Salesforce - AI for CRM

AI for CRM:
A Field Guide
to Everything You
Need To Know

2
What Is Artificial Intelligence?
Technology is evolving faster than ever. Cloud computing,
social media, and mobile devices are ubiquitous. Everyone
(and everything) is getting connected: 3 billion internet users,
5 billion mobile users, and 6 billion connected devices make
up a network of information and interaction never before
seen in history. But that’s just the beginning.
Artificial intelligence (AI) is the next major wave of innovation,
driven by advances in computing power, the ability to store
large volumes of data in the cloud at minimal cost, and easier
access to advanced algorithms. And it will be more disruptive
and powerful than any previous shift in technology.
AI is often represented by various related terms: machine
learning, deep learning, natural language processing,
predictive analytics, and so on. All of these terms point to a
future in which our platforms and systems are smart enough
to learn from our interactions and data, not only to help us
with what we ask, but also to anticipate our needs, taking
care of mundane and forgotten tasks and reminding us of
important ones. AI can connect the various nodes of our
lives (home, work, travel) into one experience that moves
seamlessly with us from house to car to office. Much of that
experience comes through our phones. Within a few years, AI
will be infused in everything digital.
As consumers, we are already using AI without even realizing
it. Google harnesses AI to autocomplete search queries,
predicting what you are searching for with great accuracy
and without human involvement. Facebook news feeds and
Amazon product recommendations are tailored for you via
machine-learning algorithms. Self-driving cars apply various
AI techniques to avoid collisions and traffic congestion.
All of these consumer apps have trained consumers to
expect more from businesses: The new standard for every
customer interaction is a smart, fast, seamless customer
experience engagement.
Introduction

3
At a high level, AI is both understanding historical data
and applying what is learned to current contexts to
make predictions. This has the potential to make every
business “smarter.”
Today, every company faces an imperative to integrate AI into
the fabric of their business in order to succeed. Until now,
however, AI has largely been out of reach for businesses.
The high cost to implement AI solutions, combined with a
shortage of data scientists and incomplete data, has made it
challenging for all but a few companies.
That’s where we come in. In 1999, Salesforce launched the
first-ever cloud CRM platform, making customer relationship
management accessible to businesses large and small.
Since then, we’ve grown into a complete Customer Success
Platform, with solutions across sales, service, marketing,
community, analytics, commerce, IoT, and apps. Now, we’re
making our platform even smarter with Salesforce Einstein.
Designed to enable companies large and small to be smarter
and more predictive about their customers, Salesforce
Einstein discovers insights, predicts outcomes, recommends
next steps, and automates tasks for business users — all while
getting smarter and smarter along the way.
Introduction: What Is Artificial Intelligence?
$153 billion
estimated market
for AI solutions
by 2020
— Bank of America Merrill Lynch

4
• How AI will impact specific business functions, including:
Sales (Chapter 3)
Customer Service (Chapter 4)
Marketing (Chapter 5)
IT (Chapter 6)
• What AI, machine learning, and deep learning
actually mean
• How AI has evolved, and why it’s suddenly a hot topic
• What AI means for business, including key challenges
and opportunities
Introduction: What Is Artificial Intelligence?
Read on to explore how you can take advantage
of a smarter future.
Sales
Service
Marketing
Platform
To help you seize the AI opportunity, we’ll explore a few themes in this e-book:

5
Artificial Intelligence (AI) is the concept of having machines “think like humans” — in other words, perform
tasks like reasoning, planning, learning, and understanding language. While no one is expecting parity with
human intelligence today or in the near future, AI has big implications in how we live our lives. The brains behind
artificial intelligence is a technology called machine learning, which is designed to make our jobs easier and more
productive.
Machine Learning is the core driver of AI, and involves computers learning from data with minimal programming.
Essentially, instead of programming rules for a machine, you program the desired outcome and train the machine to
achieve the outcome on its own by feeding it data — for example, personalized recommendations on Amazon and
Netflix. (Learn more here.) Machine learning is a broad term that encompasses related AI techniques, including:
Deep Learning which uses complex algorithms that mimic the brain’s neural network to learn a domain
with little or no human supervision. Consumer apps like Google Photos use deep learning to power face
recognition in photos.
Natural Language Processing (NLP) uses machine learning techniques to find patterns within large data sets in
order to recognize natural language. One application of NLP is sentiment analysis, where algorithms might look for
patterns in social media posts to understand how customers feel about a specific brand or product.
Big Data is the raw fuel of AI — large amounts of structured or unstructured information that provide the inputs for
surfacing patterns and making predictions.
Internet of Things (IoT) is a network of billions of digitally connected devices, from toasters to cars to houses and
jet engines, that collect and exchange data and can communicate with one another to better serve users.
Predictive Analytics is a branch of advanced analytics that is used to make predictions about unknown future
events, based on patterns in historical data. You might see this in marketing offers that become more relevant to
you each time you take action (or don’t) on an email offer.
Glossary: How to Talk About AI

6AI for CRM: Everything You Need to Know
The Path to a
Smarter World
Computing has always been about data. It’s written into the
Oxford Dictionary’s definition of a computer: “an electronic
device for storing and processing data.” From the
beginning, computers were designed to fill in gaps in human
intelligence by storing, classifying, retrieving, and applying
huge amounts of data to help us solve problems faster.
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6

AI for CRM: Everything You Need to Know
In the beginning, these were very simple problems.
One of the earliest computing prototypes, sketched
out by Belgian thinker Paul Otlet in 1934, “would
allow people to search and browse through millions
of interlinked documents, images, audio and video
files,” according to The New York Times. It was an
early vision of the internet with a poetic name: the
“Mundaneum,” a means of storing and processing
huge amounts of “mundane” data. The premise
of computing is to do what the human mind is
designed not to do: remember every little detail,
storing it so that every data point can be easily
accessed when it’s needed. (The human brain,
by contrast, is designed to focus its processing
power on what’s important, as Nobel Prize-winning
economist Daniel Kahneman explains in his book
Thinking, Fast and Slow.)
“Many, many
companies now find
themselves with
huge amounts of
data. What are we
going to do with it?”
Ascander Dost
Senior Software
Engineer & Linguist,
Salesforce
7

From the beginning, though, we dreamed of computers
that could do more than the mundane. In 1956, Prof. John
McCarthy coined the term “artificial intelligence,” describing
a world in which machines could “solve the kinds of
problems now reserved for humans.”
But in order to move from simple computing to true AI,
computers needed three things:
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This is why, while the idea isn’t new, true AI is only now
becoming a reality. The data models came first, with
simple if-this-then-that logic evolving into increasingly
complex problem-solving algorithms. The idea of
machine learning is simple:
Data models
to intelligently
classify, process,
and analyze data
Raw data
to feed the
models so
they can keep
improving
Processing
power
to drive fast,
efficient
computing The more data the machine processes, and the more
computing power it has, the faster and smarter it gets.
start with a
data model,
feed it tons
of data,
and let
it learn.

It might conclude that a cat is
a furry thing with pointy ears,
almond-shaped eyes, whiskers,
and paws. But what happens
when it comes across this image?
This is a cat, but without the
telltale pointy ears or paws.
Or what about this one?
= cat
JStone / Shutterstock.com
= cat
catwalker / Shutterstock.com
Using only two images to “teach” a machine how to identify a cat probably wouldn’t
equip that machine to accurately classify Garfield as such. However, feeding it
billions of different images of cats — in nearly every color, size, and position possible
— would make it much more likely to correctly classify an image like this:
Here’s a classic example. Let’s say you want to train a machine to recognize pictures of cats.
So you feed it two data points:
AI for CRM: Everything You Need to Know 9

Early data models lacked the volume of clean data
required to perfect their data models and effectively
“learn.” Only recently, with the surge of data readily
available via the internet, do the models have access
to the data they need to get smarter. In 2009, Stanford
University computer scientist Andrew Ng and Google
Fellow Jeff Dean led a Google research team to create a
massive “neural network” modeled after the human brain,
comprising thousands of processors and more than 1
billion connections. Then, they fed the machine random
images of cats, pulled from millions of online videos. By
identifying commonalities and filtering the images through
its brainlike neural network, the machine essentially
taught itself how to identify an image of a cat. It was an
astounding achievement for AI — and one that wouldn’t
have been possible just a few years earlier, without easy
access to those millions of thumbnail images.
But there was another limiting factor: processing power.
In the earliest days of computing, machines filled entire
rooms in university buildings. Improved ability to put more
transistors on integrated circuits meant processing capacity
doubled every two years (thank Gordon Moore and his
handy law for that observation), which packed more power
into smaller boxes, taking computers out of universities and
businesses and putting them in consumers’ hands.
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“Feed enough cat photos into a neural net, and it can learn to
identify a cat. Feed it enough photos of a cloud, and it can learn
to identify a cloud.”
— Wired, January 2016: “Artificial Intelligence Finally Entered Our Everyday World”

AI
Data
models
Processing
power
Big Data
$16.5 billion expected
market for AI in 2019
— International Data Corp
The personal computing era opened up a market for
consumer and business software, games, gadgets, and
upgrades. When the Internet followed the computer
out of the university and government buildings, we
saw that market explode. Instant connection changed
everything at an interpersonal level all the way up to an
international one. Cloud computing meant companies
didn’t have to worry about physical infrastructure to
scale. The rise of mobile built on the success of Apple’s
iPhone and then Google Android, expanding the
market of software and games (and giving the world
“the app”). Mobile also freed us from those computing
nodes at home and office and, essentially, created a
virtual world of communication and commerce on top
of the physical one.
Today, we have reached the intersection of the three
ingredients needed to create true artificial intelligence:
smarter data models, easy access to virtually unlimited
amounts of data, and cheap and powerful cloud
computing. As mentioned previously, AI is present in our
daily lives when we search Google, ride in an Uber, or
buy products on Amazon.
11

What AI Means for Business
Remember how computers have gotten smaller and smarter?
They’ve also gotten cheaper, resulting in a surge of smart
devices that are generating a growing body of business data
that, in turn, can power machine learning. The Internet of
Things encompasses an entire world of digitally connected
devices — toaster, toothbrushes, thermostats, lightbulbs, cars,
and much more — that are now being networked, talking to
each other, and businesses, and consumers. We’re talking
about a lot of connected things: 6 billion of them that, says
Gartner, will be requesting support by 2018. Those billions
of connected things mean huge volumes of customer data —
indeed, 90 percent of the world’s data was created in the last
12 months alone.
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Businesses need to be smart about the way they gather,
digest, and apply that data, which is the lifeblood of IoT
— provided it can be properly used.
But the impact of AI doesn’t stop there. Behind each of those
devices, of course, is a real customer — and the next generation
of customers expects a cohesive, intelligent experience every
time they interact with a business. When a delivery order is
delayed, they expect to be contacted with an updated ETA
and a make-good offer — such as a $5 credit or free shipping
on their next order — without having to pick up the phone
and talk to a service agent. AI makes it possible to create an
entire universe of business apps to deliver smarter customer
experiences across sales, service, and marketing interactions.
For many businesses, however, AI has been largely out of
reach. Historically, companies have faced four key challenges
when it comes to adopting AI for business:
Data
Expertise
Infrastructure
Context
13

Below, we’ll explore how companies can address each of these challenges in new ways.
The Data Challenge
For businesses, it’s not just the volume of data that matters — it’s also how all of those critical data points are
organized. Business data often resides within a hodgepodge of internal and external sources that rely on a mix
of cloud and on-premise systems. Often, these systems don’t talk to each other, leading to siloed datasets and
inconsistent data quality. Cloud-based CRM solutions like Salesforce are designed to connect all of that data to
create a single view of each individual customer — and this connected approach to data is essential to taking
advantage of the AI opportunity.
The Expertise Challenge
Beyond the data, companies must have the tools and expertise to analyze and act on it. This is difficult considering
two common problems: siloed data storage and a scarcity of data scientists. According to a McKinsey Global
Institute report, there’s a shortage of 190 thousand data scientists. Today, advances in AI tools are making it
possible for businesses to work smarter without a legion of data scientists.
The Infrastructure Challenge
Just as discrete and siloed data sources limit companies’ ability to properly leverage their data, so too do
fragmented infrastructure systems. The high cost of on-premise hardware and computing systems that have the
power to run machine-learning algorithms has prevented many companies from jumping in. Now, however, cloud
computing has made AI more accessible and affordable.
The Context Challenge
For many businesses, AI may seem not only out of reach, but also irrelevant. Popular culture has imagined AI in the
image of R2D2 and C3PO, rather than an essential component of modern business processes. Read on to learn
more about our vision for how AI will transform sales, service, marketing, and IT by automating mundane tasks and
empowering every human employee to add more value.

The result of un-analyzed, underleveraged data isn’t just
missed opportunity. It’s a significant failure to connect with
modern consumers in the way they expect (and demand).
Currently, half of all business decisions are made with
incomplete information, which disconnects the business from
the product and, therefore, from the customer. For all the data
customers are creating, less than 1% is analyzed, such that
77% of customers say they are not engaged with businesses.
Now, however, companies have the opportunity to change
this — to close the gap between business intelligence and
customer experience. New tools reveal useful insights
about the customer. These tools illustrate how AI exists
along a spectrum: the most basic tools require you to “pull”
information out of them, while the most intelligent tools
“push” information to you, anticipating what you’re going
to want to know. With machine learning, computer systems
can take all this customer data and build on it, operating
not just on what’s been programmed but also adapting to
changes. Algorithms adapt to data, developing behaviors
not programmed in advance. Learning to read and recognize
context means a digital assistant could scan emails and
extract what it knows you’ll want to know. Inherent in this
learning is the ability to make predictions about future
behavior, to know the customer more intimately, and to be
proactive rather than reactive.
6 billion connected things will
proactively request support
by 2018
— Gartner

Companies that embrace the AI opportunity will be able
to create the modern experiences their customers expect,
connecting with them on all their devices, analyzing
their data to get to know them better, and being able to
anticipate and predict in order to better serve them.
What does AI for CRM look like? Imagine being able to
capture real-time signals, wherever they occur — from a
customer’s support request to a prospect’s tweet. Then
imagine being able to analyze every data point, pulling
together data from Salesforce, external sources, and the
Internet of Things to create a complete view of every
customer. This, in turn, enables us to predict the best
next sales, marketing, or service interaction for each
customer, and then automate everything from routine
tasks to real-time customer engagement. It’s a whole new
way of connecting to your customers and prospects, with
intelligence powering a new era of customer success.

61%of employees
expect artificial
intelligence that
automates or assists
in work-related
activities to have a
major or moderate
impact on their daily
work lives
— Salesforce Research
AI has implications for every line of business. Sales will
be able to anticipate opportunities and focus on the
best leads. Customer service teams will deliver the next
generation of proactive service, preventing machine
failure or addressing FAQs in a customer community
before they have a chance to become service cases.
Marketing can build predictive journeys for every
customer, personalizing experiences like never before.
IT can embed intelligence everywhere, creating smarter
apps for employees and customers. Read on to learn
what AI means for your business.
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Smarter Sales
Imagine you’re a sales rep named James. Every morning
when you wake up, the first thing on your mind is all of your
upcoming sales calls. However, you don’t have to think too
hard because you wake up in a connected world. You check
your smartphone and your CRM automatically displays an
itinerary of your day. All of your key customer meetings are
organized in priority of opportunity value. Your smartphone
also displays each customer’s three primary pain points,
along with directions to reach the customer’s site on time,
already pre-programmed into your car’s GPS. A quick cup of
coffee, and you’re ready to start your day.
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Sales

But just as you’re about to reach your first client, your
smartphone sends you an important notification: a news
update that your client has just acquired a data analytics
company. Your sales assistant surfaces a summary of key
findings from top trending news articles, along with product
recommendations that integrate with your client’s recent
acquisition to help you move the deal forward. Within
seconds, you have full context of your deal, in-context market
updates, and a conversation starter — all of which are surfaced
automatically and embedded within your CRM experience.
After you finish talking to your client, your smartwatch vibrates,
“Great job, James! Sounds like your client liked your product
recommendation. We suggest you move this deal to stage
5. Would you like me to move forward?” With a single tap,
you move the opportunity from “Deal Qualified” to “Discuss
Pricing.” When you return to the office, you receive a push
notification reminding you, “Your meeting notes have been
uploaded successfully. The system has automatically extracted
the following action items, and suggests this follow-up email.
Would you like to send an email to the customer now?” In
one click, you’ve successfully sent an email to secure your
next meeting, without manually logging customer data or
key action items into your CRM.

“You could say to your
phone, ‘Show me leads I’m
supposed to talk to today,’
and it does those operations
for you: analyzes which ones
are at which stage, finds the
hot leads, and gives you
a ranking of which ones
you should talk to first, a
probability of converting,
and the expected monetary
value when converted.”
Chalenge Masekera
Data Scientist, Salesforce
Salespeople will benefit from AI in three ways:
Data is automatically captured, enabling reps to
discover best next steps and closest connections
Predictive sales helps reps prioritize leads and
respond faster to high-value opportunities
Digital assistants will help maintain the
relationship once it’s established by scheduling
calls and issuing reminders
The move toward making business applications
and sales tools more accessible and relevant to our
digital lifestyles will continue to expand, especially as
smartwatches and other wearable devices are further
integrated into our daily operations and interactions.
User-interaction time will shorten from minutes to
seconds as contextually aware push notifications bring
speed and intelligence to every sales experience.

Smarter Service
Here’s a vision from the present: Maria orders a gift online and
pays for two-day delivery to get it to her brother in time for his
birthday. When she calls him on his birthday, however, she
learns that the package hasn’t arrived. She calls the vendor,
but has to meander through an endless series of push-button
options to find the department she needs. Eventually, she
ends up speaking to an agent. It’s as if none of the options
she selected were recorded: She has to tell the story from the
beginning. That agent transfers her to another agent, who asks
her to tell him what happened — from the beginning — then
puts her on hold. Ultimately, she hangs up in frustration.
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Service

But with AI, service can actually anticipate a customer’s
needs, rather than simply reacting to them. Long before
Maria calls her brother, an AI-driven CRM would have
been monitoring her package’s progress, and notified a
service agent the moment it was delayed. That agent, in
turn, could have reached out to Maria proactively, letting
her know when the package would arrive and offering her
free same-day shipping on her next order.
This is possible because the interaction doesn’t start
when the customer picks up the phone, but rather
happens on a constant, ongoing basis. The conversation
between customer and business is an interaction joined
by the data the customer is producing on every digital
channel (from smartphone to connected devices to
social media), and the solutions the company finds based
on that data. This solution may come before a problem
even arises — and the customer. doesn’t have to make a
call because service is right there already.
25% of customer
service leaders were
using predictive
analytics or
best-next-action
functionality
in 2015
22

The AI-driven interaction will automatically recommend the
right content to the agent at the right time, including suggested
solutions, relevant cases, and best next actions. The agent can
introduce these actions to the customer in an organic way,
rather than bombarding her with offers she doesn’t need.
Once the issue is resolved, the agent could put a note in the
customer record instructing the system to reconnect with the
customer on a regular basis and suggest relevant upsell and
cross-sell offers when appropriate. The continuous flow of
customer data translates into a greater understanding of the
customer and a superior experience that builds brand loyalty.
Smarter service also allows a company to identify customer
churn risks and thereby prevent customer attrition. Predictive
intelligence can identify customers who are at risk for churn,
so that reps can renew or upsell with personalized offers.
Feeling neglected or ignored by a company, or forced to wade
through inefficient systems, is a sure way to alienate customers.
Companies that fail to apply AI to CRM will seem hopelessly
mired in the past.
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“Eventually, helpful AI features will be on the phone, in chat, email,
and any other type of communication people use. These kinds of
things will become commodities, and if you have highly accurate
and helpful AI, people will love your services.”
Richard Socher
Chief Scientist, Salesforce

Smarter Marketing
A new level of precision and personalization, brought about
by smarter machines using data more intelligently, applies
to marketing as well. An AI-enabled marketer can reach
every customer at the right time, knows the best audience
for every campaign, and delivers the perfect content for
every customer.
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Marketing

74% of marketers
leveraging dynamic
content (powered
by predictive
intelligence), rated it
as absolutely critical
or very important in
helping them create
cohesive customer
journeys
Today’s marketers have a wealth of data and insight
at their disposal — but that doesn’t always translate
into intelligent customer and prospect interactions.
Traditionally, marketers have lumped audiences into
broad groups based on attributes like location or industry.
Often, that’s because marketers don’t know enough about
each person — or even if they do, it’s too labor-intensive
to engage people individually with the perfect message,
content, or offer.

The AI-enabled marketer will be able to:
Leverage smart scoring to predict each customer’s
likelihood to convert
Use predictive intelligence to segment and build
audiences based on likely future actions
Automatically adapt the journey for each
individual customer
Deliver the best next product, content,
or offer — every time
Send messages at the right time, when a customer is
most likely to engage
“Market research” once relied on taking the temperature of
broad chunks of society. AI enables marketers to focus at
a granular, individual level. This depth of audience insight
will allow marketers to create and test campaigns virtually,
ensuring the ability to target and convert audiences more
effectively by surfacing the right offer to the right person at
the right time.
“Let’s say you are a
marketer and you
send out specific
emails at specific
times, and you don’t
want to go through
the same repetitive
steps over and over
again. A bot could
do that for you.”
Chalenge Masekera
Data Scientist, Salesforce

Smarter IT
We are entering what Salesforce CIO Ross Meyercord calls
“the continuum of code”: an era in which low-code and no-
code platforms are becoming more robust and ubiquitous,
enabling business users to be developers. This isn’t the end of
code, but it does mark the democratization of app-building
— an important development as apps become increasingly
central to every business function.
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27
Platform

Business apps, in particular, are held to ever higher
standards. Consumer apps are setting the bar for intuitive
user interfaces, seamless integrations, and intelligent
interactions. Business apps must be just as smart, just
as fast, and just as simple to use. Collaborating with a
partner or updating a sales quote should be just as easy
as hailing an Uber. So the question for IT becomes:
How do we enable a new generation of developers — and
non-developers — to build more intelligent apps, faster?
The answer lies in the platform. Just as Heroku enables
developers to quickly build open-ended apps in modern
languages, so too should AI platforms enable developers
to build predictive apps with minimal coding and no IT
hassle. With the power of AI, citizen data scientists can
embrace low-code solutions and build any predictive app
they dream up, even CRM-driven apps like fraud detection
or risk scoring.
In order to enable developers — and non-developers —
to build predictive apps, the best platforms must be:
Data-Ready
Platforms like Salesforce offer native
data prep, saving time and resources by
eliminating the need for ETL. This means
your CRM data is ready to go whenever your
app is.
Modeling-Ready
Machine learning should be built into the
fabric of your platform, rather than being
something you have to add on later. With
Salesforce’s trusted multi-tenant cloud,
automated machine learning is already
built in.
Production-Ready
Eliminate the need for dev ops with smart
model management and monitoring tools,
so IT can focus on building the best apps
and deliver immediate results.
With an AI-first platform like Salesforce,
companies can build intelligent apps across the
entire continuum of code — and rest assured that
intelligent data modeling, tracking, and monitoring
are built into every app.

29
Customer-Focused AI:
Salesforce Einstein
AI for everyone.
At Salesforce, we’ve focused on creating a set of AI
platform services and solving customer problems across
sales, service, marketing, and IT in a whole new way.
With Salesforce Einstein, we are solving for these kinds
of questions:
Are you selling the right product to the right
customer at the right time?
Are you servicing customers on the
right channel by the right agent?
Are you marketing on the right
channel at the right time with the
best content?
Are you building apps that leverage
the predictive power of AI?
Conclusion
“The beauty of Salesforce is that it has tons of
different applications across various verticals
and lines of business: marketing, sales, service,
IoT, healthcare, and so on. Salesforce touches
on so many different areas and has a general
platform. Hence when we solve a problem
once in a principled way the solution can be
applied to so many different companies,
improving their processes and helping them
focus on what’s actually important and exciting.
For example, a customer service expert can
focus on helping you with tough questions
specific to your org — and not on how to recover
a forgotten password for the 50th time.”
Richard Socher
Chief Scientist, Salesforce

AI Has the Power to Transform CRM
Marketing LightningServiceSales
Sales
• Spends time
visiting customers,
not entering data
• Predicts the best
next step for every
customer
• Understands what
customers need and
when they need it
Service
• Recommends a
solution before
a customer asks
• Offers cross-sells
and upsells at the
right time
• Predicts when
things will break,
before they do
Marketing
• Can reach every
customer at the
right time
• Knows the best
audience for every
campaign
• Delivers the perfect
content for
every customer
IT
• Can build predictive,
smarter apps — faster
• Leverages the power
of open-source
frameworks
• Empowers everyone
to build with AI,
faster

Salesforce Einstein enables everyone to discover new
insights; predicts likely outcomes to power smarter decision-
making; recommends best next steps; and automates
workflows so you can focus on building meaningful
relationships with every customer. It’s not bolted onto
Salesforce — it’s an integral part of our platform. Salesforce
Einstein enables every business user to:
Discover. With AI inside of Salesforce, sales reps,
service agents, and marketers will discover new
insights about customers, faster and more easily.
Predict. Knowing the likely outcome of a series of
interactions gives you an AI-powered competitive
advantage.
Recommend. What’s the next best step in a sales
process, a customer service case, or a marketing
nurture journey? AI offers it up so you can focus on
the relationship.
Automate. When certain processes are repeated
over and over with the same solution, often that task
can be automated. AI learns from past actions and
automates those tasks.
Salesforce Einstein is the first comprehensive AI for CRM,
designed to help every business be smarter and more
predictive about their customers. Einstein is powered by
machine learning, deep learning, predictive analytics,
natural language processing, and data mining.
Given our scalability and deep understanding of CRM,
Salesforce is uniquely positioned to deliver AI that
transforms the customer experience. Einstein makes AI
available to the rest of us by:
1) Democratizing AI so every business user can get smarter
and more predictive
2) Bringing intelligence to all Salesforce apps and making
the Customer Success Platform smarter
3) Allowing developers to embed intelligence in every app
Customers will now experience the pervasive benefits of
automated workflows, timely and relevant suggestions, and
adaptive applications throughout every customer touch-
point across Sales, Service, Marketing, and IT to redefine
customer success as we know it today — so that it looks
more like tomorrow.
Visit Einstein.com
to learn more.

Rights of ALBERT EINSTEIN are used with permission of The Hebrew University of Jerusalem. Represented exclusively by Greenlight. The information provided in this e-book is strictly
for the convenience of our customers and is for general informational purposes only. Publication by salesforce.com does not constitute an endorsement. Salesforce.com does not
warrant the accuracy or completeness of any information, text, graphics, links, or other items contained within this e-book. Salesforce.com does not guarantee you will achieve any
specific results if you follow any advice in the e-book. It may be advisable for you to consult with a professional such as a lawyer, accountant, architect, business advisor, or professional
engineer to get specific advice that applies to your specific situation.
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