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

Why would you build AI solutions on Salesforce?

AI is at the heart of the Salesforce platform. There isn't a cloud or prominent feature today that doesn't have predictive or analytical capabilities available. Right now, you can build advanced AI solutions using clicks, not code, across most major Salesforce applications. To some extent, this is surprising. Salesforce is a relative latecomer to the world of AI.

The Einstein platform, which is Salesforce's collective name for its various AI and analytical features, did not exist until 2016. However, once it got going, the pace of evolution has been breathtaking. In 2016 alone, Salesforce acquired 10 companies, many of which were rolled into its AI capability.

In 2019, they acquired Tableau, an undisputed market leader in analytical software. Tableau CRM, the name given to the product combining Einstein Analytics and Tableau, is poised to become the de facto standard for analyzing CRM data. Even in academic AI research, Salesforce has become a force to be reckoned with, presenting groundbreaking research on natural language processing and computer vision. It is one of the first companies committed to a vision for responsible AI, encompassing the five trusted AI principles that AI should be responsible, accountable, transparent, empowering, and inclusive.

Overall, Salesforce has made an impressive commitment to including AI features across its product portfolio and doing so in a way that honors the platform by allowing extensive point-and-click-based configuration and more in-depth code-based customization. However, this begs a simple question: Why do I need AI capabilities in my CRM in the first place? Given the already extensive customization and configuration capabilities of Salesforce, do I need to complicate the picture with artificial intelligence (AI)? As you may guess from the fact that you're reading a book about these features, my answer is a resounding yes. In the next section, I will summarize why you need integrated AI features in your CRM platform.

The value of intelligent CRM data

For most large companies today, CRM is one of the vital arteries through which critical business data flows. Put bluntly, it is the system that knows about customers. The more we know about customers and the better we can use that knowledge to serve their needs, the better our businesses will do. If we learn more about customers, we can sell them products that better fit their needs at the exact time they need them. We can address their questions and concerns proactively both before and after purchase. Not least, we will be able to respond to changes in the market so that our products and services remain relevant over time.

These points have always been true, even before there was such a thing as CRM software. What CRM has enabled companies to do is track their relationship with customers in a way that far surpasses traditional methods. Similarly, an AI-enabled CRM far surpasses a conventional CRM in building and strengthening customer relationships over time.

The first important reason for that is the increasing complexity of the relationships that companies have with consumers. Today, you need to track interactions across digital and physical channels, in-store purchases, promotional events, social media, email campaigns, website visits, online orders, mobile notifications, and potentially a whole plethora of apps and dedicated digital experiences. Some of these may also have real-world components that may generate more relationship data, such as with wearable technology. This complexity means that it is increasingly difficult for a salesperson or customer support representative to look at the customer's profile and understand what is going on and what action is appropriate at a given point in time. They need help to make sense of the actual relationship and make the right decision when dealing with the customer.

Taking this up a level, complexity of relationships generates previously unseen levels of fast-moving data in various formats that do not necessarily respond well to traditional BI/reporting treatment. Managers and marketers, therefore, can no longer rely on the conventional way of analyzing and interpreting data. They need help to aggregate, simplify, and make actionable the treasure trove of behavioral insights found in customer data. The ability to precisely target consumers and interact with them in a genuinely personalized way is at the core of why you need AI in your CRM.

On a more practical level, AI allows the automation of a wide range of traditional CRM tasks, freeing up resources to help make use of the new opportunities generated by complex and varied data. Use cases such as automated report generation, data cleanup, quality management, handling simple sales, and service requests through automated channels (such as chatbots and automating routine process steps via RPA-like technologies) all offer immediate efficiencies.

While, in theory, these technologies need not sit inside the CRM, a native capability that enables you to gain access to these tremendous benefits easily is, in most cases, a no-brainer. With a native capability, you do not have to move data around, transform it, or manage yet another set of complex integrations. You can build on your existing team's skill sets rather than have to learn entirely new technologies and limit off-platform choices to only the areas where you can make a genuine business case.

Some examples

While the Einstein platform is relatively new compared to the Salesforce platform, it has been around for long enough that we can have a look at a few cases where these benefits have been realized.

U.S. Bank is the fifth-largest bank in the United States, with 73,000 employees. They are a long-term user of Salesforce and also an early adopter of the Einstein platform. They adopted the Einstein platform's predictive capabilities across several functions within the bank, explicitly to address the issues of fast-moving and varied relationship data. By increasing the volume and quality of their data, they can see patterns that they wouldn't have been able to identify manually.

This information is brought to the front line by adding predictive analytical capabilities to the interface seen by front-line officers, enabling them to make better sense of the relationship and make the right decision with the customer. 

Accenture is the largest IT services company in the world, with more than 500,000 employees. Within the company's CRM, the Einstein platform is used to visualize and predict information relevant to winning more deals. By embedding Einstein capabilities into lightning components shown in the relevant part of the CRM, users get highly relevant and accurate information that helps them clarify the steps to take for a given opportunity and a prediction of the current win rate.

Stonewall Kitchen is a US-based specialty food company with wholesalers across 42 countries and its stores in the US. From an AI perspective, Stonewall Kitchen has gone all-in on personalizing the online retail experience. Based on the Einstein platform, they have developed a product recommendation engine that is so good that 78% of customers who get a recommendation end up adding that recommendation to their cart, and 41% go on to buy. From an e-commerce perspective, these are awe-inspiring numbers.

These are just a few examples of how different companies have leveraged the Einstein platform to improve their ability to engage with customers and serve them better. These examples, however, are just the beginning. As a relatively young platform under constant development, we can expect genuinely great solutions to come to light in the future. Maybe after reading this book, you will work on some of them. Having gained an understanding of why using the Salesforce Einstein platform may be a good idea, we will now continue to look at the components that make up the platform.