The News: Salesforce announced at its World Tour NYC event that it had added the Data Cloud Vector Database and Einstein Copilot Search to its Einstein 1 Platform. The updates are designed to let organizations unify all business data, including unstructured data such as emails, engagement transcripts, audio files, and PDFs with CRM data to enable the grounding of AI prompts and Einstein Copilot. Salesforce says this update will eliminate the need for costly and complex fine-tuning of large language models (LLMs).
In addition, Salesforce announced that Einstein Copilot Search will provide AI search capabilities to deliver precise answers from Data Cloud instantly in a conversational AI experience, which, combined with the new Data Cloud Vector Database, will enable users to access all data that is linked to Salesforce. Data Cloud Vector Database and Einstein Copilot Search each will be in pilot in February 2024, and Einstein Copilot will be generally available in February 2024. You can read the release from Salesforce on its website.
Salesforce Announces Updates to the Einstein 1 Platform
Analyst Take: Salesforce announced two updates to its Einstein 1 Platform, adding the Data Cloud Vector Database and Einstein Copilot Search features, which are designed to allow users to access all of the data linked within Salesforce, including unstructured data types, via Einstein Copilot Search.
Leveraging the Power of Vectorization
The use of a vector database allows the incorporation of more advanced features, including semantic information retrieval (which focuses on the meaning and intent of data), along with the ability to create linkages between related pieces of data. In this case, unstructured data, such as content within emails, chats, PDFs, or other formats that are not easily incorporated within a traditional spreadsheet or data file, are converted into a vector format. This vector format incorporates the meaning or intent of the data by unifying it with known structured data contained in support tickets, purchase history documents, or knowledge base information. These vectors also store metadata, enabling users to query the database using additional metadata filters for fine-grained searches.
The company says that the use of its Data Cloud Vector Database is built into the Einstein 1 Platform and is designed to enable AI, automation, and analytics across all Salesforce CRM applications. Data Cloud Vector Database is designed to eliminate the need to fine-tune LLMs, as it will enable customers to access and use all business data, including unstructured data, to enrich AI prompts across business applications and workflows.
Enhancing Einstein Copilot Search
The Data Cloud Vector Database will also power Einstein Copilot Search, Salesforce’s generative AI assistant, with AI search capabilities that can be conducted using natural language in the flow of work. This announcement opens a far greater amount of data for use by both frontend and backend workers. Prior to the infusion of Data Cloud Vector Database, workers seeking to incorporate unstructured data simply were not able to access it via search, and it was beyond the reach of Copilot AI algorithms.
For example, a contact center worker may record notes from a specific telephone interaction with a customer, accurately capturing key details. However, without the ability to ingest the full contents of that telephone interaction transcript, an AI algorithm was unable to use that information to further refine itself, with key potential insights such as tone and sentiment likely never being incorporated into the analysis of the call.
With more than 80% of company information being held in unstructured data formats, Salesforce is clearly addressing a significant need for enterprises. While the claim about not needing to fine-tune LLMs is likely oversimplifying the challenge of delivering a truly trusted and reliable generative AI experience, this functionality is a step in the right direction, in terms of making the integration and use of generative AI functionality more cost-effective and simpler to use.
Strategic Market Positioning
At Dreamforce this year, CEO Marc Benioff and the executive team repeatedly referred to Salesforce as “a data company,” which is interesting for a company that has made its reputation and living dominating the CRM market. As we have pointed out many times over the past year, Salesforce is in a very elite group of companies in terms of their mastery and understanding of AI. In the year that has been the neck-snapping and eye-popping generative AI story full of shiny objects, particularly LLMs and other foundation AI models, companies such as Salesforce that have invested in AI for more than a few years understand the key to unlocking generative AI value is in tapping and leveraging the proprietary data companies possess. During the past few months and including the New York show and analyst-only pre-briefings for it, Salesforce has shown that it has built a very sophisticated data federation/management/abstraction layer in Data Cloud, one that is every bit as comprehensive as offerings from players whose core business is data federation/management/abstraction. Here we are talking about Databricks, Snowflake, MongoDB, SingleStore, Amazon Web Services (AWS), Google, Microsoft, IBM, and more.
So is Data Cloud a Salesforce platform designed to serve the other Salesforce platforms or is the vision and strategy for Salesforce to position Data Cloud as a competing platform to these dedicated data players? It will be interesting to see how this potentially develops over 2024.
Disclosure: The Futurum Group is a research and advisory firm that engages or has engaged in research, analysis, and advisory services with many technology companies, including those mentioned in this article. The author does not hold any equity positions with any company mentioned in this article.
Analysis and opinions expressed herein are specific to the analyst individually and data and other information that might have been provided for validation, not those of The Futurum Group as a whole.
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Author Information
Keith has over 25 years of experience in research, marketing, and consulting-based fields.
He has authored in-depth reports and market forecast studies covering artificial intelligence, biometrics, data analytics, robotics, high performance computing, and quantum computing, with a specific focus on the use of these technologies within large enterprise organizations and SMBs. He has also established strong working relationships with the international technology vendor community and is a frequent speaker at industry conferences and events.
In his career as a financial and technology journalist he has written for national and trade publications, including BusinessWeek, CNBC.com, Investment Dealers’ Digest, The Red Herring, The Communications of the ACM, and Mobile Computing & Communications, among others.
He is a member of the Association of Independent Information Professionals (AIIP).
Keith holds dual Bachelor of Arts degrees in Magazine Journalism and Sociology from Syracuse University.
Mark comes to The Futurum Group from Omdia’s Artificial Intelligence practice, where his focus was on natural language and AI use cases.
Previously, Mark worked as a consultant and analyst providing custom and syndicated qualitative market analysis with an emphasis on mobile technology and identifying trends and opportunities for companies like Syniverse and ABI Research. He has been cited by international media outlets including CNBC, The Wall Street Journal, Bloomberg Businessweek, and CNET. Based in Tampa, Florida, Mark is a veteran market research analyst with 25 years of experience interpreting technology business and holds a Bachelor of Science from the University of Florida.
