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Einstein Studio: Salesforce Introduces No-Code Model Integration

Einstein Studio: Salesforce Introduces No-Code Model Integration

The News: On August 4, Salesforce announced the general availability of Einstein Studio, a solution designed to enable Salesforce customers’ data science and engineering teams to easily leverage their own AI models against their proprietary company data within Salesforce Data Cloud.

Here are the pertinent details:

  • Salesforce customers can use their company data to train their own custom AI models, or from Salesforce’s ecosystem of curated AI models, including AWS SageMaker and Google Cloud Vertex AI. They can use these models alongside turnkey large language models (LLMs) provided through Einstein GPT.
  • A key feature of the Einstein Studio solution is speedy data integration and model implementation. It provides pre-built, zero-ETL (Extract, Transform, Load) integration. Salesforce customers point and click to access their data in Salesforce Data Cloud, then build and train their custom AI models.
  • Einstein Studio’s control panel for managing use of AI models enables data governance for how customer data is exposed to AI models for training.

Read the full Einstein Studio Press Release on the Salesforce website.

Einstein Studio: Salesforce Introduces No-Code Model Integration

Analyst Take: Salesforce is a clear AI leader. One of the reasons for this leadership is that the company understands the value of the clean, machine-readable data they steward for their customers. Consider Einstein Studio. Instead of jumping on the bandwagon and creating proprietary AI models, Salesforce has tackled a more pressing issue facing enterprises hoping to leverage AI – enabling easy, AI-ready access to their proprietary data. Following are the key takeaways related to Salesforce’s strategic move in this space.

Salesforce is building market value by being a critical AI resource in the form of data steward. During the Salesforce World Tour in December 2022, CEO Marc Benioff said the company had noticed over the past few years how important data was becoming to their customers and consequently, a confounding trend was emerging: “Our customers have done something that we really didn’t like,” he said. “They’re starting to kind of set up data silos, or data warehouses, or even other data clouds outside of Salesforce with their customer data.” He said Salesforce wanted to change that by providing intelligence and value in the data by developing ways for customers to interact with it, in real time. “Once you have islands of information, it’s harder and harder to make decisions,” he said.

Currently, the enterprise market has a renewed focus on data management and data governance because of the potential of generative AI, but because of those issues Benioff mentioned, lots of enterprise data is not accessible to AI. With Data Cloud and Einstein Studio, Salesforce fills this gap, albeit for data pertinent to Salesforce apps. Regardless, Salesforce’s role as data steward and being AI-ready builds its customers value and ultimately, Salesforce’s own market value because the company becomes increasingly tougher for the competition to dislodge.

Positioning Salesforce as a developer platform and SaaS. Salesforce has invested in AI for nearly 10 years, but generally speaking, until recently, its focus has been on embedding AI into Salesforce applications. As part of this strategy, Salesforce customers enjoy the benefits of AI without necessarily requiring their own data scientists and engineers. However, Einstein Studio and Einstein GPT are developer platforms/tools targeted at IT resources. It is an interesting move for one of the world’s largest SaaS players. However, it is unclear how much emphasis the company will put on developer tools going forward.

Speed to market: to leverage AI, enterprises must solve data abstraction. When Salesforce asked customers what they needed in moving forward with AI projects, the answer was not more AI models, rather, they wanted frictionless access to their proprietary data within Salesforce Data Cloud that they could manipulate with the AI models of their choice. Data abstraction, data management, and data governance remain the key challenges for any AI initiative – where data resides, how much of it is accessible and when, and the challenges of managing it securely. The Einstein Studio solution will likely resonate because the pre-built, zero-ETL integration to clean proprietary data solves a lot of this, enabling customers to more quickly spin up AI initiatives with less resources.

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.

Other insights from The Futurum Group:

Salesforce Introduces Einstein GPT: Revolutionizing Salesforce Service Cloud

Salesforce to Integrate Einstein GPT and Data Cloud Capabilities into Workforce Automation Suite Flow

Salesforce Integrates Einstein GPT in Salesforce Sales Cloud and It’s a Game-Changer

Author Information

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.

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