Build the first agentic finance workflow your team can actually own.

Treasury workflow example. Multiple subsidiaries upload cash balances into a group system. Balances are manually reconciled. A netting calculation is performed. Residual exposures are transferred into an FX risk-management system. A person checks the hedging policy, decides which trades are needed, and executes trades manually with the bank.

Transfer pricing example. A tax accountant searches the ledger for relevant transactions, checks recharges against policy, calculates the true-up, and applies the adjustment manually.

These are the kinds of workflows this service targets: high-value, judgement-heavy processes where work crosses systems and too much coordination still depends on people copying, checking, reconciling, and deciding manually.

The workflow is redesigned so agents do most of the repeatable work, while finance professionals direct, review, approve, and improve the process.

In the treasury example, agents can collect subsidiary balances, reconcile differences, prepare the netting calculation, identify residual FX exposures, check the hedging policy, draft the recommended trades, and prepare execution or approval steps through controlled interfaces.

In the transfer-pricing example, agents can search the ledger, classify relevant transactions, compare recharges to policy, calculate the true-up, prepare the proposed adjustment, and produce an audit trail for review.

The work is not just prompting an AI model. It requires workflow redesign, interfaces into the relevant systems, deterministic calculations where needed, review points, exception handling, and audit evidence so the process can be trusted.

The AI work layer does not replace ERP, TMS, reporting, planning, or tax systems. Those systems remain authoritative.

The layer can help collect, clean, classify, and structure data before it enters controlled systems or review packs.

The source of truth stays where governance already expects it to be: ERP, TMS, tax, reporting, planning, or other approved systems.

IT needs visibility over which agents are running, what they can access, what actions they can take, and how those actions can be reversed quickly.

That means approved infrastructure, access control, logging, monitoring, deployment patterns, support ownership, and rapid undo paths.

Finance needs an audit interface: what the agent did, which evidence it used, what changed, and how a reviewer can reproduce the work rather than trust a black-box answer.

Phase one builds the agent workflow and runs it in parallel with the existing process. This proves whether agents doing the work create enough value, speed, control, and evidence quality to justify scaling.

Once ROI is clear, the capability transfers to the finance team so they can run, maintain, supervise, and improve the workflow.

Before the workflow touches production, IT controls need to be in place: access, monitoring, logs, support ownership, rollback, security, and visibility over the agent estate.

The end state is not a one-off automation. It is a finance team that can operate redesigned workflows using agents inside controlled enterprise rails.

This connects to the broader agentic organisation direction described by Deloitte and McKinsey: people owning outcomes, with AI agents executing more of the work.