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Terms of how to use it
in your everyday working
life.
I think a lot of
people are still trying
to figure that out.
As the sun sets on all the
hype and the reality of AI
starts to crystallize,
the question remains.
How do we build
the AI enterprise?
The customer is changing
the way they want
things served up to them.
So we had to
think differently,
and have one 360 view
under one single pane
of glass.
So we can personalize
every customer experience.
Creating long lasting
relationships.
Data is the fuel
that powers AI.
In fact, the
quality of the input
will absolutely determine
the quality of the output.
Now, if I might be so
bold, unified data,
that's as good as gold.
It is all about data.
Quite an important asset.
At the same time, it's
indeed a big challenge.
We've been able to
unify our customer
data across all parts of
the business, which were
traditionally siloed.
I needed a trusted
source to secure my data
and Data Cloud is
that perfect product
to take that
journey with me.
I want to know what people
are doing with my data,
don't you?
With Salesforce,
we can trust our AI
without risking
our customer data.
Collaborative AI
you can trust.
Not nice to have,
it's a must.
Slack helps my
team be more agile
and productive by enabling
seamless communication
between all of us
at any given time.
What the Einstein
1 Platform does
is collaborate
with the advisor,
bringing the human touch
to every interaction
that we can with
our customers.
It'll take their keywords
and it'll automatically
give me service replies
over here on the right.
It's like a helping hand.
Trapped insights
are now set free,
unlocked with AI,
they are ours to see.
Tableau has been an
incredible tool for us
to be able to scale.
It allows us to
surface insights
in moments that matter.
Tableau Pulse has been a
real game changer for us.
That's really
revolutionized
how we think about
a digital journey.
And as a result, we've
seen a better experience
for our customers.
Your own trusted AI,
a helpful co-pilot
at your side.
Einstein Copilot
helps the team
go from idea into
execution really quickly.
We can now create
meaningful automated
recommendations
for our customers
faster and at scale.
We were able to
close student support
cases 65 days faster.
Hundreds of hours
of manual work
taken out of
our ecosystems
across every part
of the business.
We're exceeding
the expectation
of the customer.
We're exceeding
the expectation
of profitability.
We got to break a record.
The fun, the
effects, the return.
Three, two, one.
Magic has happened
along the way,
far greater than
I ever thought.
Now that's how we ride
into the sunrise of the AI
enterprise.
Mm-hm.
Giddy up.
Please welcome CEO Slack.
Denise Dresser.
Hey, good morning, Boston.
How is everybody
feeling this morning?
[CHEERING]
That's all you've got.
That's all we've got
with the Celtics winning
the NBA?
Let's hear it.
I mean, this is, OK.
I can see where
this is going.
This is going to
take a lot of hyping.
So I'm going to be coming
back at you for this.
But first of all,
good morning.
Welcome to the World Tour.
Welcome to the
AI enterprise.
This is a very special
event for me, personally.
I'm back in my hometown.
It is so good
to be in Boston
with all of you, the
incredible energy
and people.
But I have to tell
you, I had no idea.
I wanted to do this event.
I've had it on my mind
for pretty much 10 years
or so.
I did not know that I was
going to be doing this
the morning after the
Celtics won the NBA
tournament.
So this is really, this
is a really special moment
and it's just great
to be back home.
There's nothing like
coming home and being
with all of you.
So we're going to
have a lot of fun.
But when I was thinking
about this AI event
here in this incredible
city of Boston,
this innovation and
hub for discovery,
I thought it was sort
of serendipitous.
And I thought, OK,
we're in this AI world.
It is not a fad.
It's not a trend.
It is a full
blown revolution.
Are you feeling
me with this?
Like, this is
not going away.
Yes?
Yes.
OK stay with me.
And then I thought,
well, Boston, we
know a thing or two
about revolutions.
Do we not?
Do we not?
So I think it is
so appropriate
to be talking about
AI in this city that
is a hub of innovation.
It is the city where
the mobile phone was
developed, the microwave.
Whatever you think
about microwaves,
it came from here.
The COVID 19 vaccine
came from Boston.
Fluffernutter
came from Boston.
OK, so we have it all.
So I feel this is
going to be a fun show.
I feel like I'm in good
hands with all of you
because these
are the smartest
people and the best
city in the world.
Right?
Hopefully, you already
feel like you're
in good hands with me.
It's going to be fun.
We're going to
learn a lot.
We're going to
motivate you.
We're going to
inspire you,
and we're going
to educate you.
But first, I do
want to start out
with a heartfelt
thank you.
Thank you to all
of our customers,
our partners, our
community here
in the city of Boston and
greater Massachusetts.
Our MVP'S, our
golden hoodies.
I can't get all
the way over there.
Congratulations
to our MVPs
and to everybody watching
online, because it is you
and your inspiration that
is why we do what we do.
So thank you.
And for those
of you who've
been with us
for a long time,
know that when we started
25 years ago, in 1999,
we set out to be a
different kind of company.
A company grounded
in our values.
Those values are trust,
customer success,
innovation, equality
and sustainability.
And those are
the values that
are going to guide
us, all of us,
in this new era of AI.
But everything in this new
era of AI is about data.
And therefore,
trust is paramount.
Trust is the single
most important thing.
And I want you to hear
from me that we believe
deeply and always have
that your data is not
our product and
it never will be.
Now, the values also
helped us achieve growth
and it fueled our growth.
And so we're proud
to say that we're
on path to be $38 billion
in revenue this year.
That's great.
But what we really
care about that
is the scorecard of the
success that we've all had
together, because
innovating for all of you,
our customers and
working with our partners
is again, why we
do what we do.
And 25 years later, we
are steadfast committed
to those values of
customer success,
innovation, equality
and philanthropy.
And we believe that
business can truly
be the greatest
platform for change.
Now, when we
started, those values
inspired a new
philanthropic model.
It's called the 111 model.
We give 1% of our
time, 1% of our profit
and 1% of our product
back to the world.
Now, I was not
in the room then,
but I have heard that it
was a pretty easy decision
because there was no
people, no product,
no profit.
Right?
You guys have all
heard this joke
if you've been to
an event of ours.
But it really is pretty
amazing because if you
look at it now, that
intention and that
commitment has now led to
over 700,000,000 in grants
given.
Almost 9,000,000 hours of
employee volunteer time.
I did the math.
It's 112 years.
Really kind of
incredible how much time
has been given back.
And almost 60,000
non-profit and higher Ed
institutions use
Salesforce products
for free.
So shout out
[INAUDIBLE] from an Ngo.
There's got to be
higher Ed here.
This is Boston.
You know that.
So we're really
proud of that.
And we will
continue to have
our values lead the way.
When we started,
we also started
with a bold
vision, and that
was to help you connect
with your customers
in a whole new way.
And that has never been
more relevant than now.
Because in this
AI revolution,
everything is
going to change.
How you connect
with your customers.
How you build product.
How you run your business.
And this is
what Salesforce
has done since the
very beginning,
guiding our customers
through these waves
of technological
evolution.
If you can think
about this,
it started so long
ago going from on prem
to the Cloud.
And then to mobile, and
then to social and now
with AI.
And we did start
our journey on AI
10 years ago when we
released our Einstein
product, which
is predictive AI.
It's machine learning.
But now we've moved
into generative AI.
And I heard a
stat recently
that I think captures the
point I want to make here,
which is it
took two months
to get to 100
million ChatGPT
users, subscribers.
Two months.
It took 15 years to get
to 100 million users
of the mobile phone.
It took four years to
get 100 million users
on Facebook.
My point in this
to say is if you
looked at this wave, the
cycles are compressing.
And we are moving
faster and faster
through these cycles.
But the key is,
no matter where
you are on this
journey, it's
clear that AI is
going to transform
every organization.
It's going to unlock
incredible potential.
It's going to help
all of us be better.
It's going to
augment our skills.
I really believe that.
I think we're all
going to get better.
We're going to drive
more productivity
in our organizations
and for ourselves.
That's going to lead to
obviously, improvements
in the bottom line.
But most importantly,
better customer
relationships.
And that's what we're
here to talk about.
But as we've entered this
world, it's become clear.
There's a couple of
different types of AI.
For example,
consumer AI, it's
probably the one we're
most familiar with.
And consumer AI has really
three components to it.
First, it needs to have a
user interface to access
the AI.
We've probably all
at this point in time
used ChatGPT or any of
the other products listed
in the front.
You need to have a
user interface that's
compelling to reach AI.
Then you need to have a
model, a really powerful
model to execute
the workflows.
And some of
these models are
becoming so
powerful it almost
feels like there's a
human on the other side.
It's almost moving
into sentient,
which is really, really
incredible how fast this
has moved.
And then you need
lots and lots of data.
And consumer AI is
trained on lots and lots
of public data.
Now that public data
cannot help you understand
or help your employees
understand how to engage
with your customer
and what your customer
preferences are.
And that is why consumer
AI is fundamentally
different than
enterprise AI, which
is what we're going
to be talking about.
Enterprise AI is all
about customer data,
but it has to be grounded
in your trusted customer
data.
Grounded in the context
of your business,
your customer's purchasing
behavior, service tickets,
marketing interactions.
So that we can trust it
and it can be valuable.
Now the challenge is,
organizations also
have lots and
lots of data,
but that data is
not connected.
It's not used in the
flow of business,
and it's hard to get
insights out of that.
So you end up with islands
of trapped data, which
is challenging in this
world of enterprise AI.
And I have the opportunity
to meet with many CEOs,
and they all get this.
They all see
the potential.
They want to unlock
this potential
for their businesses,
but they're concerned.
They're not only concerned
about this data challenge,
they're concerned about
security and privacy
and toxicity and
hallucinations.
And we are not going
to get to enterprise AI
if we don't trust it.
It's just not
going to happen.
And so when I
think about that
and I think about the
ability to move forward,
I feel really deeply
that Salesforce
was made for this moment.
Because Salesforce AI
is grounded in a trusted
framework with
processes to make sure
that we are using Data and
developing AI responsibly.
And that is all made
possible from the Einstein
1 Platform.
The Einstein 1 platform
is the trusted AI platform
to help you to get to
enterprise AI faster.
It helps you unite all
of your applications,
bringing all of your data
together in one place
through data cloud,
which is something
only Salesforce can do.
It has AI built right in.
It's almost like you don't
have to think about it.
It's batteries included.
And it starts with
the Customer 360.
And the reason
this matters
is because,
first of all, you
have to have a single
source of truth
to understand
your customer.
To know your
customer's preferences,
to build a relationship,
to grow your relationship.
And I think about the
Customer 360 is almost
an operating system for
growth in your business.
It can be used by anybody
across your organization,
starting out with sales.
I think about
Spotify, who has
driven 40% productivity
with the AI sales cloud.
Really just being able
to identify progress
and close deals faster.
Or how about empowering
service agents
to predict issues
before they happen
to resolve them
proactively
to build better loyalty
and better customer
engagement, which can
happen through the AI
Service Cloud.
Now, nobody knows
about loyalty
better than
marketers, right?
Am I right?
Are there marketers
here in the room?
So great marketing
is all about having
the right data,
the right insights,
reaching your customer
where they are
with the right campaign.
And that's what
Saint Jude's
did to raise $2 billion.
$2 billion for children
with cancer and terminal
illness through the AI
marketing cloud, which
is just incredible.
And every campaign is
about inspiring someone
to take action.
And that's what the
AI Commerce Cloud
does, meeting
customers right
where they are in that
moment of inspiration,
right in their
shopping cart.
And Data isn't just about
sales service, marketing,
commerce.
It's for your
whole organization,
especially in
this age of AI.
And Tableau is one of
my favorite products,
because it allows you to
see and visualize data
so quickly to
gain insights.
You don't have to
be a deep analyst.
You can get
those insights.
And I think it's
incredibly powerful.
And the Einstein
1 Platform
also powers all of our
industry solutions,
our AI powered
industry solutions.
We have 14 of them.
They're all about helping
you build applications,
or not build, excuse me,
to actually get value
faster because
you don't have
to build industry
capabilities
into the platform.
We build them for you.
And that's why today I'm
really excited because we
continue to innovate
and we are going GA
with our life
sciences cloud,
which is so exciting.
[APPLAUSE]
And what life
sciences Cloud
is going to help us
do is help patients
to be to reach,
enroll, on board
and communicate during
clinical trials.
And it's going to help
pharma and medical
professionals
to drive more
personalized
relationships through
the clinical
development all the way
to commercialization.
We're just getting started
with life sciences Cloud.
But you can come back here
at 3:00 and you'll hear
the keynote to learn
more about this product.
OK.
I don't know why they put
this last, because you
know it's my
favorite, right?
Of course, it's Slack.
Last but not least,
is my favorite, Slack.
Slack is the AI powered
command center for work.
It is where your
communication,
your collaboration, your
workflow, your documents,
your videos,
how you connect
with your customers
and your partners,
all there in a
conversational interface.
But what I love
is AI has now
entered the conversation,
which is really powerful.
Because Slack is your
long term memory.
And in fact,
here's the proof.
When I onboarded into the
CEO role six months ago,
I used Slack AI.
I used Slack AI in
a couple of ways.
And I hope this resonates
for you to understand
the power of this.
So of course,
there's no surprise.
Slack has been on Slack
since before Slack
was Slack.
So there's no
doubt about that.
But I had to
get up to speed
on 10 years worth of
work and understand
all of the context.
And as a CEO,
you kind of need
to know the history
of all the decisions
that were made.
So I used Slack AI
search generative search
capabilities to search
10 years of knowledge
within Slack about Slack.
Why did we make certain
architecture decisions?
What is so special about
the channel architecture?
All the things that
you need to know.
And then when finding
those answers,
then it took me right
into the channels
so it could summarize.
We have a product
strategy channel
that summarizes all
the decisions we make.
I was able to summarize
10 years of decisions
in seconds.
Do you know how
powerful that is?
And then starting my day
with recaps, which is,
I don't want to be
in every channel.
If you're a Slack user,
you probably don't either.
But you do need to
know what's happening
and you want to keep
your finger on the Pulse.
So I start my day with
coffee and Slack recaps.
And those are
just examples
of how I've used that
and how AI is coming
into the flow of work.
But work is getting even
more powerful in Slack.
Two weeks ago, we
released Slack lists.
Slack lists is
simply put, the way
that you take
conversations and move
them into work.
How do you create lists,
tasks and projects right
where you're working?
Which is so important
so everybody
can collaborate on the
work that is happening
in real time.
All right.
So right now you
might be like, wow,
I feel like I just
went around the world
in about five minutes.
I did cover a lot.
But here's what I
want to tell you.
If you're thinking
about where do I start?
Or I'm somewhere
on the journey,
but I don't know if I've
thought of everything.
We've got you.
I said earlier,
this is what we do.
We've been guiding
our customers
through these types
of transformations
and we're going
to do this again
with you in this AI era.
So we've built a framework
that gives you guidelines
to understand what are the
five steps that you need
to be thinking about in
leading your organization
into enterprise AI.
I'm going to
introduce them now
and then we're
going to spend
the rest of the time
going deep on these.
The first one, no surprise
unifying your apps
and data.
And once you unify
your apps and data,
how do you
collaborate with AI?
How do you discover
AI insights?
How do you put
that data to work?
But then every
organization is different.
And so how can you
give AI new skills
for your business
very specifically?
And how do you
equip AI to act?
Those are the steps.
We're going to go through
all of these in detail.
In a minute, I'm going
to introduce my colleague
Sarah to take us through
the first one, which is
unifying apps and data.
But I want you to
give a rowdy, rowdy,
we need to wake this up.
I need to give
you, you got
to give a rowdy
welcome to Sarah
to get her excited on the
first step in this journey
into enterprise AI.
All right.
I need more energy.
We need more energy.
I'm not getting
down till we get it.
All right.
All right.
Take it away.
Awesome I was
promised energy,
so I'm really glad
to see it's here now.
Thanks for that, Denise.
We just heard
how difficult
it is to connect
our data when
it lives in different
systems and silos.
But when we don't connect
our data to our CRM,
we miss out on
opportunities.
Opportunities
to transform,
to connect the dots, and
to meet our customers
expectation of a
unified view of them
across every touchpoint.
We're experiencing
a generational shift
in data.
Today, Salesforce hosts
over 250pb of data
on behalf of
our customers.
To put that into context,
that's 79,000,000 hours.
It's incredible.
That makes Salesforce one
of the world's largest
repositories of front
office enterprise data.
And we host data
about your business
and about your
customers so you
can use it to
ground your AI
and deliver a
better outcomes.
That's all possible
because of Data Cloud.
Data Cloud is Salesforce's
hyperscale data engine,
and it allows you to
connect all of that data
from any source
and activate it
in any application.
And it's completely open.
You can ingest data from
your existing data lakes
with zero data copy.
But today, we're
excited to announce
two new features
powered by Data Cloud.
First, you're able to
identify relationships
and compare them
within Data Cloud,
now with the
vector database,
live in Data Cloud.
Second, your service teams
can ingest asset data
from any source,
and then they're
able to use that to
build better, smarter,
proactive service
experiences.
And that's possible
with proactive asset
management.
But you didn't
think we were going
to stop there, right?
So today we're
excited to announce
hundreds of new
connectors in Data Cloud.
It's amazing.
So this includes Amazon,
Snowflake, Databricks.
And these are
natively built in.
They also connect to
your legacy systems,
which historically
would have required
custom integrations.
But with MuleSoft,
that's all unified.
So you're finally able
to unlock all that value
about those data
points that you've
been collecting over the
years for your customers.
That's why we built Data
Cloud, so that you're
able to unlock the
value of that data,
unify it together.
And you can
bring that data
to life in any industry
and drive better decision
making with
proactive analytics.
All right.
Again, we're going
through a lot, so
why don't we break it all
down about how this works?
Big slide.
We're going to step
through it together.
So on the left, Data
Cloud harnesses the power
of those natively
integrated connectors
in order to pull together
all of that data.
So that includes your
unstructured data.
Think knowledge
articles, think emails.
And that's why the vector
database is so important,
because you can identify
those relationships
and how that all
connects in order
to drive those actions,
AI, analytics, insights,
workflows.
In order to do
that, we have
to harmonize
that data, which
means we have to identify
all those relationships
and then unify it into
one metadata model
and create those unified
customer profiles to drive
the actions, to drive the
insights across our CRM.
And this is something that
only Salesforce can do.
That's because
Salesforce's Data Cloud is
built directly on that
Einstein 1 platform,
and it's integrated
with the metadata model.
I know that's a big word,
but it's actually not
that difficult. It's just
context about your data.
It describes how your
data can be used.
So let's take a
look at that screen.
A bunch of random
characters, letters
and numbers.
Without context, we
don't know what they are.
They could be a password.
It could be a user ID.
It could be a zip code.
Without context,
we wouldn't know.
And computers
are the same.
They need that context.
We need to tell them
this is an account ID,
this is a zip code.
Metadata simply
takes raw data
and then transforms
it into context
that can be used
anywhere in the platform.
And it's this
data and metadata
that brings business value
to AI, like Denise said.
If you go ahead
and ask ChatGPT
about the performance
of your recent campaign,
I'll bet you it's not
going to do a great job.
And that's
because it doesn't
know about your data.
It's trained
on public data.
So with Salesforce,
you can securely
inject your data
into your prompts
and then you can
give the AI hints.
Again, this is why
the vector database is
so important, so
you can retrieve
that relevant information
and then use it
in order to provide
better outcomes.
And, you know,
with Salesforce,
when you send that
prompt across,
that will ensure that data
isn't shared or retained
by that model.
Remains your data.
All right.
I hope we walked
through that together
in a way that made
a ton of sense.
But my favorite is always
when we make this real
with a demo.
How about it?
So we're going to look
at Air France KLM.
They're a large
multinational airline,
world renowned for
their customer service.
But as a merger
of two airlines,
they need to overcome
the challenge
of disparate data in order
to put it to life in AI.
So why don't we hop in
and see how Einstein 1
platform is able to
unify that data in order
to provide more
personalized experiences.
Kristin, are you ready?
All right.
I hope this crowd gets
excited by a demo.
I do.
This is my
favorite part when
I get to chat with
customers about how
this all really works.
All right.
Here we are inside of
our Salesforce instance
for air France.
And we're looking
at a contact record.
So it's Sven.
We can see he's
based in Boston.
So I hope you watched
that game last night.
I did.
We have an email
address, a phone number,
basic contact information.
And when we look
below, we see
that he's subscribed to
a monthly newsletter.
But it's pretty obvious
looking across this record
that we're missing a
lot of information.
I have no idea how Sven is
engaging with Air France
KLM.
And if I look
in the center,
as a customer service
representative,
I have no idea what
flights he's taken.
I don't even know
his home airport.
So I don't feel like
I'll do a great job when
I work with him for
whatever his issues are
for resolution.
We need data.
So let's see how Air
France KLM can unlock
all the value of that
data that they've
been collecting in
order to create more
personalized
experiences for Sven
and customers like him.
All right, here we are.
And we're looking at
data streams connected
to Data Cloud.
And we just have the one.
It is our Salesforce org.
So we got to get a
little bit more in there.
Let's click new.
All right.
We see all these data
streams at the top.
We have the
Salesforce one.
We're going to click
Marketing Cloud because we
want to bring in how we
want to actually reach out
the preferred channel
for our customers.
We scroll past that.
We see all those
other data sources
that we talked
about before.
Data lakes and
data warehouses,
like Google Cloud
and Snowflake.
Past that, we see those
MuleSoft connectors
that we talked about.
This is how we're
able to bring together
all of this data from
vast different sources.
Think preferences
for flights.
Think the way again, that
we want to reach out.
So we're going to go ahead
and hit next and continue
on integrating that
data I selected earlier.
Awesome.
Here we are inside that
harmonization step.
So on the left
here, we're looking
at where that data
resides and how
it's mapping to internal
and Salesforce, our data
model.
And we can see
that it's going
to map the right customer
to the right data.
Looks pretty good.
So I'm going to go
ahead and hit save.
Remember that screen
before that had
just the one connector?
Let's see what other
data streams we have now.
All right.
This is value.
We're able to
bring together
all of these different
data streams here in order
to create more value
out of all that data
that Air France KLM
has been connecting.
So let's see what
that actually means
in the context of Sven.
All right.
This is pretty
empty still.
So let's hit refresh.
Boom.
All right.
That is a complete
view of Sven.
It's awesome.
I know you'll
get excited when
you see that in addition
to all this contact
information, we have
those calculated insights.
It's really
just a fancy way
of saying that we're
able to take that data
and transform it into
attributes about Sven.
So, for example,
as a service rep,
I'll see that he's
now a gold member
and that means that
I'll understand what
his actual
benefits are and be
able to serve him better.
And in addition, we have
his preferred channel
and his airlines, so we're
sending the right message
about the right brands.
If I look across, there's
that flight information.
And you can see that as
a customer service rep,
I have a full view
of all the flights
that he's taking
across our airlines.
And on the right
here, we're
able to see that
he's engaging across
all different channels,
sales, service
and marketing.
This is great.
I do love this view.
But let's talk about how
to activate this data.
This is what
Salesforce does better
than anyone else.
So let's look at how Air
France KLM can take all
of this rich data
and activate it
with Salesforce Flow.
All right, here we
are inside flow.
It's Salesforce's
workflow automation tool
and it lets you turn data
into automated journeys.
So in this case, it's a
Data Cloud triggered flow.
And so it's going to be
activated when there's
a change to the data,
specifically when
someone reaches a new
membership status.
It'll then send a message
to the preferred channel.
Why don't we
check out what
that looks like for Sven?
Great.
So here we see that Sven
is getting an email sent
to him and it's
personalized to him,
because Einstein
personalization is
driven by Data Cloud.
We can see that
he's getting
an email about relevant
offers and suggestions.
For example, in this
case, the summer
deals for his gold
membership level.
I love this.
This is what it means
to unlock your data
and bring it into life in
action with Einstein 1.
All right, Denise,
why don't we
see what else we can
do with all this data?
I love it, Sarah.
Thank you.
I think it was incredible.
Great job.
Great job.
What I think can
get lost in that
is that you weren't
only just unifying
apps and data,
but you were
showing how uniquely
Data Cloud allows you
to activate that data.
And I think that
is very critical.
It's the last mile of
bringing a single source
of truth right in
the flow of work
so you don't have these
silos of trapped data
that you can't activate.
So I think it's
incredibly powerful.
OK, step one, done.
Everybody with me?
Everybody OK.
We're all
hanging in there.
OK.
Was everybody out
late last night?
Did people stay out
late watching this game?
All right, all right.
So it's all right.
I'll forgive you.
So now we're going to
go through steps two
and three.
OK stick with me.
Two and three we're
going to do together
because they're related.
So we've unified
our apps and data.
Now we're going to talk
about collaborating
with AI.
So collaborating across
your organization
and discovering
AI insights.
And so to help us
walk through that,
I'm going to invite
my colleague Krithika.
Let's try this
loud welcome again.
Let's get this
energy going.
All right.
Are you ready?
Yes, I'm ready.
All right, let's do it.
Thank you.
Denise.
I'm excited to be here
in a city of champions,
but also to talk to
you about collaborating
with AI.
AI will elevate every
part of your enterprise.
Your workflows,
your web pages.
Everywhere work happens.
That's why we're all
feeling this urgency
to implement AI.
Luckily, we've already
figured this out.
We've partnered together
with predictive AI.
Predictive AI is an
all of our core apps,
whether that's
self-service bots
for service, personalizing
your marketing offers
for marketing and
increasing forecasting
accuracy for sales.
I'm on the Sales
Cloud team,
and I can tell
you firsthand
the impact that
predictive AI has
had for our customers
across industries.
Together with
your partnership,
we are delivering over
one trillion predictions
on our platform.
So thank you.
And building on
this foundation,
we are taking it to the
next level with Einstein
Copilot, you're one
conversational AI
assistant.
Einstein Copilot is
grounded in your data
and metadata.
That's why
Einstein is smart
and it's relevant to
your organization,
because it understands
your products,
your customers,
your service
cases and your deals.
You can talk to
Einstein like you
would a coworker
or a colleague.
And what'll Einstein do?
He'll talk right back.
He'll answer
your questions.
He'll generate summaries.
He'll assist you with
all of your tasks.
This is high quality,
trusted AI that only
Salesforce can deliver.
But most importantly, what
I want you to remember
is that this is one
unified experience.
Meaning there
is one Copilot
across all your clouds.
Sales, service,
marketing, commerce,
tableau, you name it.
All of these teams
are working hard
to give you purpose
built actions
and prompt templates
so you can close deals
faster, create relevant
marketing content,
and increase commerce
conversion rates.
But I am thrilled to
be able to announce
that coming in September,
you can talk to your CRM
like you would a
coworker or a colleague
with Copilot in Slack.
Yes.
Bringing all of that
customer context,
all of that
customer data, right
where you're interacting
and moving work forward.
But ladies and
gentlemen, this
is only the beginning.
We have three other
innovations that
are also coming to you.
Record channels.
Bring all of your
Salesforce record details
into Slack and see your
Slack conversations
in Salesforce.
Removing the need
to swivel chair
between systems.
And with Slack Elevate,
you can track your deals
and take action on
your opportunities
right in Slack.
And finally, but
certainly not least,
you heard Denise talk
about Slack lists
where you can bring all
of your project management
capabilities once
again, into Slack.
All right.
So we just
learned about how
to collaborate with
AI, but you can also
collaborate with data
and insights, too.
Which brings us
to the third step
of building an
AI enterprise,
discovering AI insights.
We just learned from Sarah
how the AI enterprise
is built on a foundation
of trusted, customer
connected data.
And yet, 94% of
business leaders
tell us that they need
to get more value out
of their data.
In order to become
an AI enterprise,
you need to turn your data
into relevant, actionable
insights.
And that's why Tableau has
never been more important.
Tableau transforms the
way people see, understand
and act on their data.
Our latest two
product innovations
are going to make
trusted insights
accessible to everyone
at your organization.
Rather than opening
up that visualization
and spending all
that time trying
to connect all the dots,
receive insight summaries
in the flow of work
with Tableau Pulse.
I love Tableau Pulse.
I get it in my email,
Slack and mobile.
It services insights for
all of my top metrics.
But my favorite part it
often services insights
for questions that I
wouldn't have even thought
to ask about my data.
And with Einstein
Copilot, your teams
will now get a
conversational AI
assistant right
within Tableau.
So you can ask Copilot
all of your data analysis
questions and
Copilot will help
you build visualizations
and write calculations,
all using
natural language.
These two
innovations are going
to help your teams go
from Data to insights
to actions, fast.
All right.
Now that you've understood
how Copilot works
across all our apps,
let's bring this
to life with a live demo.
And what better
way to do that
than through the eyes of
our trailblazing customer,
Turtle Bay, a premier
resort in Oahu, Hawaii.
Turtle Bay is creating
guests for life
by personalizing every
part of their experience
for their guests from
pre-booking to post day.
And they're able
to do this at scale
with help from
Einstein Copilot,
quickly and efficiently.
The best part, their teams
are spending less time
on repetitive
tasks and more time
where it matters building
customer relationships.
The outcome, you ask?
40% increase in
annual revenue growth
for the resort.
All right,
Kristin, Ashley,
are you ready to take
this amazing crowd
through a live demo?
All right, let's do this.
Here we have Tableau
Pulse in Slack,
where I get a
personalized AI generated
digest of key insights for
the metrics that I follow.
I can quickly understand
changes in my data.
Like this insight
that occupancy rates
are declining.
That's not good.
We got to dig deeper.
Let's do that in the
Tableau Pulse mobile app.
You see questions
here that have already
been generated for me
to help me understand
the why behind my data.
The why behind your
data is so important.
And yet with
traditional BI tools,
it's time
consuming and it's
hard to get to that why.
Thanks to Tableau
Pulse, everyone
at your organization
will be a data expert.
So even someone like me
who's a beginner at data
can quickly see that the
reason this decline is
happening is
because there's
been a lack of
corporate events.
This is critical
information.
Let's send this to
our marketing teams
so they can start
to build a campaign.
With this insights, then
let's head over to Slack
to see how our marketing
teams are collaborating
on this campaign.
Pretend I'm a marketer
at Turtle Bay.
I start my day in Slack.
It's my AI powered
platform for work.
Instead of jumping
from channel
to channel to channel
to get caught up,
I use Slack AI recap.
It summarizes all of my
important information.
I see something
that catches my eye.
So let's click into
the record channel
where the conversation
is happening.
Now on this
conversation, you
might see
something familiar.
This is the insight
that we shared earlier.
This is powerful.
So I want to pause
here for a moment.
Not only are we uncovering
insights quickly,
but we're sharing them
in the flow of work
to drive collaboration
and action.
Speaking of action,
let's continue to action
on this campaign in the
record channel, which is
a wealth of information.
At the top, you see
record details coming in
straight from Salesforce.
And below that, you see
CRM data and Tableau
metrics to help the
marketers at Turtle Bay,
like me, make
better decisions.
And in this record
channel, we have lists.
So that your
teams don't need
to go to yet another
app or website
to track their
deliverables.
Everything they
need is right here.
My team at Salesforce
uses lists and we love it.
It's amazing to be able
to have conversation
on a project
and immediately
turn that conversation
into tasks
and assign owners so we
can move projects forward.
Let's continue to move
this project forward.
And we do that by
conversing with our team.
We're pulling in
all the right people
into the discussion,
as you can see.
But there's one
teammate that's missing.
Can anyone
guess who it is?
I heard it.
I'm hearing it
over here, too.
Einstein Copilot,
your conversational AI
assistant.
I'm going to ask
Copilot what resonates
with this segment.
And behind the
scenes, Einstein
is going to analyze
all of that data
that Sarah talked
about in Data Cloud.
He's going to analyze
structured data,
like booking rates.
And he's going to
use vector databases
to analyze unstructured
data like survey results.
And just like that,
we have our answer.
Now we know what resonates
with this segment.
So let's go ahead and
send this insight out
to the rest of our
marketing teams.
We have done a lot of
work on this marketing
campaign.
Let's fast forward and see
if it brought in any leads
for our sales teams.
I'm going to put
on a different hat.
Now I'm a sales
rep at Turtle Bay.
I start my day
in Seller Home.
It's my complete
view of my business.
It has my
pipeline, my tasks,
and my important to-do's.
And Einstein Copilot
is here, too.
I want to
reiterate that this
is the same Copilot you
saw the marketers working
on in Slack.
That's because with
Salesforce, you
get one Copilot that's
grounded in your customer
data regardless of
the application.
I could spin my wheels and
focus on an opportunity
that might not close.
Or I could ask Einstein
who I should focus on.
And once again, Einstein
is going to go and analyze
that data from Data
Cloud on our customers,
and he's going to
service or surface
an opportunity that
has a high propensity
to close based on the fact
that this customer has had
positive experiences
with Turtle Bay,
and has zero open
service cases.
What's more, if
I'm stuck on how
to progress the deal,
I'll ask for guidance.
And Einstein is going to
serve me a tailored close
plan with key
milestones and dates
so that I know how to move
this opportunity forward.
And this close
plan is grounded
in opportunity data and
previous interactions
with the customer.
So let's start actioning
this closed plan.
Let's do that
right from here.
I could stare at
the blinking cursor.
Think about what to say.
Go to another tab.
Find my calendar
and my availability.
Or I could ask Einstein to
generate the email for me.
And you see an email here
with scheduled meeting
times based on
my availability.
This email is
looking great,
so let's go ahead
and hit send.
Wow.
We just did so much in
a matter of minutes.
We collaborated with AI.
We discovered insights.
And we did all of
that through one
unified Copilot.
Imagine how
productive your teams
would be with this
conversational AI
assistant by their side.
Back to you, Denise.
Krithika, that
was incredible.
I mean, first of all,
seeing the power of you've
unified all of your
data and applications.
You have one single
source of truth.
And then bringing all
of that structured and
unstructured
data together,
which was never before
possible, so that you
can collaborate
more and you can
discover new insights.
And this is an
incredible unlock
for every organization.
I found it very inspiring,
especially seeing it
in Slack.
It was great.
OK, so we've brought
the data together.
We're starting to
get more insights.
We're discovering
insights.
But not every
company is the same,
as I said before.
So you want to be able to
give your AI new skills.
You want it to
learn new things
about your organization.
And maybe, just
maybe, we're
going to get into that
autonomous world where
we have agents
working for us.
So this is the
next two chapters
we're going to cover
chapter four and chapter
five.
I'm going to
introduce Carlos
to come up here and bring
a whole bunch of energy.
Carlos, let's see it.
It's like, are you ready?
You're ready.
Thank you so much
for having me.
I'm turning it
over to you.
Thank you all for
being [INAUDIBLE]
between the lobster, the
cannolis and the game
yesterday.
I couldn't be more happy
to be here in Boston
for the first time.
Thank you so much
for having us
and for having me.
I'm excited because we
are in a movement, a no
low code movement.
And it's scaling
application development
like we've never
seen before.
We're talking
about hundreds
of billions of
transactions per month.
We're talking
about millions
of custom apps built
in point and click.
It's wild.
Think about it
for a second.
But it's getting
even wilder,
because Gen AI
is fundamentally
changing the
way we develop
software and the
way we interact
with applications.
We're going from click
based UIs, user interfaces
powered by code and
transactional databases,
to Gen AI apps powered
by the most accessible
programming language of
them all, plain vanilla,
conversational natural
language in English.
I'd like to say
that differently.
If you think about
traditional software
development as
a rigid decision
tree with
predefined, if then,
do that type
of paths, then
you can think
about Gen AI apps
like a bush with
no predefined paths
where users can interact
with the applications
with many options in
many touch points.
Now the question
is, how do you
build these next
generation conversational
Gen AI apps?
And the answer to that
is Einstein 1 Studio.
You can give AI new
skills with Einstein 1
Studio, as an admin,
as a Builder persona.
Create and craft
beautiful Gen AI responses
trusted in your
enterprise data
with Prompt Builder all
the way on the left here.
Take advantage of
foundational models
or and bring your own
with Model Builder.
And finally, where it
all comes together,
where you can build
the next generation
of proactive
conversational apps,
Copilot Builder.
When you give
AI new skills,
AI can do much
more than just
provide an
information than just
answer your request.
You can also equip
AI to take action.
And you should.
This will unlock
the next levels
of productivity and
scale for any enterprise,
for any brand.
Think about this
one for a second.
We lose 41% of our day
to repetitive work,
to repetitive tasks.
That's more than
three hours a day.
It's crazy.
And that's what we can
actually help you with.
Einstein Copilot.
Now we all know that LLMs
can generate content.
We're all
familiar to that.
The beauty of
Einstein Copilot
is that we use
LLMs to understand
context and intent.
We use LLM to create
an action plan.
And we use LLMs to
take action for you
with Einstein Copilot on
those repetitive tasks
so that you can focus
on high value customer
relationships and work.
Let's unpack this
for a second.
I want to show you
how the brain works
behind the scenes.
So let's open the
lid together and walk
through these steps.
The first thing
is the trigger.
This is a data change.
Think about all of
the CRM data changes
that you may have.
This could be
previously item
that was sold
out that pops up
and it's back on stock.
Or for example,
a lead that you
are in discovery
mode with that's
back on your website
researching for a product.
These are all
great signals
to act upon to trigger
Einstein Copilot.
So let's unpack
it even further.
Let's walk through
the brain step
by step, going here
from left to right.
First thing
that happens is
that we send a Copilot
prompt to the LLM.
Think of this as context.
This is essentially, a
chunk of instructions that
allow Einstein Copilot to
start understanding what
this data change,
this trigger is about,
and how it should go
about resolving it.
But before Einstein
takes action,
it will check in
to see with you
and clarify any
intent, just
to make sure it's
on the right track.
At that point, it's going
to generate an action
plan.
And it will
select the actions
that are most relevant to
deliver the job at hand.
And if at any
moment it requires
additional information,
it will go ahead
and ask for your input.
And it will do
that autonomously,
just like any
coworker would do.
And that is how
you can complete
even multi steps
tasks end to end
with Einstein Copilot.
And I'd love to show you
this with an example.
One of our customers,
Aston Martin,
that is becoming
an AI enterprise.
The iconic automotive
brand that you know
is becoming an
AI enterprise.
The challenge
here that they had
was fragmented data.
Fragmented vehicle data,
fragmented customer data,
a little bit scattered
all over the place.
With Data Cloud, they were
able to unify this data
and activate this
data in order
to provide personalized
customer experience.
And I want to show
you an example of how
Aston Martin and any
brand could deliver
the next
generation of apps
with Einstein 1 studio.
OK, meet Luke.
This is a notification
from Aston Martin letting
him know about his
upcoming service
appointment.
It's completely
tailor made for him
as a customer.
And it was sent
in his channel
of choice, his preferred
channel of choice.
Now, what's so
special about this?
Well, the beauty
of this is
that every single
step leading to it
was done autonomously.
And that's what I want
to show you today.
So let's rewind and
actually see together
how we got here.
Here I am as a service rep
in Aston Martin's Service
Cloud Console.
This is where I start
my day in the home page,
allowing me to get
key information to get
started.
Things like open cases by
product or the temperature
of my contact center
bubbling up KPIs,
like average handle
time, or in this case,
average resolution time.
But today, I
would love for us
to drive your attention
here in the upper right
side of the screen.
I have a notification
here where Einstein
is proactively
letting me know
that one of my customers,
Luke, has an issue.
Not only was it able
to actually understand
what the problem is,
it came up with a plan
to fix it.
But wait.
Not only did it do
that, it actually
took action autonomously
on this very endeavors
that we have given
it access to.
This repetitive tasks
like create a work order,
excuse me, find the
parts of the vehicle,
assign a field technician,
and actually send
that outbound notification
that we just saw.
Think about that
for a second.
Think about all of
the repetitive tasks
that you have to deliver,
you and I every day at
work.
Think about the
time that it will
save to your employees.
Now, how were we able
to trigger proactively
Einstein Copilot?
And the answer
to that question
is this lightning flow.
This is a trigger based
lightning flow that's
listening in on
telemetry data.
So a drop on the
vehicle health score
triggers this flow.
And this flow,
in turn, will
invoke Einstein Copilot.
And Einstein Copilot
will come up with a plan.
And Einstein Copilot will
cherry pick the actions
that it needs to deliver
to solve the job at hand.
We can give Copilot skills
in the Einstein Copilot
Builder.
Buckle up.
This is my favorite piece.
I'm very proud to the
team that is building this
together with us.
Welcome to the Einstein
Copilot Builder.
This is where you can
customize your Einstein
Copilot tailored
to your business.
And we're making this very
easy for you on day one
with standard
Copilot actions,
like to do things like
case summarization
or draft and
revise an email.
But of course,
you can also
leverage custom actions
with automations
that previously are
available in your org.
Like, for example,
generate this work order.
And we're making
it very easy
for you to add an
action and equip
Copilot with skills.
OK, fantastic.
In this Copilot
Builder is where
I am going to simulate how
my end users will interact
with it.
So I'm going to go ahead
and preview the service
representative that
was interacting
with Einstein Copilot
just a second ago.
And I want to simulate
the trigger that
invoked the first turn
for Einstein Copilot,
that telemetry data
flow trigger, remember?
And we're going to run
it in the context of one
of Aston Martin's
customers.
I would choose Luke here.
OK.
Let's actually give a
little bit of space here.
So on the right
side is actually
the preview, the
experience of my end user.
And here in the center
on the plan canvas,
this is where the
AI is reasoning.
So on the right side, this
is actually the experience
of my service agent.
And as an admin, as
a Builder persona
on Copilot builder, AI
no longer is a black box.
I can see how Einstein
Copilot is reasoning.
I can see what is the plan
that Einstein Copilot came
up with to actually
solve this endeavors.
I can see the
actions that it was
able to stitch together.
I can see what data is
going through each action,
as well.
That's the power and the
beauty of the plan Canvas
here.
And of course,
remember, this
is a conversational
interface.
At any point, as
a simulated user
that I am actually
doing here,
I can interact with
it and ask it things
like, hey, when is this
actually scheduled?
Let's see if
we can actually
bring the appointment
a little bit earlier
in time, because we
know, and I know,
that my customer
is going to want
to harness his
beautiful vehicle
during the weekend.
But what's actually
happening here?
How was Einstein
co-pilot able to find
the engineering resources
as well as understand
this daytime's options?
Well, because as we've
been talking about,
Einstein Copilot sits
on top of your data
and metadata.
And it is aware of
the conversation
that is happening.
So it is able
to understand
that this week actually
refers to the days
before that very Monday.
And this allows Einstein
Copilot on day one
to know your org.
The fact that
it sits on top
of your data and
your metadata.
And that is unique
to Salesforce.
OK.
There's another thing
that is extremely
interesting to see
and to grasp here
is that the usual
service engineer
for that very customer
is not available.
And Einstein
autonomously pops up
a disambiguation situation
opportunity for me.
It's saying, hey, the
engineer that we typically
use for your customer
is not available,
but I have these
other options.
And he was able to
do that autonomously.
We did not predefine
this logic.
That is actually the
power and the beauty
of this realm.
We're going to go
ahead and choose James
as one of our stellar
service engineers.
And this is the
final dynamic plan.
At every utterance, we're
generating a dynamic plan,
cherry picking the actions
and executing them.
And remember how I added
the actions earlier.
You hold the knob.
You decide how many
actions you add or not
to Einstein Copilot.
You decide how much
autonomy or not
you give to it.
OK.
The last piece actually
on this multi-step plan
is essentially
generating this outbound
notification.
So let's see how we
created the content
and how we created this
tailor made content.
And to do that, I'll
move to Prompt Builder.
This is the tool
at your fingertips
to craft beautifully
Gen AI created
content tailored
to your business.
This is where I'm going
to basically provide
AI instructions.
Or if you want
to get fancy,
this where we're going
to do some prompt tuning.
And I have to
actually do it
in the context of
my business, right?
This is where we're
going to actually go here
and pick up a retriever.
Remember this retriever?
We're picking up
a retriever here.
Remember the
what Sarah was
doing earlier where
she was building
that unified profile?
We're doing the exact
same mechanism here.
We're basically harnessing
the customer data
that I've already
stitched up here.
But I want to also bring
in the vehicle data.
So let's go back
to Prompt Builder
and actually add this
retriever, this data graph
with the unified profile.
And finally, we want to
reference the flow that
will take in the
content and then
it will be sent
out to Luke.
Fantastic.
So let's go ahead
and actually pick up
this flow.
The piece finale,
we're getting there.
I want to preview
as a Builder persona
how my end customer
is actually
going to experience
this content.
So let's go ahead
and actually choose
one of the many customers
that Aston Martin has.
We'll check in
here with Luke,
and we can preview that.
OK.
So what's happening
here on both
of these screens is
on the left here,
we have what you call
the hydrated prompt.
Essentially,
the instructions
we send to AI with
all of the data,
all of the context,
it needs, to do what?
To give us a beautiful
Gen AI response here.
Let's go back full
circle and actually
see how Luke
will experience
this outbound
communications.
And here we go.
This is essentially
the WhatsApp
that we were able to
send on the customer's
preferred channel, letting
him know that a service
appointment and a
service engineer
will be on his
way tomorrow
to service for
his vehicle.
Thank you all.
Thank you very much.
That is how we bring
Einstein 1 Copilot to you.
[APPLAUSE]
All right, everybody,
are you still with us?
That was amazing.
I mean, hello, champions.
That was really,
really incredible.
Really incredible.
So listen, we're in
the home stretch.
We're almost done here.
So just to recap
what we saw.
Unifying apps.
Collaborating with AI.
Discovering new insights.
Giving AI new skills.
And then moving all the
way into autonomous AI,
all on the Einstein
1 Platform, grounded
in your trusted data to
help you get to enterprise
AI safely and quickly.
OK, so this is
just the beginning.
We're going to
wrap things up.
But if you weren't already
convinced to get started,
or you don't know
where to start,
we are also offering
a couple of things
to make it easy.
A starter bundle, which
includes Data Cloud
licenses and
professional services
if you want to start
out unifying your apps
and data.
You can also learn more
about this on Trailhead.
And we're offering 50%
off the certification
for Data Cloud, if
you want to do that.
So we are really coming
into the home stretch.
We are.
This is it.
This is my last slide.
This is what I want
to leave you with.
First of all, this is just
the beginning of the day
and your journey for AI.
We have 85
sessions, 25 demos.
I mentioned the keynote
for life science Cloud
here in this room at 3:00.
I want to say thank you
to our amazing sponsors.
And the last by the way,
don't miss the hands
on workshops, which
are incredible
and really bring
this to life.
But the last thing I
want to leave you with is
the AI event of the
year is happening in San
Francisco at Dreamforce,
on September 17th through
19th.
Be there, learn and
continue on your journey
with enterprise AI.
Thank you, everybody.
Have a wonderful day.
[APPLAUSE]
Hey, trailblazers,
thanks for joining us.
We'd love your feedback
on today's keynote.
Use this QR code.
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