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- Hi, I'm Nicole here
with our CIO, Juan Perez,
who just implemented
over 70 generative AI
technologies at Salesforce.
And in the next half hour,
we will hear the six steps he took
to get ready for generative AI,
and we'll see a demonstration
of how Salesforce is using
generative AI internally.
Shall we do it?
- Let's do it, Nicole.
It's great to be here with you today.
- Wonderful, let's do it!
All right, so starting with
your role in generative AI,
Juan, how did you know
that Salesforce was
ready for generative AI?
- You know, it's really
interesting, Nicole.
If you go back a year and a half ago,
we were not talking about generative AI.
Now, it's interesting.
We had spoken about AI multiple
times inside the enterprise.
In fact, Salesforce has been a leader
in implementing AI solutions
for quite some time.
But when it came to generative AI,
we just started looking at
this a year and a half ago,
and I remember vividly being
in a meeting sitting next
to Marc Benioff, who has
been the number one champion
for the implementation of
gen AI in our products,
where he just took my
notebook and write a note,
"You are no longer the CIO of the company.
You are now the Chief AI
Officer of the company."
And that was his way of saying,
"We need to become an AI enterprise."
And he has been absolutely right.
We've been working diligently
at becoming an AI enterprise
and we are committed to
continuing to do that.
- So CAIO.
- Yes!
Which by the way, became a real title.
- It became a real title?
- Absolutely! Not for me.
- Oh, not for you.
- I'm still just a CIO.
But many companies are
in fact implementing,
say, CAIO roles in the enterprise
to be able to support their AI
strategies across the board.
- Makes sense.
I mean, everyone is talking about AI,
and likely, most companies who've tuned in
have dipped their toe in the water,
maybe they've had success
or they're, you know,
checking into things.
So shall we go through
how Salesforce got ready
for generative AI in six steps?
- Absolutely. Let's do it.
- Let's do it, okay.
So, how do you even start?
- You start with the outcomes, right?
Just like any other technology
that we know how to implement,
we need to understand
what exactly are we trying to
get from these technologies.
From our perspective, we know
that generative AI can
actually have an impact
in many aspects of the business.
We truly believe that the implementation
of gen AI in the enterprise
can impact productivity,
can impact the efficiency of our people,
can ultimately free up our
people to be able to do thinking
in areas that we have
not had the opportunity
to spend time in.
It's also a great set of solutions
that can help us improve quality,
the service that we
provide to our customers,
and ultimately, you
know, provide mechanisms
by which we can improve our products
and the quality of the work that we do.
So understanding the
outcomes that you're trying
to generate from this technology
is incredibly important.
- I love that you mentioned
the customer in there
and that it can bring you
closer to your customers.
So when you start developing
with outcomes in mind,
what are those outcomes?
- Well, you know, when you think about it,
a lot of the talk about the implementation
of generative AI has focused
on productivity and efficiency.
And I recognize that these
types of technologies,
when you actually can do things faster,
when you can get responses
and answers to questions
much more quickly,
when you can get to the
root cause of a problem
by using these types
of technologies faster,
you're definitely going
to improve productivity
and efficiency.
In fact, in our own implementations
inside of Salesforce,
we've been able to see that.
But what we should not forget is
that there's a customer at the other end.
And that when you actually
can solve problems quicker,
when you can get the right information
to the right customer faster,
when you can actually
provide a better answer
to your customer's questions,
you're also improving customer service.
So to me, I think generative AI is one
of those unique technologies, Nicole,
that not only will help
us improve productivity
and efficiency, but there's a
huge customer component to it
that we should never forget.
- Fantastic.
And I know everyone wants to get closer
to their customers, right?
- Yes, absolutely.
That should not be forgotten
with the implementation of generative AI.
- Fantastic.
But how is it different?
Like, what are you doing?
You know, let's go to step two.
What is step two?
- Step two is ultimately making sure
that as you implement these
types of technologies,
you have appropriate governance,
that you build the right steps in place so
that at the end you implement
technology that's gonna work.
And that includes ensuring that
your technology is trusted.
For decades, in IT, we've been working
towards making our
technology more resilient,
making our technology more secure,
making our technology more trusted.
Well with gen AI, I think
that the bar has been raised
and we all collectively,
as IT professionals,
need to make sure that we
maintain trust in the forefront
of what we do with the
implementation of gen AI.
And you know, Nicole,
I've been really fortunate
working here at Salesforce
to live, you know, firsthand,
to see how our company has built trust
into our generative AI
solutions in our products.
We have built a phenomenal trust layer
that allows you to keep
visibility to everything
that is going on when
it comes to, you know,
generating results from generative AI.
We have audit logs that
give you a good sense
as to how you've generated
that solution at the end,
you understand the sources
of that information
that's being used to
generate the outcomes,
and also, very importantly,
we've taken the position
that the data that is used to
ultimately generate outputs
from our Gen AI solutions is your data,
it's not our data.
And we respect that immensely.
And I think that is
creating a huge separation
between our solutions and others
when it comes to the full
trusted AI environment
that we're building for our customers.
So that is important.
But simultaneously, when
it comes to governance,
I think of AI as the implementation
of any other technology that
we, as IT professionals,
need to certainly, you know,
make sure we manage effectively.
For us here at Salesforce
in the area of governance,
we formed an AI Council,
that AI Council has been very effective
at identifying the types
of solutions where gen AI can be used.
We have members from our
ethics and technology team,
we have members from the legal group,
from security, from our business units,
and this team is coming together
regularly to understand how
to best implement gen AI in the company
where we can maximize the
value of those implementations.
But secondly, we can also do it
in a responsible and trusted way.
- So it sounds like you're
doing it for your customers,
but you're also doing it
internally for the company.
- Absolutely.
At the end of the day, I am responsible
for the implementation of
technology inside the company,
and I treat generative AI as
I treat any other technology:
with the same level of attention
and with the same level of discipline
as implementing any other type
of technology solution in the enterprise.
- Makes sense.
So then, as you know, you've
set up this extra higher bar
of trust and governance.
How do you go about identifying where
to put generative AI
across your organization?
- Yeah, that's actually
the third step, right?
Making sure that after you
have some level of governance,
after you prioritize the most
critical things like trust
in the management of
your gen AI solutions,
you start really focusing on the things
that are gonna matter
most for the company.
You see, Nicole, I've always had this view
that implementing technology
for technology's sake is
a waste of time and money,
but when you implement
technology in alignment
to the company's strategy, then you win.
I think it's really important that
as you understand your company's strategy
and now you have a new great
capability like generative AI,
that you build the generative AI mindset
into your technology roadmaps,
your technology strategy,
in support of the company's strategy.
So as it relates to use cases, you know,
for me it's critical that
we identify the places
where gen AI makes the most sense.
We talked about
productivity and efficiency,
those are big terms, right?
But then you gotta get really
into the process level.
Where exactly in your business processes
you can take advantage
of these technologies
to maximize value creation?
Where exactly can you
implement those technologies
in a way that it can be
done in a responsible way,
it can be done in a manageable way,
and also very importantly
it can be scaled.
One of the challenges that I have seen
with the implementation of Gen AI
across multiple
enterprises is that we all,
including ourselves, we went out
and we started deploying
all kinds of pilots,
and then we found out that
many of them were not scalable.
We could not expand them to
the rest of the enterprise.
And now we have many of these pilots
that are sitting out
there without a strategy.
Now I think with how much we have evolved
in the use and understanding
of these technologies,
we're in a much better
position now to be able
to identify the right places
where this technology can be used,
where we can align with
the company's strategy,
and where we can actually maximize value.
- Fantastic.
And I know that there's
an internal process
that we've actually mapped out
as a best practice at
looking at two dimensions,
cognitive load and how a
repeated task drives value
for the business.
I can share the link on screen
if anyone wants to check it out.
Is that a practice that we do
to identify which repetitive tasks,
where to augment, where to automate?
- Absolutely, and it's worked well for us.
I think we, you know,
and we've done it by function,
it's important that we also
understand that every function,
every group, every business unit
within a company operates
slightly differently.
And understanding those types
of components in your decision
as to where to implement gen
AI needs to take into account
what function, what business processes
you're interested in impacting.
It's been also very important
for us here in the company to ensure
that we focus on those areas
that create the most value.
Now, at the beginning
with experimentation,
it was good to see, "Okay,
where is this going?"
"Where can we use it?"
But now that we understand
it a lot better,
it's more important now for
us to be able to identify
where we can create the most value.
- And how do you identify, you know,
so you don't end up with all those pilots,
what are you doing today in order
to identify those use cases?
- You know, the AI Council
acts as a mechanism for us
to receive an intake.
It's an intake process we have
in place to receive ideas,
to receive, you know, some
of the areas in which people
across the company want to implement AI.
And of course, we evaluate those ideas
against the capabilities that we have,
the capabilities that exist today,
whether it's in our
products or something else,
and then ultimately what
value those ideas can create.
And it's working as a team
that actually filters a lot
of these ideas to ultimately
work on the things
that matter most for the organization.
And we've had some good success.
We have received over 120, 130 ideas
as part of what the AI Council receives.
And about only 20% of
those have actually made it
to the next stage,
where we're actually actively
either implementing something
or building something to
support the organization.
- And how is data impacting that?
- So that's the next big step.
I think it's really important
to understand the state
of your data in the enterprise.
Critical.
If you want good outputs from
what you're going to be doing
with generative AI, your
overall data strategy needs
to take a completely new level of priority
and importance in the enterprise.
You know, it's interesting,
Nicole, a good CIO friend
of mine was telling me
that for a long, long time,
he tried to prioritize the
data strategy in the company
among his peers at the C level.
And in many cases, well, it
was, "Yeah, it's an IT thing,
they will deal with it over time."
Now data is becoming a real priority,
because people are
realizing that if you want
to get great value from generative AI,
you need to have solid, quality data.
And not only data that
comes from one system,
but when you want data
from multiple places,
you want data that actually
helps you compliment the outputs
that will come from these great models
that are being built and
that support generative AI.
At Salesforce, we've done two things.
One is, data is priority.
Of course, in the CRM space,
our CRM technologies have great
data because it's your data,
and at the end of the day,
it's how CRM runs, with your data.
And then secondly, we've built Data Cloud.
For me as a CIO,
after many years of
working on data strategies,
for the first time, I'm seeing the ability
to bring data from all these silos,
all these trapped data that has resided
in all these different
places for a long, long time
with a huge difficulty in
bringing it all together.
And now through zero
copy, through all the APIs
that we have built to be able to interact,
Data Cloud with all these
different data sources,
we're now in a position to
bring all these rich data
to truly maximize the
value of generative AI.
- Zero copy is so cool!
You know, the idea of being able
to access data while it resides
in another location
without duplicating it,
is that something Salesforce is doing?
- Absolutely.
We are customer zero equal, so-
- Meaning we use it before even-
- We use it before
everybody uses it, right?
And we have had some really good successes
in multiple parts of the
business by using Data Cloud,
and we are committed to
continuing to expand the use
of Data Cloud in the enterprise
as new capabilities are
built into the product,
we will be the first ones to use it
and continue to demonstrate
to our customers
how a sound data strategy supported
by great technology can
truly unleash the power
of generative AI.
- Fantastic.
Okay, so what's next?
- Well, what's next is two things.
Of course you know, we want to make sure
that we utilize generative
AI in the right places,
but we also need to make sure
that our workforce is capable
of using these great
technologies and capabilities.
And we need to go back again to the book
of implementing technology
that's been built over a long time now,
with lots of experiences,
good and bad ones,
and pull a page out of that book that says
that for effective adoption of technology,
it's really important
that you have a good
change management plan
and that you also build
a solid enablement plan
for your people.
I do think, Nicole, that in
time, gen AI will be embedded
and built into many of the
things that we use today,
many of the technologies we use today.
It will be in your phone,
it'll be in your computer,
it'll be in our CRM applications.
It'll be so embedded in
all those different tools
that it'll be just simply a
normal part of doing business.
- Table stakes.
- Absolutely.
And then at that point
in time, the whole notion
of change management and
enablement will change again
towards just simply making sure that
as improvements are made, as
enhancements become available,
that you just let our
people know in our case
that this is now available to
them and people will use it.
- And how are you getting
the team excited internally
at Salesforce?
- You know, we have multiple mechanisms
to demonstrate what we're doing.
We communicate in our channels,
we provide updates regularly
as to how we're implementing
and using Gen AI in the enterprise,
whether it's Salesforce's solutions
or our own internal
applications that we have built
to support our employees
across the enterprise.
We have lots of demos.
I know you're gonna show
a demo a little later.
We have demos that
demonstrate to our people
how this technology can truly
make 'em more efficient,
more effective in what they do,
so that then they can
free up time to take care
of our customers more effectively,
to find ways to build
more innovative products,
to create technology that
at the end fails less,
that has less defects, less problems.
These are the benefits that
I'm really excited about
when it comes to gen AI.
And I think our people are
getting it, I really do.
- I've seen that personally.
I love the features like
in Slack where I can go in
and I'm in a channel, there's
a hundred new messages
that I haven't read, and I
can hit the Summarize button
and get the main key notes.
You know, key takeaways.
- And now you can use that information
to then take other action on things
that perhaps are more important
or critical to the organization, right?
You know, we deployed a solution in Slack
that allows our people to
ask questions about IT,
questions about HR, questions about sales,
and what we have seen
in the short few months
that we've had this technology out there,
we've seen over 50,000 hours
we've been able to measure
of employee productivity improvements
by just simply using those technologies.
And it's simple, right?
When people can get the
information they need
much more efficiently, when
they can get it quicker,
and when they can then
act on that information,
it's better for the company,
it's better for the employee.
And I think there's another component
that hasn't been talked
a lot about lately,
but I think it's real gen AI
will also improve the employee experience.
And you know, we are working closely
with our employee success
team, our HR group,
to implement technology solutions
that will also help our
employees get better experiences,
whether it's onboarding,
as they join the company as new employees,
or they're working day to
day, doing their normal tasks
and they wanna have access to
information much more quickly.
- Fantastic.
So Juan, I heard you
say that we, you know,
we have it in Slack.
We're doing some things with onboarding.
Where else in the organization
are we using generative AI?
- You know, we are of course,
implementing our Salesforce solutions.
Einstein co-pilot in sales,
Einstein co-pilot in our
customer support group space.
Those technologies are starting
to provide just huge
value for the enterprise.
Our sales resources are getting
prepared for engagements
with customers using these technologies.
Reducing the amount of time
that they're having to spend trying
to understand what's going
on with a specific account
and really getting quicker
to what's most important for the customer.
"How are you solving my problems?"
"How are you giving me solutions
that can make my business better?"
Our support engineers are using
our Einstein co-pilot tools
to be able to search knowledge articles,
get responses to customer questions
much more effectively too, quickly,
and of course with better
quality at the end of the day.
So I'm seeing the application
of our own copilot tools create
huge value for the company.
I'm also seeing the use of
search capabilities in Slack.
We created a tool just
recently called Basecamp
that brings all kinds
of knowledge articles,
you know, solutions to
problems, different data points
that employees may be interested in,
and it's bringing it
all into this one place
using our gen AI capabilities.
We're providing answers faster.
So I'm seeing all of these
starting to take hold
across many parts of the enterprise.
- All right.
To give us a quick recap of
what we've covered so far,
we've talked about outcomes,
we've talked about raising the
bar in governance and trust,
identifying those use cases,
how we manage data is a little different,
then of course, prioritizing people.
What's our last step?
- Well, the last one is, you know,
this is one of those technologies
that is going to continue
to change very rapidly here.
I don't know for how long.
It may be for the next couple of years,
it may be for the next 10 years.
But one thing I've learned
in this particular world
of gen AI and that is,
whatever you're building today
is not gonna be the final solution
for the next 5, 10 years.
I think we all collectively
need to recognize
that this is a space that is
going to be changing rapidly
and as a result of that,
as IT professionals,
we're gonna have to get used
to a new operating model,
a model by which we can adjust
and change to new models faster,
where we can actually adjust
our technologies to be able
to support the new
capabilities faster as well,
and where our users are
going to be expecting them
to have access to these better tools
and capabilities much
faster than ever before.
Think about how quickly Chat GPT became
just a household name across,
you know, families all over the world.
It just happened!
I think that that's just a demonstration
of how fast this technology
is going to become pervasive
in what we do, and very importantly,
how fast we're gonna have to adjust
to be able to take full advantage of it.
- [Nicole] So true.
- Yeah.
- All right, well, how about we see a demo
and see how Salesforce is actually using
our own products internally?
Shall we do that?
- [Juan] Let's do it.
- All right, for this,
I'm gonna pass it over to Jody Farrar,
who is going to show us an overview
of how we're using Salesforce products,
and specifically, generative
AI inside Salesforce.
So our internal story.
Jody.
- Thanks Nicole.
In this demo, I'll show you
how Salesforce employees
across departments like
sales, service, operations,
and IT are more productive
with Einstein 1,
our portfolio of best in
class CRM applications,
trusted AI, and data products
that we sell to customers
and also use internally.
It all starts with data.
At Salesforce, connecting
with our customers
and having the right customer information
is critical for running our business.
From the moment a prospect
expresses interest
in one of our products,
Data Cloud helps us
access that information in Einstein 1,
like here in a customer record.
We're looking at an
example Salesforce customer
named Lauren Bailey
and we can see this record
really tells a story.
We see things we'd expect,
like record details,
customer ID, and email address
and we can also see Lauren's affinity,
which tells us about her
overall satisfaction.
We see Lauren's related
sales and service activity,
and we see Lauren's
marketing activity history.
Amazing!
How do we do this?
Let's look at a lead example.
When a prospect like
Lauren visits our website
and fills out a lead form,
the first step in our process is
to ingest this lead into Data Cloud
and we can do that in near real time.
Data Cloud helps us access
any type of data and metadata.
It can be structured data
like this lead example,
or unstructured data like a PDF or email.
Metadata is data that describes
other data in a common way,
providing context and meaning.
Like many companies,
we also have data residing
in different systems,
so we use MuleSoft's extensive library
of prebuilt connectors to access it,
and thanks to zero copy technology,
the data can reside in
its existing system,
saving us from having to duplicate it
or manage it in two places.
Data Cloud then harmonizes the data
into unified customer profiles
so that the data maps back
to specific individuals
and their related CRM records.
This harmonized data is now integrated
into Salesforce's metadata framework,
which makes it easy for
employees to view it
or action it in any
Salesforce app, record,
workflow, or AI tool.
Today, Salesforce has over
105 million unified profiles.
Today, Salesforce has over
105 million unified profiles.
Now, in this example with leads,
Data Cloud is also powering our workflows.
For example, in sales, we have workflows
for a business development representative,
someone that handles outbound
communication to prospects,
and a sales development representative,
someone that handles inbound questions.
And if we wanna make changes,
we can simply click into it
and make changes on the fly.
Once all the steps in
the flow are completed,
leads are routed to the correct
sales reps in Sales Cloud,
which helps everyone drive
the business forward.
With Data Cloud and Flow,
we've improved our lead intake time
from 25 minutes to 5 minutes.
With unified data from Data Cloud,
our employees can also
get AI powered insights
everywhere they work.
For example, Salesforce
executives often track top deals,
and now they can do that by
simply opening Tableau Pulse,
which is a proactive, personalized report,
visualizing relevant
data for every employee
so they can take action quickly.
At the top, AI automatically
generates summary insights
for key metrics.
In this example, we see that the forecast
for top deals is up this
month, but close rates
and sales activities are
trending slightly down.
Our execs can click in to learn more.
Here, we can learn more
about this month's sales activity levels.
We can ask Einstein questions
about the report using natural language
or tap on a question
that Einstein serves up.
In this report, we see
that activity levels
are highest in the U.S.
I'll tap the thumbs up
to like this insight
so I can see it first next time.
And this insight can be shared easily too.
That can happen automatically
via a workflow, or manually,
so everyone can take the right next step.
As you can see, Einstein
1 is making it easier
for everyone to get AI powered insights
so they can make better
business decisions.
Now let's take a look at sales.
Sales Cloud is helping sellers
at Salesforce be more productive.
Here on the Sales homepage,
sellers have an overview
of their business.
Sellers can each configure their homepage
to show their favorite information,
like closed business,
pipeline, activities,
top accounts, and more.
When sellers have questions,
they can talk to their CRM.
Let's ask Einstein to show us
which opportunity to focus on.
Einstein can access all
related customer data
and recommends an opportunity in seconds.
It also shares why it
made that recommendation.
In this case, our decision maker, Jackie,
had a product question.
Let's click in and follow up.
Sellers at Salesforce can also now
use Einstein Conversation Insights
when holding virtual meetings,
and Einstein will record
and transcribe the meeting
right on the record.
Einstein will also summarize the meeting
for the rep, saving time.
Now, reps can focus
on building better customer
relationships instead
of transcribing their notes
from meetings and CRM.
With Einstein Conversation Insights,
our sellers are now seeing a 50% increase
in their win rates.
in their win rates.
Now let's look at service.
At Salesforce, we have 4,000
technical support engineers
and they're using CRM, AI,
and Data and Service Cloud to
help them be more productive.
For example, when customers
start a chat from our website
or another channel, their
chat is intelligently routed
to the right technical support
engineer and service cloud.
As they chat, Einstein jumps in
and suggests a response
based on the information
from the conversation as well
as the customer's profile.
Einstein can also surface information
from the knowledge base,
which allows the support team
to respond more efficiently,
because they don't have to spend
time searching for answers.
When the chat ends,
Einstein automatically
generates a work summary,
which the team can then
edit, saving even more time.
And because customers sometimes
have the same questions,
once a case is closed, Einstein
can use the case information
to generate a new knowledge article,
which can instantly be shared
with other support engineers on the team,
helping everyone close cases faster.
Now, you might be wondering how the rest
of Salesforce is able to use AI.
With Slack,
the entire company has an AI
powered platform for work.
Let's take a look at two use cases.
First, let's talk about marketing.
Marketers use Slack every day
to do things like have conversations,
collaborate, and automate work,
and now, they can use AI in Slack too.
For example, a marketer
who is planning an event
might open a Slack channel
and discover she has 20 unread
messages, but no worries.
Slack AI can summarize
all of those messages,
saving her time.
She can just click on the diamond icon
and in seconds there's a bulleted summary
of the messages in the channel.
Fantastic!
And when she needs to schedule a meeting
with several people, she
can ask Einstein for help.
Einstein can check calendars,
recommend meeting times,
and even schedule the meeting for her.
This is currently saving employees
at Salesforce an hour of
scheduling time every week,
and those are just a couple of examples
of how Salesforce employees
are getting more work done
with AI and Slack.
So now you've seen a
behind the scenes look
at how Salesforce employees
are using Einstein 1,
our full portfolio of products
with best in class CRM,
trusted AI and data altogether
on one connected platform.
Back to you, Nicole.
- All right, thank you so much, Jody.
That was a fantastic demo.
Love seeing it come to life.
- Awesome, yes.
- Before we close out, Juan, tell me
what would you leave everyone with?
What's our key takeaway for today?
- Yeah, I think first of all, you know,
you heard my perspectives on Gen AI.
I think all of us as CIOs,
we're building our own perspectives
as we see this technology evolve.
But at the end, I really believe
that this technology
will continue to change.
So we gotta be prepared
for this constant change
in this great and exciting
space of generative AI.
- So true.
Thank you so much, Juan.
- My pleasure.
- Appreciate you being here.
- Thank you, Nicole.
- And thank you to all of our guests
who have tuned in to watch this.
We hope it was insightful for everyone
and we'll see you again next time.
If you want more information,
there should be a link on your screen,
so please go ahead and
feel free to click that.
You'll get all of the
information you need.
And well, we'll see you
on the next one.
(upbeat music)
(upbeat music continues)
(lively music)
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