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OK.
Can everyone hear me?
All right.
I got one thumbs up.
That's good.
All right.
Welcome, everyone to Day
1.5 or 2, whatever it is.
Welcome to our
session on three ways
that AI makes service
frictionless and fun.
My name is Meagan
Meyers, and I
am a director of
product marketing
here at Salesforce.
I'm on the
Service Cloud team
and I specifically
focus on AI and data.
Before we dive in,
I want to remind you
that Salesforce is a
publicly traded company.
So you should make your
purchasing decision
based on goods
and services that
are generally available.
And I also want to just
take a quick moment
and say thank you.
It's been a
wild first day.
This is my sixth
Dreamforce.
And I walked in yesterday.
I walked in today
feeling the same level
of excitement as
I did on year 1.
This is the event
that excites me,
because I get to connect
with my colleagues,
my friends, with
customers, with partners,
with trailblazers,
with all of you.
I get to hear
your stories.
I get to hear your lessons
learned, your challenges,
your use cases.
I get to learn so
much from all of you.
I get to see all
of the demos.
I get hands on.
I go to all of
the sessions.
And it's truly what makes
my job exciting, fun,
and worthwhile.
But it is not lost
on me that you're
taking a lot of time away
from your busy schedules
to be here with all of us.
You're away from
your families.
Some of you traveled all
the way around the world
to be here with us.
So thank you from the
bottom of my heart.
I appreciate it.
You could be anywhere,
but you are here with us.
So thank you.
So what do we have
on tap for you today?
First, I'm going to
introduce you to the three
ways that AI makes service
frictionless and fun.
And then I'm going
to bring my friend
and colleague,
Tim Casey on stage
to show you how
that happens.
And then get ready
to be inspired,
because we have
not one, not two,
but three incredible
service leaders
here with us today
who are going
to share their stories.
They're going
to share where
they are on their
customer service journey.
They're going
to share what
use cases they're
doing right now,
their lessons learned,
their challenges,
and even give us a hot
take on what they see
as the future of AI
and customer service,
and even share a
bit about what's
on the horizon for
customer service
AI in their organization.
And then we're going
to close it out
with some actionable
next steps and resources
before we send
you on your way
to continue on with
your Dreamforce
before Dreamfest.
So let's dive in.
So customer
service is truly
one of the hardest
jobs out there.
In fact, it is one of
the top 10 hardest jobs.
So it is no
surprise that we're
seeing an average of
a 45% turnover rate
year-over-year.
Things like fragmented
systems, a--
sorry, excuse me,
inefficient processes,
updating passwords,
all of these things
that they have to do that
are slowing them down
are really, really
taking up their time,
taking-- or monopolizing
agents time.
Another thing, another
productivity blocker
is siloed systems.
In fact, 58% of
agents say that they
have to toggle between
multiple systems just
to find the information
that they need in order
to be able to
do their job.
These fragmented workflows
are slowing agents down.
They're introducing
or increasing
the risk of human error,
and also jeopardizing
the customer experience.
At the same time,
customer expectations
continue to rise.
86% of agents say that
customer expectations
today are higher than
they were just last year.
And it's critical that
your agents are meeting
those customer
expectations,
because when they
do, customers
are more likely to
purchase or engage
with your brand again.
And that's going to
increase customer loyalty,
and it's going to
positively impact
your bottom line.
And as customer
expectations
continue to rise,
so does case volume,
and so does
case complexity.
And what we're
hearing from agents
is that they're
really struggling
to be able to
deliver on speed
and quality of service.
So it's really no wonder
that service teams
are overwhelmed. right?
But what if I
told you it didn't
have to be this way?
What if I told you
that delivering
the service of your
dreams could be easy,
that there's really
only one thing standing
in your way, and
that's the right tools.
Any guess what
that might be?
AI.
Yay.
Customer service AI
delivers fast, seamless,
and personalized service.
And when implemented
across the end-to-end
customer journey, AI
makes service frictionless
and fun.
Einstein For Service
offers 18 out-of-the-box
purpose-built solutions
built natively
on the Agentforce
platform.
Features like next
best actions, case
classification, article
recommendations, service
replies, I could
literally go on and on.
I won't, but I could.
All of these
features help agents
do all of the jobs
that need to be done.
They help them get
started fast as well.
And because they're built
natively on the Agentforce
platform, they
are extensible
across the entire
CRM, but they're also
customizable to
your unique business
needs using these low-code
features like MuleSoft
APIs, and also Apex Code.
So let's explore
the three ways
that AI helps make service
frictionless and fun.
The first is that AI
helps automate some
of these simple tasks
and deflect cases, which
helps free up agent time,
and deflect more cases so
that they can
focus on more
complex cases or high
value interactions.
Features like
Einstein Bots,
or even generative
knowledge answers in bots.
Allow or empower
your customers
to be able to answer
their own questions
or solve their own cases
or issues on their own.
And now, with AI
agents, customers
can actually address or
answer more complex issues
while engaging
with your brand
and freeing up
agents so that they
can focus on even
more complex,
high-value issues.
And as AI agents
are interacting
with your
customers, they're
gathering valuable,
critical case data
so that when the AI agent
identifies that a human is
actually needed
to intervene,
they can pass
that information
on in a seamless transfer
to the human agent.
So that brings me
to the second way
that AI helps make service
frictionless and fun,
and that AI-powered
tools built directly
into the agent
console empower
agents to deliver
efficient and effective
service.
When AI-powered tools
are built natively
into the agent
console, agents
have everything that
they need right there
in the flow of work.
They don't need to toggle
between different systems.
They're able to focus
more on the customer
than trying to find what
they need to do their job.
Features like
next best actions
will provide
recommendations
on what to do next.
Service replies will
provide personalized
responses, so the
agent can then directly
send it to the customer.
But I just mentioned that
AI agents are gathering
critical case data.
So when an AI
agent identifies
that a human
agent is needed,
the AI agent will
then summarize
the current interaction.
And when they pass it
off to the human agent
and the human agent
accepts the case,
that summary is presented
to the human agent
with a feature
that we call case--
or conversation catch up.
We use generative AI
to do that summary.
So when the human agent
gets the conversation
catch up, they're
able to start
working the
case immediately
because they have all of
the information presented
to them that they
need in order
to be able to just hit
the ground running.
And they don't need
to ask the customer
to repeat themselves,
which is literally
my least favorite
thing to do anyway.
Another reason
that AI empowers
agents to work efficiently
and effectively,
or the next evolution
of AI assist tools
is something that we
call service planners.
So the same way that
we're collecting
that critical case data,
as a case comes in,
we're collecting that case
data and generative AI
will actually create a
case-specific multi-step
plan for the agent to
start working the case.
And this allows agents to
work more productively.
It accelerates
agent onboarding,
so newer agents can
work more confidently
on more complex
cases, but it also
helps guarantee policy
or company compliance
and adherence, which
is really, really
valuable for any
company out there.
So the third way
that AI helps ensure,
or helps guarantee, or
makes service frictionless
and fun is that
AI empowers
agents to deliver more
personalized service.
When you combine
AI and data,
agents are able to deliver
tailored interactions
with ease.
They're able to see
all of the information
that they need in order
to understand who it is
that they're speaking to.
So with Data Cloud, we're
combining all of your CRM
data from sales, service,
marketing, and commerce,
and all of your
third-party data
that might not necessarily
live within your CRM,
and unifying that and
harmonizing that directly
in your CRM.
So your agent see
the CSAT score,
they see the
lifetime value,
they understand
recent transactions,
they know their web
engagements and search
history.
And that way the agent has
a better, more complete
view of who
they're talking to.
But also we're using that
data to ground the AI.
So when a recommendation
or a prediction or even
a generation is
presented to the agent,
they're that much
more confident
that what's being
presented to them
is accurate, relevant,
to the customer
that they're
interacting with.
So as you can
see, AI helps
make service efficient
and effortless
because it automates
mundane tasks.
It helps boost
agent productivity.
And it helps enhance
the customer experiences
and increases CSAT.
And there's a couple
of different ways
that this does it.
But customers like
Wiley are currently
using Agentforce Service
Agent, our AI agent,
to help handle
back-to-school surges.
And in fact, they're
seeing a 40% increase
in case resolution
with our AI agent
compared to the
traditional Einstein bots.
And Simplyhealth is using
generative AI to save over
90 hours for their
agents per week.
And Converge ICT
a telco in ASEAN
is actually seeing a 36%
increase in efficiencies
just in the first
month since deploying
their Einstein Bots.
So it's pretty
incredible the efficiency
gains that AI provides,
but really it's
the frictionless
service that it
offers that is going to
help your company overall.
So I want to invite
Tim up on the stage
to show you how all of
this really comes to life.
All right.
Thank you, Meagan.
So, everybody, my
name is Tim Casey.
I'm part of our Service
Cloud solution engineering
team.
And I've put together
a quick demonstration
to bring all of
this to life.
So let me go ahead
and cue it up,
and I'll spend just a
little bit of time level
setting.
So what we're
actually going to see
is the story of our
customer, Jonathan,
and he wants to purchase
a vacation home.
So we're going to see all
three areas that Meagan
just walked through.
We're going to see some
automate, that's where
we're going to start.
We're going to see how we
can serve up self-service,
get Jonathan
the information
that he needs
without necessarily
engaging an agent.
And from there, we'll
see how we seamlessly
transfer that
over and empower
an actual human agent.
So what we
actually see here
is an Experience
Cloud site.
And the idea is we're
offering self-service,
right?
This specific example
we're going to see
is the service
catalog where
we can guide customers
like Jonathan
to find the answers
that they need, again,
without necessarily
engaging a human agent.
In this example, he
wants to understand
how much you can afford.
And this is a great
use case for something
like Data Cloud,
right, where
we have this data
that doesn't typically
live in CRM, it
doesn't typically
live in Service Cloud,
like real-time rate
information, serving
that up or making
it actionable, and
making that usable
through something like
the service catalog,
where we can embed
those workflows right
in the site.
Now, if Jonathan
has more questions,
it's a great use case for
something like knowledge,
right?
This is something
Salesforce
has been doing
for a long time,
serving up content,
serving up knowledge.
Again, whether that
lives in Salesforce
natively or we're bringing
in third-party content.
Jonathan has
more questions.
He wants to understand
how is this going
to affect his taxes,
this is a great use case
for Gen AI.
Let's serve up an
answer on the fly
using dynamic
content that's
sourced from that
knowledge base
rather than returning
like static links,
right, to knowledge
articles or case records.
We have more
questions and we
want to engage
with the person.
It's a great
opportunity to serve up
more self-service, right.
So we've heard
a lot this week
about agent for
service agent.
This is what we see here.
We're meeting Jonathan
on the channel
of his choosing, and
deploying a service agent
that can answer his
questions, again, get him
the information he needs.
And then if he's
still ultimately needs
to connect with
a human agent,
we're going to seamlessly
pass that conversation
over.
right?
So Jonathan is
going to get
that personalized,
that contextual service
that he expects.
And that's actually
going to bring us
to our next segment here.
So when we talk
about empowering,
first I'll do
a quick review
at a high level,
covered what it looks
like to automate, right.
The goal is to deflect
some of those inquiries
from coming in, so
our human agents
can focus on those more
complex customer service
issues.
What we're going to see
here in just a moment
is this is going
to be picked up
by our actual
human agent, Amy,
and we're going
to see how she's
able to leverage
those automation,
those intelligence tools
that live inside Service
Cloud to make her
more efficient,
to make her
more productive.
Again, delivering
that service
that their
customers expect.
So what we see right here
is a service console,
and this is where Amy's
going to start her day.
So right away,
she has visibility
into some metrics around
the contact center's
performance.
She wants to
understand how
she's doing on our SLAs.
If those metrics
start to slide,
we can easily take
action on that.
What she's going
to do is go ahead
and set her presence
using Omnichannel.
And if you're
not familiar,
this is what we use
in Service Cloud
to get the right work
to the right agent
at the right time.
So with respect to
queue, skill, capacity,
availability,
we're finding
the right human person to
tackle Jonathan's issue,
pick up right where
that Agentforce service
agent left off.
And what we're
going to see
is as she accepts
that work,
she immediately
has visibility
into who this customer
is and what he represents
to her organization.
So this is what
we talk about when
we talk about a unified
profile coming in
from Data Cloud.
We're painting this full
picture, this full 360
view, again, of what
this customer represents,
serving up
calculated insights,
and then allowing
our human agents
to pick right up where
Agentforce service
agent left off,
and dive right
in using something
like a service replay,
guiding this person on
what they should be seeing
next to correctly
troubleshoot or get
Jonathan squared away.
And again, this knowledge
doesn't necessarily
need to live
in Salesforce.
We can easily bring
in external content
through something like
Unified Knowledge,
and use that to
ground these replies
in real-time.
In the background,
we've, of course,
gone ahead and logged a
case for this interaction.
The purpose here
is so, again, we
can see where we're
standing on our SLAs,
make sure we're honoring
those commitments we've
made to our customers.
We can use something
like a predictive AI
to identify how likely
is this to escalate.
We can use historical
case records
to run case classification
and, again, guide me
on how this should be--
how this should
be classified
based on interactions
we've had in the past.
The same is going to apply
with the knowledge base,
right.
Like making
recommendations on what
we've seen be
successful in the past.
As the conversation
continues to progress,
again, we're
going to continue
to see service replies
offered up that are coming
up with relevant replies.
Again, based
on the context
of the current
conversation
or information that lives
in the knowledge base.
When he says he wants to
explore how this is going
to affect his
existing heloc,
It's a great opportunity
for something
like a next best
action, like serving up
recommendations
where we can walk
the agent through
step-by-step on
not only the proper
phrasing, but what exactly
we need to do to get this
customer squared away.
It's another great
use case for something
like external data.
If we have real-time rate
information, or really
any external
or unstructured
data that
doesn't typically
live in Service Cloud,
super easy to pull that
in using something
like a flow,
using something
like Apacs,
like Meagan called out,
and make it actionable,
deploy that right
in the flow of work.
Now, as the
conversation wraps up,
another great use
case for something
like generative AI,
something that's normally
very time-consuming
with contact
centers I work with,
handling disposition,
doing that
after-conversation work.
We can use AI to identify
what was the issue, what
was the resolution.
Can you summarize
this interaction?
Go ahead and get
that case closed out,
and we can move on
to the next customer.
The last piece I'll
cover here very quickly
is if we wanted
to generate
new knowledge-based
content off
of this interaction.
Again, this is
something a lot
of customers I work with
are very excited about.
This is something we
haven't seen before.
We can create a new
knowledge article
based on the content
of this interaction.
It's going to go
ahead and drop
that in a draft status.
So it's still
subject to any review
if we're using PCS, or any
other knowledge management
process, we still get a
chance to put eyes on that
and review it before
it's something
we're actually publishing
out for our customers.
All right.
So just to wrap this
one up real quick,
I'll do a quick review.
Again, we saw how Amy
was able to leverage some
of those intelligence
and automation tools
that live inside
Salesforce to empower
her to be a more
efficient agent.
Where we're
going to go next
is I want to talk through
Meagan's last example.
So how are we going
to delight customers?
And again, it starts
with personalization.
So what we're going to
see is another example,
where this time we have
a customer, Jonathan,
who's calling
in, and we're
going to see what
that experience looks
like coming in
over the phone
and specifically
Service Cloud Voice.
So again, we're going to
see Amy, go ahead and set
her presence
using Omnichannel.
The beauty of
this is, again, we
have a single place
for presence, right?
We're not working out
of multiple systems.
And when we see
this call come in,
the experience for the
agent, the experience
for the customer is going
to be exactly the same.
When she goes
ahead, answers
that call, those
call controls
are embedded right
into the page.
Again, I have
that C360 view
of who this customer
is, what he represents
to me as an organization.
And I can start diving
in and providing
that service, again,
that contextualized,
that personalized service
that customers expect.
We see that real time
transcription coming in.
That's going to be part
of this voice call record.
The idea here is,
as an agent, again,
I don't have to
spend a bunch of time
taking notes.
We're bringing
that all in,
we're taking that off
of the agent's plate,
so they can really
focus on resolving
this customer's issue.
And we have that case
in the background.
Same idea.
We have our SLAs.
In this instance,
this customer
wants to get
pre-qualified.
It's a great use case
for something like Slack,
where I can use
expert finder,
find experts
who are outside
of my typical
organization,
collaborate in real-time,
and make sure, again, we
get this customer the
service they expect,
get him the answer he
expects, and go ahead
and get him pre-qualified.
As the conversation
wraps up,
another great use
case for Gen AI
like hey, go ahead,
summarize this back
and forth that
occurred in Slack.
Let's make that a part
of this case record.
So that's going
to be a part
of Jonathan's C360
profile going forward.
Go ahead and use
generative AI
to actually generate
a pre-approval letter.
Again, we're taking
away that overhead,
maintaining all of
these email templates.
It's a great
use case where
we can source new content
that's based or grounded
in our knowledge base.
And then we can go ahead
and get this conversation
wrapped up.
So as we
progress, Jonathan
is going to let us know
that he wants to actually
connect to an agent.
And what we're
going to see
is dynamic next
best action.
So based on a
customer keyword,
customer sentiment, some
sort of intelligence
source, we're serving
up recommendations
at the point of
impact that's again,
going to guide our
agent on what exactly
she needs to say,
what exactly she needs
to do to this
customer squared away
with an agent, so
they can proceed
on next steps for
this mortgage.
All right, just
to take us home,
I want to talk very
briefly about how it all
ties in from a supervisor
and an admin perspective
as well.
What we see is our
agent disposition
out this interaction,
same idea, issue, summary
resolution.
When we flip over to that
supervisor perspective,
what we're actually
looking at here
is the Omnichannel
wallboard.
And this is something our
customers have been asking
me for a long time.
So like holistic,
real-time view
into everything
that's going on
in that contact center
environment, right?
So regardless
of which channel
a customer is coming in
on, we have visibility.
It's the same idea.
We don't want our
supervisors swivel
chairing or jumping
back and forth
between these different
systems as well.
I have one single
place where
I can identify what my
agents are working on,
what their presence
looks like, what's
their capacity, right?
If issues are
occurring or if flags
are being raised
dynamically,
as the supervisor, I can
go ahead and review what's
already been discussed.
I can leverage
Einstein to summarize.
Like hey, what was
the back and forth
of this real-time
phone conversation?
Don't need to review
the whole thing.
Of course, I still
have the option
to listen in live.
I can provide
guidance in real-time,
and again, get this
customer squared away.
I think the real-time
visibility is awesome.
There's always
going to be a need
for historical analytics.
So what we've done with
service intelligence
is roll all up--
all of this data up
into a macro level,
so we can understand what
this customer engagement
center looks like
holistically, right?
Understanding where
we're at on our time
to answer, our handle
time, our CSAT,
and then leveraging
something
like a conversation
mining, where
we have all this
historical data that now
lives inside Salesforce.
Again, whether these
issues are coming in
over the chat,
over the phone,
over like email, right.
we can understand why are
our customers contacting
us.
Where are we
spending our time
and how much is
that costing.
And use that as a way to
identify new use cases
for self-service
and automation.
All right.
With that, I'm going to
go ahead and introduce
my friend and
colleague, Khoa Le, who
is our VP of
product management,
and he's going to go
ahead and introduce
our panelists in
just a moment.
Awesome.
[APPLAUSE]
Thank you, Tim.
Great job.
Hi, y'all.
My name is Khoa, I'm on
the Service Cloud product
management team, and
I've been working
at the junction of
customer service
and AI for about
seven years now.
And I'll say, over
the past 18 months,
nearly every
service conversation
has become an
AI conversation.
Like I'm sure
you're experiencing
the same things as well.
And we've had the
opportunity at Salesforce
to talk to so many
amazing customers
that are really
taking advantage
of the technology,
and blazing trails
to deliver value.
And so I want to be
able to introduce
some amazing
customers that we
have to come up and share
their stories today.
So please welcome Claudia
Nichols, chief customer
officer of Simplyhealth.
Kevin Quigley,
senior manager,
continuous improvement
at Wiley, and Eugene Yeo,
chief executive advisor
of Converge ICT.
And now a big
round of applause.
[APPLAUSE]
All right, so.
These three
amazing people,
real practitioners, have
delivered real value
for projects across
their organizations.
Maybe we can start
with just getting
a sense of your overall
AI strategy, how you're
thinking on it, and just
a brief summary of where
you are on your journey.
And Claude, maybe
we'll start with you?
OK thank you, Khoa.
And before I
do, can I just
say thank you
for showing up.
So we have two children
who are 9 and 12.
And when I told
them I was going
to speak at a
conference about
artificial intelligence,
and customer service.
They say, you don't
need to be nervous.
Nobody will be there.
So thank you
for showing up.
They were here.
So our story
really began--
so I'm chief customer
officer for Simplyhealth,
which is a UK-based
health insurance company,
and we help our
customers find and pay
for private health,
essentially.
So our story of
AI and automation
began from that purpose of
helping to improve access
to healthcare.
And we want to quadruple
the number of people
we help in that way.
And we can only do that
by bringing in technology.
And that's where
Salesforce is our partner
for bringing in AI.
So we have
conversational AI
that resolves 40% of
our incoming calls.
We've got generative
AI on emails, which
we do together with you.
In fact, we were the
first in the world
to implement GPT Einstein
on emails in sales.
Yeah.
Thank you for
that, by the way.
And you kind of mentioned
something there.
Wanting to be
able to scale,
and leveraging
the technology
to be able to do that.
I think that's a story
we hear all the time.
Kevin, why don't you share
your strategy for AI.
Yeah, Thanks, Khoa.
So for those
who don't know,
Wiley is a global
publishing company
across research and
learning markets,
and we're really embracing
AI across the board.
There's three parts
to our strategy, which
have to do with product
innovation, growth,
and productivity.
And productivity is the
part that I'm focused on.
You can imagine in the
customer service world.
So we've looked
over the years,
since 2019, really,
we've implemented
different types
of AI tools,
starting with those kind
of deterministic chat
bots, and then looking
at agent augmentation
or productivity
tools that empower
them to work faster, work
smarter, whatever it is.
So we had that journey.
And then in 2023,
when generative AI
started coming to
the enterprise,
we adopted that as
well and started
using it to
create these more
personalized interactions.
And now, here we are
piloting the Agentforce
Service Agent, so.
Absolutely.
Thank you for the
years of partnership.
And if you haven't
seen, Wiley and Kevin
have been all around
Dreamforce, so nothing
but gratitude there.
Eugene, would love
to hear high-level
how you're thinking
about the AI strategy.
I'm Eugene.
I'm chief
executive advisor
of a company called
Converge ICT.
We are a listed company
based in Philippines,
and our primary
business is really
providing internet
connectivity
through fiber.
So we built the largest
fiber infrastructure
in Philippines right now.
And we have one
mission in the company.
And that's really to
leave no one behind.
What do we mean by that?
We basically mean that we
want to provide everyone,
no matter whether
you can afford
or you can't afford,
we want to provide you
the best in class
internet connectivity,
because we really
believe that
with internet
connectivity,
you can really lift
a population in terms
of education, in terms
of opportunities.
And that's very
important for Philippines
and Filipinos.
So that's really
our mission.
And for us, it's about
enhancing the customer
experience to all
our customers.
And that's how
we are looking
at leveraging on AI, to
augment our contact center
staff, to augment
our delivery staff,
to be able to drive
better productivity,
and therefore drive
faster repairs, faster
installations, and better
overall experience.
So we're very early
in this journey
on AI and field services.
We have deployed it
on the 15th of July,
but we really see a lot
of gains coming out from
there.
And happy to share
that across this panel.
Absolutely.
And I love the sense of
purpose and values here.
Health, education,
connectivity.
It's so important.
It makes me think
about the moment we're
in right now.
We've heard a lot this
week about AI agents,
and how AI agents
and human agents
should be collaborating.
So, Eugene, I'd love to
get your thoughts of what
does that collaboration
look like between AI
agents and human agents?
How do those
come together.
It's all about
helping each other.
Whether it's a bot,
whether it's a human,
it's about
working together
to drive a better outcome,
a better business outcome,
a better experience.
And that's really the
purpose, the end goal,
right/ So the way that
we see it in our journey,
we leverage on AI to
really help us do,
number one, first-level
deflection of cases,
really, to try to solve
problems that can be
easily solved perhaps
by the customer.
So one of the very
simple challenges
we have in internet
connectivity
is the ONT or the
modem on your site,
and sometimes a simple
restart of the modem
will be able to resolve
the connectivity issues.
So we always try to do
those with the chat bot
and we've seen about
36% deflection of cases
just through that very
simple, automated response
to the customers.
But when the customer
has a challenge that
can't be deflected
easily, then that's
where our contact center
staff really comes in.
And the way that we
use Einstein right now,
it's really to help our
contact center staff
figure out what is the
problem at hand faster,
get suggestions
from LLM to really
be able to answer
and respond
to the customer in a
shorter period of time.
So it's a lot
about augmenting
the capabilities
of a human agent
to be able to do
their job way faster
than they could.
Absolutely.
And Claudia, I
know you and I
have talked about this
quite a bit as well,
and you've got
some good thoughts
on how AI agents
and human agents
can come together
and work together.
Yes.
So for us, it's about
explaining to people why
you're implementing
the technology,
coming back to
that purpose,
and then allowing
people to see
the value that adds.
And it is really, the
team genuinely see it
as a team member who
does the boring stuff
and who works late at
night and at the weekend.
There you go.
So one of our ladies,
so we've got mostly
women in the contact
center, and some of them
really long
standing service.
So we've got one
particular lady, Norma,
and she's been with
the company 60--
no, she is 67 years old.
She's been with the
company for 43 years.
And she's embracing this.
And she was talking about
AI and Einstein as Amy.
And thanks Amy, for
doing such a great job
as a member of the team.
So I think that anecdote
tells you quite a lot.
I think Norma and
Amy are a great team.
And it is so important to
think about how technology
and people can
work together,
thinking about the change.
Technology has created a
lot of new opportunities
for the business as well.
And Kevin, as one of
our earliest adopters
of Agentforce
Service Agent,
I'd love to hear from you
about some of the impacts
and how you think
that's actually
going to create
new opportunities
for the business at Wiley.
Yeah, sure.
So there's really
two areas of impact
I want to highlight.
One is that we're able
to provide this more
personalized experience
rather than say,
a canned experience from
traditional chat bots.
And we're able to answer
the customers inquiries
in their own terms.
So that's really great.
And then, we've also
been able to expand
that coverage,
though, right?
Because we don't have to
design the conversation
tree.
So now it's
able to respond
to virtually every topic
in our knowledge base.
And so that's-- we're
answering more questions
and we're answering them
in the customer's own
terms.
And, I mean, if
you guys have
been around
the conference,
I'm sure you've seen it,
but the result of that,
so far, has been over
40% increase in case
resolution compared
to our old chat bot.
So that's making
a difference.
That's amazing.
And on that note
of 40%, Mike,
if you'd be
willing to share
some more of the hard
metrics of how you've
seen AI impacting in
your contact center?
I know there's
amazing stats
that you shared
around email
and some of
the initiatives
that you've had as well.
So yeah.
So for us, it's resolving
40% of calls and chats.
We are now doing 40%
up to-- on emails.
And it's saving us
significant amounts
of money, too.
So year-to-date, we're
spending 1 million pounds
less, which is about
10% of the budget,
so quite significant.
Yeah.
And Eugene, obviously,
telecommunications,
connectivity, internet,
it's a regulated industry,
lots of complexity.
It's also an industry that
maybe likes older systems,
right?
So I'm curious about
your journey there
and obviously the impact
that you've seen so far
and the impact that
you're expecting
as these initiatives
scale up more
in your organization?
Yeah.
So as a regulated
industry,
it's really
challenging when
we want to adopt AI
and new technology.
We always have
to understand,
where's the
demarcation of what
we are able to share, what
we are not able to share.
So if we are going to
use ChatGPT, for example,
and implement it
in our call center,
I think that's going to
be a big challenge for us
because there's a lot
of these personal data,
sensitive data
that we are not
allowed to put in public.
At the moment, we
use a framework
like that, that's going
to be a big challenge.
And the good
thing about what
we have with
Salesforce is the fact
that it can really
demarcate what information
is sensitive and ensure
that information doesn't
get leaked out to
the public models,
so that we can keep that
information confidential.
But I think the
bigger challenge
in the telecommunications
industry
is that it's a
very old industry,
and there's a lot
of legacy systems.
And that's one of
the biggest challenge
that we had.
And one of the
big outcomes
of this transformation
with Service Cloud
is the fact that we took
nine disparate systems
and we managed
to integrate
all these systems
through MuleSoft,
so that there's one
single plane of view
for our contact
center staff,
for our field agents.
And that really
saves so much time.
Imagine the time just to
toggle between systems.
And you're locked
down in one system,
if you log in again,
that's so much time
already saved.
So AI really
brings the value
beyond just
the integration
to really drive
massive productivity.
So from just the
two plus months
that we've
implemented this,
we've seen more than
20% productivity gain.
And that's really amazing.
But I think if you give us
another six to 12 months,
I believe we should
be able to drive that
to 40% 50% easily.
I love that.
Let's get to that 50%.
And you hit
some notes there
that I just want to expand
on just a little bit.
You talked about
trust and your data.
At Salesforce.
it's something
that we think about
from the beginning,
from the first
conversation
with potential partners,
with language models,
we talk about what
is the contract
look like, how
do we actually
take on more of
the trust burden
within the walls
of Salesforce
so that customer data
stays as customers' data,
and not the
Salesforce product.
And then I think data, the
automation, and bringing
that in, all
tools that help
human agents
without AI tools
also then enhance and
accelerate the AI tools
as well.
And Kevin, I
know that's been
a big part of the journey
of adopting Agentforce
Service Agent.
I'd love to hear
how you brought
the tools together,
and how you actually
worked to scale up
Agentforce Service
Agent, how you work
to implement it?
Yeah, sure.
So there's a couple
interesting parts
of that story.
The first thing I
want to emphasize
is that it was sort of
a natural culmination,
right?
We had already
made the investment
in building out
that reference data
and our knowledge base.
And we had made
the investment
in having an Omnichannel
kind of service structure,
and having certain
metadata available
and policies documented.
So we were able to
take that investment
and start plugging it into
this agent experience.
And we were able to
design the conversational
experience with
support experts,
and with our existing CRM
experts and the product
experts, Instead
of having to design
this tedious
conversation tree branch
out every bit of logic.
So we actually
had the people,
who know what great
support looks like,
telling the AI agent what
good support should look
like in these
different scenarios
for this product.
And so that's really cool
how it brought together,
not only these
existing investments,
but it also created this
new opportunity for us
to say, here's how
we tell a human agent
to provide great service.
Here's how we're going
to tell the AI agent
to provide great service.
Absolutely.
And that point that
you just brought up,
it's a conversation that
I have with customers
all the time of what's
the skill set that we need
to bring together to make
this successful, right?
It's our admins, it's
our service reps,
it's our service
leaders, it's
folks who understand
the brand tone
and voice that we want
to be able to utilize.
So, so Claudia, you
were an early adopter,
one of the first adopters,
as you mentioned earlier,
about the generative
capabilities,
and you've obviously
brought more capabilities
to market there.
I'm curious if
there's anything
you want to share in
terms of your learnings,
of how you're able
to roll this out
in your
organization, again,
with people, with
techniques, being
able to measure success
before scaling up?
Yes.
We didn't get all of
it right straight away.
So one of the
things, we're
a regulated business, much
like what you described.
And we've got
the double whammy
of being in healthcare,
which is regulated,
and in financial services,
which is regulated,
which is a delight.
So we had to spend
a bit of time
up front on the
policy side of things.
So really documenting
very clearly
what is our AI
policy, which
was an expansion of our
existing data policy.
And you guys at
Salesforce helped us
with some examples of
how other people had
done that.
We set up an AI forum.
And, on purpose,
I included
in there the
naysayers so that they
had a place where
they could say nay,
and then we could deal
with that objections.
So it's worth
investing in that.
And then from here
on, it's really--
so we were the first
in the world on email
generative, now what
else can we do right?
I'm a bit jealous
of the Wiley
example of Agentforce.
And I'm keen that we now
lean in and see what does
that look like for us.
How do we continue to
innovate with Salesforce?
Absolutely.
Eugene, you shared
earlier the success
that you want to get to.
Do you have any tips
and best practices
about how you measure
success so far to date
that you can share
with folks who
are interested
in implementing
AI in their organization?
Ultimately, it's about
the business outcome
that you want to drive.
So we measure
success based
on business outcomes.
In this case, it's all
about customer experience.
So we look at CSAT
and MPS as the measure
of success for us.
And the good thing
is we measure it
on a daily basis, and we
look at before and after.
And after the
transformation,
we really see the
lift in MPS scores,
and really
happy with that.
So that shows that
whatever we've done,
it's really bringing
better customer
experience.
So that's how
we measure it.
Absolutely.
Can I build on that?
Because we did
the same thing.
And customer
outcomes is a part
of regulation called
consumer duty in the UK.
And we now are using AI
to do quality assurance,
not just on the sample of
2% to 3% of interactions,
but on 100% So it's
those kind of things
that you otherwise could
never afford and couldn't
do.
And that's the power
of that just blows
my mind sometimes, yeah.
Yeah.
Kevin, your business
is so cyclical, right?
So obviously, you
mentioned a number
of how a Agentforce
Service Agent is
handling some of--
a good percentage of
incoming conversations.
Like, how has that
helped your business
in terms of the
cyclicality?
Yeah, so-- I mean,
you can imagine it,
it allows our
existing support staff
to really focus on
the customers, right?
And handle those
complex issues.
It takes away
from the admin
work or the paperwork of
working with a customer,
and allows them to just
focus on that empathy,
on that problem solving,
and on those key support
skills.
So we're using--
I didn't mention
this earlier,
but we are using
capabilities
like generative
service replies,
like the generative
work summaries.
And so, we've really been
able to reduce the focus
on interacting
with the system,
and increase that ability
to focus on the customer.
And much like
both of them said,
we want to provide that
best customer experience
possible, even if
we are experiencing
a back to school
surge or whatever
the conditions are.
We don't want that
volume increase
to have a negative impact.
And making sure that we're
very efficient and very
customer-focused is one
great way to do that.
Awesome.
So great service,
even though you're
working through the surge,
with the teams that you
have assisted by AI.
Maybe to wrap us up, a bit
more open of a question.
And Eugene, I'll
start with you
and guide you
a little bit.
Earlier, we had
a conversation
about just guidance
of what you might want
to share with folks
who are looking
to implement around
people, process
and technology.
So I'd love for
you to expand
on that and share anything
else you'd love to share.
Yeah, sure.
We see AI as technology.
Again, it's a
means to an end.
It's a tool.
And it's not
everything, right?
Technology is one part.
You've got processes,
and you got people.
And I think
most of us know
that in any
transformation,
any digital
transformation,
people is going to be
the biggest challenge.
50% to 60% of the
time you really
have to educate the
folks that you have.
You have to help
them understand
that this technology is
really there to help them.
So one of the fears
that people have, and we
see that internally in
our organization as well,
is that as we implement
a lot of these automation
through AI, and they see
the capability from AI,
they get really
afraid because they
are really afraid
that they're
going to lose their job
one of these days, right?
So it's about helping
to educate them
that, hey, that's
not going to happen.
Why it doesn't happen?
Because behaviors change,
customer expectations
change, our products
change, a lot of things
change and evolve, right?
So we still need people
to decipher the problems
that's coming in, to
decipher the challenges,
but with augmentation
and support
from artificial
intelligence.
So as they start to
understand that, they
start to move towards-- we
have this adoption curve,
this is about
the trust curve.
As people start
to understand
that the tool is not
there to replace them,
but there to really help
them do their job better
and more efficiently,
they start to trust it.
They start to adopt it.
And that's where we really
see a lot of productivity
gains from this.
Absolutely.
Kevin, any last
words to share?
Yeah, just a little
bit of parting advice
is to focus on
your fundamentals.
We've been developing
these best practices
in the service
industry for a while,
and technology has
certainly evolved
what that can mean.
Things like having your
processes and policies
documented, things like
having good context data
available to your users
to provide a personalized
experience.
And so, all of
these investments
we've been making,
I think there's just
a new call to
urgency to say,
hey, those little
quality of life things
that elevate it to
the next level, that's
not optional anymore, that
is now mandatory in order
to take action on some of
these new opportunities
that AI is presenting.
So go back to
all those things,
and if there's an area
where maybe your roadmap
is a little
behind where you'd
like to be, I'd say
double down on it,
do a fresh ROI evaluation
and figure out,
OK, now that I
know this is going
to be the backbone of some
important AI grounding,
do I have new calls
to really focus
on that element.
Awesome.
Yeah, Don't forget
the fundamentals
and the foundation.
Claudia.
I'm going to go where
Eugene went is people,
because the technology
is one thing
and it's not easy, but
it's the easy part,
in many ways.
But it's the people in the
process part around it.
So we've got two top
tips to share here.
One thing was we did a
skills and wills matrix,
so we mapped out of
all of our agents
and team who had the
digital skills ready to do
this work.
And if they didn't, who
had the will to get there.
They were self-declared
and their manager
did the same.
And then we worked
through what everybody
needed to support them.
And the second
top tip is we
did a thing called speed
to happiness, which
allowed us to monitor
through data if people
were happy with the change
and the transformation
that we're asking them
to go on the journey on,
and what we could
do, identify where
we needed to help people.
So skills,
wills, happiness,
and continuous
improvement.
I love it.
Well, this has been
a global panel.
We have folks from all
over the world here.
So just want to thank
you one more time.
If we give them round
of applause, please.
[APPLAUSE]
There we go.
And before we
wrap up, just
want to call out that you
can continue your learning
journey on Salesforce.
Plus, there's a lot of
amazing content on demand.
This one will be
available to you as well.
We also have the State
of Service Report
that a lot of the
statistics that you
saw earlier in
the presentation
are there for you.
We survey customer
service leaders
from across companies,
across the world.
So you can find out
what other leaders
are thinking.
So definitely dive into
that for that full report.
And beyond that, please
join our Serviceblazer
community.
You can interact
with amazing folks,
like the three here
on stage today,
learn from their
insights, but also
share your experiences
and insights as well.
And beyond that, just
want to say thank you
for spending the
time with us.
We really appreciate
you, and hope
you have an
amazing Dreamforce.
[APPLAUSE]
Thank you all.
Really appreciate it.
[MUSIC PLAYING]
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