Agentic AI in finance and accounting brings together industry-trained AI agents, connected financial data, and the expertise of your people. It helps you close faster and control better – with accuracy, auditability, and human judgment at the center.
The growing demand for agentic AI in finance and accounting reflects the greater complexity and pressure today’s business leaders are seeing. Unlike other operational areas that have seasons or slow periods, the stream of finance work is one that never lets up: invoices arrive, transactions post, accounts have to reconcile, the period has to close, and the numbers have to be right – on time, every single time. Each task feeds the next, and even the smallest unchecked discrepancy can emerge as a painful surprise at month-end. Keeping it all accurate, controlled, and on schedule – even as the business rules and goalposts keep shifting – is the real work of a finance team.
Modern finance teams are already making great use of traditional AI for things like spotting anomalies, drafting variance commentaries, or predicting cash. As finance and accounting software evolves, agentic AI has emerged as a powerful next step. It doesn’t just flag issues; it orchestrates a response across payables, receivables, the close, and all your relevant teams – carrying approved actions forward and looping the right people in wherever judgment or sign-off is needed. It doesn’t replace the accountability at the heart of a good finance team. It extends it, across far more transactions than even the best and fastest team could ever on its own.
An AI agent is a software-based assistant that leverages a mix of AI technologies to read what’s happening, weigh the options, and carry out a specific task it was built for – always operating inside the business rules and controls you set. In finance, a single agent usually owns one chunk of the work. This could be anything from matching an invoice to its purchase order, to reconciling a sub-ledger, or applying an incoming payment to the right account.
Agentic AI is what happens when many of these agents work in a unified fashion across all your connected systems. Rather than acting alone, they share context and coordinate their work across every finance and accounting operation. Traditional AI is already good at flagging anomalies, predicting, and drafting. Agentic AI then goes on to coordinate the best response, carry approved actions through multiple steps, and keep work moving across teams and systems – all using the data, workflows, and accounting policies specific to your organization. And because finance demands so much accuracy and control, agentic systems are built to escalate judgment calls or unclear items to a nominated, qualified human, rather than pressing ahead without full clarity and transparency.
| Traditional AI | Agentic AI |
|---|---|
| Spots anomalies, risks, and patterns in financial data | Coordinates action across payables, receivables, the close, and cash |
| Produces predictions, alerts, and drafted commentary | Carries approved work forward within defined controls and policies |
| Typically supports a single analyst or process | Supports end-to-end processes from transaction to reported result |
| Helps teams make faster, better-informed decisions | Helps coordinate the response when the numbers need attention |
| Well suited to forecasting, anomaly detection, and drafting | Well suited to multi-step work spanning many teams and systems |
| Operates under direct human review and direction | Operates with human oversight while handling routine processing |
| Delivers value through insight and prediction | Delivers value through orchestration, execution, and governed action |
Finance is a high-volume, high-stakes balancing act wherein thousands of transactions – invoices, payments, journal entries, reconciliations – must be processed on time and with unrelenting accuracy. Doing this kind of repetitive, interconnected work with speed and accuracy is exactly what agentic AI is built to support. It catches what needs attention, understands what is affected, and coordinates the best next step before a small issue turns into a big problem.
Good finance depends on a range of specialists from AP and AR, to treasury, FP&A, and much more. All these teams must work in concert toward an accurate, well-controlled result. Agentic AI works in much the same way. It isn’t a single tool or application you bolt on, but several capabilities built to operate together – designed to coordinate decisions, actions, and information across the entire finance function.
Generic AI gives generic answers. But when agents are specifically built and trained on the data, rules, and process logic of finance and accounting, they can act with confidence – based on the reality of how your organization actually runs.
An AP clerk, a controller, or an FP&A analyst all need different things from the same ledger. Adaptive experiences make sure that the right dashboards, priorities, and next steps are in place for each role – cutting the time your teams spend searching for reports and chasing details.
Orchestration is what turns a range of different agents into an agentic network. It is a coordination layer that tracks business rules, shared context, and progress – directing what happens next and how agents work toward an approved goal across all of finance and accounting.
In finance, accuracy and transparency are non-negotiable. Agentic systems need clear business rules, role-based permissions, segregation of duties, auditability, on-demand explainability, and human oversight – keeping everything clear, defensible, and audit-ready.
Matches, reconciliations, and exceptions used to mean a queue of manual work – but they can now be streamlined and simplified. Here are a few situations where coordinated agents make the biggest difference:
Agents can perform line-level three-way matching against purchase orders and receipts to clear the clean ones, gather missing information, and route genuine discrepancies to the right person. Approved payments can then be scheduled on terms, keeping suppliers paid and cash predictable.
Reconciling sub-ledgers to the general ledger is steady and essential… but slow. Agents are able to match transactions continuously through the period – proposing adjustments for more clear-cut items and flagging any true exceptions for review – eliminating backlogs when it’s time for close.
Payments are sometimes going to be late or mismatched to the invoiced amount. Agents can apply receipts to the right accounts, resolve routine deductions, and even help coordinate collections. This means your people have accurate data in hand if sensitive conversations ever become necessary.
When it comes to catching errors “the sooner, the better” is the right motto. Agents watch transactions against policy and history, catching gaps or anomalies. They then coordinate the right check or escalation, getting the problem fixed and providing an audit-ready trail at any time.
Every close represents a choreography of tasks across teams. Agents track the close checklist, follow up on outstanding items, prepare recurring journal entries for review, and flag whatever’s blocking progress. This lets your expert teams spend more time doing and less time chasing.
Unfortunately, forecasts start to go stale the minute they’re delivered. Agents refresh AI-powered systems with current data from across the business. They draft variance commentary for review and analyze complex internal and external data sets to find out what’s driving trends or swings.
Agentic AI works as a connected foundation. Agents that are trained on finance processes, draw on the trusted data they have access to, to trigger approved actions and coordinate work across every system that touches your numbers.
Industry agents already understand how finance work should flow. Process mining shows how it actually flows through your business. It exposes the delays, rework, exceptions, and manual handoffs across each process and workflow. In this way, you can see (with evidence) where agents will pay off first.
Good data is the fuel that powers agents. In finance, that spans the general ledger, sub-ledgers, invoices and payments, bank data, accounting policies, and more. A unified, trusted foundation and good data management protocols let agents work from one reconciled view rather than siloed fragments.
Finance work reaches into procurement, sales, operations, and the banks and partners you transact with. Connected execution lets agents work across your systems, so a process like procure-to-pay runs seamlessly rather than stalling at each handoff between functions.
Many finance teams already automate multiple processes. Agentic AI enhances that by deciding when, where, and how each automation should run as conditions change. This means existing processes can keep improving within the business instead of breaking anytime something shifts.
Agentic systems learn as they go. As agents assess results, they refine how they operate. Forecasts get better, exceptions are fewer, and each period runs a little cleaner than the one before. The longer it works with your data and people, the better it fits into your unique operational landscape.
The degree of autonomy you strive for is entirely up to you. You decide what agents handle on their own and what waits for a person – and you widen that scope as confidence grows. It’s not a simple on/off switch, but a capability you tune to your own unique needs so you’re always firmly in control.
The capacity and potential of agentic systems is truly amazing. But what they can’t replace is human judgment. Experience, instinct, and even a healthy dose of professional skepticism are all crucial attributes that your valued and skilled team members bring. When a decision affects things like brand, reputation, and bottom line, there’s no substitute for flesh-and-blood wisdom.
In other words, the long-term aim of AI-powered systems isn’t to entirely hand the books over to software and walk away. It’s to keep qualified people in the loop whenever the stakes are high. And being “in the loop” is more than just a quick approval. It means establishing clear, enforced rules and nominating accountable human owners and experts.
Done well, a human/agentic collaboration is the gold standard. It means that resource-heavy routine processes clear themselves, and the decisions that do reach your accountants and controllers arrive with the right context already gathered and the necessary support attached. People can work faster and with more confidence. There’s always a clear, auditable trail behind the numbers. And your teams can get on with what they’re best at – liberated from a lot of the admin and fiddly tasks that slowed them down.
With a cloud-connected industry-specific platform, a lot of this comes built in – such as controls, business rules, and proven integration. The harder problems tend to crop up when teams attempt to assemble agentic capabilities themselves, without first ensuring those foundations are in place. Below are a few common challenges and some tips for navigating around them:
Speed without transparency is dangerous. An agent acting without segregation of its duties or a clear log can lead to audit and compliance risk. Insist on role-based permissions, full lineage, human approval, and on-demand explanation of black box outcomes – especially for high-stakes tasks.
In finance, it can seem like everything is labeled as urgent or critical. For AI systems, this makes it hard to know which issues to escalate to a team leader. Define priorities and enforce clear thresholds and escalation paths early on – this helps your AI models learn and takes a load off your teams.
Accounting policies, tax rules, rates, and entity structures are constantly in flux. If agents are given outdated data to work with, they’ll make bad decisions (and do so with confidence). Keep all policy and source data current and aligned with data management and governance policies at all times.
Because agents are so interesting, it’s tempting to build and spawn new ones in a silo – without integration or context. Across a function as interlinked as finance, that breeds reconciliation headaches, not efficiency. Agentic AI works best when unified as a single, connected network.
Success isn’t measured by automating as many things as possible as quickly as you can. The metrics that matter are the ones you already track – such as days to close, reconciliation backlogs, cost per invoice, on-time payment, and audit findings. Measure those, and you’ll quickly see the value agents can bring.
Finance and accounting have always used numbers to learn the truth about the business – to protect resources and give leaders the facts they need to make fast, tough decisions. For the professionals who stand behind those numbers, that’s a heavy burden. They’re the first ones in the hotseat if something goes wrong and, alas, often the last to get the kudos and glory for a big win.
Agentic AI won't take the weight of that responsibility off their shoulders. But what it can do is stand alongside them. It handles the grind, watches the details they can't catch on their own, and keeps the audit trail clear so every number holds up. The judgment stays theirs. The credit does too. But the long nights, the frantic scrambles, and the quiet dread of what might slip through? Those get a little lighter.
Discover how Infor Velocity Suite can help transform your finance and accounting functions into an essential part of your agentic enterprise.
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