Agentic AI for the CFO Office: From Cash Visibility to Controlled Action

Connect financial evidence to a controlled decision

For the CFO office, agentic AI is most useful when it connects a finance question to the evidence and follow-up work needed to resolve it. A sensible first application can assemble an exception, check the supporting records and prepare a recommendation. The authority to move money, change a supplier account or post an entry remains explicitly controlled.

The opportunity starts with a familiar problem. A group finance team sees a change in its cash outlook, but explaining it requires information from several entities, receivables teams and operating systems. A dashboard shows the variance. Someone still has to establish what caused it and what should happen next.

That gap between visibility and resolution is where an agent may help. The investment question is which part of the work it can perform reliably, with evidence that a finance owner can inspect.

What makes a finance workflow agentic?

An agentic system can select steps and use software tools as it works towards an objective. A conventional workflow follows a predetermined sequence. Anthropic makes this distinction in its engineering guidance and recommends using a simpler approach when a fixed workflow or retrieval step is sufficient.

• Anthropic: Building effective agents

For example, matching a payment reference to an invoice through a clear rule may need ordinary automation. Investigating an unmatched receipt might require looking across records, requesting missing context and choosing the next check based on what is found. That is a candidate for a bounded agentic workflow, subject to testing.

An agent does not replace the ledger, treasury platform or finance policy. Those systems and policies remain the authoritative sources for balances, permissions and approved actions.

Begin with a question finance needs to resolve

Choose a recurring decision with a named owner. “Explain material differences between expected and recorded receipts for this entity” is a more useful starting brief than “automate treasury.” It defines a question, a scope and an expected output.

In an illustrative pilot, an assistant reads approved receivables records and available transaction data. It links an overdue invoice to the latest collection note, identifies conflicting information and prepares a short exception brief. The receivables owner checks the evidence and decides whether to contact the customer or correct a record.

This example is a proposed operating model, not a claim about a delivered Bridges finance platform. Its purpose is to make the division of responsibility concrete before choosing technology.

Make the data boundary visible

A persuasive explanation can still be wrong if the underlying data is incomplete. Every brief should identify the entity, currency, reporting period, source systems and latest successful data refresh. Separate recorded transactions from forecasts and manually supplied assumptions.

If one bank feed is delayed, the assistant should show the missing coverage. It should not describe the combined view as complete or real time. If an invoice and collection note disagree, preserve the disagreement for review rather than silently selecting the more convenient answer.

Agree how the workflow handles sensitive information and access across group entities. An analyst allowed to review one subsidiary's receivables should not acquire access to every subsidiary through a conversational interface. Test permissions using the same data boundaries that apply to the underlying work.

For deployment, the relevant finance, technology and compliance owners should confirm requirements for the entity and information involved. A useful demonstration does not establish approval to transfer financial data to a new service.

Separate recommendation from authority

Give the pilot a written action boundary. The following is an illustrative review table for a receivables investigation, not a universal finance policy.

Enforce these boundaries in tool permissions and application code. An instruction in the model prompt is insufficient protection for a tool that can alter financial records. Keep read access separate from write access and log which approved identity requested each action.

Where a later release introduces an approval step, link approval to the exact proposed action and its current evidence. A changed recipient, amount or source record should invalidate the previous approval. Repeated requests must not create duplicate actions.

Give the reviewer a useful decision package

Human approval only helps when the reviewer can understand what is being approved. A long conversation transcript is a poor substitute for a clear exception brief.

Show the question, relevant records, proposed interpretation, missing evidence and recommended next step together. Let the reviewer open the source record, correct an assumption or send the case back for more information. Include an explicit route for “insufficient evidence.”

For the receivables example, the output might say that a receipt remains unmatched because the available reference does not identify an invoice. It can present possible matches without converting a guess into an accounting fact. The owner then chooses the appropriate investigation step.

The Financial Stability Institute's research on AI in banking and insurance highlights model risk, data governance, skills and third-party dependencies as areas requiring attention. Those themes are relevant inputs to a corporate finance pilot, although the paper is not a legal mandate for a GCC corporate CFO office.

• BIS / FSI: Regulating AI in the financial sector

Test the cases that make finance work difficult

Build the evaluation set with the people who reconcile and investigate records. Include normal cases and awkward ones: partial payments, duplicate documents, disputed invoices, conflicting currency information, missing references and stale data.

Start with historical cases whose resolution is known. Keep evaluation data separate from examples used to refine the workflow. Then operate in shadow mode, where the system prepares a result alongside the existing process without changing records or contacting customers.

Check whether the recommendation is supported by the retrieved evidence, whether access restrictions hold and whether uncertainty is raised when necessary. Test an unavailable integration and an unexpected document instruction. Imported documents are evidence to inspect, not permission to change the workflow's rules.

Measure useful resolution and the cost of review

Agree a baseline for the chosen exception type before the pilot. Useful measures include the time to prepare a reviewable brief, reviewer correction rates, unresolved cases at a defined age, and the effort needed to operate and support the workflow.

Record what reviewers actually do. A brief generated in seconds may create more work if its evidence is difficult to inspect. Faster preparation does not automatically create cash savings or improve collections; those outcomes depend on subsequent decisions and operating conditions.

Expand when the team can reproduce acceptable results, explain failures and maintain the controls. A successful read-only investigation may justify another exception type before it justifies any authority to execute a financial action.

What should the CFO approve first?

Approve a bounded question, an accountable finance owner, a permitted data set and an evaluation approach. Require a clear statement of what the system can read, prepare and change. Agree the conditions for pausing it and the evidence needed for the next release.

Bridges' Intelligent Automation & Agentic AI and Data Engineering & Analytics services connect workflow design with the data and controls it needs. Discuss a finance process where better evidence could shorten the path to a decision.

• Intelligent Automation & Agentic AI
• Data Engineering & Analytics
• Discuss a finance process where better evidence could shorten the path to a decision

About this article

Developed from Mohamed Elnahas's original exploration of agentic AI in financial services, rewritten for enterprise finance operations. Product-specific claims have been excluded. Examples and measures are illustrative editorial guidance, not reported client results.

• original exploration of agentic AI in financial services