Salesforce introduces the groundbreaking Einstein 1 Platform, built on a robust metadata framework.
The Einstein 1 Data Cloud supports large-scale data and high-speed automation, unifying customer data, enterprise content, and more.
The latest iteration of Einstein includes Einstein Copilot and Einstein Copilot Studio.
On September 12, 2023, Salesforce
unveiled the Einstein 1 Platform, introducing significant enhancements to the Salesforce Data Cloud and Einstein AI capabilities. The platform is built on Salesforce's underlying metadata framework. Einstein 1 is a reliable AI platform for customer-centric companies that empowers organizations to securely connect diverse datasets, enabling the creation of AI-driven applications using low-code development and the delivery of entirely novel CRM experiences.
Salesforce's original metadata framework plays a crucial role in helping companies organize and comprehend data across various Salesforce applications. This is like establishing a common language to facilitate communication among different applications built on the core platform. It then maps data from disparate systems to the Salesforce metadata framework, thus creating a unified view of enterprise data. This approach allows organizations to tailor user experiences and leverage data for various purposes using low-code platform services, including Einstein for AI predictions and content generation, Flow for automation, and Lightning for user interfaces. Importantly, these customizations are readily accessible to other core applications within the organization, eliminating the need for costly and fragile integration code.
In today's business landscape, customer data is exceedingly fragmented. On average, companies employ a staggering 1,061 different applications, yet only 29% of them are integrated. The complexity of enterprise data systems has increased, and previous computing revolutions, such as cloud computing, social media, and mobile technologies, have generated isolated pockets of customer data.
Furthermore, Salesforce ensures automatic upgrades three times a year, with the metadata framework safeguarding integrations, customizations, and security models from disruptions. This enables organizations to seamlessly incorporate, expand, and evolve their use of Salesforce as the platform evolves.
The Einstein 1 Data Cloud, which supports large-scale data and high-speed automation, paves the way for a new era of data-driven AI applications. This real-time hyperscale data engine combines and harmonizes customer data, enterprise content, telemetry data, Slack conversations, and other structured and unstructured data, culminating in a unified customer view. Currently, the platform is already processing a staggering 30 trillion transactions per month and connecting and unifying 100 billion records daily. The Data Cloud is now natively integrated with the Einstein 1 Platform, and this integration unlocks previously isolated data sources, enabling the creation of comprehensive customer profiles and the delivery of entirely fresh CRM experiences.
The Einstein 1 Platform has been expanded to support thousands of metadata-enabled objects per customer, each able to manage trillions of rows. Furthermore, Marketing Cloud and Commerce Cloud, which joined Salesforce's Customer 360 portfolio through acquisitions, have been reengineered onto the Einstein 1 Platform.
Now, massive volumes of data from external systems can be seamlessly integrated into the platform and transformed into actionable Salesforce objects. Automation at scale is achieved by triggering flows in response to changes in any object, even events from IoT devices or AI predictions, at a rate of up to 20,000 events per second. These flows can interact with any enterprise system, including legacy systems, through MuleSoft.
Analytics also benefit from this scalability, as Salesforce provides a range of insights and analytics solutions, including reports and dashboards, Tableau, CRM analytics, and Marketing Cloud reports. With the Einstein 1 Platform's common metadata schema and access model, these solutions can operate on the same data at scale, delivering valuable insights for various use cases.
Salesforce has additionally made Data Cloud accessible at no cost to every customer with Enterprise Edition or higher. This allows customers to commence data ingestion, harmonization, and exploration, leveraging Data Cloud and Tableau to extend the influence of their data across all business segments and kickstart their AI journey.
Salesforce's latest iteration of Einstein introduces a conversational AI assistant to every CRM application and customer experience. This includes:
This is an out-of-the-box conversational AI assistant integrated into every Salesforce
application's user experience. Einstein Copilot enhances productivity by assisting users within their workflow, enabling natural language inquiries, and providing pertinent, trustworthy responses grounded in proprietary company data from the Data Cloud. Furthermore, Einstein Copilot proactively takes action and offers additional options beyond the user's query.
Einstein Copilot Studio:
This feature enables companies to create a new generation of AI-powered apps with custom prompts, skills, and AI models. This can help accelerate sales processes, streamline customer service, auto-generate websites based on personalized browsing history, or transform natural language prompts into code. Einstein Copilot Studio offers configurability to make Einstein Copilot available across consumer-facing channels such as websites and messaging platforms like Slack, WhatsApp, or SMS.
Both Einstein Copilot and Einstein Copilot Studio operate within the secure Einstein Trust Layer, an AI architecture seamlessly integrated into the Einstein 1 Platform. This architecture ensures that teams can leverage generative AI while maintaining stringent data privacy and security standards.
The metadata framework within the Einstein 1 Platform expedites AI adoption by providing a flexible, dynamic, and context-rich environment for machine learning algorithms. Metadata describes the structure, relationships, and behaviors of data within the system, allowing AI models to better grasp the context of customer interactions, business processes, and interaction outcomes. This understanding enables fine-tuning of large language models over time, delivering continually improved results.