Salesforce has been assisting sales teams in finding, acquiring, and retaining clients for 25 years. This assistance covers all aspects of the sales cycle, from activity tracking and prospecting to revenue operations and forecasting.

Salesforce has released Einstein Copilot, the conversational AI assistant for CRM, to the public. They have also introduced new features to improve seller productivity and speed up the adoption of generative AI. 

Salesforce has been assisting sales teams in finding, acquiring, and retaining clients for 25 years. The platform offers customizable metadata that helps sales teams with various features, such as activity tracking, prospecting, revenue operations, and forecasting. This helps sales teams identify, win, and retain clients. Salesforce uses new analytics and testing tools to create conversational AI engagements based on an organization's business-specific data. This helps to simplify the most common and valuable seller procedures. 

Copilot Actions are pre-programmed features in Einstein Copilot that not only allow it to respond to queries using business data but also create workflows to complete tasks for users. These actions are a crucial and exclusive part of the software's metadata. Over half of the employees in the "Your Data, Your AI" survey said that AI based on data from private companies increases their trust in the product. 

Salesforce is adding new features to its action library in Sales Cloud, allowing sellers to use conversational AI. Sales representatives can use Einstein Copilot to quickly access personalized close plans, insights on why an opportunity may not close in the current month, and to check if pricing was discussed on a recent call. This eliminates the need to navigate to a separate AI tool that is not integrated with their CRM. This allows them to spend more time with customers and less time sorting through data and screens. 

Einstein Copilot features a reasoning engine that comprehends user requests. It can automate tasks from a library of actions. Each new action adds more use cases that it can handle. All of this is possible without the need to train or fine-tune a large language model (LLM).