Book Image

Architecting AI Solutions on Salesforce

By : Lars Malmqvist
Book Image

Architecting AI Solutions on Salesforce

By: Lars Malmqvist

Overview of this book

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.
Table of Contents (17 chapters)
Section 1: Salesforce and AI
Section 2: Out-of-the-Box AI Features for Salesforce
Section 3: Extending and Building AI Features
Section 4: Making the Right Decision

Using the power of built-in features

We started this book by looking at the built-in features in the core Salesforce clouds. No clouds are more used than Sales Cloud and Service Cloud, so it made sense to start our exploration there. What we saw was a range of pre-built features such as Einstein Lead Scoring, Einstein Forecasting, and Einstein Case Classification that use powerful and simple pre-built machine learning (ML) models to accomplish very specific tasks that help optimize sales or service processes.

In general, the lesson of the first few chapters is that there is a lot of value in the out-of-the-box features, and this can be realized quickly if these features happen to be a good fit for your business requirements and you don't have significant compliance or enterprise architecture constraints in terms of what you can implement. You can see a selection flow in the following diagram:

Figure 10.1 – Feature selection flow

Some features...