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)
1
Section 1: Salesforce and AI
3
Section 2: Out-of-the-Box AI Features for Salesforce
8
Section 3: Extending and Building AI Features
12
Section 4: Making the Right Decision

Summary

In this chapter, we started by looking at why we need to bring AI capabilities into our CRM. The key takeaway was that AI capabilities allow you both to personalize and improve the service you deliver to customers, both before and after purchase, in a way that represents a step change in comparison to traditional CRM. Additionally, AI allows you to automate and simplify many labor-intensive processes.

We looked at the layers of the Einstein platform and examined how we can use pre-built solutions to get a head start with AI capabilities. Equally, we looked at both the declarative and the programmatic platform services that you can use to extend the native capabilities.

Then, we took a whistle-stop tour through the different elements that make up the total Einstein offering, including sales, service, marketing, commerce, industry solutions, and platform services. This gave us a sense of both the depth and breadth of the platform as a whole.

Then, we changed tack and looked at the general question of how architecting AI solutions is different from architecting traditional solutions. We learned that seven characteristics define AI architecture, namely that AI solutions are probabilistic, model-based, data-dependent, autonomous, opaque, evolving, and ethically valent. This gave us a starting point for how to approach the deployment of these capabilities in the real world.

Finally, we learned about the fictional company Pickled Plastics Ltd., whose requirements we will be using as a reference throughout the book. And now, with the preliminaries out of the way, we will dive straight into the principal matter of the book and look at AI features for sales.