> For the complete documentation index, see [llms.txt](https://v2.dataos.info/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://v2.dataos.info/references/interfaces/ai-activation/consumer-journey.md).

# Consumer journey

For a data consumer, AI activation follows four stages.

## Stages

<table><thead><tr><th width="116.43829345703125">Stage</th><th>Example prompt</th><th>What Data Product MCP returns</th></tr></thead><tbody><tr><td>Find</td><td><code>What Data Products do we have for supplier performance?</code></td><td>Relevant Data Products or tables from the catalog, scoped to what the user can access.</td></tr><tr><td>Trust</td><td><code>Is this data fresh enough to act on?</code></td><td>Semantic fields, quality status, run history, lineage, table profile, documented limitations, and owner information.</td></tr><tr><td>Ask</td><td><code>What was quarterly revenue by customer segment?</code></td><td>A governed answer from the Data Product semantic layer, including the metric or measure used and the source model.</td></tr><tr><td>Act</td><td><code>Why did this query fail, and who owns the Data Product?</code></td><td>A clear failure reason and owner context so the user can fix the question, contact the owner, or escalate.</td></tr></tbody></table>

## How this differs from manual consumption

Manual consumption starts with a destination. The user searches the Hub, opens the Data Product, explores assets and quality, and then queries through a BI tool, API, or application.

AI activation starts with intent. The user can ask the question before knowing which product, metric, owner, table, or interface is relevant.


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