How to get your Adobe Analytics data ready for agentic AI

Adobe Analytics holds some of your most valuable business data — customer engagement across your website and apps, custom-defined metrics, calculated metrics, and audience segments built for your specific reporting needs. Getting it ready for agentic AI means giving AI agents access to a centralized, cleansed, and governed version of that data, so they can answer questions like "which segments drove the biggest jump in engagement after last week's campaign?" in seconds rather than days. Fivetran + dbt Labs delivers the complete data foundation agents need — Fivetran moves Adobe Analytics data reliably into your warehouse or data lake, and dbt transforms it into trusted, AI-ready tables.

Why Adobe Analytics data is critical for agentic AI

Adobe Analytics captures how customers actually behave across digital properties, but that insight usually stays locked inside Adobe's own reporting interface, accessible only through prebuilt reports that a digital analyst has to configure, run, and reconfigure every time the business asks a new question. That's a real cost: marketing leaders wait on an analyst to build a workspace report before they can see whether a campaign, page redesign, or segment change actually moved the needle. Because Adobe only exposes data through the reporting layer rather than raw event data, every new question can mean a new report request and another few days of turnaround. Meanwhile, engagement data tied to report suites, custom metrics, and segments accumulates constantly across brands and properties, far more than any one analyst can track by hand. This is infrastructure built for agents, not just analytics — without a centralized version of that data, the business stays stuck asking a person instead of an agent.

What agentic AI can do with Adobe Analytics data

Once Fivetran properly prepares Adobe Analytics data, an AI agent turns prebuilt reports into an on-demand source of answers.

A digital analytics lead can ask an agent to compare engagement trends across report suites for different brands or regions, getting a side-by-side view that used to require building and exporting several separate reports.

A marketing manager can ask which custom segments showed the sharpest change in engagement following a specific campaign, without waiting for an analyst to configure a new workspace panel.

A content team can track how a specific calculated metric — like engaged sessions or conversion rate for a campaign landing page — moves over time, and get alerted when it shifts significantly.

A CMO can ask for a plain-language summary of engagement trends across all report suites heading into a quarterly review, instead of waiting for someone to assemble slides from multiple exports.

Because Fivetran keeps the underlying report data current, an agent answers these questions against real, up-to-date figures rather than a report that's already a few days old by the time someone reads it. The agent works from the same custom metrics, elements, and segments your team already defined in Adobe Analytics, just made instantly queryable.

How Fivetran gets your Adobe Analytics data ready for agentic AI

Adobe Analytics only exposes data through pre-defined reports, and every report suite, custom metric, and segment your team builds lives in its own configuration inside Adobe's interface. That structure works for a single analyst running one report at a time, but it breaks down the moment an AI agent needs a consistent, centralized view across every report suite and brand you track.

Fivetran solves this by moving your Adobe Analytics data reliably into a central warehouse or data lake, running a daily rollback sync to keep figures current and accurate as Adobe's own numbers settle, so your tables stay fresh, complete, and ready for agents to query. Because Adobe Analytics data is built around custom elements, metrics, and segments unique to your reporting setup, Fivetran carries that structure through cleanly rather than forcing a generic model onto it.

Fivetran + dbt Labs deliver the rest of the stack together: dbt transforms and governs the data into clean, trusted, AI-ready tables using its full modeling, testing, and documentation capabilities.

What your Adobe Analytics data unlocks for your team

With Adobe Analytics data in a central warehouse, AI agents can unlock capabilities your team couldn't access before.

• Cross-report-suite comparison — compare engagement trends across brands, regions, or properties in one place instead of building separate reports for each.
• On-demand segment analysis — ask how a specific audience segment behaved after a campaign, without waiting for a new workspace report.
• Custom metric monitoring — track the calculated metrics your team already built and get flagged when they shift.
• Faster executive reporting — summarize engagement trends in plain language for leadership reviews, without manual report assembly.
• Open, interoperable foundation — Adobe Analytics data sits alongside your other business data, ready for any AI agent or analytics tool to use.

FAQ

What does it mean for Adobe Analytics data to be AI agent-ready?

It means Fivetran centralizes your website and app engagement data — including the custom metrics, elements, and segments your team built in Adobe Analytics — in a warehouse or data lake, and dbt models it into clean tables and governs it so an AI agent can query it accurately and combine it with other business data.

What can my team actually do with AI agents and Adobe Analytics data?

Marketing, digital analytics, and leadership teams can ask direct questions about engagement trends, segment performance, and campaign impact, and get immediate answers instead of waiting for a new report to be built.

Is Adobe Analytics data ready for AI agents out of the box?

Not without preparation. Fivetran + dbt Labs must centralize, model, and govern Adobe Analytics data before an agent can query it reliably.

How long does it take to get set up?

Not as long as building a new report from scratch. Fivetran automates the data movement, and dbt Labs' modeling framework speeds up transformation, so most teams are working with clean, queryable data within days.

How does Fivetran get Adobe Analytics data ready for AI agents?

Fivetran moves your Adobe Analytics data reliably into your warehouse or data lake, preserving the custom metrics, elements, and segments your team already built. dbt Labs then transforms and governs that data into clean, AI-ready tables using its full modeling and testing capabilities, giving teams a fast, trusted path from Adobe reports to agent-ready data.

[CTA_MODULE]