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The decision on the table is what to do with a business case, roadmap or pilot written around Einstein GPT. Starting over wastes the analysis you already paid for, while signing a renewal on old assumptions can leave you licensing features under names that no longer match your contract. The useful middle route is to keep the use cases, check them against what Salesforce sells today, and update the governance, which has moved further than the names.

Einstein GPT brought generative AI into the CRM

Einstein GPT combined the Einstein brand, which Salesforce had used for predictive features such as lead scoring, with large language models from providers including OpenAI. The promise was specific: generate content from the data already in Salesforce. Sales representatives would get drafted emails, service employees would get suggested chat replies and knowledge articles built from past cases, and marketers would get copy for email and web.

What made it different from earlier Einstein features was the output. Predictive Einstein gave you a score. Einstein GPT gave you text, which a person could send, edit or throw away. That shift is still the basis of how generative AI works in Salesforce today.

Einstein GPT and ChatGPT differ in where the answers come from

ChatGPT is a general-purpose assistant that answers from its training data and whatever you paste into it. Einstein GPT was built to answer from your Salesforce records, within the permissions of the user asking. A sales representative could only generate an email about an account they were allowed to see.

Salesforce added controls around that: sensitive fields masked before a prompt reached the model, an audit trail of what was sent, and agreements that stop model providers retaining customer data. Salesforce groups those controls as the Einstein Trust Layer, and they are still the main reason to generate CRM content inside Salesforce instead of copying records into a separate chatbot.

Einstein GPT became Agentforce in a few steps

The name did not last long. Salesforce first placed its generative features on the Einstein 1 Platform (Agentforce 360 Platform), together with Data Cloud (Data 360), the layer that unifies customer data into one profile. It then launched a conversational assistant, Einstein Copilot (Agentforce), that could answer questions and run actions. Agentforce replaced that assistant and became the umbrella for Salesforce's AI agents and generative features.

For you, that sequence explains why documents from different moments describe the same capability in three ways. The broader data foundation behind it is covered in Data Cloud as a customer foundation. Our weekly calls with Salesforce product management give us early input on new releases, which is how we keep old plans matched to what Salesforce actually ships.

Priscilla Calemi

Marketing & Community Manager

What an Einstein GPT business case means today

An Einstein GPT business case usually still holds on the use cases and needs rework on the rest. The tasks it named, such as drafting sales emails, suggesting service replies and generating knowledge articles, exist today in Agentforce and in Einstein-branded generative features, depending on your editions. The licence structure, the setup work and the governance around them have all changed.

Go through the case line by line. For each use case, confirm the current feature name, whether your contract includes it and who in your organisation owns the output. Where the original case counted time saved per task, that measure still works. Where it assumed a price, replace it with a fresh quote, because Salesforce has restructured AI pricing more than once since.

The original use cases still hold, with more autonomy

The main change since Einstein GPT is autonomy. The first generation drafted text for a person to approve. Agents can now take actions as well, such as updating a case, booking a meeting or handing a conversation to a person when it falls outside their instructions. That widens what you can automate and raises what can go wrong.

The old Einstein GPT list is still a sensible starting point, now ranked by risk. Drafts that a person reviews come first, and actions that reach customers directly come later. How to pick the first process for an agent is covered in Agentforce use cases worth starting with.

Pick up a shelved pilot where it stopped

Where an Einstein GPT pilot stalled, the cause was often data that was not ready or governance that was unclear. Both problems are still the first ones to solve. Clean the records your first use case reads, write down which fields a prompt may use, and name the owner of each template. The steps for that restart are set out in first steps with Salesforce AI.

We implement Sales Cloud, Service Cloud and Marketing Cloud ourselves, so the old plan, the data it depends on and the current AI features are reworked by one team that stays on after go-live.

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About the Author

Priscilla Calemi

Marketing & Community Manager

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