A customer asks whether their order qualifies for priority service. The CRM identifies the account, but the answer may also depend on a contract amendment, a support agreement and current operational capacity. Enterprise AI context brings those pieces together so that an answer reflects the actual customer relationship.
On September 10, 2026, Salesforce introduced its Enterprise AI Harness architecture, bringing context, actions and enterprise controls into a shared foundation. Our interpretation is that connecting systems is only part of the work: teams also need to agree what their data means.
Why connected data can still produce weak answers
A connector can retrieve records from two applications without knowing that both refer to the same customer. Different account identifiers, outdated status fields and inconsistent definitions can make an apparently complete answer misleading.
Context includes relationships as well as records. An agent needs to know which subsidiary signed a contract, which agreement is current and whether an entitlement applies to the person asking. Those relationships should be established from evidence, not guessed from similar names.
Build a small, useful context model
Start with one decision and identify the information required to support it. For a service eligibility workflow, that might include account identity, contract version, covered products and the effective date of each entitlement.
Give each important field an authoritative source and an owner. Record how conflicting values are resolved and how quickly updates need to become available. A document changed yesterday should not silently lose to an older copy in a search index.
Our article on integrating customer data with predictive AI provides a starting point for linking this foundation to analytical use cases.
Keep permissions attached to the context
Combining data should not broaden access automatically. A support employee may need to see whether an account has coverage without reading the full commercial agreement. Carry those distinctions through retrieval, generated answers and any exported summaries.
Test with different roles and accounts. Include a case where a useful document exists but the user is not permitted to see it. The system should handle that boundary without revealing the restricted content through an explanation.
Measure whether context improves decisions
Use representative questions with verified answers. Compare results before and after adding identity resolution, current document versions or shared metric definitions. Track unsupported claims and the time employees spend checking them.
Avoid treating a larger knowledge base as progress by itself. Adding irrelevant or conflicting information can make retrieval harder. A smaller collection of current, well-described sources may be more useful for a specific workflow.
A context record your team can maintain
A useful context record identifies the entity, authoritative source, business definition, effective date, last refresh and access policy. Record a conflict explicitly when two sources disagree; do not ask the model to pick the most convincing sentence.
For example, a service entitlement should resolve to a specific account and current agreement, with an explanation of how amendments are applied. Assign an owner to that resolution rule. Use this foundation for conversational analytics. Add graph-based retrieval when the questions genuinely require following relationships across documents, rather than assuming every dataset needs a graph.
Begin with a decision that matters
Map the records and documents behind one recurring customer question. Explore a knowledge repository as part of that workflow, and talk to NextBrain AI about the data relationships your team needs to make explicit.