Most Salesforce intelligent apps share one prerequisite: Einstein must be enabled in your org, and generative or agentic features also need Data Cloud and the Einstein Trust Layer in place. Here is the high-level setup an admin follows before any AI feature will appear.
- Confirm your edition and licenses
Check that your Salesforce edition includes the AI features you want. Predictive Einstein, generative features, and Agentforce have different licensing, and some need an add-on. Verify entitlements in Setup before you promise a feature to users.
- Turn on Einstein
In Setup, open the Einstein setup area and enable Einstein generative AI. This is the master switch. Many features stay hidden in their own setup pages until this is on.
- Provision Data Cloud and the Trust Layer
Generative features ground their prompts in data, so connect Data Cloud and confirm the Einstein Trust Layer settings for masking, grounding, and audit. Do this before testing with real records.
- Enable and configure the specific app
Go to the feature's own setup page, such as Einstein Lead Scoring or an Agentforce agent, and configure it. Let predictive models build, design prompt templates, or define an agent's topics and actions.
- Surface it for users and test
Add the scoring field, prompt action, or agent to the page layouts and flows where users work. Then test with a sample record and review the Trust Layer audit trail to confirm the output looks right.
Scoring and recommendation features like Einstein Lead Scoring that learn from your org history. Lowest setup effort; let the model build.
Drafting and summarization driven by prompt templates grounded in CRM data. Needs Data Cloud and Trust Layer configured.
Autonomous agents that complete multi-step tasks using topics and actions, running on the Atlas Reasoning Engine. Highest setup and governance effort.
A point-and-click way to train a custom prediction on any field or custom object without code, for predictions Salesforce does not ship out of the box.
- Generative features usually fail silently or stay hidden until Data Cloud and the Trust Layer are fully provisioned, so set those up first.
- Predictive models need enough quality history to build. A new org or a sparsely filled object can produce weak or unavailable scores.
- Generative and agentic usage consumes credits that scale with volume. Model the cost before a broad rollout, because it can exceed seat-based pricing.
- An agent can only do what its actions allow. Scope actions and permissions tightly, since an agent runs with real access to your data.