How AI Assistant Will Change the Way You Work this Year

4 min

Understanding AI Assistant

AI Assistant responds to your submitted questions by querying a database and then translating data from the database into a human-readable answer.

This internal representation of underlying data is also known as the Knowledge Graph - a comprehensive web of concepts, data, and metadata for a given answer.

The Knowledge Graph consists of sub-graphs that are referenced whenever queries submitted:

Customer operational insights.
Customer operational insights across various meta-stores.
Experience League documentation.

There are two classes of questions to consider before querying AI Assistant:

Product knowledge

Product knowledge refers to concepts and topics grounded in Experience League documentation. Product knowledge questions can be further specified into the following sub-groups:

Product knowledge	Examples
Pointed learning	What is the difference between an identity and a primary or foreign key? What are lookalike audiences?
Open discovery	How can I export this dataset? Are there schemas for healthcare customers?
Troubleshooting	Why can’t I turn on a schema owned by Adobe for profile? Why can’t I delete a segment?

Operational insights

You can ask AI Assistant questions about your operational insights in the following domains:

Domain	Supported metadata	Unsupported metadata
Attributes	Attribute name search Attribute - schema relationship Attribute - dataset relationship Attribute - audience relationship Attribute - destination relationship	Attribute class Audit Deprecation status Labels Value stored in attributes
Audiences	Audience count Audience type (streaming or batch) Creation/modification dates Activation status Profile count Duplicate audiences Audience definition search Audience - audience relationship Audience - attribute relationship Audience - dataset relationship Audience - destination relationship Name search Name and ID search	Audience overlaps Audience activation Audience - campaign relationships Audit Create/modification Labels Profile qualification trends
Dataflows	Dataflow counts Dataflow status Dataflow - dataset relationship Dataflow - source relationship	Creation/modification Dataflow-batch relationships Ingest profile count
Datasets	Dataset count Profile enable status Creation/modification date Dataset - schema relationship Dataset - audience relationship Dataset - attribute relationship Dataset - dataflow relationship Name search Name and ID search	Audit Created by Dataset - batch relationship Dataset creation/modification Dataset size Number of profiles Number of rows Value search
Destinations	Configured destination counts Destination - audience relationship Destination attribute relationship	Account set up Account credential information Unique profiles activated
Journeys	Counts Name search Name and ID search Journey status Triggered status (audience vs. events) Creation/modification dates Recurring frequency	Attributes - journey relationships Audit Creation/modification Created by Events Journey - dataset Journey - schema Offers Profile qualification trends Step events
Schemas	Schema counts Creation/modification date Schema - attribute relationship Schema - dataset relationship Schema - audience relationship Profile enable status Name search Name and ID search	Audit Creation/modification Created by Field groups Identities Identity namespaces Labels Number of profiles
Sources	Account counts Account status Active/inactive dataflows for each account Source connector - dataflow relationship Source account - dataflow relationship	Account credentials information Account set up Data ingestion metrics Number of profiles Source - batch relationships

How AI Assistant Will Change the Way You Work this Year

Understanding AI Assistant

Product knowledge

Operational insights

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