How to get your IBM Db2 data ready for agentic AI

IBM Db2 holds the records that banks, insurers, retailers, and manufacturers have trusted for decades — accounts, policies, claims, payments, and high-volume transactions. Getting your Db2 data AI agent-ready means centralizing it in a warehouse or data lake, where AI agents query your complete history, join it with data from your other systems, and answer questions on demand without adding load to your transaction systems. That centralized, cleansed, and governed version of your data is the data foundation for agentic AI. Fivetran + dbt Labs delivers it: Fivetran moves your Db2 data reliably into your warehouse or data lake, and dbt transforms it into trusted, AI-ready tables.

Why Db2 data is critical for agentic AI

Your Db2 databases hold years of transaction history, and the business runs on it. Risk, operations, and finance leaders need that history to spot trends and exposure, and your head of data must deliver it faster every quarter.

Access is the problem. Analysts wait on database administrators for extracts, because no one wants analytical queries running against core transaction systems. Reports reflect the last batch job. Combining Db2 records with newer cloud applications takes custom work, and no one person reviews decades of history across millions of accounts or claims. Questions about exposure, claim patterns, or payment delays wait in a queue.

Transaction systems process your business. They do not answer open-ended questions from agents. Agents need infrastructure for agents, not just analytics: current, complete, and separate from the systems that run without interruption.

What agentic AI does with Db2 data

Once you centralize and model your Db2 data, agents turn it into answers and actions:

• Review risk exposure. A chief risk officer asks which accounts concentrate exposure in one segment. The agent reads account and transaction history and returns the answer in minutes instead of a multi-week project.
• Flag unusual claims or payments. An operations lead receives a daily list of activity that breaks from historical patterns. The agent reviews the full record and surfaces each item with context.
• Find payment delays. A finance manager asks where settlement times grew longest. The agent compares payment history across products and regions and points to the source of the slowdown.
• Answer questions about legacy data in plain language. A head of analytics asks how customer balances shifted over 5 years and gets an answer that cites its sources, not a request to the database team.

How Fivetran gets your Db2 data ready for agentic AI

Db2 data in its raw form does not suit agent workloads. It lives inside core transaction systems, it spreads across tables and applications, and every analytical query competes with the work your business must complete without delay.

Fivetran copies your Db2 for Linux, Unix, and Windows history into your warehouse or data lake first, then keeps it current by capturing new and changed data at regular intervals. Fivetran captures deletes, so your destination reflects what you removed as well as what you added, and offers history mode on every table, so agents see how records changed over time. Data blocking at the column, table, and schema level gives you control over what leaves your database.

Fivetran supports Db2 for Linux, Unix, and Windows, and that connector is currently in beta.

Then dbt Labs takes over, turning raw Db2 data into clean, governed, analysis-ready tables, with definitions your team documents and your risk and finance teams trust. Fivetran + dbt Labs delivers the full stack: Fivetran moves your Db2 data, and dbt transforms and governs it into AI-ready tables using modeling, testing, and documentation. If you want an open, interoperable destination, Fivetran Managed Data Lake gives you one.

What your Db2 data unlocks for your team

With Db2 data in a central warehouse or data lake, AI-ready data opens up capabilities your team never had before on an open, interoperable foundation.

• Risk and exposure analysis — risk leaders see concentration across accounts and segments.
• Claims and payment monitoring — operations teams catch unusual activity the day it appears.
• Long-term trend analysis — analysts study years of history without touching transaction systems.
• Cross-system customer views — teams join Db2 records with data from newer cloud applications.
• Change tracking over time — compliance teams see how records evolved, not just their current state.

FAQ

What does it mean for Db2 data to be AI agent-ready?

AI agent-ready Db2 data lives in a warehouse or data lake, stays current, sits in clear business tables your team models, with definitions your team governs and tests. Agents query it directly, join it with other sources, and return trusted answers without touching your transaction systems.

What do risk and operations leaders get from AI agents and Db2 data?

Risk and operations leaders ask questions in plain language and get answers from their own records. Agents review exposure, flag unusual activity, and trace payment delays. They also run these checks on a schedule, so findings arrive without anyone asking.

Is Db2 data ready for AI agents out of the box?

No. You need to centralize, model, and govern Db2 data before an agent queries it reliably.

Do we need a data engineering team to set this up?

No. Fivetran automates the movement of Db2 data into your warehouse or data lake, and dbt's modeling, testing, and documentation capabilities give your team analysis-ready tables without a custom build.

How does Fivetran get Db2 data ready for AI agents?

Fivetran moves your Db2 data reliably into your warehouse or data lake and keeps it current. dbt Labs then transforms and governs that data using dbt's modeling, testing, and documentation capabilities. Together, they give you a fast path to AI-ready data from one company.

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