How to get your Microsoft Dynamics 365 Business Central data ready for agentic AI

Microsoft Dynamics 365 Business Central holds some of your most valuable business data — general ledger entries, customer and vendor records, sales and purchase orders, and inventory across your entire operation. 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 vendors have open invoices past due, and what does that mean for this week's cash flow?" in seconds rather than days. Fivetran + dbt Labs delivers the complete data foundation agents need — Fivetran moves Business Central data reliably into your warehouse or data lake, and dbt transforms it into trusted, AI-ready tables.

Why Business Central data is critical for agentic AI

Business Central runs the financial core of the business — the general ledger, accounts receivable, accounts payable, inventory, and order management all live there. But that data is built for transaction processing, not for the kind of fast, cross-functional analysis finance leaders need. Getting a straight answer about vendor exposure, customer payment behavior, or inventory value today usually means someone building a report by hand or waiting on whoever has access to run one.

That manual process does not scale as transaction volume grows across companies, entities, and locations. Cash flow forecasts, aging reports, and inventory valuations are only as current as the last time someone pulled a report, which means finance is often making decisions on numbers that are already stale. What finance actually needs is infrastructure built for agents, not just analytics — a foundation where Business Central data is always current and always ready to query.

What agentic AI can do with Business Central data

Once your ledger, customer, vendor, and order data sits in a central warehouse, an AI agent turns routine finance questions into instant answers.

A finance ops lead can ask an agent which vendors have invoices past due and what the total payable exposure looks like this week, replacing a manual aging report with a live answer.

A controller can ask an agent to reconcile general ledger entries against posted sales and purchase orders, flagging discrepancies before they surface in the monthly close.

An FP&A analyst can ask an agent to break down revenue and cost trends by customer, item, or location over any period, without waiting for someone to build a new report in Business Central.

A finance leader can ask an agent for a plain-language summary of cash position, receivables, and payables across every company in the environment, consolidated into one answer instead of several separate reports.

How Fivetran gets your Business Central data ready for agentic AI

Business Central stores your ledger, customer, vendor, and order data across many separate tables, exposed through the specific interfaces the application uses for transaction processing, not for analysis. Pulling that data together manually, keeping it current, and making it trustworthy enough for an AI agent to rely on does not scale as the business grows.

Fivetran moves your Business Central data reliably into your warehouse or data lake, keeping your ledger, customer, vendor, order, and inventory records fresh and complete with every sync. Fivetran handles the nuances of this source automatically, including incremental syncs that capture new and updated records as they happen and support for the custom subscriptions Business Central uses to capture deletes, so your finance data reflects what is actually happening in the business. Together, Fivetran + dbt Labs turn that raw data into a centralized, cleansed, and governed foundation agents can query directly — dbt's full modeling, testing, and documentation capabilities, including prebuilt quickstart models where they exist for a given connector, make your financial data genuinely ready for an agent to act on.

What your Business Central data unlocks for your team

With Business Central data in a central warehouse, AI agents unlock capabilities your finance team couldn't access before.

• Real-time payables monitoring — agents surface overdue vendor invoices and total exposure without a manual aging report.
• Automated ledger reconciliation — agents match general ledger entries against posted orders and flag discrepancies early.
• Cross-entity financial visibility — agents consolidate cash, receivables, and payables across every company in one answer.
• Customer and item-level analysis — agents break down revenue and cost trends by customer, item, or location on demand.
• Faster close support — agents answer finance's questions during close instead of waiting for a new report to be built.

FAQ

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

It means your general ledger, customer, vendor, order, and inventory data live in a centralized, cleansed, and governed warehouse or data lake where an AI agent can query the complete, current picture on demand. Without that foundation, an agent only sees whatever a single report happens to show.

What can my team actually do with AI agents and Business Central data?

Finance teams can ask agents to monitor payables and receivables, reconcile the ledger against posted orders, analyze revenue and cost by customer or item, and consolidate financial position across entities, all without building a new report first.

Is Business Central data ready for AI agents out of the box?

No. Business Central data needs to be centralized, modeled, and governed before an agent can query it reliably.

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

No. Fivetran automates the movement of Business Central 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 Business Central data ready for AI agents?

Fivetran moves your ledger, customer, vendor, and order data reliably into your warehouse or data lake, using incremental syncs and delete tracking to keep it current. dbt Labs then transforms and governs that data using its full modeling, testing, and documentation capabilities to give your team a fast path to AI-ready financial data.

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