What is the difference between a Customer 360 and customer context for AI?

Summary

• A Customer 360 is a unified customer profile: one consolidated view of attributes, history, and interactions assembled from fragmented source systems. It is the foundation.
• Customer context for AI is that profile made live, governed, and machine-consumable, so an AI agent can reason over the customer's current state and act on it, not just show it to a person.
• The shift is from a static record built for dashboards and segments to governed, real-time context an autonomous agent can query and act on continuously.
• Keeping the data in place matters: copying customer data into a separate platform to build the 360 recreates the silos and governance gaps that make context unreliable for AI.
• Databricks CustomerLake builds governed Customer 360 profiles and serves them as customer context for AI agents inside the Lakehouse, governed by Unity Catalog and reachable through Genie in natural language.

What is the difference between a Customer 360 and customer context for AI?

A Customer 360 is a unified profile: one consolidated view of a customer's attributes, history, and interactions, stitched together from many source systems. Customer context for AI is that profile made live, governed, and machine-consumable so an AI agent can read the customer's current state and act on it. The real difference is purpose and consumer. A Customer 360 was built to give people a single view in a dashboard or to define an audience. Customer context for AI is built for autonomous software: it has to be current, permission-aware, and queryable by an agent that will decide and act, not just render a screen. The 360 is the foundation; context for AI is the 360 turned into something an agent can trust and use in the moment.

What is a Customer 360?

A Customer 360 is a single, reconciled record of everything an organization knows about a customer: identity, transactions, product usage, support history, loyalty, and marketing engagement. Building one means resolving identities across systems, deduplicating records, and consolidating attributes into one profile. Traditionally, companies assembled these profiles by copying customer data into a separate customer data platform, which created a new silo and a new copy of sensitive data to govern. The result is often a static, batch-updated view: useful for reporting and segmentation, but a step removed from the systems where customers are actually served.

What is customer context for AI?

Customer context for AI is the customer profile delivered as governed, real-time context an AI agent can consume directly. Where a Customer 360 answers who a customer is, context for AI answers what should happen next for that customer, right now, within policy. It has three properties a static profile usually lacks: it stays current as behavior and signals change; it carries the governance and permissions of the underlying data, so an agent only acts on what it is allowed to; and it is exposed in a form an agent can query and reason over. That is what lets an agent analyze behavior, decide a next-best action, and personalize an experience on its own.

Key differences

Dimension	Customer 360	Customer context for AI
Primary consumer	People: analysts, marketers, dashboards	Autonomous AI agents and applications
Purpose	A single view for reporting and segmentation	Live context to decide and act
Freshness	Often batch-updated and point-in-time	Continuously current as signals change
Governance	Governed at the data layer, if at all	Permission-aware context enforced at query and action time
Data location	Frequently copied into a separate platform	Kept in place, so there is no new silo to secure
Output	A profile to look at	An action the agent can take

How Databricks approaches customer context for AI

Databricks CustomerLake is an agentic customer data platform embedded directly in the Databricks platform, so the Customer 360 and the context that serves AI are built where the data, AI, and governance already live. Because it runs inside the Lakehouse, CustomerLake does not require copying customer data into an external system to build profiles, which keeps one governed copy under Unity Catalog for security, lineage, and access control.
CustomerLake organizes the work into agents. Profile Agents build governed Customer 360 profiles using agentic identity resolution that combines deterministic, probabilistic, and agentic matching to unify records into accurate profiles that improve over time. Campaign Agents turn those profiles into activation: they analyze behavior, recommend next-best actions, and personalize messaging and channel, then send audiences to downstream marketing and advertising tools without manual data movement. Marketers reach the context in natural language through Genie, so building an audience or asking a question about a segment does not require SQL. CustomerLake is a newer, rolling-out capability rather than a long-standing generally available product, so confirm current availability and preview status with Databricks before you plan around it. For the concept and design, see Introducing CustomerLake, What defines an agentic CDP, and the CustomerLake product page.

FAQs

Is a Customer 360 still needed if I have customer context for AI?

Yes. The Customer 360 is the resolved, unified profile underneath. Customer context for AI is that profile made current, governed, and consumable by an agent. You build the 360 first, then serve it as context.

Why does copying data into a separate CDP cause problems for AI?

Copying customer data into a separate platform creates another silo and another copy of sensitive data to secure and keep in sync. Agents acting on a stale or ungoverned copy can make decisions that break policy or use outdated information. Keeping the profile in place under one governance model avoids that.

What makes context "governed" for an AI agent?

Governed context carries the permissions and lineage of the source data, so an agent only reads and acts on data it is authorized to use. On Databricks, Unity Catalog enforces this across the profiles and the agents that use them.

How do marketers use this without writing code?

Through Genie, a natural-language interface. Marketers can build audiences, explore segments, and plan campaigns by asking questions, so customer context is self-service rather than a data-engineering request.