Salesforce announced on March 7 Einstein GPT, a new generative AI technology that combines OpenAI’s enterprise-grade ChatGPT with Salesforce’s private AI models to deliver relevant and trusted AI-generated content across sales, service, marketing, commerce, and IT interactions at scale. Einstein GPT is currently in closed pilot, and Salesforce representatives declined to provide the names or total numbers of companies involved in the pilot, but are “are working to quickly bring this to market,” according to a Salesforce spokesperson.
The integration of generative AI-based models may open up a wide range of opportunities for companies seeking to improve CX, by reducing inbound and outbound interactions through “smart” automation, reducing administrative tasks and busywork that often interrupts or slows down human workers, and allowing the automatic generation of personalized and relevant content that can drive better customer engagement and satisfaction.
Salesforce announced the following offerings:
- Einstein GPT for Sales: Auto-generate sales tasks like composing emails, scheduling meetings, and preparing for the next interaction.
- Einstein GPT for Service: Generate knowledge articles from past case notes. Auto-generate personalized agent chat replies to increase customer satisfaction through personalized and expedited service interactions.
- Einstein GPT for Marketing: Dynamically generate personalized content to engage customers and prospects across email, mobile, web, and advertising.
- Einstein GPT for Slack Customer 360 apps: Deliver AI-powered customer insights in Slack-like smart summaries of sales opportunities and surface end user actions like updating knowledge articles.
- Einstein GPT for Developers: Improve developer productivity with Salesforce Research’s proprietary large language model (LLM) by using an AI chat assistant to generate code and ask questions for languages like Apex.
Salesforce and OpenAI also announced the ChatGPT for Slack app. The app provides new AI-powered conversation summaries, research tools to learn about any topic, and writing assistance to quickly draft messages.
According to the company, real-time customer data that is unified in Salesforce Data Cloud can be connected by Einstein GPT to OpenAI’s advanced AI models out of the box, or users can choose their own external model and use natural-language prompts directly within their Salesforce CRM to generate content that continuously adapts to changing customer information and needs in real time.
For example, Einstein GPT can generate personalized emails for salespeople to send to customers, and generate specific responses for customer service professionals. Using this intelligent automation allows organizations to quickly answer customer questions, generate targeted and relevant content for marketers to increase campaign response rates, and supports auto-generation of code for developers. Content generated by Einstein GPT can be reviewed and edited prior to being sent out to customers.
Despite the promise of increased efficiency, much of the initial business and consumer reporting on the use of a LLM-based conversational AI platforms has highlighted the somewhat unpredictable nature of a system trained on language, rather than content. Chat GPT, for example, lacks the ability to truly understand the complexity of human language and conversation, and is instead simply trained to generate words based on a given input. As a result, it may not always be able to provide accurate or appropriate responses, especially when faced with unexpected or unusual queries, and in the hands of careless or malevolent actors, could train a system to provide less-than-desired results.
However, Salesforce representatives pointed to the press release announcing Einstein GPT that the company is taking a cautious and measured approach with the new technology. The company says it is “embedding ethical guardrails and guidance across our products to help customers innovate responsibly, and catch potential problems before they happen.”
Salesforce introduced a new set of guidelines that are focused on the responsible development and implementation of generative AI models, which include a commitments to accuracy, safety, honesty, empowerment, and sustainability.
Ultimately, the company spokesperson indicated that the company is “still in the early days of this transformative technology, and these guidelines are very much a work in progress, but we’re committed to learning and iterating in partnership with others to find solutions.”
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.
