Architecting AI Solutions on Salesforce

Architecting AI Solutions on Salesforce

by Lars Malmqvist
Released November 2021
Publisher(s): Packt Publishing
ISBN: 9781801076012

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Book description

Use AI solutions in Salesforce to design complete enterprise solutions for sales, service, marketing, and commerce clouds and drive digital innovation in your organization

Key Features

  • Learn how to use Salesforce's AI features and capabilities to meet ever-evolving client needs
  • Get expert advice on key architectural decisions and trade-offs when designing AI-driven Salesforce solutions
  • Integrate third-party AI services into applications that modernize your solutions

Book Description

Written for Salesforce architects who want quickly implementable AI solutions for their business challenges, Architecting AI Solutions on Salesforce is a shortcut to understanding Salesforce Einstein's full capabilities – and using them.

To illustrate the full technical benefits of Salesforce's own AI solutions and components, this book will take you through a case study of a fictional company beginning to adopt AI in its Salesforce ecosystem. As you progress, you'll learn how to configure and extend the out-of-the-box features on various Salesforce clouds, their pros, cons, and limitations.

You'll also discover how to extend these features using on- and off-platform choices and how to make the best architectural choices when designing custom solutions. Later, you'll advance to integrating third-party AI services such as the Google Translation API, Microsoft Cognitive Services, and Amazon SageMaker on top of your existing solutions.

This isn't a beginners' Salesforce book, but a comprehensive overview with practical examples that will also take you through key architectural decisions and trade-offs that may impact the design choices you make.

By the end of this book, you'll be able to use Salesforce to design powerful tailor-made solutions for your customers with confidence.

What you will learn

  • Explore the Salesforce's AI components and the architectural model for Salesforce Einstein
  • Extend the out-of-the-box features using Einstein Services on major Salesforce clouds
  • Use Einstein declarative features to create your custom solutions with the right approach
  • Design AI solutions on marketing, commerce, and industry clouds
  • Use Salesforce Einstein Platform Services APIs to create custom AI solutions
  • Integrate third-party AI services such as Microsoft Cognitive Services and Amazon SageMaker into Salesforce

Who this book is for

This book is for technical and functional architects, technical decision-makers working on the Salesforce ecosystem, as well as anyone responsible for designing AI solutions in their Salesforce ecosystem. Lead and senior Salesforce developers who want to start their Salesforce architecture journey will also find this book helpful. Working knowledge of the Salesforce platform is necessary to get the most out of this book.

Table of contents

  1. Architecting AI Solutions on Salesforce
  2. Contributors
  3. About the author
  4. About the reviewers
  5. Preface
    1. Who this book is for
    2. What this book covers
    3. To get the most out of this book
    4. Download the example code files
    5. Code in Action
    6. Download the color images
    7. Conventions used
    8. Get in touch
    9. Share Your Thoughts
  6. Section 1: Salesforce and AI
  7. Chapter 1: AI Solutions on the Salesforce Einstein Platform
    1. Technical requirements
    2. Why would you build AI solutions on Salesforce?
      1. The value of intelligent CRM data
      2. Some examples
    3. What are the main components of Salesforce AI?
      1. The Platform Services layer
      2. Tableau CRM (previously called Einstein Analytics)
      3. The Lightning Platform
      4. Einstein products
      5. Third-party options
    4. What are the elements of Salesforce Einstein?
      1. Einstein for sales
      2. Einstein for Service
      3. Einstein for Marketing
      4. Einstein for Commerce
      5. Einstein for Industry Clouds
      6. Declarative Platform Services
      7. Programmatic Platform Services
    5. What's special about architecting for AI?
      1. Probabilistic
      2. Model-based
      3. Data-dependent
      4. Autonomous
      5. Opaque
      6. Evolving
      7. Ethically valent
    6. Meet Pickled Plastics Ltd.
    7. Summary
    8. Questions
  8. Section 2: Out-of-the-Box AI Features for Salesforce
  9. Chapter 2: Salesforce AI for Sales
    1. Technical requirements
    2. Introducing Sales Cloud Einstein
    3. Setting up Einstein Lead Scoring and Opportunity Scoring
      1. The basics of Einstein Lead Scoring
      2. Lead Scoring use cases
      3. Configuring Lead Scoring
      4. Architectural considerations for Lead Scoring
      5. Lead scoring at Pickled Plastics Ltd.
      6. Opportunity Scoring
    4. Learning about Einstein Forecasting
      1. The basics of Einstein Forecasting
      2. Forecasting use cases
      3. Configuring Einstein Forecasting
      4. Architectural considerations for Einstein Forecasting
    5. Diving into Einstein Activity Capture
      1. Einstein Activity Capture basics
      2. Activity Capture use cases
      3. Configuring Activity Capture
      4. Architectural considerations for Activity Capture
    6. Examining Einstein Conversation Insights
      1. Einstein Conversation Insights basics
      2. Conversation Insights use cases
      3. Configuring Conversation Insights
      4. Architectural considerations for Conversation Insights
    7. Summary
    8. Questions
  10. Chapter 3: Salesforce AI for Service
    1. Technical requirements
    2. Introducing Service Cloud Einstein
    3. Deploying Einstein Bots
      1. Einstein Bots basics
      2. Bots use cases
      3. Configuring Bots
      4. Architecture considerations for Bots
      5. Einstein Bots at Pickled Plastics Ltd.
      6. Einstein Article Recommendations basics
      7. Article Recommendations use cases
      8. Configuring Article Recommendations
      9. Architecture considerations for Article Recommendations
    4. Speeding up chat with Einstein Reply Recommendations
      1. Einstein Reply Recommendations basics
      2. Reply Recommendations use cases
      3. Configuring Reply Recommendations
      4. Architecture considerations for Reply Recommendations
      5. Alleviating manual data entry with Einstein Case Classification
      6. Case Classification basics
      7. Case Classification use cases
      8. Configuring Case classification
      9. Architectural considerations for Case classification
    5. Summary
    6. Questions
  11. Chapter 4: Salesforce AI for Marketing and Commerce
    1. Technical requirements
    2. Introducing Einstein for marketing and commerce
    3. Using Marketing Cloud Einstein
      1. Einstein Engagement Scoring
      2. Einstein Engagement Frequency
      3. Einstein Messaging Insights
      4. Einstein Copy Insights
      5. Einstein Splits
      6. Einstein Send Time Optimization
      7. Einstein Content Selection
      8. Einstein Content Tagging
      9. Einstein Recommendations
      10. Einstein Social Insights
      11. Einstein Vision for Social Studio
    4. Implementing Commerce Cloud Einstein
      1. Einstein Product Recommendations
      2. Einstein Predictive Sort
      3. Einstein Search Dictionaries
      4. Einstein Commerce Insights
    5. Summary
    6. Questions
  12. Chapter 5: Salesforce AI for Industry Clouds
    1. Technical requirements
    2. Introducing Einstein for Industry Clouds
    3. Using Health Cloud Einstein
      1. Analytics for Healthcare
      2. Analytics for Healthcare – Risk Stratification
      3. Einstein Discovery for Appointment Management
    4. Implementing Financial Services Cloud Einstein
      1. Tableau CRM for Financial Services
      2. Einstein Referral Scoring
      3. Intelligent Document Automation and Form Reader
      4. Einstein Bots for Financial Services Cloud
    5. Working with Manufacturing Cloud Einstein
      1. Tableau CRM for Manufacturing
    6. Optimizing retail compliance with Consumer Goods Cloud Einstein
      1. Analytics for Consumer Goods
      2. Einstein Visit and Visit Task Recommendations
      3. Einstein Object Detection
    7. Analyzing with Non-profit Cloud Einstein
      1. Fundraising Analytics and Performance Analytics
    8. Summary
    9. Questions
  13. Section 3: Extending and Building AI Features
  14. Chapter 6: Declarative Customization Options
    1. Technical requirements
    2. Introducing Einstein declarative features
    3. Giving timely advice with Einstein Next Best Action
      1. Overview of Einstein Next Best Action
    4. Predicting outcomes with Einstein Prediction Builder
      1. Overview of Einstein Prediction Builder
    5. Generating insights with Einstein Discovery stories
      1. Overview of Einstein Discovery
    6. Summary
    7. Questions
  15. Chapter 7: Building AI Features with Einstein Platform Services
    1. Technical requirements
    2. Introducing Einstein Platform Services
    3. Getting started with the Einstein Vision and Language Model Builder
    4. Classifying images with Einstein Vision
      1. Overview of Einstein Vision
    5. Understanding text with Einstein Language
      1. Overview of Einstein Language
    6. Summary
    7. Questions
  16. Chapter 8: Integrating Third-Party AI Services
    1. Technical requirements
    2. Introducing the examples
    3. Predicting with a custom model using AWS SageMaker
      1. Coding the machine learning model
    4. Extracting key phrases with Azure Text Analytics
      1. Coding the example on Salesforce
    5. Translating text with Google Translate
    6. Summary
    7. Questions
  17. Section 4: Making the Right Decision
  18. Chapter 9: A Salesforce AI Decision Guide
    1. Using the decision guide
    2. Choosing the right feature based on functional factors
      1. Functional fit
      2. Support for diverse technical use cases
      3. Support for declarative customization
      4. Support for code-based customization
      5. Model configurability
    3. Choosing the right feature based on structural factors
      1. Model explainability
      2. Data volumes supported
      3. Data requirements
      4. Model monitoring
      5. Model compliance
    4. Choosing the right feature based on strategic factors
      1. Size of investment
      2. Model agility
      3. Skills needed
      4. Time-to-value
    5. Applying the framework in practice
    6. Summary
    7. Questions
  19. Chapter 10: Conclusion
    1. Using the power of built-in features
    2. Extending with declarative features
    3. Knowing when to go beyond declarative features
    4. Choosing where to go from here
      1. Salesforce AI features
      2. Custom AI feature development
      3. General AI background
    5. Summary
    6. Questions
  20. Assessments
    1. Chapter 1
    2. Chapter 2
    3. Chapter 3
    4. Chapter 4
    5. Chapter 5
    6. Chapter 6
    7. Chapter 7
    8. Chapter 8
    9. Chapter 9
    10. Chapter 10
    11. Why subscribe?
  21. Other Books You May Enjoy
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Product information

  • Title: Architecting AI Solutions on Salesforce
  • Author(s): Lars Malmqvist
  • Release date: November 2021
  • Publisher(s): Packt Publishing
  • ISBN: 9781801076012