AI Agents in Finance and Accounting: What Autonomy Looks Like in Practice

A process-by-process view across Source to Pay, Order to Cash, Record to Report and FP&A

Finance leaders hear that AI agents will transform the function. A more useful question is what an agent actually does on an ordinary day in accounts payable, in collections, during the close or in the planning cycle, and which parts of the work stay with people.

This article takes the four core processes of finance and accounting in turn. For each one it looks at where the effort goes today, using published benchmarks, what an autonomous agent takes over, and where human judgement remains essential. The pattern is consistent across all four, and it points to a clear way of deciding where to start.

Sources: APQC and The Hackett Group via Corpay; FP&A Trends Survey 2026; APQC via Corporate Finance Institute.

Source to Pay: from invoice receipt to payment

Accounts payable is the most measured process in finance, which makes it a good place to see the limits of automation. APQC’s benchmarks put the median cost of processing an invoice at $6.00, the median time from receipt to payment at 15 days, and the median exception rate at about 5%. The Hackett Group’s 2025 research puts the average touchless rate at 60%, so on average four in every 10 invoices still pass through human hands. APQC also reports that top performers are roughly five times more productive per person than bottom performers.

The invoices that need people are rarely complicated. They arrive in an unfamiliar layout, reference a purchase order that has changed, or show a quantity that differs from the goods receipt. An agent reads the invoice in whatever format it arrives, compares it with the purchase order and receipt, proposes the coding and, when something does not match, contacts the supplier with a specific question. Payment is then scheduled against the agreed terms, and each step is logged. People remain responsible for policy exceptions, supplier relationships and approvals above set limits.

Order to Cash: from billing to cash in the bank

Days sales outstanding is often treated as a collections metric, though CFGI describes it more accurately as a reflection of the health of the whole order to cash process. Credit risk management, billing accuracy, dispute resolution and collections effectiveness all shape it, and CFGI points to dispute aging as a leading indicator of payment delays.

This is where agents earn their place. A cash application agent matches incoming payments to open invoices and traces short payments to the deductions or disputes behind them. When a dispute arises, it routes the case with the supporting documents already attached, so the person handling it starts with the facts rather than a search. Collections outreach reflects each customer’s payment history instead of following a single schedule. Taxilla’s order to cash blueprints, for example, reconcile orders, invoices, settlements, refunds, deductions and bank receipts, with charge validation to protect revenue.

Record to Report: a close that runs through the period

The close is where accumulated exceptions come due. APQC’s benchmarks show top teams closing in 4.8 days or fewer while the bottom quarter needs 10 days or more, and the gap is largely a story of reconciliation, exception clearing and chasing data from systems that do not talk to each other.

Agents change the timing of the work as much as the speed. Reconciliation runs continuously through the period rather than in a rush at the end. Agents match transactions at line level, draft adjusting journals for approval and surface unusual balances while there is still time to investigate. Taxilla describes the lifecycle in six stages: match, reconcile, adjust, review, approve and close, with maker-checker review and exception visibility built in. Controllers keep what matters most, which is the review and the sign-off.

FP&A: from gathering data to informing decisions

The 2026 FP&A Trends Survey of 475 finance professionals found that teams spend 47% of their time on low-value work such as data collection and validation, against 32% on insight and decision support. Only 19% of teams can run a scenario in real time or within a day, and only 11% have real-time or near-real-time access to critical data.

Agents work on exactly that imbalance. They assemble the data, refresh forecasts as actuals post, prepare first-pass variance commentary and pull the transactions behind each movement. Analysts then spend their time on the question the numbers raise and the decision they support. The survey also found that data quality is the leading barrier to deploying agents in FP&A, which makes the foundations covered later in this series central to the outcome.

What the four processes have in common

In each process the agent owns an outcome, people set the rules and handle the exceptions that call for judgement, and every action leaves a record. The processes also depend on one another. Clean payables feed accurate accruals, collected cash feeds the bank reconciliation, and a faster close feeds a forecast built on current actuals. That is why a first deployment tends to improve its neighbours as well.

Elixir Global delivers Source to Pay, Order to Cash, Record to Report and FP&A as agentic operations, drawing on the Agentic Vault, a library of pre-built AI agents designed for common finance processes. Teams begin with agents already shaped for the work, then extend the scope as results build confidence.

Frequently asked questions

Which finance process should go first?

Start where exception volume is high and outcomes are easy to measure. Accounts payable and account reconciliation both fit. Gartner’s survey of senior finance leaders found that accounts payable and receivable automation, data extraction and report creation typically return value within nine to 10 months, which gives an early deployment a realistic horizon.

How is an agent different from the straight-through rules already used in accounts payable?

Straight-through rules handle invoices that match cleanly, and the touchless rate shows how many that is. An agent works on the rest. It reads unfamiliar formats, investigates mismatches, contacts the supplier and proposes a resolution, then escalates only what falls outside its authority.

Do agents change what finance teams do day to day?

They shift effort away from keying, chasing and searching for data. Accountants and analysts spend more time on rule-setting, reviewing exceptions and explaining results, which is the work that depends on judgement and context.

Sources

Corpay, “AP Team Productivity: Benchmarks and How Automation Moves Them” (APQC and Hackett Group data). https://www.corpay.com/en-AU/resources/blog/ap-team-productivity-benchmarks

CFGI, “Demystifying DSO,” 2025. https://www.cfgi.com/wp-content/uploads/2025/08/CFGI-US-Demystifying-DSO-FINAL-1.pdf

Corporate Finance Institute, “AI Agents for Month-End Close Automation” (APQC benchmarking data). https://corporatefinanceinstitute.com/resources/artificial-intelligence-ai/ai-agents-for-month-end-close-automation/

OneStream, “The Readiness Gap: 10th Annual FP&A Trends Survey,” July 8, 2026 (survey sponsored by OneStream, n=475). https://www.onestream.com/blog/the-readiness-gap-new-10th-annual-fp-and-a-trends-survey-reveals-ai-is-outpacing-fp-and-a-s-foundations/

Gartner, “Gartner Says CFOs Must Take a More Disciplined Approach to Finance AI Investment,” September 24, 2026. https://www.gartner.com/en/newsroom/press-releases/2026-09-24-gartner-says-cfos-must-take-a-more-disciplined-approach

Taxilla, financial close automation. https://www.taxilla.com/financial-close

Taxilla, autonomous finance platform home page. https://www.taxilla.com/