AI Agents for
Business Central
A bank reconciliation that auto matches 85 percent leaves the 15 percent that takes the afternoon. That residue is the job.
- Business Central ships Copilot bank reconciliation and autonomous agents, and they cover the first mile. The residue they leave is small in percentage terms and most of the working time.
- ChatFin reads through the standard and custom API pages and OData, which are versioned. Extensions are the supported way to change BC, so nothing here depends on a page layout.
- Dimensions are read live rather than cached. Business Central carries the analysis in dimensions and they change, so a copy is a second source of truth.
- Not every Business Central team should buy an agent layer. Under about 150 purchase invoices a month with few exceptions, native capability is likely enough.
- The strongest cases are high volume with a small team, multi entity with monthly intercompany, and a close that depends on one person.
ChatFin runs finance AI agents on Microsoft Dynamics 365 Business Central. It reads through the standard and custom API pages and OData, works the residue that native matching leaves behind, groups purchase invoice exceptions by cause, and prepares close entries for a named person to approve. It posts nothing on its own.
Business Central already ships useful AI, including Copilot assisted bank reconciliation and autonomous agents, and this page does not argue with that. The point is what happens after. A reconciliation that auto matches 85 percent of lines leaves 15 percent that takes the afternoon, and those lines are where the payer name differs from the customer record, one receipt settles many invoices, or a difference turns out to be a settlement discount rather than a dispute.
Business Central ships agents. They cover the first mile
Business Central has native Copilot capability including bank reconciliation assistance and autonomous agents, and for a mid sized finance team that is a real head start. What it leaves is the second mile: the items the assistance could not match, the exceptions nobody configured a rule for, and the explanation behind a number.
| Native capability covers | What is still a person |
|---|---|
| Suggesting bank statement matches | The residue that did not match, and why |
| Automating a defined sequence | An exception outside the sequence |
| Drafting from a template | Deciding what the number means |
| Standard reports and analysis views | A question nobody built a view for |
That residue is small in percentage terms and it is most of the working time. A reconciliation that auto matches 85 percent leaves the 15 percent that takes the afternoon.
ChatFin is additive to what Business Central already does. Where Copilot bank reconciliation already clears an item there is no reason to put an agent in front of it, and ChatFin works the residue rather than redoing the easy part.
How ChatFin connects to Business Central
Through the published API layer, not through the client.
What ChatFin does once it is connected
Four jobs, weighted to what a mid sized team actually loses time on.
Work the bank reconciliation residue
The lines native matching could not clear, grouped by cause. Payer name differing from the customer, one receipt against many invoices, a reference the customer used rather than yours, or a difference that is a settlement discount.
Work the purchase invoice exception queue
Grouped into repeating patterns rather than listed by value, each routed to whoever can resolve it.
Explain a movement from the entries
Split into components that reconcile to the reported total, with the document numbers and dimensions behind each.
Prepare the recurring close entries
Accruals from open purchase orders with receipts attached, for a person to review and post.
Right sizing this for a mid sized team
Business Central teams are usually smaller, which changes the honest advice. Not every finance team on it should buy an agent layer.
| Situation | Honest recommendation |
|---|---|
| Under about 150 purchase invoices a month, few exceptions | Native capability is likely enough. Automation is worth its setup cost where the work is repetitive and large |
| High volume with a small team | This is the strongest case. The constraint is hours |
| Multi entity with intercompany every month | Strong case. Intercompany at document level is mechanical and always late |
| One person doing the whole close | Strong case, and the risk case too. Key person dependency is the real exposure |
| Heavily extended with custom logic | Scope the extensions first. That is a project of its own |
A vendor that cannot tell you when their product is not worth buying has either not deployed at scale or is not saying.
ChatFin would rather say not yet than run a pilot that fails for reasons neither side controls.
Where ChatFin underperforms on Business Central
Three, and all three sit upstream.
More on ChatFin and Business Central
Deeper reading, all live.
In build for Business Central
Business Central FP&A AI · Business Central month end close AI · Business Central accounts payable AI · Business Central accounts receivable AI
Questions Business Central teams ask first
Business Central already has AI agents. Why add anything?
Because native capability covers the first mile and the residue is where the time goes. Copilot assisted bank reconciliation suggests matches well and leaves the lines it could not resolve, which is typically where the payer name differs from the customer record, one receipt settles many invoices against the customer's own references, or a short payment is a settlement discount. ChatFin works that residue by cause rather than redoing the easy part.
Should every Business Central team buy an agent layer?
No, and it is worth being direct about that. Under roughly 150 purchase invoices a month with few exceptions, native capability is likely enough and automation will not repay its setup cost. The strongest cases are high volume against a small team, multi entity groups running intercompany every month, and a close that depends on one person, where the key person dependency is the real exposure rather than the hours.
How does ChatFin connect to Business Central?
Through the standard and custom API pages, and OData where an API page does not exist, including views your team has already defined so an agreed definition is reused rather than replaced. Dimensions are read live rather than cached, because Business Central carries the analysis in dimensions and a cached copy becomes a second source of truth for the records that change most.
Native agents do the first mile. ChatFin does the residue.
ChatFin reads Business Central through versioned API pages, works the bank reconciliation and purchase invoice residue by cause, and explains movements from the entries and dimensions behind them.
Everything is a proposal until a named person releases it, and ChatFin copies none of your master data.
Bring one month of unmatched bank lines. The comparison takes an hour.
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See it on your own Business Central data.
Bring one month of unmatched bank lines and purchase invoice exceptions. We will group both by cause, and tell you honestly if your volume does not justify this yet.