The Future of Customer Engagement: Inside Salesforce's AI Ecosystem

Salesforce’s approach to AI uniquely fuses predictive Machine Learning (ML) with generative AI that is available both programmatically and as part of its process framework, grounded by data and backstopped by a unique “Trust” layer.

To understand Salesforce's approach to AI, it is best to think of Salesforce not as a series of applications but as a customer engagement platform. This is Customer Relationship Management (CRM) in the broadest sense, as it captures all interaction points and related engagement with a customer across the customer lifecycle.

As an organization, Salesforce uniquely bridges back-office data and process with front-office engagement and experiences, with acquisitions like Slack, MuleSoft and Tableau providing the connective tissue.

In the broader context of the AI ecosystem, most of Salesforce's AI solutions sit in the application layer. In other words, Salesforce is more focused on creating end-user value and business impact than training high-parameter Large Language Models (LLMs) or developing custom hardware and infrastructure to run them. Because businesses invest in different tech stacks, Salesforce is taking more of a "marketplace" approach to AI development, enabling companies to choose from existing models (OpenAI, Anthropic, Vertex, etc.) or to bring their own. A robust set of developer tools (declarative and code-extended) will help enable the organization's preferred model.

Salesforce's AI capability set can be broken down across Machine Learning and Gen-AI. It includes out-of-the-box features and fully customizable capabilities that can be deployed in the flow of work and custom application development.

The following graphic provides a rough categorization of how AI capabilities appear on Salesforce.

For years, Salesforce has included Machine Learning capabilities throughout its platform. Most of these have been "black box" applications used to support predictions. This "democratized" AI came packaged as platform features like "Einstein Insights" or "Send Time Optimization" but included little, if any, customization capabilities.

Recently, Salesforce has introduced several Gen-AI features. The initial set includes "table-stakes" capabilities such as drafting emails in Marketing Cloud, creating catalog descriptions in Commerce Cloud, and composing sales emails in Sales Cloud. Additionally, Copilots were introduced to guide users and provide insights during key workflow-related activities.

These capabilities integrate seamlessly into the "flow of work," offering insights and guidance during routine user and administrative tasks.

The Einstein Copilot Studio enables the development or integration of both ML and Gen-AI capabilities that work together, are contextually grounded by data and content, and are accessible in workflows, programmatically via API, or even by other AI Copilots. The Studio is composed of 3 underlying builders.

1. Prompt Builder: helps you create sophisticated prompts grounded by company data using an LLM of your choosing.
2. Action Builder: gives your Copilot the ability to invoke actions, such as create and edit records, invoke workflows, research answers, and more.
3. Model Builder: guides you in building a new ML model or ingesting the outputs from common platforms data scientists use, like Google's Vertex or AWS SageMaker.

Data Cloud becomes a key part of Copilot orchestration, which provides grounding for both structured (data) and unstructured (i.e., documents) information.

And all of this is kept secure by the Einstein Trust Layer which prevents company and customer sensitive information from ever leaving Salesforce. As with other Salesforce tools, a well-trained admin can develop in Copilot Studio without code while additional mechanisms are in place to extend beyond out-of-the-box capabilities with code.

The accuracy of the response you may receive from an LLM can vary widely based on how strictly it is prompted and what context is provided to answer within. This context is called "grounding." Salesforce is enabling multiple types of grounding that can be used in the same prompt set.

• Field grounding: directly references fields from Salesforce and Data Cloud in the prompt.
• Dynamic grounding: can incorporate data from other parts of the platform (i.e., retrieve notes from the last sales interaction).
• Document-based: grounding is anticipated and would enable incorporating unstructured information as part of a response (i.e., knowledge base).

These ideas can be strung together into super-sophisticated prompts that can be exposed to internal users and external consumers alike. 

Salesforce's AI strategy is more than just a collection of features—it marks a paradigm shift in how businesses can integrate predictive modeling, generative reasoning, and business workflows into a cohesive, AI-driven framework. This orchestration is essential for developing highly effective, industry- and company-specific AI applications that drive more efficient and engaged customer interactions.

As AI continues to evolve, Salesforce's platform approach places the company at the cutting edge of innovation in customer engagement. By offering both out-of-the-box and fully customizable AI capabilities, Salesforce enables businesses to harness AI in ways that are precisely tailored to their unique needs and objectives.