can edit.
I can manipulate
a little bit.
And this is an
amazing first start.
But this is also
terrible and unusable.
It's not personalized.
It doesn't know anything
about my customer
or my company or the
products that I sell.
This reminds me of
every recruiting email
I get on LinkedIn.
It's just right
to the trash.
I can't really use this.
And it's because
it's missing context.
It's missing information
about my business.
Now, I want to include
that information.
Now, what most of you
would probably think
is I know what
I need to do.
I need to train a
large language model.
I need to spend
months of time
giving it data
and feeding all
of this data
about my business
into the large language
model to train it.
But you don't
need to do that.
You can continue
to use the prompt
in a technique
called grounding
because a prompt is more
than just a question.
A prompt is an
entire canvas
to provide
detail, context,
and instructions.
And within those
instructions,
we can ask for more
of what we want.
So, for example,
within this prompt,
I can tell them
about myself.
I'm Patrick Stokes.
I'm an investment
manager at Cumulus Bank.
I can tell the prompt
about my customer
or my company-- excuse me.
We're a one-stop solution
for personal banking
loans and investments.
I can tell the prompt
about my customer, Lauren
Bailey.
She's been a customer
for over 7 years.
She's gold tier status.
She has a checking
account, savings account,
et cetera.
I can even tell this
prompt about some
of the most recent
information.
Have you all heard that
large language models,
most of them today in
the consumer space,
they're like a
year, in fact,
maybe more out of date?
That's not good enough.
We need to know
exactly what's going on
in our business
right now so we can
put that in the prompt.
Lauren downloaded an
empowering sustainable
future white paper from
our website last week.
So we know she's
interested in sustainable
investments or a
sustainable future.
I can include information
about our newest products
that we've just launched.
We've actually
just launched
a new green energy
investment opportunity.
And then finally, I can
include information that
may be true, may not
be true at any given
time, which is we're
having an event.
So maybe we're having an
event in a few months.
Maybe we're not
in a few weeks.
And this is the type
of logic instruction
that we can
include as well.
And when we ask for
a generation based
on all of that, we
get something that
is immediately usable.
This isn't
something that we
have to download and
cut and paste and start
injecting our
data into and then
worry about where
we paste it back.
This is something I
can just click a button
and use immediately
in my application.
But there's
still a problem.
The problem is that all
of that customer data
is put in there.
Now, before I
explain how we're
going to solve
that problem,
I want you to just
consider all of the data
that Salesforce has
about your business, all
of the context
across sales,
service, commerce,
marketing,
across the Data Cloud,
all of that telemetry data
coming from Marc's
car, his two cars.
All of that
data coming in,
this is all data
that we can ground
in that prompt, that
we can add as context
to get a better generation
on the other side.
But the problem is, if you
look at that data, that's
sensitive data.
There's PII data in there.
There's Lauren
Bailey in there.
How do we protect
all of that?
So we're prompting it.
It's better than training.
But how do we protect
all of that data
from getting lost in
the large language model
and not being able to
control how we recall it
later?
And that's where the
Einstein GPT Trust
Layer comes in.
The Einstein
GPT Trust Layer
creates separation
between all
of your corporate
enterprise data
stored in your CRM, in
databases where we can
apply access controls.
And it allows you to
responsibly ground
all of your prompts
in that data
without that data ever
leaving Salesforce.
Now, we do this with
a number of methods--
secure data retrieval,
dynamic grounding,
data masking.
We do toxicity
detection and something
called zero retention.
Now, let's take a look
at just a few of those.
So if we go back
to our prompt--
let's look at
data masking.
So as I mentioned, I have
some PII data in here.
I have Lauren Bailey.
That's personally
identifiable information.
That's information that
I don't want to go out
into the prompt.
So we have a technique
called data masking.
We can simply
mask that data.
And now, that PII data
does not travel across
on the prompt with it.
But even cooler
than that is we
can take this
entire prompt.
And when we're
done with it,
we can simply erase it.
The prompt never gets
stored anywhere else.
We take that prompt.
We add the context
about our business.
We get our
generation back.
We mask all of
the PII data.
And then we
delete the prompt
so it never enters into
the LLM in a stored way.
None of that
sensitive data
is learned by the LLM.
Complete power.
Now, to show
you exactly how
this works, to dig into
the technical details
and the architecture
of how our team--
many of them over
there put this
all together-- we're going
to bring back up Srini.
Srini.
SRINI TALLAPRAGADA:
Thank you, Patrick.
[APPLAUSE]
So let me explain the
architecture, the Trusted
AI Cloud architecture.
It's grounded
in what Marc was
explaining about trust
so that your data--
the two things you
have to all remember,
we never look at your
data at Salesforce.
Also, we never
share your data
with any other customers.
We don't mingle it.
And any learning we
happens for your data
is within your
trusted boundary
for your company.
Those are
high-level concepts.
The way our
architecture builds up
is at the bottom layer.
Let me explain that.
The bottom most
layer is what
we call a trusted
infrastructure layer
hyperforce.
Hyperforce gives
us data residency,
compliance, security.
It's also net zero.
On top of that, we have
a data cloud layer.
As Marc explained and
Patrick explained,
these are lakehouse
petabyte scale.
It goes real time
data lakehouse
architecture
built natively
into the platform.
So you get to use the full
power of the platform.
And data cloud allows you
to do a unified profile.
It allows you to
do zero ETL copy.
So if you already
have your own ETL,
it allows you to
reach out onto that.
And then it has a
lot of connectors.
And using our
MuleSoft connectors--
and it has a lot
of governance.
And using our
MuleSoft connectors,
you can bring in billions
of records and petabytes
of data and
leverage all of that
in the rest of the CRM.
And the next
layer is what we
are calling open models.
There are a lot of models.
Every day, you see the
new model coming in.
What we believe is what's
where this is going to go
is there'll be
multiple models.
And some models will
be good for some tasks.
You don't need a
heavy-duty model
for every task.
At some point, based
on the sick pricing
and performance and
what it is there,
we'll pick the model.
But we don't want
you to figure it out.
Each model has
to be optimized
for security,
for compliance,
for buyers,
for everything.
We'll do it for you.
We will abstract
it for you.
We'll run a
model tournament
and give you the
best use cases.
And we'll solve
it for you.
And that's why an open
model is important.
So we'll have a lot of
Salesforce models, which
I'll go a little
bit deeper into it.
We will let you bring
your own models.
A lot of our customers
have big data science
and AI teams.
And they want to build
their own models.
We'll let you bring
your own models.
Or we are going to use
any of the partner models
with a lot of these
top of the line models.
And that's what the open
model ecosystem is there.
The next layer is
ultimately to any time
you call a model, we
want the Trust Layer,
the Trust GPT Layer.
This layer is
super important.
This allows us to
securely get data,
either from our data cloud
or your customer data.
You have to do dynamic
grounding, which is
what Patrick explained.
And then you need
to if required
do toxicity detection
or data masking
and auditing.
It's very important.
People will want to know
what all things happened.
And people want
to know what
is my audit trail of all
the prompts you're doing.
And then you want to
ensure that none of it
is retained in the models.
That's the layer.
Now, let's say this
infrastructure is there.
What does it give you?
If you are our
applications,
all our applications
will come up
with an assistant.
So if you are
a Sales Cloud,
you'll get a Sales
Cloud assistant
which will help you
close deals faster.
If you are a
Service Cloud agent,
you'll have an assistant
which will help
you close cases faster.
And as Marc said, if
you're Slack, Slack with,
I think, personally,
that is going
to be the UI for AI.
It's the entire interface.
Slack is going to wake
up and allow you to--
Slack is where the
enterprise knowledge
is there.
It will allow
you to do that.
That's what if
you're users.
That's great.
But Salesforce always
have product trailblazer.
What if you're
a trailblazer?
Now, all our trailblazers
can build builders,
whether you're a low
code or a pro code
trailblazer, we'll
have prompt builders.
You see how important
is prompting.
So you will be able to
build it using our App
Builder tools.
You'll be able to
build new generative AI
apps suddenly.
Now, if you are an
ISV on our platform,
you get the entire stack.
And you can build
a whole new class
of generative apps and
put it on the AppExchange.
And if you are
an SI partner,
you can use this stack
to implement and generate
more value for
our customers.
That's how the
entire thing is
going to come together.
And to go a
little bit deeper
into how the
GPT layer works
and how the
data flows work,
let me explain
a little bit.
In the CRM apps,
from the apps,
you are doing a prompt.
And the prompt is
going to be combined
with your company data and
a secure data retrieval.
We do the dynamic
grounding,
do any data masking
is required.
And it goes through
a secure gateway.
In the gateway,
allows you to talk
to any of the models.
They'll be
Salesforce-hosted models
in our own VPCs.
Or you may want to
call an external model
with the shared
trust boundary.
And the critical
pieces will
ensure that none of the
data is retained there.
No prompts.
Nothing is retained there.
No context is
retained in the LLM.
That is what we
call zero retention.
Once it generates,
we still
want to have
toxicity build
filters, bias filters,
and things like that.
And, obviously, for a lot
of CISOs and enterprise
data architects, they'll
want audit trails.
And that's what goes
back to the CRM map.
That's how the GPT
Trust Layer works.
Let me explain
a little bit
why I think right LLM
for the right task
is important.
So we'll have [INAUDIBLE].
We'll have great models,
best-in-class model
for specific
tasks in OpenAI,
where the data will
be still retained
in Salesforce but
with joint moderation.
Salesforce will host
in our infrastructure
globally, say, in our
VPC's multitenant models
on our side.
Or will allow
you to bring in.
And at the app layer,
in the gateway,
you will not need to know.
Our promise to you
historically has always
been that we abstract
all the complexity
as things come in.
We'll run a
model tournament.
Model A may be
very good today.
Model B will come.
And that'll be
good for this.
We will abstract
all of that.
We will do the run
the model tournament.
We'll pick the
best and cheapest.
And as things go and
keep on changing,
as this space
keeps evolving,
we'll protect you.
We'll future proof you.
That's our promise to you.
I also want to
talk a little bit
about our deep investment
in Salesforce LLMs.
I hinted at the start.
But if you see, we've
been investing in LLMs
right from 2018.
We have a world-class
AI research team.
And we published
more than 200 papers,
research papers.
These are all
peer-reviewed journals,
very hard to get into,
and more than 200 patents
in this area.
And some of these
things, models,
we were the starters.
We are the pioneers.
And some of the
models we have,
we are what we call
SOTA or State Of The Art
models we published.
And we'll continue to
invest in those models.
Use open-source models.
Use the partner models
in an open LLM system.
Pick the model
which is right
for you in the cheapest,
best way to do the job.
And handle all this
complexity for you
so you can do what you do
best, which is providing
value to your customers.
And with that,
I would like
to thank all our
AI researchers
and engineers.
And some of them
in the room,
can you please
stand up and get
a round of applause?
[APPLAUSE]
Thank you.
Next, what we
want to do is
we want to show you how
AI cloud delivers trusted
customer success for F1.
With that, please
roll the film.
[VIDEO PLAYBACK]
- From the past, the
Formula 1 experience
could have been seen more
romantic with a picnic
on the heel of a track,
watching the heroes.
The evolution from the old
days has been incredible.
Today, the F1 experience
is very special.
- F1 is the
greatest sporting
and entertainment
spectacle on the planet.
The smell of the
tires, the sound
of the car roaring past.
- As soon as the
lights go out,
you know you are watching
something history may
never see again.
- Drive to Survive
has been phenomenal
to capture new
fans interested
in the behind-the-scene
experience.
Knowing our drivers.
- It's exciting
fans worldwide.
- We have over
500 million fans.
Over a third of them are
new in the last 4 years.
- Formula 1 keep
getting bigger.
The impact keeps
getting bigger.
- Our database
is continuing
to grow 30%, 40%
year on year.
- The key for us
is to make sure
that we have the
customer at the center.
- And therefore, our
strategic partnership
with Salesforce
is imperative.
We want to make
sure that we're
designing an
experience for our fan
like no other.
We have 23 races.
But only 1% of
our fan base
actually gets
to be at a race.
- And so how do we
engage the other 99%?
- Fans have changed.
We've seen a much younger
and a much more female
fan base.
- They'll have
the TV broadcast
in front of them.
They'll have a
device showing
all of the live
content, another
showing all of that rich
data that comes through.
- Everyone has a different
relationship with Formula
1 with Customer 360.
And that single
source of truth,
we're creating these
digital experiences
that really
resonate with a fan
at an individual level.
- With AI data and
CRM, we are now
able to innovate
like never before.
- Innovation has
been always the drive
of our success.
The DNA of Formula 1.
- From an AI perspective,
we can truly talk
to our fans on a
personalized one-to-one
basis.
- Speak different
language,
have different narratives.
- Understand those
different markets.
- Be proactive, create
these magical connectors,
and bring them closer
to the action than ever
before.
The key to doing
that is data.
- We've been
collecting data
across all of
these touch points.
- We have so much
data coming in to us.
- Salesforce helps
us visualize the data
in a faster way.
- And we're
using Data Cloud
to create personalized
experiences in a way
that nobody's
ever seen before.
We like to think
of ourselves
as a fan company.
And as we grow
our fan base
and we grow our data,
working with Salesforce,
we're finding
loads of new ways
to bring those
experiences to life.
- That's really
what is all about--
the joy of our fans
embracing the unique
experience.
And Formula 1, this is
really magic for us.
[MUSIC PLAYING]
[END PLAYBACK]
SRINI TALLAPRAGADA: And
to bring it to life,
please welcome
Sanjna Parulekar,
senior director,
product marketing.
[APPLAUSE]
SANJNA PARULEKAR:
Hi, everyone.
Thank you, Srini.
So you've seen how
generative AI works.
So now, we're going
to have a little fun
and show you how you can
experience it day-to-day
and get started.
So before we
get into it, I
want to give a hand over
here for Tim and Dillon,
who are our demo drivers.
[APPLAUSE]
All right, now, Formula
1 is using AI, data,
and CRM seamlessly
behind the scenes
to provide amazing
customer experiences.
And as Marc
mentioned, we're
here to talk
about AI today.
But it all starts
with the data.
And what you're
seeing here
is the home page
for Formula 1.
It has all of their rich
fan engagement data--
every touch point,
every activation,
and every event that the
customer has been to.
But how did we get
to this neat, unique
connected view?
Well, it all starts
with connecting
to all of your data.
And Data Cloud
makes it super
simple to connect to
all of the data that
is relevant to your
ultimate customer
experience.
In this case, it's
the fan for Formula 1.
So you can connect to
any customer cloud,
any external data
source or data
lake, or even any legacy
system using MuleSoft.
And as we know, data
is the fuel for AI.
So being able to bring
in all of this data
no matter where it
sits is extremely
important for providing
that end personalization.
So once we've connected
to all of that data,
we need to make sure that
it's harmonized into one
consistent format
because you
have data sitting in a
lot of different systems.
So a single fan could
be represented in an IP
address, in maybe
a touch point
with your mobile
app or maybe
their Hotmail address
from their college account
or something like that.
But there are one customer
with one set of needs.
So with Data
Cloud, you can
harmonize all of that data
into one consistent view.
And with that,
the data is now
ready for any sort
of personalization
that we want
to start with.
And the end
result is actually
this very clean,
very unified view
of the end customer.
I can now see every single
touch point that Abby
has had with Formula 1--
the races she's attended,
what's coming next, the
purchases she's made,
really anything
about her that
gives me that full
picture of who she is.
So now that I have
this unified view
of a customer,
this is where
it gets
interesting with AI
starting with marketing.
Now, what we want to do
is create a landing page
for our upcoming event.
And I can tell you, as a
former Salesforce admin
myself and a
current marketer,
creating landing
pages is hard.
And it's hard
for two reasons.
The first is it's
difficult to personalize
it just the way you want.
And the second
is that you never
know how it's really
going to perform.
Now, this is
where Einstein GPT
is extremely
important in helping
to do this quickly.
So as I start to prompt
Einstein GPT with what
I want to do,
in this case,
creating a
landing page, all
of this content
that's being populated
on the page is
not just content
coming out of nowhere.
This is content based on
what is performed best
in the past.
So I can rest assured
as that marketer
that I'm not only
building quickly,
but I'm building for
that end personalization
that my customer is
really going to want.
So Einstein GPT is going
to help me create more
personalization on
this page, more product
recommendations.
And I even want to
add an interactive map
for our customers
and fans that
will be on site at
the Miami Grand Prix.
Now, as a
marketer, this is
where my job ends and a
developer's work begins.
So let's head over
into the developer
environment.
Now, right here,
within the IDE,
my developer can
customize this map
and make it
super interactive
for that end landing
page, which is
killer for our F1 fans.
And with just a few
prompts and descriptions
of what I want to
achieve, the code
will be automatically
generated.
And this is powered
by our own Salesforce
large language models.
Now, I want to pause
here for a second.
Because in this new
era of generative AI,
there's a lot of talk
about the future of work
and what these new roles
will be of the future.
And the future is
our trailblazers.
Our trailblazers are
building with Apex today.
And with these
tools, they'll
be more efficient
than they have ever
been before, which
is simply incredible.
So now that we've
built the page,
let's see what
it looks like.
Now, with this
page, this is
fully personalized to
the end fan, personalized
to them and their journey
at the Miami Grand Prix.
OK, so another
really special part
about F1's
business is the way
that they treat
their end fans
with their
hospitality reps.
So for them, a sales rep
is a hospitality rep.
And they pride themselves
on this trusted
relationship.
Now, this trust can
take a lifetime to build
and a moment to break
with the wrong level
of personalization or
feeling like that rep
really doesn't know them.
So Einstein GPT helps them
scale this relationship
and nurture it with
the most up-to-date
information.
So the first thing I
want Einstein GPT's help
with here is to update
my account description.
And it can help me do
this really quickly,
bringing in that
public data that's
relevant to my customer
alongside private data,
which is really important.
It'll also surface
my relevant contacts
that I might want
to reach out to
for the upcoming
Miami Grand Prix.
So it's not just
surfacing knowledge.
But it's
surfacing actions,
which helps me go faster
as that hospitality rep.
So next, I want to
compose an email
and invite my end
customer to an upcoming
race with a really
tailored email here.
Now, another
special thing that I
want to call out
about Einstein GPT
is that this
isn't just based
on everything a rep
has done in the past.
This is based on outcomes.
So this email that's
being generated
is based on
the emails that
have been most highly
performant with customers
in the past.
So we're going to go
ahead and edit that email,
bring it in to the
email composer,
and send that right
to the customer.
So the next step
of the story
is super important
because this
is where it gets very
personal with our
customer inside of Slack.
Now, the hospitality
reps within F1
are using Slack as
their centerpiece
for productivity across
all of their channels
and all of
their customers.
Now, this is the
updated sales home
page for all the Formula
1 hospitality reps.
And it's enriched with all
of the amazing data they
have in CRM about
their customer.
So I can see over here
that this end customer
that I just sent
the email to
has accepted our
invitation to the race.
And what's automatically
been created
is the Slack Canvas,
which has really
the best starting
point I could ever
ask for in terms
of information
that I want to give
to my customer.
It has some
relevant information
for VIPs, some
experiences,
on-site contacts.
But as our
hospitality rep,
I might want
to personalize
this a little bit more.
And Einstein GPT is
there for me as well.
I can ask Einstein to help
me identify what the best
experiences are at
the Miami Grand Prix
and then update
this canvas.
So I've gotten that
great first stop
with generative AI.
And I can also continue
to customize this.
So we're going
to share this
with the customer
in our joint channel
because we're
collaborating directly
with the customer here.
And we want to
make it really,
really important
for them to have
every single
experience they want.
And it looks like
they actually have
some questions for us.
And one thing that I
really love about Slack
myself at Salesforce
is using a huddle
for those quick
questions that might not
require a whole phone call
or a whole conversation.
So we get on a huddle
with the customer.
And we understand
a little bit
more deeply what they
want to experience on site
at the Formula 1 race.
Now, not only was I able
to do that Super quickly.
But after the
call is done,
I'll get a helpful summary
right in the channel
that I can use later.
So this is just
a snapshot of all
of the various
ways that Formula 1
can be using
AI cloud across
their entire business.
Back to you, Marc.
MARC BENIOFF: Very good.
Thank you very much.
[APPLAUSE]
Well, I think
we're beginning
to see the integration
between what's
going on with Salesforce,
with data, and with AI.
And as our customers are
starting to bring that
together, at
the heart of it,
it's going to be trust.
You could see
in the example
where, certainly,
there's a role
for public
information where
that public information
is not coming out
of our database.
But we want to augment
our capability.
But then as we
begin to augment
our own capabilities
inside our app,
we want to be able to
operate at the trust
layer.
So to really help us
to understand that
at the next level,
please welcome Claire.
She is the CEO
of Salesforce AI.
Clara.
[APPLAUSE]
CLARA SHIH:
Thank you, Marc.
And thanks to all of you.
I am thrilled to be
here in my new role
and to talk to you
about what we're
doing in generative AI.
Not 1 year from now,
not 5 years from now,
but today, this month,
what we're shipping.
It's really amazing just
to think that a year
ago, most of
the world didn't
know what a large
language model was.
And yet, here we are
today, all in together
with the trust
and the security
that you would expect
from Salesforce.
We are bringing
generative AI
to every application
to Salesforce,
to our platform and to
our entire ecosystem.
And we're doing
it in a trusted
way, in a way that's
rooted in the job
to be done,
highly contextual,
highly relevant,
highly secure.
And the best part is
from the very beginning,
our platform was
architected for pro code
developers, for low
code, and for no code,
which means that if you
have a big data science
team and machine
learning engineers
and you want to train
and tune your own model,
we have a solution.
But there's also
a lot of companies
that don't have a
data science team
and don't have a lot
of machine learning
engineers.
And those
companies too can
take advantage of all
of these capabilities.
We're really democratizing
generative AI.
Now, AI cloud is
powered by Einstein GPT.
And Einstein GPT can
write code for you.
It can generate flows
for you with Flow GPT.
It can write sales
emails, generate service
responses, marketing
landing pages,
a lot of what
we've already heard
about and seen so far.
Now, the amazing
thing is if you
think about the example
that Patrick showed
earlier of the sales
email, that is something
that a sales person
in your organization
could do manually.
But to get that
quality, it'd
probably be one of your
top salespeople only.
And it might take that
person several hours
or even several
days to collect
all of the context
about that customer,
their marketing
interactions,
their service support
issues that are open,
what they're doing with
their commerce, what they
have in their
commerce basket
to be able to craft
that perfect email.
And with Einstein GPT,
we do that in an instant.
And we also do it
for every seller
in your organization,
every service agent
in your organization,
every marketing manager
in your organization,
every commerce manager,
every developer, not
just the top few percent.
That is the power
of Einstein GPT.
Now, in March, we
unveiled Einstein GPT
as the world's most
trusted AI for CRM.
And since then,
we've been very busy.
Just last week
at Connections,
we launched Commerce
GPT and Marketing GPT.
Last month, we launched
Slack GPT at our New York
World tour.
We were here.
And before that, Tableau
GPT and Tableau Pulse
at our event in Las Vegas.
Well, today, we're
adding that to the mix--
Sales GPT and
Service GPT--
bringing generative AI
into the flow of work
where knowledge
workers are already
spending time and
doing it a way
that is harmonized
across the Customer 360.
Because, of course, for
us to succeed with Sales
GPT, we need the
context from marketing
and from service and
from commerce and so on.
And as we started
developing these products
with forward thinking
trailblazers,
like Vasilis and F1 and
Shohreh from AAA we'll
hear from in
just a moment,
we ask ourselves, what
is the most valuable,
highest impact place
for us to start
with generative AI?
What are the
most important
operational challenges
and bottlenecks
and opportunities
to drive consistency
and better performance
across each
of these departments?
And as we work
together, we
realize that it was
focusing on those areas
where service agents
struggle, salespeople
struggle.
Give you some examples.
In service, when there's
a customer waiting
to hear back from you on
the phone, when they're
waiting to hear back your
response in the chat,
it's very stressful
for the service agent
to have to look up
all of these product
documentation and
looking through all
these knowledge articles
to find that right answer
for that customer.
We can automate that.
The service
agent can focus
on connecting
with the customer,
to augmenting his
or her potential,
and really becoming more
than a service agent.
Same thing in sales.
Instead of the
seller trying
to spend all their
time piecing together
that perfect email
to get a prospect
to meet with
them, we automate
that so that sellers can
get out there and meet
with their customers,
really connect and drive
value and so on
and so forth.
AI Cloud drives
productivity,
drives revenue for
every workflow,
every user,
every department
across every industry,
every segment, and around
the world.
And to hear
more about that,
I think we're going
to hear from AAA.
Shohreh, thank you for
being a trailblazer.
Shohreh Abedi
is the EVP, COO,
and CTO of AAA, a
fantastic organization
here in North America.
Shohreh, thank you
so much for being
a longtime
Salesforce customer
and for joining us.
SHOHREH ABEDI: Thank you.
It's really
nice to be here.
So I am actually from
AAA, the Auto Club Group,
the second largest
club in North America.
CLARA SHIH:
Well, thank you
for being a
longtime Einstein
customer on the
predictive AI side.
Now, as we embark
on generative AI,
how are you thinking
about where to start?
SHOHREH ABEDI: So the way
we're thinking about it,
honestly, when all
of this hype started,
the first thing
that happened
is my CISO rushing
into my office spooked,
the look of spook
in his eyes, saying,
we have to shut
everything down.
You have to shut
everything down.
And I said, well, wait a
minute, slow down a bit.
I get it.
This thing is pretty
scary in terms
of a tool like this
in the hands of just
about anybody.
But at the same
time, we've
got to be able
to look at this
and see how can
we leverage it
for our benefit
but sticking
to what's core to AAA with
safety and trust in mind.
So I didn't want
to stifle all
of the creativity
of our people
because I had people
from underwriting,
from all kinds of
departments coming up
with 150 different
great ideas
that they could
do with ChatGPT.
So that was the
first thing.
But the areas
we're looking at
is three different areas.
One is customer service,
just like you mentioned,
just to be able to give
our agents more time
to actually
listen and hear
the customer versus
searching for things.
Another is in the middle
for support processing.
I can reduce all of
that processing time,
bring down the
cost, make it more
effective in quality.
The third layer is
really for my developers
within DevOps, having to
identify, troubleshoot,
and also increase our
testing capability.
CLARA SHIH: It's
just incredible.
So forward leaning in
all of these areas.
So you mentioned
earlier hype.
And there is a lot of hype
when it comes to AI what.
Advice would you have
for everyone in this room
and who's listening
on streaming on how
to cut through the
hype and really
focus on results?
SHOHREH ABEDI: So, again,
from my perspective,
to calm my CISO down, I
said, look, I get this.
But at the same
time, you've
got just about
anybody knocking down
my door right now, saying,
we've got the best model.
We've got this AI
capability or another.
What our approach
is, partnering
with partners that we
have in our collection,
such as Salesforce
and other partners,
because what we
want to do is--
we don't believe--
I don't believe
it's the right thing
to take another
layer and component
but rather fold
it into the fabric
of our platforms.
And that's really how
we're moving forward.
You want to be
able to partner
with folks that have
the wherewithal that
can go shoulder to
shoulder with you.
And that's really
what I'm looking for,
not the quickest spin-off
or someone out there
that has the
greatest thing
but then leaves
my back door
open because
that is hugely
important for a
company like us.
CLARA SHIH:
Well, thank you
for your shared
value of your trust,
your collaboration
for your business.
SHOHREH ABEDI: Thank you.
[APPLAUSE]
CLARA SHIH: Now, one of
the most powerful things
about AI is
that it learns.
It learns over time.
It continuously improves.
Or if it doesn't have
the right feedback,
it could get
worse over time,
thinking about model
and how it gets better.
And this is really where
us being the number
one AI CRM really
makes a huge difference
because there's two
aspects of this learning.
It's called
reinforcement learning.
So the top
section is a term
that probably
many of you have
heard in the industry
called reinforcement
learning from
human feedback.
When a sales email is
generated, when a service
agent response
is suggested,
does the worker use it?
Do they edit it?
Do they make
significant changes?
How does that differ
across other members
of the team?
So us having all of those
workers in Salesforce day
in and day out allows
us to figure out
what is the
reinforcement learning
from human feedback,
what is effective,
how do we use that
to tailor and improve
the next set
of suggestions
that we give to
salespeople and service
agents.
It's a big part of
reinforcement learning.
Well, there's another part
of reinforcement learning
that's very important too.
And that's
reinforcement learning
based on objective
business outcomes.
Because in addition to
being a place where--
Salesforce where we
store your customer data,
we also store your
customer outcomes.
And so we start to ask
ourselves and create data
pipelines around, did
that sales email actually
cause the sales
opportunity
to advance a stage?
And ultimately, did
it become closed one?
Did that marketing
landing page,
did that commerce
product description,
did that concierge
experience
cause the customer
to convert and buy
the items in their
shopping cart?
These customer outcomes,
this reinforcement
learning from
human feedback
and from business
outcomes will
ensure that every
customer using AI Cloud
will develop the best
models not just today
but over time for
every use case
specific to
their industry,
specific to their
task, specific
to their department.
And it's a really
exciting area
that our research team,
our engineering teams
have spent a
lot of time on.
And so with
that, let's bring
this all to life with
another customer story.
We heard from
AAA just now.
We heard from Gucci.
Let's hear from Rossignol.
Anyone who skis or
likes Alpine sports
has heard of this amazing
company based in France.
And let's hear from their
CEO, real trailblazer,
Vincent Wauters
and his team
and how they're using AI
Cloud and the combination
of AI plus data plus
CRM to transform.
[VIDEO PLAYBACK]
- What I love the most
about the mountains
is the energy that I
get every time I go up.
You get a feeling of
excitement, of discovery.
Every step you take
opens up a new horizon.
And I think that
is beautiful.
- We are very clear
on our greater purpose
and our values.
- We want to inspire
people to spend more time
in the mountains.
- We want to
carve movements
of sustainability
and human potential.
- There's always
a very strong
emotional connection
with our brand--
nostalgia, anticipation,
excitement.
- But if you want to
create and foster loyalty
with our consumer, we
need to personalize
every interaction.
Salesforce is a
very precious power
to make that happen.
- This is a unique turning
point for Rossignol.
The seasons are
getting shorter.
And it's going to
become more challenging.
- We are at
the crossroads.
We are progressing
to a new phase
where we need to deploy
the full emotional
connection with
our consumers
across both seasons.
- This brand is really the
oldest, most recognized
ski brand in the world.
- Rossignol represents
the beating heart
of the mountain.
- We make skis
for beginners.
We are making skis for the
highest level athletes.
When you have
such a wide range,
it is a curse
and a blessing.
How do we reach our
consumer with the product
that they actually
need in that moment?
Customer 360 is really
about understanding
the desires and needs
of our audiences
and how Rossignol
can deliver a better
experience.
- When that is clear,
we express ourselves
with more authenticity.
And through authenticity,
we create an inspiration.
- The world is
changing fast.
- And now, we have
almost an infinite amount
of data coming from
all the touch points.
Salesforce help us collect
those data, analyze them,
create efficiency,
create personalization.
- And now, with AI,
with Salesforce, we
are able to really
engage with our consumers
seamlessly across seasons.
We can frame it.
We can give it guidance.
They're going to get
proposed the right
product that
inspires them.
Our CRM, our data,
together with AI
is going to create a
circle, a virtuous circle
that is going to elevate
the consumer experience.
- The partnership
with Salesforce
started 12 years ago.
And now, we have an
exciting new chapter.
- This is going to be
a new era for us skiers
and snowboarders and
bikers around the world.
- I believe that
we can be agile
facing new challenges.
Salesforce will help
us to adapt and thrive
in an ever-evolving world.
[MUSIC PLAYING]
[END PLAYBACK]
CLARA SHIH:
Incredible story.
Sanjna--
[APPLAUSE]
Please help us bring it
to life by showing us
how Rossignol
uses AI Cloud.
SANJNA PARULEKAR: All
right, thank you, Clara.
Back for another
amazing demo
of how Rossignol is
using AI Cloud to deliver
these incredible
experiences.
Now, Rossignol cares
deeply about their brand
voice and tone
and bringing
that to every personalized
recommendation
they bring to
their customers.
And they're thinking
about launching
this new Heritic
bike, which
they know will be a hit
for their customers.
So it all starts
within Commerce GPT.
Now, this helpful
Guidance Center
here with the
Next Best Action
is going to help me with
that checklist of what
I need to launch a new
product and market.
And it looks like
we're actually missing
a few things here.
We're missing some
product descriptions.
And just like I said
in the previous demo,
creating these
descriptions
or creating a marketing
email, a sales email,
this takes a lot of time.
And it's no
different here.
But Einstein GPT is here
to help by automatically
generating product
descriptions that are not
just descriptive
of the product
but that are actually in
the brand voice and tone
that Rossignol wants
to bring to their end
customer.
Now, there's a catch here.
Rossignol is also
a global company.
So it's not enough
to have these product
descriptions generated.
They also need
to be localized
for the languages
that are most relevant
to their customer base.
In this case, we want
to start with Spanish.
So we can translate all
of these descriptions
with Einstein GPT in
the click of a button.
Now, normally, this would
take a lot of time--
creating the
descriptions, getting them
to the local teams,
getting them translated,
and then taking it that
extra mile of inserting
the voice and tone
of that language
into the description.
It sounds painful
because it is.
But within a few
clicks, Einstein GPT
can help them
bring this bike
to market with this
commerce experience.
Now, once this
bike is launched
and it's out there, it's
flying off the shelf.
People are so into
this new bike.
And all of our
brand managers
globally want to
make sure that they
have the right
understanding of how
their KPIs are being
affected by all
of these net new sales.
Now, these powerful
visualizations
you see here
would normally
take a lot of data
analysts, a lot of work
behind the
scenes to create
every time you
wanted to refactor
some of the elements
of the dashboard.
But with the new
Tableau Pulse,
this is available
for business users
to keep track of their
business as it happens.
It's keeping up
with the speed
of how things change
all in a trusted manner.
And even if this
dashboard isn't enough,
that business
user doesn't need
to go back to the data
analysts for changes.
Einstein GPT has your
back here as well.
You can actually
just ask a question
and natural language
here, and Einstein GPT
will generate this
additive visualization
here with a description
answering your questions.
So you can get
on with your day.
No need to meet with
those data analysts
to generate this
and wait a long time
for those insights.
Now, another element that
Rossignol cares deeply
about is the way that
they service their end
customer.
And there are two
key reasons here.
One, they want to make
sure that their service
time, their case
resolution time
is extremely short
for every case.
And two, they
want to make sure
they have happier
customers.
So this is where Einstein
GPT within service
is so important.
As this company
question is coming in
through the
service terminal,
Einstein is providing
these helpful
recommendations for
me as I go along
as a service rep.
Now, the service
manager is always
in the driver's seat.
They can accept,
edit, or decline
those recommendations.
There's always a
human in the loop.
But just the
sheer fact that
these recommendations
are in my place of work
as a service manager
is a huge time saver.
It's keeping me
from chair swiveling
between different
knowledge articles,
searching through
my whole knowledge
base for that perfect
answer for the customer.
And as the
conversation goes on,
I even have Next
Best Action giving me
those relevant,
personalized product
recommendations for
that end customer.
And the cherry on the
top here, actually,
is that as this
conversation is going on,
once I close the
case, I can easily
summarize it and
share it with the rest
of the service rep.
So, for example, if our
field service reps are
out there in the field--
they're not behind
a laptop--
they can access
this best practice
in the form of a
knowledge article
that is shared amongst
the whole team, which
is amazing.
Now, I hope between
these two stories,
these simple stories,
it's got your imagination
thinking a little bit on
how you can get started
with AI Cloud today.
The possibilities
are really
endless when you bring
AI data and CRM together.
Thank you.
[APPLAUSE]
CLARA SHIH: Thank
you, Sanjna.
So so many of
you have been
asking how you can get
started with AI Cloud.
Well, what did we do?
We talk to our customers.
And we sat down with Gucci
and AAA and Rossignol
and the Royal Bank of
Canada, so many others.
And we realized that
the world-leading,
best-in-class AI
first companies
were using a number
of solutions together.
First, CRM-- having
AI in the flow of work
where salespeople, where
service agents work,
marketing managers,
commerce managers,
developers, business
analysts, where they are,
both for the reinforcement
learning and also so
that we can automate
the prompt engineering
for their
specific job to be
done for their
industry, for
that specific customer
in their needs.
Then we saw that the
best-in-class customers
are using Slack
because it's
all about collaboration.
Once we have those
generated outputs,
it's not about
individuals.
It's about teammates,
coworkers coming together
and saying, how do we take
what the AI has produced
and make the
landing page better?
How do we make the
commerce description
better?
Let's work together
across teams.
How do we use
generative AI
to summarize all of
these conversations, all
this knowledge
that's sitting inside
of Slack that represents
our organization's brain?
Then we see these
organizations
using Tableau
and saying, well,
if I want to dig deeper
and really understand
the data that's being used
to train and fine-tune
and ground my prompts, how
do I explore that data?
How do I have
a conversation
with that data through
natural language?
And, of course,
Einstein, we've
been talking about both
the predictive aspects
of Einstein as well as
now the generative aspect
of Einstein.
And then very
important is the data.
How do we get that
data from across
the organization,
both data that's
in the cloud as well as
data that is on-premise
and use MuleSoft to bring
that together and use
Data Cloud to
unify the data,
to harmonize the
data, to create
data pipelines that can
be used in any model
to really drive results?
But it's not just
the technology.
It's also a tremendous
change management
exercise.
And that's where
Salesforce services
comes in.
Being able to
partner with you
to skill up your
organizations--
and I'll talk
about what we're
doing with our
trailblazers
in just a moment.
But everything
that you need
to get to a proof
of technology,
to make sure
that it complies
with your company
governance,
with industry
regulation, and then
to go from there
to coming up
with a strategic
plan, which use cases
are you going to start?
Will it be similar to
what Shohreh is doing?
Maybe it's different
for your organization.
We want to help you
figure that out.
And so Salesforce
services is here
to help as well as
amazing AI first partners
like Julie and our
team at Accenture,
our partner at
Deloitte, PwC,
and more coming soon.
So we're excited to
help you get started.
And as I mentioned
earlier, all of this
is shipping now.
Our engineering and
product and research
teams have been
busy at work.
They've been burning
the midnight oil
to make this happen.
This is going to be
the summer of AI.
It's going to
be even better
than the summer of love.
It's summer of AI.
And we're going to have
sales email generation.
We're already
piloting that
with a number of you.
We're going to be
shipping service reply
recommendations to augment
your service agents just
like Vasilis's
team at Gucci.
We're going to be
shipping summarization.
You just saw that
demo from Sanjna,
so on and so forth.
Every cloud busy at work.
We've mobilized the entire
team, the entire company
around AI and helping
ship that and get that
into the hands
of our customers.
And, of course, any
change management
starts with our people.
It starts with
your employees.
It starts with
our employees.
And so we're also thrilled
to be launching today
a new series of courses
through Trailhead.
It's our free online
learning platform
that any of you, any
of your employees
can start signing up for
today to start earning.
We have over 35
AI badges that
will be available,
everything
from the very basics of
what is generative AI,
how do you approach AI in
a responsible ethical way
that honors data security
and data privacy, all
the way to the most
advanced courses
around training and tuning
and prompt grounding,
some of the more
advanced concepts
that we talked about.
Really runs the gamut.
So please check this out.
This is this is going
to take all of us
and all of our employees
to reskill and learn
these new capabilities.
So with that, I will hand
it back over to Marc.
MARC BENIOFF: Very good.
Please give Clara
[INAUDIBLE]..
[APPLAUSE]
Thank you.
Well, I think that
from our perspective,
it's all about trust.
And certainly, it's all
about responsibility
going forward.
And I think as we start to
see the potential of what
AI is, starting with what
you saw in the first demo
where the AI was actually
writing its own code--
I think that when
I was talking
with Sam over that
dinner and he said--
I said to him,
I think we're
going to have to
rewrite each one
of our applications
to take
full advantage of all
these capabilities.
He turned to me and
said, no, no, you're
not going to have to
take any time to rewrite
those applications.
Don't worry
about anything.
The computer is going
to do it for you.
And it's a subtle
comment about how
he sees the future
of AI, which
is that the computers are
more powerful than ever
before.
Generative AI gives
us another generation
of capability.
And we're also going to
have these LLMs, which
do technology
that we never
thought was possible.
So for a lot of
our customers,
this is about
getting going
for the very first
time with AI.
They might have been
using predictive AI.
They might have been using
machine intelligence.
They might have been
using machine learning.
They may have even
been using deep
learning to understand
what was possible,
these neural networks.
Now, they're
really exploring
this next generation of AI
to understand how they're
going to take
the productivity
of their organizations
to a new level.
I thought it'd be a
great opportunity now
to introduce you to
Julie Sweet, who's
the CEO of Accenture.
And, Julie, thank you
so much for being here.
We're so happy
to have you.
Please give her
round of applause.
JULIE SWEET: Yeah.
[APPLAUSE]
MARC BENIOFF:
Julie, can you
give us your vision
of what you're seeing
with your customers?
You're working with
the largest customers
all over the
world and helping
them to deploy
these amazing new
next-generation
enterprise technologies.
What do you see as
the potential for AI
with them?
JULIE SWEET: Well,
thanks, Marc.
And first of all,
it's great to be here.
It's great to see
all the things
we've been talking about
really come to life.
And one of the
things that is
top of mind with
all of our clients
is this point around
trust and security.
And I would emphasize
this technology
is really early.
And one of the
big advantages
that I think are
you have by working
with Salesforce is
that trust and security
are being built in
from the beginning.
And you're
bringing together
that with the deep
expertise around the use
cases.
And that is
really resonating
with our clients who
want to make sure
that they don't get ahead
of themselves before some
of these things have
been worked out.
And yet, at the
same time, they
believe as we believe
that generative AI will
transform how
they work, how
they engage with clients.
And so I have a very
simple thing when we
talk to clients is that--
and they're saying,
how do I get started?
And so with Salesforce,
one of the first things I
say is, well, first,
make sure you're
using all the AI
that you've already
paid for because it's
incredibly powerful.
Accenture is a
user, 55,000 users.
We use the predictive AI.
We use visualization.
We have insights
in the process.
Hugely powerful.
And lots of our
Salesforce clients
are not fully using it.
So make sure
you're doing that.
Then it's all about data,
which is why the Data
Cloud is so important.
And then from there,
make sure that you're
using business criteria
to evaluate the use cases.
And that's where
the work we're doing
is so important in
the AI accelerator hub
that we just announced
with Salesforce
because that's
about bringing
the power of Salesforce
and Accenture
and all of our
industry knowledge,
our knowledge about
customers and marketing
and sales and service
with you, the clients,
together to say, how do we
create the absolute, most
powerful use cases
that have a return?
And I know that
Accenture is
thinking about
this and Salesforce
thinking about this.
We know that our clients
need a return fast.
And so we're trying
to really focus on
how do you take the
technology, the maturity
it's set today,
bring it together
with all that
Salesforce has,
and really deliver value
early to our clients.
SHOHREH ABEDI: Julie,
I want to ask you.
You're the CEO of one
of the most successful
companies in the world,
one of the largest
companies in the world.
And you're
probably thinking
about productivity in
your own organization.
You have so many
engineers yourself.
You have so many
sales and service,
marketing professionals.
How are you seeing the
future of Accenture?
What do you look at for
your own productivity
increases?
JULIE SWEET: Sure, well,
we think specifically
about AI in
three ways-- how
do we help our
clients, how
do we use it to deliver
for our clients,
and then how do we use
it to operate Accenture.
And we've been using
diagnostic AI, which
tells you what happens
in predictive AI
for years across
our business
and particularly in
our sales function
with Salesforce.
And I will tell
you that when
I became CEO in
2019, we had not
done a great job of making
sure all of our 55,000
users were using the
predictive capabilities.
And we had a phenomenal
sales officer
that I appointed, who
was deep in Salesforce,
who very smartly made sure
that we were using that.
And when the
pandemic came,
the ability to have
predictions-- and we
have proven
now for 3 years
that bottom-up
forecasting is never
as accurate as what we
do within Salesforce.
And so now, the
idea of being
able to take the power
of all of that, which
is already a part of our
process, and add on it
generative AI to draw
more connections,
be able to get to
insights faster,
we think is
hugely exciting.
But what I
would say to you
is that just like we had
to change our process
and train our
people on how
to use the
predictive AI, this
is just technology until
it becomes embedded
in how you work, in
change management
to make sure people
understand it,
and the skilling that we
were just talking about.
And I think that's
where our partnership is
so important because
it makes sure
that the technology
actually delivers value
by coming in with
a point of view
on how do you change,
how do you get people
to adopt, how do you
rewire your processes,
and very importantly,
how do you get your data.
MARC BENIOFF:
Julie, I want
to ask you about trust.
And if I could in the
last question here,
which is this idea that--
we all read this story
about this lawyer who
submitted these briefs
built on ChatGPT asking
it if it was correct,
and it wasn't.
We've all seen our
own personal examples
where we're trying to
ask it-- maybe asking
some generative
AI system, maybe
it could be inflection.
Or maybe it could be one
of these other systems
to help us to
understand something
about our business but
then wondering where
is all this data going.
Or we might be
asking ourself
how should we
be looking at
the fundamental security
of our own companies
data.
I'm sure you're thinking
a lot about that.
I know you're getting
a lot of questions
from your customers.
Where do you see this
kind of relationship
between trust
and generative AI
that we've talked
about today?
JULIE SWEET: Well,
this is a topic
that I'm really
passionate about.
We both are on the
Business Roundtable.
And actually, a
good over 2 years
ago, I led the work
on responsible AI.
That was all
about making sure
that you could be
clear about what
responsible AI was and
how to implement it.
And, in fact,
at Accenture,
we have a
compliance program
that's overseen
by our audit
committee of
our board that's
just like every other
compliance program.
And I would say one
very practical piece
of advice.
If when you
leave this room,
you are not able to
pick up the phone,
call someone
in your company
and have them tell
you where is AI
being used, what
are the risks,
how are they being
mitigated, how are they
monitored, and
who's accountable.
You do not yet have
responsible AI.
And I will tell you that
when we go into companies
and help them set
up these programs,
we have
pre-populated views.
And when we get
to Salesforce,
we say, here's all
the AI that you have
in your organization.
And here's the low risk
because it's built in.
And that is
really important.
As you move along this
journey of generative AI,
understanding
every time you're
using it where
is the risk.
And make sure that you're
partnering with companies
like Salesforce where you
can understand that it's
either low risk.
Or you absolutely
are clear on what
the risks are, how
to mitigate them,
and how to monitor it.
MARC BENIOFF: Well,
thank you, Julie, so much
for coming today.
We're really thrilled to
have a great partnership
with Accenture.
You've done so
much amazing work
with not only our
company but so
many of our customers.
Were thrilled to have you.
We're just starting
out on the future
of our journey of AI.
We have been looking
at so many companies
to invest in.
We've done some of that
work jointly as well.
We just doubled
our AI fund.
And we're looking forward
to getting our customers
to start their AI journey.
Hopefully,
they'll start them
with Salesforce
and Accenture.
Thank you for
coming today.
Thanks to all of you as
well for being with us.
JULIE SWEET: Thank you.
MARC BENIOFF: We're
so grateful for you.
And we have some
terrific demonstrations
of the technology
outside for you as well.
And welcome to
Salesforce's AI Day.
Thank you so much.
[APPLAUSE]
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