What are the main components of Salesforce AI?

The most important fact about the Einstein platform is that while it is an entity in its own right, it is also an integral part of the complete Salesforce platform. That means, first and foremost, that the core CRM data model that powers the rest of the Salesforce feature set is directly available to the Einstein platform's AI features. That also means that the core security model, user interface, administrative functions, and so forth that make up the Salesforce CRM can be used by and straightforwardly use the Einstein features. This fact is crucial to maximizing the benefit of working on CRM instead of integrating third-party solutions. The following diagram gives an overview of the platform architecture:

Figure 1.1 – Einstein platform architecture

The architecture diagram starts at the bottom level, with programmatic services that require advanced programming skills to implement, and proceeds up the stack to the pre-built solutions, which can be activated at the click of a button.

The Platform Services layer

The Platform Services layer, sometimes referred to as myEinstein, is the part of the Einstein platform that directly builds on top of the core data model to provide customizable capabilities for prediction and analysis. Overall, in keeping with the Salesforce platform, these can be divided into declarative services that you can configure via the administrative user interface and platform services that enable programmatic access to the platform:

• In the first category, we find, for instance, Einstein Prediction Builder, a point-and-click interface for making predictions about the value of fields on CRM records. This feature has extensive configurability and allows substantial tweaking of what data is used for prediction and how the system will evaluate the prediction. This feature can be maintained administratively and does not require a data scientist or a developer to implement it.
• In the second category, we find, for instance, the Einstein Vision feature. Einstein Vision is a programmatic API-based deep learning model that you can train for your particular use cases. For example, you could train a model to detect instances of your brand imagery in visual imagery. This feature requires considerable programming skills and machine learning knowledge to implement well.

Tableau CRM (previously called Einstein Analytics)

The analytics capabilities of Tableau CRM are prodigious, and they make use of many of the Einstein platform features that are discussed in this book. When considering the Einstein platform, this is often seen resting as a separate layer on top of the services layer. It is, however, well outside the scope of this book to go into any detail about this area. It deserves a large volume of its own. It is also principally focused on analyzing data to gain insight rather than using it for the types of AI-centric use cases we will be considering. Some of the pre-built solutions that we will learn about have analytics elements in them, but we will cover the specifics as and when required in these cases.

The Lightning Platform

The Lightning Platform in and of itself does not have any AI capabilities. However, you can't meaningfully operationalize the other features without them, so it deserves a mention in the overall architecture. Typically, you might bring in the predictive capability in the UI, for instance, as a field on a record that is set based on a machine learning model, or in a more elaborate scenario as a custom component, visualizing the information in a way that is particularly relevant to the context record.

However, in many cases, you may want to use the AI features directly in automation, such as a flow or process builder. A simple example might be a model that classifies incoming support cases based on which might likely escalate. If that probability is above a certain threshold, automation might alert relevant managers and assign the case to a special queue for velvet-glove treatment.

Einstein products

The last and increasingly largest category of features is found within specific Einstein products. These are prepackaged AI and analytics offerings that address particular use cases in particular clouds. It is more the rule than the exception for a Salesforce cloud to have a dedicated Einstein product offering, although some are better developed than others. There are many of these, they vary wildly, and more are added at a rapid clip release after release.

We will be going through many of these in later chapters, so we do not need to labor the point here. These solutions are, broadly speaking, less configurable than the Platform Services, but they are the obvious place to start if they fit your use case.

Third-party options

While it is generally advisable to use the platform options whenever possible, sometimes you reach a point where they do not offer the functionality you require. In those cases, you have two options:

• First, you can look at AppExchange and see if someone has created a pre-built app for you to utilize.
• Second, you can integrate third-party APIs into your solution. We will examine three options for this in Chapter 8, Integrating Third-Party AI Services, and give detailed guidance on when it is appropriate to go down that route. However, you should go down this route only when there is a much stronger fit for your requirements from going off-platform than staying on it.

With this foundation in place, let's move on to looking at the platform's various components in detail.