New Einstein 1 Platform is now accessible, and will empower companies to safely connect any data to build AI-powered apps with low-code and deliver entirely new CRM experiences. The platform features major advancements for Salesforce Data Cloud and Einstein AI capabilities, all built on Salesforce’s underlying metadata framework.
Seamlessly Bridging Data Across Applications
“A company’s AI strategy is only as good as its data strategy,” said Parker Harris, Co-Founder and CTO, Salesforce. “We pioneered the metadata framework nearly 25 years ago to seamlessly bridge data across applications.
It’s the connective tissue that fuels innovation. Now, with Data Cloud and Einstein AI native on the Einstein 1 Platform, companies can easily create AI-powered apps and workflows that supercharge productivity, reduce costs, and deliver amazing customer experiences.”
Customer Data Fragmentation Problem
Customer data is highly fragmented. On average, organisations use 1,061 different applications, yet only 29% of them are integrated. Enterprise data stacks have grown more complex, and prior computing revolutions – cloud, social, and mobile – have generated massive, siloed islands of customer data.
Organisational and Interpretative Capabilities
Salesforce’s original metadata framework helps companies organise and understand data across Salesforce applications – the equivalent of having a common language so different applications built on the core platform can communicate with each other.
It now creates a unified view of the data across an enterprise regardless of how that data is structured in disparate systems by mapping it to the Salesforce metadata framework.
Customise User Experiences
This allows organisations to customise every user experience and action their data using a variety of low-code platform services – including Einstein for AI predictions and content generation; Flow for automation; and Lightning for user interfaces.
These customizations are instantly available to the rest of the organisation’s core applications without having to write costly and brittle integration code.
Automatic Updates 3x Per Year
Additionally, Salesforce delivers automatic upgrades three times per year, and the metadata framework prevents any integrations, customizations, or security models from breaking. Organisations can easily add, extend, and build on top of Salesforce as the platform evolves.
Real-Time Hyperscale Data Engine
Data Cloud – Salesforce’s real-time hyperscale data engine – unifies and harmonises customer data, enterprise content, telemetry data, Slack conversations, and other structured and unstructured data to create a single view of the customer.
The platform is already processing 30 trillion transactions per month, and connecting and unifying 100 billion records every day.
Unlocking Siloed Data
With the new Data Cloud now natively integrated with the Einstein 1 Platform, companies can unlock siloed data in entirely new ways; create rich, unified customer profiles; and deliver entirely new CRM experiences.
Data at Scale
The platform has expanded to support thousands of metadata-enabled objects per customer, each capable of having trillions of rows. In addition, Marketing Cloud and Commerce Cloud, consumer-scale technology stacks that joined Salesforce’s Customer 360 portfolio via acquisitions, have been re-engineered onto the Einstein 1 Platform.
Automation at Scale
Now massive volumes of data can be brought into the Einstein 1 Platform from other systems and immediately made available as actionable Salesforce objects.
Flows can be triggered by any change on any object at scale, whether it’s an event coming from an IOT device, a computed insight, or an AI prediction – up to 20,000 events per second – and can interact with any system in the enterprise, including legacy systems, through MuleSoft.
Analytics at Scale
A variety of insights and analytics solutions for different use cases are offered — including Reports and Dashboards, Tableau, CRM Analytics, and Marketing Cloud Reports. With the Einstein 1 Platform’s common metadata schema and access model, all of these solutions can work on the same data at scale — providing rich insights for any use case.
Every customer with Enterprise Edition or above can now get started with Data Cloud at no cost. Customers can start ingesting, harmonising, and exploring their data with Data Cloud and Tableau to extend the power of their data across every line of business and jumpstart their AI journey.
Salesforce’s next generation of Einstein brings a conversational AI assistant to every CRM application and customer experience, including:
Einstein Copilot
A new and trusted out-of-the-box conversational AI assistant built into the user experience of every Salesforce application. Einstein Copilot will drive productivity by assisting users within their flow of work, enabling them to ask questions in natural language, and receive relevant and trustworthy answers that are grounded in secure proprietary company data from Data Cloud.
It also takes action and offers additional options beyond the user’s query – like providing a recommended action plan after a sales call, checking a consumers order status, and changing the shipping date.
Einstein Copilot Studio
An easy new way for companies to build an entirely new generation of AI-powered apps with custom prompts, skills, and AI models to close sales deals faster, streamline customer service, auto-create websites based on personalised browsing history, or turn natural language prompts into code, as well as hundreds of other business tasks.
Einstein Copilot Studio also provides configurability to make Einstein Copilot available for use across consumer-facing channels like websites to power real-time chat, or integrate with messaging platforms like Slack, WhatsApp, or SMS.
Einstein Copilot and Einstein Copilot Studio will operate within the Einstein Trust Layer, a secure AI architecture built natively into the Einstein 1 Platform that allows teams to benefit from generative AI, while preserving their company’s data privacy and security standards.
The Einstein 1 Platform’s metadata framework offers a fast, trusted path to AI by enabling a flexible, dynamic, and context-rich environment for machine learning algorithms to operate within.
Since metadata describes the structure, relationships, and behaviours of data in the system, AI models can better understand the context of customer interactions, business processes, and the outcomes of certain interactions. And, those outcomes can then be used to further fine-tune large language models to deliver results that continually improve over time.
